The essential LangChain cheat sheet: 8 concise chapters you can read in minutes, distilled from the full deck. When you're ready, drill the flashcards or test yourself under exam conditions.
LangChain is an open-source Python framework for composing applications powered by large language models. Rather than wrapping a single model call, it provides modular building blo...
Read full chapter →Prompts are how you talk to the model, and LangChain offers a small family of templates that interpolate variables, hold few-shot examples, or inject dynamic message lists. The sim...
Read full chapter →LCEL's real power lies in its composition primitives, which let you express branching, fan-out, and routing as dataflow. RunnableSequence chains runnables sequentially, while Runna...
Read full chapter →Conversational applications need to remember what the user has already said, and LangChain offers a layered set of tools, ranging from legacy Memory classes to the modern RunnableW...
Read full chapter →The retrieval stack begins with the Document class, a tiny data wrapper with two fields: page_content as a string and metadata as a dict. Every loader, splitter, retriever, and vec...
Read full chapter →A retrieval-augmented generation pipeline combines a retriever, a prompt, and an LLM so the model answers questions grounded in your own data. The canonical LCEL pattern is to buil...
Read full chapter →Tools are how an LLM takes real action in the world. The tool decorator turns any Python function into a LangChain tool — the function's name becomes the tool's name, and its docst...
Read full chapter →LangChain's observability story centers on callbacks and LangSmith. A BaseCallbackHandler subclass implements lifecycle hooks — on_llm_start, on_chat_model_start, on_llm_new_token,...
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