GitHub - param20h/PDF-Assistant-RAG: PDF-Assistant-RAG is a complete, production-ready AI document assistant that lets users upload complex PDFs, financial reports, legal contracts, and research papers — then chat with an AI that provides accurate, cited answers powered by a multi-stage Retrieval-Augmented Generation pipeline.

title	Document AI Analyst
emoji	🧠
colorFrom	indigo
colorTo	purple
sdk	docker
app_port	7860
pinned	true
license	mit
short_description	Enterprise Agentic RAG — upload PDFs and chat with AI

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Enterprise Agentic Retrieval-Augmented Generation System

Upload · Embed · Retrieve · Chat — A production-grade AI document assistant built end-to-end with an agentic RAG pipeline, streaming responses, and per-user data isolation.

## 🌟 GirlScript Summer of Code 2026

This project is an official participant in GirlScript Summer of Code 2026 (GSSoC'26) and welcomes contributions from the community.

Features · Tech Stack · Getting Started · Architecture · RAG Pipeline · API Reference · Deployment · Contributing

🤝 Contributors

Thanks to all the amazing people who have contributed to PDF-Assistant-RAG! 🎉

🌟 Want to join them? Check out CONTRIBUTING.md for contribution guidelines and look for good first issues to get started!

🌟 Overview

PDF-Assistant-RAG is a complete, production-ready AI document assistant that lets users upload complex PDFs, financial reports, legal contracts, and research papers — then chat with an AI that provides accurate, cited answers powered by a multi-stage Retrieval-Augmented Generation pipeline.

The system uses hybrid search (vector + BM25) with Reciprocal Rank Fusion and cross-encoder reranking to find the most relevant document chunks, streams AI-generated answers token-by-token, and highlights exact source citations with page numbers — all inside a modern Next.js frontend with JWT-secured per-user data isolation.

🏗️ Architecture

Contributor note: see docs/ARCHITECTURE.md for a route-by-route system map, request-flow diagrams, and Swagger/OpenAPI documentation guidance.

flowchart TD

subgraph group_backend["Backend"]
  node_backend_main["App entry<br/>FastAPI app<br/>[main.py]"]
  node_backend_routes["Routes<br/>API layer"]
  node_backend_auth["Auth<br/>JWT/bcrypt<br/>[auth.py]"]
  node_backend_documents["Documents<br/>doc lifecycle<br/>[documents.py]"]
  node_backend_chat["Chat<br/>streaming RAG<br/>[chat.py]"]
  node_backend_workspaces["Workspaces<br/>tenant scope<br/>[workspaces.py]"]
  node_backend_graph_api["Graph API<br/>knowledge graph<br/>[graph.py]"]
  node_backend_health["Health<br/>checks<br/>[health.py]"]
  node_backend_admin["Admin<br/>ops API<br/>[admin.py]"]
  node_backend_ingestion["Ingestion<br/>async pipeline"]
  node_backend_rag_retrieval["Retrieval<br/>hybrid RAG<br/>[retriever.py]"]
  node_backend_rag_generation["Generation<br/>prompt/LLM<br/>[prompts.py]"]
  node_backend_graph_rag["Graph RAG<br/>entity graph<br/>[graph_builder.py]"]
  node_backend_storage_db[("Database<br/>SQLAlchemy/PG<br/>[database.py]")]
  node_backend_storage_vector[("Vector store<br/>ChromaDB<br/>[vectorstore.py]")]
  node_backend_storage_cache[("Cache<br/>Redis/LRU<br/>[cache.py]")]
  node_backend_queue["Queue<br/>Celery/Redis<br/>[celery_app.py]"]
end

subgraph group_frontend["Frontend"]
  node_frontend_app["Next app<br/>App Router"]
  node_frontend_chat["Chat UI<br/>stream client"]
  node_frontend_documents["Docs UI<br/>document workspace"]
  node_frontend_graph["Graph view<br/>knowledge graph<br/>[KnowledgeGraph.tsx]"]
  node_frontend_state["State<br/>Zustand stores"]
  node_frontend_api["API client<br/>fetch layer<br/>[api.ts]"]
end

