Replace Ollama with llama-server (OpenAI-compatible API)

- Update llm.py to use OpenAI client with custom base_url for llama-server
- Update agents.py to use ChatOpenAI instead of ChatOllama
- Remove unused ollama imports from main.py, chunker.py, query.py
- Add LLAMA_SERVER_URL and LLAMA_MODEL_NAME env vars
- Remove ollama and langchain-ollama dependencies

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.env.example

@@ -14,12 +14,15 @@ JWT_SECRET_KEY=your-secret-key-here
 PAPERLESS_TOKEN=your-paperless-token
 BASE_URL=192.168.1.5:8000
 
-# Ollama Configuration
+# llama-server Configuration (OpenAI-compatible API)
-OLLAMA_URL=http://192.168.1.14:11434
+# If set, uses llama-server as the primary LLM backend with OpenAI as fallback
-OLLAMA_HOST=http://192.168.1.14:11434
+LLAMA_SERVER_URL=http://192.168.1.213:8080/v1
+LLAMA_MODEL_NAME=llama-3.1-8b-instruct
 
 # ChromaDB Configuration
-CHROMADB_PATH=/path/to/chromadb
+# For Docker: This is automatically set to /app/data/chromadb
+# For local development: Set to a local directory path
+CHROMADB_PATH=./data/chromadb
 
 # OpenAI Configuration
 OPENAI_API_KEY=your-openai-api-key

.gitignore

@@ -14,5 +14,7 @@ wheels/
 
 # Database files
 chromadb/
+chromadb_openai/
+chroma_db/
 database/
 *.db

.pre-commit-config.yaml

@@ -0,0 +1,6 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.2
+    hooks:
+      - id: ruff # Linter
+      - id: ruff-format # Formatter

CLAUDE.md

@@ -0,0 +1,109 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+SimbaRAG is a RAG (Retrieval-Augmented Generation) conversational AI system for querying information about Simba (a cat). It ingests documents from Paperless-NGX, stores embeddings in ChromaDB, and uses LLMs (Ollama or OpenAI) to answer questions.
+
+## Commands
+
+### Development
+
+```bash
+# Start dev environment with hot reload
+docker compose -f docker-compose.dev.yml up --build
+
+# View logs
+docker compose -f docker-compose.dev.yml logs -f raggr
+```
+
+### Database Migrations (Aerich/Tortoise ORM)
+
+```bash
+# Generate migration (must run in Docker with DB access)
+docker compose -f docker-compose.dev.yml exec raggr aerich migrate --name describe_change
+
+# Apply migrations (auto-runs on startup, manual if needed)
+docker compose -f docker-compose.dev.yml exec raggr aerich upgrade
+
+# View migration history
+docker compose exec raggr aerich history
+```
+
+### Frontend
+
+```bash
+cd raggr-frontend
+yarn install
+yarn build      # Production build
+yarn dev        # Dev server (rarely needed, backend serves frontend)
+```
+
+### Production
+
+```bash
+docker compose build raggr
+docker compose up -d
+```
+
+## Architecture
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                     Docker Compose                          │
+├─────────────────────────────────────────────────────────────┤
+│  raggr (port 8080)              │  postgres (port 5432)    │
+│  ├── Quart backend              │  PostgreSQL 16           │
+│  ├── React frontend (served)    │                          │
+│  └── ChromaDB (volume)          │                          │
+└─────────────────────────────────────────────────────────────┘
+```
+
+**Backend** (root directory):
+- `app.py` - Quart application entry, serves API and static frontend
+- `main.py` - RAG logic, document indexing, LLM interaction, LangChain agent
+- `llm.py` - LLM client with Ollama primary, OpenAI fallback
+- `aerich_config.py` - Database migration configuration
+- `blueprints/` - API routes organized as Quart blueprints
+  - `users/` - OIDC auth, JWT tokens, RBAC with LDAP groups
+  - `conversation/` - Chat conversations and message history
+  - `rag/` - Document indexing endpoints (admin-only)
+- `config/` - Configuration modules
+  - `oidc_config.py` - OIDC authentication configuration
+- `utils/` - Reusable utilities
+  - `chunker.py` - Document chunking for embeddings
+  - `cleaner.py` - PDF cleaning and summarization
+  - `image_process.py` - Image description with LLM
+  - `request.py` - Paperless-NGX API client
+- `scripts/` - Administrative and utility scripts
+  - `add_user.py` - Create users manually
+  - `user_message_stats.py` - User message statistics
+  - `manage_vectorstore.py` - Vector store management CLI
+  - `inspect_vector_store.py` - Inspect ChromaDB contents
+  - `query.py` - Query generation utilities
+- `migrations/` - Database migration files
+
+**Frontend** (`raggr-frontend/`):
+- React 19 with Rsbuild bundler
+- Tailwind CSS for styling
+- Built to `dist/`, served by backend at `/`
+
+**Auth Flow**: LLDAP → Authelia (OIDC) → Backend JWT → Frontend localStorage
+
+## Key Patterns
+
+- All endpoints are async (`async def`)
+- Use `@jwt_refresh_token_required` for authenticated endpoints
+- Use `@admin_required` for admin-only endpoints (checks `lldap_admin` group)
+- Tortoise ORM models in `blueprints/*/models.py`
+- Frontend API services in `raggr-frontend/src/api/`
+
+## Environment Variables
+
+See `.env.example`. Key ones:
+- `DATABASE_URL` - PostgreSQL connection
+- `OIDC_*` - Authelia OIDC configuration
+- `OLLAMA_URL` - Local LLM server
+- `OPENAI_API_KEY` - Fallback LLM
+- `PAPERLESS_TOKEN` / `BASE_URL` - Document source

DEV-README.md

@@ -1,110 +0,0 @@
-# Development Environment Setup
-
-This guide explains how to run the application in development mode with hot reload enabled.
-
-## Quick Start
-
-### Development Mode (Hot Reload)
-
-```bash
-# Start all services in development mode
-docker-compose -f docker-compose.dev.yml up --build
-
-# Or run in detached mode
-docker-compose -f docker-compose.dev.yml up -d --build
-```
-
-### Production Mode
-
-```bash
-# Start production services
-docker-compose up --build
-```
-
-## What's Different in Dev Mode?
-
-### Backend (Quart/Flask)
-- **Hot Reload**: Python code changes are automatically detected and the server restarts
-- **Source Mounted**: Your local `services/raggr` directory is mounted as a volume
-- **Debug Mode**: Flask runs with `debug=True` for better error messages
-- **Environment**: `FLASK_ENV=development` and `PYTHONUNBUFFERED=1` for immediate log output
-
-### Frontend (React + rsbuild)
-- **Auto Rebuild**: Frontend automatically rebuilds when files change
-- **Watch Mode**: rsbuild runs in watch mode, rebuilding to `dist/` on save
-- **Source Mounted**: Your local `services/raggr/raggr-frontend` directory is mounted as a volume
-- **Served by Backend**: Built files are served by the backend, no separate dev server
-
-## Ports
-
-- **Application**: 8080 (accessible at `http://localhost:8080` or `http://YOUR_IP:8080`)
-
-The backend serves both the API and the auto-rebuilt frontend, making it accessible from other machines on your network.
-
-## Useful Commands
-
-```bash
-# View logs
-docker-compose -f docker-compose.dev.yml logs -f
-
-# View logs for specific service
-docker-compose -f docker-compose.dev.yml logs -f raggr-backend
-docker-compose -f docker-compose.dev.yml logs -f raggr-frontend
-
-# Rebuild after dependency changes
-docker-compose -f docker-compose.dev.yml up --build
-
-# Stop all services
-docker-compose -f docker-compose.dev.yml down
-
-# Stop and remove volumes (fresh start)
-docker-compose -f docker-compose.dev.yml down -v
-```
-
-## Making Changes
-
-### Backend Changes
-1. Edit any Python file in `services/raggr/`
-2. Save the file
-3. The Quart server will automatically restart
-4. Check logs to confirm reload
-
-### Frontend Changes
-1. Edit any file in `services/raggr/raggr-frontend/src/`
-2. Save the file
-3. The browser will automatically refresh (Hot Module Replacement)
-4. No need to rebuild
-
-### Dependency Changes
-
-**Backend** (pyproject.toml):
-```bash
-# Rebuild the backend service
-docker-compose -f docker-compose.dev.yml up --build raggr-backend
-```
-
-**Frontend** (package.json):
-```bash
-# Rebuild the frontend service
-docker-compose -f docker-compose.dev.yml up --build raggr-frontend
-```
-
-## Troubleshooting
-
-### Port Already in Use
-If you see port binding errors, make sure no other services are running on ports 8080 or 3000.
-
-### Changes Not Reflected
-1. Check if the file is properly mounted (check docker-compose.dev.yml volumes)
-2. Verify the file isn't in an excluded directory (node_modules, __pycache__)
-3. Check container logs for errors
-
-### Frontend Not Connecting to Backend
-Make sure your frontend API calls point to the correct backend URL. If accessing from the same machine, use `http://localhost:8080`. If accessing from another device on the network, use `http://YOUR_IP:8080`.
-
-## Notes
-
-- Both services bind to `0.0.0.0` and expose ports, making them accessible on your network
-- Node modules and Python cache are excluded from volume mounts to use container versions
-- Database and ChromaDB data persist in Docker volumes across restarts
-- Access the app from any device on your network using your host machine's IP address

Dockerfile

@@ -25,6 +25,9 @@ RUN uv pip install --system -e .
 COPY *.py ./
 COPY blueprints ./blueprints
 COPY migrations ./migrations
+COPY utils ./utils
+COPY config ./config
+COPY scripts ./scripts
 COPY startup.sh ./
 RUN chmod +x startup.sh
 

Dockerfile.dev

@@ -0,0 +1,53 @@
+FROM python:3.13-slim
+
+WORKDIR /app
+
+# Install system dependencies, Node.js, uv, and yarn
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
+    && apt-get install -y nodejs \
+    && npm install -g yarn \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Add uv to PATH
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy dependency files
+COPY pyproject.toml ./
+
+# Install Python dependencies using uv
+RUN uv pip install --system -e .
+
+# Copy frontend package files and install dependencies
+COPY raggr-frontend/package.json raggr-frontend/yarn.lock* raggr-frontend/
+WORKDIR /app/raggr-frontend
+RUN yarn install
+
+# Copy application source code
+WORKDIR /app
+COPY . .
+
+# Build frontend
+WORKDIR /app/raggr-frontend
+RUN yarn build
+
+# Create ChromaDB and database directories
+WORKDIR /app
+RUN mkdir -p /app/chromadb /app/database
+
+# Make startup script executable
+RUN chmod +x /app/startup-dev.sh
+
+# Set environment variables
+ENV PYTHONPATH=/app
+ENV CHROMADB_PATH=/app/chromadb
+ENV PYTHONUNBUFFERED=1
+
+# Expose port
+EXPOSE 8080
+
+# Default command
+CMD ["/app/startup-dev.sh"]

README.md

@@ -1,7 +1,370 @@
-# simbarag
+# SimbaRAG 🐱
 
-**Goal:** Learn how retrieval-augmented generation works and also create a neat little tool to ask about Simba's health.
+A Retrieval-Augmented Generation (RAG) conversational AI system for querying information about Simba the cat. Built with LangChain, ChromaDB, and modern web technologies.
 
-**Current objectives:**
+## Features
 
-- [ ] Successfully use RAG to ask a question about existing information (e.g. how many teeth has Simba had extracted)
+- 🤖 **Intelligent Conversations** - LangChain-powered agent with tool use and memory
+- 📚 **Document Retrieval** - RAG system using ChromaDB vector store
+- 🔍 **Web Search** - Integrated Tavily API for real-time web searches
+- 🔐 **OIDC Authentication** - Secure auth via Authelia with LDAP group support
+- 💬 **Multi-Conversation** - Manage multiple conversation threads per user
+- 🎨 **Modern UI** - React 19 frontend with Tailwind CSS
+- 🐳 **Docker Ready** - Containerized deployment with Docker Compose
+
+## System Architecture
+
+```mermaid
+graph TB
+    subgraph "Client Layer"
+        Browser[Web Browser]
+    end
+
+    subgraph "Frontend - React"
+        UI[React UI<br/>Tailwind CSS]
+        Auth[Auth Service]
+        API[API Client]
+    end
+
+    subgraph "Backend - Quart/Python"
+        App[Quart App<br/>app.py]
+
+        subgraph "Blueprints"
+            Users[Users Blueprint<br/>OIDC + JWT]
+            Conv[Conversation Blueprint<br/>Chat Management]
+            RAG[RAG Blueprint<br/>Document Indexing]
+        end
+
+        Agent[LangChain Agent<br/>main.py]
+        LLM[LLM Client<br/>llm.py]
+    end
+
+    subgraph "Tools & Utilities"
+        Search[Simba Search Tool]
+        Web[Web Search Tool<br/>Tavily]
+    end
+
+    subgraph "Data Layer"
+        Postgres[(PostgreSQL<br/>Users & Conversations)]
+        Chroma[(ChromaDB<br/>Vector Store)]
+    end
+
+    subgraph "External Services"
+        Authelia[Authelia<br/>OIDC Provider]
+        LLDAP[LLDAP<br/>User Directory]
+        Ollama[Ollama<br/>Local LLM]
+        OpenAI[OpenAI<br/>Fallback LLM]
+        Paperless[Paperless-NGX<br/>Documents]
+        TavilyAPI[Tavily API<br/>Web Search]
+    end
+
+    Browser --> UI
+    UI --> Auth
+    UI --> API
+    API --> App
+
+    App --> Users
+    App --> Conv
+    App --> RAG
+
+    Conv --> Agent
+    Agent --> Search
+    Agent --> Web
+    Agent --> LLM
+
+    Search --> Chroma
+    Web --> TavilyAPI
+    RAG --> Chroma
+    RAG --> Paperless
+
+    Users --> Postgres
+    Conv --> Postgres
+
+    Users --> Authelia
+    Authelia --> LLDAP
+
+    LLM --> Ollama
+    LLM -.Fallback.-> OpenAI
+
+    style Browser fill:#e1f5ff
+    style UI fill:#fff3cd
+    style App fill:#d4edda
+    style Agent fill:#d4edda
+    style Postgres fill:#f8d7da
+    style Chroma fill:#f8d7da
+    style Ollama fill:#e2e3e5
+    style OpenAI fill:#e2e3e5
+```
+
+## Quick Start
+
+### Prerequisites
+
+- Docker & Docker Compose
+- PostgreSQL (or use Docker)
+- Ollama (optional, for local LLM)
+- Paperless-NGX instance (for document source)
+
+### Installation
+
+1. **Clone the repository**
+
+```bash
+git clone https://github.com/yourusername/simbarag.git
+cd simbarag
+```
+
+2. **Configure environment variables**
+
+```bash
+cp .env.example .env
+# Edit .env with your configuration
+```
+
+3. **Start the services**
+
+```bash
+# Development (local PostgreSQL only)
+docker compose -f docker-compose.dev.yml up -d
+
+# Or full Docker deployment
+docker compose up -d
+```
+
+4. **Access the application**
+
+Open `http://localhost:8080` in your browser.
+
+## Development
+
+### Local Development Setup
+
+```bash
+# 1. Start PostgreSQL
+docker compose -f docker-compose.dev.yml up -d
+
+# 2. Set environment variables
+export DATABASE_URL="postgres://raggr:raggr_dev_password@localhost:5432/raggr"
+export CHROMADB_PATH="./chromadb"
+export $(grep -v '^#' .env | xargs)
+
+# 3. Install dependencies
+pip install -r requirements.txt
+cd raggr-frontend && yarn install && yarn build && cd ..
+
+# 4. Run migrations
+aerich upgrade
+
+# 5. Start the server
+python app.py
+```
+
+See [docs/development.md](docs/development.md) for detailed development guide.
+
+## Project Structure
+
+```
+simbarag/
+├── app.py                 # Quart application entry point
+├── main.py                # RAG logic & LangChain agent
+├── llm.py                 # LLM client with Ollama/OpenAI
+├── aerich_config.py       # Database migration configuration
+│
+├── blueprints/            # API route blueprints
+│   ├── users/            # Authentication & authorization
+│   ├── conversation/     # Chat conversations
+│   └── rag/              # Document indexing
+│
+├── config/               # Configuration modules
+│   └── oidc_config.py   # OIDC authentication settings
+│
+├── utils/                # Reusable utilities
+│   ├── chunker.py       # Document chunking for embeddings
+│   ├── cleaner.py       # PDF cleaning and summarization
+│   ├── image_process.py # Image description with LLM
+│   └── request.py       # Paperless-NGX API client
+│
+├── scripts/              # Administrative scripts
+│   ├── add_user.py
+│   ├── user_message_stats.py
+│   ├── manage_vectorstore.py
+│   └── inspect_vector_store.py
+│
+├── raggr-frontend/       # React frontend
+│   └── src/
+│
+├── migrations/           # Database migrations
+│
+├── docs/                 # Documentation
+│   ├── index.md         # Documentation hub
+│   ├── development.md   # Development guide
+│   ├── deployment.md    # Deployment & migrations
+│   ├── VECTORSTORE.md   # Vector store management
+│   ├── MIGRATIONS.md    # Migration reference
+│   └── authentication.md # Authentication setup
+│
+├── docker-compose.yml        # Production compose
+├── docker-compose.dev.yml    # Development compose
+├── Dockerfile                # Production Dockerfile
+├── Dockerfile.dev            # Development Dockerfile
+├── CLAUDE.md                 # AI assistant instructions
+└── README.md                 # This file
+```
+
+## Key Technologies
+
+### Backend
+- **Quart** - Async Python web framework
+- **LangChain** - Agent framework with tool use
+- **Tortoise ORM** - Async ORM for PostgreSQL
+- **Aerich** - Database migration tool
+- **ChromaDB** - Vector database for embeddings
+- **OpenAI** - Embeddings & LLM (fallback)
+- **Ollama** - Local LLM (primary)
+
+### Frontend
+- **React 19** - UI framework
+- **Rsbuild** - Fast bundler
+- **Tailwind CSS** - Utility-first styling
+- **Axios** - HTTP client
+
+### Authentication
+- **Authelia** - OIDC provider
+- **LLDAP** - Lightweight LDAP server
+- **JWT** - Token-based auth
+
+## API Endpoints
+
+### Authentication
+- `GET /api/user/oidc/login` - Initiate OIDC login
+- `GET /api/user/oidc/callback` - OIDC callback handler
+- `POST /api/user/refresh` - Refresh JWT token
+
+### Conversations
+- `POST /api/conversation/` - Create conversation
+- `GET /api/conversation/` - List conversations
+- `GET /api/conversation/<id>` - Get conversation with messages
+- `POST /api/conversation/query` - Send message and get response
+
+### RAG (Admin Only)
+- `GET /api/rag/stats` - Vector store statistics
+- `POST /api/rag/index` - Index new documents
+- `POST /api/rag/reindex` - Clear and reindex all
+
+## Configuration
+
+### Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `DATABASE_URL` | PostgreSQL connection string | `postgres://...` |
+| `CHROMADB_PATH` | ChromaDB storage path | `./chromadb` |
+| `OLLAMA_URL` | Ollama server URL | `http://localhost:11434` |
+| `OPENAI_API_KEY` | OpenAI API key | - |
+| `PAPERLESS_TOKEN` | Paperless-NGX API token | - |
+| `BASE_URL` | Paperless-NGX base URL | - |
+| `OIDC_ISSUER` | OIDC provider URL | - |
+| `OIDC_CLIENT_ID` | OIDC client ID | - |
+| `OIDC_CLIENT_SECRET` | OIDC client secret | - |
+| `JWT_SECRET_KEY` | JWT signing key | - |
+| `TAVILY_KEY` | Tavily web search API key | - |
+
+See `.env.example` for full list.
+
+## Scripts
+
+### User Management
+```bash
+# Add a new user
+python scripts/add_user.py
+
+# View message statistics
+python scripts/user_message_stats.py
+```
+
+### Vector Store Management
+```bash
+# Show vector store statistics
+python scripts/manage_vectorstore.py stats
+
+# Index new documents from Paperless
+python scripts/manage_vectorstore.py index
+
+# Clear and reindex everything
+python scripts/manage_vectorstore.py reindex
+
+# Inspect vector store contents
+python scripts/inspect_vector_store.py
+```
+
+See [docs/vectorstore.md](docs/vectorstore.md) for details.
+
+## Database Migrations
+
+```bash
+# Generate a new migration
+aerich migrate --name "describe_your_changes"
+
+# Apply pending migrations
+aerich upgrade
+
+# View migration history
+aerich history
+
+# Rollback last migration
+aerich downgrade
+```
+
+See [docs/deployment.md](docs/deployment.md) for detailed migration workflows.
+
+## LangChain Agent
+
+The conversational agent has access to two tools:
+
+1. **simba_search** - Query the vector store for Simba's documents
+   - Used for: Medical records, veterinary history, factual information
+
+2. **web_search** - Search the web via Tavily API
+   - Used for: Recent events, external knowledge, general questions
+
+The agent automatically selects the appropriate tool based on the user's query.
+
+## Authentication Flow
+
+```
+User → Authelia (OIDC) → Backend (JWT) → Frontend (localStorage)
+                ↓
+              LLDAP
+```
+
+1. User clicks "Login"
+2. Frontend redirects to Authelia
+3. User authenticates via Authelia (backed by LLDAP)
+4. Authelia redirects back with authorization code
+5. Backend exchanges code for OIDC tokens
+6. Backend issues JWT tokens
+7. Frontend stores tokens in localStorage
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Run tests and linting
+5. Submit a pull request
+
+## Documentation
+
+- [Development Guide](docs/development.md) - Setup and development workflow
+- [Deployment Guide](docs/deployment.md) - Deployment and migrations
+- [Vector Store Guide](docs/vectorstore.md) - Managing the vector database
+- [Authentication Guide](docs/authentication.md) - OIDC and LDAP setup
+
+## License
+
+[Your License Here]
+
+## Acknowledgments
+
+- Built for Simba, the most important cat in the world 🐱
+- Powered by LangChain, ChromaDB, and the open-source community

