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Module 6 Lesson 2: Embedding Providers (Cloud vs. Local)
Choosing your engine. Comparing OpenAI cloud embeddings with local HuggingFace models for speed and privacy.
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Module 6 Lesson 3: Introduction to Vector Stores
The Semantic Database. How to store thousands of vectors so you can search them in milliseconds.
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LangChain
Module 6 Lesson 4: Similarity Search and k-Values
Fine-Tuning Retrieval. Learning how to control how many results (k) your vector store returns and what 'Score' means.
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LangChain
Module 6 Wrap-up: Storing Your AI's Memory
Hands-on: Build a local knowledge base using ChromaDB and perform semantic queries.
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LangChain
Module 7 Lesson 1: Why RAG Matters
Fighting Hallucinations. Understanding the architectural pattern of grounding AI responses in factual, retrieved context.
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Module 7 Lesson 2: The Retriever Interface
The Search Object. How LangChain standardizes vector store lookups into a 'Retriever' that can be used in any chain.
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LangChain
Module 7 Lesson 3: Building the RAG Chain
Piping Facts. Putting it all together into a single LCEL chain that retrieves context and generates an answer.
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LangChain
Module 7 Lesson 4: Designing RAG Prompts
The Art of Grounding. How to write the perfect system prompt to ensure your AI stays factual and cites its sources.
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LangChain
Module 7 Wrap-up: Building a Q&A Bot
Hands-on: Finalize your first production-ready RAG system over your own local documents.
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LangChain
Module 8 Lesson 1: Why Memory is Needed
Breaking the Amnesia. Understanding why LLMs are stateless and how we provide 'history' to simulate a conversation.
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LangChain
Module 8 Lesson 2: ConversationBufferMemory
The Raw Transcript. Using the simplest memory type to keep a literal record of every message in a conversation.
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LangChain
Module 8 Lesson 3: Summary Memory
Dense context. How to use an LLM to periodically summarize a conversation to keep the memory footprint small.
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LangChain
Module 8 Lesson 4: External Memory (Redis & Postgres)
Production State. How to move your memory from local RAM to persistent databases for multi-user applications.
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LangChain
Module 8 Wrap-up: Conversations that Stick
Hands-on: Build a persistent chatbot that remembers your name across different CLI sessions.
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LangChain
Module 9 Lesson 1: What are Tools?
The Agent's Hands. Understanding how to give an LLM the ability to execute code and interact with the physical world.
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Module 9 Lesson 2: Defining Custom Tools (The @tool Decorator)
Creating Superpowers. How to turn any Python function into a LangChain tool using a simple decorator.
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Module 9 Lesson 3: Built-in LangChain Tools
Instant Capabilities. Exploring the library of pre-made tools for web search, calculation, and database interaction.
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LangChain
Module 9 Wrap-up: Giving Your Agent Powers
Hands-on: Build a toolbox for an agent that can multiply numbers and search Wikipedia.
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