Module 7 Wrap-up: The Truth Specialist

You have reached a major milestone. You have combined Loaders (Module 5), Vector Stores (Module 6), and Chains (Module 4) to build a RAG System. This architecture is the "Bestseller" of the AI world. If you can build a reliable RAG bot, you can build 90% of current enterprise AI applications.

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Hands-on Project: The Local Knowledge Expert

1. The Goal

Create a system that processes a folder of text and PDF files and answers questions about them with Citations.

2. The Implementation Plan

1. Use DirectoryLoader to find all files in ./my_docs/.
2. Use RecursiveCharacterTextSplitter to chunk them.
3. Store them in Chroma.
4. Build an LCEL rag_chain that uses a system prompt requesting citations.
5. Test: "What is the return policy? Please cite the document name."

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Module 7 Summary

• RAG: Retrieval + Augmentation + Generation.
• Grounding: Forcing the AI to use provided context instead of its memory.
• Retriever: The chainable search object for Vector DBs.
• LCEL Pipeline: The pipe that creates a cohesive workflow.
• Hallucination Control: Using strict instructions and citations to ensure truth.

Coming Up Next...

In Module 8, we add Persistence to our conversations. We will learn about Memory and Conversation State, so our agents can remember what was said 10 minutes ago and handle multi-turn dialog.

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Module 7 Checklist

• I can describe the 3 steps of the RAG workflow.
• I have converted a Vector Store into a Retriever object.
• I have successfully used a RunnablePassthrough in a chain.
• My RAG prompt includes a "I do not know" fallback rule.
• I understand the difference between a "Document" and its "Metadata".