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WebTorrent
Chaindesk

The no-code platform for building custom LLM Agents


Chaindesk

Chaindesk provides a user-friendly solution to quickly setup a semantic search system over your personal data without any technical knowledge.

Features

  • Load data from anywhere
    • Raw text
    • Web page
    • Files
      • Word
      • Excel
      • Powerpoint
      • PDF
      • Markdown
      • Plain Text
    • Web Site (coming soon)
    • Notion (coming soon)
    • Airtable (coming soon)
  • No-code: User-friendly interface to manage your datastores and chat with your data
  • Securized API endpoint for querying your data
  • Auto sync data sources (coming soon)
  • Auto generates a ChatGPT Plugin for each datastore

Semantic Search Specs

  • Vector Datbase: Qdrant
  • Embeddigs: Openai's text-embedding-ada-002
  • Chunk size: 256 tokens

Stack

  • Next.js
  • Joy UI
  • LangchainJS
  • PostgreSQL
  • Prisma
  • Qdrant

Inspired by the ChatGPT Retrieval Plugin.

Run the project locally

Without docker compose

Minimum requirements to run the projects locally

  • Node.js v18
  • Postgres Database
  • Redis
  • Qdrant
  • GitHub App (NextAuth)
  • Email Provider (NextAuth)
  • OpenAI API Key
  • AWS S3 Credentials

Run locally (Docker)

cp .dev/databerry/app.env.example .dev/databerry/app.env
# Add your own OPENAI_API_KEY

pnpm docker:compose up

# Alternatively run app and services separately
pnpm docker:compose:deps up
pnpm docker:compose:app up

# create s3 bucket
# go to http://localhost:9090 and create bucket databerry-dev
# set bucket access policy to public
# might need to add 127.0.0.1 minio to /etc/hosts in order to access public s3 files through http://minio...

# Dev emails inbox (maildev)
# visit http://localhost:1080

You can fully rebuild dockers with :

pnpm docker:compose up --build

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