diff --git a/.gitignore b/.gitignore index fdd6dc397..4a4c6d8b1 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ __pycache__ .python-version *.egg-info venv +venv* dist build ~ @@ -16,4 +17,6 @@ verba_config.json text-generation-inference test.py cache.txt -.ruff_cache \ No newline at end of file +.ruff_cache +*_secrets.json +ollama diff --git a/CHANGELOG.md b/CHANGELOG.md index 335298941..401d7cebc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,43 +2,101 @@ All notable changes to this project will be documented in this file. +## [1.0.0] - Beautiful Verba Update + +### Added + +- Added DaisyUI +- Optimized frontend codebase +- Fully Reworked Verba Design + - Fully Responsive, optimized for all screen sizes +- Customization Capabilities + - Added Default, Darkmode, Weaviate themes + - Full text, color, image customization +- Improve Chat Interface + - Better formatting of markdown + code + - Keep conversations saved in localBrowser storage + - Better Debugging by providing more information about current states +- Improve Document Viewer Interface + - Add Pagination + - Add Sorting + - Use Aggregation for Filtering +- Improve Status Overview + - Reworked Frontend + Optimize Code + - Sort status entries + - Improve Loading Speed by using Aggregation +- Improve Component Selection for both Ingestion and RAG + - Added new configuraiton that will be passed between frontend and backend + - Cleaned codebase, merged interfaces and managers to single files + - Added clean endpoints for better code readability + - Reworked on interfaces +- Added better console and logging for ingestion +- More Configuration + - Enable/Disable Caching and Autocomplete Suggestions + - Improved verba_config.json +- Ability to enable/disable caching + autosuggestions +- Add Google Gemini as new Embedder and Generator +- Added .CSV support (all file types available in Unstructured IO) +- More test data +- Add Ollama as Generator and Embedding Component +- Add Support for Cohere R+ +- Improved WindowRetriever Context Generation +- Show RAW Context in Frontend + Save in LocalStorage +- Save Settings and Configuration in Weaviate + +### Changed + +- Changed to AppRouter framework +- Changed frontend project structure +- Changed backend project structure +- Removed Llama Generator Component + +### Fixed + +- Using Accelerator Library + ## [0.4.0] - 11.04.2024 ### Added + - Improved Docker Documentation - Improved Docker Settings - New Environment Variables for OpenAI proxies: OpenAI_BASE_URL (LiteLLM support) (https://github.com/weaviate/Verba/issues/56) - Increased version ### Changed + - Removed spaCy from project ### Fixed + - Python not working on version 3.12, 3.11, and 3.9 - GitHub Links on README - Fix Docker Default Vectorizer (https://github.com/weaviate/Verba/issues/50) - Fix requirements.txt spelling error - Minor Bug fixes - ## [0.3.1] - 15.11.2023 ### Added + - PDFReader powered by PyPDF2 - TokenChunker powered by tiktoken - Ruff Linting (set as pre-commit) - Markdown Formatting for chat messages (https://github.com/weaviate/Verba/issues/48) ### Fixed + - Added missing dependencies - Fixed restart bug - Fixed MiniLM Cuda to_device bug (https://github.com/weaviate/Verba/issues/41) - Fixed Config Issues (https://github.com/weaviate/Verba/issues/51) -- Fixed Weaviate Embedded Headers for Cohere +- Fixed Weaviate Embedded Headers for Cohere ## [0.3.0] - 12.09.2023 ### Added + - Refactor modular architecture - Add ability to import data through the frontend, CLI, and script - Add Readers (SimpleReader, PathReader, GithubReader, PDFReader) @@ -57,32 +115,37 @@ All notable changes to this project will be documented in this file. - Added technical docs and contribution guidelines ### Fixed + - Error handling for data ingestion (handling chunk size) - Schmea handling on startup ### Changed + - Removed Simple- and AdvancedEngine logic ## [0.2.3] - 05.09.2023 ### Added + - OpenAI API documentation example dataset ## [0.2.2] - 31.08.2023 ### Release! + - First version of Verba released! (many to come :) ### Added + - Verba favicon ### Fixed + - Add static files to package - Weaviate Embedded not shutting down ## [0.1.0] - 29.08.2023 ### Added -- Prepare Verba for first release - +- Prepare Verba for first release diff --git a/Dockerfile b/Dockerfile index 24b9f5a94..538b3442d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.10 +FROM python:3.11 WORKDIR /Verba COPY . /Verba RUN pip install -e '.' diff --git a/FRONTEND.md b/FRONTEND.md index 6cd20139b..97dffee21 100644 --- a/FRONTEND.md +++ b/FRONTEND.md @@ -1,15 +1,25 @@ -# 🎨 Verba - Frontend Documentation 🎨 +# Verba - Frontend Documentation -Welcome to the Verba frontend documentation. Our frontend is a React application utilizing the robust Next.js framework, designed to offer a dynamic and interactive document search and display experience. Below are the instructions to help you set up and understand the structure of the frontend, which can be found within the [frontend](./frontend/) directory of our repository. +Verba's Frontend is a [NextJS](https://nextjs.org/) application used together with [TailwindCSS](https://tailwindcss.com/) and [DaisyUI](https://daisyui.com/). ## 🚀 Setting Up the Frontend To get your local copy of the Verba frontend up and running, please follow these simple steps: +1. Clone Repository + +```git + +git clone https://github.com/weaviate/Verba.git + +``` + 1. **Node.js Requirement**: - - Confirm that Node.js version `>=18.16.0` is installed on your system. If you need to install or update Node.js, visit the official [Node.js website](https://nodejs.org/). + + - Confirm that Node.js version `>=21.3.0` is installed on your system. If you need to install or update Node.js, visit the official [Node.js website](https://nodejs.org/). 2. **Installation**: + - Navigate to the frontend directory: `cd frontend` - Run `npm install` to install the dependencies required for the project. @@ -19,25 +29,7 @@ To get your local copy of the Verba frontend up and running, please follow these ## 📦 Building Static Pages for FastAPI -If you wish to serve the frontend through FastAPI, you need to build static pages: +If you wish to serve and update the frontend through FastAPI, you need to build static pages: 1. **Build Process**: - Execute `npm run build` to generate the static production build. The output will be directed to the FastAPI folder configured to serve the static content. - -## 🎨 Styling with Tailwind CSS - -Verba's frontend is styled with Tailwind CSS, a utility-first CSS framework, along with global CSS classes for a consistent and modern design language. Here's how styling is managed: - -- **Tailwind CSS**: - - We use Tailwind CSS for responsive design and to apply styles directly in the JSX code using utility classes. - - For custom styling, the `tailwind.config.js` file can be modified. - -- **Global Styles**: - - Global stylesheets are located in the `styles` directory. These are used for overarching styles that apply to the entire application. - -- **Assets**: - - All static assets such as images and animations are housed in the `public` directory. - - These assets are referenced directly in the codebase and are automatically optimized by Next.js. - -By following these guidelines, you'll be able to contribute to the frontend of Verba effectively. We value your contributions and are excited to see the creative ways you'll enhance the Verba experience. - diff --git a/PYTHON_TUTORIAL.md b/PYTHON_TUTORIAL.md new file mode 100644 index 000000000..7c0461de4 --- /dev/null +++ b/PYTHON_TUTORIAL.md @@ -0,0 +1,70 @@ +# Installing Python and Setting Up a Virtual Environment + +Before you can use Verba, you'll need to ensure that `Python >=3.10.0` is installed on your system and that you can create a virtual environment for a safer and cleaner project setup. + +## Installing Python + +Python is required to run Verba. If you don't have Python installed, follow these steps: + +### For Windows: + +Download the latest Python installer from the official Python website. +Run the installer and make sure to check the box that says `Add Python to PATH` during installation. + +### For macOS: + +You can install Python using Homebrew, a package manager for macOS, with the following command in the terminal: + +``` +/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" +``` + +Then install Python: + +``` +brew install python +``` + +### For Linux: + +Python usually comes pre-installed on most Linux distributions. If it's not, you can install it using your distribution's package manager. You can read more about it [here](https://opensource.com/article/20/4/install-python-linux) + +## Setting Up a Virtual Environment + +It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide Python packages. + +### Install the virtualenv package: + +First, ensure you have pip installed (it comes with Python if you're using version 3.4 and above). +Install virtualenv by running: + +``` +pip install virtualenv +``` + +### Create a Virtual Environment: + +Navigate to your project's directory in the terminal. +Run the following command to create a virtual environment named venv (you can name it anything you like): + +``` +python3 -m virtualenv venv +``` + +### Activate the Virtual Environment: + +- On Windows, activate the virtual environment by running: + +``` +venv\Scripts\activate.bat +``` + +- On macOS and Linux, activate it with: + +``` +source venv/bin/activate +``` + +Once your virtual environment is activated, you'll see its name in the terminal prompt. Now you're ready to install Verba using the steps provided in the Quickstart sections. + +> Remember to deactivate the virtual environment when you're done working with Verba by simply running deactivate in the terminal. diff --git a/README.md b/README.md index 9d08074cd..667a9d671 100644 --- a/README.md +++ b/README.md @@ -1,269 +1,203 @@ -# Verba -## 🐕 The Golden RAGtriever +# Verba -Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally or through LLM providers such as OpenAI, Cohere, and HuggingFace. +## The Golden RAGtriever + +[![Weaviate](https://img.shields.io/static/v1?label=powered%20by&message=Weaviate%20%E2%9D%A4&color=green&style=flat-square)](https://weaviate.io/) +[![PyPi downloads](https://static.pepy.tech/personalized-badge/goldenverba?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/goldenverba/) [![Docker support](https://img.shields.io/badge/Docker_support-%E2%9C%93-4c1?style=flat-square&logo=docker&logoColor=white)](https://docs.docker.com/get-started/) [![Demo](https://img.shields.io/badge/Check%20out%20the%20demo!-yellow?&style=flat-square&logo=react&logoColor=white)](https://verba.weaviate.io/) + +Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google. ``` pip install goldenverba ``` -[![Weaviate](https://img.shields.io/static/v1?label=powered%20by&message=Weaviate%20%E2%9D%A4&color=green&style=flat-square)](https://weaviate.io/) -[![PyPi downloads](https://static.pepy.tech/personalized-badge/goldenverba?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/goldenverba/) [![Docker support](https://img.shields.io/badge/Docker_support-%E2%9C%93-4c1?style=flat-square&logo=docker&logoColor=white)](https://docs.docker.com/get-started/) [![Demo](https://img.shields.io/badge/Check%20out%20the%20demo!-yellow?&style=flat-square&logo=react&logoColor=white)](https://verba.weaviate.io/) - -![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba.gif) +![Demo of Verba](https://github.com/weaviate/Verba/blob/1.0.0/img/verba.gif) - [Verba](#verba) - [🎯 What Is Verba?](#what-is-verba) - - [⚙️ Under the Hood](#️under-the-hood) - - [💡 Effortless Data Import with Weaviate](#effortless-data-import-with-weaviate) - - [💥 Advanced Query Resolution with Hybrid Search](#advanced-query-resolution-with-hybrid-search) - - [🔥 Accelerate Queries with Semantic Cache](#accelerate-queries-with-semantic-cache) + - [✨ Features](#feature-lists) - [✨ Getting Started with Verba](#getting-started-with-verba) -- [🐍 Installing Python and Setting Up a Virtual Environment](#installing-python-and-setting-up-a-virtual-environment) - - [Installing Python](#installing-python) - - [Setting Up a Virtual Environment](#setting-up-a-virtual-environment) -- [📦 Choosing the Right Verba Installation Package](#choosing-the-right-verba-installation-package) - - [Default Package](#default-package) - - [HuggingFace Version](#huggingface-version) - - [Development Version](#development-version) -- [🚀 Quickstart: Deploy with pip](#quickstart-deploy-with-pip) -- [🛠️ Quickstart: Build from Source](#️quickstart-build-from-source) - [🔑 API Keys](#api-keys) - [Weaviate](#weaviate) + - [Ollama](#ollama) + - [Google](#google) + - [Unstructured](#unstructured) - [OpenAI](#openai) - - [Cohere](#cohere) - [HuggingFace](#huggingface) - - [Llama2](#llama2) - - [Unstructured](#unstructured) - - [Github](#github) -- [🐳 Quickstart: Deploy with Docker](#quickstart-deploy-with-docker) - - [Large Language Model (LLM) Costs](#large-language-model-llm-costs) -- [💾 Importing Your Data into Verba](#️importing-your-data-into-verba) -- [🛠️ Project Architecture](#️project-architecture) +- [Quickstart: Deploy with pip](#how-to-deploy-with-pip) +- [Quickstart: Build from Source](#how-to-build-from-source) +- [Quickstart: Deploy with Docker](#how-to-install-verba-with-docker) +- [💾 Verba Walkthrough](#️verba-walkthrough) - [💖 Open Source Contribution](#open-source-contribution) +- [🚩 Known Issues](#known-issues) +- [❔FAQ](#faq) ## What Is Verba? -Verba is more than just a tool—it's a personal assistant for querying and interacting with your data, **either locally or deployed via cloud**. Have questions about your documents? Need to cross-reference multiple data points? Want to gain insights from your existing knowledge base? Verba empowers you with the combined capabilities of Weaviate's context-aware database and the analytical power of Large Language Models (LLMs). Interact with your data through an intuitive chat interface that refines search results by using the ongoing conversation context to deliver even more accurate and relevant information. - -![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_screen.png) - -### Under the Hood -Verba is engineered with Weaviate's cutting-edge Generative Search technology at its core, extracting relevant context from your pool of documents to resolve queries with precision. By utilizing the power of Large Language Models, Verba doesn't just search for answers—it understands and provides responses that are contextually rich and informed by the content of your documents, all through an intuitive user interface designed for simplicity and efficiency. - -### Effortless Data Import with Weaviate -Verba offers seamless data import functionality through its frontend, supporting a diverse range of file types including `.txt`, `.md`, `.pdf` and more. Before feeding your data into Weaviate, Verba handles chunking and vectorization to optimize it for search and retrieval. Together with collaborative partners we support popular libraries such as [HuggingFace](https://github.com/huggingface), [Haystack](https://github.com/deepset-ai/haystack), [Unstructured](https://github.com/Unstructured-IO/unstructured) and many more! - -![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_import.png) - -### Advanced Query Resolution with Hybrid Search -Experience the hybrid search capabilities of Weaviate within Verba, which merges vector and lexical search methodologies for even greater precision. This dual approach not only navigates through your documents to pinpoint exact matches but also understands the nuance of context, enabling the Large Language Models to craft responses that are both comprehensive and contextually aware. It's an advanced technique that redefines document retrieval, providing you with precisely what you need, when you need it. -### Accelerate Queries with Semantic Cache -Verba enhances search efficiency with Weaviate's Semantic Cache, a sophisticated system that retains the essence of your queries, results, and dialogues. This proactive feature means that Verba anticipates your needs, using cached data to expedite future inquiries. With semantic matching, it quickly determines if your question has been asked before, delivering instant results, and even suggests auto-completions based on historical interactions, streamlining your search experience to be faster and more intuitive. +Verba is a fully-customizable personal assistant for querying and interacting with your data, **either locally or deployed via cloud**. Resolve questions around your documents, cross-reference multiple data points or gain insights from existing knowledge bases. Verba combines state-of-the-art RAG techniques with Weaviate's context-aware database. Choose between different RAG frameworks, data types, chunking & retrieving techniques, and LLM providers based on your individual use-case. + +## Feature Lists + +| 🤖 Model Support | Implemented | Description | +| --------------------------------- | ----------- | ------------------------------------------------------- | +| Ollama (e.g. Llama3) | ✅ | Local Embedding and Generation Models powered by Ollama | +| HuggingFace (e.g. MiniLMEmbedder) | ✅ | Local Embedding Models powered by HuggingFace | +| Cohere (e.g. Command R+) | ✅ | Embedding and Generation Models by Cohere | +| Google (e.g. Gemini) | ✅ | Embedding and Generation Models by Google | +| OpenAI (e.g. GPT4) | ✅ | Embedding and Generation Models by OpenAI | + +| 📁 Data Support | Implemented | Description | +| ------------------ | ----------- | ---------------------------------- | +| PDF Ingestion | ✅ | Import PDF into Verba | +| CSV/XLSX Ingestion | ✅ | Import Table Data into Verba | +| Multi-Modal | planned ⏱️ | Import Multi-Modal Data into Verba | +| UnstructuredIO | ✅ | Import Data through Unstructured | + +| ✨ RAG Features | Implemented | Description | +| ----------------------- | ----------- | ------------------------------------------------------------------------- | +| Hybrid Search | ✅ | Semantic Search combined with Keyword Search | +| Semantic Caching | ✅ | Results saved and retrieved based on semantic meaning | +| Autocomplete Suggestion | ✅ | Verba suggests autocompletion | +| Filtering | planned ⏱️ | Apply Filters (e.g. documents, document types etc.) before performing RAG | +| Advanced Querying | planned ⏱️ | Task Delegation Based on LLM Evaluation | +| Reranking | planned ⏱️ | Rerank results based on context for improved results | +| RAG Evaluation | planned ⏱️ | Interface for Evaluating RAG pipelines | +| Customizable Metadata | planned ⏱️ | Free control over Metadata | + +| 🆒 Cool Bonus | Implemented | Description | +| --------------------- | ----------- | ------------------------------------------------------- | +| Docker Support | ✅ | Verba is deployable via Docker | +| Customizable Frontend | ✅ | Verba's frontend is fully-customizable via the frontend | + +| 🤝 RAG Libraries | Implemented | Description | +| ---------------- | ----------- | ---------------------------------- | +| Haystack | planned ⏱️ | Implement Haystack RAG pipelines | +| LlamaIndex | planned ⏱️ | Implement LlamaIndex RAG pipelines | +| LangChain | planned ⏱️ | Implement LangChain RAG pipelines | + +> Something is missing? Feel free to create a new issue or discussion with your idea! + +![Showcase of Verba](https://github.com/weaviate/Verba/blob/1.0.0/img/verba_screen.png) --- # Getting Started with Verba -Starting your Verba journey is super easy, with multiple deployment options tailored to your preferences. Follow these simple steps to get Verba up and running: +You have three deployment options for Verba: + +- Install via pip -- Deploy with pip [(Quickstart)](##🚀-Quickstart:-Deploy-with-pip) ``` pip install goldenverba ``` -- Build from Source [(Quickstart)](##🛠️-Quickstart:-Build-from-Source) -``` -git clone https://github.com/weaviate/Verba -pip install -e . -``` -- Use Docker for Deployment [(Quickstart)](##🐳-Quickstart:-Deploy-with-Docker) +- Build from Source -**Prerequisites**: If you're not using Docker, ensure that you have `Python >=3.10.0` installed on your system. - -# Installing Python and Setting Up a Virtual Environment -Before you can use Verba, you'll need to ensure that `Python >=3.10.0` is installed on your system and that you can create a virtual environment for a safer and cleaner project setup. - -## Installing Python -Python is required to run Verba. If you don't have Python installed, follow these steps: - -### For Windows: -Download the latest Python installer from the official Python website. -Run the installer and make sure to check the box that says `Add Python to PATH` during installation. - -### For macOS: -You can install Python using Homebrew, a package manager for macOS, with the following command in the terminal: -``` -/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" ``` +git clone https://github.com/weaviate/Verba -Then install Python: -``` -brew install python +pip install -e . ``` -### For Linux: -Python usually comes pre-installed on most Linux distributions. If it's not, you can install it using your distribution's package manager. You can read more about it [here](https://opensource.com/article/20/4/install-python-linux) +- Use Docker for Deployment -## Setting Up a Virtual Environment -It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide Python packages. +**Prerequisites**: If you're not using Docker, ensure that you have `Python >=3.10.0` installed on your system. -### Install the virtualenv package: -First, ensure you have pip installed (it comes with Python if you're using version 3.4 and above). -Install virtualenv by running: -``` -pip install virtualenv -``` +If you're unfamiliar with Python and Virtual Environments, please read the [python tutorial guidelines](./PYTHON_TUTORIAL.md). -### Create a Virtual Environment: -Navigate to your project's directory in the terminal. -Run the following command to create a virtual environment named venv (you can name it anything you like): -``` -python3 -m virtualenv venv -``` +# API Keys -### Activate the Virtual Environment: -- On Windows, activate the virtual environment by running: -``` -venv\Scripts\activate.bat -``` +Before starting Verba you'll need to configure access to various components depending on your chosen technologies, such as OpenAI, Cohere, and HuggingFace via an `.env` file. Create this `.env` in the same directory you want to start Verba in. You can find an `.env.example` file in the [goldenverba](./goldenverba/.env.example) directory. -- On macOS and Linux, activate it with: -``` -source venv/bin/activate -``` +> Make sure to only set environment variables you intend to use, environment variables with missing or incorrect values may lead to errors. -Once your virtual environment is activated, you'll see its name in the terminal prompt. Now you're ready to install Verba using the steps provided in the Quickstart sections. +Below is a comprehensive list of the API keys and variables you may require: -> Remember to deactivate the virtual environment when you're done working with Verba by simply running deactivate in the terminal. +| Environment Variable | Value | Description | +| ------------------------------ | ---------------------------------------------------------- | --------------------------------------------------------------------------------- | +| WEAVIATE_URL_VERBA | URL to your hosted Weaviate Cluster | Connect to your [WCS](https://console.weaviate.cloud/) Cluster | +| WEAVIATE_API_KEY_VERBA | API Credentials to your hosted Weaviate Cluster | Connect to your [WCS](https://console.weaviate.cloud/) Cluster | +| OPENAI_API_KEY | Your API Key | Get Access to [OpenAI](https://openai.com/) Models | +| COHERE_API_KEY | Your API Key | Get Access to [Cohere](https://cohere.com/) Models | +| OLLAMA_URL | URL to your Ollama instance (e.g. http://localhost:11434 ) | Get Access to [Ollama](https://ollama.com/) Models | +| OLLAMA_MODEL | Model Name (e.g. llama) | Get Access to a specific [Ollama](https://ollama.com/) Model | +| UNSTRUCTURED_API_KEY | Your API Key | Get Access to [Unstructured](https://docs.unstructured.io/welcome) Data Ingestion | +| UNSTRUCTURED_API_URL | URL to Unstructured Instance | Get Access to [Unstructured](https://docs.unstructured.io/welcome) Data Ingestion | +| GITHUB_TOKEN | Your GitHub Token | Get Access to Data Ingestion via GitHub | +| GOOGLE_APPLICATION_CREDENTIALS | Google Credentials | Get Access to Google Models | +| GOOGLE_CLOUD_PROJECT | Google Cloud Project | Get Access to Google Models | +| GOOGLE_API_KEY | Your API Key | Get Access to Google Models | +| VERBA_PRODUCTION | True | Run Verba in Production Mode | -# Choosing the Right Verba Installation Package -Verba comes in several installation packages, each tailored for specific use cases and environments. Choose the package that aligns with your requirements: +## Weaviate -## Default Package -The default package is perfect for getting started quickly and includes support for popular models and services like OpenAI, Cohere, and spaCy. This package is suitable for general use and can be installed easily via pip: +Verba provides flexibility in connecting to Weaviate instances based on your needs. By default, Verba opts for [Weaviate Embedded](https://weaviate.io/developers/weaviate/installation/embedded) if it doesn't detect the `WEAVIATE_URL_VERBA` and `WEAVIATE_API_KEY_VERBA` environment variables. This local deployment is the most straightforward way to launch your Weaviate database for prototyping and testing. -``` -pip install goldenverba -``` +However, you also have other options: -> This will set you up with all you need to integrate Verba with these services without additional configuration. +**🌩️ Weaviate Cloud Service (WCS)** -## HuggingFace Version -For those looking to leverage models from the HuggingFace ecosystem, including `SentenceTransformer` and `LLama2`, the HuggingFace version is the ideal choice. This package is optimized for GPU usage to accommodate the high performance demands of these models: +If you prefer a cloud-based solution, Weaviate Cloud Service (WCS) offers a scalable, managed environment. Learn how to set up a cloud cluster and get the API keys by following the [Weaviate Cluster Setup Guide](https://weaviate.io/developers/wcs/guides/create-instance). -``` -pip install goldenverba[huggingface] -``` +**🐳 Docker Deployment** +Another robust local alternative is deploying Weaviate using Docker. For more details, consult the [Weaviate Docker Guide](https://weaviate.io/developers/weaviate/installation/docker-compose). -> Note: It's recommended to run this version on a system with a GPU to fully utilize the capabilities of the advanced models. +## Ollama -## Development Version -If you're a developer looking to contribute to Verba or need the latest features still in development, the dev version is what you're looking for. This version may be less stable but offers the cutting edge of Verba's capabilities: +Verba supports Ollama models. Download and Install Ollama on your device (https://ollama.com/download). Make sure to install your preferred LLM using `ollama run `. -``` -pip install goldenverba[dev] -``` +Tested with `llama3`, `llama3:70b` and `mistral`. The bigger models generally perform better, but need more computational power. -> Keep in mind that this version is intended for development purposes and may contain experimental features. +> Make sure Ollama Server runs in the background and that you don't ingest documents with different ollama models since their vector dimension can vary that will lead to errors -# Quickstart: Deploy with pip +## Google -1. **Initialize a new Python Environment** -``` -python3 -m virtualenv venv -``` +If you want to use the Google Features, make sure to install the Google Verba package. -2. **Install Verba** -``` -pip install goldenverba -``` +```bash +pip install goldenverba[google] -3. **Launch Verba** -``` -verba start -``` +or -4. **Access Verba** -``` -Visit localhost:8000 +pip install `.[google]` ``` -5. **Create .env file and add environment variables** +> If you're using Docker, modify the Dockerfile accordingly -# Quickstart: Build from Source +### Google Embeddings -1. **Clone the Verba repos** -``` -git clone https://github.com/weaviate/Verba.git -``` +For the Google Embeddings, Verba is using Vertex AI Studio inside Google Cloud. You can find instructions for obtaining a key [here](https://cloud.google.com/iam/docs/create-short-lived-credentials-direct). If you have the `gcloud` CLI installed, you can run the following command: `gcloud auth print-access-token`. **At the moment, this access token must be renewed every hour.** -2. **Initialize a new Python Environment** -``` -python3 -m virtualenv venv -``` - -3. **Install Verba** -``` -pip install -e . -``` +You also need to set the `GOOGLE_CLOUD_PROJECT` environment variable to the name of your project. -4. **Launch Verba** -``` -verba start -``` +### Google Gemini -5. **Access Verba** -``` -Visit localhost:8000 -``` +To use Google Gemini, you need a service account key, which is a JSON file. To obtain this, go to "project settings" in your Google Cloud console, then to "service accounts". Create a new service account, then create a new key. Download this key and place it in the route of Verba. Name it `gemini_secrets.json` to have it excluded from git automatically. Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS` to the location of this file, e.g. `gemini_secrets.json`. -6. **Create .env file and add environment variables** +You also need to set the `GOOGLE_CLOUD_PROJECT` environment variable to the name of your project. -# API Keys - -Before diving into Verba's capabilities, you'll need to configure access to various components depending on your chosen technologies, such as OpenAI, Cohere, and HuggingFace. Start by obtaining the necessary API keys and setting them up through a `.env` file based on our provided [example](./goldenverba/.env.example) , or by declaring them as environment variables on your system. If you're building from source or using Docker, make sure your `.env` file is within the goldenverba directory. -Please make sure to only include environment variables you really need. - -Below is a comprehensive list of the API keys and variables you may require: - -## Weaviate -Verba provides flexibility in connecting to Weaviate instances based on your needs. By default, Verba opts for [Weaviate Embedded](https://weaviate.io/developers/weaviate/installation/embedded) if it doesn't detect the `WEAVIATE_URL_VERBA` and `WEAVIATE_API_KEY_VERBA` environment variables. This local deployment is the most straightforward way to launch your Weaviate database for prototyping and testing. - -However, you have other compelling options to consider: - -**🌩️ Weaviate Cloud Service (WCS)** - -If you prefer a cloud-based solution, Weaviate Cloud Service (WCS) offers a scalable, managed environment. Learn how to set up a cloud cluster and get the API keys by following the [Weaviate Cluster Setup Guide](https://weaviate.io/developers/wcs/guides/create-instance). - -**🐳 Docker Deployment** -Another robust local alternative is deploying Weaviate using Docker. For more details, consult the [Weaviate Docker Guide](https://weaviate.io/developers/weaviate/installation/docker-compose). +## Unstructured -``` -WEAVIATE_URL_VERBA=URL-TO-YOUR-WEAVIATE-CLUSTER +Verba supports importing documents through Unstructured IO (e.g plain text, .pdf, .csv, and more). To use them you need the `UNSTRUCTURED_API_KEY` and `UNSTRUCTURED_API_URL` environment variable. You can get it from [Unstructured](https://unstructured.io/) -WEAVIATE_API_KEY_VERBA=API-KEY-OF-YOUR-WEAVIATE-CLUSTER -``` +> UNSTRUCTURED_API_URL is set to `https://api.unstructured.io/general/v0/general` by default ## OpenAI Verba supports OpenAI Models such as Ada, GPT3, and GPT4. To use them, you need to specify the `OPENAI_API_KEY` environment variable. You can get it from [OpenAI](https://openai.com/) -``` -OPENAI_API_KEY=YOUR-OPENAI-KEY -``` - You can also add a `OPENAI_BASE_URL` to use proxies such as LiteLLM (https://github.com/BerriAI/litellm) ``` OPENAI_BASE_URL=YOUR-OPENAI_BASE_URL ``` -## Azure OpenAI +### Azure OpenAI -To use Azure OpenAI, you need to set +To use Azure OpenAI, you need to set - The API type: + ``` OPENAI_API_TYPE="azure" ``` @@ -280,158 +214,198 @@ OPENAI_BASE_URL=http://XXX.openai.azure.com ``` AZURE_OPENAI_RESOURCE_NAME= ``` + - You need to set the models, for the embeddings and for the query. + ``` AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-ada-002" -OPENAI_MODEL="gpt-4" +OPENAI_MODEL="gpt-4" ``` -- Finally, as Azure is using per-minute quota, you might need to add a waiting time between each chunk upload. For example, if you have a limit of 240k tokens per minute, if your chunks are -400 tokens max, then 100ms between queries should be fine. If you get error 429 from weaviate, then increase this value. +- Finally, as Azure is using per-minute quota, you might need to add a waiting time between each chunk upload. For example, if you have a limit of 240k tokens per minute, if your chunks are + 400 tokens max, then 100ms between queries should be fine. If you get error 429 from weaviate, then increase this value. ``` WAIT_TIME_BETWEEN_INGESTION_QUERIES_MS="100" ``` -## Cohere +## HuggingFace -Verba supports Cohere Models, to use them, you need to specify the `COHERE_API_KEY` environment variable. You can get it from [Cohere](https://dashboard.cohere.com/) +If you want to use the HuggingFace Features, make sure to install the correct Verba package. -``` -COHERE_API_KEY=YOUR-COHERE-KEY +```bash +pip install goldenverba[huggingface] + +or + +pip install `.[huggingface]` ``` -## HuggingFace +> If you're using Docker, modify the Dockerfile accordingly + +# How to deploy with pip -Verba supports HuggingFace models, such as SentenceTransformers and Llama2. To use them you need the `HF_TOKEN` environment variable. You can get it from [HuggingFace](https://huggingface.co/) +`Python >=3.10.0` + +1. **Initialize a new Python Environment** ``` -HF_TOKEN=YOUR-HUGGINGFACE-TOKEN +python3 -m virtualenv venv ``` -### Llama2 +2. **Install Verba** + +``` +pip install goldenverba +``` -To use the Llama2 model from Meta, you first need to request access to it. Read more about accessing the [Llama model here](https://huggingface.co/blog/llama2). To enable the LLama2 model for Verba use: +3. **Launch Verba** ``` -LLAMA2-7B-CHAT-HF=True +verba start ``` -## Unstructured -Verba supports importing documents through Unstructured (e.g .pdf). To use them you need the `UNSTRUCTURED_API_KEY` environment variable. You can get it from [Unstructured](https://unstructured.io/) +> You can specify the --port and --host via flags + +4. **Access Verba** ``` -UNSTRUCTURED_API_KEY=YOUR-UNSTRUCTURED-KEY -UNSTRUCTURED_API_URL=YOUR-SELF-HOSTED-INSTANCE # If you are self hosting, in the form of `http://localhost:8000/general/v0/general` +Visit localhost:8000 ``` +5. **Create .env file and add environment variables** + +# How to build from Source +1. **Clone the Verba repos** -## Github +``` +git clone https://github.com/weaviate/Verba.git +``` + +2. **Initialize a new Python Environment** + +``` +python3 -m virtualenv venv +``` -If you want to use the Github Reader, you need the `GITHUB_TOKEN` environment variable. You can get it from [GitHub](https://github.com/) +3. **Install Verba** ``` -GITHUB_TOKEN=YOUR-GITHUB-TOKEN +pip install -e . ``` -## Status Page +4. **Launch Verba** -Once configured, you can monitor your Verba installation's health and status via the 'Status Verba' page. This dashboard provides insights into your deployment type, libraries, environment settings, Weaviate schema counts, and more. It's also your go-to for maintenance tasks like resetting Verba, clearing the cache, or managing auto-complete suggestions. +``` +verba start +``` -![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_status.png) +> You can specify the --port and --host via flags -# Quickstart: Deploy with Docker +5. **Access Verba** -Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their own software, libraries, and configuration files; they can communicate with each other through well-defined channels. All containers are run by a single operating system kernel and are thus more lightweight than virtual machines. Docker provides an additional layer of abstraction and automation of operating-system-level virtualization on Windows and Linux. +``` +Visit localhost:8000 +``` -Docker's use of containers to package software means that the application and its dependencies, libraries, and other binaries are packaged together and can be moved between environments easily. This makes it incredibly useful for developers looking to create predictable environments that are isolated from other applications. +6. **Create .env file and add environment variables** -To get started with deploying Verba using Docker, follow the steps below. If you need more detailed instructions on Docker usage, check out the [Docker Curriculum](https://docker-curriculum.com/). +# How to install Verba with Docker -If you're unfamiliar with Docker, you can learn more about it [here](https://docker-curriculum.com/). +Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. To get started with deploying Verba using Docker, follow the steps below. If you need more detailed instructions on Docker usage, check out the [Docker Curriculum](https://docker-curriculum.com/). 0. **Clone the Verba repos** -Ensure you have Git installed on your system. Then, open a terminal or command prompt and run the following command to clone the Verba repository: + Ensure you have Git installed on your system. Then, open a terminal or command prompt and run the following command to clone the Verba repository: ``` git clone https://github.com/weaviate/Verba.git ``` 1. **Set neccessary environment variables** -Make sure to set your required environment variables in the ```.env``` file. You can read more about how to set them up in the [API Keys Section](#api-keys) + Make sure to set your required environment variables in the `.env` file. You can read more about how to set them up in the [API Keys Section](#api-keys) 2. **Adjust the docker-compose file** -You can use the ```docker-compose.yml``` to add required environment variables under the ```verba``` service and can also adjust the Weaviate Docker settings to enable Authentification or change other settings of your database instance. You can read more about the Weaviate configuration in our [docker-compose documentation](https://weaviate.io/developers/weaviate/installation/docker-compose) + You can use the `docker-compose.yml` to add required environment variables under the `verba` service and can also adjust the Weaviate Docker settings to enable Authentification or change other settings of your database instance. You can read more about the Weaviate configuration in our [docker-compose documentation](https://weaviate.io/developers/weaviate/installation/docker-compose) -> Please make sure to only add environment variables that you really need. If have no authentifcation enabled in your Weaviate Cluster, make sure to not include the ```WEAVIATE_API_KEY_VERBA``` enviroment variable +> Please make sure to only add environment variables that you really need. If you have no authentifcation enabled in your Weaviate Cluster, make sure to not include the `WEAVIATE_API_KEY_VERBA` enviroment variable 2. **Deploy using Docker** -With Docker installed and the Verba repository cloned, navigate to the directory containing the Docker Compose file in your terminal or command prompt. Run the following command to start the Verba application in detached mode, which allows it to run in the background: + With Docker installed and the Verba repository cloned, navigate to the directory containing the Docker Compose file in your terminal or command prompt. Run the following command to start the Verba application in detached mode, which allows it to run in the background: + +```bash -``` docker compose up -d + +``` + +```bash + +docker compose --env-file .env up -d + ``` This command will download the necessary Docker images, create containers, and start Verba. -Remember, Docker must be installed on your system to use this method. For installation instructions and more details about Docker, visit the official Docker documentation. +Remember, Docker must be installed on your system to use this method. For installation instructions and more details about Docker, visit the official Docker documentation. 4. **Access Verba** -- You can access your local Weaviate instance at ```localhost:8080``` - -- You can access the Verba frontend at ```localhost:8000``` +- You can access your local Weaviate instance at `localhost:8080` +- You can access the Verba frontend at `localhost:8000` -If you want your Docker Instance to install a specific version of Verba (as described in the [package section](#choosing-the-right-verba-installation-package)) you can edit the ```Dockerfile``` and change the installation line. +If you want your Docker Instance to install a specific version of Verba you can edit the `Dockerfile` and change the installation line. ``` RUN pip install -e '.' ``` +## Verba Walkthrough -## Importing Your Data into Verba - -With Verba configured, you're ready to import your data and start exploring. Follow these simple steps to get your data into Verba: - -![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_data.gif) +### Overview Page -1. **Initiate the Import Process** - - Click on "Add Documents" to begin. +Once you have access to Verba, you can use the `Overview Page` to validate if all environments and libraries were correctly set and installed. You can use the Admin Console, to see all data stored in the Weaviate Collections and reset certain parts of Verba (e.g. Documents, Cache, Configuration, etc.) -2. **Select Your Data Processing Tools** - - At the top, you'll find three tabs labeled `Reader`, `Chunker`, and `Embedder`, each offering different options for handling your data. +![Demo of Verba](https://github.com/weaviate/Verba/blob/1.0.0/img/verba_status.png) -3. **Choose a Reader** - - The `Reader` is responsible for importing your data. Select from the available options: - - `SimpleReader`: For importing `.txt` and `.md` files. - - `GitHubReader`: For loading data directly from a GitHub repository by specifying the path (`owner/repo/folder_path`). - - `PDFReader`: For importing `.pdf` files. +### Import Your Data -4. **Select a Chunker** - - `Chunkers` break down your data into manageable pieces. Choose a suitable chunker: - - `WordChunker`: Chunks the text by words. - - `SentenceChunker`: Chunks the text by sentences. +With Verba configured, you're ready to import your data and start exploring. Use the `Add Documents` Page to ingest your data. You can choose between Readers that support different data types, chunking techniques, and embedding model. -5. **Pick an Embedder** - - `Embedders` are crucial for integrating your data into Weaviate. Select one based on your preference: - - `AdaEmbedder`: Utilizes OpenAI's ADA model for embedding. - - `MiniLMEmbedder`: Employs Sentence Transformers for embedding. - - `CohereEmbedder`: Uses Cohere for embedding. +![Demo of Verba](https://github.com/weaviate/Verba/blob/1.0.0/img/verba_data.png) -6. **Commence Data Ingestion** - - After setting up your preferences, click on "Import" to ingest your data into Verba. +### Query Your Data -Now your data is ready to be used within Verba, enabling you to leverage its powerful search and retrieval capabilities. +With Data imported, you can use the `Chat` page to ask any related questions. You will receive relevant chunks that are semantically relevant to your question and an answer generated by your choosen model. You can configure the RAG pipeline under the `RAG` page. -## Large Language Model (LLM) Costs - -Verba utilizes LLM models through APIs. Be advised that the usage costs for these models will be billed to the API access key you provide. Primarily, costs are incurred during data embedding and answer generation processes. +![Demo of Verba](https://github.com/weaviate/Verba/blob/1.0.0/img/verba_rag.png) ## Open Source Contribution Your contributions are always welcome! Feel free to contribute ideas, feedback, or create issues and bug reports if you find any! Before contributing, please read the [Contribution Guide](./CONTRIBUTING.md). Visit our [Weaviate Community Forum](https://forum.weaviate.io/) if you need any help! ### Project Architecture -You can learn more about Verba's architecture and implementation in its [technical documentation](./TECHNICAL.md) and [frontend documentation](./FRONTEND.md). It's recommended to read them before making any contributions. +You can learn more about Verba's architecture and implementation in its [technical documentation](./TECHNICAL.md) and [frontend documentation](./FRONTEND.md). It's recommended to have a look at them before making any contributions. + +## Known Issues + +- **Weaviate Embeeded** currently not working on Windows yet + - Will be fixed in future versions, until then please use the Docker or WCS Deployment + +## FAQ + +- **Is Verba Multi-Lingual?** + + - This depends on your choosen Embedding and Generation Model whether they support multi-lingual data. + +- **Can I use my Ollama Server with the Verba Docker?** + + - Yes, you can! Make sure the URL is set to: `OLLAMA_URL=http://host.docker.internal:11434` + +- **How to clear Weaviate Embedded Storage?** + + - Remove the directory `rm ~/.local/share/weaviate` + +- **How can I specify the port?** + - You can use the port and host flag `verba start --port 9000 -host 0.0.0.0` diff --git a/TECHNICAL.md b/TECHNICAL.md index 55a4f5350..00b09ef92 100644 --- a/TECHNICAL.md +++ b/TECHNICAL.md @@ -1,74 +1,119 @@ # Verba: The Golden RAGtriever Technical Documentation -## Introduction -This document provides a comprehensive overview of the technical architecture and implementation details of Verba. Verba is an open-source application engineered to leverage the capabilities of Retrieval-Augmented Generation (RAG) through a user-friendly interface. It is designed to work seamlessly with Weaviate's context-aware database and various Large Language Model (LLM) providers, offering advanced data interaction and query resolution features. Below is a detailed examination of Verba's modular components and their interactions. +This document provides an overview of the general modular architecture of Verba. ![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_architecture.png) ## System Architecture + Verba's system is modularly constructed, comprising five primary components: Reader, Chunkers, Embedder, Retrievers, and Generators. Each component is designed with an interface outlining its methods, input expectations, and output specifications. Component Managers oversee the operations of their respective components, ensuring smooth data flow through the system. At the core of the architecture is the Verba Manager, orchestrating the entire process from data input to answer generation. ### 1. Reader + **Purpose:** The Reader module is responsible for loading various data formats into the Verba system. -**Implementation:** -- Reader Manager: Manages different readers and maintains the current reader in use. - - SimpleReader: Loads text and markdown files. - - GithubReader: Downloads and processes text from GitHub repositories. - - PDFReader: Imports and processes data from PDF files. +**Input: List[FileData]** + +```python + +class FileData(BaseModel): + filename: str + extension: str + content: str + +``` + +**Output: List[Document]** + +```python + +class Document: + def __init__( + self, + text: str = "", + type: str = "", + name: str = "", + path: str = "", + link: str = "", + timestamp: str = "", + reader: str = "", + meta: dict = None, + ): + if meta is None: + meta = {} + self._text = text + self._type = type + self._name = name + self._path = path + self._link = link + self._timestamp = timestamp + self._reader = reader + self._meta = meta + self.chunks: list[Chunk] = [] + +``` ### 2. Chunkers + **Purpose:** Chunkers segment larger documents into smaller, manageable chunks suitable for vectorization and retrieval. -**Implementation:** -- Chunker Manager: Controls the available chunking strategies and selects the appropriate chunker as needed. - - WordChunker: Creates chunk of documents based on words - - SentenceChunker: Creates chunk of documents based on sentences +**Input & Output: List[Document]** + +```python + +class Chunk: + def __init__( + self, + text: str = "", + doc_name: str = "", + doc_type: str = "", + doc_uuid: str = "", + chunk_id: str = "", + ): + self._text = text + self._doc_name = doc_name + self._doc_type = doc_type + self._doc_uuid = doc_uuid + self._chunk_id = chunk_id + self._tokens = 0 + self._vector = None + self._score = 0 + +``` + +> The Document Class contains a list of chunks, the Chunker will append the chunks into that list. It will skip chunking if the document already contains chunks. ### 3. Embedders + **Purpose:** The Embedder takes the chunked data, transforms it into vectorized form, and ingests it into Weaviate. -**Implementation:** -- Embedder Manager: Manages the embedding process and the selection of the current embedding strategy. - - ADAEmbedder: Embeds chunks based on OpenAI's ADA Model - - MiniLMEmbedder: Embeds chunks based on Sentence Transformer - - CohereEmbedder: Embeds chunks based on Cohere's Embedding Model +**Input: List[Document]** ### 4. Retrievers + **Purpose:** Retrievers are tasked with locating the most relevant chunks based on user queries using vector search methodologies. -**Implementation:** -- Retriever Manager: Oversees the retriever components and their retrieval strategies. - - WindowRetriever: Retrieves chunks using hybrid search and adds surrounding context of chunks - - SimpleRetriever: Retrieves chunks using hybrid search only +**Output: List[Chunk]** ### 5. Generators + **Purpose:** Generators synthesize answers by utilizing the retrieved chunks and the context of user queries. -**Implementation:** -- Generator Manager: Manages different generation strategies and maintains the currently selected generator. - - GPT3Generator: Uses OpenAI's GPT3 model to generate responses - - GPT4Generator: Uses OpenAI's GPT4 model to generate responses - - CohereGenerator: Uses Cohere to generate responses - - Llama2Generator: Uses Meta's Llama2 to generate responses +**Input: str** +**Output: str** ## Core Component: Verba Manager + ### Overview + The Verba Manager is the heart of the system, holding all Component Managers and facilitating the data flow from reading to answer generation. ### Interaction with FastAPI + The FastAPI application serves as the interface to the frontend and interacts solely with the Verba Manager. This encapsulation ensures a clean and maintainable codebase where the API endpoints communicate with a single point of reference within the Verba ecosystem. - -## Data Flow Process -1. Data Ingestion: The selected Reader loads the data into the system. -2. Chunking: The chosen Chunker segments the data into smaller parts. -3. Vectorization: The Embedder transforms these chunks into vector representations. -4. Retrieval: Based on a user's query, the Retriever identifies the most relevant data chunks. -5. Answer Generation: The Generator composes an answer leveraging both the retrieved chunks and the contextual understanding of the query. - diff --git a/data/google_hackathon/Amanda_Dietrich.md b/data/google_hackathon/Amanda_Dietrich.md new file mode 100644 index 000000000..d077fa312 --- /dev/null +++ b/data/google_hackathon/Amanda_Dietrich.md @@ -0,0 +1,74 @@ +**Patient Identification Information**: +- **Name**: Amanda Dietrich +- **Date of Birth**: January 15, 2014 +- **Patient ID**: AD1412024 +- **Contact Information**: 555-234-5678 (Mother: Sarah Dietrich) + +**Date and Time of the Visit**: April 24, 2024, 10:00 AM + +**Chief Complaint**: +- The patient, a 10-year-old female, presented with complaints of increasing fatigue and intermittent night sweats persisting over the last two weeks. + +**History of Present Illness**: +- Amanda was initially diagnosed with Acute Lymphoblastic Leukemia (ALL) approximately six months prior to this visit. Despite ongoing chemotherapy, the patient's mother reports a notable intensification in symptoms, raising concerns regarding potential relapse or underlying infection. + +**Past Medical and Surgical History**: +- Known history of ALL diagnosed in October 2023. +- Previous surgical history includes an appendectomy at age five, with no complications. + +**Medications and Allergies**: +- **Medications**: Ongoing chemotherapy regimen including Methotrexate and Vincristine. +- **Allergies**: No known allergies to medications or other substances. + +**Family History**: +- Father was treated for Hodgkin's lymphoma at the age of 30; currently in remission. +- No additional familial predispositions noted. + +**Social History**: +- Resides with both parents and an older sibling. +- Educational arrangements include online schooling due to medical condition. +- No exposure to tobacco, alcohol, or illicit substances. + +**Review of Systems**: +- **General**: Reports generalized weakness and excessive sweating, particularly at night. +- **Respiratory**: Occasional cough; no reports of shortness of breath. +- **Cardiovascular**: No reported palpitations or chest pain. +- **Gastrointestinal**: Appetite and bowel patterns remain normal. +- **Neurological**: No seizures, syncope, or headaches reported. +- **Musculoskeletal**: Generalized fatigue; no specific muscle or joint pains. + +**Physical Examination**: +- **General Appearance**: Patient appears pale and fatigued but is alert and cooperative. +- **Vital Signs**: Temperature is 98.6°F, heart rate at 92 bpm, blood pressure is 100/60 mmHg, respiratory rate is 20 breaths per minute. +- **HEENT**: No lymphadenopathy noted; ocular and oral examinations are within normal limits. +- **Cardiovascular**: Heart sounds are regular without murmurs. +- **Respiratory**: Lungs clear to auscultation bilaterally. +- **Abdominal**: Abdomen is soft, non-distended, and non-tender; no hepatosplenomegaly detected. +- **Extremities**: No cyanosis, clubbing, or edema observed. + +**Assessment and Plan**: +- **Assessment**: The clinical presentation and history raise concerns for potential chemotherapy side effects or infectious complications. Differential diagnoses include chemotherapy toxicity, infection, or leukemia relapse. +- **Diagnostic Plan**: + 1. **Laboratory Tests**: Order a complete blood count, electrolytes, liver function tests, and markers of infection. + 2. **Imaging**: Recommend an immediate chest X-ray to rule out infectious processes. + 3. **Specialist Consultations**: Refer to oncology for potential adjustment of chemotherapy regimen and further evaluation. +- **Therapeutic Plan**: + 1. **Symptomatic Treatment**: Prescribe anti-emetics for nausea and analgesics for general discomfort. + 2. **Nutritional Support**: Consultation with a pediatric dietitian to optimize nutritional intake. +- **Follow-Up**: Arrange for a follow-up visit within one week or sooner if symptoms escalate. + +**Orders and Prescriptions**: +- Routine blood work as listed in the diagnostic plan. +- Refills authorized for ongoing chemotherapy medications. +- New prescriptions issued for symptomatic management of nausea and pain. + +**Side Effects of Treatment**: +- **Chemotherapy-Related**: Notable alopecia, intermittent nausea which is being managed with anti-emetics. Reports of mucositis have prompted a review and adjustment of oral care protocols. +- **Adverse Drug Reactions**: Methotrexate has caused mild liver enzyme elevations; monitoring continues. Vincristine has been associated with peripheral neuropathy; neurological evaluations are scheduled bi-monthly. + +**Progress and Follow-Up Notes**: +- **Current Status**: Amanda has shown a partial response to the initial rounds of chemotherapy but exhibits significant treatment-related side effects which are impacting her quality of life. +- **Treatment Adjustments**: Oncology consultation resulted in a reduced dose of Methotrexate to mitigate liver toxicity. Supportive treatments have been enhanced to manage + + side effects more effectively. +- **Next Steps**: Scheduled for a follow-up consultation post-lab results to assess the need for further diagnostic investigations or treatment modifications. diff --git a/data/google_hackathon/Charles_Pegler.md b/data/google_hackathon/Charles_Pegler.md new file mode 100644 index 000000000..128115acd --- /dev/null +++ b/data/google_hackathon/Charles_Pegler.md @@ -0,0 +1,69 @@ +**Patient Identification Information**: +- **Name**: Charles Pegler +- **Date of Birth**: April 16, 2016 +- **Patient ID**: CP16042016 +- **Contact Information**: 555-789-0123 (Mother: Emily Pegler) + +**Date and Time of the Visit**: April 24, 2024, 3:30 PM + +**Chief Complaint**: +- An 8-year-old male presents with persistent lower back pain and recent episodes of intermittent leg numbness. His mother expresses concern about increasing fatigue and a decrease in appetite over the last two weeks. + +**History of Present Illness**: +- Charles initially complained of mild lower back pain, which has progressively worsened over the past month. Recent development of numbness in his legs, along with unexplained weight loss and general lethargy, prompted further investigation. Initial blood tests and imaging led to a suspicion of a malignancy. + +**Past Medical and Surgical History**: +- Generally healthy prior to this episode, with routine childhood immunizations up to date. +- No previous surgeries or significant medical interventions. + +**Medications and Allergies**: +- **Medications**: None prior to diagnosis. +- **Allergies**: No known allergies. + +**Family History**: +- Mother has a history of Type 1 diabetes. +- Paternal grandfather was diagnosed with prostate cancer at age 65. +- No other significant genetic or familial diseases noted. + +**Social History**: +- Lives with parents and one younger sibling in a suburban setting. +- Attends second grade; however, recent absenteeism due to illness. +- Engages in recreational activities such as soccer and swimming, though participation has decreased due to current health. + +**Review of Systems**: +- **General**: Notable for fatigue, weight loss, and reduced appetite. +- **Neurologic**: Reports numbness in legs, no seizures or syncope. +- **Musculoskeletal**: Persistent back pain, more pronounced with physical activity. + +**Physical Examination**: +- **General Appearance**: Pale, appears fatigued, less active than expected for age. +- **Vital Signs**: Temperature 98.6°F, heart rate 98 bpm, blood pressure 102/66 mmHg, respiratory rate 20 breaths per minute. +- **Neurologic**: Alert, oriented to age-appropriate levels, diminished reflexes in lower extremities. +- **Musculoskeletal**: Tenderness and discomfort noted in the lumbar spine area, restricted range of motion due to pain. +- **Extremities**: No cyanosis, clubbing, or edema. Decreased sensation to light touch in both lower extremities. + +**Assessment and Plan**: +- **Assessment**: Recent diagnostic tests, including MRI and bone scans, indicate the presence of a malignant spinal tumor, suspected to be neuroblastoma, based on its location and radiographic characteristics. +- **Diagnostic Plan**: + 1. **Biopsy**: Schedule a guided biopsy of the spinal lesion to confirm diagnosis. + 2. **Additional Imaging**: Order full-body PET scan to check for metastases. +- **Therapeutic Plan**: + 1. **Medical Management**: Start corticosteroids to reduce spinal cord inflammation and manage pain. + 2. **Oncological Referral**: Immediate referral to pediatric oncology for evaluation and chemotherapy planning. + 3. **Support Services**: Initiate discussions with a pediatric social worker for family support and counseling. +- **Follow-Up**: Arrange for a follow-up appointment immediately post-biopsy and upon receipt of further imaging results. + +**Orders and Prescriptions**: +- MRI-guided biopsy of the spinal tumor. +- Full-body PET scan. +- Prednisone 20 mg orally once daily for inflammation and pain. + +**Side Effects of Treatment**: +- **Corticosteroids**: Charles has experienced increased irritability and some difficulty sleeping since starting prednisone. There's also been a noticeable increase in appetite. +- **Adverse Drug Reactions**: Monitoring for potential long-term effects of corticosteroid use, including impacts on growth and immune function. + +**Progress and Follow-Up Notes**: +- **Current Status**: Charles has tolerated the initial phase of treatment, but challenges with the side effects of steroids are impacting his daily life. +- **Treatment Adjustments**: Considering a potential tapering of steroids or switch to another anti-inflammatory if side effects persist. +- **Next