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

# 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

With docker compose

First cd .dev/databerry then populate the config files app.env and docker.env as needed, then run the compose command:

docker compose --env-file docker.env up

You can fully rebuild dockers with :

docker compose --env-file docker.env up --build