app.py

@@ -6,6 +6,7 @@ from tortoise.contrib.quart import register_tortoise
 
 import blueprints.conversation
 import blueprints.conversation.logic
+import blueprints.rag
 import blueprints.users
 import blueprints.users.models
 from main import consult_simba_oracle
@@ -22,12 +23,12 @@ jwt = JWTManager(app)
 # Register blueprints
 app.register_blueprint(blueprints.users.user_blueprint)
 app.register_blueprint(blueprints.conversation.conversation_blueprint)
+app.register_blueprint(blueprints.rag.rag_blueprint)
 
 
 # Database configuration with environment variable support
 DATABASE_URL = os.getenv(
-    "DATABASE_URL",
+    "DATABASE_URL", "postgres://raggr:raggr_dev_password@localhost:5432/raggr"
-    "postgres://raggr:raggr_dev_password@localhost:5432/raggr"
 )
 
 TORTOISE_CONFIG = {
@@ -123,10 +124,17 @@ async def get_messages():
             }
         )
 
+    name = conversation.name
+    if len(messages) > 8:
+        name = await blueprints.conversation.logic.rename_conversation(
+            user=user,
+            conversation=conversation,
+        )
+
     return jsonify(
         {
             "id": str(conversation.id),
-            "name": conversation.name,
+            "name": name,
             "messages": messages,
             "created_at": conversation.created_at.isoformat(),
             "updated_at": conversation.updated_at.isoformat(),

blueprints/conversation/__init__.py

@@ -0,0 +1,172 @@
+import datetime
+
+from quart import Blueprint, jsonify, request
+from quart_jwt_extended import (
+    get_jwt_identity,
+    jwt_refresh_token_required,
+)
+
+import blueprints.users.models
+
+from .agents import main_agent
+from .logic import (
+    add_message_to_conversation,
+    get_conversation_by_id,
+    rename_conversation,
+)
+from .models import (
+    Conversation,
+    PydConversation,
+    PydListConversation,
+)
+
+conversation_blueprint = Blueprint(
+    "conversation_api", __name__, url_prefix="/api/conversation"
+)
+
+
+@conversation_blueprint.post("/query")
+@jwt_refresh_token_required
+async def query():
+    current_user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=current_user_uuid)
+    data = await request.get_json()
+    query = data.get("query")
+    conversation_id = data.get("conversation_id")
+    conversation = await get_conversation_by_id(conversation_id)
+    await conversation.fetch_related("messages")
+    await add_message_to_conversation(
+        conversation=conversation,
+        message=query,
+        speaker="user",
+        user=user,
+    )
+
+    # Build conversation history from recent messages (last 10 for context)
+    recent_messages = (
+        conversation.messages[-10:]
+        if len(conversation.messages) > 10
+        else conversation.messages
+    )
+
+    messages_payload = [
+        {
+            "role": "system",
+            "content": """You are a helpful cat assistant named Simba that understands veterinary terms. When there are questions to you specifically, they are referring to Simba the cat. Answer the user in as if you were a cat named Simba. Don't act too catlike. Be assertive.
+
+SIMBA FACTS (as of January 2026):
+- Name: Simba
+- Species: Feline (Domestic Short Hair / American Short Hair)
+- Sex: Male, Neutered
+- Date of Birth: August 8, 2016 (approximately 9 years 5 months old)
+- Color: Orange
+- Current Weight: 16 lbs (as of 1/8/2026)
+- Owner: Ryan Chen
+- Location: Long Island City, NY
+- Veterinarian: Court Square Animal Hospital
+
+Medical Conditions:
+- Hypertrophic Cardiomyopathy (HCM): Diagnosed 12/11/2025. Concentric left ventricular hypertrophy with no left atrial dilation. Grade II-III/VI systolic heart murmur. No cardiac medications currently needed. Must avoid Domitor, acepromazine, and ketamine during anesthesia.
+- Dental Issues: Prior extraction of teeth 307 and 407 due to resorption. Tooth 107 extracted on 1/8/2026. Early resorption lesions present on teeth 207, 309, and 409.
+
+Recent Medical Events:
+- 1/8/2026: Dental cleaning and tooth 107 extraction. Prescribed Onsior for 3 days. Oravet sealant applied.
+- 12/11/2025: Echocardiogram confirming HCM diagnosis. Pre-op bloodwork was normal.
+- 12/1/2025: Visited for decreased appetite/nausea. Received subcutaneous fluids and Cerenia.
+
+Diet & Lifestyle:
+- Diet: Hill's I/D wet and dry food
+- Supplements: Plaque Off
+- Indoor only cat, only pet in the household
+
+Upcoming Appointments:
+- Rabies Vaccine: Due 2/19/2026
+- Routine Examination: Due 6/1/2026
+- FVRCP-3yr Vaccine: Due 10/2/2026
+
+IMPORTANT: When users ask factual questions about Simba's health, medical history, veterinary visits, medications, weight, or any information that would be in documents, you MUST use the simba_search tool to retrieve accurate information before answering. Do not rely on general knowledge - always search the documents for factual questions.""",
+        }
+    ]
+
+    # Add recent conversation history
+    for msg in recent_messages[:-1]:  # Exclude the message we just added
+        role = "user" if msg.speaker == "user" else "assistant"
+        messages_payload.append({"role": role, "content": msg.text})
+
+    # Add current query
+    messages_payload.append({"role": "user", "content": query})
+
+    payload = {"messages": messages_payload}
+
+    response = await main_agent.ainvoke(payload)
+    message = response.get("messages", [])[-1].content
+    await add_message_to_conversation(
+        conversation=conversation,
+        message=message,
+        speaker="simba",
+        user=user,
+    )
+    return jsonify({"response": message})
+
+
+@conversation_blueprint.route("/<conversation_id>")
+@jwt_refresh_token_required
+async def get_conversation(conversation_id: str):
+    conversation = await Conversation.get(id=conversation_id)
+    current_user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=current_user_uuid)
+    await conversation.fetch_related("messages")
+
+    # Manually serialize the conversation with messages
+    messages = []
+    for msg in conversation.messages:
+        messages.append(
+            {
+                "id": str(msg.id),
+                "text": msg.text,
+                "speaker": msg.speaker.value,
+                "created_at": msg.created_at.isoformat(),
+            }
+        )
+    name = conversation.name
+    if len(messages) > 8 and "datetime" in name.lower():
+        name = await rename_conversation(
+            user=user,
+            conversation=conversation,
+        )
+        print(name)
+
+    return jsonify(
+        {
+            "id": str(conversation.id),
+            "name": name,
+            "messages": messages,
+            "created_at": conversation.created_at.isoformat(),
+            "updated_at": conversation.updated_at.isoformat(),
+        }
+    )
+
+
+@conversation_blueprint.post("/")
+@jwt_refresh_token_required
+async def create_conversation():
+    user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=user_uuid)
+    conversation = await Conversation.create(
+        name=f"{user.username} {datetime.datetime.now().timestamp}",
+        user=user,
+    )
+
+    serialized_conversation = await PydConversation.from_tortoise_orm(conversation)
+    return jsonify(serialized_conversation.model_dump())
+
+
+@conversation_blueprint.get("/")
+@jwt_refresh_token_required
+async def get_all_conversations():
+    user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=user_uuid)
+    conversations = Conversation.filter(user=user)
+    serialized_conversations = await PydListConversation.from_queryset(conversations)
+
+    return jsonify(serialized_conversations.model_dump())

blueprints/conversation/agents.py

@@ -0,0 +1,88 @@
+import os
+from typing import cast
+
+from langchain.agents import create_agent
+from langchain.chat_models import BaseChatModel
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+from tavily import AsyncTavilyClient
+
+from blueprints.rag.logic import query_vector_store
+
+# Configure LLM with llama-server or OpenAI fallback
+llama_url = os.getenv("LLAMA_SERVER_URL")
+if llama_url:
+    llama_chat = ChatOpenAI(
+        base_url=llama_url,
+        api_key="not-needed",
+        model=os.getenv("LLAMA_MODEL_NAME", "llama-3.1-8b-instruct"),
+    )
+else:
+    llama_chat = None
+
+openai_fallback = ChatOpenAI(model="gpt-5-mini")
+model_with_fallback = cast(
+    BaseChatModel,
+    llama_chat.with_fallbacks([openai_fallback]) if llama_chat else openai_fallback,
+)
+client = AsyncTavilyClient(os.getenv("TAVILY_KEY"), "")
+
+
+@tool
+async def web_search(query: str) -> str:
+    """Search the web for current information using Tavily.
+
+    Use this tool when you need to:
+    - Find current information not in the knowledge base
+    - Look up recent events, news, or updates
+    - Verify facts or get additional context
+    - Search for information outside of Simba's documents
+
+    Args:
+        query: The search query to look up on the web
+
+    Returns:
+        Search results from the web with titles, content, and source URLs
+    """
+    response = await client.search(query=query, search_depth="basic")
+    results = response.get("results", [])
+
+    if not results:
+        return "No results found for the query."
+
+    formatted = "\n\n".join(
+        [
+            f"**{result['title']}**\n{result['content']}\nSource: {result['url']}"
+            for result in results[:5]
+        ]
+    )
+    return formatted
+
+
+@tool(response_format="content_and_artifact")
+async def simba_search(query: str):
+    """Search through Simba's medical records, veterinary documents, and personal information.
+
+    Use this tool whenever the user asks questions about:
+    - Simba's health history, medical records, or veterinary visits
+    - Medications, treatments, or diagnoses
+    - Weight, diet, or physical characteristics over time
+    - Veterinary recommendations or advice
+    - Ryan's (the owner's) information related to Simba
+    - Any factual information that would be found in documents
+
+    Args:
+        query: The user's question or information need about Simba
+
+    Returns:
+        Relevant information from Simba's documents
+    """
+    print(f"[SIMBA SEARCH] Tool called with query: {query}")
+    serialized, docs = await query_vector_store(query=query)
+    print(f"[SIMBA SEARCH] Found {len(docs)} documents")
+    print(f"[SIMBA SEARCH] Serialized result length: {len(serialized)}")
+    print(f"[SIMBA SEARCH] First 200 chars: {serialized[:200]}")
+    return serialized, docs
+
+
+main_agent = create_agent(model=model_with_fallback, tools=[simba_search, web_search])

blueprints/conversation/logic.py

@@ -1,9 +1,10 @@
 import tortoise.exceptions
-
+from langchain_openai import ChatOpenAI
-from .models import Conversation, ConversationMessage
 
 import blueprints.users.models
 
+from .models import Conversation, ConversationMessage, RenameConversationOutputSchema
+
 
 async def create_conversation(name: str = "") -> Conversation:
     conversation = await Conversation.create(name=name)
@@ -58,3 +59,22 @@ async def get_conversation_transcript(
         messages.append(f"{message.speaker} at {message.created_at}: {message.text}")
 
     return "\n".join(messages)
+
+
+async def rename_conversation(
+    user: blueprints.users.models.User,
+    conversation: Conversation,
+) -> str:
+    messages: str = await get_conversation_transcript(
+        user=user, conversation=conversation
+    )
+
+    llm = ChatOpenAI(model="gpt-4o-mini")
+    structured_llm = llm.with_structured_output(RenameConversationOutputSchema)
+
+    prompt = f"Summarize the following conversation into a sassy one-liner title:\n\n{messages}"
+    response = structured_llm.invoke(prompt)
+    new_name: str = response.get("title", "")
+    conversation.name = new_name
+    await conversation.save()
+    return new_name

