Where Vector Databases Fit in the AI Stack

In the previous lessons, we explored the "What" and "Why" of vector databases. Now, we focus on the "Where."

If you were building a traditional web app, you'd have a simple stack: Frontend -> Backend -> Database. However, in the age of Agentic AI and Retrieval-Augmented Generation (RAG), the architecture has shifted. We have introduced new components like LLMs, Embedding Models, and specialized middleware.

In this lesson, we will deconstruct the "Modern AI Stack" and see how the vector database acts as the central Nervous System for information retrieval.

1. The V-L-M Stack: A New Architectural Standard

A popular way to visualize the AI stack is through the V-L-M model:

V (Vector Database): The long-term memory.
L (Large Language Model): The reasoning engine.
M (Middleware/Orchestration): The glue (LangChain, LangGraph, etc.).

The Traditional vs. AI Flow

Traditional Flow:

User requests "Settings."
API queries Postgres SELECT * FROM settings WHERE user_id=?.
Backend returns JSON to Frontend.

Production AI Flow:

User asks "How do I change my billing address?"
Orchestrator converts the question into a Vector.
Orchestrator queries the Vector Database for the "Account Management" documentation.
Orchestrator sends the retrieved text + User question to the LLM.
LLM generates a human-readable instruction.
Backend returns the AI response to the user.

graph TD
    subgraph Client
    A[Frontend: React/Next.js]
    end
    subgraph Application_Layer
    B[FastAPI / Node.js]
    C[Orchestrator: LangChain]
    end
    subgraph AI_Infrastructure
    D[Embedding Model: OpenAI/HuggingFace]
    E[Vector DB: Pinecone/Chroma]
    F[LLM: Claude/GPT-4]
    end
    
    A -- Query --> B
    B --> C
    C -- 1. Embed --> D
    D -- Vector --> C
    C -- 2. Retrieve --> E
    E -- Context --> C
    C -- 3. Reason --> F
    F -- Answer --> C
    C --> B
    B -- Final Response --> A

2. Ingestion vs. Retrieval Pipelines

A critical concept for any AI Engineer is the distinction between the Ingestion Pipeline (Offline/Sync) and the Retrieval Pipeline (Online).

The Ingestion Pipeline (The Write Path)

This is how your data gets into the vector database. It typically happens as a background worker or a CI/CD process.

Load: Read PDFs, Slack messages, or database rows.
Chunk: Split a 50-page PDF into 500-word segments (semantic chunks).
Embed: Send chunks to an embedding model.
Index: Store the vectors + original text (metadata) in the vector database.

The Retrieval Pipeline (The Read Path)

This is what happens when a user types a query. It must be optimized for latency.

Embed Query: Convert the user's input into the same vector space as your data.
Similarity Search: Find the $K$ most similar vectors in the DB.
Augment: Build a prompt including those results.
Generate: Call the LLM and stream the answer.

3. The Backend: FastAPI and the Vector Database

In production, you don't call the vector database directly from your frontend. You wrap it in a secure API. Let's look at a production-ready snippet using FastAPI and a generic vector store client pattern.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import ollama # For local embeddings
from vector_client import VectorStore # Conceptual client

app = FastAPI(title="AI Knowledge API")

class QueryRequest(BaseModel):
    text: str
    top_k: int = 5

@app.post("/search")
async def search_knowledge(request: QueryRequest):
    try:
        # 1. Generate Embedding
        # Using a local model for cost-efficiency
        response = ollama.embeddings(model="mxbai-embed-large", prompt=request.text)
        query_vector = response["embedding"]
        
        # 2. Query Vector DB
        # We search our 'documentation' collection
        results = VectorStore.query(
            collection="docs",
            vector=query_vector,
            limit=request.top_k,
            include_metadata=True
        )
        
        # 3. Format and Return
        return {
            "query": request.text,
            "matches": [
                {"text": r.text, "score": r.score, "source": r.metadata["url"]}
                for r in results
            ]
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

4. The Frontend: Displaying Semantic Results

When building UIs for vector search, we need to handle "Fuzzy Matches." Unlike a traditional database, we often get a Similarity Score.

A common React pattern is to show "Confidence Levels" or highlight the specific "Source" that the AI retrieved from the vector store.

// React Component for Search Results
const SearchResults = ({ results }) => {
  return (
    <div className="space-y-4">
      {results.map((res, index) => (
        <div key={index} className="p-4 border rounded-lg bg-gray-900 border-blue-500/30">
          <div className="flex justify-between items-center mb-2">
            <span className="text-sm font-bold text-blue-400">
              Match Confidence: {(res.score * 100).toFixed(1)}%
            </span>
            <a href={res.source} className="text-xs text-gray-400 underline">Source Link</a>
          </div>
          <p className="text-gray-200 text-sm leading-relaxed">
            "{res.text.substring(0, 200)}..."
          </p>
        </div>
      ))}
    </div>
  );
};

5. Security and Access Control in the Stack

One of the biggest mistakes in AI architecture is ignoring Identity and Access Management (IAM).

Because vector databases are "Fuzzy," you cannot easily say SELECT * WHERE user_id = 5. If User A searches for "My salary," and the vector database retrieves a document about User B's salary because they are semantically similar, you have a major security breach.

In a production AI stack, the Vector Database must support Metadata Filtering: When searching, you must always include a filter: db.search(vector=q, filter={"tenant_id": current_user.org_id})

This ensures that the "Similarity" math stays within the boundaries of the user's data.

6. The Developer Workflow: Local to Cloud

How do you develop this stack?

Local (Development): Use Chroma (Module 5) running in Docker. Use Ollama for embeddings to keep costs at zero.
Staging/Production: Use Pinecone (Module 6) for managed scaling or OpenSearch (Module 7) for enterprise-grade hybrid search and compliance.

Summary and Key Takeaways

The Vector Database is not just "another database"; it is the bridge between Unstructured Information and LLM Reasoning.

Orchestration (LangChain/LangGraph) connects the components.
Ingestion processes raw files into vectors.
Retrieval finds the right context at query time.
Metadata Filtering is the primary way we enforce security and multi-tenancy.

In the next lesson, we will wrap up Module 1 with Real-World Use Cases, going beyond simple chatbots to explore recommendation systems, anomaly detection, and autonomous agents.

Exercise: Stack Mapping

Think about an application you want to build (e.g., a technical support bot for a specific software).

Identify your Data Sources (Ingestion).
Choose your Embedding Model (Text, Image, or both?).
Pick your Vector Database (Local Chroma or Managed Pinecone?).
Draw a simple diagram (using pen and paper or Excalidraw) showing how a user query flows through these components.

Mapping the flow before you code is common practice for production AI systems.

The Modern AI Stack: Where Vector Databases Live