Steps**: Await results from the biopsy and PET scan to tailor the next phase of chemotherapy. Ensure supportive care measures are in place to manage symptoms and improve quality of life. +- **Support Measures**: Increased engagement with pediatric pain management and psychological support services to help Charles and his family cope with the diagnosis and treatment. \ No newline at end of file diff --git a/data/google_hackathon/Emiliy_Torres.md b/data/google_hackathon/Emiliy_Torres.md new file mode 100644 index 000000000..bee8a11fd --- /dev/null +++ b/data/google_hackathon/Emiliy_Torres.md @@ -0,0 +1,68 @@ +**Patient Identification Information**: +- **Name**: Emily Torres +- **Date of Birth**: August 9, 2015 +- **Patient ID**: ET09082015 +- **Contact Information**: 555-123-4567 (Father: Michael Torres) + +**Date and Time of the Visit**: April 24, 2024, 2:00 PM + +**Chief Complaint**: +- A 9-year-old female presents with a 2-month history of progressive right thigh pain and recent onset of night sweats. + +**History of Present Illness**: +- Emily initially experienced mild, intermittent pain in her right thigh, which has progressively worsened and is now constant. The pain increases with physical activity and at night. Over the last three weeks, she has also developed night sweats and intermittent fever, prompting further evaluation. + +**Past Medical and Surgical History**: +- No previous significant illnesses or surgeries. Routine vaccinations are up to date. + +**Medications and Allergies**: +- **Medications**: Acetaminophen as needed for pain. +- **Allergies**: No known drug allergies. + +**Family History**: +- Maternal aunt diagnosed with breast cancer at age 45. +- No other known family history of cancer or genetic disorders. + +**Social History**: +- Lives with both parents and one older brother. +- Attends fourth grade at a local school; however, attendance has decreased due to symptoms. +- Active in gymnastics until recently when pain limited participation. + +**Review of Systems**: +- **General**: Fatigue, fever, and weight loss. +- **Musculoskeletal**: Persistent and localized pain in the right thigh. +- **Hematologic**: Bruising noted on legs with no known trauma. +- **Other Systems**: No complaints related to respiratory, cardiovascular, or gastrointestinal systems. + +**Physical Examination**: +- **General Appearance**: Appears pale and lethargic, less playful than expected for age. +- **Vital Signs**: Temperature 100.4°F, heart rate 110 bpm, blood pressure 100/60 mmHg, respiratory rate 22 breaths per minute. +- **Musculoskeletal**: Swelling and tenderness localized to the right thigh; restricted range of motion in the right hip joint. +- **Skin**: Notable bruising on lower extremities. +- **Lymph Nodes**: No palpable lymphadenopathy. + +**Assessment and Plan**: +- **Assessment**: Symptoms and physical findings suggest a possible bone tumor, with differential diagnosis including osteosarcoma. +- **Diagnostic Plan**: + 1. **Imaging**: Immediate MRI of the right thigh to assess the extent of the lesion. + 2. **Biopsy**: Schedule a biopsy to confirm the diagnosis. + 3. **Laboratory Tests**: Complete blood count, ESR, CRP to evaluate for infection or other inflammatory processes. +- **Therapeutic Plan**: + 1. **Pain Management**: Prescribe ibuprofen for pain relief, to be taken with food. + 2. **Referral**: Refer to pediatric oncology for evaluation and treatment planning post-diagnosis. + 3. **Supportive Care**: Initiate referral to a child psychologist to support psychological well-being. +- **Follow-Up**: Arrange for a follow-up appointment immediately after imaging and biopsy results are available. + +**Orders and Prescriptions**: +- MRI of the right thigh. +- Biopsy of the thigh lesion. +- Ibuprofen 200 mg, take 1 tablet orally every 6-8 hours as needed for pain. + +**Side Effects of Treatment**: +- **Ibuprofen**: Monitor for gastrointestinal discomfort or signs of bleeding. Advise parents on signs of allergic reaction. + +**Progress and Follow-Up Notes**: +- **Current Status**: Emily's symptoms have been managed with analgesics, but she continues to experience significant pain affecting her daily activities. +- **Treatment Adjustments**: Consideration for stronger analgesics or adjunct therapies such as physical therapy. +- **Next Steps**: Review biopsy and MRI results to confirm the diagnosis and stage the disease. Discuss the potential treatment options including chemotherapy, surgery, and radiation based on oncological assessment. +- **Support Measures**: Engage with educational liaison to manage school absences and maintain educational progress. \ No newline at end of file diff --git a/data/google_hackathon/The Influence of Detoxification Agents on the Intensity of Side Effects Caused by Medium-high Doses of Methotrexate in Children with Acute Lymphoblastic Leukemia: Case Series.pdf b/data/google_hackathon/The Influence of Detoxification Agents on the Intensity of Side Effects Caused by Medium-high Doses of Methotrexate in Children with Acute Lymphoblastic Leukemia: Case Series.pdf new file mode 100644 index 000000000..785088239 Binary files /dev/null and b/data/google_hackathon/The Influence of Detoxification Agents on the Intensity of Side Effects Caused by Medium-high Doses of Methotrexate in Children with Acute Lymphoblastic Leukemia: Case Series.pdf differ diff --git a/data/openai/gpt4-o.txt b/data/openai/gpt4-o.txt new file mode 100644 index 000000000..e47e33718 --- /dev/null +++ b/data/openai/gpt4-o.txt @@ -0,0 +1,51 @@ +Introducing GPT-4o +GPT-4o is our newest flagship model that provides GPT-4-level intelligence but is much faster and improves on its capabilities across text, voice, and vision. + +Today, GPT-4o is much better than any existing model at understanding and discussing the images you share. For example, you can now take a picture of a menu in a different language and talk to GPT-4o to translate it, learn about the food's history and significance, and get recommendations. In the future, improvements will allow for more natural, real-time voice conversation and the ability to converse with ChatGPT via real-time video. For example, you could show ChatGPT a live sports game and ask it to explain the rules to you. We plan to launch a new Voice Mode with these new capabilities in an alpha in the coming weeks, with early access for Plus users as we roll out more broadly. + +To make advanced AI more accessible and useful worldwide, GPT-4o's language capabilities are improved across quality and speed. ChatGPT also now supports more than 50 languages(opens in a new window) across sign-up and login, user settings, and more. + +We are beginning to roll out GPT-4o to ChatGPT Plus and Team users, with availability for Enterprise users coming soon. We are also starting to roll out to ChatGPT Free with usage limits today. Plus users will have a message limit that is up to 5x greater than free users, and Team and Enterprise users will have even higher limits. + +Bringing more intelligence and advanced tools for free +Our mission includes making advanced AI tools available to as many people as possible. Every week, more than a hundred million people use ChatGPT. We are starting to roll out more intelligence and advanced tools to ChatGPT Free users over the coming weeks. + +When using GPT-4o, ChatGPT Free users will now have access to features such as: + +Experience GPT-4 level intelligence + +Get responses(opens in a new window) from both the model and the web + +Analyze data(opens in a new window) and create charts + +Chat about photos you take + +Upload files(opens in a new window) for assistance summarizing, writing or analyzing + +Discover and use GPTs and the GPT Store + +Build a more helpful experience with Memory + + + + + + + + + +There will be a limit on the number of messages that free users can send with GPT-4o depending on usage and demand. When the limit is reached, ChatGPT will automatically switch to GPT-3.5 so users can continue their conversations. + + + +Streamlining your workflow in the new desktop app +For both free and paid users, we're also launching a new ChatGPT desktop app for macOS that is designed to integrate seamlessly into anything you’re doing on your computer. With a simple keyboard shortcut (Option + Space), you can instantly ask ChatGPT a question. You can also take and discuss screenshots directly in the app. + + +You can now have voice conversations with ChatGPT directly from your computer, starting with Voice Mode that has been available in ChatGPT at launch, with GPT-4o’s new audio and video capabilities coming in the future. Whether you want to brainstorm a new idea for your company, prepare for an interview or have a topic you’d like to discuss, tap the headphone icon in the bottom right corner of the desktop app to start a voice conversation. + +mac-desktop-wave +We're rolling out the macOS app to Plus users starting today, and we will make it more broadly available in the coming weeks. We also plan to launch a Windows version later this year. + +A simplified look and feel for ChatGPT +We’re introducing a new look and feel for ChatGPT that's designed to be friendlier and more conversational. You’ll notice a new home screen, message layout and more. \ No newline at end of file diff --git a/data/test/file_example_XLSX_50.xlsx b/data/test/file_example_XLSX_50.xlsx new file mode 100644 index 000000000..ca420719d Binary files /dev/null and b/data/test/file_example_XLSX_50.xlsx differ diff --git a/data/test/patient_notes.txt b/data/test/patient_notes.txt new file mode 100644 index 000000000..b0ceb795a --- /dev/null +++ b/data/test/patient_notes.txt @@ -0,0 +1,33 @@ +An undiagnosed case of acute myeloid leukemia +A 43-year-old female patient reported to the Department of Periodontology, with the chief complaint of generalized bleeding gums, and gingival swelling from last 2 months. Patient gave no history of fever, malaise, weight loss in the recent past. Patient was not under any medication for chronic illness. Extra oral examination reveals bilateral submandibular and deep cervical lymphadenopathy; lymph nodes were indurated and tender on palpation. Systemic examination reveals mild hepatomegaly and splenomegaly.\nIntraoral examination revealed generalized enlargement of the maxillary and mandibular gingiva involving the buccal, lingual, and palatal aspects [Figure –]. The gingiva was swollen, inflamed, erythematous, and devoid of stippling. On palpation gingiva was firm and edematous, tender and bleeding on slight provocation. There was a fair amount of plaque and calculus, but did not justify the amount of enlargement. Orthopantomogram did not reveal any significant findings.\nThe following differential diagnoses were considered: Inflammatory enlargement, conditioned enlargement, systemic enlargement, and neoplastic enlargement.\nComplete blood count [] revealed marked increase in white blood cells, decrease in red blood cells with decrease in hemoglobin levels, and low platelets count; indicative of leukocytosis, anemia, and thrombocytopenia.\nPeripheral smear depicted numerous blast cells, mainly immature leukocytes with few erythrocytes and thrombocytes [].\nThese results confirmed the final diagnosis of acute myeloid leukemia. Oral hygiene instruction was given to the patient. Patient was instructed to use soft bristle toothbrush, as gingiva was inflamed, friable and edematous use of hard or regular brush is not recommended. Thrice a day Chlorhexidine (0.2%) was prescribed as oral rinse.[] Scaling and root planning was postponed since the treatment needs a minimum platelet count of 60 000 in this condition. Patient was immediately referred to the oncology centre for further management, where bone marrow biopsy confirms acute myeloid leukemia M5 and chemotherapy started. Patient did not respond well to chemotherapy and succumbed to the disease within 10 days of admission. + +Oral manifestations of acute myelomonocytic leukemia: a case report and review of the classification of leukemias +Abstract +Background: Oral signs and symptoms may indicate a serious underlying systemic disease. The most frequently observed oral findings of leukemia are mucosal bleeding and ulceration, petechiae, and gingival hyperplasia. This case report describes a 53-year-old male who presented with gingival enlargement and bleeding, fatigue, and recent weight loss as initial manifestations of acute myelomonocytic leukemia. + +Methods: A gingival biopsy was performed, revealing the presence of a hypercellular infiltrate of atypical myeloid and monocytic cells. Further work-up consisted of a complete blood count, bone marrow biopsy, and immunohistochemical and histochemical analysis of biopsy material and flow cytometry of peripheral blood. + +Results: Flow cytometry results confirmed that the infiltrate was of a myelomonocytic origin, and a diagnosis of acute myelomonocytic leukemia was rendered. The patient responded well to a chemotherapeutic induction regimen of cytosine arabinoside and idarubicin hydrochloride, with regression of gingival enlargement and remission of disease. The patient continued with consolidation chemotherapy and an autologous bone marrow transplant, but eventually died 22 months after initial diagnosis. + +Conclusions: Oral health care professionals, especially periodontists, must recognize that gingival enlargement may represent an initial manifestation of an underlying systemic disease. Acute myelogenous leukemia is a hematological disorder with a predilection for gingival involvement. + +Oral pathoses as diagnostic indicators in leukemia +A retrospective chart review of 500 leukemia patients was carried out to analyze the role of oral pathoses in the presentation of leukemia and to determine those factors which affect the frequency of oral involvement during the initial clinical phases of the disease. The data indicate that oral pathoses were frequent signs or symptoms in patients with undiagnosed acute leukemia but were less prevalent in patients with undiagnosed chronic leukemia. Neither age nor sex appeared to be a significant factor affecting oral involvement. Oral signs of thrombocytopenia were the most prevalent complaint of patients seeking diagnosis for their leukemia because of an oral problem, and they were also most frequently responsible for oral problems found at initial physical examination. Head and neck lymphadenopathy was also a frequent presenting sign or symptom. Dentists were responsible for initiating the diagnosis of leukemia in a significant number of patients with acute nonlymphoblastic leukemia. + +The clinicopathologic and histopathologic features of leukemic gingival and cutaneous "infiltrates" were studied in 1,076 adults hospitalized for cancer chemotherapy. Leukemic gingival hyperplasia was present in 3.6 percent and leukemia cutis in 3.1 percent of the patients. Only 7.6 percent of those with leukemic "infiltrates" had simultaneous gingival and skin involvement. Histologically, the lesions consisted of extravasated and proliferating monocytoid or myeloid cells. The infiltrative-proliferative tendency was most pronounced in patients with, in a sharply descending order, acute monocytic leukemia, acute myelomonocytic leukemia, and acute myelocytic leukemia. Formation of lesions in the skin and gingiva was unrelated to sex, age, and white blood cell count. Gingival lesions were particularly prone to infectious complications in patients with poor oral hygiene. Aside from the morphologic predisposition, the propensity for adults with leukemia to develop chemotherapeutically reversible malignant mucocutaneous lesions was highly individualistic and unpredictable. + +Acute myeloid leukemias (AMLs) are infrequent, yet highly malignant neoplasms responsible for a large number of cancer-related deaths. The incidence has been near stable over the last years. It continuously shows 2 peaks in occurrence in early childhood and later adulthood. With an incidence of 3.7 per 100,000 persons and an age-dependent mortality of 2.7 to nearly 18 per 100,000 persons, there is a rising awareness in the Western world of AML's special attributes resulting from an ever-aging population. To objectively describe epidemiologic data on this patient population, recent publications were evaluated to make transparent the current trends and facts. A review of the literature is presented, reflecting highlights of current research with respect to AML etiology. To estimate outcome and discuss informed treatment decisions with AML patients of different age groups and different biologic risk categories, it is mandatory to consider that the outcome results reported in clinical trials were until now heavily biased toward younger patients, whereas the overall dismal prognosis documented in population-based studies most likely reflects the exclusion of older patients from aggressive treatment. The etiology for most cases of AML is unclear, but a growing knowledge concerning leukemogenenic agents within chemotherapy regimens for other malignancies is already available. This includes specific associations of the most frequent balanced translocations in AML, including the "good-risk" abnormalities comprised by the core binding factor leukemias (i.e., AML with the translocation (8;21) and inversion of chromosome 16, and acute promyelocytic leukemia with the translocation (15;17)). In contrast to these genetic alterations, epigenetic lesions, e.g., promoter silencing by hypermethylation of the p15/INK4b and other genes, are increasingly recognized as important in the pathogenesis of AML. + +Background: The aim of this cross-sectional study was to evaluate the oral health of adult patients with newly diagnosed acute leukemia. + +Methods: Patients with initially diagnosed acute myeloid (AML) or lymphocytic (ALL) leukemia and a matched healthy control (HC) group were included. The oral investigation comprised inspection of the oral mucosa; the decayed (D), missing (M), and filled (F) teeth (DMF-T) index; and a detailed periodontal status. Subgingival biofilm samples were analyzed (polymerase chain reaction) for the presence of selected potentially periodontal pathogenic bacteria. Statistical analysis was performed using Fisher's exact test, chi-squared test, and Mann-Whitney U test (significance level α = 5%). + +Results: Thirty-nine patients with leukemia (AML 26, ALL 13) and 38 HCs were included. Oral mucosal findings were present in 62% of L compared to 0% of HC patients, whereby gingival hyperplasia was the most detected finding. Furthermore, a higher caries prevalence in leukemia patients was shown (D value 3.64 ± 3.98 vs. 0.72 ± 1.72, p < 0.01). The periodontal parameters were poorer in leukemia patients. No substantial differences in microbiological findings of selected bacteria were detected within L group and between L and HC patients. + +Conclusion: The high prevalence of oral diseases supports the demand of an early and consequent dental treatment of leukemia patients, especially considering subsequent therapy. + +Keywords: Acute lymphocytic leukemia; Acute myeloid leukemia; Caries; Leukemia; Periodontal bacteria; Periodontitis. + +Leukaemia is a neoplastic disease characterized by an excessive proliferation of immature white blood cells and their precursors. Oral lesions may be the presenting feature of acute leukaemias, which can be rapidly fatal if left untreated. This report describes the case of a 22-year-old female who presented to a dental emergency department with oral manifestations of acute leukaemia. It outlines the role of oral lesions in the diagnosis of acute leukaemias and emphasizes the importance of immediate referral by general dental practitioners. + +Leukemia is a neoplastic disease characterized by an excessive proliferation of immature white blood cells and their precursors. Oral lesions may be the presenting feature of acute leukemia, which can be rapidly fatal if left untreated. Although many cases of gingival enlargement in patients with acute myeloid leukemia's have been reported in the literature, cases diagnosed by the oral manifestations in India are very few. This report describes the case of a 43-year-old female who presented with gingival bleeding and gingival enlargement. Within a month she developed signs and symptoms of systemic disease such as, and splenomegaly, and upon further investigation, she was diagnosed with acute myeloid leukemia to which she succumbed within 10 days after diagnosis. The need for early diagnosis and referral of this fatal disease are also underline. \ No newline at end of file diff --git a/data/test/test_json.json b/data/test/test_json.json new file mode 100644 index 000000000..6280256e0 --- /dev/null +++ b/data/test/test_json.json @@ -0,0 +1,11 @@ +{ + "text": "\n\n -->\n\nimport PreviewUnit from '../../_snippets/preview.mdx'\n\n\n\n##   Overview\n\nNow that you've learned about what chunking is, and why it is important, you are ready to start looking at practical chunking techniques. Here, we start by looking at **fixed-size** chunking techniques, including some example implementations.\n\n##   Fixed-size chunking\n\nAs the name suggests, fixed-size chunking refers to the process of splitting texts into chunks of a fixed size, or at least based on size. Using fixed size chunking, you might split an article into a set of chunks of 100 words each, or a set of 200 characters each.\n\nThis may be the most common chunking technique due to its simplicity and effectiveness.\n\n###   Implementations\n\nFixed-size chunking is implemented by splitting texts into chunks of a fixed number of units. The units may be composed of words, characters, or even *tokens*, and the number of units per chunk is fixed (to a maximum), with an optional overlap.\n\nA \"token\" in this context is a unit of text that will be processed by a model by being substituted with a number. In modern tranformer models, a token is commonly a \"subword\" unit composed of a few characters.\n\nOne pseudocode implementation of fixed-size chunking is:\n\n```python\n# Given a text of length L\n# Split the text into chunks of size N units (e.g. tokens, characters, words)\n# Optionally, add an overlap of M units at the beginning or end of each chunk (from the previous or next chunk)\n# This should typically result in a list of chunks of length L // N + 1\n```\n\nAnd implementing in Python, it may look like:\n\n\n\n\n\n\n\nWhich can be modified to include an overlap (in this case, at the beginning of each chunk):\n\n\n\n\n\n\n\nThis is far from the only way to implement fixed-size chunking, but it is one possible, relatively simple, implementation.\n\nConsider how *you* might implement fixed-size chunking. What would your pseudocode (or code) look like?\n\n###   Examples\n\nWe are ready to look at some concrete examples of fixed-size chunking. Let's take a look at three examples, with a chunk size of 5 words, 25 words and 100 words, respectively.\n\nWe'll use an excerpt from the Pro Git book*. More specifically, we'll use text of the What is Git? chapter.\n\nHere is one example using our chunking function from above:\n\n\n\n\n\n\n\nThis will result in outputs like these. Take a look at the first few chunks at each size - what do you notice?\n\nConsider which of these chunk sizes would be most appropriate for search. Why do you think so? What are the tradeoffs?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHopefully, these concrete examples start to illustrate some of the ideas that we discussed above.\n\nImmediately, it strikes me that the smaller chunks are very granular, to the point where they may not contain enough information to be useful for search. On the other hand, the larger chunks begin to retain more information as they get to lengths that are similar to a typical paragraph.\n\nNow imagine these chunks becoming even longer. As chunks become longer, the corresponding vector embeddings would start to become more general. This would eventually reach a point where they cease to be useful in terms of searching for information.\n\nAt these sizes, you typically will not need to employ character-based or sub-word token-based chunking, as splitting words at these boundaries in a group of words will not typically be meaningful.\n\nFor search with fixed-size chunks, if you don't have any other factors, try a size of around 100-200 words, and a 20% overlap.\n\n##   Notes\n\n\n*Available through the Creative Commons Attribution-Non Commercial-Share Alike 3.0 license.\n\n\n  Review\n\n\n\nAny quiz questions\n\n###   Review exercise\n\nTry out ...\n\n###   Key takeaways\n\nAdd summary\n\n\n\n\n", + "type": "Documentation", + "name": "chunking-how_1", + "path": "developers/academy/standalone/chunking/20_how_1.mdx", + "link": "https://weaviate.io/developers/academy/standalone/chunking/how_1", + "timestamp": "2024-02-08 21:19:39", + "reader": "JSON", + "meta": {}, + "chunks": [] +} \ No newline at end of file diff --git a/data/test/text_md.md b/data/test/text_md.md new file mode 100644 index 000000000..9d08074cd --- /dev/null +++ b/data/test/text_md.md @@ -0,0 +1,437 @@ +# Verba +## 🐕 The Golden RAGtriever + +Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally or through LLM providers such as OpenAI, Cohere, and HuggingFace. + +``` +pip install goldenverba +``` + +[![Weaviate](https://img.shields.io/static/v1?label=powered%20by&message=Weaviate%20%E2%9D%A4&color=green&style=flat-square)](https://weaviate.io/) +[![PyPi downloads](https://static.pepy.tech/personalized-badge/goldenverba?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/goldenverba/) [![Docker support](https://img.shields.io/badge/Docker_support-%E2%9C%93-4c1?style=flat-square&logo=docker&logoColor=white)](https://docs.docker.com/get-started/) [![Demo](https://img.shields.io/badge/Check%20out%20the%20demo!-yellow?&style=flat-square&logo=react&logoColor=white)](https://verba.weaviate.io/) + +![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba.gif) + +- [Verba](#verba) + - [🎯 What Is Verba?](#what-is-verba) + - [⚙️ Under the Hood](#️under-the-hood) + - [💡 Effortless Data Import with Weaviate](#effortless-data-import-with-weaviate) + - [💥 Advanced Query Resolution with Hybrid Search](#advanced-query-resolution-with-hybrid-search) + - [🔥 Accelerate Queries with Semantic Cache](#accelerate-queries-with-semantic-cache) +- [✨ Getting Started with Verba](#getting-started-with-verba) +- [🐍 Installing Python and Setting Up a Virtual Environment](#installing-python-and-setting-up-a-virtual-environment) + - [Installing Python](#installing-python) + - [Setting Up a Virtual Environment](#setting-up-a-virtual-environment) +- [📦 Choosing the Right Verba Installation Package](#choosing-the-right-verba-installation-package) + - [Default Package](#default-package) + - [HuggingFace Version](#huggingface-version) + - [Development Version](#development-version) +- [🚀 Quickstart: Deploy with pip](#quickstart-deploy-with-pip) +- [🛠️ Quickstart: Build from Source](#️quickstart-build-from-source) +- [🔑 API Keys](#api-keys) + - [Weaviate](#weaviate) + - [OpenAI](#openai) + - [Cohere](#cohere) + - [HuggingFace](#huggingface) + - [Llama2](#llama2) + - [Unstructured](#unstructured) + - [Github](#github) +- [🐳 Quickstart: Deploy with Docker](#quickstart-deploy-with-docker) + - [Large Language Model (LLM) Costs](#large-language-model-llm-costs) +- [💾 Importing Your Data into Verba](#️importing-your-data-into-verba) +- [🛠️ Project Architecture](#️project-architecture) +- [💖 Open Source Contribution](#open-source-contribution) + +## What Is Verba? +Verba is more than just a tool—it's a personal assistant for querying and interacting with your data, **either locally or deployed via cloud**. Have questions about your documents? Need to cross-reference multiple data points? Want to gain insights from your existing knowledge base? Verba empowers you with the combined capabilities of Weaviate's context-aware database and the analytical power of Large Language Models (LLMs). Interact with your data through an intuitive chat interface that refines search results by using the ongoing conversation context to deliver even more accurate and relevant information. + +![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_screen.png) + +### Under the Hood +Verba is engineered with Weaviate's cutting-edge Generative Search technology at its core, extracting relevant context from your pool of documents to resolve queries with precision. By utilizing the power of Large Language Models, Verba doesn't just search for answers—it understands and provides responses that are contextually rich and informed by the content of your documents, all through an intuitive user interface designed for simplicity and efficiency. + +### Effortless Data Import with Weaviate +Verba offers seamless data import functionality through its frontend, supporting a diverse range of file types including `.txt`, `.md`, `.pdf` and more. Before feeding your data into Weaviate, Verba handles chunking and vectorization to optimize it for search and retrieval. Together with collaborative partners we support popular libraries such as [HuggingFace](https://github.com/huggingface), [Haystack](https://github.com/deepset-ai/haystack), [Unstructured](https://github.com/Unstructured-IO/unstructured) and many more! + +![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_import.png) + +### Advanced Query Resolution with Hybrid Search +Experience the hybrid search capabilities of Weaviate within Verba, which merges vector and lexical search methodologies for even greater precision. This dual approach not only navigates through your documents to pinpoint exact matches but also understands the nuance of context, enabling the Large Language Models to craft responses that are both comprehensive and contextually aware. It's an advanced technique that redefines document retrieval, providing you with precisely what you need, when you need it. + +### Accelerate Queries with Semantic Cache +Verba enhances search efficiency with Weaviate's Semantic Cache, a sophisticated system that retains the essence of your queries, results, and dialogues. This proactive feature means that Verba anticipates your needs, using cached data to expedite future inquiries. With semantic matching, it quickly determines if your question has been asked before, delivering instant results, and even suggests auto-completions based on historical interactions, streamlining your search experience to be faster and more intuitive. + +--- + +# Getting Started with Verba + +Starting your Verba journey is super easy, with multiple deployment options tailored to your preferences. Follow these simple steps to get Verba up and running: + +- Deploy with pip [(Quickstart)](##🚀-Quickstart:-Deploy-with-pip) +``` +pip install goldenverba +``` +- Build from Source [(Quickstart)](##🛠️-Quickstart:-Build-from-Source) +``` +git clone https://github.com/weaviate/Verba + +pip install -e . +``` +- Use Docker for Deployment [(Quickstart)](##🐳-Quickstart:-Deploy-with-Docker) + +**Prerequisites**: If you're not using Docker, ensure that you have `Python >=3.10.0` installed on your system. + +# Installing Python and Setting Up a Virtual Environment +Before you can use Verba, you'll need to ensure that `Python >=3.10.0` is installed on your system and that you can create a virtual environment for a safer and cleaner project setup. + +## Installing Python +Python is required to run Verba. If you don't have Python installed, follow these steps: + +### For Windows: +Download the latest Python installer from the official Python website. +Run the installer and make sure to check the box that says `Add Python to PATH` during installation. + +### For macOS: +You can install Python using Homebrew, a package manager for macOS, with the following command in the terminal: +``` +/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" +``` + +Then install Python: +``` +brew install python +``` + +### For Linux: +Python usually comes pre-installed on most Linux distributions. If it's not, you can install it using your distribution's package manager. You can read more about it [here](https://opensource.com/article/20/4/install-python-linux) + +## Setting Up a Virtual Environment +It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide Python packages. + +### Install the virtualenv package: +First, ensure you have pip installed (it comes with Python if you're using version 3.4 and above). +Install virtualenv by running: +``` +pip install virtualenv +``` + +### Create a Virtual Environment: +Navigate to your project's directory in the terminal. +Run the following command to create a virtual environment named venv (you can name it anything you like): +``` +python3 -m virtualenv venv +``` + +### Activate the Virtual Environment: +- On Windows, activate the virtual environment by running: +``` +venv\Scripts\activate.bat +``` + +- On macOS and Linux, activate it with: +``` +source venv/bin/activate +``` + +Once your virtual environment is activated, you'll see its name in the terminal prompt. Now you're ready to install Verba using the steps provided in the Quickstart sections. + +> Remember to deactivate the virtual environment when you're done working with Verba by simply running deactivate in the terminal. + +# Choosing the Right Verba Installation Package +Verba comes in several installation packages, each tailored for specific use cases and environments. Choose the package that aligns with your requirements: + +## Default Package +The default package is perfect for getting started quickly and includes support for popular models and services like OpenAI, Cohere, and spaCy. This package is suitable for general use and can be installed easily via pip: + +``` +pip install goldenverba +``` + +> This will set you up with all you need to integrate Verba with these services without additional configuration. + +## HuggingFace Version +For those looking to leverage models from the HuggingFace ecosystem, including `SentenceTransformer` and `LLama2`, the HuggingFace version is the ideal choice. This package is optimized for GPU usage to accommodate the high performance demands of these models: + +``` +pip install goldenverba[huggingface] +``` + +> Note: It's recommended to run this version on a system with a GPU to fully utilize the capabilities of the advanced models. + +## Development Version +If you're a developer looking to contribute to Verba or need the latest features still in development, the dev version is what you're looking for. This version may be less stable but offers the cutting edge of Verba's capabilities: + +``` +pip install goldenverba[dev] +``` + +> Keep in mind that this version is intended for development purposes and may contain experimental features. + +# Quickstart: Deploy with pip + +1. **Initialize a new Python Environment** +``` +python3 -m virtualenv venv +``` + +2. **Install Verba** +``` +pip install goldenverba +``` + +3. **Launch Verba** +``` +verba start +``` + +4. **Access Verba** +``` +Visit localhost:8000 +``` + +5. **Create .env file and add environment variables** + +# Quickstart: Build from Source + +1. **Clone the Verba repos** +``` +git clone https://github.com/weaviate/Verba.git +``` + +2. **Initialize a new Python Environment** +``` +python3 -m virtualenv venv +``` + +3. **Install Verba** +``` +pip install -e . +``` + +4. **Launch Verba** +``` +verba start +``` + +5. **Access Verba** +``` +Visit localhost:8000 +``` + +6. **Create .env file and add environment variables** + +# API Keys + +Before diving into Verba's capabilities, you'll need to configure access to various components depending on your chosen technologies, such as OpenAI, Cohere, and HuggingFace. Start by obtaining the necessary API keys and setting them up through a `.env` file based on our provided [example](./goldenverba/.env.example) , or by declaring them as environment variables on your system. If you're building from source or using Docker, make sure your `.env` file is within the goldenverba directory. +Please make sure to only include environment variables you really need. + +Below is a comprehensive list of the API keys and variables you may require: + +## Weaviate +Verba provides flexibility in connecting to Weaviate instances based on your needs. By default, Verba opts for [Weaviate Embedded](https://weaviate.io/developers/weaviate/installation/embedded) if it doesn't detect the `WEAVIATE_URL_VERBA` and `WEAVIATE_API_KEY_VERBA` environment variables. This local deployment is the most straightforward way to launch your Weaviate database for prototyping and testing. + +However, you have other compelling options to consider: + +**🌩️ Weaviate Cloud Service (WCS)** + +If you prefer a cloud-based solution, Weaviate Cloud Service (WCS) offers a scalable, managed environment. Learn how to set up a cloud cluster and get the API keys by following the [Weaviate Cluster Setup Guide](https://weaviate.io/developers/wcs/guides/create-instance). + +**🐳 Docker Deployment** +Another robust local alternative is deploying Weaviate using Docker. For more details, consult the [Weaviate Docker Guide](https://weaviate.io/developers/weaviate/installation/docker-compose). + +``` +WEAVIATE_URL_VERBA=URL-TO-YOUR-WEAVIATE-CLUSTER + +WEAVIATE_API_KEY_VERBA=API-KEY-OF-YOUR-WEAVIATE-CLUSTER +``` + +## OpenAI + +Verba supports OpenAI Models such as Ada, GPT3, and GPT4. To use them, you need to specify the `OPENAI_API_KEY` environment variable. You can get it from [OpenAI](https://openai.com/) + +``` +OPENAI_API_KEY=YOUR-OPENAI-KEY +``` + +You can also add a `OPENAI_BASE_URL` to use proxies such as LiteLLM (https://github.com/BerriAI/litellm) + +``` +OPENAI_BASE_URL=YOUR-OPENAI_BASE_URL +``` + +## Azure OpenAI + +To use Azure OpenAI, you need to set + +- The API type: +``` +OPENAI_API_TYPE="azure" +``` + +- The key and the endpoint: + +``` +OPENAI_API_KEY= +OPENAI_BASE_URL=http://XXX.openai.azure.com +``` + +- Azure OpenAI ressource name, which is XXX if your endpoint is XXX.openai.azure.com + +``` +AZURE_OPENAI_RESOURCE_NAME= +``` +- You need to set the models, for the embeddings and for the query. +``` +AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-ada-002" +OPENAI_MODEL="gpt-4" +``` + +- Finally, as Azure is using per-minute quota, you might need to add a waiting time between each chunk upload. For example, if you have a limit of 240k tokens per minute, if your chunks are +400 tokens max, then 100ms between queries should be fine. If you get error 429 from weaviate, then increase this value. + +``` +WAIT_TIME_BETWEEN_INGESTION_QUERIES_MS="100" +``` + +## Cohere + +Verba supports Cohere Models, to use them, you need to specify the `COHERE_API_KEY` environment variable. You can get it from [Cohere](https://dashboard.cohere.com/) + +``` +COHERE_API_KEY=YOUR-COHERE-KEY +``` + +## HuggingFace + +Verba supports HuggingFace models, such as SentenceTransformers and Llama2. To use them you need the `HF_TOKEN` environment variable. You can get it from [HuggingFace](https://huggingface.co/) + +``` +HF_TOKEN=YOUR-HUGGINGFACE-TOKEN +``` + +### Llama2 + +To use the Llama2 model from Meta, you first need to request access to it. Read more about accessing the [Llama model here](https://huggingface.co/blog/llama2). To enable the LLama2 model for Verba use: + +``` +LLAMA2-7B-CHAT-HF=True +``` + +## Unstructured +Verba supports importing documents through Unstructured (e.g .pdf). To use them you need the `UNSTRUCTURED_API_KEY` environment variable. You can get it from [Unstructured](https://unstructured.io/) + +``` +UNSTRUCTURED_API_KEY=YOUR-UNSTRUCTURED-KEY +UNSTRUCTURED_API_URL=YOUR-SELF-HOSTED-INSTANCE # If you are self hosting, in the form of `http://localhost:8000/general/v0/general` +``` + + + +## Github + +If you want to use the Github Reader, you need the `GITHUB_TOKEN` environment variable. You can get it from [GitHub](https://github.com/) + +``` +GITHUB_TOKEN=YOUR-GITHUB-TOKEN +``` + +## Status Page + +Once configured, you can monitor your Verba installation's health and status via the 'Status Verba' page. This dashboard provides insights into your deployment type, libraries, environment settings, Weaviate schema counts, and more. It's also your go-to for maintenance tasks like resetting Verba, clearing the cache, or managing auto-complete suggestions. + +![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_status.png) + +# Quickstart: Deploy with Docker + +Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their own software, libraries, and configuration files; they can communicate with each other through well-defined channels. All containers are run by a single operating system kernel and are thus more lightweight than virtual machines. Docker provides an additional layer of abstraction and automation of operating-system-level virtualization on Windows and Linux. + +Docker's use of containers to package software means that the application and its dependencies, libraries, and other binaries are packaged together and can be moved between environments easily. This makes it incredibly useful for developers looking to create predictable environments that are isolated from other applications. + +To get started with deploying Verba using Docker, follow the steps below. If you need more detailed instructions on Docker usage, check out the [Docker Curriculum](https://docker-curriculum.com/). + +If you're unfamiliar with Docker, you can learn more about it [here](https://docker-curriculum.com/). + +0. **Clone the Verba repos** +Ensure you have Git installed on your system. Then, open a terminal or command prompt and run the following command to clone the Verba repository: + +``` +git clone https://github.com/weaviate/Verba.git +``` + +1. **Set neccessary environment variables** +Make sure to set your required environment variables in the ```.env``` file. You can read more about how to set them up in the [API Keys Section](#api-keys) + +2. **Adjust the docker-compose file** +You can use the ```docker-compose.yml``` to add required environment variables under the ```verba``` service and can also adjust the Weaviate Docker settings to enable Authentification or change other settings of your database instance. You can read more about the Weaviate configuration in our [docker-compose documentation](https://weaviate.io/developers/weaviate/installation/docker-compose) + +> Please make sure to only add environment variables that you really need. If have no authentifcation enabled in your Weaviate Cluster, make sure to not include the ```WEAVIATE_API_KEY_VERBA``` enviroment variable + +2. **Deploy using Docker** +With Docker installed and the Verba repository cloned, navigate to the directory containing the Docker Compose file in your terminal or command prompt. Run the following command to start the Verba application in detached mode, which allows it to run in the background: + +``` +docker compose up -d +``` + +This command will download the necessary Docker images, create containers, and start Verba. +Remember, Docker must be installed on your system to use this method. For installation instructions and more details about Docker, visit the official Docker documentation. + +4. **Access Verba** + +- You can access your local Weaviate instance at ```localhost:8080``` + +- You can access the Verba frontend at ```localhost:8000``` + + +If you want your Docker Instance to install a specific version of Verba (as described in the [package section](#choosing-the-right-verba-installation-package)) you can edit the ```Dockerfile``` and change the installation line. + +``` +RUN pip install -e '.' +``` + + +## Importing Your Data into Verba + +With Verba configured, you're ready to import your data and start exploring. Follow these simple steps to get your data into Verba: + +![Demo of Verba](https://github.com/weaviate/Verba/blob/dev/img/verba_data.gif) + +1. **Initiate the Import Process** + - Click on "Add Documents" to begin. + +2. **Select Your Data Processing Tools** + - At the top, you'll find three tabs labeled `Reader`, `Chunker`, and `Embedder`, each offering different options for handling your data. + +3. **Choose a Reader** + - The `Reader` is responsible for importing your data. Select from the available options: + - `SimpleReader`: For importing `.txt` and `.md` files. + - `GitHubReader`: For loading data directly from a GitHub repository by specifying the path (`owner/repo/folder_path`). + - `PDFReader`: For importing `.pdf` files. + +4. **Select a Chunker** + - `Chunkers` break down your data into manageable pieces. Choose a suitable chunker: + - `WordChunker`: Chunks the text by words. + - `SentenceChunker`: Chunks the text by sentences. + +5. **Pick an Embedder** + - `Embedders` are crucial for integrating your data into Weaviate. Select one based on your preference: + - `AdaEmbedder`: Utilizes OpenAI's ADA model for embedding. + - `MiniLMEmbedder`: Employs Sentence Transformers for embedding. + - `CohereEmbedder`: Uses Cohere for embedding. + +6. **Commence Data Ingestion** + - After setting up your preferences, click on "Import" to ingest your data into Verba. + +Now your data is ready to be used within Verba, enabling you to leverage its powerful search and retrieval capabilities. + +## Large Language Model (LLM) Costs + +Verba utilizes LLM models through APIs. Be advised that the usage costs for these models will be billed to the API access key you provide. Primarily, costs are incurred during data embedding and answer generation processes. + +## Open Source Contribution + +Your contributions are always welcome! Feel free to contribute ideas, feedback, or create issues and bug reports if you find any! Before contributing, please read the [Contribution Guide](./CONTRIBUTING.md). Visit our [Weaviate Community Forum](https://forum.weaviate.io/) if you need any help! + +### Project Architecture +You can learn more about Verba's architecture and implementation in its [technical documentation](./TECHNICAL.md) and [frontend documentation](./FRONTEND.md). It's recommended to read them before making any contributions. + diff --git a/data/test/text_pdf.pdf b/data/test/text_pdf.pdf new file mode 100644 index 000000000..3d570b857 Binary files /dev/null and b/data/test/text_pdf.pdf differ diff --git a/data/test/username.csv b/data/test/username.csv new file mode 100644 index 000000000..006ac8e60 --- /dev/null +++ b/data/test/username.csv @@ -0,0 +1,7 @@ +Username; Identifier;First name;Last name +booker12;9012;Rachel;Booker +grey07;2070;Laura;Grey +johnson81;4081;Craig;Johnson +jenkins46;9346;Mary;Jenkins +smith79;5079;Jamie;Smith + diff --git a/docker-compose.yml b/docker-compose.yml index c7ac21215..2a365ed96 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -11,14 +11,15 @@ services: environment: - WEAVIATE_URL_VERBA=http://weaviate:8080 - OPENAI_API_KEY=$OPENAI_API_KEY + - COHERE_API_KEY=$COHERE_API_KEY + - OLLAMA_URL=http://host.docker.internal:11434 + - OLLAMA_MODEL=$OLLAMA_MODEL + - UNSTRUCTURED_API_KEY=$UNSTRUCTURED_API_KEY + - UNSTRUCTURED_API_URL=$UNSTRUCTURED_API_URL + - GITHUB_TOKEN=$GITHUB_TOKEN ## IF YOU WANT AUTHENTICATION, UNCOMENT BELLOW ## AND ADD WEAVIATE SERVICE #- WEAVIATE_API_KEY_VERBA=adminkey - #- COHERE_API_KEY=YOUR-COHERE-KEY - #- UNSTRUCTURED_API_KEY=YOUR-UNSTRUCTURED-KEY - #- GITHUB_TOKEN=YOUR-GITHUB-TOKEN - #- HF_TOKEN=YOUR-HUGGINGFACE-TOKEN - #- LLAMA2-7B-CHAT-HF=ENABLE-LLAMA2?-(True or False) volumes: - ./data:/data/ @@ -40,7 +41,7 @@ services: - '8080' - --scheme - http - image: semitechnologies/weaviate:1.21.2 + image: semitechnologies/weaviate:1.24.2 ports: - 8080:8080 - 3000:8080 @@ -55,10 +56,10 @@ services: start_period: 10s environment: OPENAI_APIKEY: $OPENAI_API_KEY + COHERE_APIKEY: $COHERE_API_KEY QUERY_DEFAULTS_LIMIT: 25 AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true' PERSISTENCE_DATA_PATH: '/var/lib/weaviate' - # DEFAULT_VECTORIZER_MODULE: 'text2vec-openai' ENABLE_MODULES: 'text2vec-openai, generative-openai, qna-openai, text2vec-cohere' CLUSTER_HOSTNAME: 'node1' #######IF YOU WANT AUTHENTICATION UNCOMMENT BELOW######## diff --git a/frontend/app/api.ts b/frontend/app/api.ts new file mode 100644 index 000000000..5434f66a2 --- /dev/null +++ b/frontend/app/api.ts @@ -0,0 +1,29 @@ +export const detectHost = async (): Promise => { + const checkUrl = async (url: string): Promise => { + try { + const response = await fetch(url); + if (!response.ok) { + throw new Error(`HTTP status ${response.status}`); + } + return true; + } catch (error) { + console.error(`Failed to fetch from ${url}:`, error); + return false; + } + }; + + const localUrl = "http://localhost:8000/api/health"; + const rootUrl = "/api/health"; + + const isLocalHealthy = await checkUrl(localUrl); + if (isLocalHealthy) { + return "http://localhost:8000"; + } + + const isRootHealthy = await checkUrl(rootUrl); + if (isRootHealthy) { + return ""; + } + + throw new Error("Both health checks failed, please check the Verba Server"); +}; diff --git a/frontend/app/components/Chat/ChatComponent.tsx b/frontend/app/components/Chat/ChatComponent.tsx new file mode 100644 index 000000000..8dc2458a7 --- /dev/null +++ b/frontend/app/components/Chat/ChatComponent.tsx @@ -0,0 +1,105 @@ +"use client"; + +import React, { useState } from "react"; +import ChatInterfaceComponent from "./ChatInterface"; +import { SettingsConfiguration } from "../Settings/types"; +import { DocumentChunk } from "../Document/types"; + +import ChunksComponent from "../Document/ChunksComponent"; +import DocumentComponent from "../Document/DocumentComponent"; + +import { RAGConfig } from "../RAG/types"; + +import InfoComponent from "../Navigation/InfoComponent"; +import { + document_interface_info, + chunk_interface_info, + chat_interface_info, +} from "@/app/info"; + +interface ChatComponentProps { + settingConfig: SettingsConfiguration; + APIHost: string | null; + setCurrentPage: (p: any) => void; + RAGConfig: RAGConfig | null; + production: boolean; +} + +const ChatComponent: React.FC = ({ + APIHost, + settingConfig, + setCurrentPage, + RAGConfig, + production, +}) => { + const [chunks, setChunks] = useState([]); + const [context, setContext] = useState(""); + const [chunkTime, setChunkTime] = useState(0); + const [selectedChunk, setSelectedChunk] = useState( + null + ); + + return ( +
+ {/* Chat Interface */} +
+ + +
+ +
+ {/* Chunk Selection */} +
+ + +
+ + {/* Document Viewer */} +
+ + +
+
+
+ ); +}; + +export default ChatComponent; diff --git a/frontend/app/components/Chat/ChatInterface.tsx b/frontend/app/components/Chat/ChatInterface.tsx new file mode 100644 index 000000000..128924029 --- /dev/null +++ b/frontend/app/components/Chat/ChatInterface.tsx @@ -0,0 +1,583 @@ +"use client"; +import React, { useState, useEffect, useRef } from "react"; +import PulseLoader from "react-spinners/PulseLoader"; +import { IoMdSend } from "react-icons/io"; +import { DocumentChunk } from "../Document/types"; +import { Message, QueryPayload } from "./types"; +import { getWebSocketApiHost } from "./util"; +import ChatMessage from "./ChatMessage"; +import { SettingsConfiguration } from "../Settings/types"; +import { IoIosRefresh } from "react-icons/io"; +import { AiFillRobot } from "react-icons/ai"; + +import StatusLabel from "./StatusLabel"; + +import ComponentStatus from "../Status/ComponentStatus"; + +import { RAGConfig } from "../RAG/types"; + +interface ChatInterfaceComponentProps { + settingConfig: SettingsConfiguration; + APIHost: string | null; + setChunks: (c: DocumentChunk[]) => void; + setChunkTime: (t: number) => void; + setCurrentPage: (p: any) => void; + setContext: (c: string) => void; + RAGConfig: RAGConfig | null; + production: boolean; +} + +const ChatInterfaceComponent: React.FC = ({ + APIHost, + settingConfig, + setChunks, + setChunkTime, + setCurrentPage, + setContext, + production, + RAGConfig, +}) => { + const [previewText, setPreviewText] = useState(""); + const lastMessageRef = useRef(null); + + const [socket, setSocket] = useState(null); + + const [userInput, setUserInput] = useState(""); + const [messages, setMessages] = useState([]); + const [isFetching, setIsFetching] = useState(false); + const [fetchingStatus, setFetchingStatus] = useState< + "DONE" | "CHUNKS" | "RESPONSE" + >("DONE"); + const [isFetchingSuggestion, setIsFetchingSuggestions] = useState(false); + const [suggestions, setSuggestions] = useState([]); + + const [showNotification, setShowNotification] = useState(false); + const [notificationText, setNotificationText] = useState(""); + const [notificationState, setNotificationState] = useState<"GOOD" | "BAD">( + "GOOD" + ); + + const handleCopyToBillboard = (text: string) => { + navigator.clipboard.writeText(text).then( + function () { + triggerNotification("Copied message"); + }, + function (err) { + console.error("Unable to copy text: ", err); + } + ); + }; + + const triggerNotification = (text: string, warning?: boolean) => { + if (warning) { + setNotificationState("BAD"); + } else { + setNotificationState("GOOD"); + } + + if (showNotification) { + setNotificationText(text); + return; + } + + setNotificationText(text); + setShowNotification(true); + + // Hide the notification after 3 seconds + setTimeout(() => { + setShowNotification(false); + setNotificationText(""); + }, 3000); + }; + + // Setup WebSocket and messages + useEffect(() => { + setMessages(getMessagesFromLocalStorage("VERBA_CONVERSATION")); + setChunks(getChunksFromLocalStorage("VERBA_CHUNKS")); + setContext(getContextFromLocalStorage("VERBA_CONTEXT")); + + const socketHost = getWebSocketApiHost(); + const localSocket = new WebSocket(socketHost); + + localSocket.onopen = () => { + console.log("WebSocket connection opened to " + socketHost); + triggerNotification("WebSocket Online"); + }; + + localSocket.onmessage = (event) => { + let data; + + try { + data = JSON.parse(event.data); + } catch (e) { + console.error("Received data is not valid JSON:", event.data); + return; // Exit early if data isn't valid JSON + } + const newMessageContent = data.message; + setPreviewText((prev) => prev + newMessageContent); + + if (data.finish_reason === "stop") { + setIsFetching(false); + setFetchingStatus("DONE"); + const full_text = data.full_text; + if (data.cached) { + const distance = data.distance; + setMessages((prev) => [ + ...prev, + { + type: "system", + content: full_text, + cached: true, + distance: distance, + }, + ]); + } else { + setMessages((prev) => [ + ...prev, + { type: "system", content: full_text }, + ]); + } + setPreviewText(""); + } + }; + + localSocket.onerror = (error) => { + console.error("WebSocket Error:", error); + triggerNotification("WebSocket Error: " + error, true); + setIsFetching(false); + setFetchingStatus("DONE"); + }; + + localSocket.onclose = (event) => { + if (event.wasClean) { + console.log( + `WebSocket connection closed cleanly, code=${event.code}, reason=${event.reason}` + ); + } else { + console.error("WebSocket connection died"); + } + triggerNotification("WebSocket Connection Offline", true); + setIsFetching(false); + setFetchingStatus("DONE"); + }; + + setSocket(localSocket); + + return () => { + if (localSocket.readyState !== WebSocket.CLOSED) { + localSocket.close(); + } + }; + }, []); + + // Scroll to latest message + useEffect(() => { + if (messages.length > 1) { + saveMessagesToLocalStorage("VERBA_CONVERSATION", messages); + } + if (lastMessageRef.current) { + const lastMessage = messages[messages.length - 1]; + lastMessageRef.current.scrollIntoView({ behavior: "smooth" }); + } + }, [messages]); + + const handleSuggestionClick = async (suggestion: string) => { + // Update the userInput with the clicked suggestion + setUserInput(suggestion); + setSuggestions([]); + }; + + const streamResponses = (query?: string, context?: string) => { + if (socket?.readyState === WebSocket.OPEN) { + const data = JSON.stringify({ + query: query, + context: context, + conversation: messages, + }); + socket.send(data); + } else { + console.error("WebSocket is not open. ReadyState:", socket?.readyState); + } + }; + + const handleKeyDown = (e: any) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); // Prevent new line + handleSendMessage(e); // Submit form + } + }; + + const saveMessagesToLocalStorage = (key: string, value: Message[]) => { + if (typeof window !== "undefined") { + // Check if window is defined + localStorage.setItem(key, JSON.stringify(value)); + } + }; + + const saveContextToLocalStorage = (key: string, value: string) => { + if (typeof window !== "undefined") { + // Check if window is defined + localStorage.setItem(key, value); + } + }; + + const getMessagesFromLocalStorage = (key: string) => { + if (typeof window !== "undefined") { + const saved = localStorage.getItem(key); + if (saved && JSON.parse(saved).length > 0) { + return JSON.parse(saved); + } + } + return [ + { + type: "system", + content: settingConfig.Customization.settings.intro_message.text, + }, + ]; // Return a default value or null if not found + }; + + const removeMessagesFromLocalStorage = (key: string) => { + if (typeof window !== "undefined") { + localStorage.removeItem(key); + } + }; + + const saveChunksToLocalStorage = (key: string, value: DocumentChunk[]) => { + if (typeof window !== "undefined") { + // Check if window is defined + localStorage.setItem(key, JSON.stringify(value)); + } + }; + + const getChunksFromLocalStorage = (key: string) => { + try { + if (typeof window !== "undefined") { + const saved = localStorage.getItem(key); + if (saved && JSON.parse(saved).length > 0) { + return JSON.parse(saved); + } + } + } catch (e) { + console.error("Failed to load chunks from local storage:", e); + return []; // Exit early if data isn't valid JSON + } + return []; // Return a default value or null if not found + }; + + const getContextFromLocalStorage = (key: string) => { + try { + if (typeof window !== "undefined") { + const saved = localStorage.getItem(key); + if (saved) { + return saved; + } + } + } catch (e) { + console.error("Failed to load context from local storage:", e); + return ""; // Exit early if data isn't valid JSON + } + return ""; // Return a default value or null if not found + }; + + const removeChunksFromLocalStorage = (key: string) => { + if (typeof window !== "undefined") { + localStorage.removeItem(key); + } + }; + + const handleSendMessage = async (e?: React.FormEvent) => { + e?.preventDefault(); + + setSuggestions([]); + + if (APIHost === null) { + triggerNotification("No connection to server"); + return; + } + + if (isFetching) { + return; + } + + const sendInput = userInput; + + if (sendInput.trim()) { + setMessages((prev) => [...prev, { type: "user", content: sendInput }]); + setUserInput(""); + + const textarea = document.getElementById("reset"); + if (textarea !== null) { + // Check if the element is not null + textarea.style.height = ""; // Reset height + textarea.style.width = ""; // Reset width + } else { + console.error('The element with ID "target" was not found in the DOM.'); + } + + try { + // Start the API call + setIsFetching(true); + setFetchingStatus("CHUNKS"); + + // Start both API calls in parallel + const response = await fetch(APIHost + "/api/query", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ query: sendInput }), + }); + + const data: QueryPayload = await response.json(); + + if (data) { + if (data.error !== "") { + triggerNotification(data.error, true); + } + + setChunks(data.chunks); + saveChunksToLocalStorage("VERBA_CHUNKS", data.chunks); + setSuggestions([]); + setChunkTime(data.took); + + if (data.context) { + streamResponses(sendInput, data.context); + setContext(data.context); + saveContextToLocalStorage("VERBA_CONTEXT", data.context); + setFetchingStatus("RESPONSE"); + } + } else { + triggerNotification( + "Failed to fetch from API: No data received", + true + ); + setIsFetching(false); + setFetchingStatus("DONE"); + } + } catch (error) { + console.error("Failed to fetch from API:", error); + triggerNotification("Failed to fetch from API: " + error, true); + setIsFetching(false); + setFetchingStatus("DONE"); + } + } + }; + + const fetchSuggestions = async (query: string) => { + if ( + isFetchingSuggestion || + query === "" || + isFetching || + !settingConfig.Chat.settings.suggestion.checked + ) { + setSuggestions([]); + return; + } + + try { + setIsFetchingSuggestions(true); + const response = await fetch(APIHost + "/api/suggestions", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ query }), + }); + + const data = await response.json(); + + if (data) { + setSuggestions(data.suggestions); + setIsFetchingSuggestions(false); + } else { + setIsFetchingSuggestions(false); + } + } catch (error) { + console.error("Failed to fetch suggestions:", error); + setIsFetchingSuggestions(false); + } + }; + + const escapeRegExp = (string: string) => { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + }; + + const renderBoldedSuggestion = (suggestion: string, userInput: string) => { + const escapedUserInput = escapeRegExp(userInput); + const parts = suggestion.split(new RegExp(`(${escapedUserInput})`, "gi")); + return ( +
+ {parts.map((part, i) => ( +

