/databerry

The no-code platform for connecting custom data to large language models

Primary LanguageTypeScriptMIT LicenseMIT


WebTorrent
Databerry

Connect your data to large language models!


WebTorrent

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

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
# Create .env.local
cp .env.example .env.local

# Install dependencies
pnpm install

# Generate DB tables
pnpm prisma:migrate:dev

# Run server
pnpm dev

# Run worker process
pnpm worker:datasource-loader