Enterprise RAG Platform

Processes documents from various sources (SharePoint, Confluence, S3)

Service: AWS Lambda / OCI Functions

Splits documents into meaningful chunks preserving context

Service: LangChain / LlamaIndex

Stores embeddings for semantic search

Service: Pinecone / Weaviate / pgvector

Generates answers from retrieved context

Service: Claude / GPT-4 / OCI GenAI

Set up infrastructure and basic pipeline

Build the retrieval and generation pipeline

Make it production-ready

RAG Query Implementation

Basic RAG query with citation support

from langchain.vectorstores import Pinecone
from langchain.chat_models import ChatAnthropic

def query_rag(question: str, top_k: int = 5):
    # Retrieve relevant documents
    docs = vectorstore.similarity_search(question, k=top_k)
    
    # Build context from retrieved docs
    context = "\n\n".join([d.page_content for d in docs])
    
    # Generate answer with citations
    response = llm.invoke(
        f"Based on the following context, answer the question.\n\n"
        f"Context:\n{context}\n\n"
        f"Question: {question}\n\n"
        f"Provide the answer with source citations."
    )
    
    return response, docs