blueprints/conversation/models.py

@@ -1,11 +1,18 @@
 import enum
+from dataclasses import dataclass
 
-from tortoise.models import Model
 from tortoise import fields
 from tortoise.contrib.pydantic import (
-    pydantic_queryset_creator,
     pydantic_model_creator,
+    pydantic_queryset_creator,
 )
+from tortoise.models import Model
+
+
+@dataclass
+class RenameConversationOutputSchema:
+    title: str
+    justification: str
 
 
 class Speaker(enum.Enum):

blueprints/rag/__init__.py

@@ -0,0 +1,47 @@
+from quart import Blueprint, jsonify
+from quart_jwt_extended import jwt_refresh_token_required
+
+from .logic import get_vector_store_stats, index_documents, vector_store
+from blueprints.users.decorators import admin_required
+
+rag_blueprint = Blueprint("rag_api", __name__, url_prefix="/api/rag")
+
+
+@rag_blueprint.get("/stats")
+@jwt_refresh_token_required
+async def get_stats():
+    """Get vector store statistics."""
+    stats = get_vector_store_stats()
+    return jsonify(stats)
+
+
+@rag_blueprint.post("/index")
+@admin_required
+async def trigger_index():
+    """Trigger indexing of documents from Paperless-NGX. Admin only."""
+    try:
+        await index_documents()
+        stats = get_vector_store_stats()
+        return jsonify({"status": "success", "stats": stats})
+    except Exception as e:
+        return jsonify({"status": "error", "message": str(e)}), 500
+
+
+@rag_blueprint.post("/reindex")
+@admin_required
+async def trigger_reindex():
+    """Clear and reindex all documents. Admin only."""
+    try:
+        # Clear existing documents
+        collection = vector_store._collection
+        all_docs = collection.get()
+
+        if all_docs["ids"]:
+            collection.delete(ids=all_docs["ids"])
+
+        # Reindex
+        await index_documents()
+        stats = get_vector_store_stats()
+        return jsonify({"status": "success", "stats": stats})
+    except Exception as e:
+        return jsonify({"status": "error", "message": str(e)}), 500

blueprints/rag/fetchers.py

@@ -0,0 +1,75 @@
+import os
+import tempfile
+
+import httpx
+
+
+class PaperlessNGXService:
+    def __init__(self):
+        self.base_url = os.getenv("BASE_URL")
+        self.token = os.getenv("PAPERLESS_TOKEN")
+        self.url = f"http://{os.getenv('BASE_URL')}/api/documents/?tags__id=8"
+        self.headers = {"Authorization": f"Token {os.getenv('PAPERLESS_TOKEN')}"}
+
+    def get_data(self):
+        print(f"Getting data from: {self.url}")
+        r = httpx.get(self.url, headers=self.headers)
+        results = r.json()["results"]
+
+        nextLink = r.json().get("next")
+
+        while nextLink:
+            r = httpx.get(nextLink, headers=self.headers)
+            results += r.json()["results"]
+            nextLink = r.json().get("next")
+
+        return results
+
+    def get_doc_by_id(self, doc_id: int):
+        url = f"http://{os.getenv('BASE_URL')}/api/documents/{doc_id}/"
+        r = httpx.get(url, headers=self.headers)
+        return r.json()
+
+    def download_pdf_from_id(self, id: int) -> str:
+        download_url = f"http://{os.getenv('BASE_URL')}/api/documents/{id}/download/"
+        response = httpx.get(
+            download_url, headers=self.headers, follow_redirects=True, timeout=30
+        )
+        response.raise_for_status()
+        # Use a temporary file for the downloaded PDF
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+        temp_file.write(response.content)
+        temp_file.close()
+        temp_pdf_path = temp_file.name
+        pdf_to_process = temp_pdf_path
+        return pdf_to_process
+
+    def upload_cleaned_content(self, document_id, data):
+        PUTS_URL = f"http://{os.getenv('BASE_URL')}/api/documents/{document_id}/"
+        r = httpx.put(PUTS_URL, headers=self.headers, data=data)
+        r.raise_for_status()
+
+    def upload_description(self, description_filepath, file, title, exif_date: str):
+        POST_URL = f"http://{os.getenv('BASE_URL')}/api/documents/post_document/"
+        files = {"document": ("description_filepath", file, "application/txt")}
+        data = {
+            "title": title,
+            "create": exif_date,
+            "document_type": 3,
+            "tags": [7],
+        }
+
+        r = httpx.post(POST_URL, headers=self.headers, data=data, files=files)
+        r.raise_for_status()
+
+    def get_tags(self):
+        GET_URL = f"http://{os.getenv('BASE_URL')}/api/tags/"
+        r = httpx.get(GET_URL, headers=self.headers)
+        data = r.json()
+        return {tag["id"]: tag["name"] for tag in data["results"]}
+
+    def get_doctypes(self):
+        GET_URL = f"http://{os.getenv('BASE_URL')}/api/document_types/"
+        r = httpx.get(GET_URL, headers=self.headers)
+        data = r.json()
+        return {doctype["id"]: doctype["name"] for doctype in data["results"]}

blueprints/rag/logic.py

@@ -0,0 +1,101 @@
+import datetime
+import os
+
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+from langchain_openai import OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from .fetchers import PaperlessNGXService
+
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+vector_store = Chroma(
+    collection_name="simba_docs",
+    embedding_function=embeddings,
+    persist_directory=os.getenv("CHROMADB_PATH", ""),
+)
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,  # chunk size (characters)
+    chunk_overlap=200,  # chunk overlap (characters)
+    add_start_index=True,  # track index in original document
+)
+
+
+def date_to_epoch(date_str: str) -> float:
+    split_date = date_str.split("-")
+    date = datetime.datetime(
+        int(split_date[0]),
+        int(split_date[1]),
+        int(split_date[2]),
+        0,
+        0,
+        0,
+    )
+
+    return date.timestamp()
+
+
+async def fetch_documents_from_paperless_ngx() -> list[Document]:
+    ppngx = PaperlessNGXService()
+    data = ppngx.get_data()
+    doctypes = ppngx.get_doctypes()
+    documents = []
+    for doc in data:
+        metadata = {
+            "created_date": date_to_epoch(doc["created_date"]),
+            "filename": doc["original_file_name"],
+            "document_type": doctypes.get(doc["document_type"], ""),
+        }
+        documents.append(Document(page_content=doc["content"], metadata=metadata))
+
+    return documents
+
+
+async def index_documents():
+    documents = await fetch_documents_from_paperless_ngx()
+
+    splits = text_splitter.split_documents(documents)
+    await vector_store.aadd_documents(documents=splits)
+
+
+async def query_vector_store(query: str):
+    retrieved_docs = await vector_store.asimilarity_search(query, k=2)
+    serialized = "\n\n".join(
+        (f"Source: {doc.metadata}\nContent: {doc.page_content}")
+        for doc in retrieved_docs
+    )
+    return serialized, retrieved_docs
+
+
+def get_vector_store_stats():
+    """Get statistics about the vector store."""
+    collection = vector_store._collection
+    count = collection.count()
+    return {
+        "total_documents": count,
+        "collection_name": collection.name,
+    }
+
+
+def list_all_documents(limit: int = 10):
+    """List documents in the vector store with their metadata."""
+    collection = vector_store._collection
+    results = collection.get(limit=limit, include=["metadatas", "documents"])
+
+    documents = []
+    for i, doc_id in enumerate(results["ids"]):
+        documents.append(
+            {
+                "id": doc_id,
+                "metadata": results["metadatas"][i]
+                if results.get("metadatas")
+                else None,
+                "content_preview": results["documents"][i][:200]
+                if results.get("documents")
+                else None,
+            }
+        )
+
+    return documents

blueprints/users/__init__.py

@@ -7,7 +7,7 @@ from quart_jwt_extended import (
 )
 from .models import User
 from .oidc_service import OIDCUserService
-from oidc_config import oidc_config
+from config.oidc_config import oidc_config
 import secrets
 import httpx
 from urllib.parse import urlencode
@@ -60,7 +60,7 @@ async def oidc_login():
             "client_id": oidc_config.client_id,
             "response_type": "code",
             "redirect_uri": oidc_config.redirect_uri,
-            "scope": "openid email profile",
+            "scope": "openid email profile groups",
             "state": state,
             "code_challenge": code_challenge,
             "code_challenge_method": "S256",
@@ -115,7 +115,9 @@ async def oidc_callback():
         token_response = await client.post(token_endpoint, data=token_data)
 
         if token_response.status_code != 200:
-            return jsonify({"error": f"Failed to exchange code for token: {token_response.text}"}), 400
+            return jsonify(
+                {"error": f"Failed to exchange code for token: {token_response.text}"}
+            ), 400
 
         tokens = token_response.json()
 
@@ -141,7 +143,13 @@ async def oidc_callback():
     return jsonify(
         access_token=access_token,
         refresh_token=refresh_token,
-        user={"id": str(user.id), "username": user.username, "email": user.email},
+        user={
+            "id": str(user.id),
+            "username": user.username,
+            "email": user.email,
+            "groups": user.ldap_groups,
+            "is_admin": user.is_admin(),
+        },
     )
 
 

blueprints/users/decorators.py

@@ -0,0 +1,26 @@
+"""
+Authentication decorators for role-based access control.
+"""
+
+from functools import wraps
+from quart import jsonify
+from quart_jwt_extended import jwt_refresh_token_required, get_jwt_identity
+from .models import User
+
+
+def admin_required(fn):
+    """
+    Decorator that requires the user to be an admin (member of lldap_admin group).
+    Must be used on async route handlers.
+    """
+
+    @wraps(fn)
+    @jwt_refresh_token_required
+    async def wrapper(*args, **kwargs):
+        user_id = get_jwt_identity()
+        user = await User.get_or_none(id=user_id)
+        if not user or not user.is_admin():
+            return jsonify({"error": "Admin access required"}), 403
+        return await fn(*args, **kwargs)
+
+    return wrapper

blueprints/users/models.py

@@ -12,8 +12,13 @@ class User(Model):
     email = fields.CharField(max_length=100, unique=True)
 
     # OIDC fields
-    oidc_subject = fields.CharField(max_length=255, unique=True, null=True, index=True)  # "sub" claim from OIDC
+    oidc_subject = fields.CharField(
-    auth_provider = fields.CharField(max_length=50, default="local")  # "local" or "oidc"
+        max_length=255, unique=True, null=True, index=True
+    )  # "sub" claim from OIDC
+    auth_provider = fields.CharField(
+        max_length=50, default="local"
+    )  # "local" or "oidc"
+    ldap_groups = fields.JSONField(default=[])  # LDAP groups from OIDC claims
 
     created_at = fields.DatetimeField(auto_now_add=True)
     updated_at = fields.DatetimeField(auto_now=True)
@@ -21,6 +26,14 @@ class User(Model):
     class Meta:
         table = "users"
 
+    def has_group(self, group: str) -> bool:
+        """Check if user belongs to a specific LDAP group."""
+        return group in (self.ldap_groups or [])
+
+    def is_admin(self) -> bool:
+        """Check if user is an admin (member of lldap_admin group)."""
+        return self.has_group("lldap_admin")
+
     def set_password(self, plain_password: str):
         self.password = bcrypt.hashpw(
             plain_password.encode("utf-8"),

blueprints/users/oidc_service.py

@@ -1,6 +1,7 @@
 """
 OIDC User Management Service
 """
+
 from typing import Dict, Any, Optional
 from uuid import uuid4
 from .models import User
@@ -31,10 +32,10 @@ class OIDCUserService:
             # Update user info from latest claims (optional)
             user.email = claims.get("email", user.email)
             user.username = (
-                claims.get("preferred_username")
+                claims.get("preferred_username") or claims.get("name") or user.username
-                or claims.get("name")
-                or user.username
             )
+            # Update LDAP groups from claims
+            user.ldap_groups = claims.get("groups", [])
             await user.save()
             return user
 
@@ -47,6 +48,7 @@ class OIDCUserService:
                 user.oidc_subject = oidc_subject
                 user.auth_provider = "oidc"
                 user.password = None  # Clear password
+                user.ldap_groups = claims.get("groups", [])
                 await user.save()
                 return user
 
@@ -58,14 +60,17 @@ class OIDCUserService:
             or f"user_{oidc_subject[:8]}"
         )
 
+        # Extract LDAP groups from claims
+        groups = claims.get("groups", [])
+
         user = await User.create(
             id=uuid4(),
             username=username,
-            email=email
+            email=email or f"{oidc_subject}@oidc.local",  # Fallback if no email claim
-            or f"{oidc_subject}@oidc.local",  # Fallback if no email claim
             oidc_subject=oidc_subject,
             auth_provider="oidc",
             password=None,
+            ldap_groups=groups,
         )
 
         return user

config/oidc_config.py

@@ -1,6 +1,7 @@
 """
 OIDC Configuration for Authelia Integration
 """
+
 import os
 from typing import Dict, Any
 from authlib.jose import jwt

docker-compose.dev.yml

@@ -1,5 +1,3 @@
-version: "3.8"
-
 services:
   postgres:
     image: postgres:16-alpine
@@ -17,55 +15,56 @@ services:
       timeout: 5s
       retries: 5
 
-  raggr-backend:
+  # raggr service disabled - run locally for development
-    build:
+  # raggr:
-      context: ./services/raggr
+  #   build:
-      dockerfile: Dockerfile.dev
+  #     context: .
-    image: torrtle/simbarag:dev
+  #     dockerfile: Dockerfile.dev
-    ports:
+  #   image: torrtle/simbarag:dev
-      - "8080:8080"
+  #   ports:
-    env_file:
+  #     - "8080:8080"
-      - .env
+  #   env_file:
-    environment:
+  #     - .env
-      - PAPERLESS_TOKEN=${PAPERLESS_TOKEN}
+  #   environment:
-      - BASE_URL=${BASE_URL}
+  #     - PAPERLESS_TOKEN=${PAPERLESS_TOKEN}
-      - OLLAMA_URL=${OLLAMA_URL:-http://localhost:11434}
+  #     - BASE_URL=${BASE_URL}
-      - CHROMADB_PATH=/app/chromadb
+  #     - OLLAMA_URL=${OLLAMA_URL:-http://localhost:11434}
-      - OPENAI_API_KEY=${OPENAI_API_KEY}
+  #     - CHROMADB_PATH=/app/data/chromadb
-      - JWT_SECRET_KEY=${JWT_SECRET_KEY}
+  #     - OPENAI_API_KEY=${OPENAI_API_KEY}
-      - OIDC_ISSUER=${OIDC_ISSUER}
+  #     - JWT_SECRET_KEY=${JWT_SECRET_KEY}
-      - OIDC_CLIENT_ID=${OIDC_CLIENT_ID}
+  #     - OIDC_ISSUER=${OIDC_ISSUER}
-      - OIDC_CLIENT_SECRET=${OIDC_CLIENT_SECRET}
+  #     - OIDC_CLIENT_ID=${OIDC_CLIENT_ID}
-      - OIDC_REDIRECT_URI=${OIDC_REDIRECT_URI}
+  #     - OIDC_CLIENT_SECRET=${OIDC_CLIENT_SECRET}
-      - OIDC_USE_DISCOVERY=${OIDC_USE_DISCOVERY:-true}
+  #     - OIDC_REDIRECT_URI=${OIDC_REDIRECT_URI}
-      - DATABASE_URL=postgres://raggr:raggr_dev_password@postgres:5432/raggr
+  #     - OIDC_USE_DISCOVERY=${OIDC_USE_DISCOVERY:-true}
-      - FLASK_ENV=development
+  #     - DATABASE_URL=postgres://raggr:raggr_dev_password@postgres:5432/raggr
-      - PYTHONUNBUFFERED=1
+  #     - FLASK_ENV=development
-    depends_on:
+  #     - PYTHONUNBUFFERED=1
-      postgres:
+  #     - NODE_ENV=development
-        condition: service_healthy
+  #     - TAVILY_KEY=${TAVILIY_KEY}
-    volumes:
+  #   depends_on:
-      # Mount source code for hot reload
+  #     postgres:
-      - ./services/raggr:/app
+  #       condition: service_healthy
-      # Exclude node_modules and Python cache
+  #   volumes:
-      - /app/raggr-frontend/node_modules
+  #     - chromadb_data:/app/data/chromadb
-      - /app/__pycache__
+  #     - ./migrations:/app/migrations  # Bind mount for migrations (bidirectional)
-      # Persist data
+  #   develop:
-      - chromadb_data:/app/chromadb
+  #     watch:
-    command: sh -c "chmod +x /app/startup-dev.sh && /app/startup-dev.sh"
+  #       # Sync+restart on any file change in root directory
-
+  #       - action: sync+restart
-  raggr-frontend:
+  #         path: .
-    build:
+  #         target: /app
-      context: ./services/raggr/raggr-frontend
+  #         ignore:
-      dockerfile: Dockerfile.dev
+  #           - __pycache__/
-    environment:
+  #           - "*.pyc"
-      - NODE_ENV=development
+  #           - "*.pyo"
-    volumes:
+  #           - "*.pyd"
-      # Mount source code for hot reload
+  #           - .git/
-      - ./services/raggr/raggr-frontend:/app
+  #           - chromadb/
-      # Exclude node_modules to use container's version
+  #           - node_modules/
-      - /app/node_modules
+  #           - raggr-frontend/dist/
-    command: sh -c "yarn build && yarn watch:build"
+  #           - docs/
+  #           - .venv/
 
