Project 4: RAG Agent with a Real Vector Database

Build a PydanticAI RAG agent that uses a production vector database and returns validated responses.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12-18 hours
Language Python
Prerequisites RAG basics, vector DB familiarity
Key Topics vector DBs, retrieval, schema validation

1. Learning Objectives

By completing this project, you will:

  1. Integrate a vector database (Chroma, Pinecone, etc.).
  2. Index documents with metadata.
  3. Retrieve top-k evidence for questions.
  4. Validate answers with Pydantic schemas.
  5. Measure retrieval latency and accuracy.

2. Theoretical Foundation

2.1 Real Vector Databases

Production systems require persistence, metadata filters, and scalable retrieval beyond in-memory indexes.

3. Project Specification

3.1 What You Will Build

A RAG agent that uses a vector DB backend and outputs schema-validated answers with citations.

3.2 Functional Requirements

  1. Vector DB integration with ingestion pipeline.
  2. Metadata filters for targeted retrieval.
  3. Answer schema with citation fields.
  4. Latency metrics for retrieval.
  5. Evaluation on a test set.

3.3 Non-Functional Requirements

  • Deterministic evaluation with fixed seeds.
  • Clear error handling for DB failures.
  • Configurable index settings.

4. Solution Architecture

4.1 Components

Component Responsibility
Ingestor Load and chunk docs
Vector DB Store embeddings
Retriever Query top-k
Answerer Produce validated output
Evaluator Measure accuracy

5. Implementation Guide

5.1 Project Structure

LEARN_PYDANTIC_AI/P04-rag-vector-db/
├── src/
│   ├── ingest.py
│   ├── index.py
│   ├── retrieve.py
│   ├── answer.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Ingestion + indexing (4-6h)

  • Ingest and embed documents.
  • Checkpoint: documents indexed with metadata.

Phase 2: Retrieval + answers (4-6h)

  • Retrieve top-k chunks.
  • Validate answer schema.
  • Checkpoint: outputs include citations.

Phase 3: Evaluation (3-6h)

  • Measure latency and accuracy.
  • Checkpoint: report shows retrieval metrics.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit retrieval filter correctness
Integration DB index/retrieve flow
Regression output schema validation

6.2 Critical Test Cases

  1. Metadata filters restrict results correctly.
  2. Output validates against schema.
  3. Retrieval latency within target.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Missing metadata poor filters enforce metadata schema
Slow retrieval high latency tune index parameters
Schema errors invalid output add retry logic

8. Extensions & Challenges

Beginner

  • Add a local vector DB option.
  • Add simple CLI.

Intermediate

  • Add reranking.
  • Add hybrid search.

Advanced

  • Add sharding or multi-tenant indexes.
  • Add monitoring dashboards.

9. Real-World Connections

  • Enterprise RAG relies on scalable vector DBs.
  • Production systems need validated outputs.

10. Resources

  • Vector DB documentation
  • PydanticAI guides

11. Self-Assessment Checklist

  • I can integrate a vector database.
  • I can validate RAG outputs.
  • I can measure retrieval latency.

12. Submission / Completion Criteria

Minimum Completion:

  • Vector DB backed RAG
  • Schema-validated output

Full Completion:

  • Evaluation metrics
  • Error handling

Excellence:

  • Reranking or hybrid search
  • Monitoring dashboards

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_PYDANTIC_AI.md.