Module 15 Wrap-up: The Memory Master

You have learned that for an agent to be useful in an enterprise, it needs Long-Term Memory (Vector DBs) and it must be Scalable (Caching/Queueing). Now, you are going to build a Grounded Knowledge Agent.

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Hands-on Exercise: The Policy Expert

The Goal: Build an agent that "Learns" from a text file. If you ask it a question about that file, it must retrieve the relevant paragraph before thinking.

1. Requirements

pip install chromadb langchain openai

2. The Logic (Python)

import chromadb
from langchain_openai import OpenAIEmbeddings

# 1. SETUP LOCAL VECTOR DB
client = chromadb.Client()
collection = client.create_collection(name="policy_docs")

# 2. ADD DATA
# In real life, you'd load a PDF. Here we add a string.
collection.add(
    documents=["Our company holiday policy allows for 25 days of paid leave per year."],
    ids=["id1"],
    metadatas=[{"type": "hr"}]
)

# 3. THE RAG AGENT FUNCTION
def policy_agent(query):
    # A. Retrieve
    results = collection.query(query_texts=[query], n_results=1)
    context = results['documents'][0][0]
    
    # B. Augment & Generate
    prompt = f"User Question: {query}\n\nRelevant Context: {context}\n\nAnswer only based on context."
    # response = llm.call(prompt)
    return f"Based on our records: {context}"

# 4. TEST
print(policy_agent("How many vacation days do I get?"))

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Module 15 Summary

• Vector Databases allow agents to access massive knowledge bases without "Token Bloat."
• Semantic Caching reduces costs and latency for repetitive queries.
• Monitoring ensures the technical and financial health of your system.
• CI/CD automates the testing of prompts and tools to prevent regressions.

Coming Up Next...

In Module 16, we enter the world of Agentic UX. We will learn how to design interfaces that make sense for asynchronous, multi-step agents—moving from simple chat boxes to Generative UI.

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Module 15 Checklist

• I have installed ChromaDB or set up a Pinecone account.
• I can explain the difference between a "Vector" and "Metadata."
• I have integrated a Semantic Cache into my test code.
• I understand the benefit of a Correlation ID in production logs.
• I can describe a "Shadow Deployment" strategy.