ToolDix study guide · Hands-on lab
Build a RAG Agent with LangChain
LangChain
A guided implementation of retrieval and agentic RAG using loaders, embeddings, vector stores, tools, and a model-driven workflow.
Start with the source
The original publisher hosts the complete material and the current terms of use.
How to use this resource
Why it matters
It makes a useful comparison between a fixed retrieval chain and an agent that decides when and how to retrieve.
First practical move
Log every retrieved document and tool call, then add a test where the corpus cannot answer the question.
Good fit for
Developers comparing deterministic and agentic retrieval
Source and publishing context
This page is an original ToolDix editorial guide. We do not reproduce the source's full article, course media, figures, or book pages. Official LangChain documentation; check current package APIs and project licensing.
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