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

Home Page: https://databerry.ai

License: MIT License

Shell 0.18% JavaScript 6.21% TypeScript 90.87% CSS 2.23% Dockerfile 0.51%

databerry's Introduction


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.

Features

  • Load data from anywhere
    • Raw text
    • Web page
    • Files
      • Word
      • Excel
      • Powerpoint
      • PDF
      • Mardown
      • 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

databerry's People

Contributors

gmpetrov avatar

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