Tutorials teach syntax. We teach how production systems actually work.

1# Production RAG Pipeline with Semantic Reranking & Citations
2import asyncio
3from langchain_core.prompts import ChatPromptTemplate
4from langchain_community.vectorstores import Chroma
5from codeparallels.retrieval import CohereReranker, StreamingTokenHandler
6
7async def execute_agentic_retrieval(query: str, tenant_id: str):
8    """
9    Executes multi-stage hybrid search across partitioned vector indices
10    with strict tenant isolation and contextual citation verification.
11    """
12    vector_store = Chroma(persist_directory=f"/var/data/indices/{tenant_id}")
13    
14    # 1. Parallel dense embeddings + sparse BM25 retrieval
15    initial_candidates = await vector_store.a_similarity_search(
16        query, k=25, filter={"is_active": True}
17    )
18    
19    # 2. Cross-encoder semantic reranking to eliminate hallucination
20    reranker = CohereReranker(model="rerank-english-v3.0", top_n=4)
21    scored_documents = await reranker.rerank(query, initial_candidates)
22    
23    # 3. Stream token generation with dynamic latency budget
24    stream_handler = StreamingTokenHandler(p95_target_ms=180)
25    return await stream_handler.stream_with_citations(
26        query=query, 
27        context=scored_documents,
28        temperature=0.2
29    )