Building Production RAG Pipelines with LangChain

Retrieval-Augmented Generation (RAG) has become the de facto pattern for grounding LLMs in proprietary data. But building a demo RAG system and deploying one to production are vastly different challenges.

This post covers the architecture decisions, tradeoffs, and lessons learned from building RAG pipelines that process 10K+ documents and serve thousands of queries daily.

The RAG Stack

A production RAG system has three core layers:

1. Document Processing - Ingestion, chunking, metadata extraction
2. Embedding & Storage - Vector generation and indexing
3. Retrieval & Generation - Search, reranking, and LLM synthesis

Let's break down each layer.

Document Chunking: The Foundation

Most RAG failures stem from poor chunking strategies. The classic approach—fixed-size chunks with overlap—works for simple text but fails on:

• Code blocks (syntax gets mangled)
• Tables (structure is lost)
• Multi-paragraph context (semantic boundaries ignored)

Semantic Chunking

We implemented semantic chunking using LangChain's RecursiveCharacterTextSplitter:

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", ". ", " ", ""],
    length_function=len,
)

Tradeoff: Semantic chunking increases storage by ~15% but improves retrieval precision by 23% in our benchmarks.

Embeddings: Quality vs Cost

We tested three embedding models:

• text-embedding-ada-002 (OpenAI) - $0.0001/1K tokens
• text-embedding-3-small (OpenAI) - $0.00002/1K tokens
• all-MiniLM-L6-v2 (Open source) - Free, self-hosted

Winner: text-embedding-3-small achieved 92% of ada-002's quality at 1/5th the cost.

Caching Strategy

Embedding the same document twice is wasted compute and money. We cache embeddings in Redis:

def get_or_create_embedding(text: str) -> List[float]:
    cache_key = f"emb:{hashlib.md5(text.encode()).hexdigest()}"
    cached = redis_client.get(cache_key)
    
    if cached:
        return json.loads(cached)
    
    embedding = openai.embeddings.create(
        model="text-embedding-3-small",
        input=text
    ).data[0].embedding
    
    redis_client.setex(cache_key, 86400, json.dumps(embedding))
    return embedding

Vector Search: Pinecone vs Self-Hosted

We evaluated:

• Pinecone (managed)
• Qdrant (self-hosted)
• pgvector (PostgreSQL extension)

Decision: Pinecone for production. Managed infrastructure, automatic scaling, and sub-100ms queries justified the cost ($0.07/1M queries).

Hybrid Search

Pure semantic search misses exact keyword matches. We combine vector search with BM25:

# Vector search (semantic)
semantic_results = index.query(
    vector=query_embedding,
    top_k=20,
    include_metadata=True
)

# BM25 search (keyword)
keyword_results = bm25.get_top_n(query, documents, n=20)

# Combine with weighted scoring
final_results = merge_and_rerank(semantic_results, keyword_results)

Result: 40% reduction in irrelevant retrievals.

Reranking: The Secret Weapon

Retrieving 20 candidates and returning the top 5 after reranking drastically improves quality:

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2")

scores = reranker.predict([
    (query, doc.page_content) for doc in retrieved_docs
])

top_docs = sorted(
    zip(retrieved_docs, scores), 
    key=lambda x: x[1], 
    reverse=True
)[:5]

Cost: Adds 200ms latency but worth it for 40% better relevance.

Production Lessons

1. Async Everything

Blocking on OpenAI API calls kills throughput. Use asyncio:

async def process_document(doc: Document):
    chunks = await chunker.split(doc.content)
    embeddings = await asyncio.gather(*[
        get_embedding(chunk) for chunk in chunks
    ])
    await vector_store.upsert(chunks, embeddings)

2. Circuit Breakers

OpenAI rate limits hit hard. Implement exponential backoff:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=60)
)
async def call_openai_with_retry(prompt: str):
    return await openai.chat.completions.create(...)

3. Cost Monitoring

Track every API call:

import prometheus_client as prom

embedding_cost = prom.Counter(
    "embedding_cost_usd",
    "Total embedding cost in USD"
)

def track_embedding_cost(tokens: int):
    cost = tokens * 0.00002 / 1000
    embedding_cost.inc(cost)

What's Next

• Query expansion with synonym generation
• User feedback loops for retrieval tuning
• Multi-modal RAG (images, tables, charts)
• Streaming responses for better UX

Conclusion

Production RAG is about tradeoffs:

• Speed vs Quality: Reranking adds latency but improves results
• Cost vs Control: Managed services cost more but save engineering time
• Complexity vs Reliability: More components = more failure modes

Build incrementally. Start simple, measure everything, optimize bottlenecks.

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Code: github.com/yourusername/rag-pipeline

Questions? Reach out on LinkedIn