+ {part} +

+ ))} +
+ ); + }; + + return ( +
+ {/*Chat Messages*/} +
+
+ {RAGConfig && ( +
+ +
+ )} +
+ + + +
+ +
+ {messages.map((message, index) => ( +
+ +
+ ))} + {/* Render the preview message if available */} + {previewText && ( + + )} +
+ + {isFetching && ( +
+ +

+ {fetchingStatus === "CHUNKS" && "Retrieving chunks"} + {fetchingStatus === "RESPONSE" && "Generating answer"} +

+
+ )} +
+ + {/*Chat Input*/} +
+
+ + +
+ +
+
+
+ +
+ {suggestions.map((suggestion, index) => ( + + ))} +
+ + {/*Chat Notification*/} +
+

{notificationText}

+
+
+ ); +}; + +export default ChatInterfaceComponent; diff --git a/frontend/app/components/Chat/ChatMessage.tsx b/frontend/app/components/Chat/ChatMessage.tsx new file mode 100644 index 000000000..2d4c69730 --- /dev/null +++ b/frontend/app/components/Chat/ChatMessage.tsx @@ -0,0 +1,80 @@ +"use client"; + +import React from "react"; +import { Message } from "./types"; +import ReactMarkdown from "react-markdown"; +import { FaDatabase } from "react-icons/fa"; +import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; +import { + oneDark, + oneLight, +} from "react-syntax-highlighter/dist/cjs/styles/prism"; +import { SettingsConfiguration } from "../Settings/types"; + +interface ChatMessageProps { + message: Message; + handleCopyToBillboard: (m: string) => void; + settingConfig: SettingsConfiguration; +} + +const ChatMessage: React.FC = ({ + message, + handleCopyToBillboard, + settingConfig, +}) => { + return ( +
+
+ {message.cached && } + {message.type === "system" ? ( + + {String(children).replace(/\n$/, "")} + + ) : ( + + {children} + + ); + }, + }} + > + {message.content} + + ) : ( +
{message.content}
// Use whitespace-pre-wrap for user messages + )} +
+
+ {message.type === "system" && ( + + )} +
+
+ ); +}; + +export default ChatMessage; diff --git a/frontend/app/components/Chat/StatusLabel.tsx b/frontend/app/components/Chat/StatusLabel.tsx new file mode 100644 index 000000000..ab7cf4e7e --- /dev/null +++ b/frontend/app/components/Chat/StatusLabel.tsx @@ -0,0 +1,29 @@ +"use client"; + +import React from "react"; + +interface StatusLabelProps { + status: boolean; + true_text: string; + false_text: string; +} + +const StatusLabel: React.FC = ({ + status, + true_text, + false_text, +}) => { + return ( +
+

+ {status ? true_text : false_text} +

+
+ ); +}; + +export default StatusLabel; diff --git a/frontend/app/components/Chat/types.ts b/frontend/app/components/Chat/types.ts new file mode 100644 index 000000000..8e1bdb6fe --- /dev/null +++ b/frontend/app/components/Chat/types.ts @@ -0,0 +1,19 @@ +import { DocumentChunk } from "../Document/types"; + +export interface Message { + type: "user" | "system"; + content: string; + cached?: boolean; + distance?: string; +} + +export type QueryPayload = { + chunks: DocumentChunk[]; + context: string; + error: string; + took: number; +}; + +export type Segment = + | { type: "text"; content: string } + | { type: "code"; language: string; content: string }; diff --git a/frontend/app/components/Chat/util.ts b/frontend/app/components/Chat/util.ts new file mode 100644 index 000000000..61727c21e --- /dev/null +++ b/frontend/app/components/Chat/util.ts @@ -0,0 +1,9 @@ +export const getWebSocketApiHost = () => { + if (process.env.NODE_ENV === "development") { + return "ws://localhost:8000/ws/generate_stream"; + } + // If you're serving the app directly through FastAPI, generate the WebSocket URL based on the current location. + const protocol = window.location.protocol === "https:" ? "wss:" : "ws:"; + const host = window.location.host; + return `${protocol}//${host}/ws/generate_stream`; +}; diff --git a/frontend/app/components/Document/ChunksComponent.tsx b/frontend/app/components/Document/ChunksComponent.tsx new file mode 100644 index 000000000..8d49029fa --- /dev/null +++ b/frontend/app/components/Document/ChunksComponent.tsx @@ -0,0 +1,140 @@ +"use client"; +import React, { useEffect } from "react"; +import { DocumentChunk } from "../Document/types"; +import CountUp from "react-countup"; + +import { RAGConfig } from "../RAG/types"; +import ComponentStatus from "../Status/ComponentStatus"; + +import { FaSearch } from "react-icons/fa"; +import { FaDatabase } from "react-icons/fa"; + +import { IoSparkles } from "react-icons/io5"; + +import UserModalComponent from "../Navigation/UserModal"; + +interface ChunksComponentComponentProps { + chunks: DocumentChunk[]; + selectedChunk: DocumentChunk | null; + chunkTime: number; + setSelectedChunk: (c: DocumentChunk | null) => void; + setCurrentPage: (p: any) => void; + context: string; + RAGConfig: RAGConfig | null; + production: boolean; +} + +const ChunksComponent: React.FC = ({ + chunks, + selectedChunk, + chunkTime, + context, + setSelectedChunk, + setCurrentPage, + RAGConfig, + production, +}) => { + useEffect(() => { + if (chunks && chunks.length > 0) { + setSelectedChunk(chunks[0]); + } else { + setSelectedChunk(null); + } + }, [chunks]); + + const openContextModal = () => { + const modal = document.getElementById("context_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + return ( +
+ {/*Chunks*/} +
+
+
+ {RAGConfig && ( +
+ + +
+ )} +
+ {chunks && chunks.length > 0 && ( +
+

+ {chunks.length} chunks retrieved in {chunkTime} seconds. +

+
+ )} +
+
+ {chunks && + chunks.map((chunk, index) => ( + +
+
+

+ {chunk.doc_name} +

+

+ {chunk.doc_type} +

+
+ + ))} + {context !== "" && ( + + )} +
+
+ + + ); +}; + +export default ChunksComponent; diff --git a/frontend/app/components/Document/DocumentComponent.tsx b/frontend/app/components/Document/DocumentComponent.tsx new file mode 100644 index 000000000..60556a9a2 --- /dev/null +++ b/frontend/app/components/Document/DocumentComponent.tsx @@ -0,0 +1,348 @@ +"use client"; +import React, { useState, useEffect, useRef } from "react"; +import { DocumentChunk, Document, DocumentPayload } from "../Document/types"; +import ReactMarkdown from "react-markdown"; +import PulseLoader from "react-spinners/PulseLoader"; +import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; +import { + oneDark, + oneLight, +} from "react-syntax-highlighter/dist/cjs/styles/prism"; +import { SettingsConfiguration } from "../Settings/types"; +import { FormattedDocument } from "../Document/types"; +import { splitDocument } from "./util"; +import { FaExternalLinkAlt } from "react-icons/fa"; +import { MdOutlineSimCardDownload } from "react-icons/md"; +import { HiMiniSparkles } from "react-icons/hi2"; +import { MdDelete } from "react-icons/md"; + +import UserModalComponent from "../Navigation/UserModal"; + +interface DocumentComponentProps { + settingConfig: SettingsConfiguration; + APIhost: string | null; + selectedChunk: DocumentChunk | null; + setSelectedChunk: (s: any | null) => void; + selectedDocument: Document | null; + deletable: boolean; + setDocuments?: (d: Document[] | null) => void; + setTriggerReset?: (b: any) => void; + production: boolean; +} + +const DocumentComponent: React.FC = ({ + APIhost, + selectedChunk, + settingConfig, + selectedDocument, + deletable, + setSelectedChunk, + setDocuments, + setTriggerReset, + production, +}) => { + const [currentDocument, setCurrentDocument] = useState(null); + const [formattedDocument, setFormattedDocument] = + useState(null); + const [isFetching, setIsFetching] = useState(false); + const [showWholeDocument, setWholeDocument] = useState(false); + + const chunkRef = useRef(null); + + useEffect(() => { + if (selectedChunk != null && APIhost != null) { + fetchDocuments(); + } else { + setCurrentDocument(null); + } + }, [selectedChunk]); + + const fetchDocuments = async () => { + if (selectedChunk != null && APIhost != null) { + try { + setIsFetching(true); + + const response = await fetch(APIhost + "/api/get_document", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + document_id: selectedChunk.doc_uuid, + }), + }); + const data: DocumentPayload = await response.json(); + + if (data) { + if (data.error !== "") { + setCurrentDocument(null); + console.error(data.error); + setFormattedDocument(null); + setIsFetching(false); + setWholeDocument(false); + } else { + setCurrentDocument(data.document); + setFormattedDocument( + splitDocument(data.document.text, selectedChunk.text) + ); + setIsFetching(false); + if (chunkRef.current) { + chunkRef.current.scrollIntoView({ behavior: "smooth" }); + } + if ( + selectedChunk.text !== "" && + data.document.text.length > + settingConfig.Chat.settings.max_document_size.value + ) { + setWholeDocument(false); + } else { + setWholeDocument(true); + } + } + } + } catch (error) { + console.error("Failed to fetch document:", error); + setIsFetching(false); + } + } + }; + + const handleSourceClick = () => { + // Open a new tab with the specified URL + window.open(currentDocument?.link, "_blank", "noopener,noreferrer"); + }; + + const handleDeleteDocument = async (uuid: string) => { + try { + console.log("DELETING " + uuid); + fetch(APIhost + "/api/delete_document", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ document_id: uuid }), + }); + setCurrentDocument(null); + setSelectedChunk(null); + if (setDocuments) { + setDocuments(null); + } + if (setTriggerReset) { + setTriggerReset((prev: boolean) => !prev); + } + } catch (error) { + console.error("Failed to delete document:", error); + } + }; + + const handleDocumentShow = () => { + setWholeDocument((prev) => !prev); + }; + + const openDeleteModal = () => { + const modal = document.getElementById("delete_document_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + if (currentDocument !== null && !isFetching) { + return ( +
+ {/*Title*/} +
+
+

+ {currentDocument.name} +

+

+ {currentDocument.type} +

+
+
+ {formattedDocument && formattedDocument.substring !== "" && ( +
+ +
+ )} + {currentDocument.link !== "" && ( +
+ +
+ )} + {deletable && !production && ( +
+ +
+ )} +
+
+ + {/*Text*/} + {formattedDocument && ( +
+ {showWholeDocument && formattedDocument.beginning !== "" && ( + + {String(children).replace(/\n$/, "")} + + ) : ( + + {children} + + ); + }, + }} + > + {formattedDocument.beginning} + + )} + {formattedDocument.substring !== "" && ( +
+
+
+ +

Context Used

+
+
+ + {String(children).replace(/\n$/, "")} + + ) : ( + + {children} + + ); + }, + }} + > + {formattedDocument.substring} + +
+ )} + {showWholeDocument && formattedDocument.ending !== "" && ( + + {String(children).replace(/\n$/, "")} + + ) : ( + + {children} + + ); + }, + }} + > + {formattedDocument.ending} + + )} +
+ )} + + +
+ ); + } else { + return ( +
+
+ {isFetching && ( +
+ +

Loading Document...

+
+ )} +
+
+ ); + } +}; + +export default DocumentComponent; diff --git a/frontend/app/components/Document/DocumentSearchComponent.tsx b/frontend/app/components/Document/DocumentSearchComponent.tsx new file mode 100644 index 000000000..80a699d8c --- /dev/null +++ b/frontend/app/components/Document/DocumentSearchComponent.tsx @@ -0,0 +1,291 @@ +"use client"; +import React, { useState, useEffect } from "react"; +import { + DocumentChunk, + Document, + AllDocumentsPayload, +} from "../Document/types"; +import { FaSearch } from "react-icons/fa"; +import PulseLoader from "react-spinners/PulseLoader"; +import { SettingsConfiguration } from "../Settings/types"; +import { IoIosRefresh } from "react-icons/io"; + +import { FaDatabase } from "react-icons/fa"; + +import { RAGConfig } from "../RAG/types"; +import ComponentStatus from "../Status/ComponentStatus"; + +interface DocumentSearchComponentProps { + APIHost: string | null; + selectedDocument: DocumentChunk | null; + setSelectedDocument: (c: DocumentChunk | null) => void; + settingConfig: SettingsConfiguration; + documents: Document[] | null; + setDocuments: (d: Document[] | null) => void; + triggerReset: boolean; + setCurrentPage: (p: any) => void; + RAGConfig: RAGConfig | null; + production: boolean; +} + +const DocumentSearchComponent: React.FC = ({ + APIHost, + selectedDocument, + settingConfig, + setSelectedDocument, + documents, + setDocuments, + triggerReset, + setCurrentPage, + RAGConfig, + production, +}) => { + const [userInput, setUserInput] = useState(""); + const [page, setPage] = useState(1); + + const pageSize = 100; + + const [documentsTook, setDocumentsTook] = useState(0); + const [docTypes, setDocTypes] = useState([]); + const [currentEmbedder, setCurrentEmbedder] = useState(null); + const [selectedType, setSelectedType] = useState(null); + + const [isFetching, setIsFetching] = useState(false); + + const nextPage = () => { + if (!documents) { + return; + } + + if (documents.length < pageSize) { + setPage(1); + } else { + setPage((prev) => prev + 1); + } + }; + + const previousPage = () => { + if (!documents) { + return; + } + if (page == 1) { + setPage(1); + } else { + setPage((prev) => prev - 1); + } + }; + + const fetchAllDocuments = async (_userInput?: string) => { + try { + setIsFetching(true); + + const response = await fetch(APIHost + "/api/get_all_documents", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + query: _userInput ? _userInput : "", + doc_type: selectedType ? selectedType : "", + page: page, + pageSize: pageSize, + }), + }); + + const data: AllDocumentsPayload = await response.json(); + + if (data) { + if (data.error !== "") { + console.error(data.error); + setIsFetching(false); + setDocuments(null); + } else { + setDocuments(data.documents); + setDocTypes(data.doc_types); + setCurrentEmbedder(data.current_embedder); + setDocumentsTook(data.took); + setIsFetching(false); + } + } + } catch (error) { + console.error("Failed to fetch document:", error); + setIsFetching(false); + } + }; + + useEffect(() => { + if (documents != null && APIHost != null) { + fetchAllDocuments(userInput); + } else { + setDocuments(null); + setIsFetching(false); + } + }, [page, selectedType]); + + useEffect(() => { + if (APIHost != null) { + fetchAllDocuments(userInput); + } else { + setDocuments(null); + setIsFetching(false); + } + }, [triggerReset]); + + const handleSearch = () => { + fetchAllDocuments(userInput); + }; + + const clearSearch = () => { + setUserInput(""); + fetchAllDocuments(""); + }; + + const handleKeyDown = (e: any) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); // Prevent new line + handleSearch(); // Submit form + } + }; + + const handleDocumentClick = (d: Document) => { + const new_chunk: DocumentChunk = { + text: "", + doc_name: d.name, + chunk_id: 0, + doc_uuid: d.uuid, + doc_type: d.type, + score: 0, + }; + setSelectedDocument(new_chunk); + }; + + return ( +
+
+ {/*Search Bar*/} +
+ + +
+
+ +
+ + +
+
+
+
+
+
+ +
+
+ {page > 1 && ( + + )} + + {documents && documents.length >= pageSize && ( + + )} +
+
+ {documents && documents.length > 0 && ( +
+

+ {documents.length} documents retrieved in {documentsTook} seconds + with {currentEmbedder}. +

+
+ )} +
+ {documents && + documents.map((document, index) => ( + + ))} +
+ + {isFetching && ( +
+ +

Loading Document

+
+ )} +
+
+ ); +}; + +export default DocumentSearchComponent; diff --git a/frontend/app/components/Document/DocumentViewerComponent.tsx b/frontend/app/components/Document/DocumentViewerComponent.tsx new file mode 100644 index 000000000..ad71a9918 --- /dev/null +++ b/frontend/app/components/Document/DocumentViewerComponent.tsx @@ -0,0 +1,68 @@ +"use client"; + +import React, { useState } from "react"; +import { SettingsConfiguration } from "../Settings/types"; +import { DocumentChunk, Document } from "../Document/types"; + +import DocumentComponent from "../Document/DocumentComponent"; +import DocumentSearchComponent from "./DocumentSearchComponent"; + +import { RAGConfig } from "../RAG/types"; + +interface DocumentViewerComponentProps { + settingConfig: SettingsConfiguration; + APIHost: string | null; + setCurrentPage: (p: any) => void; + RAGConfig: RAGConfig | null; + production: boolean; +} + +const DocumentViewerComponent: React.FC = ({ + APIHost, + settingConfig, + setCurrentPage, + RAGConfig, + production, +}) => { + const [documents, setDocuments] = useState([]); + const [documentTime, setDocumentTime] = useState(0); + const [selectedDocument, setSelectedDocument] = + useState(null); + const [triggerReset, setTriggerReset] = useState(false); + + return ( +
+ {/* Chat Interface */} +
+ +
+ +
+ +
+
+ ); +}; + +export default DocumentViewerComponent; diff --git a/frontend/app/components/Document/types.ts b/frontend/app/components/Document/types.ts new file mode 100644 index 000000000..f8112be96 --- /dev/null +++ b/frontend/app/components/Document/types.ts @@ -0,0 +1,38 @@ +export type DocumentChunk = { + text: string; + doc_name: string; + chunk_id: number; + doc_uuid: string; + doc_type: string; + score: number; +}; + +export type DocumentPayload = { + error: string; + document: Document; +}; + +export type AllDocumentsPayload = { + error: string; + documents: Document[]; + doc_types: string[]; + current_embedder: string; + took: number; +}; + +export type Document = { + text: string; + name: string; + chunks: number; + uuid: string; + type: string; + class: string; + timestamp: string; + link: string; +}; + +export type FormattedDocument = { + beginning: string; + substring: string; + ending: string; +}; diff --git a/frontend/app/components/Document/util.ts b/frontend/app/components/Document/util.ts new file mode 100644 index 000000000..636863f11 --- /dev/null +++ b/frontend/app/components/Document/util.ts @@ -0,0 +1,28 @@ +import { FormattedDocument } from "./types"; + +export function splitDocument( + fullText: string, + searchSubstring: string | null +): FormattedDocument { + if (searchSubstring === null || searchSubstring === "") { + const beginning = fullText; + const substring = ""; + const ending = ""; + + return { beginning, substring, ending }; + } + + const startIndex = fullText.indexOf(searchSubstring); + if (startIndex === -1) { + // Substring not found, handle as appropriate, maybe return null or the full text as beginning + return { beginning: fullText, substring: "", ending: "" }; + } + + const endIndex = startIndex + searchSubstring.length; + + const beginning = fullText.substring(0, startIndex); + const substring = fullText.substring(startIndex, endIndex); + const ending = fullText.substring(endIndex); + + return { beginning, substring, ending }; +} diff --git a/frontend/app/components/Navigation/InfoComponent.tsx b/frontend/app/components/Navigation/InfoComponent.tsx new file mode 100644 index 000000000..202fda57d --- /dev/null +++ b/frontend/app/components/Navigation/InfoComponent.tsx @@ -0,0 +1,33 @@ +"use client"; + +import React from "react"; +import { FaInfo } from "react-icons/fa"; + +import { SettingsConfiguration } from "../Settings/types"; + +interface InfoComponentProps { + settingConfig: SettingsConfiguration; + tooltip_text: string; + display_text: string; +} + +const InfoComponent: React.FC = ({ + tooltip_text, + display_text, + settingConfig, +}) => { + return ( +
+
+ +
+

{display_text}

+
+ ); +}; + +export default InfoComponent; diff --git a/frontend/app/components/Navigation/NavButton.tsx b/frontend/app/components/Navigation/NavButton.tsx new file mode 100644 index 000000000..4d3ab8c68 --- /dev/null +++ b/frontend/app/components/Navigation/NavButton.tsx @@ -0,0 +1,44 @@ +"use client"; + +import React from "react"; +import { FaStar } from "react-icons/fa"; + +interface NavbarButtonProps { + Icon: typeof FaStar; + iconSize: number; + title: string; + currentPage: string; + setCurrentPage: ( + page: "CHAT" | "DOCUMENTS" | "STATUS" | "ADD" | "SETTINGS" | "RAG" + ) => void; + setPage: "CHAT" | "DOCUMENTS" | "STATUS" | "ADD" | "SETTINGS" | "RAG"; + APIHost: string | null; + hide: boolean; +} + +const NavbarButton: React.FC = ({ + Icon, + iconSize, + title, + currentPage, + setPage, + setCurrentPage, + APIHost, + hide, +}) => { + return ( + + ); +}; + +export default NavbarButton; diff --git a/frontend/app/components/Navigation/NavbarComponent.tsx b/frontend/app/components/Navigation/NavbarComponent.tsx new file mode 100644 index 000000000..c12fdcae4 --- /dev/null +++ b/frontend/app/components/Navigation/NavbarComponent.tsx @@ -0,0 +1,262 @@ +"use client"; + +import React, { useState, useEffect } from "react"; + +import { IoChatbubbleSharp } from "react-icons/io5"; +import { IoDocumentSharp } from "react-icons/io5"; +import { HiOutlineStatusOnline } from "react-icons/hi"; +import { IoMdAddCircle } from "react-icons/io"; +import { IoSettingsSharp } from "react-icons/io5"; +import { FaGithub } from "react-icons/fa"; +import { IoBuildSharp } from "react-icons/io5"; +import { LuMenu } from "react-icons/lu"; + +import NavbarButton from "./NavButton"; +import { getGitHubStars } from "./util"; + +interface NavbarProps { + imageSrc: string; + title: string; + subtitle: string; + version: string; + currentPage: string; + APIHost: string | null; + production: boolean; + setCurrentPage: ( + page: "CHAT" | "DOCUMENTS" | "STATUS" | "ADD" | "SETTINGS" | "RAG" + ) => void; +} + +const formatGitHubNumber = (num: number): string => { + if (num >= 1000) { + return (num / 1000).toFixed(1).replace(/\.0$/, "") + "k"; + } + return num.toString(); +}; + +const Navbar: React.FC = ({ + imageSrc, + title, + subtitle, + APIHost, + version, + currentPage, + setCurrentPage, + production, +}) => { + const [gitHubStars, setGitHubStars] = useState("0"); + const icon_size = 18; + + useEffect(() => { + // Declare an asynchronous function inside the useEffect + const fetchGitHubStars = async () => { + try { + // Await the asynchronous call to getGitHubStars + const response: number = await getGitHubStars(); + + if (response) { + // Now response is the resolved value of the promise + const formatedStars = formatGitHubNumber(response); + setGitHubStars(formatedStars); + } + } catch (error) { + console.error("Failed to fetch GitHub stars:", error); + } + }; + + // Call the async function + fetchGitHubStars(); + }, []); + + const handleGitHubClick = () => { + // Open a new tab with the specified URL + window.open( + "https://github.com/weaviate/verba", + "_blank", + "noopener,noreferrer" + ); + }; + + return ( +
+ {/* Logo, Title, Subtitle */} +
+ +
+

{title}

+

+ {subtitle} +

+
+
+ +
+
+ + {/* Pages */} +
+ + + + + + + + + + +

+ {version} +

+
+ + {/* Menu */} +
+
+
    +
  • +
    + + + +
      +
    • { + setCurrentPage("CHAT"); + }} + > + Chat +
      • +
    • { + setCurrentPage("DOCUMENTS"); + }} + > + Documents +
      • + {!production && ( +
    • { + setCurrentPage("STATUS"); + }} + > + Status +
      • + )} + + {!production && ( +
    • { + setCurrentPage("ADD"); + }} + > + Add Documents +
      • + )} + + {!production && ( +
    • { + setCurrentPage("RAG"); + }} + > + RAG +
      • + )} + + {!production && ( +
    • { + setCurrentPage("SETTINGS"); + }} + > + Settings +
      • + )} + +
    • + GitHub +
      • +
    • + {version} +
      • +
    +
    +
    • +
+
+
+
+
+ ); +}; + +export default Navbar; diff --git a/frontend/app/components/Navigation/UserModal.tsx b/frontend/app/components/Navigation/UserModal.tsx new file mode 100644 index 000000000..058bfde77 --- /dev/null +++ b/frontend/app/components/Navigation/UserModal.tsx @@ -0,0 +1,49 @@ +"use client"; + +import React from "react"; + +interface UserModalComponentProps { + modal_id: string; + title: string; + text: string; + triggerAccept?: (a: any) => void; + triggerValue?: any; + triggerString?: string; +} + +const UserModalComponent: React.FC = ({ + title, + modal_id, + text, + triggerAccept, + triggerString, + triggerValue, +}) => { + return ( + +
+

{title}

+

{text}

+
+
+ {triggerAccept && triggerValue && triggerString && ( + + )} + +
+
+
+ + ); +}; + +export default UserModalComponent; diff --git a/frontend/app/components/Navigation/util.ts b/frontend/app/components/Navigation/util.ts new file mode 100644 index 000000000..fb3674446 --- /dev/null +++ b/frontend/app/components/Navigation/util.ts @@ -0,0 +1,21 @@ +export async function getGitHubStars(): Promise { + try { + const response: any = await fetch( + "https://api.github.com/repos/weaviate/verba", + { + method: "GET", + } + ); + + const data: any = await response.json(); + + if (data) { + return data.stargazers_count; + } else { + return 0; + } + } catch (error) { + console.error("Failed to perform search:", error); + return 0; + } +} diff --git a/frontend/app/components/RAG/NumberFieldRAGComponent.tsx b/frontend/app/components/RAG/NumberFieldRAGComponent.tsx new file mode 100644 index 000000000..f4791acb1 --- /dev/null +++ b/frontend/app/components/RAG/NumberFieldRAGComponent.tsx @@ -0,0 +1,62 @@ +"use client"; + +import React from "react"; + +import { NumberFieldSetting } from "../Settings/types"; +import { RAGConfig } from "./types"; + +interface NumberFieldRAGComponentProps { + title: string; + NumberFieldSetting: NumberFieldSetting; + RAGConfig: RAGConfig; + RAGConfigTitle: string; + RAGComponentTitle: string; + setRAGConfig: (r_: any) => void; +} + +const NumberFieldRAGComponent: React.FC = ({ + title, + NumberFieldSetting, + RAGConfig, + RAGConfigTitle, + RAGComponentTitle, + setRAGConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.value; + setRAGConfig((prevConfig: any) => { + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + console.log(newConfig[RAGConfigTitle].components[RAGComponentTitle]); + newConfig[RAGConfigTitle].components[RAGComponentTitle].config[ + title + ].value = newText; + return newConfig; + }); + }; + + return ( +
+
+

{NumberFieldSetting.description}

+
+
+ +
+
+ ); +}; + +export default NumberFieldRAGComponent; diff --git a/frontend/app/components/RAG/RAGComponent.tsx b/frontend/app/components/RAG/RAGComponent.tsx new file mode 100644 index 000000000..817897e0a --- /dev/null +++ b/frontend/app/components/RAG/RAGComponent.tsx @@ -0,0 +1,252 @@ +"use client"; + +import React, { useState } from "react"; +import { SettingsConfiguration } from "../Settings/types"; +import RAGConfigComponent from "./RAGConfigComponent"; +import { RAGConfig, ConsoleMessage, ImportResponse } from "./types"; +import { FaFileImport } from "react-icons/fa"; +import { MdCancel } from "react-icons/md"; + +import PulseLoader from "react-spinners/PulseLoader"; +import { Settings } from "../Settings/types"; + +import { processFiles } from "./util"; + +interface RAGComponentProps { + settingConfig: SettingsConfiguration; + APIHost: string | null; + RAGConfig: RAGConfig | null; + setRAGConfig: (r_: RAGConfig | null) => void; + showComponents: string[]; + buttonTitle: string; + settingTemplate: string; + baseSetting: Settings; + setCurrentPage: (p: any) => void; +} + +const RAGComponent: React.FC = ({ + APIHost, + setCurrentPage, + settingConfig, + RAGConfig, + setRAGConfig, + showComponents, + buttonTitle, + baseSetting, + settingTemplate, +}) => { + const [currentRAGSettings, setCurrentRAGSettings] = useState( + JSON.parse(JSON.stringify(RAGConfig)) + ); + const [files, setFiles] = useState(null); + const [textValues, setTextValues] = useState([]); + const [isFetching, setIsFetching] = useState(false); + + const [consoleLog, setConsoleLog] = useState([]); + + const saveSettings = () => { + setRAGConfig(currentRAGSettings); + if (buttonTitle === "Import") { + importData(); + } else { + importConfig(); + setCurrentPage("CHAT"); + } + }; + + const importData = async () => { + setIsFetching(true); + + if (!files && textValues.length <= 0) { + setIsFetching(false); + return; + } + + try { + addToConsole("INFO", "Importing..."); + const fileData = files ? await processFiles(files) : []; + setFiles(null); + if (fileData) { + const payload = { + config: { + RAG: currentRAGSettings, + SETTING: { selectedTheme: settingTemplate, themes: baseSetting }, + }, + data: fileData, + textValues: textValues, + }; + + const response = await fetch(APIHost + "/api/import", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(payload), + }); + + const data: ImportResponse = await response.json(); + + if (data) { + for (let i = 0; i < data.logging.length; i++) { + setConsoleLog((oldItems) => [...oldItems, data.logging[i]]); + } + setIsFetching(false); + } else { + setIsFetching(false); + } + } else { + setIsFetching(false); + } + } catch (error) { + console.error("Failed to fetch from API:", error); + setIsFetching(false); + } + }; + + const importConfig = async () => { + if (!APIHost) { + return; + } + + setIsFetching(true); + + try { + const payload = { + config: { + RAG: currentRAGSettings, + SETTING: { selectedTheme: settingTemplate, themes: baseSetting }, + }, + }; + + const response = await fetch(APIHost + "/api/set_config", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(payload), + }); + + const data: ImportResponse = await response.json(); + + if (data) { + console.log(data); + setIsFetching(false); + } else { + setIsFetching(false); + } + } catch (error) { + console.error("Failed to fetch from API:", error); + setIsFetching(false); + } + }; + + const resetSettings = () => { + setCurrentRAGSettings(JSON.parse(JSON.stringify(RAGConfig))); + setConsoleLog([]); + setTextValues([]); + setFiles(null); + }; + + const addToConsole = ( + t: "INFO" | "WARNING" | "SUCCESS" | "ERROR", + m: string + ) => { + const consoleMsg: ConsoleMessage = { type: t, message: m }; + setConsoleLog((oldItems) => [...oldItems, consoleMsg]); + }; + + return ( +
+ {currentRAGSettings && + Object.entries(currentRAGSettings).map( + ([key, value]) => + showComponents.includes(key) && ( +
+ +
+ ) + )} + {currentRAGSettings ? ( +
+
+ + +
+ {buttonTitle === "Import" && consoleLog.length > 0 && ( +
+ {consoleLog && + consoleLog.map((msg, index) => ( + 
+                    
+                      ({msg.type}) {msg.message}
+                    
+
+ ))} +
+ )} +
+ ) : ( +
+
+ +

Loading Components...

