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

IAmStoxe/databerry