 volumes:
   chromadb_data:

docker-compose.yml

@@ -3,6 +3,8 @@ version: "3.8"
 services:
   postgres:
     image: postgres:16-alpine
+    ports:
+      - "5432:5432"
     environment:
       - POSTGRES_USER=${POSTGRES_USER:-raggr}
       - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-changeme}
@@ -18,15 +20,16 @@ services:
 
   raggr:
     build:
-      context: ./services/raggr
+      context: .
       dockerfile: Dockerfile
     image: torrtle/simbarag:latest
-    network_mode: host
+    ports:
+      - "8080:8080"
     environment:
       - PAPERLESS_TOKEN=${PAPERLESS_TOKEN}
       - BASE_URL=${BASE_URL}
       - OLLAMA_URL=${OLLAMA_URL:-http://localhost:11434}
-      - CHROMADB_PATH=/app/chromadb
+      - CHROMADB_PATH=/app/data/chromadb
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - JWT_SECRET_KEY=${JWT_SECRET_KEY}
       - OIDC_ISSUER=${OIDC_ISSUER}
@@ -35,11 +38,12 @@ services:
       - OIDC_REDIRECT_URI=${OIDC_REDIRECT_URI}
       - OIDC_USE_DISCOVERY=${OIDC_USE_DISCOVERY:-true}
       - DATABASE_URL=${DATABASE_URL:-postgres://raggr:changeme@postgres:5432/raggr}
+      - TAVILY_KEY=${TAVILIY_KEY}
     depends_on:
       postgres:
         condition: service_healthy
     volumes:
-      - chromadb_data:/app/chromadb
+      - chromadb_data:/app/data/chromadb
     restart: unless-stopped
 
 volumes:

docs/TASKS.md

@@ -0,0 +1,53 @@
+# Tasks & Feature Requests
+
+## Feature Requests
+
+### YNAB Integration (Admin-Only)
+- **Description**: Integration with YNAB (You Need A Budget) API to enable financial data queries and insights
+- **Requirements**:
+  - Admin-guarded endpoint (requires `lldap_admin` group)
+  - YNAB API token configuration in environment variables
+  - Sync budget data, transactions, and categories
+  - Store YNAB data for RAG queries
+- **Endpoints**:
+  - `POST /api/admin/ynab/sync` - Trigger YNAB data sync
+  - `GET /api/admin/ynab/status` - Check sync status and last update
+  - `GET /api/admin/ynab/budgets` - List available budgets
+- **Implementation Notes**:
+  - Use YNAB API v1 (https://api.youneedabudget.com/v1)
+  - Consider rate limiting (200 requests per hour)
+  - Store transaction data in PostgreSQL with appropriate indexing
+  - Index transaction descriptions and categories in ChromaDB for RAG queries
+
+### Money Insights
+- **Description**: AI-powered financial insights and analysis based on YNAB data
+- **Features**:
+  - Spending pattern analysis
+  - Budget vs. actual comparisons
+  - Category-based spending trends
+  - Anomaly detection (unusual transactions)
+  - Natural language queries like "How much did I spend on groceries last month?"
+  - Month-over-month and year-over-year comparisons
+- **Implementation Notes**:
+  - Leverage existing LangChain agent architecture
+  - Add custom tools for financial calculations
+  - Use LLM to generate insights and summaries
+  - Create visualizations or data exports for frontend display
+
+## Backlog
+
+- [ ] YNAB API client module
+- [ ] YNAB data models (Budget, Transaction, Category, Account)
+- [ ] Database schema for financial data
+- [ ] YNAB sync background job/scheduler
+- [ ] Financial insights LangChain tools
+- [ ] Admin UI for YNAB configuration
+- [ ] Frontend components for money insights display
+
+## Technical Debt
+
+_To be added_
+
+## Bugs
+
+_To be added_

docs/VECTORSTORE.md

@@ -0,0 +1,97 @@
+# Vector Store Management
+
+This document describes how to manage the ChromaDB vector store used for RAG (Retrieval-Augmented Generation).
+
+## Configuration
+
+The vector store location is controlled by the `CHROMADB_PATH` environment variable:
+
+- **Development (local)**: Set in `.env` to a local path (e.g., `/path/to/chromadb`)
+- **Docker**: Automatically set to `/app/data/chromadb` and persisted via Docker volume
+
+## Management Commands
+
+### CLI (Command Line)
+
+Use the `scripts/manage_vectorstore.py` script for vector store operations:
+
+```bash
+# Show statistics
+python scripts/manage_vectorstore.py stats
+
+# Index documents from Paperless-NGX (incremental)
+python scripts/manage_vectorstore.py index
+
+# Clear and reindex all documents
+python scripts/manage_vectorstore.py reindex
+
+# List documents
+python scripts/manage_vectorstore.py list 10
+python scripts/manage_vectorstore.py list 20 --show-content
+```
+
+### Docker
+
+Run commands inside the Docker container:
+
+```bash
+# Show statistics
+docker compose exec raggr python scripts/manage_vectorstore.py stats
+
+# Reindex all documents
+docker compose exec raggr python scripts/manage_vectorstore.py reindex
+```
+
+### API Endpoints
+
+The following authenticated endpoints are available:
+
+- `GET /api/rag/stats` - Get vector store statistics
+- `POST /api/rag/index` - Trigger indexing of new documents
+- `POST /api/rag/reindex` - Clear and reindex all documents
+
+## How It Works
+
+1. **Document Fetching**: Documents are fetched from Paperless-NGX via the API
+2. **Chunking**: Documents are split into chunks of ~1000 characters with 200 character overlap
+3. **Embedding**: Chunks are embedded using OpenAI's `text-embedding-3-large` model
+4. **Storage**: Embeddings are stored in ChromaDB with metadata (filename, document type, date)
+5. **Retrieval**: User queries are embedded and similar chunks are retrieved for RAG
+
+## Troubleshooting
+
+### "Error creating hnsw segment reader"
+
+This indicates a corrupted index. Solution:
+
+```bash
+python scripts/manage_vectorstore.py reindex
+```
+
+### Empty results
+
+Check if documents are indexed:
+
+```bash
+python scripts/manage_vectorstore.py stats
+```
+
+If count is 0, run:
+
+```bash
+python scripts/manage_vectorstore.py index
+```
+
+### Different results in Docker vs local
+
+Docker and local environments use separate ChromaDB instances. To sync:
+
+1. Index inside Docker: `docker compose exec raggr python scripts/manage_vectorstore.py reindex`
+2. Or mount the same volume for both environments
+
+## Production Considerations
+
+1. **Volume Persistence**: Use Docker volumes or persistent storage for ChromaDB
+2. **Backup**: Regularly backup the ChromaDB data directory
+3. **Reindexing**: Schedule periodic reindexing to keep data fresh
+4. **Monitoring**: Monitor the `/api/rag/stats` endpoint for document counts

docs/authentication.md

@@ -0,0 +1,274 @@
+# Authentication Architecture
+
+This document describes the authentication stack for SimbaRAG: LLDAP → Authelia → OAuth2/OIDC.
+
+## Overview
+
+```
+┌─────────┐     ┌──────────┐     ┌──────────────┐     ┌──────────┐
+│  LLDAP  │────▶│ Authelia │────▶│ OAuth2/OIDC  │────▶│ SimbaRAG │
+│ (Users) │     │  (IdP)   │     │   (Flow)     │     │  (App)   │
+└─────────┘     └──────────┘     └──────────────┘     └──────────┘
+```
+
+| Component | Role |
+|-----------|------|
+| **LLDAP** | Lightweight LDAP server storing users and groups |
+| **Authelia** | Identity provider that authenticates against LLDAP and issues OIDC tokens |
+| **SimbaRAG** | Relying party that consumes OIDC tokens and manages sessions |
+
+## OIDC Configuration
+
+### Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `OIDC_ISSUER` | Authelia server URL | Required |
+| `OIDC_CLIENT_ID` | Client ID registered in Authelia | Required |
+| `OIDC_CLIENT_SECRET` | Client secret for token exchange | Required |
+| `OIDC_REDIRECT_URI` | Callback URL after authentication | Required |
+| `OIDC_USE_DISCOVERY` | Enable automatic discovery | `true` |
+| `JWT_SECRET_KEY` | Secret for signing backend JWTs | Required |
+
+### Discovery
+
+When `OIDC_USE_DISCOVERY=true`, the application fetches endpoints from:
+
+```
+{OIDC_ISSUER}/.well-known/openid-configuration
+```
+
+This provides:
+
+- Authorization endpoint
+- Token endpoint
+- JWKS URI for signature verification
+- Supported scopes and claims
+
+## Authentication Flow
+
+### 1. Login Initiation
+
+```
+GET /api/user/oidc/login
+```
+
+1. Generate PKCE code verifier and challenge (S256)
+2. Generate CSRF state token
+3. Store state in session storage
+4. Return authorization URL for frontend redirect
+
+### 2. Authorization
+
+User is redirected to Authelia where they:
+
+1. Enter LDAP credentials
+2. Complete MFA if configured
+3. Consent to requested scopes
+
+### 3. Callback
+
+```
+GET /api/user/oidc/callback?code=...&state=...
+```
+
+1. Validate state matches stored value (CSRF protection)
+2. Exchange authorization code for tokens using PKCE verifier
+3. Verify ID token signature using JWKS
+4. Validate claims (issuer, audience, expiration)
+5. Create or update user in database
+6. Issue backend JWT tokens (access + refresh)
+
+### 4. Token Refresh
+
+```
+POST /api/user/refresh
+Authorization: Bearer <refresh_token>
+```
+
+Issues a new access token without re-authentication.
+
+## User Model
+
+```python
+class User(Model):
+    id = UUIDField(primary_key=True)
+    username = CharField(max_length=255)
+    password = BinaryField(null=True)  # Nullable for OIDC-only users
+    email = CharField(max_length=100, unique=True)
+
+    # OIDC fields
+    oidc_subject = CharField(max_length=255, unique=True, null=True)
+    auth_provider = CharField(max_length=50, default="local")  # "local" or "oidc"
+    ldap_groups = JSONField(default=[])  # LDAP groups from OIDC claims
+
+    created_at = DatetimeField(auto_now_add=True)
+    updated_at = DatetimeField(auto_now=True)
+
+    def has_group(self, group: str) -> bool:
+        """Check if user belongs to a specific LDAP group."""
+        return group in (self.ldap_groups or [])
+
+    def is_admin(self) -> bool:
+        """Check if user is an admin (member of lldap_admin group)."""
+        return self.has_group("lldap_admin")
+```
+
+### User Provisioning
+
+The `OIDCUserService` handles automatic user creation:
+
+1. Extract claims from ID token (`sub`, `email`, `preferred_username`)
+2. Check if user exists by `oidc_subject`
+3. If not, check by email for migration from local auth
+4. Create new user or update existing
+
+## JWT Tokens
+
+Backend issues its own JWTs after OIDC authentication:
+
+| Token Type | Purpose | Typical Lifetime |
+|------------|---------|------------------|
+| Access Token | API authorization | 15 minutes |
+| Refresh Token | Obtain new access tokens | 7 days |
+
+### Claims
+
+```json
+{
+  "identity": "<user-uuid>",
+  "type": "access|refresh",
+  "exp": 1234567890,
+  "iat": 1234567890
+}
+```
+
+## Protected Endpoints
+
+All API endpoints use the `@jwt_refresh_token_required` decorator for basic authentication:
+
+```python
+@blueprint.route("/example")
+@jwt_refresh_token_required
+async def protected_endpoint():
+    user_id = get_jwt_identity()
+    # ...
+```
+
+---
+
+## Role-Based Access Control (RBAC)
+
+RBAC is implemented using LDAP groups passed through Authelia as OIDC claims. Users in the `lldap_admin` group have admin privileges.
+
+### Architecture
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                        LLDAP                                │
+│  Groups: lldap_admin, lldap_user                           │
+└─────────────────────────────────────────────────────────────┘
+                              │
+                              ▼
+┌─────────────────────────────────────────────────────────────┐
+│                       Authelia                              │
+│  Scope: groups → Claim: groups = ["lldap_admin"]           │
+└─────────────────────────────────────────────────────────────┘
+                              │
+                              ▼
+┌─────────────────────────────────────────────────────────────┐
+│                       SimbaRAG                              │
+│  1. Extract groups from ID token                            │
+│  2. Store in User.ldap_groups                               │
+│  3. Check membership with @admin_required decorator         │
+└─────────────────────────────────────────────────────────────┘
+```
+
+### Authelia Configuration
+
+Ensure Authelia is configured to pass the `groups` claim:
+
+```yaml
+identity_providers:
+  oidc:
+    clients:
+      - client_id: simbarag
+        scopes:
+          - openid
+          - profile
+          - email
+          - groups  # Required for RBAC
+```
+
+### Admin-Only Endpoints
+
+The `@admin_required` decorator protects privileged endpoints:
+
+```python
+from blueprints.users.decorators import admin_required
+
+@blueprint.post("/admin-action")
+@admin_required
+async def admin_only_endpoint():
+    # Only users in lldap_admin group can access
+    ...
+```
+
+**Protected endpoints:**
+
+| Endpoint | Access | Description |
+|----------|--------|-------------|
+| `POST /api/rag/index` | Admin | Trigger document indexing |
+| `POST /api/rag/reindex` | Admin | Clear and reindex all documents |
+| `GET /api/rag/stats` | All users | View vector store statistics |
+
+### User Response
+
+The OIDC callback returns group information:
+
+```json
+{
+  "access_token": "...",
+  "refresh_token": "...",
+  "user": {
+    "id": "uuid",
+    "username": "john",
+    "email": "john@example.com",
+    "groups": ["lldap_admin", "lldap_user"],
+    "is_admin": true
+  }
+}
+```
+
+---
+
+## Security Considerations
+
+### Current Gaps
+
+| Issue | Risk | Mitigation |
+|-------|------|------------|
+| In-memory session storage | State lost on restart, not scalable | Use Redis for production |
+| No token revocation | Tokens valid until expiry | Implement blacklist or short expiry |
+| No audit logging | Cannot track auth events | Add event logging |
+| Single JWT secret | Compromise affects all tokens | Rotate secrets, use asymmetric keys |
+
+### Recommendations
+
+1. **Use Redis** for OIDC state storage in production
+2. **Implement logout** with token blacklisting
+3. **Add audit logging** for authentication events
+4. **Rotate JWT secrets** regularly
+5. **Use short-lived access tokens** (15 min) with refresh
+
+---
+
+## File Reference
+
+| File | Purpose |
+|------|---------|
+| `services/raggr/oidc_config.py` | OIDC client configuration and discovery |
+| `services/raggr/blueprints/users/models.py` | User model definition with group helpers |
+| `services/raggr/blueprints/users/oidc_service.py` | User provisioning from OIDC claims |
+| `services/raggr/blueprints/users/__init__.py` | Auth endpoints and flow |
+| `services/raggr/blueprints/users/decorators.py` | Auth decorators (`@admin_required`) |

