RAG Assistant

A minimal end‑to‑end Retrieval‑Augmented Generation (RAG) app.
Upload a PDF on the client, create embeddings on the backend (MongoDB Atlas Vector Search), and ask questions that are answered with OpenAI using the most relevant chunks.

✨ Features

• PDF upload & chunking on the client (UI built with React + shadcn/ui + lucide icons).
• Embeddings with @langchain/community using the local HF model Xenova/all-MiniLM-L6-v2.
• MongoDB Atlas Vector Search for storing and retrieving chunks.
• OpenAI chat completion (gpt-4.1) to generate answers from retrieved context.
• Rate limiting on vector routes (4 requests / 5 minutes per IP).
• Local persistence for counters and chat history via localStorage.

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🧱 Architecture

client/
  └─ React UI (RAGProcessor, DocumentUpload, ChatInterface, stats cards)
backend/
  ├─ Express app + routers
  ├─ Embedding pipeline (PDFLoader -> TextSplitter -> Embeddings -> MongoDB)
  └─ Search pipeline (embed query -> $vectorSearch -> compose context -> OpenAI)

Data Flow

1. Client
   • User uploads a PDF.
   • RAGProcessor.chunkDocument(file, 800, 200, cb) chunks it and shows stats.
   • User asks a question → POST {VITE_BASE_API}/vector/search with { query }.
2. Backend
   • POST /api/vector/create-embedding: multer saves the file, service creates embeddings and stores documents in MongoDB.
   • POST /api/vector/search: embeds the query, runs $vectorSearch, builds a context string, and calls OpenAI for the final answer.
3. Client
   • Displays the answer + maintains a lightweight chat history and counters in localStorage.

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🧰 Tech Stack

• Client: React, shadcn/ui (Button, Card, Textarea, etc.), lucide-react, Vite env (VITE_BASE_API).
• Backend: Node.js, Express, Multer, CORS.
• LangChain: @langchain/community PDFLoader, text splitters, HF Transformers embeddings.
• Vector DB: MongoDB Atlas Vector Search (MongoDBAtlasVectorSearch).
• LLM: OpenAI Chat Completions (gpt-4.1).

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📁 Important Code Paths (Backend)

• Server bootstrap

  • createApp(config, MongoDbclient, OpenAInit) sets up JSON, CORS, routes, and global error middleware.
  • server() initializes Mongo, OpenAI, then starts Express on config.PORT.

• Routes

  • POST /api/vector/create-embedding → file upload (upload.single("file")) → embeddingController.create().
  • POST /api/vector/search → JSON { query } → embeddingController.search().
  • Each route is rate‑limited: 4 requests per 5 minutes.

• Embedding Service (embeddingService)

  • Loads a PDF: PDFLoader(filePath)
  • Splits with RecursiveCharacterTextSplitter({ chunkSize: 800, chunkOverlap: 200 })
  • Embeds with HuggingFaceTransformersEmbeddings("Xenova/all-MiniLM-L6-v2")
  • Persists using MongoDBAtlasVectorSearch.addDocuments(...)

• Search Service

  • Embeds the query with the same HF model.
  • $vectorSearch pipeline returns top matches (limit 5).
  • Concatenates context and calls OpenAI (model: "openai/gpt-4.1").

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🔐 Environment Variables

Client

VITE_BASE_API=http://localhost:4000/api

Backend

PORT=4000
ALLOW_ORIGIN=http://localhost:5173

# OpenAI
OPENAI_API_KEY=sk-...
OPENAI_ENDPOINT=https://api.openai.com/v1

# MongoDB
MONGODB_URI=mongodb+srv://<user>:<pass>@<cluster>/<db>?retryWrites=true&w=majority
MONGODB_ATLAS_DB=your_db_name
MONGODB_ATLAS_COLLECTION=your_collection_name

InitDb() should use MONGODB_URI (or your chosen var) to connect and provide a MongoClient.

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▶️ Running Locally

1) Backend

cd backend
npm i        # or npm i / yarn
npm run dev     # or  pnpm dev
# server starts on PORT, e.g., http://localhost:4000

2) Client

cd client
npm i
npm run dev
# app starts e.g. on http://localhost:5173

Ensure CORS origins align: ALLOW_ORIGIN should include your client URL.

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📡 API Endpoints

Create Embeddings

POST /api/vector/create-embedding

• Body: multipart/form-data with file=<PDF>
• Rate limit: 4 req / 5 min

cURL

curl -X POST http://localhost:4000/api/vector/create-embedding \
  -H "Accept: application/json" \
  -F "file=@/path/to/document.pdf"

Response

{ "msg": "embeddings created successfully" }

or

{ "msg": "vector embedding created" }

Vector Search

POST /api/vector/search

• Body: application/json

{ "query": "What does the document say about X?" }

cURL

curl -X POST http://localhost:4000/api/vector/search \
  -H "Content-Type: application/json" \
  -d '{"query":"Summarize section 3"}'

Response

{
  "data": {
    "answer": "… LLM answer based on retrieved context …",
    "sources": [
      { "pageContent": "...", "metadata": { ... } },
      ...
    ]
  },
  "msg": "vector search successful"
}

The client expects response.data.answer in its current implementation.

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🧩 Client Notes

• Displays counters/statistics from state or localStorage:
  • documents uploaded (document),
  • questions answered (answers),
  • chunks created (chunk).
• Only one document is processed at a time in the current UI.
• ChatInterface shows the latest answer and preserves a simple chat history in localStorage.

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⚙️ Configuration & Tuning

• Chunking: Adjust in the client (RAGProcessor.chunkDocument(file, 800, 200)) and/or backend split logic for consistency.
• Top‑K: $vectorSearch currently returns limit: 5. Tweak numCandidates and limit for accuracy vs. cost.
• Model Choice: The embedding model is local (Xenova/...) while the generation model is OpenAI (gpt-4.1). You can swap or unify them as needed.
• Rate Limiting: Adjust windowMs and limit in VectorEmbeddingRouter.

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🧯 Error Handling

• Centralized with GlobalErrorMiddleware and GlobalErrorHandler (custom).
• Controllers catch errors and pass enriched details to the middleware.
• On the client, toast() shows user‑friendly messages for processing/generation errors.

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🔒 Security Considerations

• No authentication is built in; add auth middleware before exposing publicly.
• Validate mime types and file sizes for uploads (multer config).
• Sanitize/limit query input to avoid prompt abuse.
• Enforce CORS carefully for production.
• Don’t log secrets; rotate OPENAI_API_KEY if leaked.

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🚧 Known Limitations

• Client UI processes a single PDF at a time.
• Embeddings are created from PDFs only (no plain text or other formats in current route).
• Simple prompt template; no citations highlighting/snippets beyond the raw source list.
• No streaming responses on the client.

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📜 License

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🙌 Acknowledgements

• LangChain community packages
• MongoDB Atlas Vector Search
• OpenAI API
• Xenova Transformers (ONNX/JS embeddings)