+
+
+ )} +
+ ); +}; + +export default RAGComponent; diff --git a/frontend/app/components/RAG/RAGConfigComponent.tsx b/frontend/app/components/RAG/RAGConfigComponent.tsx new file mode 100644 index 000000000..6a4978eac --- /dev/null +++ b/frontend/app/components/RAG/RAGConfigComponent.tsx @@ -0,0 +1,232 @@ +"use client"; + +import React, { useState } from "react"; +import { SettingsConfiguration } from "../Settings/types"; + +import { RAGConfig, RAGComponentClass } from "./types"; + +import { TextFieldSetting, NumberFieldSetting } from "../Settings/types"; + +import { FaCheck } from "react-icons/fa"; + +import TextFieldRAGComponent from "./TextFieldRAGComponent"; +import NumberFieldRAGComponent from "./NumberFieldRAGComponent"; +import { text } from "stream/consumers"; + +interface RAGConfigComponentProps { + settingConfig: SettingsConfiguration; + APIHost: string | null; + files: FileList | null; + RAGConfig: RAGConfig; + RAGConfigTitle: string; + RAGComponents: RAGComponentClass; + setRAGConfig: (r_: any) => void; + setFiles: (f: FileList | null) => void; + setTextValues: (t: string[]) => void; + textValues: string[]; +} + +const RAGConfigComponent: React.FC = ({ + APIHost, + files, + settingConfig, + textValues, + RAGConfig, + RAGConfigTitle, + RAGComponents, + setRAGConfig, + setFiles, + setTextValues, +}) => { + const [currentText, setCurrentText] = useState( + textValues ? textValues[0] : "" + ); + + const onSelectComponent = (_selected: string) => { + setRAGConfig((prevConfig: any) => { + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + newConfig[RAGConfigTitle].selected = _selected; + return newConfig; + }); + }; + + const handleUploadFiles = (event: React.ChangeEvent) => { + if (event.target.files && event.target.files[0]) { + setFiles(event.target.files); + } + }; + + const renderSettingComponent = ( + title: any, + setting_type: TextFieldSetting | NumberFieldSetting + ) => { + if (!RAGConfig) { + return null; + } + + switch (setting_type.type) { + case "text": + return ( + + ); + case "number": + return ( + + ); + default: + return null; + } + }; + + return ( +
+
+
+

Select a {RAGConfigTitle}

+
+ +
+ {RAGComponents && + Object.entries(RAGComponents.components).map(([key, value]) => ( + + ))} +
+ +
+
+

You selected:

+

+ {RAGComponents.selected} +

+
+

+ {RAGComponents.components[RAGComponents.selected].description} +

+
+ +
+ {RAGConfig && + Object.entries( + RAGComponents.components[RAGComponents.selected].config + ).map(([key, settingValue]) => + renderSettingComponent(key, settingValue) + )} +
+ + {RAGComponents.components[RAGComponents.selected].type === "UPLOAD" && ( +
+
+
+ + +
+ {files && ( +
+ +
+ )} +
+ {files && ( +
+

Files:

+
+ {files && + Array.from(files).map((file, index) => ( +

{file.name}

+ ))} +
+
+ )} +
+ )} + + {RAGComponents.components[RAGComponents.selected].type === "URL" && ( +
+
+ {(textValues.length > 0 && currentText !== textValues[0]) || + (currentText != "" && textValues.length <= 0) ? ( +

*Enter URL

+ ) : ( +

Enter URL

+ )} +
+
+
+ +
+ +
+
+ )} + +
+
+
+ ); +}; + +export default RAGConfigComponent; diff --git a/frontend/app/components/RAG/TextFieldRAGComponent.tsx b/frontend/app/components/RAG/TextFieldRAGComponent.tsx new file mode 100644 index 000000000..7393ed854 --- /dev/null +++ b/frontend/app/components/RAG/TextFieldRAGComponent.tsx @@ -0,0 +1,62 @@ +"use client"; + +import React from "react"; + +import { TextFieldSetting } from "../Settings/types"; +import { RAGConfig } from "./types"; + +interface TextFieldComponentProps { + title: string; + TextFieldSetting: TextFieldSetting; + RAGConfig: RAGConfig; + RAGConfigTitle: string; + RAGComponentTitle: string; + setRAGConfig: (r_: any) => void; +} + +const TextFieldRAGComponent: React.FC = ({ + title, + TextFieldSetting, + RAGConfig, + RAGConfigTitle, + RAGComponentTitle, + setRAGConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.value; + setRAGConfig((prevConfig: any) => { + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + console.log(newConfig[RAGConfigTitle].components[RAGComponentTitle]); + newConfig[RAGConfigTitle].components[RAGComponentTitle].config[ + title + ].text = newText; + return newConfig; + }); + }; + + return ( +
+
+

{TextFieldSetting.description}

+
+
+ +
+
+ ); +}; + +export default TextFieldRAGComponent; diff --git a/frontend/app/components/RAG/types.ts b/frontend/app/components/RAG/types.ts new file mode 100644 index 000000000..db43686c3 --- /dev/null +++ b/frontend/app/components/RAG/types.ts @@ -0,0 +1,53 @@ +import { + TextFieldSetting, + NumberFieldSetting, + SettingsPayload, +} from "../Settings/types"; + +export type RAGResponse = { + data: { RAG: RAGConfig; SETTING: SettingsPayload }; + error: string; +}; + +export type ImportResponse = { + logging: ConsoleMessage[]; +}; + +export type ConsoleMessage = { + type: "INFO" | "WARNING" | "SUCCESS" | "ERROR"; + message: string; +}; + +export type FileData = { + filename: string; + extension: string; + content: string; +}; + +export type RAGConfig = { + [componentTitle: string]: RAGComponentClass; +}; + +export type RAGComponentClass = { + selected: string; + components: RAGComponent; +}; + +export type RAGComponent = { + [key: string]: RAGComponentConfig; +}; + +export type RAGComponentConfig = { + name: string; + variables: string[]; + library: string[]; + description: string[]; + selected: string; + config: RAGSetting; + type: string; + available: boolean; +}; + +export type RAGSetting = { + [key: string]: TextFieldSetting | NumberFieldSetting; +}; diff --git a/frontend/app/components/RAG/util.ts b/frontend/app/components/RAG/util.ts new file mode 100644 index 000000000..879a8b3b8 --- /dev/null +++ b/frontend/app/components/RAG/util.ts @@ -0,0 +1,36 @@ +import { FileData } from "./types"; + +export function arrayBufferToBase64(buffer: ArrayBuffer): string { + let binary = ""; + const bytes = new Uint8Array(buffer); + const len = bytes.byteLength; + for (let i = 0; i < len; i++) { + binary += String.fromCharCode(bytes[i]); + } + return btoa(binary); // Encode the binary string to base64 +} + +export function processFiles(fileList: FileList): Promise { + return Promise.all( + Array.from(fileList).map( + (file) => + new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => { + const arrayBuffer = reader.result as ArrayBuffer; + const content = arrayBufferToBase64(arrayBuffer); + const filename = file.name; + const extension = file.name.split(".").pop() || ""; + resolve({ + filename, + extension, + content, + }); + }; + reader.onerror = () => reject(reader.error); + + reader.readAsArrayBuffer(file); + }) + ) + ); +} diff --git a/frontend/app/components/Settings/CheckFieldComponent.tsx b/frontend/app/components/Settings/CheckFieldComponent.tsx new file mode 100644 index 000000000..dc61b8a80 --- /dev/null +++ b/frontend/app/components/Settings/CheckFieldComponent.tsx @@ -0,0 +1,53 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, CheckboxSetting } from "./types"; + +interface CheckComponent { + title: string; + CheckboxSetting: CheckboxSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const CheckComponent: React.FC = ({ + title, + CheckboxSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.checked; + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].checked = newText; + + // Return the updated copy + return newConfig; + }); + }; + + return ( +
+
+

{CheckboxSetting.description}

+
+
+ +
+
+ ); +}; + +export default CheckComponent; diff --git a/frontend/app/components/Settings/ColorFieldComponent.tsx b/frontend/app/components/Settings/ColorFieldComponent.tsx new file mode 100644 index 000000000..200158007 --- /dev/null +++ b/frontend/app/components/Settings/ColorFieldComponent.tsx @@ -0,0 +1,66 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, ColorSetting } from "./types"; +import { HexColorPicker } from "react-colorful"; + +interface ColorFieldComponentProps { + title: string; + ColorSetting: ColorSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const ColorFieldComponent: React.FC = ({ + title, + ColorSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleChange = (e: string) => { + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].color = e; + + // Return the updated copy + return newConfig; + }); + }; + + return ( +
+
+

{ColorSetting.description}

+
+
+
+ + { + handleChange(newColor); + }} + /> +
+
+
+ ); +}; + +export default ColorFieldComponent; diff --git a/frontend/app/components/Settings/ImageFieldComponent.tsx b/frontend/app/components/Settings/ImageFieldComponent.tsx new file mode 100644 index 000000000..721939932 --- /dev/null +++ b/frontend/app/components/Settings/ImageFieldComponent.tsx @@ -0,0 +1,77 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, ImageFieldSetting } from "./types"; + +interface ImageFieldComponentProps { + title: string; + ImageFieldSetting: ImageFieldSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const ImageFieldComponent: React.FC = ({ + title, + ImageFieldSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleImageChange = (event: React.ChangeEvent) => { + if (event.target.files && event.target.files[0]) { + const reader = new FileReader(); + + reader.onload = (e) => { + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].src = e.target?.result as string; + + // Return the updated copy + return newConfig; + }); + }; + reader.readAsDataURL(event.target.files[0]); + } + }; + + return ( +
+
+

{ImageFieldSetting.description}

+
+
+
+
+ Logo +
+
+ + +
+
+
+
+ ); +}; + +export default ImageFieldComponent; diff --git a/frontend/app/components/Settings/NumberFieldComponent.tsx b/frontend/app/components/Settings/NumberFieldComponent.tsx new file mode 100644 index 000000000..6b2799e1a --- /dev/null +++ b/frontend/app/components/Settings/NumberFieldComponent.tsx @@ -0,0 +1,56 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, NumberFieldSetting } from "./types"; + +interface NumberFieldComponentProps { + title: string; + NumberFieldSetting: NumberFieldSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const NumberFieldComponent: React.FC = ({ + title, + NumberFieldSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.value; + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].value = newText; + + // Return the updated copy + return newConfig; + }); + }; + + return ( +
+
+

{NumberFieldSetting.description}

+
+
+ +
+
+ ); +}; + +export default NumberFieldComponent; diff --git a/frontend/app/components/Settings/SelectFieldComponent.tsx b/frontend/app/components/Settings/SelectFieldComponent.tsx new file mode 100644 index 000000000..876f93bc9 --- /dev/null +++ b/frontend/app/components/Settings/SelectFieldComponent.tsx @@ -0,0 +1,56 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, SelectSetting } from "./types"; + +interface SelectComponentProps { + title: string; + SelectSetting: SelectSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const SelectComponent: React.FC = ({ + title, + SelectSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.value; + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].value = newText; + + // Return the updated copy + return newConfig; + }); + }; + + return ( +
+
+

{SelectSetting.description}

+
+
+ +
+
+ ); +}; + +export default SelectComponent; diff --git a/frontend/app/components/Settings/SettingsButton.tsx b/frontend/app/components/Settings/SettingsButton.tsx new file mode 100644 index 000000000..6b7b92277 --- /dev/null +++ b/frontend/app/components/Settings/SettingsButton.tsx @@ -0,0 +1,37 @@ +"use client"; + +import React from "react"; +import { FaStar } from "react-icons/fa"; + +interface SettingsButtonProps { + Icon: typeof FaStar; + iconSize: number; + title: string; + currentSetting: string; + setSetting: (page: "Customization" | "Chat" | "") => void; + setSettingString: "Customization" | "Chat" | ""; +} + +const SettingsButton: React.FC = ({ + Icon, + iconSize, + title, + currentSetting, + setSetting, + setSettingString, +}) => { + return ( + + ); +}; + +export default SettingsButton; diff --git a/frontend/app/components/Settings/SettingsComponent.tsx b/frontend/app/components/Settings/SettingsComponent.tsx new file mode 100644 index 000000000..fb092c1d9 --- /dev/null +++ b/frontend/app/components/Settings/SettingsComponent.tsx @@ -0,0 +1,303 @@ +"use client"; + +import React, { useState, useEffect } from "react"; +import { + SettingsConfiguration, + TextFieldSetting, + ImageFieldSetting, + CheckboxSetting, + ColorSetting, + BaseSettings, + Settings, + SelectSetting, + NumberFieldSetting, +} from "./types"; +import { FaPaintBrush } from "react-icons/fa"; +import { IoChatbubbleSharp } from "react-icons/io5"; +import { FaCheckCircle } from "react-icons/fa"; +import { MdCancel } from "react-icons/md"; +import { LuMenu } from "react-icons/lu"; + +import TextFieldComponent from "./TextFieldComponent"; +import ImageFieldComponent from "./ImageFieldComponent"; +import ColorFieldComponent from "./ColorFieldComponent"; +import SelectComponent from "./SelectFieldComponent"; +import CheckComponent from "./CheckFieldComponent"; +import NumberFieldComponent from "./NumberFieldComponent"; + +import SettingButton from "./SettingsButton"; + +interface SettingsComponentProps { + settingTemplate: string; + setSettingTemplate: (s: string) => void; + + baseSetting: Settings; + setBaseSetting: (b: any) => void; +} + +const SettingsComponent: React.FC = ({ + settingTemplate, + setSettingTemplate, + baseSetting, + setBaseSetting, +}) => { + const [setting, setSetting] = useState<"Customization" | "Chat" | "">( + "Customization" + ); + const [currentSettingsConfig, setCurrentSettingsConfig] = + useState( + JSON.parse(JSON.stringify(baseSetting[settingTemplate])) + ); + + const [availableTemplate, setAvailableTemplate] = useState( + Object.keys(BaseSettings) + ); + + useEffect(() => { + setAvailableTemplate(Object.keys(BaseSettings)); + setCurrentSettingsConfig( + JSON.parse(JSON.stringify(baseSetting[settingTemplate])) + ); + }, [baseSetting, settingTemplate]); + + const iconSize = 20; + + const applyChanges = () => { + setBaseSetting((prevSetting: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevSetting)); + + // Updating the copied state + newConfig[settingTemplate] = currentSettingsConfig; + + // Return the updated copy + return newConfig; + }); + }; + + const revertChanges = () => { + setCurrentSettingsConfig( + JSON.parse(JSON.stringify(baseSetting[settingTemplate])) + ); + }; + + const renderSettingComponent = ( + title: any, + setting_type: + | TextFieldSetting + | ImageFieldSetting + | CheckboxSetting + | ColorSetting + | SelectSetting + | NumberFieldSetting + ) => { + if (setting === "") { + return null; + } + + switch (setting_type.type) { + case "text": + return ( + + ); + case "image": + return ( + + ); + case "check": + return ( + + ); + case "select": + return ( + + ); + case "color": + return ( + + ); + case "number": + return ( + + ); + default: + return null; + } + }; + + const handleTemplateChange = (e: React.ChangeEvent) => { + const name = e.target.value; + setSettingTemplate(name); // Update the selected template state + }; + + return ( +
+ {/* Setting Options */} +
+
+

+ Settings +

+
+ + +
+
+ {setting != "" && ( +
+

+ Description +

+
+

+ {" "} + {BaseSettings[settingTemplate][setting] + ? BaseSettings[settingTemplate][setting].description + : ""} +

+
+
+ )} +
+ + {/* Configuration Options */} +
+
+
+
    +
  • +
    + + Settings + + +
    +
    • +
+
+

+ Configuration +

+ +
+
+

{setting}

+ {setting != "" && ( +
+

+ Description +

+
+

+ {" "} + {BaseSettings[settingTemplate][setting] + ? BaseSettings[settingTemplate][setting].description + : ""} +

+
+
+ )} +
+ {setting && + Object.entries( + BaseSettings[settingTemplate][setting].settings + ).map(([key, settingValue]) => + renderSettingComponent(key, settingValue) + )} +
+
+ + +
+
+
+
+ ); +}; + +export default SettingsComponent; diff --git a/frontend/app/components/Settings/TextFieldComponent.tsx b/frontend/app/components/Settings/TextFieldComponent.tsx new file mode 100644 index 000000000..139a93cae --- /dev/null +++ b/frontend/app/components/Settings/TextFieldComponent.tsx @@ -0,0 +1,56 @@ +"use client"; + +import React from "react"; +import { SettingsConfiguration, TextFieldSetting } from "./types"; + +interface TextFieldComponentProps { + title: string; + TextFieldSetting: TextFieldSetting; + setting: "Customization" | "Chat"; + + settingsConfig: SettingsConfiguration; + setSettingsConfig: (settings: any) => void; +} + +const TextFieldComponent: React.FC = ({ + title, + TextFieldSetting, + setting, + settingsConfig, + setSettingsConfig, +}) => { + const handleChange = (e: React.ChangeEvent) => { + const newText = e.target.value; + setSettingsConfig((prevConfig: any) => { + // Creating a deep copy of prevConfig to avoid mutating the original state directly + const newConfig = JSON.parse(JSON.stringify(prevConfig)); + + // Updating the copied state + newConfig[setting].settings[title].text = newText; + + // Return the updated copy + return newConfig; + }); + }; + + return ( +
+
+

{TextFieldSetting.description}

+
+
+ +
+
+ ); +}; + +export default TextFieldComponent; diff --git a/frontend/app/components/Settings/types.ts b/frontend/app/components/Settings/types.ts new file mode 100644 index 000000000..714a4fdb5 --- /dev/null +++ b/frontend/app/components/Settings/types.ts @@ -0,0 +1,587 @@ +export interface SettingsPayload { + selectedTheme: string; + themes: Settings; +} + +interface MetaInformation { + title: string; + description: string; +} + +export interface Settings { + [key: string]: SettingsConfiguration; +} + +export interface SettingsConfiguration { + Customization: CustomizationSettings; + Chat: ChatSettings; +} + +// Setting Options + +export interface CustomizationSettings extends MetaInformation { + settings: { + title: TextFieldSetting; + subtitle: TextFieldSetting; + intro_message: TextFieldSetting; + placeholder_message: TextFieldSetting; + image: ImageFieldSetting; + primary_color: ColorSetting; + secondary_color: ColorSetting; + warning_color: ColorSetting; + bg_color: ColorSetting; + bg_alt_color: ColorSetting; + text_color: ColorSetting; + text_alt_color: ColorSetting; + button_color: ColorSetting; + button_hover_color: ColorSetting; + bg_console: ColorSetting; + text_console: ColorSetting; + font: SelectSetting; + theme: "light" | "dark"; + }; +} + +export interface ChatSettings extends MetaInformation { + settings: { + caching: CheckboxSetting; + suggestion: CheckboxSetting; + info_button: CheckboxSetting; + max_document_size: NumberFieldSetting; + }; +} + +// Setting Fields + +export interface TextFieldSetting { + type: "text"; + text: string; + description: string; +} + +export interface NumberFieldSetting { + type: "number"; + value: number; + description: string; +} + +export interface ImageFieldSetting { + type: "image"; + src: string; + description: string; +} + +export interface CheckboxSetting { + type: "check"; + checked: boolean; + description: string; +} + +export interface ColorSetting { + type: "color"; + color: string; + description: string; +} + +export interface SelectSetting { + type: "select"; + options: string[]; + value: string; + description: string; +} + +// Base Settings + +const AvailableFonts: string[] = [ + "Inter", + "Plus_Jakarta_Sans", + "Open_Sans", + "PT_Mono", +]; + +const BaseFonts: SelectSetting = { + value: "Plus_Jakarta_Sans", + type: "select", + options: AvailableFonts, + description: "Text Font", +}; + +const BaseCustomization: CustomizationSettings = { + title: "Customization", + description: + "Customize the layout of your Verba by changing the title, subtitle, logo, and colors of the app.", + settings: { + title: { text: "Verba", type: "text", description: "Title of the Page" }, + subtitle: { + text: "The Golden RAGtriever", + type: "text", + description: "Subtitle of the Page", + }, + intro_message: { + text: "Welcome to Verba, your open-source RAG application!", + type: "text", + description: "Intro Message", + }, + placeholder_message: { + text: "Ask Verba anything!", + type: "text", + description: "Input Placeholder", + }, + image: { + src: "https://github.com/weaviate/Verba/blob/main/frontend/public/favicon.png?raw=true", + type: "image", + description: "Logo of the Page", + }, + primary_color: { + color: "#FDFF91", + type: "color", + description: "Primary Color", + }, + secondary_color: { + color: "#90FFA8", + type: "color", + description: "Secondary Color", + }, + warning_color: { + color: "#FF8399", + type: "color", + description: "Accent Color", + }, + bg_color: { + color: "#FEF7F7", + type: "color", + description: "Background Color", + }, + bg_alt_color: { + color: "#FFFFFF", + type: "color", + description: "Alternative Background Color", + }, + text_color: { color: "#161616", type: "color", description: "Text Color" }, + text_alt_color: { + color: "#8E8E8E", + type: "color", + description: "Alternative Text Color", + }, + button_color: { + color: "#EFEFEF", + type: "color", + description: "Button Color", + }, + button_hover_color: { + color: "#DCDCDC", + type: "color", + description: "Button Hover Color", + }, + bg_console: { + color: "#3F3E40", + type: "color", + description: "Console Background", + }, + text_console: { + color: "#FFFFFF", + type: "color", + description: "Console Text", + }, + font: BaseFonts, + theme: "light", + }, +}; + +const CustomCustomization: CustomizationSettings = { + title: "Customization", + description: + "Customize the layout of your Verba by changing the title, subtitle, logo, and colors of the app.", + settings: { + title: { text: "My RAG", type: "text", description: "Title of the Page" }, + subtitle: { + text: "Powered by Weaviate", + type: "text", + description: "Subtitle of the Page", + }, + intro_message: { + text: "Welcome to Verba, your open-source RAG application!", + type: "text", + description: "Intro Message", + }, + placeholder_message: { + text: "Ask Verba anything!", + type: "text", + description: "Input Placeholder", + }, + image: { + src: "https://github.com/weaviate/Verba/blob/main/frontend/public/favicon.png?raw=true", + type: "image", + description: "Logo of the Page", + }, + primary_color: { + color: "#FDFF91", + type: "color", + description: "Primary Color", + }, + secondary_color: { + color: "#90FFA8", + type: "color", + description: "Secondary Color", + }, + warning_color: { + color: "#FF8399", + type: "color", + description: "Accent Color", + }, + bg_color: { + color: "#FEF7F7", + type: "color", + description: "Background Color", + }, + bg_alt_color: { + color: "#FFFFFF", + type: "color", + description: "Alternative Background Color", + }, + text_color: { color: "#161616", type: "color", description: "Text Color" }, + text_alt_color: { + color: "#8E8E8E", + type: "color", + description: "Alternative Text Color", + }, + button_color: { + color: "#EFEFEF", + type: "color", + description: "Button Color", + }, + button_hover_color: { + color: "#DCDCDC", + type: "color", + description: "Button Hover Color", + }, + bg_console: { + color: "#3F3E40", + type: "color", + description: "Console Background", + }, + text_console: { + color: "#FFFFFF", + type: "color", + description: "Console Text", + }, + font: BaseFonts, + theme: "light", + }, +}; + +const DarkModeCustomization: CustomizationSettings = { + title: "Customization", + description: + "Customize the layout of your Verba by changing the title, subtitle, logo, and colors of the app.", + settings: { + title: { text: "Verba", type: "text", description: "Title of the Page" }, + subtitle: { + text: "The Dark RAGtriever", + type: "text", + description: "Subtitle of the Page", + }, + intro_message: { + text: "Welcome to the dark mode version of Verba! Spooky, right?", + type: "text", + description: "Intro Message", + }, + placeholder_message: { + text: "Ask anything!", + type: "text", + description: "Input Placeholder", + }, + image: { + src: "https://github.com/weaviate/Verba/blob/main/frontend/public/favicon.png?raw=true", + type: "image", + description: "Logo of the Page", + }, + primary_color: { + color: "#BB86FC", + type: "color", + description: "Primary Color", + }, + secondary_color: { + color: "#008F82", + type: "color", + description: "Secondary Color", + }, + warning_color: { + color: "#FF8399", + type: "color", + description: "Accent Color", + }, + bg_color: { + color: "#202020", + type: "color", + description: "Background Color", + }, + bg_alt_color: { + color: "#2F2929", + type: "color", + description: "Alternative Background Color", + }, + text_color: { color: "#ffffff", type: "color", description: "Text Color" }, + text_alt_color: { + color: "#999999", + type: "color", + description: "Alternative Text Color", + }, + button_color: { + color: "#3C3C3C", + type: "color", + description: "Button Color", + }, + button_hover_color: { + color: "#2C2C2C", + type: "color", + description: "Button Hover Color", + }, + bg_console: { + color: "#2F2929", + type: "color", + description: "Console Background", + }, + text_console: { + color: "#FFFFFF", + type: "color", + description: "Console Text", + }, + font: { + value: "Open_Sans", + type: "select", + options: AvailableFonts, + description: "Text Font", + }, + theme: "dark", + }, +}; + +const GoogleHackathonCustomization: CustomizationSettings = { + title: "Customization", + description: + "Customize the layout of your Verba by changing the title, subtitle, logo, and colors of the app.", + settings: { + title: { + text: "Prinses Maxima Centrum", + type: "text", + description: "Title of the Page", + }, + subtitle: { + text: "Medical RAG", + type: "text", + description: "Subtitle of the Page", + }, + intro_message: { + text: "Welcome to Medical Retrieval Augmented Generation! We ingested medical data, like clinical reports, research and more to improve access to healthcare. Try it out by asking questions related to diseases, symptoms and medical conditions.", + type: "text", + description: "Intro Message", + }, + placeholder_message: { + text: "Ask a medical question", + type: "text", + description: "Input Placeholder", + }, + image: { + src: "https://www.prinsesmaximacentrum.nl/img/logo/prinses-maxima-centrum-logo-en.svg", + type: "image", + description: "Logo of the Page", + }, + primary_color: { + color: "#FF963F", + type: "color", + description: "Primary Color", + }, + secondary_color: { + color: "#53E797", + type: "color", + description: "Secondary Color", + }, + warning_color: { + color: "#F11F78", + type: "color", + description: "Accent Color", + }, + bg_color: { + color: "#F1F1F1", + type: "color", + description: "Background Color", + }, + bg_alt_color: { + color: "#FFFFFF", + type: "color", + description: "Alternative Background Color", + }, + text_color: { color: "#000F1D", type: "color", description: "Text Color" }, + text_alt_color: { + color: "#818181", + type: "color", + description: "Alternative Text Color", + }, + button_color: { + color: "#E4E4E4", + type: "color", + description: "Button Color", + }, + button_hover_color: { + color: "#CCCCCC", + type: "color", + description: "Button Hover Color", + }, + bg_console: { + color: "#3F3E40", + type: "color", + description: "Console Background", + }, + text_console: { + color: "#FFFFFF", + type: "color", + description: "Console Text", + }, + font: { + value: "Open_Sans", + type: "select", + options: AvailableFonts, + description: "Text Font", + }, + theme: "light", + }, +}; + +const WeaviateCustomization: CustomizationSettings = { + title: "Customization", + description: + "Customize the layout of your Verba by changing the title, subtitle, logo, and colors of the app.", + settings: { + title: { + text: "Weaviate Verba", + type: "text", + description: "Title of the Page", + }, + subtitle: { + text: "Chatbot for Weaviate", + type: "text", + description: "Subtitle of the Page", + }, + intro_message: { + text: "Welcome to Weaviate, your AI-Native vector database. How can I help you with Weaviate today?", + type: "text", + description: "Intro Message", + }, + placeholder_message: { + text: "Ask all questions related to Weaviate", + type: "text", + description: "Input Placeholder", + }, + image: { + src: "https://github.com/weaviate/Verba/blob/1.0.0/frontend/public/weaviate.png?raw=true", + type: "image", + description: "Logo of the Page", + }, + primary_color: { + color: "#6BDF4A", + type: "color", + description: "Primary Color", + }, + secondary_color: { + color: "#7AD6EB", + type: "color", + description: "Secondary Color", + }, + warning_color: { + color: "#F4404E", + type: "color", + description: "Accent Color", + }, + bg_color: { + color: "#EDEDED", + type: "color", + description: "Background Color", + }, + bg_alt_color: { + color: "#ffffff", + type: "color", + description: "Alternative Background Color", + }, + text_color: { color: "#130C49", type: "color", description: "Text Color" }, + text_alt_color: { + color: "#8196A6", + type: "color", + description: "Alternative Text Color", + }, + button_color: { + color: "#E6E3E3", + type: "color", + description: "Button Color", + }, + button_hover_color: { + color: "#FFFFFF", + type: "color", + description: "Button Hover Color", + }, + bg_console: { + color: "#3F3E40", + type: "color", + description: "Console Background", + }, + text_console: { + color: "#FFFFFF", + type: "color", + description: "Console Text", + }, + font: { + value: "Inter", + type: "select", + options: AvailableFonts, + description: "Text Font", + }, + theme: "light", + }, +}; + +const BaseChat: ChatSettings = { + title: "Chat Settings", + description: + "Customize chat settings like caching generated answers in Weaviate or let Weaviate give you autocomplete suggestions.", + settings: { + caching: { checked: true, type: "check", description: "Enable Caching" }, + suggestion: { + checked: true, + type: "check", + description: "Enable Autocompletion", + }, + info_button: { + checked: true, + type: "check", + description: "Enable Help Information", + }, + max_document_size: { + value: 10000, + type: "number", + description: "Max characters to show Documents", + }, + }, +}; + +export const BaseSettings: Settings = { + Default: { + Customization: BaseCustomization, + Chat: BaseChat, + }, + DarkMode: { + Customization: DarkModeCustomization, + Chat: BaseChat, + }, + Weaviate: { + Customization: WeaviateCustomization, + Chat: BaseChat, + }, + GoogleHackathon: { + Customization: GoogleHackathonCustomization, + Chat: BaseChat, + }, + Custom: { + Customization: CustomCustomization, + Chat: BaseChat, + }, +}; diff --git a/frontend/app/components/Status/AdminConsole.tsx b/frontend/app/components/Status/AdminConsole.tsx new file mode 100644 index 000000000..28dbef180 --- /dev/null +++ b/frontend/app/components/Status/AdminConsole.tsx @@ -0,0 +1,204 @@ +"use client"; +import React from "react"; +import { SettingsConfiguration } from "../Settings/types"; + +import { SchemaStatus } from "./types"; +import { MdDelete } from "react-icons/md"; + +import StatusLabel from "../Chat/StatusLabel"; +import StatusCard from "./StatusCard"; +import PulseLoader from "react-spinners/PulseLoader"; + +import UserModalComponent from "../Navigation/UserModal"; + +interface AdminConsoleComponentProps { + type: string | null; + connected: string; + schemas: SchemaStatus | null; + isFetching: boolean; + settingConfig: SettingsConfiguration; + reset_verba: (m: string) => void; +} + +const AdminConsoleComponent: React.FC = ({ + type, + connected, + isFetching, + schemas, + settingConfig, + reset_verba, +}) => { + const openResetVerba = () => { + const modal = document.getElementById("reset_verba_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + const openResetDocuments = () => { + const modal = document.getElementById("reset_documents_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + const openResetCache = () => { + const modal = document.getElementById("reset_cache_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + const openResetSuggestions = () => { + const modal = document.getElementById("reset_suggestions_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + const openConfigSuggestions = () => { + const modal = document.getElementById("reset_config_modal"); + if (modal instanceof HTMLDialogElement) { + modal.showModal(); + } + }; + + return ( +
+
+
+

Admin Console

+
+ + +
+
+ + {isFetching && ( +
+ +

Loading Stats

+
+ )} + + {connected === "Online" && ( +
+ + + + + +
+ )} + +
+ {schemas && + Object.entries(schemas).map(([key, value]) => ( + + ))} +
+
+ + + + + + +
+ ); +}; + +export default AdminConsoleComponent; diff --git a/frontend/app/components/Status/ComponentStatus.tsx b/frontend/app/components/Status/ComponentStatus.tsx new file mode 100644 index 000000000..2620ab3ac --- /dev/null +++ b/frontend/app/components/Status/ComponentStatus.tsx @@ -0,0 +1,35 @@ +"use client"; + +import React from "react"; +import { FaStar } from "react-icons/fa"; + +interface ComponentStatusProps { + component_name: string; + Icon: typeof FaStar; + changeTo: string; + changePage: (p: string) => void; + disable: boolean; +} + +const ComponentStatus: React.FC = ({ + component_name, + Icon, + changeTo, + changePage, + disable, +}) => { + return ( + + ); +}; + +export default ComponentStatus; diff --git a/frontend/app/components/Status/StatusCard.tsx b/frontend/app/components/Status/StatusCard.tsx new file mode 100644 index 000000000..a86a8a01a --- /dev/null +++ b/frontend/app/components/Status/StatusCard.tsx @@ -0,0 +1,30 @@ +"use client"; + +import React from "react"; + +interface StatusCardProps { + title: string; + value: number | null; + checked: boolean; +} + +const StatusCard: React.FC = ({ title, value, checked }) => { + return ( +
+
+

{title}

+
+ {value !== null ? ( +

{value}

+ ) : ( +

{checked ? "Available" : "Not Available"}

+ )} +
+
+
+ ); +}; + +export default StatusCard; diff --git a/frontend/app/components/Status/StatusComponent.tsx b/frontend/app/components/Status/StatusComponent.tsx new file mode 100644 index 000000000..c7b7e722b --- /dev/null +++ b/frontend/app/components/Status/StatusComponent.tsx @@ -0,0 +1,149 @@ +"use client"; + +import React, { useState, useEffect } from "react"; +import { SettingsConfiguration } from "../Settings/types"; + +import AdminConsoleComponent from "./AdminConsole"; +import StatusConsoleComponent from "./StatusConsole"; + +import { StatusPayload, Status, SchemaStatus } from "./types"; + +interface StatusComponentComponentProps { + settingConfig: SettingsConfiguration; + fetchHost: () => void; + APIHost: string | null; +} + +const StatusComponent: React.FC = ({ + APIHost, + settingConfig, + fetchHost, +}) => { + const [type, setType] = useState(null); + const [connected, setConnected] = useState<"Online" | "Offline">("Offline"); + const [libraries, setLibraries] = useState(null); + const [variables, setVariables] = useState(null); + const [schemas, setSchemas] = useState(null); + const [isFetching, setIsFetching] = useState(false); + + useEffect(() => { + if (APIHost != null) { + fetchStatus(); + } else { + console.warn("Missing API Host for retrieving status"); + setConnected("Offline"); + setType(null); + setLibraries(null); + setVariables(null); + setSchemas(null); + setIsFetching(false); + } + }, []); + + const fetchStatus = async () => { + if (!APIHost) { + return; + } + + try { + setIsFetching(true); + + const response = await fetch(APIHost + "/api/get_status", { + method: "GET", + }); + const data: StatusPayload = await response.json(); + + if (data) { + if (data.error) { + console.log(data.error); + } + + setType(data.type); + setConnected("Online"); + setLibraries(data.libraries); + setVariables(data.variables); + setSchemas(data.schemas); + setIsFetching(false); + } else { + console.warn("Status could not be retrieved"); + } + } catch (error) { + console.error("Failed to fetch document:", error); + setConnected("Offline"); + setType(null); + setLibraries(null); + setVariables(null); + setSchemas(null); + setIsFetching(false); + } + }; + + const removeMessagesFromLocalStorage = (key: string) => { + if (typeof window !== "undefined") { + localStorage.removeItem(key); + } + }; + + const reset_verba = async (mode: string) => { + try { + setType(null); + setLibraries(null); + setVariables(null); + setSchemas(null); + setIsFetching(true); + removeMessagesFromLocalStorage("VERBA_CONVERSATION"); + removeMessagesFromLocalStorage("VERBA_CHUNKS"); + removeMessagesFromLocalStorage("VERBA_CONTEXT"); + + const response = await fetch(APIHost + "/api/reset", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ resetMode: mode }), + }); + + if (response) { + await fetchHost(); + await fetchStatus(); + } + } catch (error) { + console.error("Failed to delete document:", error); + } + }; + + return ( +
+
+ +
+ +
+ +
+ +
+ +
+
+ ); +}; + +export default StatusComponent; diff --git a/frontend/app/components/Status/StatusConsole.tsx b/frontend/app/components/Status/StatusConsole.tsx new file mode 100644 index 000000000..516883a29 --- /dev/null +++ b/frontend/app/components/Status/StatusConsole.tsx @@ -0,0 +1,56 @@ +"use client"; +import React from "react"; +import { SettingsConfiguration } from "../Settings/types"; + +import { Status } from "./types"; +import StatusCard from "./StatusCard"; +import PulseLoader from "react-spinners/PulseLoader"; + +interface StatusConsoleComponentProps { + title: string; + status: Status | null; + isFetching: boolean; + settingConfig: SettingsConfiguration; +} + +const StatusConsoleComponent: React.FC = ({ + title, + status, + isFetching, + settingConfig, +}) => { + return ( +
+
+
+

{title}

+
+ + {isFetching && ( +
+ +
+ )} + +
+ {status && + Object.entries(status).map(([key, value]) => ( + + ))} +
+
+
+ ); +}; + +export default StatusConsoleComponent; diff --git a/frontend/app/components/Status/types.ts b/frontend/app/components/Status/types.ts new file mode 100644 index 000000000..b43416a65 --- /dev/null +++ b/frontend/app/components/Status/types.ts @@ -0,0 +1,21 @@ +export type StatusPayload = { + type: string; + variables: Status; + libraries: Status; + schemas: SchemaStatus; + error: string; +}; + +export type HealthPayload = { + message: string; + production: boolean; + gtag: string; +}; + +export type Status = { + [key: string]: boolean; +}; + +export type SchemaStatus = { + [key: string]: number; +}; diff --git a/frontend/app/favicon.ico b/frontend/app/favicon.ico deleted file mode 100644 index 718d6fea4..000000000 Binary files a/frontend/app/favicon.ico and /dev/null differ diff --git a/frontend/app/globals.css b/frontend/app/globals.css index 04ec4a1fa..86caa3715 100644 --- a/frontend/app/globals.css +++ b/frontend/app/globals.css @@ -2,269 +2,6 @@ @tailwind components; @tailwind utilities; -@keyframes popIn { - 0% { - transform: scale(0.3); - } - - 60% { - transform: scale(1.025); - } - - 100% { - transform: scale(1); - } -} - -@keyframes popInLate { - 0% { - transform: scale(0); - } - - 40% { - transform: scale(0); - } - - 80% { - transform: scale(1.025); - } - - 100% { - transform: scale(1); - } -} - -@keyframes popInMoreLate { - 0% { - transform: scale(0); - } - - 60% { - transform: scale(0); - } - - 75% { - transform: scale(1.025); - } - - 100% { - transform: scale(1); - } -} - -@keyframes pressIn { - 0% { - transform: scale(0.85); - opacity: 0; - } - - 50% { - transform: scale(1.025); - } - - 100% { - transform: scale(1); - opacity: 1; - } -} - -.animate-pop-in { - animation: popIn 0.5s ease-out; -} - -.animate-pop-in-late { - animation: popInLate 0.5s ease-out; -} - -.animate-pop-more-late { - animation: popInMoreLate 0.9s ease-out; -} - -.hover-container { - transition: transform 0.2s ease-in-out; -} - -.hover-container:hover { - transform: scale(1.025); -} - -.animate-press-in { - animation: pressIn 0.6s ease-out; -} - -/* Setting the font-family to PT Mono for all markdown content */ -.my-markdown-styles { - font-family: "PT Mono", monospace; - line-height: 1.5; - white-space: pre-line; -} - -/* Headers */ -.my-markdown-styles h1, -.my-markdown-styles h2, -.my-markdown-styles h3, -.my-markdown-styles h4, -.my-markdown-styles h5, -.my-markdown-styles h6 { - font-weight: bold; - margin-top: 1em; - margin-bottom: 0.5em; -} - -.my-markdown-styles h1 { - font-size: 2em; - padding-top: 0.5em; - padding-bottom: 0.5em; -} - -.my-markdown-styles h2 { - font-size: 1.75em; - padding-top: 0.5em; - padding-bottom: 0.5em; -} - -.my-markdown-styles h3 { - font-size: 1.5em; -} - -.my-markdown-styles h4 { - font-size: 1.25em; -} - -.my-markdown-styles h5 { - font-size: 1.125em; -} - -.my-markdown-styles h6 { - font-size: 1em; -} - -/* Blockquotes */ -.my-markdown-styles blockquote { - border-left: 4px solid #ddd; - padding-left: 1em; - color: #666; - margin: 1em 0; -} - -/* Inline code */ -.my-markdown-styles code { - background-color: #edebeb; - color: rgb(44, 161, 58); - border-radius: 3px; - padding: 0.2em 0.4em; -} - -.my-markdown-styles .extract code { - background-color: rgb(0, 0, 0, 0); - color: rgb(0, 0, 0, 100); - border-radius: 3px; - padding: 0.2em 0.4em; -} - -/* Bold and italic */ -.my-markdown-styles strong { - font-weight: bold; -} - -.my-markdown-styles em { - font-style: italic; -} - -/* Links */ -.my-markdown-styles a { - color: #007acc; - text-decoration: none; -} - -.my-markdown-styles a:hover { - text-decoration: underline; -} - -/* Lists */ -.my-markdown-styles ul, -.my-markdown-styles ol { - padding-left: 2em; - margin: 1em 0; -} - -.dot { - display: inline-block; - width: 8px; - height: 8px; - margin-right: 5px; - background-color: #333; - border-radius: 50%; - animation: dotBounce 1s infinite; -} - -.dot:nth-child(2) { - animation-delay: 0.2s; -} - -.dot:nth-child(3) { - animation-delay: 0.4s; -} - -@keyframes dotBounce { - - 0%, - 80%, - 100% { - transform: translateY(0); - } - - 40% { - transform: translateY(-10px); - } -} - -.rounded-indicator { - display: inline-block; - padding: 4px 12px; - border-radius: 20px; - box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); -} - -.neon-text { - background: linear-gradient(-45deg, - #ff0000, - #ff7f00, - #ffff00, - #00ff00, - #00ffff, - #0000ff, - #8b00ff, - #ff0000); - background-size: 300% 300%; - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - animation: neonGradient 10s ease infinite; -} - -@keyframes neonGradient { - 0% { - background-position: 25% 50%; - } - - 50% { - background-position: 100% 75%; - } - - 100% { - background-position: 25% 50%; - } -} - -.fade-in-out { - opacity: 0; - transition: opacity 0.3s ease-in-out; -} - -button:hover .fade-in-out { - opacity: 1; -} - -.hide { - opacity: 0; +.react-colorful { + height: 100px; } \ No newline at end of file diff --git a/frontend/app/info.ts b/frontend/app/info.ts new file mode 100644 index 000000000..c683a9972 --- /dev/null +++ b/frontend/app/info.ts @@ -0,0 +1,22 @@ +import { Inter, Plus_Jakarta_Sans, Open_Sans, PT_Mono } from "next/font/google"; + +// Fonts +const inter = Inter({ subsets: ["latin"] }); +const plus_jakarta_sans = Plus_Jakarta_Sans({ subsets: ["latin"] }); +const open_sans = Open_Sans({ subsets: ["latin"] }); +const pt_mono = PT_Mono({ subsets: ["latin"], weight: "400" }); +export type FontKey = "Inter" | "Plus_Jakarta_Sans" | "Open_Sans" | "PT_Mono"; + +export const fonts: Record = { + Inter: inter, + Plus_Jakarta_Sans: plus_jakarta_sans, + Open_Sans: open_sans, + PT_Mono: pt_mono, +}; + +export const chat_interface_info = + "Use the Chat Interface to interact with your data. Your query will be used to retrieve relevant information and to construct a response. You can choose between different Large Language Models (LLM) to create a response."; +export const chunk_interface_info = + "Use the Chunk Interface to browse through relevant chunks of your data, based on your last query. You can choose between different embeddings and retrieval techniques."; +export const document_interface_info = + "Use the Document Viewer to inspect your data and extracts of context that were used to generate responses to your queries. You can switch between showing the whole document and only showing the specific extract."; diff --git a/frontend/app/layout.tsx b/frontend/app/layout.tsx index 3723b2418..43fa26eed 100644 --- a/frontend/app/layout.tsx +++ b/frontend/app/layout.tsx @@ -1,18 +1,22 @@ -import "./globals.css"; import type { Metadata } from "next"; +import "./globals.css"; + +import { detectHost } from "./api"; export const metadata: Metadata = { - title: "Create Next App", - description: "Generated by create next app", + title: "Verba", + description: "The GoldenRAGtriever", }; export default function RootLayout({ children, -}: { +}: Readonly<{ children: React.ReactNode; -}) { +}>) { return ( + + {children} ); diff --git a/frontend/app/page.tsx b/frontend/app/page.tsx new file mode 100644 index 000000000..a23d323f5 --- /dev/null +++ b/frontend/app/page.tsx @@ -0,0 +1,288 @@ +"use client"; + +import React, { useState, useEffect } from "react"; +import Navbar from "./components/Navigation/NavbarComponent"; +import SettingsComponent from "./components/Settings/SettingsComponent"; +import ChatComponent from "./components/Chat/ChatComponent"; +import DocumentViewerComponent from "./components/Document/DocumentViewerComponent"; +import StatusComponent from "./components/Status/StatusComponent"; +import { Settings, BaseSettings } from "./components/Settings/types"; +import RAGComponent from "./components/RAG/RAGComponent"; +import { HealthPayload } from "./components/Status/types"; +import { RAGConfig, RAGResponse } from "./components/RAG/types"; +import { detectHost } from "./api"; +import { GoogleAnalytics } from "@next/third-parties/google"; +import { fonts, FontKey } from "./info"; +import PulseLoader from "react-spinners/PulseLoader"; + +export default function Home() { + // Page States + const [currentPage, setCurrentPage] = useState< + "CHAT" | "DOCUMENTS" | "STATUS" | "ADD" | "SETTINGS" | "RAG" + >("CHAT"); + + const [production, setProduction] = useState(false); + const [gtag, setGtag] = useState(""); + + // Settings + const [settingTemplate, setSettingTemplate] = useState("Default"); + const [baseSetting, setBaseSetting] = useState(null); + + const fontKey = baseSetting + ? (baseSetting[settingTemplate].Customization.settings.font + .value as FontKey) + : null; // Safely cast if you're sure, or use a check + const fontClassName = fontKey ? fonts[fontKey]?.className || "" : ""; + + // RAG Config + const [RAGConfig, setRAGConfig] = useState(null); + + const [APIHost, setAPIHost] = useState(null); + + const fetchHost = async () => { + try { + const host = await detectHost(); + setAPIHost(host); + if (host === "" || host === "http://localhost:8000") { + try { + const health_response = await fetch(host + "/api/health", { + method: "GET", + }); + + const health_data: HealthPayload = await health_response.json(); + + if (health_data) { + setProduction(health_data.production); + setGtag(health_data.gtag); + } else { + console.warn("Could not retrieve health data"); + } + + const response = await fetch(host + "/api/config", { + method: "GET", + }); + const data: RAGResponse = await response.json(); + + if (data) { + if (data.error) { + console.error(data.error); + } + + if (data.data.RAG) { + setRAGConfig(data.data.RAG); + } + if (data.data.SETTING.themes) { + setBaseSetting(data.data.SETTING.themes); + setSettingTemplate(data.data.SETTING.selectedTheme); + } else { + setBaseSetting(BaseSettings); + setSettingTemplate("Default"); + } + } else { + console.warn("Configuration could not be retrieved"); + } + } catch (error) { + console.error("Failed to fetch configuration:", error); + setRAGConfig(null); + } + } + } catch (error) { + console.error("Error detecting host:", error); + setAPIHost(null); // Optionally handle the error by setting the state to an empty string or a specific error message + } + }; + + useEffect(() => { + fetchHost(); + }, []); + + const importConfig = async () => { + if (!APIHost || !baseSetting) { + return; + } + + try { + const payload = { + config: { + RAG: RAGConfig, + SETTING: { selectedTheme: settingTemplate, themes: baseSetting }, + }, + }; + + const response = await fetch(APIHost + "/api/set_config", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(payload), + }); + } catch (error) { + console.error("Failed to update config:", error); + } + }; + + useEffect(() => { + importConfig(); + }, [baseSetting, settingTemplate]); + + useEffect(() => { + if (baseSetting) { + document.documentElement.style.setProperty( + "--primary-verba", + baseSetting[settingTemplate].Customization.settings.primary_color.color + ); + document.documentElement.style.setProperty( + "--secondary-verba", + baseSetting[settingTemplate].Customization.settings.secondary_color + .color + ); + document.documentElement.style.setProperty( + "--warning-verba", + baseSetting[settingTemplate].Customization.settings.warning_color.color + ); + document.documentElement.style.setProperty( + "--bg-verba", + baseSetting[settingTemplate].Customization.settings.bg_color.color + ); + document.documentElement.style.setProperty( + "--bg-alt-verba", + baseSetting[settingTemplate].Customization.settings.bg_alt_color.color + ); + document.documentElement.style.setProperty( + "--text-verba", + baseSetting[settingTemplate].Customization.settings.text_color.color + ); + document.documentElement.style.setProperty( + "--text-alt-verba", + baseSetting[settingTemplate].Customization.settings.text_alt_color.color + ); + document.documentElement.style.setProperty( + "--button-verba", + baseSetting[settingTemplate].Customization.settings.button_color.color + ); + document.documentElement.style.setProperty( + "--button-hover-verba", + baseSetting[settingTemplate].Customization.settings.button_hover_color + .color + ); + document.documentElement.style.setProperty( + "--bg-console-verba", + baseSetting[settingTemplate].Customization.settings.bg_console.color + ); + document.documentElement.style.setProperty( + "--text-console-verba", + baseSetting[settingTemplate].Customization.settings.text_console.color + ); + } + }, [baseSetting, settingTemplate]); + + return ( +
+ {gtag !== "" && } + + {baseSetting ? ( +
+ + + {currentPage === "CHAT" && ( + + )} + + {currentPage === "DOCUMENTS" && ( + + )} + + {currentPage === "STATUS" && !production && ( + + )} + + {currentPage === "ADD" && !production && ( + + )} + + {currentPage === "RAG" && !production && ( + + )} + + {currentPage === "SETTINGS" && !production && ( + + )} + +
+ +
+
+ ) : ( +
+ +

Loading Verba

+
+ )} +
+ ); +} diff --git a/frontend/components/ChatComponent.tsx b/frontend/components/ChatComponent.tsx deleted file mode 100644 index 5b3cf87e5..000000000 --- a/frontend/components/ChatComponent.tsx +++ /dev/null @@ -1,297 +0,0 @@ -import React, { useState, useEffect, useRef } from "react"; -import Typewriter from "typewriter-effect"; -import ReactMarkdown from "react-markdown"; -import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; -import { oneLight } from "react-syntax-highlighter/dist/cjs/styles/prism"; -import PulseLoader from "react-spinners/PulseLoader"; -import { FaCopy } from 'react-icons/fa'; -import { BsDatabaseFillCheck } from 'react-icons/bs'; - -interface ChatComponentProps { - onUserMessageSubmit: Message[]; - isFetching: boolean; - setHandleGenerateStreamMessageRef: (ref: React.MutableRefObject) => void; - setIsFetching: (isFetching: boolean) => void; - apiHost: string; -} - -export interface Message { - type: "user" | "system"; - content: string; - typewriter: boolean; - cached?: boolean; - distance?: string; -} - -export function ChatComponent({ - onUserMessageSubmit, - isFetching, - setHandleGenerateStreamMessageRef, - apiHost, - setIsFetching, -}: ChatComponentProps) { - const [messageHistory, setMessageHistory] = useState([]); - const lastMessageRef = useRef(null); - const [socket, setSocket] = useState(null); - const handleGenerateStreamMessageRef = useRef(null); - const [previewMessage, setPreviewMessage] = useState(""); - const [showNotification, setShowNotification] = useState(false); - - const getWebSocketApiHost = () => { - if (process.env.NODE_ENV === 'development') { - return 'ws://localhost:8000/ws/generate_stream'; - } - - // If you're serving the app directly through FastAPI, generate the WebSocket URL based on the current location. - const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:'; - const host = window.location.host; - return `${protocol}//${host}/ws/generate_stream`; - }; - - const handleCopyToBillboard = (text: string) => { - navigator.clipboard.writeText(text).then( - function () { - console.log('Text successfully copied to clipboard!'); - - // Show the notification - setShowNotification(true); - - // Hide the notification after 3 seconds - setTimeout(() => { - setShowNotification(false); - }, 3000); - }, - function (err) { - console.error('Unable to copy text: ', err); - } - ); - }; - - - useEffect(() => { - // Pass the ref up to the parent - setHandleGenerateStreamMessageRef(handleGenerateStreamMessageRef); - }, [setHandleGenerateStreamMessageRef]); - - useEffect(() => { - const localSocket = new WebSocket(getWebSocketApiHost()); - - localSocket.onopen = () => { - console.log("WebSocket connection opened."); - }; - - localSocket.onmessage = (event) => { - let data; - - try { - data = JSON.parse(event.data); - } catch (e) { - console.error("Received data is not valid JSON:", event.data); - return; // Exit early if data isn't valid JSON - } - const newMessageContent = data.message; - setPreviewMessage(prev => prev + newMessageContent); - - if (data.finish_reason === "stop") { - setIsFetching(false); - const full_text = data.full_text - if (data.cached) { - const distance = data.distance - setMessageHistory(prev => [...prev, { type: "system", content: full_text, typewriter: false, cached: true, distance: distance }]); - } else { - setMessageHistory(prev => [...prev, { type: "system", content: full_text, typewriter: false }]); - } - setPreviewMessage(""); // Reset for the next message - } - }; - - localSocket.onerror = (error) => { - console.error("WebSocket Error:", error); - }; - - localSocket.onclose = (event) => { - if (event.wasClean) { - console.log(`WebSocket connection closed cleanly, code=${event.code}, reason=${event.reason}`); - } else { - console.error("WebSocket connection died"); - } - }; - - setSocket(localSocket); - - return () => { - if (localSocket.readyState !== WebSocket.CLOSED) { - localSocket.close(); - } - }; - }, []); - - - handleGenerateStreamMessageRef.current = (query?: string, context?: string) => { - if (socket?.readyState === WebSocket.OPEN) { - console.log(messageHistory) - const data = JSON.stringify({ query: query, context: context, conversation: messageHistory }); - socket.send(data); - } else { - console.error("WebSocket is not open. ReadyState:", socket?.readyState); - } - }; - - type Segment = - | { type: "text"; content: string } - | { type: "code"; language: string; content: string }; - - // Helper function to parse message into segments - const parseMessage = (content: string) => { - const regex = /```(.*?)\n([\s\S]*?)```/g; // Matches code blocks and the specified language - const segments: Segment[] = []; - let lastEndIndex = 0; - - content.replace(regex, (match, language, codeContent, index) => { - if (index > lastEndIndex) { - segments.push({ - type: "text", - content: content.slice(lastEndIndex, index), - }); - } - - segments.push({ - type: "code", - language: language.trim(), - content: codeContent.trim(), - }); - lastEndIndex = index + match.length; - return match; - }); - - if (lastEndIndex < content.length) { - segments.push({ type: "text", content: content.slice(lastEndIndex) }); - } - - return segments; - }; - - useEffect(() => { - if ( - onUserMessageSubmit.length && - (messageHistory.length === 0 || - onUserMessageSubmit[onUserMessageSubmit.length - 1].content !== - messageHistory[messageHistory.length - 1].content) - ) { - const newMessage = onUserMessageSubmit[onUserMessageSubmit.length - 1]; - setMessageHistory((prev) => [...prev, newMessage]); - } - }, [onUserMessageSubmit]); - - useEffect(() => { - if (lastMessageRef.current) { - const lastMessage = messageHistory[messageHistory.length - 1]; - - // If it's a system message with the Typewriter effect - if (lastMessage.type === "system") { - // Calculate the delay needed based on the message length and Typewriter delay - const delay = lastMessage.content.length * 25; - setTimeout(() => { - lastMessageRef.current?.scrollIntoView({ behavior: "smooth" }); - }, delay); - } else { - lastMessageRef.current.scrollIntoView({ behavior: "smooth" }); - } - } - }, [messageHistory]); - - return ( -
- {messageHistory.map((message, index) => ( -
-
- {message.type === "system" && ()} - {message.type === "system" && message.cached && ()} -
- - {message.type === "system" - ? parseMessage(message.content).map((segment, segIndex) => { - if (segment.type === "text") { - return ( -
- - {segment.content} - -
- ); - } else if (segment.type === "code") { - return ( - - {segment.content} - - ); - } - return null; - }) - : message.content} - -
- ))} - - {/* Render the preview message if available */} - {previewMessage && ( -
- - {parseMessage(previewMessage).map((segment, segIndex) => { - if (segment.type === "text") { - return ( -