docs/deployment.md

@@ -0,0 +1,188 @@
+# Deployment & Migrations Guide
+
+This document covers database migrations and deployment workflows for SimbaRAG.
+
+## Migration Workflow
+
+Migrations are managed by [Aerich](https://github.com/tortoise/aerich), the migration tool for Tortoise ORM.
+
+### Key Principles
+
+1. **Generate migrations in Docker** - Aerich needs database access to detect schema changes
+2. **Migrations auto-apply on startup** - Both `startup.sh` and `startup-dev.sh` run `aerich upgrade`
+3. **Commit migrations to git** - Migration files must be in the repo for production deploys
+
+### Generating a New Migration
+
+#### Development (Recommended)
+
+With `docker-compose.dev.yml`, your local `services/raggr` directory is synced to the container. Migrations generated inside the container appear on your host automatically.
+
+```bash
+# 1. Start the dev environment
+docker compose -f docker-compose.dev.yml up -d
+
+# 2. Generate migration (runs inside container, syncs to host)
+docker compose -f docker-compose.dev.yml exec raggr aerich migrate --name describe_your_change
+
+# 3. Verify migration was created
+ls services/raggr/migrations/models/
+
+# 4. Commit the migration
+git add services/raggr/migrations/
+git commit -m "Add migration: describe_your_change"
+```
+
+#### Production Container
+
+For production, migration files are baked into the image. You must generate migrations in dev first.
+
+```bash
+# If you need to generate a migration from production (not recommended):
+docker compose exec raggr aerich migrate --name describe_your_change
+
+# Copy the file out of the container
+docker cp $(docker compose ps -q raggr):/app/migrations/models/ ./services/raggr/migrations/
+```
+
+### Applying Migrations
+
+Migrations apply automatically on container start via the startup scripts.
+
+**Manual application (if needed):**
+
+```bash
+# Dev
+docker compose -f docker-compose.dev.yml exec raggr aerich upgrade
+
+# Production
+docker compose exec raggr aerich upgrade
+```
+
+### Checking Migration Status
+
+```bash
+# View applied migrations
+docker compose exec raggr aerich history
+
+# View pending migrations
+docker compose exec raggr aerich heads
+```
+
+### Rolling Back
+
+```bash
+# Downgrade one migration
+docker compose exec raggr aerich downgrade
+
+# Downgrade to specific version
+docker compose exec raggr aerich downgrade -v 1
+```
+
+## Deployment Workflows
+
+### Development
+
+```bash
+# Start with watch mode (auto-restarts on file changes)
+docker compose -f docker-compose.dev.yml up
+
+# Or with docker compose watch (requires Docker Compose v2.22+)
+docker compose -f docker-compose.dev.yml watch
+```
+
+The dev environment:
+- Syncs `services/raggr/` to `/app` in the container
+- Rebuilds frontend on changes
+- Auto-applies migrations on startup
+
+### Production
+
+```bash
+# Build and deploy
+docker compose build raggr
+docker compose up -d
+
+# View logs
+docker compose logs -f raggr
+
+# Verify migrations applied
+docker compose exec raggr aerich history
+```
+
+### Fresh Deploy (New Database)
+
+On first deploy with an empty database, `startup-dev.sh` runs `aerich init-db` instead of `aerich upgrade`. This creates all tables from the current models.
+
+For production (`startup.sh`), ensure the database exists and run:
+
+```bash
+# If aerich table doesn't exist yet
+docker compose exec raggr aerich init-db
+
+# Or if migrating from existing schema
+docker compose exec raggr aerich upgrade
+```
+
+## Troubleshooting
+
+### "No migrations found" on startup
+
+The `migrations/models/` directory is empty or not copied into the image.
+
+**Fix:** Ensure migrations are committed and the Dockerfile copies them:
+```dockerfile
+COPY migrations ./migrations
+```
+
+### Migration fails with "relation already exists"
+
+The database has tables but aerich doesn't know about them (fresh aerich setup on existing DB).
+
+**Fix:** Fake the initial migration:
+```bash
+# Mark initial migration as applied without running it
+docker compose exec raggr aerich upgrade --fake
+```
+
+### Model changes not detected
+
+Aerich compares models against the last migration's state. If state is out of sync:
+
+```bash
+# Regenerate migration state (dangerous - review carefully)
+docker compose exec raggr aerich migrate --name fix_state
+```
+
+### Database connection errors
+
+Ensure PostgreSQL is healthy before running migrations:
+
+```bash
+# Check postgres status
+docker compose ps postgres
+
+# Wait for postgres then run migrations
+docker compose exec raggr bash -c "sleep 5 && aerich upgrade"
+```
+
+## File Reference
+
+| File | Purpose |
+|------|---------|
+| `pyproject.toml` | Aerich config (`[tool.aerich]` section) |
+| `migrations/models/` | Migration files |
+| `startup.sh` | Production startup (runs `aerich upgrade`) |
+| `startup-dev.sh` | Dev startup (runs `aerich upgrade` or `init-db`) |
+| `app.py` | Contains `TORTOISE_CONFIG` |
+| `aerich_config.py` | Aerich initialization configuration |
+
+## Quick Reference
+
+| Task | Command |
+|------|---------|
+| Generate migration | `docker compose -f docker-compose.dev.yml exec raggr aerich migrate --name name` |
+| Apply migrations | `docker compose exec raggr aerich upgrade` |
+| View history | `docker compose exec raggr aerich history` |
+| Rollback | `docker compose exec raggr aerich downgrade` |
+| Fresh init | `docker compose exec raggr aerich init-db` |

docs/development.md

@@ -0,0 +1,258 @@
+# Development Guide
+
+This guide explains how to run SimbaRAG in development mode.
+
+## Quick Start
+
+### Option 1: Local Development (Recommended)
+
+Run PostgreSQL in Docker and the application locally for faster iteration:
+
+```bash
+# 1. Start PostgreSQL
+docker compose -f docker-compose.dev.yml up -d
+
+# 2. Set environment variables
+export DATABASE_URL="postgres://raggr:raggr_dev_password@localhost:5432/raggr"
+export CHROMADB_PATH="./chromadb"
+export $(grep -v '^#' .env | xargs)  # Load other vars from .env
+
+# 3. Install dependencies (first time)
+pip install -r requirements.txt
+cd raggr-frontend && yarn install && yarn build && cd ..
+
+# 4. Run migrations
+aerich upgrade
+
+# 5. Start the server
+python app.py
+```
+
+The application will be available at `http://localhost:8080`.
+
+### Option 2: Full Docker Development
+
+Run everything in Docker with hot reload (slower, but matches production):
+
+```bash
+# Uncomment the raggr service in docker-compose.dev.yml first!
+
+# Start all services
+docker compose -f docker-compose.dev.yml up --build
+
+# View logs
+docker compose -f docker-compose.dev.yml logs -f raggr
+```
+
+## Project Structure
+
+```
+raggr/
+├── app.py                    # Quart application entry point
+├── main.py                   # RAG logic and LangChain agent
+├── llm.py                    # LLM client (Ollama + OpenAI fallback)
+├── aerich_config.py          # Database migration configuration
+│
+├── blueprints/               # API route blueprints
+│   ├── users/               # Authentication (OIDC, JWT, RBAC)
+│   ├── conversation/        # Chat conversations and messages
+│   └── rag/                 # Document indexing (admin only)
+│
+├── config/                   # Configuration modules
+│   └── oidc_config.py       # OIDC authentication settings
+│
+├── utils/                    # Reusable utilities
+│   ├── chunker.py           # Document chunking for embeddings
+│   ├── cleaner.py           # PDF cleaning and summarization
+│   ├── image_process.py     # Image description with LLM
+│   └── request.py           # Paperless-NGX API client
+│
+├── scripts/                  # Administrative scripts
+│   ├── add_user.py          # Create users manually
+│   ├── user_message_stats.py # User message statistics
+│   ├── manage_vectorstore.py # Vector store management
+│   ├── inspect_vector_store.py # Inspect ChromaDB contents
+│   └── query.py             # Query generation utilities
+│
+├── raggr-frontend/          # React frontend
+│   └── src/                # Frontend source code
+│
+├── migrations/              # Database migrations
+└── docs/                    # Documentation
+```
+
+## Making Changes
+
+### Backend Changes
+
+**Local development:**
+1. Edit Python files
+2. Save
+3. Restart `python app.py` (or use a tool like `watchdog` for auto-reload)
+
+**Docker development:**
+1. Edit Python files
+2. Files are synced via Docker watch mode
+3. Container automatically restarts
+
+### Frontend Changes
+
+```bash
+cd raggr-frontend
+
+# Development mode with hot reload
+yarn dev
+
+# Production build (for testing)
+yarn build
+```
+
+The backend serves built files from `raggr-frontend/dist/`.
+
+### Database Model Changes
+
+When you modify Tortoise ORM models:
+
+```bash
+# Generate migration
+aerich migrate --name "describe_your_change"
+
+# Apply migration
+aerich upgrade
+
+# View history
+aerich history
+```
+
+See [deployment.md](deployment.md) for detailed migration workflows.
+
+### Adding Dependencies
+
+**Backend:**
+```bash
+# Add to requirements.txt or use uv
+pip install package-name
+pip freeze > requirements.txt
+```
+
+**Frontend:**
+```bash
+cd raggr-frontend
+yarn add package-name
+```
+
+## Useful Commands
+
+### Database
+
+```bash
+# Connect to PostgreSQL
+docker compose -f docker-compose.dev.yml exec postgres psql -U raggr -d raggr
+
+# Reset database
+docker compose -f docker-compose.dev.yml down -v
+docker compose -f docker-compose.dev.yml up -d
+aerich init-db
+```
+
+### Vector Store
+
+```bash
+# Show statistics
+python scripts/manage_vectorstore.py stats
+
+# Index new documents from Paperless
+python scripts/manage_vectorstore.py index
+
+# Clear and reindex everything
+python scripts/manage_vectorstore.py reindex
+```
+
+See [vectorstore.md](vectorstore.md) for details.
+
+### Scripts
+
+```bash
+# Add a new user
+python scripts/add_user.py
+
+# View message statistics
+python scripts/user_message_stats.py
+
+# Inspect vector store contents
+python scripts/inspect_vector_store.py
+```
+
+## Environment Variables
+
+Copy `.env.example` to `.env` and configure:
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `DATABASE_URL` | PostgreSQL connection | `postgres://user:pass@localhost:5432/db` |
+| `CHROMADB_PATH` | ChromaDB storage path | `./chromadb` |
+| `OLLAMA_URL` | Ollama server URL | `http://localhost:11434` |
+| `OPENAI_API_KEY` | OpenAI API key (fallback LLM) | `sk-...` |
+| `PAPERLESS_TOKEN` | Paperless-NGX API token | `...` |
+| `BASE_URL` | Paperless-NGX URL | `https://paperless.example.com` |
+| `OIDC_ISSUER` | OIDC provider URL | `https://auth.example.com` |
+| `OIDC_CLIENT_ID` | OIDC client ID | `simbarag` |
+| `OIDC_CLIENT_SECRET` | OIDC client secret | `...` |
+| `JWT_SECRET_KEY` | JWT signing key | `random-secret` |
+| `TAVILY_KEY` | Tavily web search API key | `tvly-...` |
+
+## Troubleshooting
+
+### Port Already in Use
+
+```bash
+# Find and kill process on port 8080
+lsof -ti:8080 | xargs kill -9
+
+# Or change the port in app.py
+```
+
+### Database Connection Errors
+
+```bash
+# Check if PostgreSQL is running
+docker compose -f docker-compose.dev.yml ps postgres
+
+# View PostgreSQL logs
+docker compose -f docker-compose.dev.yml logs postgres
+```
+
+### Frontend Not Building
+
+```bash
+cd raggr-frontend
+rm -rf node_modules dist
+yarn install
+yarn build
+```
+
+### ChromaDB Errors
+
+```bash
+# Clear and recreate ChromaDB
+rm -rf chromadb/
+python scripts/manage_vectorstore.py reindex
+```
+
+### Import Errors After Reorganization
+
+Ensure you're in the project root directory when running scripts, or use:
+
+```bash
+# Add project root to Python path
+export PYTHONPATH="${PYTHONPATH}:$(pwd)"
+python scripts/your_script.py
+```
+
+## Hot Tips
+
+- Use `python -m pdb app.py` for debugging
+- Enable Quart debug mode in `app.py`: `app.run(debug=True)`
+- Check API logs: They appear in the terminal running `python app.py`
+- Frontend logs: Open browser DevTools console
+- Use `docker compose -f docker-compose.dev.yml down -v` for a clean slate

docs/index.md

@@ -0,0 +1,203 @@
+# SimbaRAG Documentation
+
+Welcome to the SimbaRAG documentation! This guide will help you understand, develop, and deploy the SimbaRAG conversational AI system.
+
+## Getting Started
+
+New to SimbaRAG? Start here:
+
+1. Read the main [README](../README.md) for project overview and architecture
+2. Follow the [Development Guide](development.md) to set up your environment
+3. Learn about [Authentication](authentication.md) setup with OIDC and LDAP
+
+## Documentation Structure
+
+### Core Guides
+
+- **[Development Guide](development.md)** - Local development setup, project structure, and workflows
+- **[Deployment Guide](deployment.md)** - Database migrations, deployment workflows, and troubleshooting
+- **[Vector Store Guide](VECTORSTORE.md)** - Managing ChromaDB, indexing documents, and RAG operations
+- **[Migrations Guide](MIGRATIONS.md)** - Database migration reference
+- **[Authentication Guide](authentication.md)** - OIDC, Authelia, LLDAP configuration and user management
+
+### Quick Reference
+
+| Task | Documentation |
+|------|---------------|
+| Set up local dev environment | [Development Guide → Quick Start](development.md#quick-start) |
+| Run database migrations | [Deployment Guide → Migration Workflow](deployment.md#migration-workflow) |
+| Index documents | [Vector Store Guide → Management Commands](VECTORSTORE.md#management-commands) |
+| Configure authentication | [Authentication Guide](authentication.md) |
+| Run administrative scripts | [Development Guide → Scripts](development.md#scripts) |
+
+## Common Tasks
+
+### Development
+
+```bash
+# Start local development
+docker compose -f docker-compose.dev.yml up -d
+export DATABASE_URL="postgres://raggr:raggr_dev_password@localhost:5432/raggr"
+export CHROMADB_PATH="./chromadb"
+python app.py
+```
+
+### Database Migrations
+
+```bash
+# Generate migration
+aerich migrate --name "your_change"
+
+# Apply migrations
+aerich upgrade
+
+# View history
+aerich history
+```
+
+### Vector Store Management
+
+```bash
+# Show statistics
+python scripts/manage_vectorstore.py stats
+
+# Index new documents
+python scripts/manage_vectorstore.py index
+
+# Reindex everything
+python scripts/manage_vectorstore.py reindex
+```
+
+## Architecture Overview
+
+SimbaRAG is built with:
+
+- **Backend**: Quart (async Python), LangChain, Tortoise ORM
+- **Frontend**: React 19, Rsbuild, Tailwind CSS
+- **Database**: PostgreSQL (users, conversations)
+- **Vector Store**: ChromaDB (document embeddings)
+- **LLM**: Ollama (primary), OpenAI (fallback)
+- **Auth**: Authelia (OIDC), LLDAP (user directory)
+
+See the [README](../README.md#system-architecture) for detailed architecture diagram.
+
+## Project Structure
+
+```
+simbarag/
+├── app.py                  # Quart app entry point
+├── main.py                 # RAG & LangChain agent
+├── llm.py                  # LLM client
+├── blueprints/             # API routes
+├── config/                 # Configuration
+├── utils/                  # Utilities
+├── scripts/                # Admin scripts
+├── raggr-frontend/         # React UI
+├── migrations/             # Database migrations
+├── docs/                   # This documentation
+├── docker-compose.yml      # Production Docker setup
+└── docker-compose.dev.yml  # Development Docker setup
+```
+
+## Key Concepts
+
+### RAG (Retrieval-Augmented Generation)
+
+SimbaRAG uses RAG to answer questions about Simba:
+
+1. Documents are fetched from Paperless-NGX
+2. Documents are chunked and embedded using OpenAI
+3. Embeddings are stored in ChromaDB
+4. User queries are embedded and matched against the store
+5. Relevant chunks are passed to the LLM for context
+6. LLM generates an answer using retrieved context
+
+### LangChain Agent
+
+The conversational agent has two tools:
+
+- **simba_search**: Queries the vector store for Simba's documents
+- **web_search**: Searches the web via Tavily API
+
+The agent automatically selects tools based on the query.
+
+### Authentication Flow
+
+1. User initiates OIDC login via Authelia
+2. Authelia authenticates against LLDAP
+3. Backend receives OIDC tokens and issues JWT
+4. Frontend stores JWT in localStorage
+5. Subsequent requests use JWT for authorization
+
+## Environment Variables
+
+Key environment variables (see `.env.example` for complete list):
+
+| Variable | Purpose |
+|----------|---------|
+| `DATABASE_URL` | PostgreSQL connection |
+| `CHROMADB_PATH` | Vector store location |
+| `OLLAMA_URL` | Local LLM server |
+| `OPENAI_API_KEY` | OpenAI for embeddings/fallback |
+| `PAPERLESS_TOKEN` | Document source API |
+| `OIDC_*` | Authentication configuration |
+| `TAVILY_KEY` | Web search API |
+
+## API Endpoints
+
+### Authentication
+- `GET /api/user/oidc/login` - Start OIDC flow
+- `GET /api/user/oidc/callback` - OIDC callback
+- `POST /api/user/refresh` - Refresh JWT
+
+### Conversations
+- `POST /api/conversation/` - Create conversation
+- `GET /api/conversation/` - List conversations
+- `POST /api/conversation/query` - Chat message
+
+### RAG (Admin Only)
+- `GET /api/rag/stats` - Vector store stats
+- `POST /api/rag/index` - Index documents
+- `POST /api/rag/reindex` - Reindex all
+
+## Troubleshooting
+
+### Common Issues
+
+| Issue | Solution |
+|-------|----------|
+| Port already in use | Check if services are running: `lsof -ti:8080` |
+| Database connection error | Ensure PostgreSQL is running: `docker compose ps` |
+| ChromaDB errors | Clear and reindex: `python scripts/manage_vectorstore.py reindex` |
+| Import errors | Check you're in `services/raggr/` directory |
+| Frontend not building | `cd raggr-frontend && yarn install && yarn build` |
+
+See individual guides for detailed troubleshooting.
+
+## Contributing
+
+1. Read the [Development Guide](development.md)
+2. Set up your local environment
+3. Make changes and test locally
+4. Generate migrations if needed
+5. Submit a pull request
+
+## Additional Resources
+
+- [LangChain Documentation](https://python.langchain.com/)
+- [ChromaDB Documentation](https://docs.trychroma.com/)
+- [Quart Documentation](https://quart.palletsprojects.com/)
+- [Tortoise ORM Documentation](https://tortoise.github.io/)
+- [Authelia Documentation](https://www.authelia.com/)
+
+## Need Help?
+
+- Check the relevant guide in this documentation
+- Review troubleshooting sections
+- Check application logs: `docker compose logs -f`
+- Inspect database: `docker compose exec postgres psql -U raggr`
+
+---
+
+**Documentation Version**: 1.0
+**Last Updated**: January 2026

