Second Brain API

A personal knowledge base application using FastAPI, Milvus, and Gemini for storing and retrieving information using RAG (Retrieval Augmented Generation).

Features

• Store questions and answers with vector embeddings
• Query the knowledge base using natural language
• Stream responses for real-time interaction
• Vector similarity search using Milvus
• RAG-powered responses using Gemini

Prerequisites

• Python 3.8+
• Milvus server running locally or accessible
• Gemini API key

Project Structure

app/
  ├── __init__.py
  ├── main.py
  ├── config.py
  ├── routes/
  │   ├── __init__.py
  │   ├── health.py
  │   └── knowledge.py
  └── services/
      ├── __init__.py
      ├── milvus_service.py
      └── gemini_service.py

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables: Create a .env file with the following variables:

# Milvus Configuration
MILVUS_HOST=localhost
MILVUS_PORT=19530
MILVUS_COLLECTION_NAME=second_brain

# Gemini Configuration
GEMINI_API_KEY=your_gemini_api_key_here

# Vector Configuration
VECTOR_DIMENSION=768
VECTOR_INDEX_TYPE=IVF_FLAT
VECTOR_METRIC_TYPE=COSINE

Running the Application

Start the server using uvicorn:

uvicorn app.main:app --reload

The application will be available at:

• http://localhost:8000
• API documentation: http://localhost:8000/docs

API Endpoints

Health Check

• GET /health - Check application health

Knowledge Base

• POST /knowledge/add - Add a new document (question and answer)
• POST /knowledge/query - Query the knowledge base
• POST /knowledge/query/stream - Stream the response from querying

Example Usage

1. Add a document:

curl -X POST "http://localhost:8000/knowledge/add" \
     -H "Content-Type: application/json" \
     -d '{"question": "What is Python?", "answer": "Python is a high-level programming language..."}'

2. Query the knowledge base:

curl -X POST "http://localhost:8000/knowledge/query" \
     -H "Content-Type: application/json" \
     -d '{"question": "Tell me about Python"}'

3. Stream query response:

curl -X POST "http://localhost:8000/knowledge/query/stream" \
     -H "Content-Type: application/json" \
     -d '{"question": "Tell me about Python"}'