{segment.content}

- ); - } else if (segment.type === "code") { - return ( - - {segment.content} - - ); - } - return null; - })} - -
- )} - - {isFetching && ( -
- -
- )} - {showNotification && ( -
- Text copied! -
- )} -
- ); -} diff --git a/frontend/components/ConfigModal.tsx b/frontend/components/ConfigModal.tsx deleted file mode 100644 index be8a0f8d3..000000000 --- a/frontend/components/ConfigModal.tsx +++ /dev/null @@ -1,137 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { TbVectorTriangle } from "react-icons/tb"; -import { LuFileOutput } from "react-icons/lu"; -import { MdOutlineQuestionAnswer } from "react-icons/md"; -import { Component } from "../components/ImportModalComponent"; - - -interface ConfigModalProps { - component: string; - apiHost: string; - production: boolean; - onGeneratorSelect: (streamable: boolean) => void; -} - -const ConfigModal: React.FC = ({ component = "embedders", apiHost, onGeneratorSelect, production }) => { - - const [components, setComponents] = useState([]); - const [selectedComponent, setSelectedComponent] = useState(); - const [isListVisible, setListVisible] = useState(false); - const [isTooltipVisible, setTooltipVisible] = useState(false); - - const mainButtonRef = React.useRef(null); - - const toggleListVisibility = () => { - setListVisible(prevState => !prevState); - }; - - const handleComponentSelection = async (selectedComponent: Component) => { - if (production == true) { - return - } - setSelectedComponent(selectedComponent); - toggleListVisibility() - try { - const response = await fetch(`${apiHost}/api/set_component`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ "component": component, "selected_component": selectedComponent.name }), - }); - // Handle the response if necessary - } catch (error) { - console.error("Error setting component:", error); - } - }; - - useEffect(() => { - if (component === "generators") { - onGeneratorSelect(selectedComponent?.streamable ? selectedComponent.streamable : false); - } - }, [selectedComponent]); - - useEffect(() => { - // Fetch the list of readers from your API when the component mounts - const fetchReaders = async () => { - try { - const response = await fetch(`${apiHost}/api/get_component`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json' - }, - body: JSON.stringify({ "component": component }) - }); - - const data = await response.json(); - setSelectedComponent(data.selected_component) - setComponents(data.components); - - - } catch (error) { - console.error("Error fetching readers:", error); - } - } - fetchReaders(); - }, [apiHost]); - - return ( -
-
{/* Wrapped button and tooltip in flex container */} - -
setTooltipVisible(true)} // Show tooltip on mouse enter - onMouseLeave={() => setTooltipVisible(false)} > - - {/* Tooltip */} - {isTooltipVisible && ( -
- {selectedComponent?.description} -
- )} -
-
- { - isListVisible && ( -
-
- {components.filter(_component => _component.name !== selectedComponent?.name).map(_component => ( -
- - {!_component.available && ( -

{_component.message}

- )} -
- ))} -
-
- ) - } -
- ); -}; - -export default ConfigModal; \ No newline at end of file diff --git a/frontend/components/CoolButton.tsx b/frontend/components/CoolButton.tsx deleted file mode 100644 index 5830ee06b..000000000 --- a/frontend/components/CoolButton.tsx +++ /dev/null @@ -1,48 +0,0 @@ -// CoolButton.tsx -import React from 'react'; -import { FaCopy } from 'react-icons/fa'; // Importing the React icon from FontAwesome - - -interface CoolButtonProps { - main: string; - sub: string; - onClick: () => void; - available?: boolean; - clipboard: boolean; - subBgColor?: string; // Optional prop to control the background color of the sub span - mainBgColor?: string; - isActive?: boolean; // optional prop for button active state - title?: string; // optional prop for hover tooltip -} - -const CoolButton: React.FC = ({ main, sub, onClick, clipboard, available = true, subBgColor = 'green', mainBgColor = 'yellow', isActive = false, title = '' }) => { - - const handleCopyToBillboard = () => { - navigator.clipboard.writeText(main).then( - function () { - console.log('Text successfully copied to clipboard!'); - }, - function (err) { - console.error('Unable to copy text: ', err); - } - ); - }; - - return ( - - ); -}; - -export default CoolButton; \ No newline at end of file diff --git a/frontend/components/DocumentComponent.tsx b/frontend/components/DocumentComponent.tsx deleted file mode 100644 index e498f32da..000000000 --- a/frontend/components/DocumentComponent.tsx +++ /dev/null @@ -1,129 +0,0 @@ -import { useEffect, useRef, useState } from "react"; -import React from 'react'; -import ReactMarkdown from "react-markdown"; -import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; -import { oneLight } from "react-syntax-highlighter/dist/cjs/styles/prism"; -import { GrDocumentDownload } from 'react-icons/gr'; - -interface DocumentComponentProps { - title: string; - text: string; - type?: string; - extract?: string; - docLink?: string; -} - -const MemoizedSyntaxHighlighter = React.memo(SyntaxHighlighter); - -export function DocumentComponent({ - title, - text, - type = "Documentation", - extract, - docLink, -}: DocumentComponentProps) { - const extractRef = useRef(null); - const containerRef = useRef(null) // Add a ref for the container - const [displayedChunks, setDisplayedChunks] = useState(1); - const [hasScrolledToExtract, setHasScrolledToExtract] = useState(false); - const CHUNK_SIZE = 1500; // Number of characters per chunk. Adjust as needed. - - - useEffect(() => { - setDisplayedChunks(1); - }, [text]); - - const start = extract ? text.indexOf(extract) : -1; - const extractChunk = start !== -1 ? Math.ceil(start / CHUNK_SIZE) : 0; - - useEffect(() => { - if (extract && extractChunk > displayedChunks) { - setDisplayedChunks(extractChunk + 1); // Load up to the chunk containing the extract - } - }, [extract, extractChunk]); - - useEffect(() => { - if (extractRef.current && !hasScrolledToExtract) { - const element = extractRef.current as HTMLElement; - element.scrollIntoView({ - behavior: "smooth", - block: "center", - }); - setHasScrolledToExtract(true); // Set to true after scrolling - } - }, [text, extract]); - - useEffect(() => { - setHasScrolledToExtract(false); // Reset the flag when text or extract changes - }, [text, extract]) - - const RenderMarkdown = React.memo(function RenderMarkdown({ text }: { text: string; }) { - return ( - - {String(children).replace(/\n$/, "")} - - ) : ( - - {children} - - ); - }, - }} - > - {text} - - ); - }); - - const handleLoadMore = () => { - setDisplayedChunks(prev => prev + 1); - } - - if (!title) return
; - - const end = extract ? start + extract.length : -1; - - return ( -
- -
- - - {extract && ( -
- -
- )} - - - {/* Load More Button */} - {displayedChunks * CHUNK_SIZE < text.length && ( -
- -
- )} -
-
- ); -} diff --git a/frontend/components/ImportModalComponent.tsx b/frontend/components/ImportModalComponent.tsx deleted file mode 100644 index 7e506142a..000000000 --- a/frontend/components/ImportModalComponent.tsx +++ /dev/null @@ -1,378 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { FaPlus, FaTimes } from "react-icons/fa"; -import { useDropzone } from 'react-dropzone'; -import CoolButton from "../components/CoolButton"; -import HashLoader from "react-spinners/HashLoader"; - -type ImportModalProps = { - onClose: () => void; - apiHost: string; -} - -export type Component = { - name: string; - description: string; - input_form: 'UPLOAD' | 'INPUT' | 'CHUNKER' | 'TEXT'; - available: boolean; - message: string; - units?: number, - overlap?: number - streamable?: boolean -}; - -const ImportModal: React.FC = ({ onClose, apiHost }) => { - const [readers, setReaders] = useState([]); - const [chunker, setChunker] = useState([]); - const [embedder, setEmbedder] = useState([]); - const [selectedOption, setSelectedOption] = useState("Reader"); - const [selectedReader, setSelectedReader] = useState(null); - const [selectedChunker, setSelectedChunker] = useState(null); - const [chunkUnits, setChunkUnits] = useState(100); - const [chunkOverlap, setChunkOverlap] = useState(50); - const [selectedEmbedder, setSelectedEmbedder] = useState(null); - - const [loadingState, setLoadingState] = useState(false); - - const [droppedFiles, setDroppedFiles] = useState([]); - const [filePath, setFilePath] = useState(""); - - const [docType, setDocType] = useState("Documentation"); - const [inputFileKey, setInputFileKey] = useState(Date.now()); // Using the current timestamp as the initial key - const [apiResponse, setApiResponse] = useState<{ status: number, status_msg: string } | null>(null); - - useEffect(() => { - // Fetch the list of readers from your API when the component mounts - const fetchReaders = async () => { - try { - const response = await fetch(apiHost + '/api/get_components'); - const data = await response.json(); - setReaders(data.readers); - setChunker(data.chunker); - setEmbedder(data.embedder); - - if (data.readers && data.readers.length > 0) { - setSelectedReader(data.default_values.last_reader); - handleChunkerSelection(data.default_values.last_chunker) - setSelectedEmbedder(data.default_values.last_embedder) - setDocType(data.default_values.last_document_type) - } - } catch (error) { - console.error("Error fetching readers:", error); - } - } - fetchReaders(); - }, [apiHost]); - - const handleFilePathInput = (event: React.ChangeEvent) => { - setFilePath(event.target.value); - }; - - const handleImport = async () => { - if (!selectedReader || !selectedChunker || !selectedEmbedder) { - console.error("Missing component"); - return; - } - - setLoadingState(true); - - const fileNames: string[] = droppedFiles.map(file => file.name); - - // Read the File objects to get their bytes and convert them to base64 strings - const promises = droppedFiles.map(file => { - return new Promise((resolve) => { - const reader = new FileReader(); - reader.onloadend = () => { - // Convert ArrayBuffer to base64 string - const base64String = btoa( - Array.from(new Uint8Array(reader.result as ArrayBuffer)).map( - (byte) => String.fromCharCode(byte) - ).join('') - ); - resolve(base64String); - }; - reader.readAsArrayBuffer(file); - }); - }); - - const fileBytesBase64 = await Promise.all(promises); - - let payload = { - reader: selectedReader.name, - chunker: selectedChunker.name, - embedder: selectedEmbedder.name, - fileBytes: fileBytesBase64, - fileNames: fileNames, - filePath: filePath, - document_type: docType, - chunkUnits: chunkUnits, - chunkOverlap: chunkOverlap, - - }; - - try { - const response = await fetch(`${apiHost}/api/load_data`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json' - }, - body: JSON.stringify(payload) - }); - - const data = await response.json(); - - setLoadingState(false); - - if (response.ok) { - // Clear files and path after successful import - setDroppedFiles([]); - - // Reset the input file by changing its key - setInputFileKey(Date.now()); - } else { - console.error("Error from server:", data); - } - - setApiResponse(data); - - // Handle the response as needed, e.g., show a success message - } catch (error) { - console.error("Error importing data:", error); - // Handle the error as needed, e.g., show an error message - } - }; - - const onDrop = async (acceptedFiles: File[]) => { - setDroppedFiles(prevFiles => [...prevFiles, ...acceptedFiles]); - }; - - const { getRootProps, getInputProps } = useDropzone({ - onDrop, - accept: { - 'text/txt': ['.txt', '.md', '.mdx', '.pdf'], - 'text/json': ['.json'] - } - }); - - const handleChunkerSelection = (chunk: Component) => { - setSelectedChunker(chunk); - if (chunk.units !== undefined) { - setChunkUnits(chunk.units); - } - if (chunk.overlap !== undefined) { - setChunkOverlap(chunk.overlap); - } - }; - - - const handleChunkerUnitsSelection = (value: number) => { - setChunkUnits(value); - if (selectedChunker) { - selectedChunker.units = value; - setSelectedChunker({ ...selectedChunker }); - } - }; - - const handleChunkerOverlapSelection = (value: number) => { - setChunkOverlap(value); - if (selectedChunker) { - selectedChunker.overlap = value; - setSelectedChunker({ ...selectedChunker }); - } - }; - - const handleRemoveAllFiles = () => { - setDroppedFiles([]); - }; - - return ( -
-
-
-
- - - - - -
-
- {/* Left column for the list of readers */} -
-
    - {selectedOption === "Reader" ? ( - readers.map((reader, index) => ( - setSelectedReader(reader)} - subBgColor={reader.available ? 'green' : 'red'} - isActive={reader.name === selectedReader?.name} - available={reader.available} - title={reader.name} - /> - )) - ) : selectedOption === "Chunking" ? ( - chunker.map((chunk, index) => ( - handleChunkerSelection(chunk)} - mainBgColor='cyan' - subBgColor={chunk.available ? 'green' : 'red'} - isActive={chunk.name === selectedChunker?.name} - available={chunk.available} - title={chunk.name} - /> - )) - ) : selectedOption === "Embedding" ? ( - embedder.map((embedder, index) => ( - setSelectedEmbedder(embedder)} - mainBgColor='fuchsia' - subBgColor={embedder.available ? 'green' : 'red'} - isActive={embedder.name === selectedEmbedder?.name} - title={embedder.name} - /> - )) - ) : null} -
-
- {/* Right column for configurations */} -
-
- {selectedOption === "Reader" && selectedReader && selectedReader.input_form === 'UPLOAD' ? ( -
-

📁 Upload documents

-
- -

Drop your files here, or click to select files

-
-
- {droppedFiles.length > 0 && ( -
- {droppedFiles.length} files selected - -
- )} -
-
- - setDocType(e.target.value)} - className="mt-1 focus:ring-yellow p-3 - -500 focus:border-yellow-500 block w-full shadow-sm sm:text-sm border-gray-300 rounded-md" - placeholder="Documentation" - /> -
-
- ) : selectedOption === "Reader" && selectedReader && selectedReader.input_form === 'INPUT' ? ( -
-

Provide Path

- -
- - setDocType(e.target.value)} - className="mt-1 focus:ring-yellow p-3 - -500 focus:border-yellow-500 block w-full shadow-sm sm:text-sm border-gray-300 rounded-md" - placeholder="Documentation" - /> -
-
- ) : selectedOption === "Chunking" && selectedChunker?.input_form === 'CHUNKER' ? ( -
-

Units

- setChunkUnits(Number(e.target.value))} - className='p-2 rounded-lg w-1/2' - /> -

Overlap

- setChunkOverlap(Number(e.target.value))} - className='p-2 rounded-lg w-1/2' - /> -
- ) : selectedOption === "Embedding" && selectedEmbedder?.input_form === 'TEXT' ? ( -
- -
- ) : null} -
-
-

{selectedOption === "Reader" ? selectedReader?.description : selectedOption === "Chunking" ? selectedChunker?.description : selectedEmbedder?.description}

-
-
- -
-
-
- { - apiResponse && ( -
- {apiResponse.status_msg} -
- ) - } -
-
- ); -} - -export default ImportModal; diff --git a/frontend/components/Navbar.tsx b/frontend/components/Navbar.tsx deleted file mode 100644 index a47b69e2e..000000000 --- a/frontend/components/Navbar.tsx +++ /dev/null @@ -1,38 +0,0 @@ -"use client"; - -import Link from "next/link"; -import { useRouter } from "next/router"; - -const Navbar = () => { - const router = useRouter(); - - const links = [ - { name: "Search", path: "/" }, - { name: "Documents", path: "/document_explorer" }, - { name: "Status", path: "/status" }, - ]; - - return ( - - ); -}; - -export default Navbar; diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 889d982d4..c42f30800 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,24 +1,26 @@ { "name": "verba", - "version": "0.3.1", + "version": "1.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "verba", - "version": "0.3.1", + "version": "1.0.0", "dependencies": { "@mdx-js/mdx": "^2.3.0", "@mdx-js/react": "^2.3.0", + "@next/third-parties": "^14.2.3", "@types/node": "20.5.0", "@types/react": "18.2.20", "@types/react-dom": "18.2.7", "autoprefixer": "10.4.15", "eslint": "8.47.0", "eslint-config-next": "13.4.13", - "next": "13.4.13", + "next": "^14.2.0", "postcss": "8.4.27", "react": "18.2.0", + "react-colorful": "^5.6.1", "react-countup": "^6.4.2", "react-dom": "18.2.0", "react-dropzone": "^14.2.3", @@ -33,9 +35,12 @@ "typewriter-effect": "^2.20.1" }, "devDependencies": { + "@tailwindcss/typography": "^0.5.12", "@types/react-syntax-highlighter": "^15.5.7", "@types/react-window": "^1.8.5", - "@welldone-software/why-did-you-render": "^7.0.1" + "@welldone-software/why-did-you-render": "^7.0.1", + "daisyui": "^4.10.1", + "prettier": "^3.2.5" } }, "node_modules/@aashutoshrathi/word-wrap": { @@ -731,9 +736,9 @@ } }, "node_modules/@next/env": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/env/-/env-13.4.13.tgz", - "integrity": "sha512-fwz2QgVg08v7ZL7KmbQBLF2PubR/6zQdKBgmHEl3BCyWTEDsAQEijjw2gbFhI1tcKfLdOOJUXntz5vZ4S0Polg==" + "version": "14.2.0", + "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.0.tgz", + "integrity": "sha512-4+70ELtSbRtYUuyRpAJmKC8NHBW2x1HMje9KO2Xd7IkoyucmV9SjgO+qeWMC0JWkRQXgydv1O7yKOK8nu/rITQ==" }, "node_modules/@next/eslint-plugin-next": { "version": "13.4.13", @@ -744,9 +749,9 @@ } }, "node_modules/@next/swc-darwin-arm64": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-13.4.13.tgz", - "integrity": "sha512-ZptVhHjzUuivnXMNCJ6lER33HN7lC+rZ01z+PM10Ows21NHFYMvGhi5iXkGtBDk6VmtzsbqnAjnx4Oz5um0FjA==", + "version": "14.2.0", + "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.0.tgz", + "integrity": "sha512-kHktLlw0AceuDnkVljJ/4lTJagLzDiO3klR1Fzl2APDFZ8r+aTxNaNcPmpp0xLMkgRwwk6sggYeqq0Rz9K4zzA==", "cpu": [ "arm64" ], @@ -758,124 +763,16 @@ "node": ">= 10" } }, - "node_modules/@next/swc-darwin-x64": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-13.4.13.tgz", - "integrity": "sha512-t9nTiWCLApw8W4G1kqJyYP7y6/7lyal3PftmRturIxAIBlZss9wrtVN8nci50StDHmIlIDxfguYIEGVr9DbFTg==", - "cpu": [ - "x64" - ], - "optional": true, - "os": [ - "darwin" - ], - "engines": { - "node": ">= 10" - } - }, - "node_modules/@next/swc-linux-arm64-gnu": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-13.4.13.tgz", - 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} - }, - "node_modules/@next/swc-linux-x64-musl": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-13.4.13.tgz", - "integrity": "sha512-+Y4LLhOWWZQIDKVwr2R17lq2KSN0F1c30QVgGIWfnjjHpH8nrIWHEndhqYU+iFuW8It78CiJjQKTw4f51HD7jA==", - "cpu": [ - "x64" - ], - "optional": true, - "os": [ - "linux" - ], - "engines": { - "node": ">= 10" - } - }, - "node_modules/@next/swc-win32-arm64-msvc": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-13.4.13.tgz", - "integrity": "sha512-rWurdOR20uxjfqd1X9vDAgv0Jb26KjyL8akF9CBeFqX8rVaBAnW/Wf6A2gYEwyYY4Bai3T7p1kro6DFrsvBAAw==", - "cpu": [ - "arm64" - ], - "optional": true, - "os": [ - "win32" - ], - "engines": { - "node": ">= 10" - } - }, - "node_modules/@next/swc-win32-ia32-msvc": { - "version": "13.4.13", - "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-13.4.13.tgz", - "integrity": "sha512-E8bSPwRuY5ibJ3CzLQmJEt8qaWrPYuUTwnrwygPUEWoLzD5YRx9SD37oXRdU81TgGwDzCxpl7z5Nqlfk50xAog==", - 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"version": "0.3.1", + "version": "1.0.0", "private": true, "scripts": { "dev": "next dev", - "build": "next build && next export && cp -r out/* ../goldenverba/server/frontend/out/ && rm -r out", + "build": "next build && cp -r out/* ../goldenverba/server/frontend/out/ && rm -r out", "start": "next start -H 0.0.0.0 -p 8080", "lint": "next lint" }, "dependencies": { "@mdx-js/mdx": "^2.3.0", "@mdx-js/react": "^2.3.0", + "@next/third-parties": "^14.2.3", "@types/node": "20.5.0", "@types/react": "18.2.20", "@types/react-dom": "18.2.7", "autoprefixer": "10.4.15", "eslint": "8.47.0", "eslint-config-next": "13.4.13", - "next": "13.4.13", + "next": "^14.2.0", "postcss": "8.4.27", "react": "18.2.0", + "react-colorful": "^5.6.1", "react-countup": "^6.4.2", "react-dom": "18.2.0", - "react-dropzone": "^14.2.3", "react-icons": "^4.11.0", "react-markdown": "^8.0.7", "react-spinners": "^0.13.8", @@ -34,8 +35,10 @@ "typewriter-effect": "^2.20.1" }, "devDependencies": { + "@tailwindcss/typography": "^0.5.12", "@types/react-syntax-highlighter": "^15.5.7", "@types/react-window": "^1.8.5", - "@welldone-software/why-did-you-render": "^7.0.1" + "daisyui": "^4.10.1", + "prettier": "^3.2.5" } -} \ No newline at end of file +} diff --git a/frontend/pages/_app.tsx b/frontend/pages/_app.tsx deleted file mode 100644 index 52b2299a0..000000000 --- a/frontend/pages/_app.tsx +++ /dev/null @@ -1,88 +0,0 @@ -import type { AppProps } from "next/app"; -import Navbar from "../components/Navbar"; -import { useState, useEffect } from "react"; -import Head from 'next/head'; -import Script from "next/script"; - -import { getApiHost } from "@/pages"; - -import { PT_Mono } from "next/font/google"; - -const pt_mono = PT_Mono({ weight: "400", subsets: ["latin"] }); - -import "../app/globals.css"; - -const bgUrl = process.env.NODE_ENV === 'production' - ? 'static/' - : '/'; - -const apiHost = getApiHost() - -function MyApp({ Component, pageProps }: AppProps) { - - const [googleTag, setGoogleTag] = useState(""); - - useEffect(() => { - document.body.style.backgroundImage = `url('${bgUrl + 'background.png'}')`; - document.body.style.backgroundPosition = "center"; - document.body.style.backgroundSize = "cover"; - document.body.style.backgroundRepeat = "no-repeat"; - }, []); - - useEffect(() => { - const fetchEnvironment = async () => { - try { - const response = await fetch(apiHost + "/api/get_google_tag", { - method: "GET", - headers: { - "Content-Type": "application/json", - }, - }); - const response_data = await response.json(); - - if (response_data.tag) { - console.log("Google Tag Set!") - } else { - console.log("No Google Tag found.") - } - - // Update the document title and text - setGoogleTag(response_data.tag); - } catch (error) { - console.error("Failed to fetch document:", error); - } - } - - fetchEnvironment(); - }, []); - - return ( -
- {googleTag && ( - <> - - - )} - - - - - -
- ); -} - -export default MyApp; diff --git a/frontend/pages/document_explorer.tsx b/frontend/pages/document_explorer.tsx deleted file mode 100644 index 6588209f1..000000000 --- a/frontend/pages/document_explorer.tsx +++ /dev/null @@ -1,296 +0,0 @@ -import { useState, useEffect } from "react"; -import { DocumentComponent } from "../components/DocumentComponent"; -import { Virtuoso } from "react-virtuoso"; -import { getApiHost } from "@/pages"; -import ImportModalComponent from "../components/ImportModalComponent"; -import ConfigModal from "../components/ConfigModal"; -import { FaPlus } from "react-icons/fa"; -import CoolButton from "../components/CoolButton"; - -type Document = { - doc_name: string; - doc_type: string; - doc_link: string; - _additional: { id: string }; -}; - -const apiHost = getApiHost() -const bgUrl = process.env.NODE_ENV === 'production' - ? 'static/' - : '/'; - - -export default function DocumentOnly() { - const [searchQuery, setSearchQuery] = useState(""); - const [uniqueDocTypes, setUniqueDocTypes] = useState([]); - const [selectedDocType, setSelectedDocType] = useState("All types"); - const [showModal, setShowModal] = useState(false); - const [showDeleteModal, setShowDeleteModal] = useState(false); - const [isDeleting, setIsDeleting] = useState(false); - const [documentTitle, setDocumentTitle] = useState(""); - const [documentText, setDocumentText] = useState(""); - const [documentType, setDocumentType] = useState("Documentation"); - const [documentLink, setDocumentLink] = useState("#"); - const [documents, setDocuments] = useState([]); - const [production, setProduction] = useState(false); - const [focusedDocument, setFocusedDocument] = useState(null); - const [currentEmbedder, setCurrentEmbedder] = useState(""); - - const generatorStreamable = (streamable: boolean) => { - console.log("Set Streamable") - } - - useEffect(() => { - const fetchProduction = async () => { - try { - const response = await fetch(apiHost + "/api/get_production", { - method: "GET", - headers: { - "Content-Type": "application/json", - }, - }); - const response_data = await response.json(); - - if (response_data.production == true) { - console.log("In Production Mode") - } else { - console.log("In Normal Mode") - } - - // Update the document title and text - setProduction(response_data.production); - } catch (error) { - console.error("Failed to fetch document:", error); - } - } - - fetchProduction(); - }, []); - - const fetchDocuments = async (query = "") => { - try { - const endpoint = query - ? `${apiHost}/api/search_documents` - : `${apiHost}/api/get_all_documents`; - - const body = { "query": query, "doc_type": selectedDocType == "All types" ? "" : selectedDocType }; - - const response = await fetch(endpoint, { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify(body), - }); - - const data = await response.json(); - - // Assuming the data is an array of documents - setDocuments(data.documents); - setCurrentEmbedder(data.current_embedder) - - if (data.doc_types) { - setUniqueDocTypes(data.doc_types); - } - - } catch (error) { - console.error(`Failed to fetch documents: ${error}`); - } - }; - - useEffect(() => { - fetchDocuments(); - }, []); - - useEffect(() => { - const fetchDocument = async () => { - if (focusedDocument && focusedDocument._additional.id) { - try { - const response = await fetch(apiHost + "/api/get_document", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - document_id: focusedDocument._additional.id, - }), - }); - const documentData = await response.json(); - - // Update the document title and text - setDocumentTitle(documentData.document.properties.doc_name); - setDocumentText(documentData.document.properties.text); - setDocumentType(documentData.document.properties.doc_type); - setDocumentLink(documentData.document.properties.doc_link); - } catch (error) { - console.error("Failed to fetch document:", error); - } - } - }; - - fetchDocument(); - }, [focusedDocument]); - - const handleDeleteDocument = async () => { - try { - setIsDeleting(true) - const response = await fetch(`${apiHost}/api/delete_document`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ document_id: focusedDocument?._additional.id }), - }); - const data = await response.json(); - console.log(data); // Log the response for debugging - setShowDeleteModal(false); // Hide the delete confirmation modal - setIsDeleting(false); - setFocusedDocument(null) - setDocumentTitle(""); - setDocumentText(""); - fetchDocuments(); // Refresh the document list - } catch (error) { - console.error(`Failed to delete document: ${error}`); - } - }; - - // Handle form submission - const handleSearch = async (e: React.FormEvent) => { - e.preventDefault(); - const form = e.currentTarget; - const formData = new FormData(form); - const query = formData.get("searchInput"); // 'searchInput' is the name attribute of input field - - fetchDocuments(query as string); - }; - - return ( -
- {showModal && setShowModal(false)} apiHost={apiHost} />} -
-
-
{/* <-- flexbox container */} -
-
- Verba Logo -
-
-
-

Verba

-
- - The - - - Golden - - - RAGtriever - -
-
-
- - -
-
-
-
-
-
-
{/* Container for the title and button */} -

📚 Documents

- {focusedDocument && ( - - )} -
- -

Search through all your {documents.length} imported documents embedded by {currentEmbedder}

-
- -
- -
- - -
-
-
- {documents.length > 0 && ( - ( - setFocusedDocument(documents[index])} - title={documents[index].doc_name} - /> - )} - /> - )} -
-
- -
-
-
- {showDeleteModal && ( -
-
-

⚠️ Warning

-

Do you want to remove {focusedDocument?.doc_name}

-
- - -
-
-
- )} -
- ); -} \ No newline at end of file diff --git a/frontend/pages/index.tsx b/frontend/pages/index.tsx deleted file mode 100644 index df1b106c6..000000000 --- a/frontend/pages/index.tsx +++ /dev/null @@ -1,456 +0,0 @@ -import { useState, useEffect, useCallback, useRef } from "react"; -import { ChatComponent, Message } from "../components/ChatComponent"; -import { DocumentComponent } from "../components/DocumentComponent"; -import ImportModalComponent from "../components/ImportModalComponent"; -import ConfigModal from "../components/ConfigModal"; -import { FaPlus } from "react-icons/fa"; -import CountUp from 'react-countup'; - -export const getApiHost = () => { - if (process.env.NODE_ENV === 'development') { - return 'http://localhost:8000'; - } - return ""; -}; - -export const getApiHostNoHttp = () => { - if (process.env.NODE_ENV === 'development') { - return 'localhost:8000'; - } - return ""; -}; - -export const apiHost = getApiHost(); -const bgUrl = process.env.NODE_ENV === 'production' - ? 'static/' - : '/'; - - -type DocumentChunk = { - text: string; - doc_name: string; - chunk_id: number; - doc_uuid: string; - doc_type: string; - score: number; -}; - -export default function Home() { - const [showModal, setShowModal] = useState(false); - const [suggestions, setSuggestions] = useState([]); - const [userInput, setUserInput] = useState(""); - const [streamable, setStreamable] = useState(false); - const [documentTitle, setDocumentTitle] = useState(""); - const [documentText, setDocumentText] = useState(""); - const [documentLink, setDocumentLink] = useState("#"); - const [documentChunks, setDocumentChunks] = useState([]); - const [apiStatus, setApiStatus] = useState('Offline'); // API status - const [focusedDocument, setFocusedDocument] = useState( - null - ); - const [messages, setMessages] = useState([]); - const [production, setProduction] = useState(false); - const [isFetching, setIsFetching] = useState(false); - const [isFetchingSuggestion, setIsFetchingSuggestions] = useState(false); - const handleGenerateStreamMessageRef = useRef(null); - - // Function for checking the health of the API - const checkApiHealth = useCallback(async () => { - try { - // Change ENDPOINT based on your setup (Default to localhost:8000) - const response = await fetch(apiHost + '/api/health'); - - if (response.status === 200) { - setApiStatus('Online'); - } else { - setApiStatus('Offline'); - } - } catch (error) { - setApiStatus('Offline'); - } - }, []); - - useEffect(() => { - const fetchProduction = async () => { - try { - const response = await fetch(apiHost + "/api/get_production", { - method: "GET", - headers: { - "Content-Type": "application/json", - }, - }); - const response_data = await response.json(); - - if (response_data.production == true) { - console.log("In Production Mode") - } else { - console.log("In Normal Mode") - } - - // Update the document title and text - setProduction(response_data.production); - } catch (error) { - console.error("Failed to fetch document:", error); - } - } - - fetchProduction(); - }, []); - - // UseEffect hook to check the API health on initial load - useEffect(() => { - checkApiHealth(); - }, [checkApiHealth]); - - const handleGenerateMessage = async (query?: string, context?: string) => { - - try { - console.log(messages) - const answerResponse = await fetch(apiHost + "/api/generate", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ query: query, context: context, conversation: messages }), // context will be added once we get it from the first API call, - }); - - const answerData = await answerResponse.json(); - - if (answerData.system) { - setMessages((prev) => [ - ...prev, - { type: "system", content: answerData.system, typewriter: true }, - ]); - } - } catch (error) { - console.error("Failed to fetch from API:", error); - } finally { - setIsFetching(false); - } - } - - const setHandleGenerateStreamMessage = (ref: React.MutableRefObject) => { - handleGenerateStreamMessageRef.current = ref.current; - }; - - const handleGenerateStreamMessage = (query?: string, context?: string) => { - if (handleGenerateStreamMessageRef.current) { - handleGenerateStreamMessageRef.current(query, context); - } - }; - - const generatorStreamable = (streamable: boolean) => { - setStreamable(streamable) - } - - const handleSendMessage = async (e?: React.FormEvent, message?: string) => { - e?.preventDefault(); - - const sendInput = message || userInput; - - if (sendInput.trim()) { - setMessages((prev) => [...prev, { type: "user", content: sendInput, typewriter: true }]); - - // Clear the suggestions list - setSuggestions([]); - setUserInput(""); - - // Start the API call - setIsFetching(true); - - try { - checkApiHealth(); - - // Start both API calls in parallel - const queryResponse = await fetch(apiHost + "/api/query", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ query: sendInput }), - }) - - const queryData = await queryResponse.json(); - checkApiHealth(); - setDocumentChunks([]); - setDocumentChunks(queryData.documents); - setSuggestions([]); - - if (queryData.context != "") { - if (streamable) { - handleGenerateStreamMessage(sendInput, queryData.context) - } else { - handleGenerateMessage(sendInput, queryData.context) - } - } else { - setIsFetching(false) - setMessages((prev) => [ - ...prev, - { type: "system", content: "Your Verba has no data yet :(", typewriter: true }, - ]); - } - - } catch (error) { - checkApiHealth(); - console.error("Failed to fetch from API:", error); - } - } - }; - - useEffect(() => { - // Now, set the first chunk as the focused document inside this useEffect - if (documentChunks && documentChunks.length > 0) { - setFocusedDocument(documentChunks[0]); - } - }, [documentChunks]); - - useEffect(() => { - const fetchDocument = async () => { - if (focusedDocument && focusedDocument.doc_uuid) { - try { - const response = await fetch(apiHost + "/api/get_document", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ document_id: focusedDocument.doc_uuid }), - }); - const documentData = await response.json(); - - // Update the document title and text - setDocumentTitle(documentData.document.properties.doc_name); - setDocumentText(documentData.document.properties.text); - setDocumentLink(documentData.document.properties.doc_link); - } catch (error) { - console.error("Failed to fetch document:", error); - } - } - }; - - fetchDocument(); - }, [focusedDocument]); - - function debounce(func: (...args: any[]) => void, wait: number) { - let timeout: number | null = null; - - return function executedFunction(...args: any[]) { - const later = () => { - if (timeout !== null) { - clearTimeout(timeout); - } - func(...args); - }; - - if (timeout !== null) { - clearTimeout(timeout); - } - timeout = window.setTimeout(later, wait); - }; - } - - const delay = (ms: number) => new Promise(resolve => setTimeout(resolve, ms)); - - const fetchSuggestions = async (query: string) => { - try { - setIsFetchingSuggestions(true); - const response = await fetch(apiHost + "/api/suggestions", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ query }), - }); - - const data = await response.json(); - - if (userInput != '') { - setSuggestions(data.suggestions); - } - await delay(1000); - setIsFetchingSuggestions(false) - } catch (error) { - console.error("Failed to fetch suggestions:", error); - setIsFetchingSuggestions(false) - } - }; - - // Debounce the fetchSuggestions function to prevent rapid requests - const debouncedFetchSuggestions = debounce(fetchSuggestions, 1500); - - const handleInputChange = (e: React.ChangeEvent) => { - setUserInput(e.target.value); - if (!isFetchingSuggestion) { - fetchSuggestions(e.target.value); - } - }; - - const handleSuggestionClick = async (suggestion: string) => { - // Update the userInput with the clicked suggestion - setUserInput(suggestion); - handleSendMessage(undefined, suggestion); - }; - - const escapeRegExp = (string: string) => { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string - }; - - const renderBoldedSuggestion = (suggestion: string, userInput: string) => { - const escapedUserInput = escapeRegExp(userInput); - const parts = suggestion.split(new RegExp(`(${escapedUserInput})`, 'gi')); - return ( - - {parts.map((part, i) => ( - - {part} - - ))} - - ); - }; - - return ( -
- {showModal && { - setShowModal(false) - }} apiHost={apiHost} />} -
-
-
{/* <-- flexbox container */} -
-
- Verba Logo -
-
-
-

Verba

-
- - The - - - Golden - - - RAGtriever - -
-
-
-
-
- -
-
- - - -
-
-
-
- {documentChunks.map((chunk, index) => ( - - ))} -
-
-
- - {/* Header */} - - - {/* ChatComponent */} - - - {/* Input area */} -
- -
-
- {suggestions.map((suggestion, index) => ( -
handleSuggestionClick(suggestion)} - > - {renderBoldedSuggestion(suggestion, userInput)} -
- ))} -
-
-
- -
-
-
-
- ); -} \ No newline at end of file diff --git a/frontend/pages/status.tsx b/frontend/pages/status.tsx deleted file mode 100644 index f82ea134c..000000000 --- a/frontend/pages/status.tsx +++ /dev/null @@ -1,245 +0,0 @@ -import { useState, useEffect } from "react"; -import CoolButton from "../components/CoolButton"; - -export default function StatusPage() { - - const getApiHost = () => { - if (process.env.NODE_ENV === 'development') { - return 'http://localhost:8000'; - } - return ""; - }; - const apiHost = getApiHost() - - const bgUrl = process.env.NODE_ENV === 'production' - ? 'static/' - : '/'; - - // Define states to hold the retrieved values - const [type, setType] = useState(""); - const [connected, setConnected] = useState("Offline"); - const [libraries, setLibraries] = useState({}); - const [variables, setVariables] = useState({}); - const [schemas, setSchemas] = useState({}); - const [showModal, setShowModal] = useState(false); - const [showCacheModal, setCacheModal] = useState(false); - const [showSuggestionModal, setSuggestioneModal] = useState(false); - const [isLoading, setIsLoading] = useState(false); - - const handleReset = () => { - setIsLoading(true); // Start loading - fetch(apiHost + "/api/reset") - .then(response => response.json()) - .then(data => { - console.log('Reset successful:', data); - setShowModal(false); - setIsLoading(false); - window.location.reload(); // Refresh page - }) - .catch(error => { - console.error('Error during reset:', error); - setIsLoading(false); // Stop loading if there's an error - }); - }; - - const handleCacheReset = () => { - setIsLoading(true); // Start loading - fetch(apiHost + "/api/reset_cache") - .then(response => response.json()) - .then(data => { - console.log('Reset successful:', data); - setCacheModal(false); - setIsLoading(false); - window.location.reload(); // Refresh page - }) - .catch(error => { - console.error('Error during reset:', error); - setIsLoading(false); // Stop loading if there's an error - }); - }; - - const handleSuggestionReset = () => { - setIsLoading(true); // Start loading - fetch(apiHost + "/api/reset_suggestion") - .then(response => response.json()) - .then(data => { - console.log('Reset successful:', data); - setSuggestioneModal(false); - setIsLoading(false); - window.location.reload(); // Refresh page - }) - .catch(error => { - console.error('Error during reset:', error); - setIsLoading(false); // Stop loading if there's an error - }); - }; - - useEffect(() => { - fetch(apiHost + "/api/get_status") - .then(response => response.json()) - .then(data => { - setType(data.type); - setLibraries(data.libraries); - setVariables(data.variables); - setSchemas(data.schemas) - setConnected("Online") - }) - .catch(error => { - console.error("Error fetching the data:", error); - }); - }, []); - - return ( -
-
-
-
{/* <-- flexbox container */} -
-
- Verba Logo -
-
-
-

Verba

-
- - The - - - Golden - - - RAGtriever - -
-
-
-
-
{/* Adjusted container for the three sections */} - {/* First Section */} -
-

🐕 Verba Status

-

This view shows whether your Verba Client is connected to the Backend and which Deployment of Weaviate you are using

-
-
- console.log("Connected button clicked")} /> - console.log("Type button clicked")} /> -
-
- {/* Second Section */} -
-

📚 Libraries & Variables

-

This interface lists all available libraries and defined variables.

-

- To set environment variables, create a .env file and define your variables (e.g., OPENAI_API_KEY=Your-Key). Alternatively, you may configure them system-wide. Click here for detailed instructions. -

-

- To install any required libraries that are not present, use the command pip install Your-Library (for example, pip install spacy). -

-
-
- {Object.entries(libraries).map(([key, value]) => ( - console.log(`${key} button clicked`)} - /> - ))} - {Object.entries(variables).map(([key, value]) => ( - console.log(`${key} button clicked`)} - /> - ))} -
-
- {/* Third Section */} -
-
{/* Container for the title and button */} -

📝 Schemas & Objects

- -
-

This view shows all schemas and their object count

- - - -
-
- {Object.entries(schemas).map(([key, value]) => ( - console.log(`${key} button clicked`)} - /> - ))} -
-
-
-
- {showModal && ( -
-
-

⚠️ Warning

-

Are you sure? This will remove ALL existing data on your Weaviate Instance.

-
- - -
-
-
- )} - {showCacheModal && ( -
-
-

⚠️ Warning

-

Are you sure? This will remove all cached data.

