add: custom GPT

Custom GPT/README.md

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+# Conversational Retrieval with LangChain and OpenAI
+
+This directory contains a Python script that implements a conversational retrieval system using LangChain and OpenAI's API. The script allows users to query a collection of documents and receive responses based on the retrieved information.
+
+## Features
+
+- Load documents from a specified directory.
+- Create and persist a vector store index for efficient querying.
+- Engage in conversational interactions, maintaining chat history.
+- Easily exit the program.
+
+## Requirements
+
+- Python 3.7+
+- Required packages:
+  - `openai`
+  - `langchain`
+  - `chromadb`
+
+You can install the required packages using pip:
+
+```bash
+pip install openai langchain chromadb
+```
+## Setup
+1. Clone the Repository:
+    ```bash
+    git clone https://github.com/king04aman/custom-gpt.git
+    cd your_repository
+    ```
+2. Set the OpenAI API Key:
+Replace `your_api_key_here` in the script with your actual OpenAI API key. You can also set the environment variable directly in your terminal:
+    ```bash
+    export OPENAI_API_KEY="your_api_key_here"
+    ```
+3. Prepare Your Data:
+Place your documents in a folder named `data/`. The script will load all documents from this directory.
+
+## Usage
+Run the script from the command line:
+```bash
+python main.py
+```
+### Command Line Arguments
+You can provide an initial query as a command line argument:
+```bash
+python main.py "Your initial query here"
+```
+### Interactive Mode
+If no initial query is provided, the script will prompt you to enter queries interactively. Type your question and press Enter to get a response. Type `quit`, `q`, or exit to `exit` the program.
+
+### Persistence
+- Set the `PERSIST` variable to `True` in the script to enable saving the vector store index to disk for reuse in future sessions.
+- The index will be saved in a directory named `persist/`.
+
+## Example
+```bash
+Prompt (type 'quit' to exit): What is the significance of data persistence?
+Response: [Your response here based on the documents]
+```
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Contributing
+Feel free to submit issues or pull requests. Contributions are welcome!

Custom GPT/example-env

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+OPENAI_API_KEY = "your_api_key"

Custom GPT/main.py

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+import os
+import openai
+import sys
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import DirectoryLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.vectorstores import Chroma
+
+
+os.environ["OPENAI_API_KEY"] = "your_api_key_here"
+PERSIST = False
+
+query = sys.argv[1] if len(sys.argv) > 1 else None
+
+if PERSIST and os.path.exists("persist"):
+    print("Reusing index...\n")
+    vectorstore = Chroma(persist_directory="persist", embedding_function=OpenAIEmbeddings())
+    index = VectorStoreIndexWrapper(vectorstore=vectorstore)
+else:
+    loader = DirectoryLoader("data/")
+    index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory": "persist"}).from_loaders([loader]) if PERSIST else VectorstoreIndexCreator().from_loaders([loader])
+
+chain = ConversationalRetrievalChain.from_llm(
+    llm=ChatOpenAI(model="gpt-3.5-turbo"),
+    retriever=index.vectorstore.as_retriever(search_kwargs={"k": 1}),
+)
+
+chat_history = []
+
+while True:
+    if not query:
+        query = input("Prompt (type 'quit' to exit): ")
+    if query.lower() in ['quit', 'q', 'exit']:
+        print("Exiting the program...")
+        sys.exit()
+
+    result = chain({"question": query, "chat_history": chat_history})
+
+    print("Response:", result['answer'])
+
+    chat_history.append((query, result['answer']))
+    query = None