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LangChain
Mental Model and Core
What is LangChain? The shift from standard programming to orchestrating LLMs.
✓LLMs vs Chat Models
Understanding the difference between raw text completion models and conversational Chat Models.
✓Prompts and Output Parsers
Using PromptTemplates to inject variables and Output Parsers to guarantee JSON output.
✓LCEL (LangChain Expression Language)
The modern pipe syntax (|) for chaining components together seamlessly.
✓Document Loaders and Splitters
Loading external data (PDFs/Web) and chunking it to fit the LLM context window.
✓Vector Stores and Embeddings
Converting text into numerical arrays (Embeddings) to perform semantic searches.
✓Retrievers and RAG
Retrieval-Augmented Generation (RAG): Connecting the Vector Store to the LLM.
✓Memory
Giving chat bots conversational history using Buffer and Summary memory.
✓Agents and Tools
Giving the LLM the ability to think, decide, and execute external functions.
✓Callbacks and Tracing (LangSmith)
Debugging LangChain applications with LangSmith and implementing streaming responses.
✓LangChain Cheat Sheet
Quick reference guide for Prompts, RAG, LCEL chains, and Agents.
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