subgraph group_ops["Ops & Integrations"]
  node_integrations_bot["Discord bot<br/>integration<br/>[bot.py]"]
  node_ops_obs["Observability<br/>metrics/tracing<br/>[observability.py]"]
  node_ops_deploy["Deploy stack<br/>containers<br/>[docker-compose.yml]"]
end

node_frontend_app -->|"uses"| node_frontend_api
node_frontend_api -->|"requests"| node_backend_main
node_frontend_chat -->|"reads/writes"| node_frontend_state
node_frontend_documents -->|"reads/writes"| node_frontend_state
node_frontend_graph -->|"loads"| node_frontend_api
node_backend_main -->|"mounts"| node_backend_routes
node_backend_routes -->|"exposes"| node_backend_auth
node_backend_routes -->|"exposes"| node_backend_documents
node_backend_routes -->|"exposes"| node_backend_chat
node_backend_routes -->|"exposes"| node_backend_workspaces
node_backend_routes -->|"exposes"| node_backend_graph_api
node_backend_routes -->|"exposes"| node_backend_health
node_backend_routes -->|"exposes"| node_backend_admin
node_backend_documents -->|"enqueues"| node_backend_queue
node_backend_queue -->|"runs"| node_backend_ingestion
node_backend_ingestion -->|"persists"| node_backend_storage_db
node_backend_ingestion -->|"indexes"| node_backend_storage_vector
node_backend_ingestion -->|"caches"| node_backend_storage_cache
node_backend_chat -->|"retrieves"| node_backend_rag_retrieval
node_backend_rag_retrieval -->|"searches"| node_backend_storage_vector
node_backend_rag_retrieval -->|"filters"| node_backend_storage_db
node_backend_rag_retrieval -->|"supplies context"| node_backend_rag_generation
node_backend_rag_generation -->|"streams"| node_backend_chat
node_backend_graph_api -->|"queries"| node_backend_graph_rag
node_backend_graph_rag -->|"persists"| node_backend_storage_db
node_backend_auth -->|"authenticates"| node_backend_storage_db
node_backend_workspaces -->|"scopes"| node_backend_storage_db
node_backend_health -->|"checks"| node_backend_storage_db
node_backend_health -->|"checks"| node_backend_storage_cache
node_backend_health -->|"checks"| node_backend_queue
node_backend_health -->|"checks"| node_backend_storage_vector
node_ops_obs -->|"observes"| node_backend_main
node_ops_deploy -->|"runs"| node_backend_main
node_ops_deploy -->|"runs"| node_frontend_app
node_integrations_bot -.->|"integrates"| node_backend_routes

