Databerry

The no-code platform for building custom LLM Agents

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

📄 Documentation

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