Chaindesk provides a user-friendly solution to quickly setup a semantic search system over your personal data without any technical knowledge.
- Load data from anywhere
- Raw text
- Web page
- Files
- Word
- Excel
- Powerpoint
- 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
- Vector Datbase: Qdrant
- Embeddigs: Openai's text-embedding-ada-002
- Chunk size: 256 tokens
- Next.js
- Joy UI
- LangchainJS
- PostgreSQL
- Prisma
- Qdrant
Inspired by the ChatGPT Retrieval Plugin.
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
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:1080You can fully rebuild dockers with :
pnpm docker:compose up --build