Vector Databases: The Infinite Memory

An agent's Short-Term Memory is the Context Window (Module 2). But what happens if your agent needs to know 1,000 corporate documents or millions of previous user chats? You can't put that in the prompt. You need a Vector Database.

1. What is a Vector?

A Vector (or Embedding) is a list of numbers that represents the Meaning of a sentence.

• "The dog is happy" and "The canine is joyful" have very similar vectors, even though they use different words.

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2. RAG (Retrieval Augmented Generation)

Vector DBs are the engine of RAG.

1. User asks: "What is our vacation policy?"
2. System: Converts the question to a Vector.
3. Vector DB: Searches for the top 3 most similar paragraphs in the employee handbook.
4. Agent: Receives the question AND those 3 paragraphs.
5. Result: The agent answers accurately without having to "know" the policy during training.

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3. Top Vector Databases for Agents

• ChromaDB: Open-source and runs locally on your machine. Excellent for small/medium projects.
• Pinecone: Fully managed, high-performance cloud database. The standard for tech startups.
• PGVector: An extension for Postgres. Best if you already have a database and want to keep everything in one place.

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4. Visualizing the Memory Retrieval

graph TD
    User[Human Query] --> Emb[Embedding Model]
    Emb --> V[Vector Search]
    V <--> DB[(Vector Database)]
    V --> Context[Top 3 Relevant Chunks]
    Context --> Agent[Agent Brain]
    Agent --> Final[Grounded Answer]

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5. Cleaning the "Knowledge Base"

A Vector DB is only as good as the data you put in.

• Chunking: Don't just upload a 100-page PDF. Break it into 500-word chunks so the search results are precise.
• Metadata: Add tags like department:HR or date:2024. This allows your agent to say: "Search the Vector DB, but only look at HR documents from 2024."

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Key Takeaways

• Vector Databases provide the long-term knowledge for your agent.
• Embeddings map text to mathematical meanings.
• RAG combines retrieval with LLM reasoning to prevent hallucinations.
• Metadata filtering is the secret to high-accuracy vector search.