index.html

@@ -0,0 +1,81 @@
+<!doctype html>
+
+
+<html lang="en">
+    <head>
+        <meta charset="utf-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+        <meta name="author" content="Paperless-ngx project and contributors">
+        <meta name="robots" content="noindex,nofollow">
+
+        <title>
+            
+    Paperless-ngx sign in
+
+        </title>
+        <link href="/static/bootstrap.min.css" rel="stylesheet">
+        <link href="/static/base.css" rel="stylesheet">
+    </head>
+
+    <body class="text-center">
+        <div class="position-absolute top-50 start-50 translate-middle">
+            <form class="form-accounts" id="form-account" method="post">
+                <input type="hidden" name="csrfmiddlewaretoken" value="KLQ3mMraTFHfK9sMmc6DJcNIS6YixeHnSJiT3A12LYB49HeEXOpx5RnY9V6uPSrD">
+                
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+        <path d="M2596.5,706.4c-5.7,0-11,1-15.8,3s-9,5-12.5,8.9v-9.4h-19.4v93.6h19.4v-52c0-8.6,2.1-15.3,6.3-20c4.2-4.7,9.5-7.1,15.9-7.1 c7.8,0,13.4,2.3,16.8,6.7c3.4,4.5,5.1,11.3,5.1,20.5v52h19.4v-56.8c0-12.8-3.2-22.6-9.5-29.3 C2615.8,709.8,2607.3,706.4,2596.5,706.4z" transform="translate(0)"/>
+        <path d="M2733.8,717.7c-3.6-3.4-7.9-6.1-13.1-8.2s-10.6-3.1-16.2-3.1c-8.7,0-16.5,2.1-23.5,6.3s-12.5,10-16.5,17.3 c-4,7.3-6,15.4-6,24.4c0,8.9,2,17.1,6,24.3c4,7.3,9.5,13,16.5,17.2s14.9,6.3,23.5,6.3c5.6,0,11-1,16.2-3.1 c5.1-2.1,9.5-4.8,13.1-8.2v24.4c0,8.5-2.5,14.8-7.6,18.7c-5,3.9-11,5.9-18,5.9c-6.7,0-12.4-1.6-17.3-4.7c-4.8-3.1-7.6-7.7-8.3-13.8 h-19.4c0.6,7.7,2.9,14.2,7.1,19.5s9.6,9.3,16.2,12c6.6,2.7,13.8,4,21.7,4c12.8,0,23.5-3.4,32-10.1c8.6-6.7,12.8-17.1,12.8-31.1 V708.9h-19.2V717.7z M2732.2,770.1c-2.5,4.7-6,8.3-10.4,11.2c-4.4,2.7-9.4,4-14.9,4c-5.7,0-10.8-1.4-15.2-4.3s-7.8-6.7-10.2-11.4 c-2.3-4.8-3.5-9.8-3.5-15.2c0-5.5,1.1-10.6,3.5-15.3s5.8-8.5,10.2-11.3s9.5-4.2,15.2-4.2c5.5,0,10.5,1.4,14.9,4s7.9,6.3,10.4,11 s3.8,10,3.8,15.8S2734.7,765.4,2732.2,770.1z" transform="translate(0)"/>
+        <polygon points="2867.9,708.9 2846.5,708.9 2820.9,741.9 2795.5,708.9 2773.1,708.9 2809.1,755 2771.5,802.5 2792.9,802.5  2820.1,767.9 2847.2,802.6 2869.6,802.6 2832,754.4 	" transform="translate(0)"/>
+        <path d="M757.6,293.7c-20-10.8-42.6-16.2-67.8-16.2H600c-8.5,39.2-21.1,76.4-37.6,111.3c-9.9,20.8-21.1,40.6-33.6,59.4v207.2h88.9 V521.5h72c25.2,0,47.8-5.4,67.8-16.2s35.7-25.6,47.1-44.2c11.4-18.7,17.1-39.1,17.1-61.3c0.1-22.7-5.6-43.3-17-61.9 C793.3,319.2,777.6,304.5,757.6,293.7z M716.6,434.3c-9.3,8.9-21.6,13.3-36.7,13.3l-62.2,0.4v-92.5l62.2-0.4 c15.1,0,27.3,4.4,36.7,13.3c9.4,8.9,14,19.9,14,32.9C730.6,414.5,726,425.4,716.6,434.3z" transform="translate(0)"/>
+    </g>
+</svg>
+
+                
+                
+                
+    <p>
+      Please sign in.
+      
+    </p>
+
+                
+
+                
+		
+    
+        
+        
+        <div class="form-floating form-stacked-top">
+            <input type="text" name="login" id="inputUsername" placeholder="Username" class="form-control" autocorrect="off" autocapitalize="none" required autofocus>
+            <label for="inputUsername">Username</label>
+        </div>
+        <div class="form-floating form-stacked-bottom">
+            <input type="password" name="password" id="inputPassword" placeholder="Password" class="form-control" required>
+            <label for="inputPassword">Password</label>
+        </div>
+        <div class="d-grid mt-3">
+            <button class="btn btn-lg btn-primary" type="submit">Sign in</button>
+        </div>
+        
+    
+
+            </form>
+            
+    
+    
+    
+
+        </div>
+    </body>
+</html>

llm.py

@@ -0,0 +1,46 @@
+import os
+import logging
+
+from openai import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+
+
+class LLMClient:
+    def __init__(self):
+        llama_url = os.getenv("LLAMA_SERVER_URL")
+        if llama_url:
+            self.client = OpenAI(base_url=llama_url, api_key="not-needed")
+            self.model = os.getenv("LLAMA_MODEL_NAME", "llama-3.1-8b-instruct")
+            self.PROVIDER = "llama_server"
+            logging.info("Using llama_server as LLM backend")
+        else:
+            self.client = OpenAI()
+            self.model = "gpt-4o-mini"
+            self.PROVIDER = "openai"
+            logging.info("Using OpenAI as LLM backend")
+
+    def chat(
+        self,
+        prompt: str,
+        system_prompt: str,
+    ):
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=[
+                {
+                    "role": "system",
+                    "content": system_prompt,
+                },
+                {"role": "user", "content": prompt},
+            ],
+        )
+        return response.choices[0].message.content
+
+
+if __name__ == "__main__":
+    client = LLMClient()
+    print(client.chat(prompt="Hello!", system_prompt="You are a helpful assistant."))

main.py

@@ -1,30 +1,20 @@
+import argparse
 import datetime
 import logging
 import os
 import sqlite3
-
-import argparse
-import chromadb
-import ollama
-
 import time
 
-
-from request import PaperlessNGXService
-from chunker import Chunker
-from cleaner import pdf_to_image, summarize_pdf_image
-from llm import LLMClient
-from query import QueryGenerator
-
-
 from dotenv import load_dotenv
 
-_dotenv_loaded = load_dotenv()
+import chromadb
+from utils.chunker import Chunker
+from utils.cleaner import pdf_to_image, summarize_pdf_image
+from llm import LLMClient
+from scripts.query import QueryGenerator
+from utils.request import PaperlessNGXService
 
-# Configure ollama client with URL from environment or default to localhost
+_dotenv_loaded = load_dotenv()
-ollama_client = ollama.Client(
-    host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
-)
 
 client = chromadb.PersistentClient(path=os.getenv("CHROMADB_PATH", ""))
 simba_docs = client.get_or_create_collection(name="simba_docs2")
@@ -186,7 +176,7 @@ def consult_oracle(
 def llm_chat(input: str, transcript: str = "") -> str:
     system_prompt = "You are a helpful assistant that understands veterinary terms."
     transcript_prompt = f"Here is the message transcript thus far {transcript}."
-    prompt = f"""Answer the user in  as if you were a cat named Simba. Don't act too catlike. Be assertive.
+    prompt = f"""Answer the user in as if you were a cat named Simba. Don't act too catlike. Be assertive.
     {transcript_prompt if len(transcript) > 0 else ""}
     Respond to this prompt: {input}"""
     output = llm_client.chat(prompt=prompt, system_prompt=system_prompt)

migrations/models/1_20260131214411_None.py

@@ -0,0 +1,72 @@
+from tortoise import BaseDBAsyncClient
+
+RUN_IN_TRANSACTION = True
+
+
+async def upgrade(db: BaseDBAsyncClient) -> str:
+    return """
+        CREATE TABLE IF NOT EXISTS "users" (
+    "id" UUID NOT NULL PRIMARY KEY,
+    "username" VARCHAR(255) NOT NULL,
+    "password" BYTEA,
+    "email" VARCHAR(100) NOT NULL UNIQUE,
+    "oidc_subject" VARCHAR(255) UNIQUE,
+    "auth_provider" VARCHAR(50) NOT NULL DEFAULT 'local',
+    "ldap_groups" JSONB NOT NULL,
+    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updated_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP
+);
+CREATE INDEX IF NOT EXISTS "idx_users_oidc_su_5aec5a" ON "users" ("oidc_subject");
+CREATE TABLE IF NOT EXISTS "conversations" (
+    "id" UUID NOT NULL PRIMARY KEY,
+    "name" VARCHAR(255) NOT NULL,
+    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updated_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "user_id" UUID REFERENCES "users" ("id") ON DELETE CASCADE
+);
+CREATE TABLE IF NOT EXISTS "conversation_messages" (
+    "id" UUID NOT NULL PRIMARY KEY,
+    "text" TEXT NOT NULL,
+    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "speaker" VARCHAR(10) NOT NULL,
+    "conversation_id" UUID NOT NULL REFERENCES "conversations" ("id") ON DELETE CASCADE
+);
+COMMENT ON COLUMN "conversation_messages"."speaker" IS 'USER: user\nSIMBA: simba';
+CREATE TABLE IF NOT EXISTS "aerich" (
+    "id" SERIAL NOT NULL PRIMARY KEY,
+    "version" VARCHAR(255) NOT NULL,
+    "app" VARCHAR(100) NOT NULL,
+    "content" JSONB NOT NULL
+);"""
+
+
+async def downgrade(db: BaseDBAsyncClient) -> str:
+    return """
+        """
+
+
+MODELS_STATE = (
+    "eJztmm1v4jgQx78Kyquu1KtatnRX1emkQOkttwuceNinXhWZ2ICviZ1NnG1R1e9+tkmIkz"
+    "gUKFDY401bxh5s/zzO/Mfpo+FSiJzgpEbJT+QHgGFKjMvSo0GAi/gf2vbjkgE8L2kVBgYG"
+    "jnSwlZ6yBQwC5gOb8cYhcALETRAFto+9aDASOo4wUpt3xGSUmEKCf4TIYnSE2Bj5vOHmlp"
+    "sxgegBBfFH784aYuTA1LwxFGNLu8UmnrT1+42ra9lTDDewbOqELkl6exM2pmTWPQwxPBE+"
+    "om2ECPIBQ1BZhphltOzYNJ0xNzA/RLOpwsQA0RCEjoBh/D4MiS0YlORI4sf5H8YSeDhqgR"
+    "YTJlg8Pk1XlaxZWg0xVO2D2Tl6e/FGrpIGbOTLRknEeJKOgIGpq+SagJS/cyhrY+DrUcb9"
+    "MzD5RFfBGBsSjkkMxSBjQKtRM1zwYDmIjNiYfyxXKnMwfjY7kiTvJVFSHtfTqG9FTeVpm0"
+    "CaILR9JJZsAZYHecVbGHaRHmbaM4MURq4n8R87CpivAbaJM4kOwRy+vUaz3u2Zzb/FStwg"
+    "+OFIRGavLlrK0jrJWI8uMlsx+5LSl0bvQ0l8LH1vt+rZ2J/16303xJxAyKhF6L0FoHJeY2"
+    "sMJrWxoQdX3Ni052FjX3Vjo8kr+xog31ougyguL0gj0dy2uImrJw2Reod32pwhYOThXVMf"
+    "4RH5iCYSYYPPAxBblywi0dGPvmZXoSXWZBY+uJ+pETUo+Or4mhCbZk+zWzOv6oZkOAD23T"
+    "3woVUA00VBAEYoyAOtRp7XHzvImUkzPUtVwDWn37ibT5UitpIVLVOFUYpevsktu1kLIHzd"
+    "MBpbjDSHzjMqWIG4mBi21I08iOK9FsUMPWhSfo9b9Sjj/vsiiuel8vrXXiqLx9L3qGl+fZ"
+    "PK5J/arT/j7opUrn1qVw8K+VcUUnmFHHgI3OnEgCgg6yR0c1IgtbuK+ysfHaPfrXcuSyKj"
+    "/0O6jWbVvCwF2B0AY7EtTlWZZ6cLFJlnp4U1pmjKHCA10Sz3mNe4rvOZv6cS1s5ceL1Qym"
+    "bvz3aW4rOaVhMuy2rbTSo5WTNopFtcSxRrNXG0D9ps/7WZ2MdlLy1Vn33RaFu4uPRAENxT"
+    "XxOZVUyAP9HDVL0yMAcTNq1/drWk18GrCr2qyi2OrNpomZ1veskb91fjtvqtVzczdJELsL"
+    "NMlM4c1hOiz5/4dQbo2eliomee6snJHoqhbQXh4F9kayqHYpJZv5WAZoN0uzw3cuC5lh9b"
+    "nk9/Ylgk2vVAc47be4oaDrWB84I0lOZaWSRMK8VRWskFqQOBZ418GnqaO7y/uu2WHmnGLQ"
+    "O0T/gqbyC22XHJwQG73Rjem9vNpHix8vkXCdk7g8wzVXzB4SLhf3KRcHjV9kts7OwmP1cQ"
+    "PvcaJPd/Jet5F7LLYnS770BM5GN7bGhq56jleF71DJI+O1M+N0jBdby2ehaYM8EQ7fyrim"
+    "j5Juq38tn5u/P3by/O3/MuciYzy7s5D4NGq/dMtSwOgvaKq1jrKS6HWjmRzvxoLCOYp933"
+    "E+BGajk+IkNEk96LJbLi8lryeGO3DmuTx0tk2/Wnl6f/AHvgrXs="
+)

mkdocs.yml

@@ -0,0 +1,25 @@
+site_name: SimbaRAG Documentation
+site_description: Documentation for SimbaRAG - RAG-powered conversational AI
+
+theme:
+  name: material
+  features:
+    - content.code.copy
+    - navigation.sections
+    - navigation.expand
+
+markdown_extensions:
+  - admonition
+  - pymdownx.highlight:
+      anchor_linenums: true
+  - pymdownx.superfences
+  - pymdownx.tabbed:
+      alternate_style: true
+  - tables
+  - toc:
+      permalink: true
+
+nav:
+  - Home: index.md
+  - Architecture:
+      - Authentication: authentication.md

pyproject.toml

@@ -0,0 +1,42 @@
+[project]
+name = "raggr"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "chromadb>=1.1.0",
+    "python-dotenv>=1.0.0",
+    "flask>=3.1.2",
+    "httpx>=0.28.1",
+    "openai>=2.0.1",
+    "pydantic>=2.11.9",
+    "pillow>=10.0.0",
+    "pymupdf>=1.24.0",
+    "black>=25.9.0",
+    "pillow-heif>=1.1.1",
+    "flask-jwt-extended>=4.7.1",
+    "bcrypt>=5.0.0",
+    "pony>=0.7.19",
+    "flask-login>=0.6.3",
+    "quart>=0.20.0",
+    "tortoise-orm>=0.25.1",
+    "quart-jwt-extended>=0.1.0",
+    "pre-commit>=4.3.0",
+    "tortoise-orm-stubs>=1.0.2",
+    "aerich>=0.8.0",
+    "tomlkit>=0.13.3",
+    "authlib>=1.3.0",
+    "asyncpg>=0.30.0",
+    "langchain-openai>=1.1.6",
+    "langchain>=1.2.0",
+    "langchain-chroma>=1.0.0",
+    "langchain-community>=0.4.1",
+    "jq>=1.10.0",
+    "tavily-python>=0.7.17",
+]
+
+[tool.aerich]
+tortoise_orm = "app.TORTOISE_CONFIG"
+location = "./migrations"
+src_folder = "./."