click node_backend_main "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/main.py"
click node_backend_routes "https://github.com/param20h/pdf-assistant-rag/tree/dev/backend/app/routes"
click node_backend_auth "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/auth.py"
click node_backend_documents "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/documents.py"
click node_backend_chat "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/chat.py"
click node_backend_workspaces "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/workspaces.py"
click node_backend_graph_api "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/graph.py"
click node_backend_health "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/health.py"
click node_backend_admin "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/routes/admin.py"
click node_backend_ingestion "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/services/document_ingestion.py"
click node_backend_rag_retrieval "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/rag/retriever.py"
click node_backend_rag_generation "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/rag/prompts.py"
click node_backend_graph_rag "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/rag/graph_builder.py"
click node_backend_storage_db "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/database.py"
click node_backend_storage_vector "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/rag/vectorstore.py"
click node_backend_storage_cache "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/cache.py"
click node_backend_queue "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/celery_app.py"
click node_frontend_app "https://github.com/param20h/pdf-assistant-rag/tree/dev/frontend/src/app"
click node_frontend_chat "https://github.com/param20h/pdf-assistant-rag/tree/dev/frontend/src/components/chat"
click node_frontend_documents "https://github.com/param20h/pdf-assistant-rag/tree/dev/frontend/src/components/document"
click node_frontend_graph "https://github.com/param20h/pdf-assistant-rag/blob/dev/frontend/src/components/graph/KnowledgeGraph.tsx"
click node_frontend_state "https://github.com/param20h/pdf-assistant-rag/tree/dev/frontend/src/store"
click node_frontend_api "https://github.com/param20h/pdf-assistant-rag/blob/dev/frontend/src/lib/api.ts"
click node_integrations_bot "https://github.com/param20h/pdf-assistant-rag/blob/dev/bots/discord/bot.py"
click node_ops_obs "https://github.com/param20h/pdf-assistant-rag/blob/dev/backend/app/observability.py"
click node_ops_deploy "https://github.com/param20h/pdf-assistant-rag/blob/dev/docker-compose.yml"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_backend_main,node_backend_routes,node_backend_auth,node_backend_documents,node_backend_chat,node_backend_workspaces,node_backend_graph_api,node_backend_health,node_backend_admin,node_backend_ingestion,node_backend_rag_retrieval,node_backend_rag_generation,node_backend_graph_rag,node_backend_storage_db,node_backend_storage_vector,node_backend_storage_cache,node_backend_queue toneBlue
class node_frontend_app,node_frontend_chat,node_frontend_documents,node_frontend_graph,node_frontend_state,node_frontend_api toneAmber
class node_integrations_bot,node_ops_obs,node_ops_deploy toneMint

🔄 System Flow Overview

User uploads a document via the Next.js frontend.
FastAPI queues a Celery ingestion task backed by Redis.
The worker chunks the document, generates local embeddings (cached via Redis/LRU), builds a BM25 index, and stores vectors in ChromaDB.
At query time, hybrid search merges vector and BM25 results via Reciprocal Rank Fusion.
A cross-encoder reranker refines the top candidates.
The agent assembles a prompt and calls the HuggingFace Inference API.
Streamed SSE tokens are returned to the frontend chat panel.

🛠 Tech Stack

Backend

	Technology	Purpose
	FastAPI	Async web framework + routing
	Python 3.11	Runtime environment
	PostgreSQL / SQLite	Relational database (SQLAlchemy ORM)
	JWT + bcrypt	Authentication & password hashing
	ChromaDB	Local vector store (embeddings)
	HuggingFace Inference API	LLM answer generation
	sentence-transformers	Local embedding model (all-MiniLM-L6-v2)

Frontend

	Technology	Purpose
	Next.js 14	React framework (App Router)
	TypeScript	Frontend language
	Tailwind CSS	Utility-first styling

AI / ML Pipeline

	Technology	Purpose
	sentence-transformers (all-MiniLM-L6-v2)	Generates vector embeddings for document chunks
	ChromaDB	Stores + retrieves embeddings locally
	HuggingFace Inference API	Generates answers from retrieved context
	BAAI/bge-reranker-v2-m3	Cross-encoder reranking for retrieval quality
	Knowledge Graph (GraphRAG)	Entity extraction + relationship graphs
	PyMuPDF + pdfplumber + python-docx	Document text extraction

DevOps & Tooling

	Technology	Purpose
	Docker Multi-Stage	Containerised deployment
	GitHub Actions	CI/CD (E2E, security, deploy)
	Playwright	E2E + visual regression tests
	Prometheus + Grafana	Metrics & observability
	HuggingFace Spaces	Production deployment

✨ Key Features

🆕 Recent Updates

🤖 Discord Bot Integration
⚡ Celery + Redis Background PDF Processing
📧 Email Verification Workflow
🧠 RAGAS Evaluation Pipeline
🚀 Response Caching with Redis
🐳 Optimized Docker Deployment

👤 Users

🔐 JWT-secured register, login & email verification
📄 Upload PDF, DOCX, TXT, and Markdown
🌐 URL ingestion via web crawler
💬 Ask questions in natural language
🌊 Streaming AI responses token-by-token
📚 Inline source citations with page numbers
📥 Export chat as Markdown, TXT, or PDF
🗂️ Per-user complete data isolation