-
- - -
-
-
- )} - {showSuggestionModal && ( -
-
-

⚠️ Warning

-

Are you sure? This will remove all suggestions

-
- - -
-
-
- )} -
- ); -} \ No newline at end of file diff --git a/frontend/public/background.png b/frontend/public/background.png deleted file mode 100644 index 67621b79f..000000000 Binary files a/frontend/public/background.png and /dev/null differ diff --git a/frontend/public/icon.ico b/frontend/public/icon.ico new file mode 100644 index 000000000..750e270c0 Binary files /dev/null and b/frontend/public/icon.ico differ diff --git a/frontend/public/weaviate.png b/frontend/public/weaviate.png new file mode 100644 index 000000000..021b47948 Binary files /dev/null and b/frontend/public/weaviate.png differ diff --git a/frontend/tailwind.config.ts b/frontend/tailwind.config.ts index 76a933223..1025fd0be 100644 --- a/frontend/tailwind.config.ts +++ b/frontend/tailwind.config.ts @@ -1,4 +1,6 @@ import type { Config } from "tailwindcss"; +const colors = require('tailwindcss/colors') + const config: Config = { purge: { @@ -31,13 +33,37 @@ const config: Config = { theme: { screens: { sm: '100px', - md: '1024px', - lg: '1480px', + md: '800px', + lg: '1330px', full: '1700px', - xl: '2000px', - '2xl': '1536px', + }, + colors: { + transparent: 'transparent', + current: 'currentColor', + black: colors.black, + white: colors.white, + gray: colors.gray, + emerald: colors.emerald, + indigo: colors.indigo, + green: colors.green, + blue: colors.blue, + yellow: colors.yellow, + red: colors.red }, extend: { + colors: { + "bg-verba": "var(--bg-verba, #FEF7F7)", + "bg-alt-verba": "var(--bg-alt-verba, #FFFFFF)", + "button-verba": "var(--button-verba, #EFEFEF)", + "button-hover-verba": "var(--button-hover-verba, #DCDCDC)", + "primary-verba": "var(--primary-verba, #FDFF91)", + "secondary-verba": "var(--secondary-verba, #90FFA8)", + "warning-verba": "var(--warning-verba, #FF8399)", + "text-verba": "var(--text-verba, #161616)", + "text-alt-verba": "var(--text-alt-verba, #8E8E8E)", + "bg-console-verba": "var(--bg-console-verba, #3F3E40)", + "text-console-verba": "var(--text-console-verba, #FFFFFF)" + }, backgroundImage: { "gradient-radial": "radial-gradient(var(--tw-gradient-stops))", "gradient-conic": @@ -45,6 +71,10 @@ const config: Config = { }, }, }, - plugins: [], + plugins: [require("daisyui"), require('@tailwindcss/typography')], + + daisyui: { + themes: ["light", "dark"], + }, }; export default config; diff --git a/frontend/wdyr.ts b/frontend/wdyr.ts deleted file mode 100644 index 4bbaebc81..000000000 --- a/frontend/wdyr.ts +++ /dev/null @@ -1,10 +0,0 @@ -/// - -import React from 'react'; - -if (process.env.NODE_ENV === 'development') { - const whyDidYouRender = require('@welldone-software/why-did-you-render'); - whyDidYouRender(React, { - trackAllPureComponents: true, - }); -} \ No newline at end of file diff --git a/goldenverba/.env.example b/goldenverba/.env.example index d8f797842..100e88118 100644 --- a/goldenverba/.env.example +++ b/goldenverba/.env.example @@ -15,12 +15,16 @@ COHERE_API_KEY= # YOUR-UNSTRUCTURED-KEY UNSTRUCTURED_API_KEY= +# UNSTRUCTURED_API_URL= # YOUR-GITHUB-TOKEN GITHUB_TOKEN= -# YOUR-HUGGINGFACE-TOKEN -HF_TOKEN= +# OLLAMA_URL=http://localhost:11434 +# OLLAMA_MODEL=llama3 -# ENABLE-LLAMA2?-(True or False) -# LLAMA2-7B-CHAT-HF= \ No newline at end of file +# GOOGLE ENVIRONMENT VARIABLES + +# GOOGLE_APPLICATION_CREDENTIALS= +# GOOGLE_CLOUD_PROJECT= +# GOOGLE_API_KEY= \ No newline at end of file diff --git a/goldenverba/components/chunking/chunk.py b/goldenverba/components/chunk.py similarity index 100% rename from goldenverba/components/chunking/chunk.py rename to goldenverba/components/chunk.py diff --git a/goldenverba/components/chunking/tiktokenchunker.py b/goldenverba/components/chunking/TokenChunker.py similarity index 58% rename from goldenverba/components/chunking/tiktokenchunker.py rename to goldenverba/components/chunking/TokenChunker.py index ceeea4042..16975d864 100644 --- a/goldenverba/components/chunking/tiktokenchunker.py +++ b/goldenverba/components/chunking/TokenChunker.py @@ -6,9 +6,9 @@ with contextlib.suppress(Exception): import tiktoken -from goldenverba.components.chunking.chunk import Chunk -from goldenverba.components.chunking.interface import Chunker -from goldenverba.components.reader.document import Document +from goldenverba.components.chunk import Chunk +from goldenverba.components.interfaces import Chunker +from goldenverba.components.document import Document class TokenChunker(Chunker): @@ -20,20 +20,11 @@ def __init__(self): super().__init__() self.name = "TokenChunker" self.requires_library = ["tiktoken"] - self.default_units = 250 - self.default_overlap = 50 - self.description = "Chunk documents by tokens powered by tiktoken. You can specify how many tokens should overlap between chunks to improve retrieval." + self.description = "Chunks documents by word tokens. Choose between the chunk size and their overlap." self.encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") - def chunk( - self, documents: list[Document], units: int, overlap: int - ) -> list[Document]: - """Chunk verba documents into chunks based on units and overlap - @parameter: documents : list[Document] - List of Verba documents - @parameter: units : int - How many units per chunk (words, sentences, etc.) - @parameter: overlap : int - How much overlap between the chunks - @returns list[str] - List of documents that contain the chunks. - """ + def chunk(self, documents: list[Document], logging: list[dict]) -> list[Document]: + for document in tqdm( documents, total=len(documents), desc="Chunking documents" ): @@ -43,7 +34,10 @@ def chunk( encoded_tokens = self.encoding.encode(document.text, disallowed_special=()) - if units > len(encoded_tokens) or units < 1: + if ( + self.config["units"].value > len(encoded_tokens) + or self.config["units"].value < 1 + ): doc_chunk = Chunk( text=document.text, doc_name=document.name, @@ -51,18 +45,24 @@ def chunk( chunk_id=0, ) - if overlap >= units: + if self.config["overlap"].value >= self.config["units"].value: msg.warn( - f"Overlap value is greater than unit (Units {units}/ Overlap {overlap})" + f"Overlap value is greater than unit (Units {self.config['units'].value}/ Overlap {self.config['overlap'].value})" ) - continue + logging.append( + { + "type": "ERROR", + "message": f"Overlap value is greater than unit (Units {self.config['units'].value}/ Overlap {self.config['overlap'].value})", + } + ) + self.config["overlap"].value = self.config["units"].value - 1 i = 0 split_id_counter = 0 while i < len(encoded_tokens): # Overlap start_i = i - end_i = min(i + units, len(encoded_tokens)) + end_i = min(i + self.config["units"].value, len(encoded_tokens)) chunk_tokens = encoded_tokens[start_i:end_i] chunk_text = self.encoding.decode(chunk_tokens) @@ -80,6 +80,8 @@ def chunk( if end_i == len(encoded_tokens): break - i += units - overlap # Step forward, considering overlap + i += ( + self.config["units"].value - self.config["overlap"].value + ) # Step forward, considering overlap - return documents + return documents, logging diff --git a/goldenverba/components/chunking/interface.py b/goldenverba/components/chunking/interface.py deleted file mode 100644 index fe57ff360..000000000 --- a/goldenverba/components/chunking/interface.py +++ /dev/null @@ -1,27 +0,0 @@ -from goldenverba.components.component import VerbaComponent -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm - - -class Chunker(VerbaComponent): - """ - Interface for Verba Chunking. - """ - - def __init__(self): - super().__init__() - self.input_form = InputForm.CHUNKER.value # Default for all Chunkers - self.default_units = 100 - self.default_overlap = 50 - - def chunk( - self, documents: list[Document], units: int, overlap: int - ) -> list[Document]: - """Chunk verba documents into chunks based on units and overlap. - - @parameter: documents : list[Document] - List of Verba documents - @parameter: units : int - How many units per chunk (words, sentences, etc.) - @parameter: overlap : int - How much overlap between the chunks - @returns list[str] - List of documents that contain the chunks. - """ - raise NotImplementedError("chunk method must be implemented by a subclass.") diff --git a/goldenverba/components/chunking/manager.py b/goldenverba/components/chunking/manager.py deleted file mode 100644 index df08cd982..000000000 --- a/goldenverba/components/chunking/manager.py +++ /dev/null @@ -1,60 +0,0 @@ -import tiktoken -from wasabi import msg - -from goldenverba.components.chunking.interface import Chunker -from goldenverba.components.chunking.tiktokenchunker import TokenChunker -from goldenverba.components.reader.document import Document - - -class ChunkerManager: - def __init__(self): - self.chunker: dict[str, Chunker] = { - "TokenChunker": TokenChunker(), - } - self.selected_chunker: Chunker = self.chunker["TokenChunker"] - - def chunk( - self, documents: list[Document], units: int, overlap: int - ) -> list[Document]: - """Chunk verba documents into chunks based on n and overlap. - - @parameter: documents : list[Document] - List of Verba documents - @parameter: units : int - How many units per chunk (words, sentences, etc.) - @parameter: overlap : int - How much overlap between the chunks - @returns list[str] - List of documents that contain the chunks. - """ - chunked_docs = self.selected_chunker.chunk(documents, units, overlap) - msg.good("Chunking completed") - if self.check_chunks(chunked_docs): - return chunked_docs - return [] - - def set_chunker(self, chunker: str) -> bool: - if chunker in self.chunker: - self.selected_chunker = self.chunker[chunker] - return True - else: - msg.warn(f"Chunker {chunker} not found") - return False - - def get_chunkers(self) -> dict[str, Chunker]: - return self.chunker - - def check_chunks(self, documents: list[Document]) -> bool: - """Checks token count of chunks which are hardcapped to 1000 tokens per chunk - @parameter: documents : list[Document] - List of Verba documents - @returns bool - Whether the chunks are within the token range. - """ - encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") - - for document in documents: - chunks = document.chunks - for chunk in chunks: - tokens = encoding.encode(chunk.text, disallowed_special=()) - chunk.set_tokens(tokens) - if len(tokens) > 1000: - raise Exception( - "Chunk detected with more than 1000 tokens which exceeds the maximum size. Please reduce size of your chunk." - ) - - return True diff --git a/goldenverba/components/component.py b/goldenverba/components/component.py deleted file mode 100644 index b546f4972..000000000 --- a/goldenverba/components/component.py +++ /dev/null @@ -1,10 +0,0 @@ -class VerbaComponent: - """ - Base Class for Verba Readers, Chunkers, Embedders, Retrievers, and Generators. - """ - - def __init__(self): - self.name = "" - self.requires_env = [] - self.requires_library = [] - self.description = "" diff --git a/goldenverba/components/reader/document.py b/goldenverba/components/document.py similarity index 97% rename from goldenverba/components/reader/document.py rename to goldenverba/components/document.py index aec3e8f16..f225a221d 100644 --- a/goldenverba/components/reader/document.py +++ b/goldenverba/components/document.py @@ -1,4 +1,4 @@ -from goldenverba.components.chunking.chunk import Chunk +from goldenverba.components.chunk import Chunk class Document: diff --git a/goldenverba/components/embedding/ADAEmbedder.py b/goldenverba/components/embedding/ADAEmbedder.py index 0161c3cd1..570f57ab5 100644 --- a/goldenverba/components/embedding/ADAEmbedder.py +++ b/goldenverba/components/embedding/ADAEmbedder.py @@ -1,7 +1,7 @@ from weaviate import Client -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.reader.document import Document +from goldenverba.components.interfaces import Embedder +from goldenverba.components.document import Document class ADAEmbedder(Embedder): @@ -13,14 +13,11 @@ def __init__(self): super().__init__() self.name = "ADAEmbedder" self.requires_env = ["OPENAI_API_KEY"] - self.requires_library = ["openai"] self.description = "Embeds and retrieves objects using OpenAI's ADA model" self.vectorizer = "text2vec-openai" def embed( - self, - documents: list[Document], - client: Client, + self, documents: list[Document], client: Client, logging: list[dict] ) -> bool: """Embed verba documents and its chunks to Weaviate @parameter: documents : list[Document] - List of Verba documents @@ -28,4 +25,4 @@ def embed( @parameter: batch_size : int - Batch Size of Input @returns bool - Bool whether the embedding what successful. """ - return self.import_data(documents, client) + return self.import_data(documents, client, logging) diff --git a/goldenverba/components/embedding/CohereEmbedder.py b/goldenverba/components/embedding/CohereEmbedder.py index 92979a017..158c42c93 100644 --- a/goldenverba/components/embedding/CohereEmbedder.py +++ b/goldenverba/components/embedding/CohereEmbedder.py @@ -1,7 +1,7 @@ from weaviate import Client -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.reader.document import Document +from goldenverba.components.interfaces import Embedder +from goldenverba.components.document import Document class CohereEmbedder(Embedder): @@ -22,6 +22,7 @@ def embed( self, documents: list[Document], client: Client, + logging: list[dict], ) -> bool: """Embed verba documents and its chunks to Weaviate @parameter: documents : list[Document] - List of Verba documents @@ -29,4 +30,4 @@ def embed( @parameter: batch_size : int - Batch Size of Input @returns bool - Bool whether the embedding what successful. """ - return self.import_data(documents, client) + return self.import_data(documents, client, logging) diff --git a/goldenverba/components/embedding/GoogleEmbedder.py b/goldenverba/components/embedding/GoogleEmbedder.py new file mode 100644 index 000000000..3026622cb --- /dev/null +++ b/goldenverba/components/embedding/GoogleEmbedder.py @@ -0,0 +1,30 @@ +from weaviate import Client +from goldenverba.components.interfaces import Embedder +from goldenverba.components.document import Document + + +class GoogleEmbedder(Embedder): + """ + GoogleEmbedder for Verba. + """ + + def __init__(self): + super().__init__() + self.name = "GoogleEmbedder" + self.requires_env = ["GOOGLE_API_KEY"] + self.description = "Embeds and retrieves objects using Google's text-embedding-preview-0409 model" + self.vectorizer = "text2vec-palm" + + def embed( + self, + documents: list[Document], + client: Client, + logging: list[dict], + ) -> bool: + """Embed verba documents and its chunks to Weaviate + @parameter: documents : list[Document] - List of Verba documents + @parameter: client : Client - Weaviate Client + @parameter: batch_size : int - Batch Size of Input + @returns bool - Bool whether the embedding what successful. + """ + return self.import_data(documents, client, logging) diff --git a/goldenverba/components/embedding/MiniLMEmbedder.py b/goldenverba/components/embedding/MiniLMEmbedder.py index c97b1699b..38ffa993f 100644 --- a/goldenverba/components/embedding/MiniLMEmbedder.py +++ b/goldenverba/components/embedding/MiniLMEmbedder.py @@ -1,9 +1,15 @@ from tqdm import tqdm -from wasabi import msg from weaviate import Client -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.reader.document import Document +try: + import torch + from accelerate import Accelerator + from transformers import AutoModel, AutoTokenizer +except: + pass + +from goldenverba.components.interfaces import Embedder +from goldenverba.components.document import Document class MiniLMEmbedder(Embedder): @@ -14,41 +20,34 @@ class MiniLMEmbedder(Embedder): def __init__(self): super().__init__() self.name = "MiniLMEmbedder" - self.requires_library = ["torch", "transformers"] + self.requires_library = ["torch", "transformers", "accelerate"] self.description = "Embeds and retrieves objects using SentenceTransformer's all-MiniLM-L6-v2 model" self.vectorizer = "MiniLM" self.model = None self.tokenizer = None try: - import torch - from transformers import AutoModel, AutoTokenizer - - def get_device(): - if torch.cuda.is_available(): - return torch.device("cuda") - elif torch.backends.mps.is_available(): - return torch.device("mps") - else: - return torch.device("cpu") + accelerator = Accelerator() - self.device = get_device() + self.device = accelerator.device self.model = AutoModel.from_pretrained( "sentence-transformers/all-MiniLM-L6-v2", device_map=self.device ) + + self.model = accelerator.prepare(self.model) + self.tokenizer = AutoTokenizer.from_pretrained( "sentence-transformers/all-MiniLM-L6-v2", device_map=self.device ) - self.model = self.model.to(self.device) except Exception as e: - msg.warn(str(e)) pass def embed( self, documents: list[Document], client: Client, + logging: list[dict], ) -> bool: """Embed verba documents and its chunks to Weaviate @parameter: documents : list[Document] - List of Verba documents @@ -59,15 +58,13 @@ def embed( for document in tqdm( documents, total=len(documents), desc="Vectorizing document chunks" ): - for chunk in document.chunks: - chunk.set_vector(self.vectorize_chunk(chunk.text)) + for chunk in tqdm(document.chunks, total=len(document.chunks), desc="Vectorizing Chunks"): + chunk.set_vector(self.vectorize_chunk(document.name + " : " + chunk.text)) - return self.import_data(documents, client) + return self.import_data(documents, client, logging) def vectorize_chunk(self, chunk) -> list[float]: try: - import torch - text = chunk tokens = self.tokenizer.tokenize(text) diff --git a/goldenverba/components/embedding/OllamaEmbedder.py b/goldenverba/components/embedding/OllamaEmbedder.py new file mode 100644 index 000000000..b18812566 --- /dev/null +++ b/goldenverba/components/embedding/OllamaEmbedder.py @@ -0,0 +1,56 @@ +from tqdm import tqdm +from weaviate import Client +import os +import requests +import json + +from goldenverba.components.interfaces import Embedder +from goldenverba.components.document import Document + + +class OllamaEmbedder(Embedder): + + def __init__(self): + super().__init__() + self.name = "OllamaEmbedder" + self.requires_env = ["OLLAMA_URL", "OLLAMA_MODEL"] + self.description = "Embeds and retrieves objects using Ollama and the model specified in the environment variable 'OLLAMA_MODEL'" + self.vectorizer = "OLLAMA" + self.url = os.environ.get("OLLAMA_URL", "") + self.model = os.environ.get("OLLAMA_MODEL", "") + + def embed( + self, + documents: list[Document], + client: Client, + logging: list[dict], + ) -> bool: + """Embed verba documents and its chunks to Weaviate + @parameter: documents : list[Document] - List of Verba documents + @parameter: client : Client - Weaviate Client + @parameter: batch_size : int - Batch Size of Input + @returns bool - Bool whether the embedding what successful. + """ + for document in tqdm( + documents, total=len(documents), desc="Vectorizing document chunks" + ): + for chunk in tqdm(document.chunks, total=len(document.chunks), desc="Vectorizing Chunks"): + chunk.set_vector(self.vectorize_chunk(document.name + " : " + chunk.text)) + + return self.import_data(documents, client, logging) + + def vectorize_chunk(self, chunk) -> list[float]: + try: + embeddings = [] + embedding_url = self.url + "/api/embeddings" + data = {"model": self.model, "prompt": chunk} + response = requests.post(embedding_url, json=data) + json_data = json.loads(response.text) + embeddings = json_data.get("embedding", []) + return embeddings + + except Exception: + raise + + def vectorize_query(self, query: str) -> list[float]: + return self.vectorize_chunk(query) diff --git a/goldenverba/components/embedding/manager.py b/goldenverba/components/embedding/manager.py deleted file mode 100644 index b74cfeffa..000000000 --- a/goldenverba/components/embedding/manager.py +++ /dev/null @@ -1,40 +0,0 @@ -from wasabi import msg -from weaviate import Client - -from goldenverba.components.embedding.ADAEmbedder import ADAEmbedder -from goldenverba.components.embedding.CohereEmbedder import CohereEmbedder -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.embedding.MiniLMEmbedder import MiniLMEmbedder -from goldenverba.components.reader.document import Document - - -class EmbeddingManager: - def __init__(self): - self.embedders: dict[str, Embedder] = { - "MiniLMEmbedder": MiniLMEmbedder(), - "ADAEmbedder": ADAEmbedder(), - "CohereEmbedder": CohereEmbedder(), - } - self.selected_embedder: Embedder = self.embedders["ADAEmbedder"] - - def embed( - self, documents: list[Document], client: Client, batch_size: int = 100 - ) -> bool: - """Embed verba documents and its chunks to Weaviate - @parameter: documents : list[Document] - List of Verba documents - @parameter: client : Client - Weaviate Client - @parameter: batch_size : int - Batch Size of Input - @returns bool - Bool whether the embedding what successful. - """ - return self.selected_embedder.embed(documents, client) - - def set_embedder(self, embedder: str) -> bool: - if embedder in self.embedders: - self.selected_embedder = self.embedders[embedder] - return True - else: - msg.warn(f"Embedder {embedder} not found") - return False - - def get_embedders(self) -> dict[str, Embedder]: - return self.embedders diff --git a/goldenverba/components/generation/CohereGenerator.py b/goldenverba/components/generation/CohereGenerator.py index 93067b8fd..3fb33953b 100644 --- a/goldenverba/components/generation/CohereGenerator.py +++ b/goldenverba/components/generation/CohereGenerator.py @@ -1,10 +1,8 @@ -import asyncio import os -from collections.abc import Iterator +import aiohttp +import json -from wasabi import msg - -from goldenverba.components.generation.interface import Generator +from goldenverba.components.interfaces import Generator class CohereGenerator(Generator): @@ -14,106 +12,79 @@ class CohereGenerator(Generator): def __init__(self): super().__init__() - self.name = "CohereGenerator" - self.description = "Generator using Cohere's command model" - self.requires_library = ["cohere"] + self.name = "CommandR+" + self.description = "Generator using Cohere's command-r-plus model" self.requires_env = ["COHERE_API_KEY"] - self.streamable = False - self.model_name = "command" - self.context_window = 3000 - - async def generate( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> str: - """Generate an answer based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns str - Answer generated by the Generator. - """ - if conversation is None: - conversation = {} - message, _conversation = self.prepare_messages(queries, context, conversation) - - try: - import cohere - - co = cohere.Client(os.getenv("COHERE_API_KEY")) - - # This is your synchronous chat function call. - def synchronous_chat_call(): - # ... setup your parameters for the call ... - return co.chat( - chat_history=_conversation, - message=message, - model="command", - temperature=0.1, - ) - - # This is your async wrapper function. - async def asynchronous_chat_call(): - chat_obj = await asyncio.to_thread(synchronous_chat_call) - return chat_obj - - chat_obj = await asynchronous_chat_call() - - system_msg = str(chat_obj.text) - - except Exception: - raise - - return system_msg + self.streamable = True + self.model = "command-r-plus" + self.context_window = 10000 + self.url = "https://api.cohere.ai/v1/chat" async def generate_stream( self, queries: list[str], context: list[str], conversation: dict = None, - ) -> Iterator[dict]: + ): """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context @parameter: queries : list[str] - List of queries @parameter: context : list[str] - List of contexts @parameter: conversation : dict - Conversational context @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. """ + + if os.environ.get("COHERE_API_KEY") == None: + yield { + "message": "Missing Cohere API Key", + "finish_reason": "stop", + } + + + if conversation is None: conversation = {} message, _conversation = self.prepare_messages(queries, context, conversation) + data = { + "model": self.model, + "chat_history": _conversation, # Assuming this structure from your context + "message": message, + "stream": True + } + + headers = { + 'Authorization': f'Bearer {os.getenv("COHERE_API_KEY")}', + 'Content-Type': 'application/json', + 'Accept': 'application/json' + } + try: - import cohere - from cohere.responses.chat import StreamEnd, StreamTextGeneration - - co = cohere.Client(os.getenv("COHERE_API_KEY")) - - async for chunk in co.chat( - chat_history=_conversation, - stream=True, - message=message, - model="command", - temperature=0.1, - ): - if isinstance(chunk, StreamTextGeneration): - yield { - "message": chunk.text, - "finish_reason": "", - } - elif isinstance(chunk, StreamEnd): - yield { - "message": "", - "finish_reason": "stop", - } + async with aiohttp.ClientSession() as session: + async with session.post(self.url, json=data, headers=headers) as response: + if response.status == 200: + async for line in response.content: + if line.strip(): + json_data = json.loads(line.decode('utf-8')) + message = json_data.get("text", "") + finish_reason = "stop" if json_data.get("finish_reason", "") == "COMPLETE" else "" + + yield { + "message": message, + "finish_reason": finish_reason, + } + else: + error_message = await response.text() + yield { + "message": f"HTTP Error {response.status}: {error_message}", + "finish_reason": "stop", + } except Exception as e: - raise - msg.warn(str(e)) yield { - "message": "", + "message": str(e), "finish_reason": "stop", } + raise def prepare_messages( self, queries: list[str], context: list[str], conversation: dict[str, str] diff --git a/goldenverba/components/generation/GPT3Generator.py b/goldenverba/components/generation/GPT3Generator.py index f8ff20dae..f8070b543 100644 --- a/goldenverba/components/generation/GPT3Generator.py +++ b/goldenverba/components/generation/GPT3Generator.py @@ -8,6 +8,6 @@ class GPT3Generator(GPT4Generator): def __init__(self): super().__init__() - self.name = "GPT3Generator" - self.description = "Generator using OpenAI's GPT-3.5-turbo-1106 model" - self.model_name = "gpt-3.5-turbo-1106" + self.name = "GPT3" + self.description = "Generator using OpenAI's gpt-3.5-turbo-0125 model" + self.model_name = "gpt-3.5-turbo-0125" diff --git a/goldenverba/components/generation/GPT4Generator.py b/goldenverba/components/generation/GPT4Generator.py index 0c9ae2abc..361c48a4c 100644 --- a/goldenverba/components/generation/GPT4Generator.py +++ b/goldenverba/components/generation/GPT4Generator.py @@ -1,13 +1,10 @@ -import asyncio import os -from collections.abc import Iterator - from dotenv import load_dotenv - -from goldenverba.components.generation.interface import Generator +from goldenverba.components.interfaces import Generator load_dotenv() + class GPT4Generator(Generator): """ GPT4 Generator. @@ -15,77 +12,34 @@ class GPT4Generator(Generator): def __init__(self): super().__init__() - self.name = "GPT4Generator" - self.description = "Generator using OpenAI's GPT-4-1106-preview model" + self.name = "GPT4-O" + self.description = "Generator using the new OpenAI's gpt4-o model" self.requires_library = ["openai"] self.requires_env = ["OPENAI_API_KEY"] self.streamable = True - self.model_name = os.getenv("OPENAI_MODEL","gpt-4-1106-preview") + self.model_name = os.getenv("OPENAI_MODEL", "gpt-4o") self.context_window = 10000 - async def generate( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> str: - """Generate an answer based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns str - Answer generated by the Generator. - """ - if conversation is None: - conversation = {} - messages = self.prepare_messages(queries, context, conversation) - - try: - import openai - - openai.api_key = os.getenv("OPENAI_API_KEY") - - if "OPENAI_API_TYPE" in os.environ: - openai.api_type = os.getenv("OPENAI_API_TYPE") - if "OPENAI_API_BASE" in os.environ: - openai.api_base = os.getenv("OPENAI_API_BASE") - if "OPENAI_API_VERSION" in os.environ: - openai.api_version = os.getenv("OPENAI_API_VERSION") - - chat_completion_arguments = { - "model":self.model_name, - "messages":messages - } - if openai.api_type=="azure": - chat_completion_arguments["deployment_id"]=self.model_name - - - base_url = os.environ.get("OPENAI_BASE_URL", "") - if base_url: - openai.api_base = base_url - - - completion = await asyncio.to_thread( - openai.ChatCompletion.create, **chat_completion_arguments - ) - system_msg = str(completion["choices"][0]["message"]["content"]) - - except Exception: - raise - - return system_msg - async def generate_stream( self, queries: list[str], context: list[str], conversation: dict = None, - ) -> Iterator[dict]: + ): """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context @parameter: queries : list[str] - List of queries @parameter: context : list[str] - List of contexts @parameter: conversation : dict - Conversational context @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. """ + + url = os.environ.get("OPENAI_API_KEY", "") + if url == "": + yield { + "message": "Missing OpenAI API Key", + "finish_reason": "stop", + } + if conversation is None: conversation = {} messages = self.prepare_messages(queries, context, conversation) @@ -106,13 +60,13 @@ async def generate_stream( openai.api_version = os.getenv("OPENAI_API_VERSION") chat_completion_arguments = { - "model":self.model_name, - "messages":messages, - "stream":True, - "temperature":0.0 + "model": self.model_name, + "messages": messages, + "stream": True, + "temperature": 0.0, } - if openai.api_type=="azure": - chat_completion_arguments["deployment_id"]=self.model_name + if openai.api_type == "azure": + chat_completion_arguments["deployment_id"] = self.model_name completion = await openai.ChatCompletion.acreate( **chat_completion_arguments diff --git a/goldenverba/components/generation/GeminiGenerator.py b/goldenverba/components/generation/GeminiGenerator.py new file mode 100644 index 000000000..f3c8847cb --- /dev/null +++ b/goldenverba/components/generation/GeminiGenerator.py @@ -0,0 +1,159 @@ +import os + +try: + import vertexai.preview + from vertexai.preview.generative_models import GenerativeModel, Content, Part +except: + pass + +from wasabi import msg + +from dotenv import load_dotenv + +from goldenverba.components.interfaces import Generator + +load_dotenv() + + +class GeminiGenerator(Generator): + """ + Gemini Generator. + """ + + def __init__(self): + super().__init__() + self.name = "Gemini" + self.description = "Generator using Google's Gemini 1.5 Pro model" + self.requires_library = ["vertexai"] + self.requires_env = [ + "GOOGLE_APPLICATION_CREDENTIALS", + "GOOGLE_CLOUD_PROJECT", + ] + self.streamable = True + self.model_name = os.getenv("GEMINI_MODEL", "gemini-1.5-pro-preview-0409") + self.context_window = 10000 + + async def generate_stream( + self, + queries: list[str], + context: list[str], + conversation: dict = None, + ): + """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context + @parameter: queries : list[str] - List of queries + @parameter: context : list[str] - List of contexts + @parameter: conversation : dict - Conversational context + @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. + """ + + url = os.environ.get("GOOGLE_CLOUD_PROJECT", "") + if url == "": + yield { + "message": "Missing GOOGLE_CLOUD_PROJECT", + "finish_reason": "stop", + } + + if conversation is None: + conversation = {} + messages = self.prepare_messages(queries, context, conversation) + + try: + project_id = os.getenv("GOOGLE_CLOUD_PROJECT") + + REGION = "us-central1" + vertexai.init(project=project_id, location=REGION) + + generative_multimodal_model = GenerativeModel( + "gemini-1.5-pro-preview-0409", + ) + + completion = await generative_multimodal_model.generate_content_async( + stream=True, contents=messages + ) + + iter = completion.__aiter__() + + try: + while True: + chunk = await iter.__anext__() + if len(chunk.candidates) > 0: + if len(chunk.candidates[0].content.parts) > 0: + yield { + "message": chunk.candidates[0].content.parts[0].text, + "finish_reason": chunk.candidates[0].finish_reason, + } + else: + yield { + "message": " < Canceled due SAFETY REASONS >", + "finish_reason": "", + } + + except StopAsyncIteration: + yield { + "message": "", + "finish_reason": "stop", + } + pass + + except Exception: + raise + + def prepare_messages( + self, queries: list[str], context: list[str], conversation: dict[str, str] + ): + """ + Prepares a list of messages formatted for a Retrieval Augmented Generation chatbot system, including system instructions, previous conversation, and a new user query with context. + + @parameter queries: A list of strings representing the user queries to be answered. + @parameter context: A list of strings representing the context information provided for the queries. + @parameter conversation: A list of previous conversation messages that include the role and content. + + @returns A list of message dictionaries formatted for the chatbot. This includes an initial system message, the previous conversation messages, and the new user query encapsulated with the provided context. + + Each message in the list is a dictionary with 'role' and 'content' keys, where 'role' is either 'system' or 'user', and 'content' contains the relevant text. This will depend on the LLM used. + """ + messages = [] + + for message in conversation: + messages.append( + Content(role=message.type, parts=[Part.from_text(message.content)]) + ) + + query = " ".join(queries) + user_context = " ".join(context) + + messages.append( + Content( + role="user", + parts=[ + Part.from_text( + f"{user_context} Please answer this query: '{query}' with this provided context. Only use the context if it is necessary to answer the question." + ) + ], + ) + ) + + messages = self.ensure_user_model_alteration(messages) + + return messages + + def ensure_user_model_alteration(self, messages): + current_role: str = "" + + new_messages: list[Content] = [] + + for message in messages: + if message.role == "system": + message.role = "model" + + if messages[0].role == "model": + messages = messages[1:] + + for message in messages: + if message.role == current_role: + new_messages[-1] = message + else: + new_messages.append(message) + current_role = message.role + + return new_messages diff --git a/goldenverba/components/generation/Llama2Generator.py b/goldenverba/components/generation/Llama2Generator.py deleted file mode 100644 index b13b7a78a..000000000 --- a/goldenverba/components/generation/Llama2Generator.py +++ /dev/null @@ -1,178 +0,0 @@ -import asyncio -import os -from collections.abc import Iterator - -from wasabi import msg - -from goldenverba.components.generation.interface import Generator - - -class Llama2Generator(Generator): - """ - Llama2Generator Generator. - """ - - def __init__(self): - super().__init__() - self.name = "Llama2Generator" - self.description = "Generator using Meta's Llama-2-7b-chat-hf model" - self.requires_library = ["huggingface_hub", "transformers"] - self.requires_env = ["HF_TOKEN", "LLAMA2-7B-CHAT-HF"] - self.streamable = True - self.model = None - self.tokenizer = None - self.device = None - self.context_window = 3000 - if os.environ.get("LLAMA2-7B-CHAT-HF", "").lower() == "true": - try: - import torch - from transformers import AutoModelForCausalLM, AutoTokenizer - - def get_device(): - if torch.cuda.is_available(): - return torch.device("cuda") - elif torch.backends.mps.is_available(): - return torch.device("mps") - else: - return torch.device("cpu") - - self.device = get_device() - - self.model = AutoModelForCausalLM.from_pretrained( - "meta-llama/Llama-2-7b-chat-hf", - device_map=self.device, - ) - self.tokenizer = AutoTokenizer.from_pretrained( - "meta-llama/Llama-2-7b-chat-hf", - device_map=self.device, - ) - self.model = self.model.to(self.device) - msg.info("Loading Llama Model") - except Exception as e: - msg.warn(str(e)) - - async def generate_stream( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> Iterator[dict]: - """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. - """ - if conversation is None: - conversation = {} - messages = self.prepare_messages(queries, context, conversation) - - try: - import torch - - prompt = messages - output_length = 500 - - # Ensure the tokenizer has a padding token defined - if self.tokenizer.pad_token is None: - self.tokenizer.pad_token = self.tokenizer.eos_token - - input = self.tokenizer( - prompt, - return_tensors="pt", - padding=True, - truncation=True, - max_length=3500, # Specify a max_length value - ) - - input = {k: v.to(self.device) for k, v in input.items()} - attention_mask = input.get( - "attention_mask", None - ) # Get the attention mask, if provided - input_len = input["input_ids"].shape[1] - msg.info(f"Tokenized finished with {input_len} tokens") - - position_ids = torch.arange( - 0, input_len, dtype=torch.long, device=self.device - ).unsqueeze(0) - - for output_len in range(output_length): - updated_position_ids = torch.arange( - 0, input_len + output_len, dtype=torch.long, device=self.device - ).unsqueeze(0) - output = await asyncio.to_thread( - lambda: self.model.generate( - input_ids=input["input_ids"], - attention_mask=attention_mask, - position_ids=updated_position_ids, - max_length=input_len + output_len + 1, - do_sample=True, - temperature=0.1, - pad_token_id=self.tokenizer.pad_token_id, - eos_token_id=self.tokenizer.eos_token_id, - ) - ) - current_token_id = output[0][-1] - if current_token_id == self.tokenizer.eos_token_id: - break - current_token = self.tokenizer.convert_ids_to_tokens( - [current_token_id], skip_special_tokens=False - ) - if type(current_token) == list: - current_token = " ".join(current_token) - current_token = current_token.replace("<0x0A>", "\n").replace("▁", " ") - # Update input for next iteration - input["input_ids"] = torch.cat( - (input["input_ids"], current_token_id.view(1, 1)), dim=1 - ) - attention_mask = torch.cat( - ( - attention_mask, - torch.ones((1, 1), dtype=torch.long, device=self.device), - ), - dim=1, - ) # Update the attention_mask - position_ids = torch.cat( - ( - position_ids, - torch.tensor([[input_len + output_len]], device=self.device), - ), - dim=1, - ) - yield { - "message": current_token, - "finish_reason": "", - } - yield { - "message": "", - "finish_reason": "stop", - } - - except Exception: - raise - - def prepare_messages(self, queries, context, conversation): - """ - Prepares a list of messages formatted for a Retrieval Augmented Generation chatbot system, including system instructions, previous conversation, and a new user query with context. - - @parameter queries: A list of strings representing the user queries to be answered. - @parameter context: A list of strings representing the context information provided for the queries. - @parameter conversation: A list of previous conversation messages that include the role and content. - - @returns A list or of message dictionaries or whole prompts formatted for the chatbot. This includes an initial system message, the previous conversation messages, and the new user query encapsulated with the provided context. - - Each message in the list is a dictionary with 'role' and 'content' keys, where 'role' is either 'system' or 'user', and 'content' contains the relevant text. This will depend on the LLM used. - """ - llama_prompt = """ - [INST] <> - You are a Retrieval Augmented Generation chatbot. Answer user queries using only the provided context. If the context does not provide enough information, say so. If the answer requires code examples encapsulate them with ```programming-language-name ```. Don't do pseudo-code. \n<>\n\n - """ - - query = " ".join(queries) - user_context = " ".join(context) - - llama_prompt += ( - f"Answer this query: '{query}' with this context: {user_context} [/INST] " - ) - - return llama_prompt diff --git a/goldenverba/components/generation/OllamaGenerator.py b/goldenverba/components/generation/OllamaGenerator.py new file mode 100644 index 000000000..045d2f55e --- /dev/null +++ b/goldenverba/components/generation/OllamaGenerator.py @@ -0,0 +1,106 @@ +import os + +import json +import aiohttp + +from goldenverba.components.interfaces import Generator + + +class OllamaGenerator(Generator): + def __init__(self): + super().__init__() + self.name = "Ollama" + self.description = "Generator using a local running Ollama Model specified in the ` OLLAMA_MODEL` variable" + self.requires_env = ["OLLAMA_URL", "OLLAMA_MODEL"] + self.streamable = True + self.context_window = 10000 + + async def generate_stream( + self, + queries: list[str], + context: list[str], + conversation: dict = None, + ): + """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context + @parameter: queries : list[str] - List of queries + @parameter: context : list[str] - List of contexts + @parameter: conversation : dict - Conversational context + @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. + """ + + url = os.environ.get("OLLAMA_URL", "") + model = os.environ.get("OLLAMA_MODEL", "") + if url == "": + yield { + "message": "Missing Ollama URL", + "finish_reason": "stop", + } + + url += "/api/chat" + + if conversation is None: + conversation = {} + messages = self.prepare_messages(queries, context, conversation) + + try: + data = {"model": model, "messages": messages} + async with aiohttp.ClientSession() as session: + async with session.post(url, json=data) as response: + async for line in response.content: + if line.strip(): # Ensure line is not just whitespace + json_data = json.loads( + line.decode("utf-8") + ) # Decode bytes to string then to JSON + message = json_data.get("message", {}).get("content", "") + finish_reason = ( + "stop" if json_data.get("done", False) else "" + ) + + yield { + "message": message, + "finish_reason": finish_reason, + } + else: + yield { + "message": "", + "finish_reason": "stop", + } + + except Exception: + raise + + def prepare_messages( + self, queries: list[str], context: list[str], conversation: dict[str, str] + ) -> dict[str, str]: + """ + Prepares a list of messages formatted for a Retrieval Augmented Generation chatbot system, including system instructions, previous conversation, and a new user query with context. + + @parameter queries: A list of strings representing the user queries to be answered. + @parameter context: A list of strings representing the context information provided for the queries. + @parameter conversation: A list of previous conversation messages that include the role and content. + + @returns A list of message dictionaries formatted for the chatbot. This includes an initial system message, the previous conversation messages, and the new user query encapsulated with the provided context. + + Each message in the list is a dictionary with 'role' and 'content' keys, where 'role' is either 'system' or 'user', and 'content' contains the relevant text. This will depend on the LLM used. + """ + messages = [ + { + "role": "system", + "content": self.system_message, + } + ] + + for message in conversation: + messages.append({"role": message.type, "content": message.content}) + + query = " ".join(queries) + user_context = " ".join(context) + + messages.append( + { + "role": "user", + "content": f"With this provided context: '{user_context}' Please answer this query: '{query}'", + } + ) + + return messages diff --git a/goldenverba/components/generation/interface.py b/goldenverba/components/generation/interface.py deleted file mode 100644 index 5fdd268c4..000000000 --- a/goldenverba/components/generation/interface.py +++ /dev/null @@ -1,66 +0,0 @@ -from collections.abc import Iterator - -from goldenverba.components.component import VerbaComponent - - -class Generator(VerbaComponent): - """ - Interface for Verba Generators. - """ - - def __init__(self): - super().__init__() - self.streamable = False - self.context_window = 4000 - - async def generate( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> str: - """Generate an answer based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns str - Answer generated by the Generator. - """ - if conversation is None: - conversation = {} - raise NotImplementedError("generate method must be implemented by a subclass.") - - async def generate_stream( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> Iterator[dict]: - """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. - """ - if conversation is None: - conversation = {} - raise NotImplementedError( - "generate_stream method must be implemented by a subclass." - ) - - def prepare_messages( - self, queries: list[str], context: list[str], conversation: dict[str, str] - ) -> any: - """ - Prepares a list of messages formatted for a Retrieval Augmented Generation chatbot system, including system instructions, previous conversation, and a new user query with context. - - @parameter queries: A list of strings representing the user queries to be answered. - @parameter context: A list of strings representing the context information provided for the queries. - @parameter conversation: A list of previous conversation messages that include the role and content. - - @returns A list or of message dictionaries or whole prompts formatted for the chatbot. This includes an initial system message, the previous conversation messages, and the new user query encapsulated with the provided context. - - Each message in the list is a dictionary with 'role' and 'content' keys, where 'role' is either 'system' or 'user', and 'content' contains the relevant text. This will depend on the LLM used. - """ - raise NotImplementedError( - "prepare_messages method must be implemented by a subclass." - ) diff --git a/goldenverba/components/generation/manager.py b/goldenverba/components/generation/manager.py deleted file mode 100644 index 4c8f399a7..000000000 --- a/goldenverba/components/generation/manager.py +++ /dev/null @@ -1,119 +0,0 @@ -from collections.abc import Iterator - -import tiktoken -from wasabi import msg - -from goldenverba.components.generation.CohereGenerator import CohereGenerator -from goldenverba.components.generation.GPT3Generator import GPT3Generator -from goldenverba.components.generation.GPT4Generator import GPT4Generator -from goldenverba.components.generation.interface import Generator -from goldenverba.components.generation.Llama2Generator import Llama2Generator - - -class GeneratorManager: - def __init__(self): - self.generators: dict[str, Generator] = { - "GPT4Generator": GPT4Generator(), - "GPT3Generator": GPT3Generator(), - "CohereGenerator": CohereGenerator(), - "Llama2Generator": Llama2Generator(), - } - self.selected_generator: Generator = self.generators["GPT3Generator"] - - async def generate( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> str: - """Generate an answer based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns str - Answer generated by the Generator. - """ - if conversation is None: - conversation = {} - return await self.selected_generator.generate( - queries, - context, - self.truncate_conversation_items( - conversation, int(self.selected_generator.context_window * 0.375) - ), - ) - - async def generate_stream( - self, - queries: list[str], - context: list[str], - conversation: dict = None, - ) -> Iterator[dict]: - """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context - @parameter: queries : list[str] - List of queries - @parameter: context : list[str] - List of contexts - @parameter: conversation : dict - Conversational context - @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. - """ - if conversation is None: - conversation = {} - async for result in self.selected_generator.generate_stream( - queries, - context, - self.truncate_conversation_items( - conversation, int(self.selected_generator.context_window * 0.375) - ), - ): - yield result - - def truncate_conversation_dicts( - self, conversation_dicts: list[dict[str, any]], max_tokens: int - ) -> list[dict[str, any]]: - """ - Truncate a list of conversation dictionaries to fit within a specified maximum token limit. - - @parameter conversation_dicts: List[Dict[str, any]] - A list of conversation dictionaries that may contain various keys, where 'content' key is present and contains text data. - @parameter max_tokens: int - The maximum number of tokens that the combined content of the truncated conversation dictionaries should not exceed. - - @returns List[Dict[str, any]]: A list of conversation dictionaries that have been truncated so that their combined content respects the max_tokens limit. The list is returned in the original order of conversation with the most recent conversation being truncated last if necessary. - - """ - encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") - accumulated_tokens = 0 - truncated_conversation_dicts = [] - - # Start with the newest conversations - for item_dict in reversed(conversation_dicts): - item_tokens = encoding.encode(item_dict["content"], disallowed_special=()) - - # If adding the entire new item exceeds the max tokens - if accumulated_tokens + len(item_tokens) > max_tokens: - # Calculate how many tokens we can add from this item - remaining_space = max_tokens - accumulated_tokens - truncated_content = encoding.decode(item_tokens[:remaining_space]) - - # Create a new truncated item dictionary - truncated_item_dict = { - "type": item_dict["type"], - "content": truncated_content, - "typewriter": item_dict["typewriter"], - } - - truncated_conversation_dicts.append(truncated_item_dict) - break - - truncated_conversation_dicts.append(item_dict) - accumulated_tokens += len(item_tokens) - - # The list has been built in reverse order so we reverse it again - return list(reversed(truncated_conversation_dicts)) - - def set_generator(self, generator: str) -> bool: - if generator in self.generators: - self.selected_generator = self.generators[generator] - return True - else: - msg.warn(f"Generator {generator} not found") - return False - - def get_generators(self) -> dict[str, Generator]: - return self.generators diff --git a/goldenverba/components/embedding/interface.py b/goldenverba/components/interfaces.py similarity index 52% rename from goldenverba/components/embedding/interface.py rename to goldenverba/components/interfaces.py index 20e982459..a24af8d42 100644 --- a/goldenverba/components/embedding/interface.py +++ b/goldenverba/components/interfaces.py @@ -1,4 +1,7 @@ -import re +from goldenverba.components.document import Document +from goldenverba.components.chunk import Chunk +from goldenverba.components.types import InputText, FileData, InputNumber + import os import time from dotenv import load_dotenv @@ -7,9 +10,11 @@ from wasabi import msg from weaviate import Client -from goldenverba.components.component import VerbaComponent -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm +try: + import tiktoken +except Exception: + msg.warn("tiktoken not installed, your base installation might be corrupted.") + from goldenverba.components.schema.schema_generation import ( EMBEDDINGS, VECTORIZERS, @@ -18,6 +23,114 @@ load_dotenv() + +class VerbaComponent: + """ + Base Class for Verba Readers, Chunkers, Embedders, Retrievers, and Generators. + """ + + def __init__(self): + self.name = "" + self.requires_env = [] + self.requires_library = [] + self.description = "" + self.config = {} + self.type = "" + + def get_meta(self, envs, libs) -> dict: + _metadata = { + "name": self.name, + "variables": self.requires_env, + "library": self.requires_library, + "description": self.description, + "type": self.type, + "config": {_c: self.config[_c].model_dump() for _c in self.config}, + "available": self.check_available(envs, libs), + } + return _metadata + + def set_config(self, new_config: dict): + for _k in new_config: + if _k in self.config: + if self.config[_k].type == "text": + if self.config[_k].text != new_config[_k].get("text", ""): + self.config[_k].text = new_config[_k].get("text", "") + msg.info( + f"Updating {self.name} config ({_k}) {self.config[_k].text} -> {new_config[_k].get('text','')}" + ) + if self.config[_k].type == "number": + if self.config[_k].value != int(new_config[_k].get("value", 0)): + msg.info( + f"Updating {self.name} config ({_k}) {self.config[_k].value} -> {new_config[_k].get('value',0)}" + ) + self.config[_k].value = int(new_config[_k].get("value", 0)) + + def check_available(self, envs, libs) -> bool: + if self.requires_env: + for _env in self.requires_env: + if _env not in envs or not envs.get(_env, False): + return False + if self.requires_library: + for _lib in self.requires_library: + if _lib not in libs or not libs.get(_lib, False): + return False + return True + + +class Reader(VerbaComponent): + """ + Interface for Verba Readers. + """ + + def __init__(self): + super().__init__() + self.type = "UPLOAD" # "TEXT" + self.config = { + "document_type": InputText( + type="text", + text="Document", + description="Choose a label for your documents for filtering", + ) + } + + def load( + self, fileData: list[FileData], textValues: list[str], logging: list[dict] + ) -> tuple[list[Document], list[str]]: + """Ingest data into Weaviate + @parameter: fileData : list[FileData] - List of filename and bytes pairs + @parameter: textValues : list[str] - List of strings, e.g. URLs etc + @returns tuple[list[Document], list[str]] - A tuple of a list of documents and a list of strings displayed as logging on the frontend. + """ + raise NotImplementedError("load method must be implemented by a subclass.") + + +class Chunker(VerbaComponent): + """ + Interface for Verba Chunking. + """ + + def __init__(self): + super().__init__() + self.config = { + "units": InputNumber( + type="number", value=100, description="Choose the units per chunks" + ), + "overlap": InputNumber( + type="number", + value=50, + description="Choose the units for overlap between chunks", + ), + } + + def chunk(self, documents: list[Document], logging: list[dict]) -> list[Document]: + """Chunk verba documents into chunks based on units and overlap. + + @parameter: documents : list[Document] - List of Verba documents + @returns list[str] - List of documents that contain the chunks. + """ + raise NotImplementedError("chunk method must be implemented by a subclass.") + + class Embedder(VerbaComponent): """ Interface for Verba Embedding. @@ -25,10 +138,14 @@ class Embedder(VerbaComponent): def __init__(self): super().__init__() - self.input_form = InputForm.TEXT.value # Default for all Embedders self.vectorizer = "" - def embed(documents: list[Document], client: Client, batch_size: int = 100) -> bool: + def embed( + documents: list[Document], + client: Client, + logging: list[dict], + batch_size: int = 100, + ) -> bool: """Embed verba documents and its chunks to Weaviate @parameter: documents : list[Document] - List of Verba documents @parameter: client : Client - Weaviate Client @@ -38,9 +155,7 @@ def embed(documents: list[Document], client: Client, batch_size: int = 100) -> b raise NotImplementedError("embed method must be implemented by a subclass.") def import_data( - self, - documents: list[Document], - client: Client, + self, documents: list[Document], client: Client, logging: list[dict] ) -> bool: """Import verba documents and its chunks to Weaviate @parameter: documents : list[Document] - List of Verba documents @@ -86,7 +201,7 @@ def import_data( "timestamp": str(document.timestamp), } - class_name = "Document_" + strip_non_letters(self.vectorizer) + class_name = "VERBA_Document_" + strip_non_letters(self.vectorizer) uuid = client.batch.add_data_object(properties, class_name) for chunk in document.chunks: @@ -108,30 +223,41 @@ def import_data( "doc_type": chunk.doc_type, "chunk_id": chunk.chunk_id, } - class_name = "Chunk_" + strip_non_letters(self.vectorizer) + class_name = "VERBA_Chunk_" + strip_non_letters( + self.vectorizer + ) # Check if vector already exists if chunk.vector is None: - client.batch.add_data_object(properties, class_name) + try: + client.batch.add_data_object(properties, class_name) + except Exception as e: + msg.fail(f"Error adding chunk to Weaviate: {e}") else: client.batch.add_data_object( properties, class_name, vector=chunk.vector ) - - wait_time_ms = int(os.getenv("WAIT_TIME_BETWEEN_INGESTION_QUERIES_MS","0")) - if wait_time_ms>0: - time.sleep(float(wait_time_ms)/1000) + + wait_time_ms = int( + os.getenv("WAIT_TIME_BETWEEN_INGESTION_QUERIES_MS", "0") + ) + if wait_time_ms > 0: + time.sleep(float(wait_time_ms) / 1000) self.check_document_status( client, uuid, document.name, - "Document_" + strip_non_letters(self.vectorizer), - "Chunk_" + strip_non_letters(self.vectorizer), + "VERBA_Document_" + strip_non_letters(self.vectorizer), + "VERBA_Chunk_" + strip_non_letters(self.vectorizer), len(document.chunks), + logging, ) - return True + return logging except Exception as e: + logging.append( + {"type": "ERROR", "message": f"Embedding not successful: {str(e)}"} + ) raise Exception(e) def check_document_status( @@ -142,6 +268,7 @@ def check_document_status( doc_class_name: str, chunk_class_name: str, chunk_count: int, + logging: list[dict], ): """Verifies that imported documents and its chunks exist in the database, if not, remove everything that was added and rollback @parameter: client : Client - Weaviate Client @@ -207,8 +334,8 @@ def remove_document( msg.warn(f"Deleted document {doc_name} and its chunks") def remove_document_by_id(self, client: Client, doc_id: str): - doc_class_name = "Document_" + strip_non_letters(self.vectorizer) - chunk_class_name = "Chunk_" + strip_non_letters(self.vectorizer) + doc_class_name = "VERBA_Document_" + strip_non_letters(self.vectorizer) + chunk_class_name = "VERBA_Chunk_" + strip_non_letters(self.vectorizer) client.data_object.delete(uuid=doc_id, class_name=doc_class_name) @@ -220,20 +347,23 @@ def remove_document_by_id(self, client: Client, doc_id: str): msg.warn(f"Deleted document {doc_id} and its chunks") def get_document_class(self) -> str: - return "Document_" + strip_non_letters(self.vectorizer) + return "VERBA_Document_" + strip_non_letters(self.vectorizer) def get_chunk_class(self) -> str: - return "Chunk_" + strip_non_letters(self.vectorizer) + return "VERBA_Chunk_" + strip_non_letters(self.vectorizer) def get_cache_class(self) -> str: - return "Cache_" + strip_non_letters(self.vectorizer) + return "VERBA_Cache_" + strip_non_letters(self.vectorizer) - def search_documents(self, client: Client, query: str, doc_type: str) -> list: + def search_documents( + self, client: Client, query: str, doc_type: str, page: int, pageSize: int + ) -> list: """Search for documents from Weaviate @parameter query_string : str - Search query @returns list - Document list. """ - doc_class_name = "Document_" + strip_non_letters(self.vectorizer) + doc_class_name = "VERBA_Document_" + strip_non_letters(self.vectorizer) + offset = pageSize * (page - 1) if doc_type == "" or doc_type is None: query_results = ( @@ -243,7 +373,8 @@ def search_documents(self, client: Client, query: str, doc_type: str) -> list: ) .with_bm25(query, properties=["doc_name"]) .with_additional(properties=["id"]) - .with_limit(100) + .with_limit(pageSize) + .with_offset(offset) .do() ) else: @@ -260,11 +391,13 @@ def search_documents(self, client: Client, query: str, doc_type: str) -> list: "valueText": doc_type, } ) + .with_offset(offset) .with_additional(properties=["id"]) .with_limit(100) .do() ) + # TODO Better Error Handling, what if error occur? results = query_results["data"]["Get"][doc_class_name] return results @@ -317,15 +450,17 @@ def retrieve_semantic_cache( .with_limit(1) ).do() - if "data" in match_results and len(match_results["data"]["Get"][self.get_cache_class()]) > 0 and ( - query - == match_results["data"]["Get"][self.get_cache_class()][0]["query"] + if ( + "data" in match_results + and len(match_results["data"]["Get"][self.get_cache_class()]) > 0 + and ( + query + == match_results["data"]["Get"][self.get_cache_class()][0]["query"] + ) ): msg.good("Direct match from cache") return ( - match_results["data"]["Get"][self.get_cache_class()][0][ - "system" - ], + match_results["data"]["Get"][self.get_cache_class()][0]["system"], 0.0, ) @@ -391,3 +526,106 @@ def add_to_semantic_cache(self, client: Client, query: str, system: str): ) else: client.batch.add_data_object(properties, self.get_cache_class()) + + +class Retriever(VerbaComponent): + """ + Interface for Verba Retrievers. + """ + + def __init__(self): + super().__init__() + + def retrieve( + self, + queries: list[str], + client: Client, + embedder: Embedder, + ) -> tuple[list[Chunk], str]: + """Ingest data into Weaviate + @parameter: queries : list[str] - List of queries + @parameter: client : Client - Weaviate client + @parameter: embedder : Embedder - Current selected Embedder + @returns tuple(list[Chunk],str) - List of retrieved chunks and the context string. + """ + raise NotImplementedError("load method must be implemented by a subclass.") + + def cutoff_text(self, text: str, content_length: int) -> str: + encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") + + # Tokenize the input text + encoded_tokens = encoding.encode(text, disallowed_special=()) + + # Check if we need to truncate + if len(encoded_tokens) > content_length: + encoded_tokens = encoded_tokens[:content_length] + truncated_text = encoding.decode(encoded_tokens) + msg.info(f"Truncated Context to {content_length} tokens") + return truncated_text + else: + msg.info(f"Retrieved Context of {len(encoded_tokens)} tokens") + return text + + +class Generator(VerbaComponent): + """ + Interface for Verba Generators. + """ + + def __init__(self): + super().__init__() + self.streamable = False + self.context_window = 4000 + self.system_message = "You are Verba, The Golden RAGtriever, a chatbot for Retrieval Augmented Generation (RAG). You will receive a user query and context pieces that have a semantic similarity to that specific query. Please answer these user queries only with their provided context. If the provided documentation does not provide enough information, say so. If the user asks questions about you as a chatbot specifially, answer them naturally. If the answer requires code examples encapsulate them with ```programming-language-name ```. Don't do pseudo-code." + + async def generate( + self, + queries: list[str], + context: list[str], + conversation: dict = None, + ) -> str: + """Generate an answer based on a list of queries and list of contexts, and includes conversational context + @parameter: queries : list[str] - List of queries + @parameter: context : list[str] - List of contexts + @parameter: conversation : dict - Conversational context + @returns str - Answer generated by the Generator. + """ + if conversation is None: + conversation = {} + raise NotImplementedError("generate method must be implemented by a subclass.") + + async def generate_stream( + self, + queries: list[str], + context: list[str], + conversation: dict = None, + ): + """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context + @parameter: queries : list[str] - List of queries + @parameter: context : list[str] - List of contexts + @parameter: conversation : dict - Conversational context + @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. + """ + if conversation is None: + conversation = {} + raise NotImplementedError( + "generate_stream method must be implemented by a subclass." + ) + + def prepare_messages( + self, queries: list[str], context: list[str], conversation: dict[str, str] + ) -> any: + """ + Prepares a list of messages formatted for a Retrieval Augmented Generation chatbot system, including system instructions, previous conversation, and a new user query with context. + + @parameter queries: A list of strings representing the user queries to be answered. + @parameter context: A list of strings representing the context information provided for the queries. + @parameter conversation: A list of previous conversation messages that include the role and content. + + @returns A list or of message dictionaries or whole prompts formatted for the chatbot. This includes an initial system message, the previous conversation messages, and the new user query encapsulated with the provided context. + + Each message in the list is a dictionary with 'role' and 'content' keys, where 'role' is either 'system' or 'user', and 'content' contains the relevant text. This will depend on the LLM used. + """ + raise NotImplementedError( + "prepare_messages method must be implemented by a subclass." + ) diff --git a/goldenverba/components/managers.py b/goldenverba/components/managers.py new file mode 100644 index 000000000..2a2c85de8 --- /dev/null +++ b/goldenverba/components/managers.py @@ -0,0 +1,352 @@ +from wasabi import msg +from weaviate import Client + +from goldenverba.components.document import Document +from goldenverba.components.chunk import Chunk +from goldenverba.components.types import FileData +from goldenverba.components.interfaces import ( + Reader, + Chunker, + Embedder, + Retriever, + Generator, +) + +from goldenverba.components.reader.BasicReader import BasicReader +from goldenverba.components.reader.GitReader import GitHubReader +from goldenverba.components.reader.UnstructuredAPI import UnstructuredReader + +from goldenverba.components.chunking.TokenChunker import TokenChunker + +from goldenverba.components.embedding.ADAEmbedder import ADAEmbedder +from goldenverba.components.embedding.CohereEmbedder import CohereEmbedder +from goldenverba.components.embedding.MiniLMEmbedder import MiniLMEmbedder +from goldenverba.components.embedding.GoogleEmbedder import GoogleEmbedder +from goldenverba.components.embedding.OllamaEmbedder import OllamaEmbedder + +from goldenverba.components.retriever.WindowRetriever import WindowRetriever + +from goldenverba.components.generation.GeminiGenerator import GeminiGenerator +from goldenverba.components.generation.CohereGenerator import CohereGenerator +from goldenverba.components.generation.GPT3Generator import GPT3Generator +from goldenverba.components.generation.GPT4Generator import GPT4Generator +from goldenverba.components.generation.OllamaGenerator import OllamaGenerator + +import time + + +try: + import tiktoken +except Exception: + msg.warn("tiktoken not installed, your base installation might be corrupted.") + + +class ReaderManager: + def __init__(self): + self.readers: dict[str, Reader] = { + "BasicReader": BasicReader(), + "GitHubReader": GitHubReader(), + "UnstructuredAPI": UnstructuredReader(), + } + self.selected_reader: str = "BasicReader" + + def load( + self, fileData: list[FileData], textValues: list[str], logging: list[dict] + ) -> tuple[list[Document], list[str]]: + + start_time = time.time() # Start timing + + if len(fileData) > 0: + logging.append( + { + "type": "INFO", + "message": f"Importing {len(fileData)} files with {self.selected_reader}", + } + ) + else: + logging.append( + { + "type": "INFO", + "message": f"Importing {textValues} with {self.selected_reader}", + } + ) + + documents, logging = self.readers[self.selected_reader].load( + fileData, textValues, logging + ) + + elapsed_time = round(time.time() - start_time, 2) # Calculate elapsed time + + msg.good(f"Loaded {len(documents)} documents in {elapsed_time}s") + logging.append( + { + "type": "SUCCESS", + "message": f"Loaded {len(documents)} documents in {elapsed_time}s", + } + ) + + return documents, logging + + def set_reader(self, reader: str): + if reader in self.readers: + msg.info(f"Setting READER to {reader}") + self.selected_reader = reader + else: + msg.warn(f"Reader {reader} not found") + + def get_readers(self) -> dict[str, Reader]: + return self.readers + + +class ChunkerManager: + def __init__(self): + self.chunker: dict[str, Chunker] = { + "TokenChunker": TokenChunker(), + } + self.selected_chunker: str = "TokenChunker" + + def chunk(self, documents: list[Document], logging: list[dict]) -> list[Document]: + logging.append( + { + "type": "INFO", + "message": f"Starting Chunking with {self.selected_chunker}", + } + ) + start_time = time.time() # Start timing + chunked_docs, logging = self.chunker[self.selected_chunker].chunk( + documents, logging + ) + if self.check_chunks(chunked_docs): + elapsed_time = round(time.time() - start_time, 2) # Calculate elapsed time + msg.good( + f"Chunking completed with {sum([len(document.chunks) for document in chunked_docs])} chunks in {elapsed_time}s" + ) + logging.append( + { + "type": "SUCCESS", + "message": f"Chunking completed with {sum([len(document.chunks) for document in chunked_docs])} chunks in {elapsed_time}s", + } + ) + return chunked_docs, logging + return [] + + def set_chunker(self, chunker: str) -> bool: + if chunker in self.chunker: + msg.info(f"Setting CHUNKER to {chunker}") + self.selected_chunker = chunker + return True + else: + msg.warn(f"Chunker {chunker} not found") + return False + + def get_chunkers(self) -> dict[str, Chunker]: + return self.chunker + + def check_chunks(self, documents: list[Document]) -> bool: + """Checks token count of chunks which are hardcapped to 1000 tokens per chunk + @parameter: documents : list[Document] - List of Verba documents + @returns bool - Whether the chunks are within the token range. + """ + encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") + + for document in documents: + chunks = document.chunks + for chunk in chunks: + tokens = encoding.encode(chunk.text, disallowed_special=()) + chunk.set_tokens(tokens) + if len(tokens) > 1000: + raise Exception( + "Chunk detected with more than 1000 tokens which exceeds the maximum size. Please reduce size of your chunk." + ) + + return True + + +class EmbeddingManager: + def __init__(self): + self.embedders: dict[str, Embedder] = { + "GoogleEmbedder": GoogleEmbedder(), + "MiniLMEmbedder": MiniLMEmbedder(), + "ADAEmbedder": ADAEmbedder(), + "CohereEmbedder": CohereEmbedder(), + "OllamaEmbedder": OllamaEmbedder(), + } + self.selected_embedder: str = "ADAEmbedder" + + def embed( + self, + documents: list[Document], + client: Client, + logging: list[dict], + batch_size: int = 100, + ) -> bool: + """Embed verba documents and its chunks to Weaviate + @parameter: documents : list[Document] - List of Verba documents + @parameter: client : Client - Weaviate Client + @parameter: batch_size : int - Batch Size of Input + @returns bool - Bool whether the embedding what successful. + """ + start_time = time.time() # Start timing + logging.append( + { + "type": "INFO", + "message": f"Starting Embedding with {self.selected_embedder}", + } + ) + successful_embedding = self.embedders[self.selected_embedder].embed( + documents, client, logging + ) + elapsed_time = round(time.time() - start_time, 2) # Calculate elapsed time + msg.good( + f"Embedding completed with {len(documents)} Documents and {sum([len(document.chunks) for document in documents])} chunks in {elapsed_time}s" + ) + logging.append( + { + "type": "SUCCESS", + "message": f"Embedding completed with {len(documents)} Documents and {sum([len(document.chunks) for document in documents])} chunks in {elapsed_time}s", + } + ) + return successful_embedding + + def set_embedder(self, embedder: str) -> bool: + if embedder in self.embedders: + msg.info(f"Setting EMBEDDER to {embedder}") + self.selected_embedder = embedder + return True + else: + msg.warn(f"Embedder {embedder} not found") + return False + + def get_embedders(self) -> dict[str, Embedder]: + return self.embedders + + +class RetrieverManager: + def __init__(self): + self.retrievers: dict[str, Retriever] = { + "WindowRetriever": WindowRetriever(), + } + self.selected_retriever: str = "WindowRetriever" + + def retrieve( + self, + queries: list[str], + client: Client, + embedder: Embedder, + generator: Generator, + ) -> list[Chunk]: + """Ingest data into Weaviate + @parameter: queries : list[str] - List of queries + @parameter: client : Client - Weaviate client + @parameter: embedder : Embedder - Current selected Embedder + @returns list[Chunk] - List of retrieved chunks. + """ + chunks, context = self.retrievers[self.selected_retriever].retrieve( + queries, client, embedder + ) + managed_context = self.retrievers[self.selected_retriever].cutoff_text( + context, generator.context_window + ) + return chunks, managed_context + + def set_retriever(self, retriever: str) -> bool: + if retriever in self.retrievers: + msg.info(f"Setting RETRIEVER to {retriever}") + self.selected_retriever = retriever + return True + else: + msg.warn(f"Retriever {retriever} not found") + return False + + def get_retrievers(self) -> dict[str, Retriever]: + return self.retrievers + + +class GeneratorManager: + def __init__(self): + self.generators: dict[str, Generator] = { + "Gemini": GeminiGenerator(), + "GPT4-O": GPT4Generator(), + "GPT3": GPT3Generator(), + "Ollama": OllamaGenerator(), + "Command R+": CohereGenerator(), + } + self.selected_generator: str = "GPT3" + + async def generate_stream( + self, + queries: list[str], + context: list[str], + conversation: dict = None, + ): + """Generate a stream of response dicts based on a list of queries and list of contexts, and includes conversational context + @parameter: queries : list[str] - List of queries + @parameter: context : list[str] - List of contexts + @parameter: conversation : dict - Conversational context + @returns Iterator[dict] - Token response generated by the Generator in this format {system:TOKEN, finish_reason:stop or empty}. + """ + if conversation is None: + conversation = {} + async for result in self.generators[self.selected_generator].generate_stream( + queries, + context, + self.truncate_conversation_dicts( + conversation, + int(self.generators[self.selected_generator].context_window * 0.375), + ), + ): + yield result + + def truncate_conversation_dicts( + self, conversation_dicts: list[dict[str, any]], max_tokens: int + ) -> list[dict[str, any]]: + """ + Truncate a list of conversation dictionaries to fit within a specified maximum token limit. + + @parameter conversation_dicts: List[Dict[str, any]] - A list of conversation dictionaries that may contain various keys, where 'content' key is present and contains text data. + @parameter max_tokens: int - The maximum number of tokens that the combined content of the truncated conversation dictionaries should not exceed. + + @returns List[Dict[str, any]]: A list of conversation dictionaries that have been truncated so that their combined content respects the max_tokens limit. The list is returned in the original order of conversation with the most recent conversation being truncated last if necessary. + + """ + encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") + accumulated_tokens = 0 + truncated_conversation_dicts = [] + + # Start with the newest conversations + for item_dict in reversed(conversation_dicts): + item_tokens = encoding.encode(item_dict["content"], disallowed_special=()) + + # If adding the entire new item exceeds the max tokens + if accumulated_tokens + len(item_tokens) > max_tokens: + # Calculate how many tokens we can add from this item + remaining_space = max_tokens - accumulated_tokens + truncated_content = encoding.decode(item_tokens[:remaining_space]) + + # Create a new truncated item dictionary + truncated_item_dict = { + "type": item_dict["type"], + "content": truncated_content, + "typewriter": item_dict["typewriter"], + } + + truncated_conversation_dicts.append(truncated_item_dict) + break + + truncated_conversation_dicts.append(item_dict) + accumulated_tokens += len(item_tokens) + + # The list has been built in reverse order so we reverse it again + return list(reversed(truncated_conversation_dicts)) + + def set_generator(self, generator: str) -> bool: + if generator in self.generators: + msg.info(f"Setting GENERATOR to {generator}") + self.selected_generator = generator + return True + else: + msg.warn(f"Generator {generator} not found") + return False + + def get_generators(self) -> dict[str, Generator]: + return self.generators diff --git a/goldenverba/components/reader/BasicReader.py b/goldenverba/components/reader/BasicReader.py new file mode 100644 index 000000000..158062f6e --- /dev/null +++ b/goldenverba/components/reader/BasicReader.py @@ -0,0 +1,117 @@ +import base64 +import json +from datetime import datetime +import io + +from wasabi import msg + +from goldenverba.components.document import Document +from goldenverba.components.interfaces import Reader +from goldenverba.components.types import FileData + +try: + from pypdf import PdfReader +except Exception: + msg.warn("pypdf not installed, your base installation might be corrupted.") + + +class BasicReader(Reader): + """ + The BasicReader reads .txt, .md, .mdx, .json and .pdf files. + """ + + def __init__(self): + super().__init__() + self.name = "BasicReader" + self.description = "Imports plain text, pdf, markdown, and json files." + self.requires_library = ["pypdf"] + + def load( + self, fileData: list[FileData], textValues: list[str], logging: list[dict] + ) -> tuple[list[Document], list[str]]: + + documents = [] + + for file in fileData: + msg.info(f"Loading in {file.filename}") + logging.append({"type": "INFO", "message": f"Importing {file.filename}"}) + + decoded_bytes = base64.b64decode(file.content) + + if file.extension in ["txt", "md", "mdx"]: + try: + original_text = decoded_bytes.decode("utf-8") + document = Document( + name=file.filename, + text=original_text, + type=self.config["document_type"].text, + timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), + reader=self.name, + ) + documents.append(document) + + except Exception as e: + msg.warn(f"Failed to load {file.filename} : {str(e)}") + logging.append( + { + "type": "WARNING", + "message": f"Failed to load {file.filename} : {str(e)}", + } + ) + + elif file.extension == "json": + try: + decoded_bytes = base64.b64decode(file.content) + original_text = decoded_bytes.decode("utf-8") + json_obj = json.loads(original_text) + document = Document.from_json(json_obj) + documents.append(document) + + except Exception as e: + msg.warn(f"Failed to load {file.filename} : {str(e)}") + logging.append( + { + "type": "WARNING", + "message": f"Failed to load {file.filename} : {str(e)}", + } + ) + + elif file.extension == "pdf": + try: + pdf_bytes = io.BytesIO(base64.b64decode(file.content)) + + full_text = "" + reader = PdfReader(pdf_bytes) + + for page in reader.pages: + full_text += page.extract_text() + "\n\n" + + document = Document( + name=file.filename, + text=full_text, + type=self.config["document_type"].text, + timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), + reader=self.name, + ) + documents.append(document) + except Exception as e: + msg.warn(f"Failed to load {file.filename} : {str(e)}") + logging.append( + { + "type": "WARNING", + "message": f"Failed to load {file.filename} : {str(e)}", + } + ) + + else: + msg.warn( + f"{file.filename} with extension {file.extension} not supported by BasicReader." + ) + logging.append( + { + "type": "WARNING", + "message": f"{file.filename} with extension {file.extension} not supported by BasicReader.", + } + ) + + return documents, logging diff --git a/goldenverba/components/reader/GitReader.py b/goldenverba/components/reader/GitReader.py new file mode 100644 index 000000000..8b8045f1a --- /dev/null +++ b/goldenverba/components/reader/GitReader.py @@ -0,0 +1,164 @@ +import base64 +import json +from datetime import datetime +import requests +import os +import re + +from wasabi import msg + +from goldenverba.components.document import Document +from goldenverba.components.interfaces import Reader +from goldenverba.components.types import FileData + + +class GitHubReader(Reader): + """ + The GithubReader downloads files from Github and ingests them into Weaviate. + """ + + def __init__(self): + super().