raggr-frontend/Dockerfile.dev

@@ -8,8 +8,11 @@ COPY package.json yarn.lock* ./
 # Install dependencies
 RUN yarn install
 
+# Copy application source code
+COPY . .
+
 # Expose rsbuild dev server port (default 3000)
 EXPOSE 3000
 
-# The actual source code will be mounted as a volume
+# Default command
-# CMD will be specified in docker-compose
+CMD ["sh", "-c", "yarn build && yarn watch:build"]

raggr-frontend/src/api/conversationService.ts

@@ -37,7 +37,7 @@ class ConversationService {
     conversation_id: string,
   ): Promise<QueryResponse> {
     const response = await userService.fetchWithRefreshToken(
-      `${this.baseUrl}/query`,
+      `${this.conversationBaseUrl}/query`,
       {
         method: "POST",
         body: JSON.stringify({ query, conversation_id }),

raggr-frontend/src/components/ChatScreen.tsx

@@ -40,6 +40,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
   const [selectedConversation, setSelectedConversation] =
     useState<Conversation | null>(null);
   const [sidebarCollapsed, setSidebarCollapsed] = useState<boolean>(false);
+  const [isLoading, setIsLoading] = useState<boolean>(false);
 
   const messagesEndRef = useRef<HTMLDivElement>(null);
   const simbaAnswers = ["meow.", "hiss...", "purrrrrr", "yowOWROWWowowr"];
@@ -80,6 +81,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
       setConversations(parsedConversations);
       setSelectedConversation(parsedConversations[0]);
       console.log(parsedConversations);
+      console.log("JELLYFISH@");
     } catch (error) {
       console.error("Failed to load messages:", error);
     }
@@ -104,11 +106,18 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
 
   useEffect(() => {
     const loadMessages = async () => {
+      console.log(selectedConversation);
+      console.log("JELLYFISH");
       if (selectedConversation == null) return;
       try {
         const conversation = await conversationService.getConversation(
           selectedConversation.id,
         );
+        // Update the conversation title in case it changed
+        setSelectedConversation({
+          id: conversation.id,
+          title: conversation.name,
+        });
         setMessages(
           conversation.messages.map((message) => ({
             text: message.text,
@@ -120,14 +129,15 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
       }
     };
     loadMessages();
-  }, [selectedConversation]);
+  }, [selectedConversation?.id]);
 
   const handleQuestionSubmit = async () => {
-    if (!query.trim()) return; // Don't submit empty messages
+    if (!query.trim() || isLoading) return; // Don't submit empty messages or while loading
 
     const currMessages = messages.concat([{ text: query, speaker: "user" }]);
     setMessages(currMessages);
     setQuery(""); // Clear input immediately after submission
+    setIsLoading(true);
 
     if (simbaMode) {
       console.log("simba mode activated");
@@ -142,6 +152,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
           },
         ]),
       );
+      setIsLoading(false);
       return;
     }
 
@@ -162,6 +173,8 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
       if (error instanceof Error && error.message.includes("Session expired")) {
         setAuthenticated(false);
       }
+    } finally {
+      setIsLoading(false);
     }
   };
 
@@ -180,9 +193,11 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
   return (
     <div className="h-screen flex flex-row bg-[#F9F5EB]">
       {/* Sidebar - Expanded */}
-      <aside className={`hidden md:flex md:flex-col bg-white border-r border-gray-200 p-4 overflow-y-auto transition-all duration-300 ${sidebarCollapsed ? 'w-20' : 'w-64'}`}>
+      <aside
+        className={`hidden md:flex md:flex-col bg-[#F9F5EB] border-r border-gray-200 p-4 overflow-y-auto transition-all duration-300 ${sidebarCollapsed ? "w-20" : "w-64"}`}
+      >
         {!sidebarCollapsed ? (
-          <>
+          <div className="bg-[#F9F5EB]">
             <div className="flex flex-row items-center gap-2 mb-6">
               <img
                 src={catIcon}
@@ -205,7 +220,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
                 logout
               </button>
             </div>
-          </>
+          </div>
         ) : (
           <div className="flex flex-col items-center gap-4">
             <img
@@ -243,7 +258,18 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
         </header>
 
         {/* Messages area */}
-        <div className="flex-1 overflow-y-auto px-4 py-4">
+        {selectedConversation && (
+          <div className="sticky top-0 mx-auto w-full">
+            <div className="bg-[#F9F5EB] text-black px-6 w-full py-3">
+              <h2 className="text-lg font-semibold">
+                {selectedConversation.title || "Untitled Conversation"}
+              </h2>
+            </div>
+          </div>
+        )}
+        <div className="flex-1 overflow-y-auto relative px-4 py-6">
+          {/* Floating conversation name */}
+
           <div className="max-w-2xl mx-auto flex flex-col gap-4">
             {showConversations && (
               <div className="md:hidden">
@@ -260,6 +286,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
               }
               return <QuestionBubble key={index} text={msg.text} />;
             })}
+            {isLoading && <AnswerBubble text="" loading={true} />}
             <div ref={messagesEndRef} />
           </div>
         </div>
@@ -273,6 +300,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
               handleKeyDown={handleKeyDown}
               handleQuestionSubmit={handleQuestionSubmit}
               setSimbaMode={setSimbaMode}
+              isLoading={isLoading}
             />
           </div>
         </footer>

raggr-frontend/src/components/MessageInput.tsx

@@ -0,0 +1,56 @@
+import { useEffect, useState, useRef } from "react";
+
+type MessageInputProps = {
+  handleQueryChange: (event: React.ChangeEvent<HTMLTextAreaElement>) => void;
+  handleKeyDown: (event: React.ChangeEvent<HTMLTextAreaElement>) => void;
+  handleQuestionSubmit: () => void;
+  setSimbaMode: (sdf: boolean) => void;
+  query: string;
+  isLoading: boolean;
+};
+
+export const MessageInput = ({
+  query,
+  handleKeyDown,
+  handleQueryChange,
+  handleQuestionSubmit,
+  setSimbaMode,
+  isLoading,
+}: MessageInputProps) => {
+  return (
+    <div className="flex flex-col gap-4 sticky bottom-0 bg-[#3D763A] p-6 rounded-xl">
+      <div className="flex flex-row justify-between grow">
+        <textarea
+          className="p-3 sm:p-4 border border-blue-200 rounded-md grow bg-[#F9F5EB] min-h-[44px] resize-y"
+          onChange={handleQueryChange}
+          onKeyDown={handleKeyDown}
+          value={query}
+          rows={2}
+          placeholder="Type your message... (Press Enter to send, Shift+Enter for new line)"
+        />
+      </div>
+      <div className="flex flex-row justify-between gap-2 grow">
+        <button
+          className={`p-3 sm:p-4 min-h-[44px] border border-blue-400 rounded-md flex-grow text-sm sm:text-base ${
+            isLoading
+              ? "bg-gray-400 cursor-not-allowed opacity-50"
+              : "bg-[#EDA541] hover:bg-blue-400 cursor-pointer"
+          }`}
+          onClick={() => handleQuestionSubmit()}
+          type="submit"
+          disabled={isLoading}
+        >
+          {isLoading ? "Sending..." : "Submit"}
+        </button>
+      </div>
+      <div className="flex flex-row justify-center gap-2 grow items-center">
+        <input
+          type="checkbox"
+          onChange={(event) => setSimbaMode(event.target.checked)}
+          className="w-5 h-5 cursor-pointer"
+        />
+        <p className="text-sm sm:text-base">simba mode?</p>
+      </div>
+    </div>
+  );
+};

scripts/index_immich.py

@@ -1,18 +1,21 @@
-import httpx
-import os
-from pathlib import Path
 import logging
-import tempfile
+import os
-
-from image_process import describe_simba_image
-from request import PaperlessNGXService
 import sqlite3
 
+import httpx
+from dotenv import load_dotenv
+import sys
+from pathlib import Path
+
+# Add parent directory to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from utils.image_process import describe_simba_image
+from utils.request import PaperlessNGXService
+
 logging.basicConfig(level=logging.INFO)
 
 
-from dotenv import load_dotenv
-
 load_dotenv()
 
 # Configuration from environment variables
@@ -89,7 +92,7 @@ if __name__ == "__main__":
             image_date = description.image_date
 
             description_filepath = os.path.join(
-                "/Users/ryanchen/Programs/raggr", f"SIMBA_DESCRIBE_001.txt"
+                "/Users/ryanchen/Programs/raggr", "SIMBA_DESCRIBE_001.txt"
             )
             file = open(description_filepath, "w+")
             file.write(image_description)

scripts/inspect_vector_store.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""CLI tool to inspect the vector store contents."""
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+
+from blueprints.rag.logic import (
+    get_vector_store_stats,
+    index_documents,
+    list_all_documents,
+)
+
+# Load .env from the root directory
+root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+env_path = os.path.join(root_dir, ".env")
+load_dotenv(env_path)
+
+
+def print_stats():
+    """Print vector store statistics."""
+    stats = get_vector_store_stats()
+    print("=== Vector Store Statistics ===")
+    print(f"Collection Name: {stats['collection_name']}")
+    print(f"Total Documents: {stats['total_documents']}")
+    print()
+
+
+def print_documents(limit: int = 10, show_content: bool = False):
+    """Print documents in the vector store."""
+    docs = list_all_documents(limit=limit)
+    print(f"=== Documents (showing {len(docs)} of {limit} requested) ===\n")
+
+    for i, doc in enumerate(docs, 1):
+        print(f"Document {i}:")
+        print(f"  ID: {doc['id']}")
+        print(f"  Metadata: {doc['metadata']}")
+        if show_content:
+            print(f"  Content Preview: {doc['content_preview']}")
+        print()
+
+
+async def run_index():
+    """Run the indexing process."""
+    print("Starting indexing process...")
+    await index_documents()
+    print("Indexing complete!")
+    print_stats()
+
+
+def main():
+    import asyncio
+
+    parser = argparse.ArgumentParser(description="Inspect the vector store contents")
+    parser.add_argument(
+        "--stats", action="store_true", help="Show vector store statistics"
+    )
+    parser.add_argument(
+        "--list", type=int, metavar="N", help="List N documents from the vector store"
+    )
+    parser.add_argument(
+        "--show-content",
+        action="store_true",
+        help="Show content preview when listing documents",
+    )
+    parser.add_argument(
+        "--index",
+        action="store_true",
+        help="Index documents from Paperless-NGX into the vector store",
+    )
+
+    args = parser.parse_args()
+
+    # Handle indexing first if requested
+    if args.index:
+        asyncio.run(run_index())
+        return
+
+    # If no arguments provided, show stats by default
+    if not any([args.stats, args.list]):
+        args.stats = True
+
+    if args.stats:
+        print_stats()
+
+    if args.list:
+        print_documents(limit=args.list, show_content=args.show_content)
+
+
+if __name__ == "__main__":
+    main()

scripts/manage_vectorstore.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""Management script for vector store operations."""
+
+import argparse
+import asyncio
+import sys
+
+from blueprints.rag.logic import (
+    get_vector_store_stats,
+    index_documents,
+    list_all_documents,
+    vector_store,
+)
+
+
+def stats():
+    """Show vector store statistics."""
+    stats = get_vector_store_stats()
+    print("=== Vector Store Statistics ===")
+    print(f"Collection: {stats['collection_name']}")
+    print(f"Total Documents: {stats['total_documents']}")
+
+
+async def index():
+    """Index documents from Paperless-NGX."""
+    print("Starting indexing process...")
+    print("Fetching documents from Paperless-NGX...")
+    await index_documents()
+    print("✓ Indexing complete!")
+    stats()
+
+
+async def reindex():
+    """Clear and reindex all documents."""
+    print("Clearing existing documents...")
+    collection = vector_store._collection
+    all_docs = collection.get()
+
+    if all_docs["ids"]:
+        print(f"Deleting {len(all_docs['ids'])} existing documents...")
+        collection.delete(ids=all_docs["ids"])
+        print("✓ Cleared")
+    else:
+        print("Collection is already empty")
+
+    await index()
+
+
+def list_docs(limit: int = 10, show_content: bool = False):
+    """List documents in the vector store."""
+    docs = list_all_documents(limit=limit)
+    print(f"\n=== Documents (showing {len(docs)}) ===\n")
+
+    for i, doc in enumerate(docs, 1):
+        print(f"Document {i}:")
+        print(f"  ID: {doc['id']}")
+        print(f"  Metadata: {doc['metadata']}")
+        if show_content:
+            print(f"  Content: {doc['content_preview']}")
+        print()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Manage vector store for RAG system",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s stats                    # Show vector store statistics
+  %(prog)s index                    # Index new documents from Paperless-NGX
+  %(prog)s reindex                  # Clear and reindex all documents
+  %(prog)s list 10                  # List first 10 documents
+  %(prog)s list 20 --show-content   # List 20 documents with content preview
+        """,
+    )
+
+    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
+
+    # Stats command
+    subparsers.add_parser("stats", help="Show vector store statistics")
+
+    # Index command
+    subparsers.add_parser("index", help="Index documents from Paperless-NGX")
+
+    # Reindex command
+    subparsers.add_parser("reindex", help="Clear and reindex all documents")
+
+    # List command
+    list_parser = subparsers.add_parser("list", help="List documents in vector store")
+    list_parser.add_argument(
+        "limit", type=int, default=10, nargs="?", help="Number of documents to list"
+    )
+    list_parser.add_argument(
+        "--show-content", action="store_true", help="Show content preview"
+    )
+
+    args = parser.parse_args()
+
+    if not args.command:
+        parser.print_help()
+        sys.exit(1)
+
+    try:
+        if args.command == "stats":
+            stats()
+        elif args.command == "index":
+            asyncio.run(index())
+        elif args.command == "reindex":
+            asyncio.run(reindex())
+        elif args.command == "list":
+            list_docs(limit=args.limit, show_content=args.show_content)
+    except KeyboardInterrupt:
+        print("\n\nOperation cancelled by user")
+        sys.exit(1)
+    except Exception as e:
+        print(f"\n❌ Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/query.py

@@ -1,18 +1,11 @@
 import json
-import os
 from typing import Literal
 import datetime
-from ollama import Client
 
 from openai import OpenAI
 
 from pydantic import BaseModel, Field
 
-# Configure ollama client with URL from environment or default to localhost
-ollama_client = Client(
-    host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
-)
-
 # This uses inferred filters — which means using LLM to create the metadata filters
 