🤖 RAG Pipeline

🔪 Smart recursive text chunking (configurable size & overlap)
🧠 Local embeddings — no data leaves your machine
⚡ Embedding cache (Redis + LRU) — skip redundant computation
🔍 Hybrid search — vector + BM25 merged via RRF
🏆 Cross-encoder reranking for precision answers
🖼️ Image caption extraction from PDF figures
🔗 URL extraction from PDF link annotations
🗺️ Knowledge graph (GraphRAG) per document

⚙️ Engineering

🚀 Async FastAPI with SSE streaming
🗄️ PostgreSQL metadata + ChromaDB vectors
🔄 Celery + Redis async ingestion pipeline
🐳 Multi-stage Docker with CPU & GPU profiles
📊 Prometheus metrics + Grafana dashboard
🩺 Deep health endpoint — DB, Redis, Celery, ChromaDB
🔒 Rate limiting, CORS, file validation, JWT expiry
🧪 Playwright E2E + visual regression tests

📁 Project Structure

PDF-Assistant-RAG/
│
├── backend/
│   ├── app/
│   │   ├── main.py                 # FastAPI app — lifespan, middleware, routers
│   │   ├── config.py               # Pydantic settings (env vars)
│   │   ├── models.py               # SQLAlchemy ORM models
│   │   ├── schemas.py              # Pydantic request/response schemas
│   │   ├── database.py             # Engine, session, migrations
│   │   ├── auth.py                 # JWT helpers
│   │   ├── tasks.py                # Celery task definitions
│   │   │
│   │   ├── routes/
│   │   │   ├── auth.py             # Register, login, OAuth
│   │   │   ├── documents.py        # Upload, list, delete, status
│   │   │   ├── chat.py             # Ask, stream, history, export
│   │   │   ├── health.py           # Deep health check endpoint
│   │   │   ├── admin.py            # Admin stats
│   │   │   └── workspaces.py       # Workspace management
│   │   │
│   │   ├── rag/
│   │   │   ├── chunker.py          # PDF/DOCX/TXT extraction + chunking
│   │   │   ├── embeddings.py       # Local embeddings + Redis/LRU cache
│   │   │   ├── vectorstore.py      # ChromaDB operations
│   │   │   ├── bm25.py             # BM25 index per document
│   │   │   ├── retriever.py        # Hybrid search + RRF + reranking
│   │   │   ├── reranker.py         # Cross-encoder reranker
│   │   │   ├── vision.py           # Image caption extraction
│   │   │   ├── url_extractor.py    # PDF URL/link extraction
│   │   │   ├── graph_builder.py    # Knowledge graph (GraphRAG)
│   │   │   ├── agent.py            # LLM answer generation
│   │   │   └── summarizer.py       # Document summarisation
│   │   │
│   │   └── services/
│   │       └── document_ingestion.py  # End-to-end ingestion pipeline
│   │
│   ├── tests/                      # pytest test suite
│   ├── requirements.txt
│   └── migrate_add_extracted_urls.py
│
├── frontend/
│   ├── src/
│   │   ├── app/                    # Next.js App Router pages
│   │   ├── components/             # React components
│   │   ├── store/                  # Zustand state stores
│   │   ├── lib/                    # API client, auth, utilities
│   │   └── services/               # API service layer
│   ├── e2e/                        # Playwright E2E + snapshot tests
│   ├── next.config.ts
│   └── playwright.config.ts
│
├── docs/
│   └── ARCHITECTURE.md
│
├── .github/
│   └── workflows/
│       ├── ci.yml                  # Backend CI
│       ├── e2e.yml                 # Playwright E2E + visual regression
│       ├── deploy.yml              # Docker build (main branch)
│       └── devsecops.yml           # Security scans
│
├── docker-compose.yml              # CPU + GPU + debug profiles + log rotation
├── Dockerfile                      # Multi-stage backend build
├── frontend/Dockerfile             # Multi-stage frontend build (nginx)
└── .env.example

