Journal AI Backend

A FastAPI-based backend service that provides AI-enhanced journaling capabilities with semantic search, summarization, and interactive chat features.

⚡ Tech Stack

• FastAPI - High-performance Python backend
• Google Gemini LLM - AI-driven journal insights
• Qdrant + SentenceTransformers - Vector search for context retrieval
• Versioning + Testing - Scalable API

🚀 Features

• Journal entry creation and management
• Semantic search using Qdrant vector database
• AI-powered journal summarization using Google's Gemini Pro
• Interactive chat with your journal entries
• Firebase authentication
• Structured error handling and logging
• API versioning

🛠 Tech Stack

• Framework: FastAPI
• Authentication: Firebase Admin SDK
• Vector Database: Qdrant
• AI Models:
  • Google Gemini 1.5 Pro (for chat and summarization)
  • Sentence Transformer (all-MiniLM-L6-v2 for embeddings)
• Logging: Custom colored logging with rotation
• Validation: Pydantic

📁 Project Structure

src/
├── api/
│   └── v1/
│       ├── endpoints/
│       │   └── journals.py
│       └── router.py
├── core/
│   ├── config.py
│   ├── firebase.py
│   ├── logger.py
│   └── prompt_templates.py
├── models/
│   ├── journal.py
│   └── response.py
├── services/
│   ├── gemini_service.py
│   └── qdrant_service.py
├── utils/
│   └── journal_validator.py
└── main.py

⚙️ Environment Variables

Create a .env file with:

QDRANT_URL=http://localhost:6333
FIREBASE_CREDENTIALS_BASE64=<base64_encoded_firebase_credentials>
GEMINI_API_KEY=<your_gemini_api_key>

🚀 Getting Started

1. Clone the repository

git clone https://github.com/Badar25/Journal-backend

2. Install dependencies

pip install -r requirements.txt

3. Start Qdrant (using Docker)

docker run -p 6333:6333 qdrant/qdrant

4. Run the application

uvicorn src.main:app --reload --host 0.0.0.0 --port 8000

📌 API Endpoints

Authentication

All endpoints require Firebase authentication token in the header:

Authorization: Bearer <firebase_token>

Journals

Create Journal

• POST /v1/journals/
• Body:

{
    "title": "string",
    "content": "string"
}

Update Journal

• PUT /v1/journals/{journal_id}
• Body:

{
    "title": "string (optional)",
    "content": "string (optional)"
}

Get Journals

• GET /v1/journals/
• Query Params: days (optional, int)

Get Summary

• GET /v1/journals/summary
• Query Params: days (optional, int, default=7)

Search Journals

• GET /v1/journals/search
• Query Params: query (required), limit (optional, default=3)

🔒 Validation Rules

Journal Content

• Title: Max 200 characters
• Content: Max 999 words
• At least one field (title or content) must be provided

⚠️ Error Codes

Code	Description
TOKEN_EXPIRED	Authentication token has expired
TOKEN_REVOKED	Authentication token has been revoked
TOKEN_INVALID	Invalid authentication token
AUTH_ERROR	General authentication failure
EMPTY_FIELDS	Required fields are empty
TITLE_TOO_LONG	Title exceeds 200 characters
CONTENT_TOO_LONG	Content exceeds 999 words
MISSING_FIELDS	Required fields are missing
JOURNAL_NOT_FOUND	Requested journal doesn't exist
UNAUTHORIZED	Not authorized to access the journal
GEMINI_ERROR	AI processing error
SEARCH_ERROR	Vector search failed
INITIALIZATION_ERROR	Service initialization failed

📊 Response Format

All API responses follow this structure:

{
    "success": boolean,
    "message": string,
    "data": object | null,
    "error": string | null
}

🧪 Testing

Run tests using pytest:

pytest tests -v