__init__() + self.name = "GitHubReader" + self.type = "URL" + self.requires_env = ["GITHUB_TOKEN"] + self.description = "Retrieves all text files (.txt, .md, .mdx, .json) from a GitHub Repository and imports them into Verba. Use this format {owner}/{repo}/{branch}/{folder}" + + def load( + self, fileData: list[FileData], textValues: list[str], logging: list[dict] + ) -> tuple[list[Document], list[str]]: + + if len(textValues) <= 0: + logging.append({"type": "ERROR", "message": f"No GitHub Link detected"}) + return [], logging + elif textValues[0] == "": + logging.append({"type": "ERROR", "message": f"Empty GitHub URL"}) + return [], logging + + github_link = textValues[0] + + if not self.is_valid_github_path(github_link): + logging.append( + { + "type": "ERROR", + "message": f"GitHub URL {github_link} not matching pattern: owner/repo/branch/folder", + } + ) + return [], logging + + documents = [] + docs, logging = self.fetch_docs(github_link, logging) + + for _file in docs: + try: + logging.append({"type": "INFO", "message": f"Downloading {_file}"}) + content, link, _path = self.download_file(github_link, _file) + if ".json" in _file: + json_obj = json.loads(str(content)) + try: + document = Document.from_json(json_obj) + except Exception as e: + raise Exception(f"Loading JSON failed {e}") + + elif ".txt" in _file or ".md" in _file or ".mdx" in _file: + document = Document( + text=content, + type=self.config["document_type"].text, + name=_file, + link=link, + path=_path, + timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), + reader=self.name, + ) + + documents.append(document) + + except Exception as e: + msg.warn(f"Couldn't load, skipping {_file}: {str(e)}") + logging.append( + { + "type": "WARNING", + "message": f"Couldn't load, skipping {_file}: {str(e)}", + } + ) + continue + + return documents, logging + + def fetch_docs(self, path: str, logging) -> list: + """Fetch filenames from Github + @parameter path : str - Path to a GitHub repository + @returns list - List of document names. + """ + split = path.split("/") + owner = split[0] + repo = split[1] + branch = split[2] if len(split) > 2 else "main" + folder_path = "/".join(split[3:]) if len(split) > 3 else "" + + url = f"https://api.github.com/repos/{owner}/{repo}/git/trees/{branch}?recursive=1" + headers = { + "Authorization": f"token {os.environ.get('GITHUB_TOKEN', '')}", + "Accept": "application/vnd.github.v3+json", + } + response = requests.get(url, headers=headers) + response.raise_for_status() # Raise an exception for HTTP errors + + files = [ + item["path"] + for item in response.json()["tree"] + if item["path"].startswith(folder_path) + and ( + item["path"].endswith(".md") + or item["path"].endswith(".mdx") + or item["path"].endswith(".txt") + or item["path"].endswith(".json") + ) + ] + msg.info( + f"Fetched {len(files)} filenames from {url} (checking folder {folder_path})" + ) + logging.append( + { + "type": "SUCCESS", + "message": f"Fetched {len(files)} filenames from {url} (checking folder {folder_path})", + } + ) + return files, logging + + def download_file(self, path: str, file_path: str) -> str: + """Download files from Github based on filename + @parameter path : str - Path to a GitHub repository + @parameter file_path : str - Path of the file in repo + @returns str - Content of the file. + """ + split = path.split("/") + owner = split[0] + repo = split[1] + branch = split[2] if len(split) > 2 else "main" + + url = f"https://api.github.com/repos/{owner}/{repo}/contents/{file_path}?ref={branch}" + headers = { + "Authorization": f"token {os.environ.get('GITHUB_TOKEN', '')}", + "Accept": "application/vnd.github.v3+json", + } + response = requests.get(url, headers=headers) + response.raise_for_status() + + content_b64 = response.json()["content"] + link = response.json()["html_url"] + path = response.json()["path"] + content = base64.b64decode(content_b64).decode("utf-8") + msg.info(f"Downloaded {url}") + return (content, link, path) + + def is_valid_github_path(self, path): + # Regex pattern to match {owner}/{repo}/{branch}/{folder} + # {folder} is optional and can include subfolders + pattern = r"^([^/]+)/([^/]+)/([^/]+)(/[^/]*)*$" + + # Match the pattern with the provided path + match = re.match(pattern, path) + + # Return True if the pattern matches, False otherwise + return bool(match) diff --git a/goldenverba/components/reader/UnstructuredAPI.py b/goldenverba/components/reader/UnstructuredAPI.py new file mode 100644 index 000000000..773994af6 --- /dev/null +++ b/goldenverba/components/reader/UnstructuredAPI.py @@ -0,0 +1,112 @@ +import base64 +import os +from datetime import datetime +import io + +import requests +from wasabi import msg + +from goldenverba.components.document import Document +from goldenverba.components.interfaces import Reader +from goldenverba.components.types import FileData + + +class UnstructuredReader(Reader): + """ + Unstructured API Reader + """ + + def __init__(self): + super().__init__() + self.file_types = [".pdf"] + self.requires_env = ["UNSTRUCTURED_API_KEY"] + self.name = "UnstructuredAPI" + self.description = "Uses the Unstructured API to import multiple file types such as plain text and documents (.pdf, .csv). Requires an Unstructured API Key" + + def load( + self, fileData: list[FileData], textValues: list[str], logging: list[dict] + ) -> tuple[list[Document], list[str]]: + + documents = [] + + url = os.environ.get( + "UNSTRUCTURED_API_URL", "https://api.unstructured.io/general/v0/general" + ) + api_key = os.environ.get("UNSTRUCTURED_API_KEY", "") + + if api_key == "": + logging.append( + {"type": "ERROR", "message": f"No Unstructed API Key detected"} + ) + msg.warn(f"No Unstructed API Key detected") + return documents, logging + + headers = { + "accept": "application/json", + "unstructured-api-key": api_key, + } + + data = { + "strategy": "auto", + } + + for file in fileData: + msg.info(f"Loading in {file.filename}") + logging.append({"type": "INFO", "message": f"Importing {file.filename}"}) + + file_bytes = io.BytesIO(base64.b64decode(file.content)) + file_data = {"files": (file.filename, file_bytes)} + + try: + response = requests.post( + url, headers=headers, data=data, files=file_data + ) + json_response = response.json() + + if "detail" in json_response: + msg.warn( + f"Failed to load {file.filename} : {json_response['detail']}" + ) + logging.append( + { + "type": "ERROR", + "message": f"Failed to load {file.filename} : {json_response['detail']}", + } + ) + continue + + full_content = "" + for chunk in json_response: + if "text" in chunk: + text = str(chunk["text"]) + full_content += text + " " + + if full_content == "": + msg.warn(f"Empty Text for {file.filename}") + logging.append( + { + "type": "WARNING", + "message": f"Empty Text for {file.filename}", + } + ) + continue + + document = Document( + name=file.filename, + text=full_content, + type=self.config["document_type"].text, + timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), + reader=self.name, + ) + documents.append(document) + + except Exception as e: + msg.warn(f"Failed to load {file.filename} : {str(e)}") + logging.append( + { + "type": "WARNING", + "message": f"Failed to load {file.filename} : {str(e)}", + } + ) + + return documents, logging diff --git a/goldenverba/components/reader/githubreader.py b/goldenverba/components/reader/githubreader.py deleted file mode 100644 index 37e0ab147..000000000 --- a/goldenverba/components/reader/githubreader.py +++ /dev/null @@ -1,147 +0,0 @@ -import base64 -import json -import os -from datetime import datetime - -import requests -from wasabi import msg - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm, Reader - - -class GithubReader(Reader): - """ - The GithubReader downloads files from Github and ingests them into Weaviate. - """ - - def __init__(self): - super().__init__() - self.name = "GithubReader" - self.requires_env = ["GITHUB_TOKEN"] - self.description = "Downloads only text files from a GitHub repository and ingests it into Verba. Use this format {owner}/{repo}/{branch}/{folder}" - self.input_form = InputForm.INPUT.value - - def load( - self, - bytes: list[str] = None, - contents: list[str] = None, - paths: list[str] = None, - fileNames: list[str] = None, - document_type: str = "Documentation", - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - if fileNames is None: - fileNames = [] - if paths is None: - paths = [] - if contents is None: - contents = [] - if bytes is None: - bytes = [] - documents = [] - - # If paths exist - if len(paths) > 0: - for path in paths: - if path != "": - files = self.fetch_docs(path) - - for _file in files: - try: - content, link, _path = self.download_file(path, _file) - except Exception as e: - msg.warn(f"Couldn't load, skipping {_file}: {str(e)}") - continue - - if ".json" in _file: - json_obj = json.loads(str(content)) - try: - document = Document.from_json(json_obj) - except Exception as e: - raise Exception(f"Loading JSON failed {e}") - - else: - document = Document( - text=content, - type=document_type, - name=_file, - link=link, - path=_path, - timestamp=str( - datetime.now().strftime("%Y-%m-%d %H:%M:%S") - ), - reader=self.name, - ) - documents.append(document) - - msg.good(f"Loaded {len(documents)} documents") - return documents - - def fetch_docs(self, path: str) -> list: - """Fetch filenames from Github - @parameter path : str - Path to a GitHub repository - @returns list - List of document names. - """ - split = path.split("/") - owner = split[0] - repo = split[1] - branch = split[2] if len(split) > 2 else "main" - folder_path = "/".join(split[3:]) if len(split) > 3 else "" - - url = f"https://api.github.com/repos/{owner}/{repo}/git/trees/{branch}?recursive=1" - headers = { - "Authorization": f"token {os.environ.get('GITHUB_TOKEN', '')}", - "Accept": "application/vnd.github.v3+json", - } - response = requests.get(url, headers=headers) - response.raise_for_status() # Raise an exception for HTTP errors - - files = [ - item["path"] - for item in response.json()["tree"] - if item["path"].startswith(folder_path) - and ( - item["path"].endswith(".md") - or item["path"].endswith(".mdx") - or item["path"].endswith(".txt") - or item["path"].endswith(".json") - ) - ] - msg.info( - f"Fetched {len(files)} filenames from {url} (checking folder {folder_path})" - ) - return files - - def download_file(self, path: str, file_path: str) -> str: - """Download files from Github based on filename - @parameter path : str - Path to a GitHub repository - @parameter file_path : str - Path of the file in repo - @returns str - Content of the file. - """ - split = path.split("/") - owner = split[0] - repo = split[1] - branch = split[2] if len(split) > 2 else "main" - - url = f"https://api.github.com/repos/{owner}/{repo}/contents/{file_path}?ref={branch}" - headers = { - "Authorization": f"token {os.environ.get('GITHUB_TOKEN', '')}", - "Accept": "application/vnd.github.v3+json", - } - response = requests.get(url, headers=headers) - response.raise_for_status() - - content_b64 = response.json()["content"] - link = response.json()["html_url"] - path = response.json()["path"] - content = base64.b64decode(content_b64).decode("utf-8") - msg.info(f"Downloaded {url}") - return (content, link, path) diff --git a/goldenverba/components/reader/interface.py b/goldenverba/components/reader/interface.py deleted file mode 100644 index fd74b464b..000000000 --- a/goldenverba/components/reader/interface.py +++ /dev/null @@ -1,39 +0,0 @@ -from enum import Enum - -from goldenverba.components.component import VerbaComponent -from goldenverba.components.reader.document import Document - - -class InputForm(Enum): - UPLOAD = "UPLOAD" # Input Form to upload text files directly - INPUT = "INPUT" # Simple Text Input in Frontend - CHUNKER = "CHUNKER" # Default Input for Chunkers - TEXT = "TEXT" # Default Input for Embedder - - -class Reader(VerbaComponent): - """ - Interface for Verba Readers. - """ - - def __init__(self): - super().__init__() - self.file_types = [] - self.input_form = InputForm.UPLOAD.value - - def load( - bytes: list[str], - contents: list[str], - paths: list[str], - fileNames: list[str], - document_type: str, - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - raise NotImplementedError("load method must be implemented by a subclass.") diff --git a/goldenverba/components/reader/manager.py b/goldenverba/components/reader/manager.py deleted file mode 100644 index 8bfc20eed..000000000 --- a/goldenverba/components/reader/manager.py +++ /dev/null @@ -1,58 +0,0 @@ -from wasabi import msg - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.githubreader import GithubReader -from goldenverba.components.reader.interface import Reader -from goldenverba.components.reader.pdfreader import PDFReader -from goldenverba.components.reader.simplereader import SimpleReader -from goldenverba.components.reader.unstructuredpdf import UnstructuredPDF - - -class ReaderManager: - def __init__(self): - self.readers: dict[str, Reader] = { - "SimpleReader": SimpleReader(), - "PDFReader": PDFReader(), - "GithubReader": GithubReader(), - "UnstructuredPDF": UnstructuredPDF(), - } - self.selected_reader: Reader = self.readers["SimpleReader"] - - def load( - self, - bytes: list[str] = None, - contents: list[str] = None, - paths: list[str] = None, - fileNames: list[str] = None, - document_type: str = "Documentation", - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - if fileNames is None: - fileNames = [] - if paths is None: - paths = [] - if contents is None: - contents = [] - if bytes is None: - bytes = [] - return self.selected_reader.load( - bytes, contents, paths, fileNames, document_type - ) - - def set_reader(self, reader: str) -> bool: - if reader in self.readers: - self.selected_reader = self.readers[reader] - return True - else: - msg.warn(f"Reader {reader} not found") - return False - - def get_readers(self) -> dict[str, Reader]: - return self.readers diff --git a/goldenverba/components/reader/pdfreader.py b/goldenverba/components/reader/pdfreader.py deleted file mode 100644 index 7dcc3c99b..000000000 --- a/goldenverba/components/reader/pdfreader.py +++ /dev/null @@ -1,145 +0,0 @@ -import base64 -import glob -import os -from datetime import datetime -from pathlib import Path - -from wasabi import msg - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm, Reader - -try: - from PyPDF2 import PdfReader -except Exception: - msg.warn("PyPDF2 not installed, your base installation might be corrupted.") - - -class PDFReader(Reader): - """ - The PDFReader reads .pdf files using Unstructured. - """ - - def __init__(self): - super().__init__() - self.file_types = [".pdf"] - self.requires_library = ["PyPDF2"] - self.name = "PDFReader" - self.description = "Reads PDF files using the PyPDF2 library" - self.input_form = InputForm.UPLOAD.value - - def load( - self, - bytes: list[str] = None, - contents: list[str] = None, - paths: list[str] = None, - fileNames: list[str] = None, - document_type: str = "Documentation", - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - if fileNames is None: - fileNames = [] - if paths is None: - paths = [] - if contents is None: - contents = [] - if bytes is None: - bytes = [] - documents = [] - - # If paths exist - if len(paths) > 0: - for path in paths: - if path != "": - data_path = Path(path) - if data_path.exists(): - if data_path.is_file(): - documents += self.load_file(data_path, document_type) - else: - documents += self.load_directory(data_path, document_type) - else: - msg.warn(f"Path {data_path} does not exist") - - # If bytes exist - if len(bytes) > 0 and len(bytes) == len(fileNames): - for byte, fileName in zip(bytes, fileNames): - decoded_bytes = base64.b64decode(byte) - with open(f"{fileName}", "wb") as file: - file.write(decoded_bytes) - - documents += self.load_file(f"{fileName}", document_type) - os.remove(f"{fileName}") - - # If content exist - if len(contents) > 0 and len(contents) == len(fileNames): - for content, fileName in zip(contents, fileNames): - document = Document( - name=fileName, - text=content, - type=document_type, - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - - msg.good(f"Loaded {len(documents)} documents") - return documents - - def load_file(self, file_path: Path, document_type: str) -> list[Document]: - """Loads .pdf file - @param file_path : Path - Path to file - @param document_type : str - Document Type - @returns list[Document] - Lists of documents. - """ - documents = [] - full_text = "" - reader = PdfReader(file_path) - - for page in reader.pages: - full_text += page.extract_text() + "\n\n" - - document = Document( - text=full_text, - type=document_type, - name=str(file_path), - link=str(file_path), - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - msg.good(f"Loaded {str(file_path)}") - return documents - - def load_directory(self, dir_path: Path, document_type: str) -> list[Document]: - """Loads .pdf files from a directory and its subdirectories. - - @param dir_path : Path - Path to directory - @param document_type : str - Document Type - @returns list[Document] - List of documents - """ - # Initialize an empty dictionary to store the file contents - documents = [] - - # Convert dir_path to string, in case it's a Path object - dir_path_str = str(dir_path) - - # Loop through each file type - for file_type in self.file_types: - # Use glob to find all the files in dir_path and its subdirectories matching the current file_type - files = glob.glob(f"{dir_path_str}/**/*{file_type}", recursive=True) - - # Loop through each file - for file in files: - msg.info(f"Reading {str(file)}") - with open(file, encoding="utf-8"): - documents += self.load_file(file, document_type=document_type) - - msg.good(f"Loaded {len(documents)} documents") - return documents diff --git a/goldenverba/components/reader/simplereader.py b/goldenverba/components/reader/simplereader.py deleted file mode 100644 index 42e046bf1..000000000 --- a/goldenverba/components/reader/simplereader.py +++ /dev/null @@ -1,177 +0,0 @@ -import base64 -import glob -import json -from datetime import datetime -from pathlib import Path - -from wasabi import msg - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm, Reader - - -class SimpleReader(Reader): - """ - The SimpleReader reads .txt, .md, .mdx, and .json files. It can handle both paths, content and bytes. - """ - - def __init__(self): - super().__init__() - self.file_types = [".txt", ".md", ".mdx", ".json"] - self.name = "SimpleReader" - self.description = "Reads text, markdown, and json files." - self.input_form = InputForm.UPLOAD.value - - def load( - self, - bytes: list[str] = None, - contents: list[str] = None, - paths: list[str] = None, - fileNames: list[str] = None, - document_type: str = "Documentation", - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - if fileNames is None: - fileNames = [] - if paths is None: - paths = [] - if contents is None: - contents = [] - if bytes is None: - bytes = [] - documents = [] - - # If paths exist - if len(paths) > 0: - for path in paths: - if path != "": - data_path = Path(path) - if data_path.exists(): - if data_path.is_file(): - documents += self.load_file(data_path, document_type) - else: - documents += self.load_directory(data_path, document_type) - else: - msg.warn(f"Path {data_path} does not exist") - - # If bytes exist - if len(bytes) > 0 and len(bytes) == len(fileNames): - for byte, fileName in zip(bytes, fileNames): - decoded_bytes = base64.b64decode(byte) - try: - original_text = decoded_bytes.decode("utf-8") - except UnicodeDecodeError: - msg.fail( - f"Error decoding text for file {fileName}. The file might not be a text file." - ) - continue - - if ".json" in fileName: - json_obj = json.loads(original_text) - try: - document = Document.from_json(json_obj) - except Exception as e: - raise Exception(f"Loading JSON failed {e}") - - else: - document = Document( - name=fileName, - text=original_text, - type=document_type, - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - - # If content exist - if len(contents) > 0 and len(contents) == len(fileNames): - for content, fileName in zip(contents, fileNames): - document = Document( - name=fileName, - text=content, - type=document_type, - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - - msg.good(f"Loaded {len(documents)} documents") - return documents - - def load_file(self, file_path: Path, document_type: str) -> list[Document]: - """Loads text file - @param file_path : Path - Path to file - @param document_type : str - Document Type - @returns list[Document] - Lists of documents. - """ - documents = [] - - if file_path.suffix not in self.file_types: - msg.warn(f"{file_path.suffix} not supported") - return [] - - with open(file_path, encoding="utf-8") as f: - msg.info(f"Reading {str(file_path)}") - - if file_path.suffix == ".json": - json_obj = json.loads(f.read()) - try: - document = Document.from_json(json_obj) - except Exception as e: - raise Exception(f"Loading JSON failed {e}") - - else: - document = Document( - text=f.read(), - type=document_type, - name=str(file_path), - link=str(file_path), - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - msg.good(f"Loaded {str(file_path)}") - return documents - - def load_directory(self, dir_path: Path, document_type: str) -> list[Document]: - """Loads text files from a directory and its subdirectories. - - @param dir_path : Path - Path to directory - @param document_type : str - Document Type - @returns list[Document] - List of documents - """ - # Initialize an empty dictionary to store the file contents - documents = [] - - # Convert dir_path to string, in case it's a Path object - dir_path_str = str(dir_path) - - # Loop through each file type - for file_type in self.file_types: - # Use glob to find all the files in dir_path and its subdirectories matching the current file_type - files = glob.glob(f"{dir_path_str}/**/*{file_type}", recursive=True) - - # Loop through each file - for file in files: - msg.info(f"Reading {str(file)}") - with open(file, encoding="utf-8") as f: - document = Document( - text=f.read(), - type=document_type, - name=str(file), - link=str(file), - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - - documents.append(document) - - msg.good(f"Loaded {len(documents)} documents") - return documents diff --git a/goldenverba/components/reader/unstructuredpdf.py b/goldenverba/components/reader/unstructuredpdf.py deleted file mode 100644 index a75a3025c..000000000 --- a/goldenverba/components/reader/unstructuredpdf.py +++ /dev/null @@ -1,215 +0,0 @@ -import base64 -import glob -import os -from datetime import datetime -from pathlib import Path - -import requests -from wasabi import msg - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.interface import InputForm, Reader - - -class UnstructuredPDF(Reader): - """ - The PDFReader reads .pdf files using Unstructured. - """ - - def __init__(self): - super().__init__() - self.file_types = [".pdf"] - self.requires_env = ["UNSTRUCTURED_API_KEY"] - self.name = "UnstructuredPDF" - self.description = "Reads PDF files powered by unstructured.io" - self.input_form = InputForm.UPLOAD.value - - def load( - self, - bytes: list[str] = None, - contents: list[str] = None, - paths: list[str] = None, - fileNames: list[str] = None, - document_type: str = "Documentation", - ) -> list[Document]: - """Ingest data into Weaviate - @parameter: bytes : list[str] - List of bytes - @parameter: contents : list[str] - List of string content - @parameter: paths : list[str] - List of paths to files - @parameter: fileNames : list[str] - List of file names - @parameter: document_type : str - Document type - @returns list[Document] - Lists of documents. - """ - if fileNames is None: - fileNames = [] - if paths is None: - paths = [] - if contents is None: - contents = [] - if bytes is None: - bytes = [] - documents = [] - - # If paths exist - if len(paths) > 0: - for path in paths: - if path != "": - data_path = Path(path) - if data_path.exists(): - if data_path.is_file(): - documents += self.load_file(data_path, document_type) - else: - documents += self.load_directory(data_path, document_type) - else: - msg.warn(f"Path {data_path} does not exist") - - # If bytes exist - if len(bytes) > 0 and len(bytes) == len(fileNames): - for byte, fileName in zip(bytes, fileNames): - documents += self.load_bytes(byte, fileName, document_type) - - # If content exist - if len(contents) > 0 and len(contents) == len(fileNames): - for content, fileName in zip(contents, fileNames): - document = Document( - name=fileName, - text=content, - type=document_type, - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - - msg.good(f"Loaded {len(documents)} documents") - return documents - - def load_bytes(self, bytes_string, fileName, document_type: str) -> list[Document]: - """Loads a pdf bytes file - @param bytes_string : str - PDF File bytes coming from the frontend - @param fileName : str - Filename - @param document_type : str - Document Type - @returns list[Document] - Lists of documents. - """ - documents = [] - - url = os.environ.get( - "UNSTRUCTURED_API_URL", "https://api.unstructured.io/general/v0/general" - ) - - headers = { - "accept": "application/json", - "unstructured-api-key": os.environ.get("UNSTRUCTURED_API_KEY", ""), - } - - data = { - "strategy": "auto", - } - - decoded_bytes = base64.b64decode(bytes_string) - with open("reconstructed.pdf", "wb") as file: - file.write(decoded_bytes) - - file_data = {"files": open("reconstructed.pdf", "rb")} - - response = requests.post(url, headers=headers, data=data, files=file_data) - - json_response = response.json() - - full_content = "" - - for chunk in json_response: - if "text" in chunk: - text = chunk["text"] - full_content += text + " " - - document = Document( - text=full_content, - type=document_type, - name=str(fileName), - link=str(fileName), - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - msg.good(f"Loaded {str(fileName)}") - os.remove("reconstructed.pdf") - return documents - - def load_file(self, file_path: Path, document_type: str) -> list[Document]: - """Loads .pdf file - @param file_path : Path - Path to file - @param document_type : str - Document Type - @returns list[Document] - Lists of documents. - """ - documents = [] - - if file_path.suffix not in self.file_types: - msg.warn(f"{file_path.suffix} not supported") - return [] - - url = os.environ.get( - "UNSTRUCTURED_API_URL", "https://api.unstructured.io/general/v0/general" - ) - - headers = { - "accept": "application/json", - "unstructured-api-key": os.environ.get("UNSTRUCTURED_API_KEY", ""), - } - - data = { - "strategy": "auto", - } - - file_data = {"files": open(file_path, "rb")} - - response = requests.post(url, headers=headers, data=data, files=file_data) - - file_data["files"].close() - - json_response = response.json() - - full_content = "" - - for chunk in json_response: - if "text" in chunk: - text = chunk["text"] - full_content += text + " " - - document = Document( - text=full_content, - type=document_type, - name=str(file_path), - link=str(file_path), - timestamp=str(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), - reader=self.name, - ) - documents.append(document) - msg.good(f"Loaded {str(file_path)}") - return documents - - def load_directory(self, dir_path: Path, document_type: str) -> list[Document]: - """Loads .pdf files from a directory and its subdirectories. - - @param dir_path : Path - Path to directory - @param document_type : str - Document Type - @returns list[Document] - List of documents - """ - # Initialize an empty dictionary to store the file contents - documents = [] - - # Convert dir_path to string, in case it's a Path object - dir_path_str = str(dir_path) - - # Loop through each file type - for file_type in self.file_types: - # Use glob to find all the files in dir_path and its subdirectories matching the current file_type - files = glob.glob(f"{dir_path_str}/**/*{file_type}", recursive=True) - - # Loop through each file - for file in files: - msg.info(f"Reading {str(file)}") - with open(file, encoding="utf-8"): - documents += self.load_file(file, document_type=document_type) - - msg.good(f"Loaded {len(documents)} documents") - return documents diff --git a/goldenverba/components/retriever/SimpleRetriever.py b/goldenverba/components/retriever/SimpleRetriever.py deleted file mode 100644 index 77d8ca921..000000000 --- a/goldenverba/components/retriever/SimpleRetriever.py +++ /dev/null @@ -1,88 +0,0 @@ -from weaviate import Client -from weaviate.gql.get import HybridFusion - -from goldenverba.components.chunking.chunk import Chunk -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.retriever.interface import Retriever - - -class SimpleRetriever(Retriever): - """ - SimpleRetriver that retrieves chunks through hybrid search, no reranking or additional logic. - """ - - def __init__(self): - super().__init__() - self.description = "SimpleRetriever uses Hybrid Search to retrieve relevant chunks to the user's query" - self.name = "SimpleRetriever" - - def retrieve( - self, - queries: list[str], - client: Client, - embedder: Embedder, - ) -> list[Chunk]: - """Ingest data into Weaviate - @parameter: queries : list[str] - List of queries - @parameter: client : Client - Weaviate client - @parameter: embedder : Embedder - Current selected Embedder - @returns list[Chunk] - List of retrieved chunks. - """ - chunk_class = embedder.get_chunk_class() - needs_vectorization = embedder.get_need_vectorization() - chunks = [] - - for query in queries: - query_results = ( - client.query.get( - class_name=chunk_class, - properties=[ - "text", - "doc_name", - "chunk_id", - "doc_uuid", - "doc_type", - ], - ) - .with_additional(properties=["score"]) - .with_autocut(2) - ) - - if needs_vectorization: - vector = embedder.vectorize_query(query) - query_results = query_results.with_hybrid( - query=query, - vector=vector, - fusion_type=HybridFusion.RELATIVE_SCORE, - properties=[ - "text", - ], - ).do() - - else: - query_results = query_results.with_hybrid( - query=query, - fusion_type=HybridFusion.RELATIVE_SCORE, - properties=[ - "text", - ], - ).do() - - for chunk in query_results["data"]["Get"][chunk_class]: - chunk_obj = Chunk( - chunk["text"], - chunk["doc_name"], - chunk["doc_type"], - chunk["doc_uuid"], - chunk["chunk_id"], - ) - chunk_obj.set_score(chunk["_additional"]["score"]) - chunks.append(chunk_obj) - - sorted_chunks = self.sort_chunks(chunks) - - context = "" - for chunk in sorted_chunks: - context += chunk.text + " " - - return sorted_chunks, context diff --git a/goldenverba/components/retriever/WindowRetriever.py b/goldenverba/components/retriever/WindowRetriever.py index 1512d4d87..6a26baf64 100644 --- a/goldenverba/components/retriever/WindowRetriever.py +++ b/goldenverba/components/retriever/WindowRetriever.py @@ -1,9 +1,8 @@ from weaviate import Client from weaviate.gql.get import HybridFusion -from goldenverba.components.chunking.chunk import Chunk -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.retriever.interface import Retriever +from goldenverba.components.chunk import Chunk +from goldenverba.components.interfaces import Embedder, Retriever class WindowRetriever(Retriever): @@ -13,7 +12,7 @@ class WindowRetriever(Retriever): def __init__(self): super().__init__() - self.description = "WindowRetriever uses Hybrid Search to retrieve relevant chunks and adds their surrounding context" + self.description = "Retrieve relevant chunks and their surrounding context using Semantic and Keyword Search (Hybrid)" self.name = "WindowRetriever" def retrieve( @@ -45,7 +44,7 @@ def retrieve( ], ) .with_additional(properties=["score"]) - .with_autocut(2) + .with_autocut(1) ) if needs_vectorization: @@ -79,9 +78,9 @@ def retrieve( chunk_obj.set_score(chunk["_additional"]["score"]) chunks.append(chunk_obj) - sorted_chunks = self.sort_chunks(chunks) + sorted_chunks = sorted(chunks, key=lambda x: x.score, reverse=True) - context = self.combine_context(sorted_chunks, client, embedder) + context = self.combine_context(chunks, client, embedder) return sorted_chunks, context @@ -97,12 +96,15 @@ def combine_context( for chunk in chunks: if chunk.doc_name not in doc_name_map: - doc_name_map[chunk.doc_name] = {} + doc_name_map[chunk.doc_name] = {"score": 0, "chunks":{}} - doc_name_map[chunk.doc_name][chunk.chunk_id] = chunk + doc_name_map[chunk.doc_name]["chunks"][chunk.chunk_id] = chunk + doc_name_map[chunk.doc_name]["score"] += float(chunk.score) + + doc_name_map = dict(sorted(doc_name_map.items(), key=lambda item: item[1]['score'], reverse=True)) for doc in doc_name_map: - chunk_map = doc_name_map[doc] + chunk_map = doc_name_map[doc]["chunks"] window = 2 added_chunks = {} for chunk in chunk_map: @@ -171,15 +173,17 @@ def combine_context( for chunk in added_chunks: if chunk not in doc_name_map[doc]: - doc_name_map[doc][chunk] = added_chunks[chunk] + doc_name_map[doc]["chunks"][chunk] = added_chunks[chunk] for doc in doc_name_map: sorted_dict = { - k: doc_name_map[doc][k] - for k in sorted(doc_name_map[doc], key=lambda x: int(x)) + k: doc_name_map[doc]["chunks"][k] + for k in sorted(doc_name_map[doc]["chunks"], key=lambda x: int(x)) } + context += "--- Document " + doc + " ---" + "\n\n" + for chunk in sorted_dict: - context += sorted_dict[chunk].text + context += "Chunk "+ str(sorted_dict[chunk].chunk_id) + "\n\n" + sorted_dict[chunk].text + "\n\n" return context diff --git a/goldenverba/components/retriever/interface.py b/goldenverba/components/retriever/interface.py deleted file mode 100644 index a46db7627..000000000 --- a/goldenverba/components/retriever/interface.py +++ /dev/null @@ -1,49 +0,0 @@ -import tiktoken -from wasabi import msg -from weaviate import Client - -from goldenverba.components.chunking.chunk import Chunk -from goldenverba.components.component import VerbaComponent -from goldenverba.components.embedding.interface import Embedder - - -class Retriever(VerbaComponent): - """ - Interface for Verba Retrievers. - """ - - def __init__(self): - super().__init__() - - def retrieve( - self, - queries: list[str], - client: Client, - embedder: Embedder, - ) -> tuple[list[Chunk], str]: - """Ingest data into Weaviate - @parameter: queries : list[str] - List of queries - @parameter: client : Client - Weaviate client - @parameter: embedder : Embedder - Current selected Embedder - @returns tuple(list[Chunk],str) - List of retrieved chunks and the context string. - """ - raise NotImplementedError("load method must be implemented by a subclass.") - - def sort_chunks(self, chunks: list[Chunk]) -> list[Chunk]: - return sorted(chunks, key=lambda chunk: (chunk.doc_uuid, int(chunk.chunk_id))) - - def cutoff_text(self, text: str, content_length: int) -> str: - encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") - - # Tokenize the input text - encoded_tokens = encoding.encode(text, disallowed_special=()) - - # Check if we need to truncate - if len(encoded_tokens) > content_length: - encoded_tokens = encoded_tokens[:content_length] - truncated_text = encoding.decode(encoded_tokens) - msg.info(f"Truncated Context to {content_length} tokens") - return truncated_text - else: - msg.info(f"Retrieved Context of {len(encoded_tokens)} tokens") - return text diff --git a/goldenverba/components/retriever/manager.py b/goldenverba/components/retriever/manager.py deleted file mode 100644 index cedf05bed..000000000 --- a/goldenverba/components/retriever/manager.py +++ /dev/null @@ -1,48 +0,0 @@ -from wasabi import msg -from weaviate import Client - -from goldenverba.components.chunking.chunk import Chunk -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.generation.interface import Generator -from goldenverba.components.retriever.interface import Retriever -from goldenverba.components.retriever.SimpleRetriever import SimpleRetriever -from goldenverba.components.retriever.WindowRetriever import WindowRetriever - - -class RetrieverManager: - def __init__(self): - self.retrievers: dict[str, Retriever] = { - "WindowRetriever": WindowRetriever(), - "SimpleRetriever": SimpleRetriever(), - } - self.selected_retriever: Retriever = self.retrievers["WindowRetriever"] - - def retrieve( - self, - queries: list[str], - client: Client, - embedder: Embedder, - generator: Generator, - ) -> list[Chunk]: - """Ingest data into Weaviate - @parameter: queries : list[str] - List of queries - @parameter: client : Client - Weaviate client - @parameter: embedder : Embedder - Current selected Embedder - @returns list[Chunk] - List of retrieved chunks. - """ - chunks, context = self.selected_retriever.retrieve(queries, client, embedder) - managed_context = self.selected_retriever.cutoff_text( - context, generator.context_window - ) - return chunks, managed_context - - def set_retriever(self, retriever: str) -> bool: - if retriever in self.retrievers: - self.selected_retriever = self.retrievers[retriever] - return True - else: - msg.warn(f"Retriever {retriever} not found") - return False - - def get_retrievers(self) -> dict[str, Retriever]: - return self.retrievers diff --git a/goldenverba/components/schema/schema_generation.py b/goldenverba/components/schema/schema_generation.py index ade9a022c..7301a565d 100644 --- a/goldenverba/components/schema/schema_generation.py +++ b/goldenverba/components/schema/schema_generation.py @@ -6,8 +6,15 @@ load_dotenv() -VECTORIZERS = {"text2vec-openai", "text2vec-cohere"} # Needs to match with Weaviate modules -EMBEDDINGS = {"MiniLM"} # Custom Vectors +VECTORIZERS = { + "text2vec-openai", + "text2vec-cohere", +} # Needs to match with Weaviate modules +EMBEDDINGS = {"MiniLM", "OLLAMA"} # Custom Vectors + +google_project = os.getenv("GOOGLE_CLOUD_PROJECT") +if google_project != None: + VECTORIZERS.add("text2vec-palm") def strip_non_letters(s: str): @@ -27,20 +34,28 @@ def verify_vectorizer( skip_properties = [] modified_schema = schema.copy() - #adding specific config for Azure OpenAI + # adding specific config for Azure OpenAI vectorizer_config = None - if os.getenv("OPENAI_API_TYPE") == "azure" and vectorizer=="text2vec-openai": + if os.getenv("OPENAI_API_TYPE") == "azure" and vectorizer == "text2vec-openai": resourceName = os.getenv("AZURE_OPENAI_RESOURCE_NAME") model = os.getenv("AZURE_OPENAI_EMBEDDING_MODEL") if resourceName is None or model is None: - raise Exception("AZURE_OPENAI_RESOURCE_NAME and AZURE_OPENAI_EMBEDDING_MODEL should be set when OPENAI_API_TYPE is azure. Resource name is XXX in http://XXX.openai.azure.com") - vectorizer_config = { - "text2vec-openai": { - "deploymentId": model, - "resourceName": resourceName - } + raise Exception( + "AZURE_OPENAI_RESOURCE_NAME and AZURE_OPENAI_EMBEDDING_MODEL should be set when OPENAI_API_TYPE is azure. Resource name is XXX in http://XXX.openai.azure.com" + ) + vectorizer_config = { + "text2vec-openai": {"deploymentId": model, "resourceName": resourceName} } + # adding specific config for Google + if vectorizer == "text2vec-palm": + if google_project is not None: + vectorizer_config = { + "text2vec-palm": { + "projectId": google_project, + } + } + # Verify Vectorizer if vectorizer in VECTORIZERS: modified_schema["classes"][0]["vectorizer"] = vectorizer @@ -72,7 +87,10 @@ def add_suffix(schema: dict, vectorizer: str) -> tuple[dict, str]: modified_schema = schema.copy() # Verify Vectorizer and add suffix modified_schema["classes"][0]["class"] = ( - modified_schema["classes"][0]["class"] + "_" + strip_non_letters(vectorizer) + "VERBA_" + + modified_schema["classes"][0]["class"] + + "_" + + strip_non_letters(vectorizer) ) return modified_schema, modified_schema["classes"][0]["class"] @@ -81,9 +99,9 @@ def reset_schemas( client: Client = None, vectorizer: str = None, ): - doc_name = "Document_" + strip_non_letters(vectorizer) - chunk_name = "Chunk_" + strip_non_letters(vectorizer) - cache_name = "Cache_" + strip_non_letters(vectorizer) + doc_name = "VERBA_Document_" + strip_non_letters(vectorizer) + chunk_name = "VERBA_Chunk_" + strip_non_letters(vectorizer) + cache_name = "VERBA_Cache_" + strip_non_letters(vectorizer) client.schema.delete_class(doc_name) client.schema.delete_class(chunk_name) @@ -107,6 +125,7 @@ def init_schemas( init_documents(client, vectorizer, force, check) init_cache(client, vectorizer, force, check) init_suggestion(client, vectorizer, force, check) + init_config(client, vectorizer, force, check) return True except Exception as e: msg.fail(f"Schema initialization failed {str(e)}") @@ -318,7 +337,7 @@ def init_suggestion( SCHEMA_SUGGESTION = { "classes": [ { - "class": "Suggestion", + "class": "VERBA_Suggestion", "description": "List of possible prompts", "properties": [ { @@ -332,7 +351,7 @@ def init_suggestion( } suggestion_schema = SCHEMA_SUGGESTION - suggestion_name = "Suggestion" + suggestion_name = "VERBA_Suggestion" if client.schema.exists(suggestion_name): if check: @@ -354,3 +373,47 @@ def init_suggestion( msg.good(f"{suggestion_name} schema created") return suggestion_schema + +def init_config( + client: Client, vectorizer: str = None, force: bool = False, check: bool = False +) -> dict: + """Initializes the Configuration schema""" + SCHEMA_CONFIG = { + "classes": [ + { + "class": "VERBA_Config", + "description": "Configuration JSON", + "properties": [ + { + "name": "config", + "dataType": ["text"], + "description": "JSON String Config", + }, + ], + } + ] + } + + config_schema = SCHEMA_CONFIG + config_name = "VERBA_Config" + + if client.schema.exists(config_name): + if check: + return config_schema + if not force: + user_input = input( + f"{config_name} class already exists, do you want to delete it? (y/n): " + ) + else: + user_input = "y" + if user_input.strip().lower() == "y": + client.schema.delete_class(config_name) + client.schema.create(config_schema) + msg.good(f"{config_name} schema created") + else: + msg.warn(f"Skipped deleting {config_name} schema, nothing changed") + else: + client.schema.create(config_schema) + msg.good(f"{config_name} schema created") + + return config_schema diff --git a/goldenverba/components/schema/tests/test_schema_generation.py b/goldenverba/components/schema/tests/test_schema_generation.py deleted file mode 100644 index 40258ea7f..000000000 --- a/goldenverba/components/schema/tests/test_schema_generation.py +++ /dev/null @@ -1,42 +0,0 @@ -from goldenverba.components.schema import schema_generation -from goldenverba.components.util import setup_client - -client = setup_client() - - -def test_init_documents_no_vectorizer(): - doc, chunk = schema_generation.init_documents(client, force=True) - - assert doc == schema_generation.SCHEMA_DOCUMENT - assert chunk == schema_generation.SCHEMA_CHUNK - - -def test_init_documents_unknown_vectorizer(): - doc, chunk = schema_generation.init_documents(client, "cohere", force=True) - - assert doc == schema_generation.SCHEMA_DOCUMENT - assert chunk == schema_generation.SCHEMA_CHUNK - - -def test_init_documents_vectorizer(): - doc, chunk = schema_generation.init_documents( - client, vectorizer="text2vec-openai", force=True - ) - - assert doc == schema_generation.SCHEMA_DOCUMENT - assert chunk["classes"][0]["vectorizer"] == "text2vec-openai" - - -def test_init_cache_no_vectorizer(): - cache = schema_generation.init_cache(client, force=True) - - assert cache == schema_generation.SCHEMA_CACHE - - -def test_init_cache_vectorizer(): - cache = schema_generation.init_cache( - client, vectorizer="text2vec-openai", force=True - ) - - assert cache == schema_generation.SCHEMA_CACHE - assert cache["classes"][0]["vectorizer"] == "text2vec-openai" diff --git a/goldenverba/components/tests/data/test.txt b/goldenverba/components/tests/data/test.txt deleted file mode 100644 index 273c1a9ff..000000000 --- a/goldenverba/components/tests/data/test.txt +++ /dev/null @@ -1 +0,0 @@ -This is a test. \ No newline at end of file diff --git a/goldenverba/components/tests/data/text.md b/goldenverba/components/tests/data/text.md deleted file mode 100644 index 273c1a9ff..000000000 --- a/goldenverba/components/tests/data/text.md +++ /dev/null @@ -1 +0,0 @@ -This is a test. \ No newline at end of file diff --git a/goldenverba/components/tests/test_readers.py b/goldenverba/components/tests/test_readers.py deleted file mode 100644 index 3cb82d500..000000000 --- a/goldenverba/components/tests/test_readers.py +++ /dev/null @@ -1,52 +0,0 @@ -import base64 -from pathlib import Path - -import pytest - -from goldenverba.components.reader.document import Document -from goldenverba.components.reader.simplereader import SimpleReader - - -@pytest.fixture -def byte_data(): - return [base64.b64encode(b"Test text content.").decode("utf-8")] - - -@pytest.fixture -def file_names(): - # Get the absolute directory path of this test file. - test_dir = Path(__file__).parent - # Build an absolute path for the test file. - return [str(test_dir / "./data/test.txt")] - - -@pytest.fixture -def content_data(): - return ["Test text content."] - - -def test_simple_reader_load_from_bytes(byte_data, file_names): - reader = SimpleReader() - documents = reader.load(bytes=byte_data, fileNames=file_names) - assert isinstance(documents, list) - assert len(documents) == 1 - assert isinstance(documents[0], Document) - assert documents[0].text == "Test text content." - - -def test_simple_reader_load_from_paths(file_names): - reader = SimpleReader() - documents = reader.load(paths=file_names) - assert isinstance(documents, list) - assert len(documents) == 1 - assert isinstance(documents[0], Document) - assert documents[0].text == "This is a test." - - -def test_simple_reader_load_from_contents(content_data, file_names): - reader = SimpleReader() - documents = reader.load(contents=content_data, fileNames=file_names) - assert isinstance(documents, list) - assert len(documents) == 1 - assert isinstance(documents[0], Document) - assert documents[0].text == "Test text content." diff --git a/goldenverba/components/tests/test_word_chunking.py b/goldenverba/components/tests/test_word_chunking.py deleted file mode 100644 index cdb754ee5..000000000 --- a/goldenverba/components/tests/test_word_chunking.py +++ /dev/null @@ -1,70 +0,0 @@ -from goldenverba.components.chunking.wordchunker import WordChunker -from goldenverba.components.reader.document import Document - -chunker = WordChunker() - - -def test_split_length_greater_than_doc_length(): - documents = [Document(text="This is a test")] - modified_documents = chunker.chunk(documents, 50, 5) - assert modified_documents[0].chunks == [] - - -def test_split_length_less_than_one(): - documents = [Document(text="This is a test")] - modified_documents = chunker.chunk(documents, 0, 5) - assert modified_documents[0].chunks == [] - - -def test_split_overlap_greater_than_split_length(): - documents = [Document(text="This is a test")] - modified_documents = chunker.chunk(documents, 5, 5) - assert modified_documents[0].chunks == [] - - -def test_overlap(): - documents = [Document(text="This is a test sentence. Another test sentence.")] - modified_documents = chunker.chunk(documents, 5, 2) - assert modified_documents[0].chunks[0].text == "This is a test sentence" - assert modified_documents[0].chunks[1].text == "test sentence. Another test" - assert modified_documents[0].chunks[2].text == "Another test sentence." - - -def test_empty_doc(): - documents = [Document(text="")] - modified_documents = chunker.chunk(documents, 5, 2) - assert modified_documents[0].chunks == [] - - -def test_non_overlapping_windows(): - documents = [ - Document(text="This is a short sentence. Another short one. And another.") - ] - modified_documents = chunker.chunk(documents, 5, 0) - assert modified_documents[0].chunks[0].text == "This is a short sentence" - assert modified_documents[0].chunks[1].text == ". Another short one." - assert modified_documents[0].chunks[2].text == "And another." - - -def test_overlapping_windows(): - documents = [ - Document(text="This is a short sentence. Another short one. And another.") - ] - modified_documents = chunker.chunk(documents, 5, 2) - assert len(modified_documents[0].chunks) == 4 - - -def test_varying_lengths(): - documents = [ - Document(text="This is a short sentence. Another short one. And another.") - ] - modified_documents = chunker.chunk(documents, 3, 1) - assert len(modified_documents[0].chunks) == 6 - - -def test_long_lengths(): - documents = [ - Document(text="This is a short sentence. Another short one. And another.") - ] - modified_documents = chunker.chunk(documents, 11, 2) - assert len(modified_documents[0].chunks) == 2 diff --git a/goldenverba/components/types.py b/goldenverba/components/types.py new file mode 100644 index 000000000..615324e0e --- /dev/null +++ b/goldenverba/components/types.py @@ -0,0 +1,20 @@ +from pydantic import BaseModel +from typing import Literal + + +class InputText(BaseModel): + type: Literal["text"] + text: str + description: str + + +class InputNumber(BaseModel): + type: Literal["number"] + value: int + description: str + + +class FileData(BaseModel): + filename: str + extension: str + content: str diff --git a/goldenverba/server/ConfigManager.py b/goldenverba/server/ConfigManager.py deleted file mode 100644 index 90ba5244a..000000000 --- a/goldenverba/server/ConfigManager.py +++ /dev/null @@ -1,115 +0,0 @@ -import json -import os - -from wasabi import msg - - -class Config: - def __init__( - self, reader: str, chunker: str, embedder: str, retriever: str, generator: str - ): - self.reader = reader - self.chunker = chunker - self.embedder = embedder - self.retriever = retriever - self.generator = generator - - def initalized(self) -> bool: - return ( - self.reader == "" - or self.chunker == "" - or self.embedder == "" - or self.retriever == "" - or self.generator == "" - ) - - -class ConfigManager: - def __init__( - self, - filename="verba_config.json", - ): - self.filename = filename - self.config: Config = None - # Load the config if exists or create one if not - if os.path.exists(self.filename): - self.load_config() - else: - self.default_config() - self.save_config() - - def default_config(self): - """Create a default config.""" - msg.info("New Config initialized") - self.config = Config( - reader="", - chunker="", - embedder="", - retriever="", - generator="", - ) - - def load_config(self): - """Load config from file.""" - msg.good("Config loaded") - with open(self.filename) as file: - json_obj = json.load(file) - self.config = Config( - reader=json_obj["reader"], - chunker=json_obj["chunker"], - embedder=json_obj["embedder"], - retriever=json_obj["retriever"], - generator=json_obj["generator"], - ) - - def save_config(self): - """Save config to file.""" - msg.good("Saved Config") - with open(self.filename, "w") as file: - json_obj = { - "reader": self.config.reader, - "chunker": self.config.chunker, - "embedder": self.config.embedder, - "retriever": self.config.retriever, - "generator": self.config.generator, - } - json.dump(json_obj, file, indent=4) - - def set_reader(self, reader: str): - self.config.reader = reader - - def get_reader(self): - return self.config.reader - - def set_chunker(self, chunker: str): - self.config.chunker = chunker - - def get_chunker(self): - return self.config.chunker - - def set_embedder(self, embedder: str): - self.config.embedder = embedder - - def get_embedder(self): - return self.config.embedder - - def set_retriever(self, retriever: str): - self.config.retriever = retriever - - def get_retriever(self): - return self.config.retriever - - def set_generator(self, generator: str): - self.config.generator = generator - - def get_generator(self): - return self.config.generator - - def initialized(self) -> bool: - if self.config is not None: - return self.config.initalized() - else: - return False - - def get_config(self): - return self.config diff --git a/goldenverba/server/api.py b/goldenverba/server/api.py index 593a18dce..af284382b 100644 --- a/goldenverba/server/api.py +++ b/goldenverba/server/api.py @@ -1,4 +1,4 @@ -from fastapi import FastAPI, Request, WebSocket, status +from fastapi import FastAPI, WebSocket, status from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse, JSONResponse from fastapi.staticfiles import StaticFiles @@ -7,23 +7,27 @@ from pathlib import Path from dotenv import load_dotenv -from pydantic import BaseModel from starlette.websockets import WebSocketDisconnect from wasabi import msg # type: ignore[import] +import time from goldenverba import verba_manager -from goldenverba.components.chunking.interface import Chunker -from goldenverba.components.embedding.interface import Embedder -from goldenverba.components.generation.interface import Generator -from goldenverba.components.reader.interface import Reader -from goldenverba.components.retriever.interface import Retriever -from goldenverba.server.ConfigManager import ConfigManager -from goldenverba.server.util import setup_managers +from goldenverba.server.types import ( + ResetPayload, + ConfigPayload, + QueryPayload, + GeneratePayload, + GetDocumentPayload, + SearchQueryPayload, + ImportPayload, +) +from goldenverba.server.util import get_config, set_config, setup_managers load_dotenv() # Check if runs in production production_key = os.environ.get("VERBA_PRODUCTION", "") +tag = os.environ.get("VERBA_GOOGLE_TAG", "") if production_key == "True": msg.info("API runs in Production Mode") production = True @@ -31,80 +35,7 @@ production = False manager = verba_manager.VerbaManager() -config_manager = ConfigManager() - -readers = manager.reader_get_readers() -chunker = manager.chunker_get_chunker() -embedders = manager.embedder_get_embedder() -retrievers = manager.retriever_get_retriever() -generators = manager.generator_get_generator() - -setup_managers( - manager, config_manager, readers, chunker, embedders, retrievers, generators -) -config_manager.save_config() - - -def create_reader_payload(key: str, reader: Reader) -> dict: - available, message = manager.check_verba_component(reader) - - return { - "name": key, - "description": reader.description, - "input_form": reader.input_form, - "available": available, - "message": message, - } - - -def create_chunker_payload(key: str, chunker: Chunker) -> dict: - available, message = manager.check_verba_component(chunker) - - return { - "name": key, - "description": chunker.description, - "input_form": chunker.input_form, - "units": chunker.default_units, - "overlap": chunker.default_overlap, - "available": available, - "message": message, - } - - -def create_embedder_payload(key: str, embedder: Embedder) -> dict: - available, message = manager.check_verba_component(embedder) - - return { - "name": key, - "description": embedder.description, - "input_form": embedder.input_form, - "available": available, - "message": message, - } - - -def create_retriever_payload(key: str, retriever: Retriever) -> dict: - available, message = manager.check_verba_component(retriever) - - return { - "name": key, - "description": retriever.description, - "available": available, - "message": message, - } - - -def create_generator_payload(key: str, generator: Generator) -> dict: - available, message = manager.check_verba_component(generator) - - return { - "name": key, - "description": generator.description, - "available": available, - "message": message, - "streamable": generator.streamable, - } - +setup_managers(manager) # FastAPI App app = FastAPI() @@ -137,92 +68,27 @@ def create_generator_payload(key: str, generator: Generator) -> dict: app.mount("/static", StaticFiles(directory=BASE_DIR / "frontend/out"), name="app") -class QueryPayload(BaseModel): - query: str - - -class ConversationItem(BaseModel): - type: str - content: str - typewriter: bool - - -class GeneratePayload(BaseModel): - query: str - context: str - conversation: list[ConversationItem] - - -class SearchQueryPayload(BaseModel): - query: str - doc_type: str - - -class GetDocumentPayload(BaseModel): - document_id: str - - -class LoadPayload(BaseModel): - reader: str - chunker: str - embedder: str - fileBytes: list[str] - fileNames: list[str] - filePath: str - document_type: str - chunkUnits: int - chunkOverlap: int - - -class GetComponentPayload(BaseModel): - component: str - - -class SetComponentPayload(BaseModel): - component: str - selected_component: str - - @app.get("/") @app.head("/") async def serve_frontend(): return FileResponse(os.path.join(BASE_DIR, "frontend/out/index.html")) - -@app.get("/status") -async def catch_status(): - # Check if the path corresponds to a file that exists in the static directory - file_path = BASE_DIR / "frontend/out" / "status.html" - if os.path.isfile(file_path): - return FileResponse(file_path) - # Otherwise, serve index.html - return FileResponse(os.path.join(BASE_DIR, "frontend/out/index.html")) - - -@app.get("/document_explorer") -async def catch_explorer(): - # Check if the path corresponds to a file that exists in the static directory - file_path = BASE_DIR / "frontend/out" / "document_explorer.html" - if os.path.isfile(file_path): - return FileResponse(file_path) - # Otherwise, serve index.html - return FileResponse(os.path.join(BASE_DIR, "frontend/out/index.html")) - +### GET # Define health check endpoint @app.get("/api/health") -async def root(): +async def health_check(): try: if manager.client.is_ready(): return JSONResponse( - content={ - "message": "Alive!", - } + content={"message": "Alive!", "production": production, "gtag": tag} ) else: return JSONResponse( content={ "message": "Database not ready!", + "production": production, + "gtag": tag, }, status_code=status.HTTP_503_SERVICE_UNAVAILABLE, ) @@ -231,295 +97,199 @@ async def root(): return JSONResponse( content={ "message": f"Healthcheck failed with {str(e)}", + "production": production, + "gtag": tag, }, status_code=status.HTTP_503_SERVICE_UNAVAILABLE, ) - -# Define health check endpoint -@app.get("/api/get_google_tag") -async def get_google_tag(): - tag = os.environ.get("VERBA_GOOGLE_TAG", "") - - if tag: - msg.good("Google Tag available!") - - return JSONResponse( - content={ - "tag": tag, - } - ) - - -@app.get("/api/get_production") -async def get_production(): - return JSONResponse( - content={ - "production": production, - } - ) - - -# Get Readers, Chunkers, and Embedders -@app.get("/api/get_components") -async def get_components(): - msg.info("Retrieving components") - - data = {"readers": [], "chunker": [], "embedder": []} - - for key in readers: - current_reader = readers[key] - current_reader_data = create_reader_payload(key, current_reader) - data["readers"].append(current_reader_data) - - for key in chunker: - current_chunker = chunker[key] - current_chunker_data = create_chunker_payload(key, current_chunker) - data["chunker"].append(current_chunker_data) - - for key in embedders: - current_embedder = embedders[key] - current_embedder_data = create_embedder_payload(key, current_embedder) - data["embedder"].append(current_embedder_data) - +# Get Status meta data +@app.get("/api/get_status") +async def get_status(): try: - data["default_values"] = { - "last_reader": create_reader_payload( - config_manager.get_reader(), readers[config_manager.get_reader()] - ), - "last_chunker": create_chunker_payload( - config_manager.get_chunker(), chunker[config_manager.get_chunker()] - ), - "last_embedder": create_embedder_payload( - config_manager.get_embedder(), embedders[config_manager.get_embedder()] - ), - "last_document_type": "Documentation", - } - except KeyError: - # Reset Config - msg.warn("Mismatched Config detected, resetting managers") - config_manager.default_config() - config_manager.save_config() - setup_managers( - manager, config_manager, readers, chunker, embedders, retrievers, generators - ) - config_manager.save_config() - data["default_values"] = { - "last_reader": create_reader_payload( - config_manager.get_reader(), readers[config_manager.get_reader()] - ), - "last_chunker": create_chunker_payload( - config_manager.get_chunker(), chunker[config_manager.get_chunker()] - ), - "last_embedder": create_embedder_payload( - config_manager.get_embedder(), embedders[config_manager.get_embedder()] - ), - "last_document_type": "Documentation", - } - - return JSONResponse(content=data) - - -@app.post("/api/get_component") -async def get_component(payload: GetComponentPayload): - msg.info(f"Retrieving {payload.component} components") - - data = {"components": []} - - if payload.component == "embedders": - data["selected_component"] = create_embedder_payload( - manager.embedder_manager.selected_embedder.name, - manager.embedder_manager.selected_embedder, + schemas = manager.get_schemas() + sorted_schemas = dict( + sorted(schemas.items(), key=lambda item: item[1], reverse=True) ) - for key in embedders: - current_embedder = embedders[key] - current_embedder_data = create_embedder_payload(key, current_embedder) - data["components"].append(current_embedder_data) - - elif payload.component == "retrievers": - data["selected_component"] = create_retriever_payload( - manager.retriever_manager.selected_retriever.name, - manager.retriever_manager.selected_retriever, + sorted_libraries = dict( + sorted( + manager.installed_libraries.items(), + key=lambda item: (not item[1], item[0]), + ) ) - - for key in retrievers: - current_retriever = retrievers[key] - current_retriever_data = create_retriever_payload(key, current_retriever) - data["components"].append(current_retriever_data) - - elif payload.component == "generators": - data["selected_component"] = create_generator_payload( - manager.generator_manager.selected_generator.name, - manager.generator_manager.selected_generator, + sorted_variables = dict( + sorted( + manager.environment_variables.items(), + key=lambda item: (not item[1], item[0]), + ) ) - for key in generators: - current_generator = generators[key] - current_generator_data = create_generator_payload(key, current_generator) - data["components"].append(current_generator_data) - - return JSONResponse(content=data) - - -@app.post("/api/set_component") -async def set_component(payload: SetComponentPayload): - if production: - return JSONResponse(content={}) - - msg.info(f"Setting {payload.component} to {payload.selected_component}") - - if payload.component == "embedders": - manager.embedder_manager.set_embedder(payload.selected_component) - config_manager.set_embedder(payload.selected_component) - - elif payload.component == "retrievers": - manager.retriever_manager.set_retriever(payload.selected_component) - config_manager.set_retriever(payload.selected_component) + data = { + "type": manager.weaviate_type, + "libraries": sorted_libraries, + "variables": sorted_variables, + "schemas": sorted_schemas, + "error": "", + } - elif payload.component == "generators": - manager.generator_manager.set_generator(payload.selected_component) - config_manager.set_generator(payload.selected_component) + msg.info("Status Retrieved") + return JSONResponse(content=data) + except Exception as e: + data = { + "type": "", + "libraries": {}, + "variables": {}, + "schemas": {}, + "error": f"Status retrieval failed: {str(e)}", + } + msg.fail(f"Status retrieval failed: {str(e)}") + return JSONResponse(content=data) - config_manager.save_config() +# Get Configuration +@app.get("/api/config") +async def retrieve_config(): + try: + config = get_config(manager) + msg.info("Config Retrieved") + return JSONResponse(status_code=200, content={"data": config, "error": ""}) - return JSONResponse(content={}) + except Exception as e: + msg.warn(f"Could not retrieve configuration: {str(e)}") + return JSONResponse( + status_code=500, + content={ + "data": {}, + "error": f"Could not retrieve configuration: {str(e)}", + }, + ) +### WEBSOCKETS -# Get Status meta data -@app.get("/api/get_status") -async def get_status(): - msg.info("Retrieving status") +@app.websocket("/ws/generate_stream") +async def websocket_generate_stream(websocket: WebSocket): + await websocket.accept() + while True: # Start a loop to keep the connection alive. + try: + data = await websocket.receive_text() + # Parse and validate the JSON string using Pydantic model + payload = GeneratePayload.model_validate_json(data) + msg.good(f"Received generate stream call for {payload.query}") + full_text = "" + async for chunk in manager.generate_stream_answer( + [payload.query], [payload.context], payload.conversation + ): + full_text += chunk["message"] + if chunk["finish_reason"] == "stop": + chunk["full_text"] = full_text + await websocket.send_json(chunk) - data = { - "type": manager.weaviate_type, - "libraries": manager.installed_libraries, - "variables": manager.environment_variables, - "schemas": manager.get_schemas(), - } + except WebSocketDisconnect: + msg.warn("WebSocket connection closed by client.") + break # Break out of the loop when the client disconnects - return JSONResponse(content=data) + except Exception as e: + msg.fail(f"WebSocket Error: {str(e)}") + await websocket.send_json( + {"message": e, "finish_reason": "stop", "full_text": str(e)} + ) + msg.good("Succesfully streamed answer") +### POST # Reset Verba -@app.get("/api/reset") -async def reset_verba(): +@app.post("/api/reset") +async def reset_verba(payload: ResetPayload): if production: return JSONResponse(status_code=200, content={}) - msg.info("Resetting verba") + try: + if payload.resetMode == "VERBA": + manager.reset() + elif payload.resetMode == "DOCUMENTS": + manager.reset_documents() + elif payload.resetMode == "CACHE": + manager.reset_cache() + elif payload.resetMode == "SUGGESTIONS": + manager.reset_suggestion() + elif payload.resetMode == "CONFIG": + manager.reset_config() + + msg.info(f"Resetting Verba ({payload.resetMode})") - manager.reset() + except Exception as e: + msg.warn(f"Failed to reset Verba {str(e)}") return JSONResponse(status_code=200, content={}) +# Receive query and return chunks and query answer +@app.post("/api/import") +async def import_data(payload: ImportPayload): -# Reset Verba -@app.get("/api/reset_cache") -async def reset_cache(): - if production: - return JSONResponse(status_code=200, content={}) - msg.info("Resetting cache") - - manager.reset_cache() - - return JSONResponse(status_code=200, content={}) - + logging = [] -# Reset Verba suggestions -@app.get("/api/reset_suggestion") -async def reset_suggestion(): if production: - return JSONResponse(status_code=200, content={}) - msg.info("Resetting suggestions") + logging.append( + {"type": "ERROR", "message": "Can't import when in production mode"} + ) + return JSONResponse( + content={ + "logging": logging, + } + ) - manager.reset_suggestion() + try: + set_config(manager, payload.config) + documents, logging = manager.import_data( + payload.data, payload.textValues, logging + ) - return JSONResponse(status_code=200, content={}) + return JSONResponse( + content={ + "logging": logging, + } + ) + + except Exception as e: + logging.append({"type": "ERROR", "message": str(e)}) + return JSONResponse( + content={ + "logging": logging, + } + ) +@app.post("/api/set_config") +async def update_config(payload: ConfigPayload): -# Receive query and return chunks and query answer -@app.post("/api/load_data") -async def load_data(payload: LoadPayload): if production: return JSONResponse( content={ "status": "200", - "status_msg": "Can't add data when in production mode", + "status_msg": "Config can't be updated in Production Mode", } ) - manager.reader_set_reader(payload.reader) - manager.chunker_set_chunker(payload.chunker) - manager.embedder_set_embedder(payload.embedder) - - config_manager.set_reader(payload.reader) - config_manager.set_chunker(payload.chunker) - config_manager.set_embedder(payload.embedder) - config_manager.save_config() - - # Set new default values based on user input - current_chunker = manager.chunker_get_chunker()[payload.chunker] - current_chunker.default_units = payload.chunkUnits - current_chunker.default_overlap = payload.chunkOverlap - - msg.info( - f"Received Data to Import: READER({payload.reader}, Documents {len(payload.fileBytes)}, Type {payload.document_type}) CHUNKER ({payload.chunker}, UNITS {payload.chunkUnits}, OVERLAP {payload.chunkOverlap}), EMBEDDER ({payload.embedder})" - ) - - if payload.fileBytes or payload.filePath: - try: - documents = manager.import_data( - payload.fileBytes, - [], - [payload.filePath], - payload.fileNames, - payload.document_type, - payload.chunkUnits, - payload.chunkOverlap, - ) - - if documents is None: - return JSONResponse( - content={ - "status": 200, - "status_msg": "No documents imported", - } - ) - - document_count = len(documents) - chunks_count = sum([len(document.chunks) for document in documents]) + try: + set_config(manager, payload.config) + except Exception as e: + msg.warn(f"Failed to set new Config {str(e)}") - return JSONResponse( - content={ - "status": 200, - "status_msg": f"Succesfully imported {document_count} documents and {chunks_count} chunks", - } - ) - except Exception as e: - msg.fail(f"Loading data failed {str(e)}") - return JSONResponse( - content={ - "status": "400", - "status_msg": str(e), - } - ) return JSONResponse( content={ "status": "200", - "status_msg": "No documents received", + "status_msg": "Config Updated", } ) - # Receive query and return chunks and query answer @app.post("/api/query") async def query(payload: QueryPayload): msg.good(f"Received query: {payload.query}") + start_time = time.time() # Start timing try: chunks, context = manager.retrieve_chunks([payload.query]) - results = [ + retrieved_chunks = [ { "text": chunk.text, "doc_name": chunk.doc_name, @@ -531,91 +301,39 @@ async def query(payload: QueryPayload): for chunk in chunks ] - msg.good(f"Succesfully processed query: {payload.query}") + elapsed_time = round(time.time() - start_time, 2) # Calculate elapsed time + msg.good(f"Succesfully processed query: {payload.query} in {elapsed_time}s") if len(chunks) == 0: return JSONResponse( content={ - "documents": [], + "chunks": [], + "took": 0, "context": "", + "error": "No Chunks Available", } ) return JSONResponse( content={ - "documents": results, + "error": "", + "chunks": retrieved_chunks, "context": context, + "took": elapsed_time, } ) except Exception as e: - msg.fail(f"Query failed: {str(e)}") - return JSONResponse( - content={ - "system": f"Something went wrong! {str(e)}", - "context": "", - "documents": [], - } - ) - - -# Receive query and return chunks and query answer -@app.post("/api/generate") -async def generate(payload: GeneratePayload): - msg.good(f"Received generate call for: {payload.query}") - try: - answer = await manager.generate_answer( - [payload.query], [payload.context], payload.conversation - ) - - msg.good(f"Succesfully generated answer: {payload.query}") - - return JSONResponse( - content={ - "system": answer, - } - ) - - except Exception as e: - raise - msg.fail(f"Answer Generation failed: {str(e)}") + msg.warn(f"Query failed: {str(e)}") return JSONResponse( content={ - "system": f"Something went wrong! {str(e)}", + "chunks": [], + "took": 0, + "context": "", + "error": f"Something went wrong: {str(e)}", } ) - -@app.websocket("/ws/generate_stream") -async def websocket_generate_stream(websocket: WebSocket): - await websocket.accept() - while True: # Start a loop to keep the connection alive. - try: - data = await websocket.receive_text() - # Parse and validate the JSON string using Pydantic model - payload = GeneratePayload.model_validate_json(data) - msg.good(f"Received generate stream call for {payload.query}") - full_text = "" - async for chunk in manager.generate_stream_answer( - [payload.query], [payload.context], payload.conversation - ): - full_text += chunk["message"] - if chunk["finish_reason"] == "stop": - chunk["full_text"] = full_text - await websocket.send_json(chunk) - - except WebSocketDisconnect: - msg.warn("WebSocket connection closed by client.") - break # Break out of the loop when the client disconnects - - except Exception as e: - msg.fail(f"WebSocket Error: {e}") - await websocket.send_json( - {"message": e, "finish_reason": "stop", "full_text": e} - ) - msg.good("Succesfully streamed answer") - - # Retrieve auto complete suggestions based on user input @app.post("/api/suggestions") async def suggestions(payload: QueryPayload): @@ -634,67 +352,102 @@ async def suggestions(payload: QueryPayload): } ) - # Retrieve specific document based on UUID @app.post("/api/get_document") async def get_document(payload: GetDocumentPayload): + # TODO Standarize Document Creation msg.info(f"Document ID received: {payload.document_id}") try: document = manager.retrieve_document(payload.document_id) + document_properties = document.get("properties", {}) + document_obj = { + "class": document.get("class", "No Class"), + "id": document.get("id", payload.document_id), + "chunks": document_properties.get("chunk_count", 0), + "link": document_properties.get("doc_link", ""), + "name": document_properties.get("doc_name", "No name"), + "type": document_properties.get("doc_type", "No type"), + "text": document_properties.get("text", "No text"), + "timestamp": document_properties.get("timestamp", ""), + } + msg.good(f"Succesfully retrieved document: {payload.document_id}") return JSONResponse( content={ - "document": document, + "error": "", + "document": document_obj, } ) except Exception as e: msg.fail(f"Document retrieval failed: {str(e)}") return JSONResponse( content={ - "document": {}, + "error": str(e), + "document": None, } ) - -## Retrieve all documents imported to Weaviate +## Retrieve and search documents imported to Weaviate @app.post("/api/get_all_documents") async def get_all_documents(payload: SearchQueryPayload): + # TODO Standarize Document Creation msg.info("Get all documents request received") + start_time = time.time() # Start timing try: - documents = manager.retrieve_all_documents(payload.doc_type) - msg.good(f"Succesfully retrieved document: {len(documents)} documents") + if payload.query == "": + documents = manager.retrieve_all_documents( + payload.doc_type, payload.page, payload.pageSize + ) + else: + documents = manager.search_documents( + payload.query, payload.doc_type, payload.page, payload.pageSize + ) - doc_types = {document["doc_type"] for document in documents} + if not documents: + return JSONResponse( + content={ + "documents": [], + "doc_types": [], + "current_embedder": manager.embedder_manager.selected_embedder, + "error": f"No Results found!", + "took": 0, + } + ) - return JSONResponse( - content={ - "documents": documents, - "doc_types": list(doc_types), - "current_embedder": manager.embedder_manager.selected_embedder.name, - } - ) - except Exception as e: - msg.fail(f"All Document retrieval failed: {str(e)}") - return JSONResponse( - content={ - "documents": [], - "doc_types": [], - "current_embedder": manager.embedder_manager.selected_embedder.name, - } + documents_obj = [] + for document in documents: + + _additional = document["_additional"] + + documents_obj.append( + { + "class": "No Class", + "uuid": _additional.get("id", "none"), + "chunks": document.get("chunk_count", 0), + "link": document.get("doc_link", ""), + "name": document.get("doc_name", "No name"), + "type": document.get("doc_type", "No type"), + "text": document.get("text", "No text"), + "timestamp": document.get("timestamp", ""), + } + ) + + elapsed_time = round(time.time() - start_time, 2) # Calculate elapsed time + msg.good( + f"Succesfully retrieved document: {len(documents)} documents in {elapsed_time}s" ) + doc_types = manager.retrieve_all_document_types() -## Search for documentation -@app.post("/api/search_documents") -async def search_documents(payload: SearchQueryPayload): - try: - documents = manager.search_documents(payload.query, payload.doc_type) return JSONResponse( content={ - "documents": documents, - "current_embedder": manager.embedder_manager.selected_embedder.name, + "documents": documents_obj, + "doc_types": list(doc_types), + "current_embedder": manager.embedder_manager.selected_embedder, + "error": "", + "took": elapsed_time, } ) except Exception as e: @@ -702,15 +455,18 @@ async def search_documents(payload: SearchQueryPayload): return JSONResponse( content={ "documents": [], - "current_embedder": manager.embedder_manager.selected_embedder.name, + "doc_types": [], + "current_embedder": manager.embedder_manager.selected_embedder, + "error": f"All Document retrieval failed: {str(e)}", + "took": 0, } ) - -# Retrieve specific document based on UUID +# Delete specific document based on UUID @app.post("/api/delete_document") async def delete_document(payload: GetDocumentPayload): if production: + msg.warn("Can't delete documents when in Production Mode") return JSONResponse(status_code=200, content={}) msg.info(f"Document ID received: {payload.document_id}") diff --git a/goldenverba/server/cli.py b/goldenverba/server/cli.py index aacc09295..c6ee9d33b 100644 --- a/goldenverba/server/cli.py +++ b/goldenverba/server/cli.py @@ -1,96 +1,35 @@ import click import uvicorn from dotenv import load_dotenv -from wasabi import msg - -from goldenverba.verba_manager import VerbaManager load_dotenv() - @click.group() def cli(): """Main command group for verba.""" pass - @cli.command() @click.option( "--port", default=8000, help="FastAPI Port", ) -def start(port): - """ - Run the FastAPI application. - """ - uvicorn.run("goldenverba.server.api:app", host="0.0.0.0", port=port, reload=True) - - -@cli.command() -@click.option( - "--reader", - default="SimpleReader", - help="Reader", -) -@click.option( - "--type", - default="Documentation", - help="Document Type", -) @click.option( - "--chunker", - default="WordChunker", - help="Chunker", + "--host", + default="0.0.0.0", + help="FastAPI Host", ) @click.option( - "--units", - default=100, - help="Units per chunk", + "--prod/--no-prod", + default=False, + help="Run in production mode.", ) -@click.option( - "--overlap", - default=50, - help="Overlap of units per chunk", -) -@click.option( - "--embedder", - default="ADAEmbedder", - help="Embedder", -) -@click.option( - "--path", - help="Path to data", -) -def load(reader, type, chunker, units, overlap, embedder, path): +def start(port, host, prod): """ Run the FastAPI application. """ - manager = VerbaManager() - manager.reader_set_reader(reader) - manager.chunker_set_chunker(chunker) - manager.embedder_set_embedder(embedder) - - manager.import_data( - bytes=[], - contents=[], - paths=[path], - fileNames=[path], - document_type=type, - units=units, - overlap=overlap, - ) - - -@cli.command() -def reset(): - """ - Delete all schemas. - """ - manager = VerbaManager() - manager.reset() - msg.warn("Verba Resetted") - + uvicorn.run("goldenverba.server.api:app", host=host, port=port, reload=(not prod)) if __name__ == "__main__": cli() diff --git a/goldenverba/server/frontend/out/404.html b/goldenverba/server/frontend/out/404.html index 7396e834e..ae6c9059b 100644 --- a/goldenverba/server/frontend/out/404.html +++ b/goldenverba/server/frontend/out/404.html @@ -1 +1 @@ -404: This page could not be found