🚀 Getting Started

Prerequisites

Python 3.11+
Node.js 20+
Docker + Docker Compose (recommended)
HuggingFace API token — huggingface.co/settings/tokens (free)

1. Clone the Repository

git clone https://github.com/param20h/PDF-Assistant-RAG.git
cd PDF-Assistant-RAG

2. Configure Environment

cp .env.example .env

Edit .env:

SECRET_KEY=your-strong-random-secret
DATABASE_URL=postgresql://pdf_rag_user:pdf_rag_pass@localhost:5432/pdf_rag
HF_TOKEN=hf_your_huggingface_token_here
UPLOAD_DIR=./data/uploads
CHROMA_PERSIST_DIR=./data/chroma_db
CELERY_BROKER_URL=redis://localhost:6379/0
CELERY_RESULT_BACKEND=redis://localhost:6379/1

Get your free HuggingFace token at huggingface.co/settings/tokens

Email Verification Setup (optional)

FRONTEND_URL=http://localhost:3000
MAIL_USERNAME=your_smtp_username
MAIL_PASSWORD=your_smtp_or_gmail_app_password
MAIL_FROM=your_sender_email@example.com
MAIL_SERVER=smtp.gmail.com
MAIL_PORT=587
MAIL_STARTTLS=True
MAIL_SSL_TLS=False

Without SMTP settings, registration returns a local verification link so contributors can test without email credentials.

3. Run with Docker (recommended)

# CPU-only (no GPU needed)
docker compose --profile cpu up --build

# GPU-accelerated (requires NVIDIA Container Toolkit)
docker compose --profile gpu up --build

# Also start pgAdmin at http://localhost:5050
docker compose --profile cpu --profile debug up --build

Service	URL
Frontend	http://localhost:3000
Backend API	http://localhost:7860
API Docs	http://localhost:7860/docs
pgAdmin	http://localhost:5050 (debug profile)

4. Run Locally (without Docker)

# Backend
cd backend
python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 7860

# Celery worker (separate terminal)
celery -A app.celery_app.celery_app worker --loglevel=info

# Frontend (separate terminal)
cd frontend
npm install
npm run dev

5. Set up crawl4ai (URL Upload Feature — optional)

crawl4ai-setup

🧠 RAG Pipeline

                    ┌─────────────────────────────────────────────┐
                    │         PDF / DOCX / TXT / MD Upload        │
                    └───────────────────┬─────────────────────────┘
                                        │
                                        ▼
                    ┌─────────────────────────────────────────────┐
                    │   PyMuPDF / pdfplumber / python-docx Parser │
                    │   + Image caption extraction                │
                    │   + PDF URL/link annotation extraction      │
                    └───────────────────┬─────────────────────────┘
                                        │
                                        ▼
                    ┌─────────────────────────────────────────────┐
                    │      Recursive Character Text Splitter      │
                    │   chunk_size=1000  |  overlap=200           │
                    └───────────────────┬─────────────────────────┘
                                        │
                                        ▼
                    ┌─────────────────────────────────────────────┐
                    │  all-MiniLM-L6-v2  (local embeddings)       │
                    │  384-dim · Redis + LRU cache (24h TTL)      │
                    └──────────────┬──────────────────────────────┘
                                   │
                    ┌──────────────┴──────────────┐
                    ▼                             ▼
         ┌──────────────────┐         ┌─────────────────────┐
         │ ChromaDB vectors │         │  BM25 keyword index │
         │ (per-user coll.) │         │  (per-document .pkl)│
         └──────────────────┘         └─────────────────────┘

                          ── At Query Time ──

  User Question ──▶ Embed (cached) ──▶ Vector Search (Top-K=20)
                         │
                         ├──▶ BM25 Search (Top-K=20)
                         │
                         ▼
              Reciprocal Rank Fusion (RRF, k=60)
                         │
                         ▼
          BGE-Reranker-v2-m3 Cross-Encoder (Top-K=8)
                         │
                         ▼
        Prompt Assembly (system + context + question)
                         │
                         ▼
        Qwen2.5-72B-Instruct (HF Inference API)
                         │
                         ▼
        Streamed SSE tokens ──▶ Frontend ChatPanel

