Retrieval-Augmented Generation (RAG) Architecture

Introduction to RAG Architecture

This architecture enhances Large Language Models with dynamic knowledge retrieval from external data sources. It combines Embedding Generation for semantic understanding, Vector Database (e.g., Pinecone/Weaviate) for efficient similarity search, Semantic Search for context retrieval, and Response Synthesis by the LLM to generate accurate, up-to-date responses. The system includes Data Ingestion Pipelines for document processing, Query Transformation for optimized retrieval, and Hybrid Search capabilities combining semantic and keyword approaches. Security is maintained through encrypted data storage and access controls.

High-Level System Diagram

The RAG pipeline begins with Data Sources (APIs/DBs/files) feeding into a Document Processor that chunks and cleans content. An Embedding Model (e.g., OpenAI/text-embedding-ada-002) converts chunks to vectors stored in a Vector Database. User queries are transformed to embeddings for Semantic Search, with retrieved documents passed to the LLM for response generation. The Cache Layer stores frequent queries, while Feedback Mechanisms improve retrieval quality. Components are color-coded: blue for data flow, orange for processing, green for storage, and purple for augmentation.

Key Components

• Data Sources: APIs, databases, PDFs, wikis (structured/unstructured)
• Document Processor: Chunking, cleaning, metadata extraction (e.g., LangChain)
• Embedding Models: text-embedding-ada-002, BERT, or custom fine-tuned models
• Vector Database: Pinecone, Weaviate, or Milvus for similarity search
• Query Transformer: Query expansion/reformulation for better retrieval
• Retriever: Hybrid (dense + sparse) search with optional re-ranking
• LLM Synthesizer: GPT-4, Claude, or Llama-2 for response generation
• Cache Layer: Redis for frequent query-response pairs
• Feedback System: Clickstream analysis and retrieval scoring
• Monitoring: Recall@K metrics, latency tracking, and LLM eval metrics

Benefits of RAG Architecture

• Dynamic Knowledge: Overcomes LLM knowledge cutoffs with live data
• Verifiability: Sources can be cited to validate responses
• Cost-Efficiency: Smaller LLM context windows than full fine-tuning
• Domain Adaptability: Swap vector DB contents for different use cases
• Transparency: Audit trail from source document to generated response
• Hybrid Search: Combines semantic understanding with keyword precision

Implementation Considerations

• Chunking Strategy: Optimal size balance (e.g., 512 tokens) with overlap
• Embedding Model: Match to domain (general vs. specialized embeddings)
• Vector DB Selection: Consider scale (Pinecone for large, Chroma for small)
• Hybrid Search: Combine BM25 (keyword) with vector similarity
• Re-ranking: Cross-encoders (e.g., BERT) to improve top-K results
• Prompt Engineering: Clear instructions for source utilization
• Cache Policy: TTL-based caching of common queries
• Metadata Filtering: Apply date/author filters during retrieval
• Evaluation Metrics: Track MRR@K, precision@K, and hallucination rates

Example Configuration: Pinecone Vector DB Setup

# Initialize Pinecone index
import pinecone
pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")

# Create index
pinecone.create_index(
    name="rag-demo",
    dimension=1536,  # OpenAI embedding size
    metric="cosine",
    pods=1,
    pod_type="p1.x1"
)

# Upsert embeddings
index = pinecone.Index("rag-demo")
vectors = []
for doc in processed_docs:
    vectors.append({
        "id": doc["doc_id"],
        "values": generate_embeddings(doc["text"]),  # Using OpenAI API
        "metadata": {"source": doc["source"], "timestamp": doc["date"]}
    })
index.upsert(vectors=vectors)

# Query example
query_embedding = generate_embeddings("What is RAG architecture?")
results = index.query(
    vector=query_embedding,
    top_k=5,
    include_metadata=True,
    filter={"timestamp": {"$gte": "2022-01-01"}}  # Metadata filtering
)
                

Example RAG Service Implementation

from fastapi import FastAPI
from pydantic import BaseModel
import openai
import pinecone

app = FastAPI()
pinecone.init(api_key="PINECONE_KEY", environment="us-west1-gcp")
index = pinecone.Index("rag-index")
openai.api_key = "OPENAI_KEY"

class Query(BaseModel):
    text: str
    filters: dict = None

@app.post("/query")
async def rag_query(query: Query):
    # Generate query embedding
    embedding = openai.Embedding.create(
        input=query.text,
        model="text-embedding-ada-002"
    )["data"][0]["embedding"]
    
    # Vector DB search
    results = index.query(
        vector=embedding,
        top_k=3,
        include_metadata=True,
        filter=query.filters
    )
    
    # Build context
    context = "\n\n".join([
        f"Source: {match['metadata']['source']}\nContent: {match['metadata']['text']}"
        for match in results["matches"]
    ])
    
    # LLM synthesis
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "Use these sources to answer. Cite sources when possible."},
            {"role": "user", "content": f"Question: {query.text}\nContext: {context}"}
        ],
        temperature=0.3
    )
    
    return {
        "answer": response.choices[0].message.content,
        "sources": [match["metadata"]["source"] for match in results["matches"]]
    }

# Document processing endpoint would similarly chunk and embed new documents