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.Verba

404

This page could not be found.

\ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/0Hcan7GeSF1TuouAgzveb/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/0Hcan7GeSF1TuouAgzveb/_buildManifest.js deleted file mode 100644 index a6da8a3ad..000000000 --- a/goldenverba/server/frontend/out/_next/static/0Hcan7GeSF1TuouAgzveb/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__BUILD_MANIFEST=function(e,s){return{__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/":[e,s,"static/chunks/pages/index-587c8a189d07a085.js"],"/_error":["static/chunks/pages/_error-b190518070b21757.js"],"/about":["static/chunks/pages/about-5eabeb8efb0f0a5d.js"],"/document_explorer":[e,"static/chunks/280-74dc7793334d9da5.js",s,"static/chunks/pages/document_explorer-55171382bcb992de.js"],sortedPages:["/","/_app","/_error","/about","/document_explorer"]}}("static/chunks/611-ee9176c9cb6e0276.js","static/chunks/172-a717b482738a1ff0.js"),self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/1NMt8wGsnxJQdeutSmA9v/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/1NMt8wGsnxJQdeutSmA9v/_buildManifest.js deleted file mode 100644 index 44247aa56..000000000 --- a/goldenverba/server/frontend/out/_next/static/1NMt8wGsnxJQdeutSmA9v/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/":["static/chunks/pages/index-491c7585514c00a2.js"],"/_error":["static/chunks/pages/_error-b190518070b21757.js"],"/document_explorer":["static/chunks/246-8cba9c3e138a3cf5.js","static/chunks/pages/document_explorer-896e2841c08a0cf2.js"],"/status":["static/chunks/pages/status-5eecffe6be63ff44.js"],sortedPages:["/","/_app","/_error","/document_explorer","/status"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/5fzR3LERKY5UlE9ayIEWy/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/5fzR3LERKY5UlE9ayIEWy/_buildManifest.js deleted file mode 100644 index 0b51bc97f..000000000 --- a/goldenverba/server/frontend/out/_next/static/5fzR3LERKY5UlE9ayIEWy/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__BUILD_MANIFEST=function(e,s){return{__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/":[e,s,"static/chunks/pages/index-587c8a189d07a085.js"],"/_error":["static/chunks/pages/_error-b190518070b21757.js"],"/about":["static/chunks/pages/about-5eabeb8efb0f0a5d.js"],"/document_explorer":[e,"static/chunks/280-74dc7793334d9da5.js",s,"static/chunks/pages/document_explorer-55171382bcb992de.js"],sortedPages:["/","/_app","/_error","/about","/document_explorer"]}}("static/chunks/611-ee9176c9cb6e0276.js","static/chunks/172-f543cedc6ac6c1ac.js"),self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git 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1cfd45a09..000000000 --- a/goldenverba/server/frontend/out/_next/static/A4tVXjpGyJ80L5yK9Jv_a/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/":["static/chunks/pages/index-491c7585514c00a2.js"],"/_error":["static/chunks/pages/_error-b190518070b21757.js"],"/document_explorer":["static/chunks/246-8cba9c3e138a3cf5.js","static/chunks/pages/document_explorer-ccca135591a6180e.js"],"/status":["static/chunks/pages/status-7d60916a01bd3f1e.js"],sortedPages:["/","/_app","/_error","/document_explorer","/status"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/BrKix8hVmzKkq4nA40XDY/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/BrKix8hVmzKkq4nA40XDY/_buildManifest.js deleted file mode 100644 index 19effd0fe..000000000 --- a/goldenverba/server/frontend/out/_next/static/BrKix8hVmzKkq4nA40XDY/_buildManifest.js +++ 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-1 +0,0 @@ -self.__BUILD_MANIFEST=function(e,s){return{__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/":[e,s,"static/chunks/pages/index-56e8765bfd3090f9.js"],"/_error":["static/chunks/pages/_error-b190518070b21757.js"],"/about":["static/chunks/pages/about-772e84e976153ce1.js"],"/document_explorer":[e,"static/chunks/280-e826f6179b5d8b6c.js",s,"static/chunks/pages/document_explorer-c8a428b01649242c.js"],sortedPages:["/","/_app","/_error","/about","/document_explorer"]}}("static/chunks/866-cfe4bf209f60b238.js","static/chunks/172-11387f2fea15f8f4.js"),self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); \ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/Ej9O_hZ3RDAnJYJlOxclF/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/Ej9O_hZ3RDAnJYJlOxclF/_buildManifest.js deleted file mode 100644 index d5ae210fc..000000000 --- a/goldenverba/server/frontend/out/_next/static/Ej9O_hZ3RDAnJYJlOxclF/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ 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b/goldenverba/server/frontend/out/_next/static/LgB67yeDij4th8wsrC2eS/_ssgManifest.js deleted file mode 100644 index 5b3ff592f..000000000 --- a/goldenverba/server/frontend/out/_next/static/LgB67yeDij4th8wsrC2eS/_ssgManifest.js +++ /dev/null @@ -1 +0,0 @@ -self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB() \ No newline at end of file diff --git a/goldenverba/server/frontend/out/_next/static/NnjBs5HkyOiVgbYuj7tdV/_buildManifest.js b/goldenverba/server/frontend/out/_next/static/NnjBs5HkyOiVgbYuj7tdV/_buildManifest.js deleted file mode 100644 index 3d5282678..000000000 --- a/goldenverba/server/frontend/out/_next/static/NnjBs5HkyOiVgbYuj7tdV/_buildManifest.js +++ /dev/null @@ -1 +0,0 @@ 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