📡 API Reference

Method	Endpoint	Auth	Description
`POST`	`/api/v1/auth/register`	❌	Create a new user account
`POST`	`/api/v1/auth/login`	❌	Login and receive JWT tokens
`GET`	`/api/v1/auth/me`	✅	Get current user profile
`POST`	`/api/v1/documents/upload`	✅	Upload PDF/DOCX/TXT and enqueue ingestion (`202`)
`POST`	`/api/v1/documents/urlupload`	✅	Crawl a URL and ingest as document
`GET`	`/api/v1/documents/`	✅	List documents (pagination + `?q=` name filter)
`GET`	`/api/v1/documents/{id}`	✅	Get document metadata (incl. extracted URLs)
`GET`	`/api/v1/documents/{id}/status`	✅	Poll ingestion progress
`DELETE`	`/api/v1/documents/{id}`	✅	Soft-delete document
`POST`	`/api/v1/chat/ask/stream`	✅	Ask a question (SSE streaming)
`GET`	`/api/v1/chat/history/{doc_id}`	✅	Get chat history for a document
`DELETE`	`/api/v1/chat/history/{doc_id}`	✅	Clear chat history
`GET`	`/api/v1/chat/export/{doc_id}`	✅	Export transcript as MD / TXT / PDF
`GET`	`/api/v1/chat/sessions`	✅	List chat sessions
`POST`	`/api/v1/chat/sessions`	✅	Create chat session
`GET`	`/api/v1/health/status`	❌	Deep health check (DB, Redis, Celery, ChromaDB)
`GET`	`/api/health`	❌	Basic liveness check

Full interactive docs at /docs (Swagger UI) when running locally.

📦 Environment Variables

Variable	Required	Default	Description
`SECRET_KEY`	✅	—	JWT signing secret. Generate: `python -c "import secrets; print(secrets.token_urlsafe(32))"`
`HF_TOKEN`	✅	—	HuggingFace API token for LLM inference
`DATABASE_URL`	❌	`sqlite:///./data/app.db`	SQLAlchemy connection string (SQLite or PostgreSQL)
`CELERY_BROKER_URL`	❌	`redis://localhost:6379/0`	Redis broker for Celery
`CELERY_RESULT_BACKEND`	❌	`redis://localhost:6379/1`	Redis backend for Celery results
`REDIS_URL`	❌	—	Redis URL for response + embedding cache
`UPLOAD_DIR`	❌	`./data/uploads`	File storage directory
`CHROMA_PERSIST_DIR`	❌	`./data/chroma_db`	ChromaDB persistence directory
`EMBEDDING_MODEL`	❌	`sentence-transformers/all-MiniLM-L6-v2`	Local embedding model
`EMBEDDING_CACHE_TTL`	❌	`86400`	Embedding cache TTL in seconds (24h)
`LLM_MODEL`	❌	`Qwen/Qwen2.5-72B-Instruct`	HuggingFace model for answer generation
`LLM_TEMPERATURE`	❌	`0.3`	LLM sampling temperature
`RERANKER_MODEL`	❌	`BAAI/bge-reranker-v2-m3`	Cross-encoder reranker model
`USE_HYBRID_SEARCH`	❌	`True`	Enable BM25 + vector hybrid search
`RRF_K`	❌	`60`	RRF smoothing constant
`CHUNK_SIZE`	❌	`1000`	Characters per document chunk
`CHUNK_OVERLAP`	❌	`200`	Overlap between consecutive chunks
`TOP_K_RETRIEVAL`	❌	`20`	Candidates retrieved from vector store
`TOP_K_RERANK`	❌	`8`	Final chunks after reranking
`VISION_PROVIDER`	❌	—	Set to `openai` to use GPT-4o-mini for image captions
`OPENAI_API_KEY`	❌	—	Required when `VISION_PROVIDER=openai`
`ENVIRONMENT`	❌	`development`	Set to `production` to lock CORS
`FRONTEND_URL`	❌	`http://localhost:3000`	Public frontend URL for OAuth + email links
`NEXT_PUBLIC_API_URL`	❌	`http://localhost:7860`	Backend URL injected at frontend build time

