RAGify Docs API 📚🤖

A comprehensive API for scraping documentation from any URL and asking intelligent questions about it using Retrieval-Augmented Generation (RAG) powered by LangChain and Groq.

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🎯 Overview

RAGify Docs API is a developer-friendly tool that automates the process of extracting knowledge from documentation websites. Instead of manually reading through documentation, you can now:

• 🔗 Provide a documentation URL
• 🤔 Ask any question about the content
• 🧠 Get intelligent, sourced answers powered by AI

Built with LangChain, FastAPI, and Groq's powerful LLM, this tool handles the heavy lifting of document loading, chunking, embedding, retrieval, and generation—all with caching for performance.

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🚀 Quick Start

Prerequisites

• Python 3.12 or higher
• Groq API key (free tier available at console.groq.com)

Installation

1. Clone or navigate to the project directory:

   cd RAGify-Docs-API
   

2. Create a virtual environment:

   python -m venv .venv
   .venv\Scripts\activate  # On Windows
   # or
   source .venv/bin/activate  # On macOS/Linux
   

3. Install dependencies:

   pip install -r requirements.txt
   # or
   pip install -e .
   

4. Set up environment variables: Create a .env file in the project root:

   GROQ_API_KEY=your_groq_api_key_here
   

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📖 Usage

Option 1: FastAPI Server

Start the API server:

uvicorn app:app --reload

The API will be available at http://localhost:8000

API Documentation:

• Interactive Docs: http://localhost:8000/docs (Swagger UI)
• Alternative Docs: http://localhost:8000/redoc

Option 2: Interactive CLI

Run the interactive mode:

python main.py

You'll be prompted to:

1. Enter a documentation URL (e.g., https://docs.langchain.com/oss/python/langchain/overview)
2. Ask questions about the scraped documentation
3. View answers with source URLs

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🔌 API Endpoints

GET /

Welcome endpoint with basic information.

Response:

{
  "message": "Welcome to the RAGify Docs API. Use the /ragify endpoint to ask questions about documentation from a given URL."
}

POST /ragify

Main endpoint for asking questions about documentation.

Request Body:

{
  "url": "https://docs.langchain.com/oss/python/langchain/overview",
  "query": "What is LangChain?"
}

Response:

{
  "answer": "LangChain is a framework for developing applications powered by language models...",
  "sources": [
    "https://docs.langchain.com/oss/python/langchain/overview",
    "https://docs.langchain.com/oss/python/langchain/guides"
  ]
}

Status Codes:

• 200 — Success
• 500 — Error (RAG initialization or chain invocation failed)

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│             FastAPI Server                   │
│          (app.py with CORS)                 │
└────────────┬────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────┐
│      URL Caching Layer                      │
│   (Avoid re-processing same URLs)           │
└────────────┬────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────┐
│         RAG Pipeline (main.py)              │
├─────────────────────────────────────────────┤
│  1. RecursiveUrlLoader → Scrape docs        │
│  2. RecursiveCharacterTextSplitter          │
│  3. HuggingFace Embeddings                  │
│  4. InMemoryVectorStore                     │
│  5. MMR Retriever (k=5, diverse results)    │
│  6. ChatGroq LLM (openai/gpt-oss-120b)      │
│  7. LangChain RAG Chain                     │
└─────────────────────────────────────────────┘

Component Details

Component	Purpose
RecursiveUrlLoader	Crawls documentation pages recursively with custom HTML extraction
RecursiveCharacterTextSplitter	Splits documents (1000 chars/chunk, 200 char overlap)
HuggingFace Embeddings	Converts text to vectors using sentence-transformers/all-MiniLM-L6-v2
InMemoryVectorStore	Stores embeddings for semantic search
MMR Retriever	Returns diverse, relevant documents (5 best from 10 candidates)
ChatGroq	LLM for generating answers (model: openai/gpt-oss-120b, temp: 0.2)

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💻 Example Usage

Using cURL

curl -X POST "http://localhost:8000/ragify" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://docs.langchain.com/oss/python/langchain/overview",
    "query": "What is the purpose of LangChain?"
  }'

Using Python Requests

import requests

response = requests.post(
    "http://localhost:8000/ragify",
    json={
        "url": "https://docs.langchain.com/oss/python/langchain/overview",
        "query": "How do I create a RAG chain?"
    }
)

print(response.json())

Using JavaScript/Fetch

fetch("http://localhost:8000/ragify", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    url: "https://docs.langchain.com/oss/python/langchain/overview",
    query: "What are chains in LangChain?"
  })
})
.then(res => res.json())
.then(data => console.log(data));

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⚙️ Configuration

Environment Variables

Create a .env file to configure the application:

# Groq API Key (required)
GROQ_API_KEY=your_api_key_here

# Optional: Customize embedding model
# EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Optional: Customize LLM model
# LLM_MODEL=openai/gpt-oss-120b

Tunable Parameters (in main.py)

# Chunk size and overlap
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,      # Increase for longer context
    chunk_overlap=200     # Increase for better continuity
)

# Retriever settings
retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={
        "k": 5,           # Number of results to return
        "fetch_k": 10,    # Candidates to consider
        "lambda_mult": 0.5  # 1.0 = relevance, 0.0 = diversity
    }
)

# LLM settings
llm = ChatGroq(
    model="openai/gpt-oss-120b",
    temperature=0.2      # Lower = factual, Higher = creative
)

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🛠️ Development & Debugging

Run with hot-reload

uvicorn app:app --reload

Run tests (if added)

pytest

Check for issues

# Verify dependencies
pip check

# Lint code
pylint app.py main.py

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🎉 Happy RAGifying

Build smarter, faster, and more informed applications with RAGify Docs API.

RAGify Docs API — Because great documentation deserves great answers.