Multi-stage Reasoning with LangChain using Azure Databricks and Azure OpenAI

Why it matters

Chaining is the lightest form of composition, and you need to recognize where it stops being enough for an agent system.

What to practise

Deploy gpt-4o and text-embedding-ada-002 to an Azure OpenAI resource, then work in an Azure Databricks notebook on serverless compute. Embed three sample documents into a FAISS vector store, build a question-answering chain with create_retrieval_chain and AzureChatOpenAI, and pipe its result into a second chain with {"summary": qa_chain1} | prompt2 | llm | StrOutputParser(). Watch that each stage runs once, in order, with no state between calls. The exercise uses an API key, an older preview API version, and the legacy langchain_classic chains. Two printed cells need a fix: the embeddings call lacks a comma after the API version, and FAISS imports from langchain_community.vectorstores.

How it fits

The pipe composition is the runnable chain of 2.4.13. Ask which step would need branching, retries or a pause before 2.4.13 moves it to LangGraph or Agent Framework. Databricks, FAISS and embeddings are background here.