Module 6 Wrap-up: The Librarian

You have successfully turned "Words" into "Vectors" and "Folders" into "Indexes." You now possess the core infrastructure required to solve the biggest problem in AI: Hallucinations. By storing facts in a vector store, you give your model a "Reference Book" to read from.

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Hands-on Exercise: The Local Semantic Search

1. The Goal

Build a script that:

1. Takes 3 text Strings (e.g. news about space, cooking, and football).
2. Stores them in a Chroma vector store.
3. Asks a question that doesn't use the exact words (e.g., "Tell me about celestial bodies").
4. Verifies the "Space" document is returned.

2. The Implementation Plan

• Install Chroma: pip install langchain-chroma
• Use your OpenAIEmbeddings from Lesson 1.
• Experiment with k=1 to get just the single best result.

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Module 6 Summary

• Embeddings: Mathematical meaning (vectors).
• Vector Store: The semantic database (Chroma, FAISS, Pinecone).
• Similarity: Math-based relevance (Distance scores).
• k value: Controls the quantity of retrieved context.
• Persistence: Saving the memory to disk.

Coming Up Next...

In Module 7, we combine everything into RAG (Retrieval-Augmented Generation). We will link our Vector Store (Module 6) with our Chains (Module 4) to build an agent that can answer questions about any data you give it.

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Module 6 Checklist

• I can explain the difference between a string and a vector.
• I have chosen my preferred embedding model (OpenAI vs. Local).
• I understand that k=4 means I am retrieving 4 text chunks.
• I have successfully saved a vector store to my local drive.
• I know that switching embedding models requires a full database rebuild.