How do you build a production-ready RAG pipeline with LangChain?
Production RAG pipeline: (1) Document loading with UnstructuredLoader, (2) Chunking with RecursiveCharacterTextSplitter (chunk_size=1000, overlap=200), (3) Embedding with OpenAIEmbeddings, (4) Storage in Pinecone/Chroma vectorstore, (5) Retrieval with similarity search (top-k=5), (6) Reranking with Cohere Reranker, (7) Answer generation with ChatOpenAI + prompt template. Monitor retrieval quality with RAGAS evaluation framework.
How do you handle prompt versioning and management in production LangChain apps?
Use LangSmith Hub for centralized prompt storage and versioning. Alternatively, store prompts in a database or YAML files with version tags. Key practices: (1) Never hardcode prompts in code — externalize them, (2) A/B test prompts with different versions, (3) Track which prompt version produced each response, (4) Use PromptTemplate with input_variables for reusable prompts. LangSmith provides prompt testing and comparison tools.
What is LangGraph and when should you use it over LangChain agents?
LangGraph is a library built on LangChain for building stateful multi-agent workflows as graphs. Use LangGraph when: (1) You need complex branching logic and loops, (2) Multiple agents collaborate with shared state, (3) You need human-in-the-loop review steps, (4) Your workflow has conditional paths based on intermediate results. For simple single-agent tasks, standard LangChain agents are sufficient.
How do you reduce LangChain LLM costs in production?
Cost reduction strategies: (1) Caching — use LangChain's built-in cache (SQLiteCache or RedisCache) to avoid duplicate API calls, (2) Model routing — use GPT-3.5 for simple queries, GPT-4 only for complex reasoning, (3) Context compression — use ContextualCompressionRetriever to reduce tokens sent to LLM, (4) Batch processing — use .batch() instead of individual .invoke() calls, (5) Token counting — set max_tokens limits and monitor with LangSmith.
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