 

scripts/test_query.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+"""Test the query_vector_store function."""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+
+from blueprints.rag.logic import query_vector_store
+
+# Load .env from the root directory
+root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+env_path = os.path.join(root_dir, ".env")
+load_dotenv(env_path)
+
+
+async def test_query(query: str):
+    """Test a query against the vector store."""
+    print(f"Query: {query}\n")
+    result, docs = await query_vector_store(query)
+    print(f"Found {len(docs)} documents\n")
+    print("Serialized result:")
+    print(result)
+    print("\n" + "=" * 80 + "\n")
+
+
+async def main():
+    queries = [
+        "What is Simba's weight?",
+        "What medications is Simba taking?",
+        "Tell me about Simba's recent vet visits",
+    ]
+
+    for query in queries:
+        await test_query(query)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

scripts/user_message_stats.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Script to show how many messages each user has written
+"""
+
+import asyncio
+from tortoise import Tortoise
+from blueprints.users.models import User
+from blueprints.conversation.models import Speaker
+import os
+
+
+async def get_user_message_stats():
+    """Get message count statistics per user"""
+
+    # Initialize database connection
+    database_url = os.getenv("DATABASE_URL", "sqlite://raggr.db")
+    await Tortoise.init(
+        db_url=database_url,
+        modules={
+            "models": [
+                "blueprints.users.models",
+                "blueprints.conversation.models",
+            ]
+        },
+    )
+
+    print("\n📊 User Message Statistics\n")
+    print(
+        f"{'Username':<20} {'Total Messages':<15} {'User Messages':<15} {'Conversations':<15}"
+    )
+    print("=" * 70)
+
+    # Get all users
+    users = await User.all()
+
+    total_users = 0
+    total_messages = 0
+
+    for user in users:
+        # Get all conversations for this user
+        conversations = await user.conversations.all()
+
+        if not conversations:
+            continue
+
+        total_users += 1
+
+        # Count messages across all conversations
+        user_message_count = 0
+        total_message_count = 0
+
+        for conversation in conversations:
+            messages = await conversation.messages.all()
+            total_message_count += len(messages)
+
+            # Count only user messages (not assistant responses)
+            user_messages = [msg for msg in messages if msg.speaker == Speaker.USER]
+            user_message_count += len(user_messages)
+
+        total_messages += user_message_count
+
+        print(
+            f"{user.username:<20} {total_message_count:<15} {user_message_count:<15} {len(conversations):<15}"
+        )
+
+    print("=" * 70)
+    print("\n📈 Summary:")
+    print(f"   Total active users: {total_users}")
+    print(f"   Total user messages: {total_messages}")
+    print(
+        f"   Average messages per user: {total_messages / total_users if total_users > 0 else 0:.1f}\n"
+    )
+
+    await Tortoise.close_connections()
+
+
+if __name__ == "__main__":
+    asyncio.run(get_user_message_stats())

services/raggr/.dockerignore

@@ -1,16 +0,0 @@
-.git
-.gitignore
-README.md
-.env
-.DS_Store
-chromadb/
-chroma_db/
-raggr-frontend/node_modules/
-__pycache__/
-*.pyc
-*.pyo
-*.pyd
-.Python
-.venv/
-venv/
-.pytest_cache/

services/raggr/.python-version

@@ -1 +0,0 @@
-3.13

services/raggr/Dockerfile.dev

@@ -1,33 +0,0 @@
-FROM python:3.13-slim
-
-WORKDIR /app
-
-# Install system dependencies and uv
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    && rm -rf /var/lib/apt/lists/* \
-    && curl -LsSf https://astral.sh/uv/install.sh | sh
-
-# Add uv to PATH
-ENV PATH="/root/.local/bin:$PATH"
-
-# Copy dependency files
-COPY pyproject.toml ./
-
-# Install Python dependencies using uv
-RUN uv pip install --system -e .
-
-# Create ChromaDB and database directories
-RUN mkdir -p /app/chromadb /app/database
-
-# Expose port
-EXPOSE 8080
-
-# Set environment variables
-ENV PYTHONPATH=/app
-ENV CHROMADB_PATH=/app/chromadb
-ENV PYTHONUNBUFFERED=1
-
-# The actual source code will be mounted as a volume
-# No CMD here - will be specified in docker-compose

services/raggr/blueprints/conversation/__init__.py

@@ -1,72 +0,0 @@
-import datetime
-
-from quart_jwt_extended import (
-    jwt_refresh_token_required,
-    get_jwt_identity,
-)
-
-from quart import Blueprint, jsonify
-from .models import (
-    Conversation,
-    PydConversation,
-    PydListConversation,
-)
-
-import blueprints.users.models
-
-conversation_blueprint = Blueprint(
-    "conversation_api", __name__, url_prefix="/api/conversation"
-)
-
-
-@conversation_blueprint.route("/<conversation_id>")
-async def get_conversation(conversation_id: str):
-    conversation = await Conversation.get(id=conversation_id)
-    await conversation.fetch_related("messages")
-
-    # Manually serialize the conversation with messages
-    messages = []
-    for msg in conversation.messages:
-        messages.append(
-            {
-                "id": str(msg.id),
-                "text": msg.text,
-                "speaker": msg.speaker.value,
-                "created_at": msg.created_at.isoformat(),
-            }
-        )
-
-    return jsonify(
-        {
-            "id": str(conversation.id),
-            "name": conversation.name,
-            "messages": messages,
-            "created_at": conversation.created_at.isoformat(),
-            "updated_at": conversation.updated_at.isoformat(),
-        }
-    )
-
-
-@conversation_blueprint.post("/")
-@jwt_refresh_token_required
-async def create_conversation():
-    user_uuid = get_jwt_identity()
-    user = await blueprints.users.models.User.get(id=user_uuid)
-    conversation = await Conversation.create(
-        name=f"{user.username} {datetime.datetime.now().timestamp}",
-        user=user,
-    )
-
-    serialized_conversation = await PydConversation.from_tortoise_orm(conversation)
-    return jsonify(serialized_conversation.model_dump())
-
-
-@conversation_blueprint.get("/")
-@jwt_refresh_token_required
-async def get_all_conversations():
-    user_uuid = get_jwt_identity()
-    user = await blueprints.users.models.User.get(id=user_uuid)
-    conversations = Conversation.filter(user=user)
-    serialized_conversations = await PydListConversation.from_queryset(conversations)
-
-    return jsonify(serialized_conversations.model_dump())

services/raggr/llm.py

@@ -1,73 +0,0 @@
-import os
-
-from ollama import Client
-from openai import OpenAI
-
-import logging
-from dotenv import load_dotenv
-
-load_dotenv()
-
-logging.basicConfig(level=logging.INFO)
-
-TRY_OLLAMA = os.getenv("TRY_OLLAMA", False)
-
-
-class LLMClient:
-    def __init__(self):
-        try:
-            self.ollama_client = Client(
-                host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=1.0
-            )
-            self.ollama_client.chat(
-                model="gemma3:4b", messages=[{"role": "system", "content": "test"}]
-            )
-            self.PROVIDER = "ollama"
-            logging.info("Using Ollama as LLM backend")
-        except Exception as e:
-            print(e)
-            self.openai_client = OpenAI()
-            self.PROVIDER = "openai"
-            logging.info("Using OpenAI as LLM backend")
-
-    def chat(
-        self,
-        prompt: str,
-        system_prompt: str,
-    ):
-        # Instituting a fallback if my gaming PC is not on
-        if self.PROVIDER == "ollama":
-            try:
-                response = self.ollama_client.chat(
-                    model="gemma3:4b",
-                    messages=[
-                        {
-                            "role": "system",
-                            "content": system_prompt,
-                        },
-                        {"role": "user", "content": prompt},
-                    ],
-                )
-                output = response.message.content
-                return output
-            except Exception as e:
-                logging.error(f"Could not connect to OLLAMA: {str(e)}")
-
-        response = self.openai_client.responses.create(
-            model="gpt-4o-mini",
-            input=[
-                {
-                    "role": "system",
-                    "content": system_prompt,
-                },
-                {"role": "user", "content": prompt},
-            ],
-        )
-        output = response.output_text
-
-        return output
-
-
-if __name__ == "__main__":
-    client = Client()
-    client.chat(model="gemma3:4b", messages=[{"role": "system", "promp": "hack"}])

services/raggr/migrations/models/0_20251225052005_init.py

@@ -1,71 +0,0 @@
-from tortoise import BaseDBAsyncClient
-
-RUN_IN_TRANSACTION = True
-
-
-async def upgrade(db: BaseDBAsyncClient) -> str:
-    return """
-        CREATE TABLE IF NOT EXISTS "users" (
-    "id" UUID NOT NULL PRIMARY KEY,
-    "username" VARCHAR(255) NOT NULL,
-    "password" BYTEA,
-    "email" VARCHAR(100) NOT NULL UNIQUE,
-    "oidc_subject" VARCHAR(255) UNIQUE,
-    "auth_provider" VARCHAR(50) NOT NULL DEFAULT 'local',
-    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updated_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP
-);
-CREATE INDEX IF NOT EXISTS "idx_users_oidc_su_5aec5a" ON "users" ("oidc_subject");
-CREATE TABLE IF NOT EXISTS "conversations" (
-    "id" UUID NOT NULL PRIMARY KEY,
-    "name" VARCHAR(255) NOT NULL,
-    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updated_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "user_id" UUID REFERENCES "users" ("id") ON DELETE CASCADE
-);
-CREATE TABLE IF NOT EXISTS "conversation_messages" (
-    "id" UUID NOT NULL PRIMARY KEY,
-    "text" TEXT NOT NULL,
-    "created_at" TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "speaker" VARCHAR(10) NOT NULL,
-    "conversation_id" UUID NOT NULL REFERENCES "conversations" ("id") ON DELETE CASCADE
-);
-COMMENT ON COLUMN "conversation_messages"."speaker" IS 'USER: user\nSIMBA: simba';
-CREATE TABLE IF NOT EXISTS "aerich" (
-    "id" SERIAL NOT NULL PRIMARY KEY,
-    "version" VARCHAR(255) NOT NULL,
-    "app" VARCHAR(100) NOT NULL,
-    "content" JSONB NOT NULL
-);"""
-
-
-async def downgrade(db: BaseDBAsyncClient) -> str:
-    return """
-        """
-
-
-MODELS_STATE = (
-    "eJztmmtP4zgUhv9KlE+MxCLoUGaEViulpex0Z9qO2nR3LjuK3MRtvSROJnYGKsR/X9u5J0"
-    "56AUqL+gXosU9sPz7OeY/Lveq4FrTJSdvFv6BPAEUuVi+VexUDB7I/pO3Higo8L23lBgom"
-    "tnAwMz1FC5gQ6gOTssYpsAlkJgsS00deNBgObJsbXZN1RHiWmgKMfgbQoO4M0jn0WcP3H8"
-    "yMsAXvIIk/ejfGFEHbys0bWXxsYTfowhO28bh7dS168uEmhunagYPT3t6Czl2cdA8CZJ1w"
-    "H942gxj6gEIrsww+y2jZsSmcMTNQP4DJVK3UYMEpCGwOQ/19GmCTM1DESPzH+R/qGngYao"
-    "4WYcpZ3D+Eq0rXLKwqH6r9QRsevb14I1bpEjrzRaMgoj4IR0BB6Cq4piDF7xLK9hz4cpRx"
-    "/wJMNtFNMMaGlGMaQzHIGNBm1FQH3Bk2xDM6Zx8bzWYNxr+1oSDJegmULovrMOr7UVMjbO"
-    "NIU4SmD/mSDUDLIK9YC0UOlMPMexaQWpHrSfzHjgJma7AG2F5Eh6CGr97tdUa61vvMV+IQ"
-    "8tMWiDS9w1sawrooWI8uCluRPET5p6t/UPhH5dug3ynGftJP/6byOYGAugZ2bw1gZc5rbI"
-    "3B5DY28KwNNzbvedjYF93YaPKZfSXQN9bLIBmXR6SRaG5b3MTNkwZPvdMbac7gMMrwrl0f"
-    "ohn+CBcCYZfNA2BTliwi0TGOHrOr0FJrOgsf3CZqJBsUbHVsTZCG2VMbtbWrjioYToB5cw"
-    "t8y6iA6UBCwAySMtBW5Hn9cQjtRJrJWWYFXC984m6+VarYClZuw80wytErNzkNp2gBmK3b"
-    "isbmI9XQWaKCMxBXE8NGdiMPonivRTGFd5KUrzOrHGXcf19EcV0q73zRc1k8lr5HPe3Lm1"
-    "wm/zTo/xl3z0jl9qdB66CQX6OQKitk4kFwIxMDvIDs4MApSYHc7mbcX/joqONRZ3ip8Iz+"
-    "Lx51ey3tUiHImQB1tS3OVZlnpysUmWenlTUmbyocoGyiWe81L3F9ynf+nkpYs3Dh9UgpW7"
-    "w/21mKSzWtJFzW1bbPqeREzSCRbnEtUa3V+NE+aLP912Z8H9e9tMz67ItG28LFpQcIuXV9"
-    "SWS2EAb+Qg4z61WAOVnQsP7Z1ZJeBq/F9WpWbjFkrW5fG36VS964fzZuW1/1jlagCx2A7H"
-    "WiNHF4mhBdfuKfMkDPTlcTPXWqpyR7XGSZBgkm/0FTUjlUkyz6bQS0GKTb5fksB55p+bnh"
-    "+e4vZFWJdjnQkuP23qKq7ZrAfkQaynNtrhKmzeoobZa1+aG4fZ3F7eHrn1exscntcqlIWX"
-    "Y1X/pfh6e5n99lgbTde3kN+sicq5J6Lmo5rqvoQNpnZ0q6Lq64IpZWdBxzIRiinX9RYSe+"
-    "HfmtcXb+7vz924vz96yLmElieVfzMuj29SUVHD8I0muXav2RcTnUb6mcY0djHREXdt9PgM"
-    "9SX7ARKcSS9P7XaNCvvE6NXQogx5gt8LuFTHqs2IjQH7uJtYYiX3X9dz/Fr3kKuZk/oCW7"
-    "eN3mZeHD/9BpOYI="
-)

services/raggr/pyproject.toml

@@ -1,12 +0,0 @@
-[project]
-name = "raggr"
-version = "0.1.0"
-description = "Add your description here"
-readme = "README.md"
-requires-python = ">=3.13"
-dependencies = ["chromadb>=1.1.0", "python-dotenv>=1.0.0", "flask>=3.1.2", "httpx>=0.28.1", "ollama>=0.6.0", "openai>=2.0.1", "pydantic>=2.11.9", "pillow>=10.0.0", "pymupdf>=1.24.0", "black>=25.9.0", "pillow-heif>=1.1.1", "flask-jwt-extended>=4.7.1", "bcrypt>=5.0.0", "pony>=0.7.19", "flask-login>=0.6.3", "quart>=0.20.0", "tortoise-orm>=0.25.1", "quart-jwt-extended>=0.1.0", "pre-commit>=4.3.0", "tortoise-orm-stubs>=1.0.2", "aerich>=0.8.0", "tomlkit>=0.13.3", "authlib>=1.3.0", "asyncpg>=0.30.0"]
-
-[tool.aerich]
-tortoise_orm = "app.TORTOISE_CONFIG"
-location = "./migrations"
-src_folder = "./."

services/raggr/raggr-frontend/src/components/MessageInput.tsx

@@ -1,43 +0,0 @@
-import { useEffect, useState, useRef } from "react";
-
-type MessageInputProps = {
-    handleQueryChange: (event: React.ChangeEvent<HTMLTextAreaElement>) => void;
-    handleKeyDown: (event: React.ChangeEvent<HTMLTextAreaElement>) => void;
-    handleQuestionSubmit: () => void;
-    setSimbaMode: (sdf: boolean) => void;
-    query: string;
-}
-
-export const MessageInput = ({query, handleKeyDown, handleQueryChange, handleQuestionSubmit, setSimbaMode}: MessageInputProps) => {
-    return (
-        <div className="flex flex-col gap-4 sticky bottom-0 bg-[#3D763A] p-6 rounded-xl">
-            <div className="flex flex-row justify-between grow">
-                <textarea
-                    className="p-3 sm:p-4 border border-blue-200 rounded-md grow bg-[#F9F5EB] min-h-[44px] resize-y"
-                    onChange={handleQueryChange}
-                    onKeyDown={handleKeyDown}
-                    value={query}
-                    rows={2}
-                    placeholder="Type your message... (Press Enter to send, Shift+Enter for new line)"
-                />
-            </div>
-            <div className="flex flex-row justify-between gap-2 grow">
-                <button
-                    className="p-3 sm:p-4 min-h-[44px] border border-blue-400 bg-[#EDA541] hover:bg-blue-400 cursor-pointer rounded-md flex-grow text-sm sm:text-base"
-                    onClick={() => handleQuestionSubmit()}
-                    type="submit"
-                >
-                    Submit
-                </button>
-            </div>
-            <div className="flex flex-row justify-center gap-2 grow items-center">
-                <input
-                    type="checkbox"
-                    onChange={(event) => setSimbaMode(event.target.checked)}
-                    className="w-5 h-5 cursor-pointer"
-                />
-                <p className="text-sm sm:text-base">simba mode?</p>
-            </div>
-        </div>
-    );
-}

startup-dev.sh

@@ -2,13 +2,12 @@
 set -e
 
 echo "Initializing directories..."
-mkdir -p /app/chromadb
+mkdir -p /app/data/chromadb
 
-echo "Waiting for frontend to build..."
+echo "Rebuilding frontend..."
-while [ ! -f /app/raggr-frontend/dist/index.html ]; do
+cd /app/raggr-frontend
-    sleep 1
+yarn build
-done
+cd /app
-echo "Frontend built successfully!"
 
 echo "Setting up database..."
 # Give PostgreSQL a moment to be ready (healthcheck in docker-compose handles this)
@@ -22,8 +21,5 @@ else
     aerich init-db
 fi
 
-echo "Starting reindex process..."
-python main.py "" --reindex || echo "Reindex failed, continuing anyway..."
-
 echo "Starting Flask application in debug mode..."
 python app.py

utils/chunker.py

@@ -3,7 +3,6 @@ from math import ceil
 import re
 from typing import Union
 from uuid import UUID, uuid4
-from ollama import Client
 from chromadb.utils.embedding_functions.openai_embedding_function import (
     OpenAIEmbeddingFunction,
 )
@@ -13,10 +12,6 @@ from llm import LLMClient
 
 load_dotenv()
 
-ollama_client = Client(
-    host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=1.0
-)
-
 
 def remove_headers_footers(text, header_patterns=None, footer_patterns=None):
     if header_patterns is None:

utils/cleaner.py

@@ -8,7 +8,7 @@ import ollama
 from PIL import Image
 import fitz
 
-from request import PaperlessNGXService
+from .request import PaperlessNGXService
 
 load_dotenv()