🤖 RAG Documentation System DEMO

# Advanced RAG System Components

1. Document Processing Pipeline
   ├── Markdown parser for FusionAuth docs
   ├── Code block extraction and indexing
   ├── Metadata enrichment (tags, categories, versions)
   └── Chunking strategy (semantic boundaries)

2. Vector Database (ChromaDB)
   ├── 1,000+ documentation pages indexed
   ├── Embedding model: OpenAI text-embedding-ada-002
   ├── Semantic similarity search
   └── Hybrid search with BM25 ranking

3. Query Processing
   ├── Intent classification
   ├── Query expansion and reformulation
   ├── Context window optimization
   └── Source attribution tracking

4. Response Generation
   ├── GPT-4 for answer synthesis
   ├── Code example generation
   ├── Confidence scoring
   └── Source citation system

from chromadb import Client
from openai import OpenAI
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

class RAGSystem:
    def __init__(self, config):
        self.vector_store = ChromaDB(config.db_path)
        self.llm_client = OpenAI(api_key=config.api_key)
        self.embedder = SentenceTransformer(config.model)
        
    async def query(self, question: str) -> RAGResponse:
        # 1. Generate query embedding
        query_embedding = self.embedder.encode(question)
        
        # 2. Semantic search in vector store
        results = self.vector_store.similarity_search(
            query_embedding, 
            k=5,
            metadata_filter={"type": "documentation"}
        )
        
        # 3. Build context from retrieved documents
        context = self.build_context(results)
        
        # 4. Generate response with GPT-4
        response = await self.llm_client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"}
            ],
            temperature=0.1
        )
        
        # 5. Return structured response with sources
        return RAGResponse(
            answer=response.choices[0].message.content,
            sources=[doc.metadata for doc in results],
            confidence=self.calculate_confidence(results)
        )

# Search endpoint
POST /api/search
{
  "query": "How to configure SAML SSO?",
  "filters": {
    "category": "authentication",
    "version": "latest"
  },
  "max_results": 5
}

# Response format
{
  "answer": "To configure SAML SSO in FusionAuth...",
  "sources": [
    {
      "title": "SAML v2 Configuration",
      "url": "/docs/v1/tech/samlv2/",
      "relevance": 0.95
    }
  ],
  "confidence": 0.92,
  "response_time_ms": 1847
}