📜 Scripts

Backend (`backend/`)

Command	Description
`uvicorn app.main:app --reload`	Start FastAPI with hot reload
`celery -A app.celery_app.celery_app worker --loglevel=info`	Start Celery worker
`python migrate_add_extracted_urls.py`	Run URL extraction column migration
`python scripts/run_ragas_eval.py --user-id <id>`	Run RAGAS evaluation (vector vs GraphRAG)

Frontend (`frontend/`)

Command	Description
`npm run dev`	Start Next.js dev server
`npm run build`	Production build
`npm run test`	Run Vitest unit tests
`npm run test:e2e`	Run Playwright E2E tests
`npx playwright test e2e/snapshots.spec.ts --update-snapshots`	Regenerate visual regression baselines

Docker

Command	Description
`docker compose --profile cpu up --build`	Full stack — CPU only
`docker compose --profile gpu up --build`	Full stack — GPU accelerated
`docker compose --profile debug up`	Also start pgAdmin at http://localhost:5050
`docker compose down`	Stop all containers

GPU profile requires NVIDIA Container Toolkit.

🌐 Deployment

HuggingFace Spaces

Fork this repo and create a new Space at huggingface.co/new-space (SDK: Docker)
Set Space secrets: HF_TOKEN, SECRET_KEY, DATABASE_URL
Push to the hf remote:

git remote add hf https://<username>:<HF_TOKEN>@huggingface.co/spaces/<username>/<space-name>
git push hf main

Self-Hosted / VPS

docker compose --profile cpu up -d --build
# App at http://your-server:7860
# Frontend at http://your-server:3000

🤝 Contributing

This project is participating in GirlScript Summer of Code! We welcome contributors of all skill levels.

Branch Strategy:

Branch	Purpose
`main`	Production — HuggingFace deployed (admin only)
`dev`	All contributor PRs target here
`feature/` / `fix/` / `docs/*`	Your working branches

# Always branch from dev
git checkout -b feature/my-feature upstream/dev

Quick links:

📄 License

Distributed under the MIT License. See LICENSE for more information.

Built with 💙 by the open-source community

If you found this project helpful, please give it a ⭐ — it helps contributors discover it!

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Name		Name	Last commit message	Last commit date
Latest commit History 889 Commits
.github		.github
.pytest_tmp		.pytest_tmp
backend		backend
bots/discord		bots/discord
docs		docs
frontend		frontend
migrations		migrations
scripts		scripts
.coveragerc		.coveragerc
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.secrets.baseline		.secrets.baseline
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
docker-compose.yml		docker-compose.yml
grafana_dashboard.json		grafana_dashboard.json
license		license
pyrightconfig.json		pyrightconfig.json
render.yaml		render.yaml

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Enterprise Agentic Retrieval-Augmented Generation System

🤝 Contributors

🌟 Overview

🏗️ Architecture

🔄 System Flow Overview

🛠 Tech Stack

Backend

Frontend

AI / ML Pipeline

DevOps & Tooling

✨ Key Features

🆕 Recent Updates

👤 Users

🤖 RAG Pipeline

⚙️ Engineering

📁 Project Structure

🚀 Getting Started

Prerequisites

1. Clone the Repository

2. Configure Environment

Email Verification Setup (optional)

3. Run with Docker (recommended)

4. Run Locally (without Docker)

5. Set up crawl4ai (URL Upload Feature — optional)

🧠 RAG Pipeline

📡 API Reference

📦 Environment Variables

📜 Scripts

Backend (backend/)

Frontend (frontend/)

Docker

🌐 Deployment

HuggingFace Spaces

Self-Hosted / VPS

🤝 Contributing

📄 License

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