Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
uv run python -m backend.app ---> http://127.0.0.1:8000/docs#/
sudo systemctl start nginx ---> http://localhost
Docker:
docker compose up -d --build
docker compose up -d
git clone https://github.com/spha-code/Production-RAG-MCP locally
cd Production-RAG-MCP --> uv init --> uv add fastapi uvicorn sentence-transformers chromadb
A production-ready Retrieval-Augmented Generation (RAG) system with FastAPI, ChromaDB, Sentence Transformers, and a lightweight web UI.
Supports document ingestion (PDF, DOCX, TXT, CSV), semantic search, and MCP-compatible tool exposure.
- FastAPI backend with lifecycle-managed embeddings
- ChromaDB vector store
- Sentence-Transformers embeddings
- File upload (PDF, DOCX, TXT, MD, CSV)
- Folder ingestion with progress tracking
- Chunking + metadata handling
- Simple web-based chat widget
- MCP-compatible /mcp endpoint
- Ready for MLOps, CI/CD, and scaling
Production-RAG-MCP/ ├── backend/ # Python service + ML + MCP │ ├── app.py # FastAPI entry: mounts routers │ ├── routes/ # API endpoints │ │ ├── __init__.py │ │ └── upload.py # /upload endpoint │ ├── middleware/ │ │ ├── __init__.py │ │ └── auth.py # JWT / API-key guard │ ├── rag/ │ │ ├── __init__.py │ │ └── schemas.py # Pydantic models │ ├── mcp/ │ │ ├── __init__.py │ │ └── tools.py # MCP tool descriptor + handler │ ├── tests/ │ │ ├── __init__.py │ │ └── test_app.py │ ├── alembic/ # DB migrations (if Postgres added) │ │ └── versions/ │ ├── requirements-lock.txt │ ├── requirements-dev.txt # dev-only deps │ ├── .env.example # template env vars │ └── Dockerfile ├── mlops/ # MLOps pipelines & monitoring │ ├── data/ # raw / labelled datasets │ ├── notebooks/ # EDA & embedding quality checks │ ├── pipelines/ │ │ ├── embed_validation.py # offline eval job │ │ └── retrain_trigger.py # scheduled / event retraining │ ├── features/ │ │ └── chunker.py # shared text-split logic │ ├── monitoring/ │ │ ├── data_drift.py # detect embedding drift │ │ └── answer_quality.py # LLM-as-judge scoring │ ├── configs/ │ │ └── embed_config.yaml # model, chunk size, overlap │ ├── tests/ │ │ └── test_pipelines.py │ ├── Dockerfile.pipeline # Airflow / Prefect runner image │ └── README.md # usage docs ├── web/ # Next.js site & embeddable widget │ ├── pages/ │ ├── components/ │ │ └── ChatWidget.tsx │ ├── public/ │ ├── styles/ │ └── package.json ├── ios/ # SwiftUI Xcode project │ └── ProductionRAG/ ├── android/ # Kotlin Android-Studio project │ └── app/ ├── iac/ # AWS SAM infra │ └── template.yaml ├── .github/ │ └── workflows/ │ └── ci-cd.yaml ├── scripts/ │ ├── build.sh │ └── deploy.sh ├── docker-compose.yml # local dev stack ├── pyproject.toml ├── uv.lock ├── LICENSE ├── CHANGELOG.md ├── SECURITY.md ├── README.md └── .env # .gitignore
uv add google-genai python-dotenv
cd backend ---> uv add PyPDF2 python-docx pandas chardet
List all documents: http://127.0.0.1:8000/upload/documents
Test the API: http://127.0.0.1:8000/docs (Swagger UI)
Health check: http://127.0.0.1:8000/upload/test
| Method | Path | Description | Request | Response |
|---|---|---|---|---|
| Core RAG | ||||
| POST | /chat | Semantic search + generative answer | { "query": "string", "k": 3 } | { "chunks": ["concise answer"] } |
| GET | /mcp | MCP-compatible tool descriptor | — | { "tools": [{ "name": "semantic_search", … }] } |
| Single-file Upload | ||||
| POST | /upload/ | Upload one document (PDF/DOCX/TXT/MD/CSV) | multipart/form-data (file, metadata, chunk_size, chunk_overlap) | { "document_id", "filename", "total_chunks", "message" } |
| Bulk / Folder Upload | ||||
| POST | /upload/upload/folder | Queue an entire folder for ingestion | folder_path, metadata, chunk_size, chunk_overlap, recursive, max_files | { "status": "processing_started", "total_files" } |
| Document Management | ||||
| GET | /upload/documents | List every ingested document | — | { "total_documents", "documents": [ … ] } |
| GET | /upload/documents/{id} | Get single document metadata | — | { "document_id", "filename", "file_type", "upload_time", … } |
| GET | /upload/documents/{id}/download | Download original file | — | file stream (Content-Disposition: attachment) |
| DELETE | /upload/documents/{id} | Delete document + chunks + file | — | { "status": "success", "message" } |
| Utility | ||||
| GET | /upload/supported-types | Supported extensions & human names | — | { "supported_extensions": [".pdf", …], "formats": { "pdf": "PDF" } } |
| GET | /upload/test | Health ping | — | { "status": "ok" } |
app.include_router(upload.router)
uv add llama-cpp-python
Create file: backend/local_llm.py
in app.py
from local_llm import ask_local as ask_gemini # same function signature
mkdir backend/models uv run huggingface-cli login uv run hf download bartowski/Meta-Llama-3.1-8B-Instruct-GGUF Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf --local-dir models