Pattern [14]

Knowledge Retrieval (RAG)

≈ Database Querying / Search Index Integration (Elasticsearch)

> Agentic Definition

Retrieval-Augmented Generation (RAG) equips agents with the ability to query external knowledge bases (Vector DBs, Search Indices) to ground their answers in fact and access proprietary data.

> Description

Retrieval-Augmented Generation (RAG) equips agents with the ability to query external knowledge bases (Vector DBs, Search Indices) to ground their answers in fact and access proprietary data.

≈ How It Maps to Database Query / Search Index

Fetching data from storage to populate a view or answer a request.

≠ Key Divergence

RAG involves semantic search (vector embeddings) rather than keyword match. It creates a prompt context, not just a data object. The "Schema" is the semantic meaning of the data chunks.

> Key Takeaway

Adapt: "Data Access Layer" now implies Vector DBs and Embedding Models. You are optimizing for "Context Relevance," not just query performance.

The Code

Before: SQL Query

SQL Query
1-- SQL Query
2SELECT answer FROM faq WHERE topic = 'pricing';

After: Agentic RAG

Agentic RAG
1# Semantic Retrieval
2docs = retriever.get_relevant_documents(
3    "What is the pricing model?"
4)
5
6# Augment Context
7prompt = f"Context: {docs}\nQuestion: What is the pricing model?"
8
9# Generate Answer
10response = llm.generate(prompt)

Production Notes

  • You must evaluate the Retrieval (Did we get the right doc?) and the Generation (Did the LLM use the doc correctly?). This is known as "RAG Triad" evaluation.
  • Vector search + LLM generation is slower than a DB lookup. Latency optimization requires careful indexing and caching strategies.

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Frequently Asked Questions

When should I use the Knowledge Retrieval (RAG) pattern?

Retrieval-Augmented Generation (RAG) equips agents with the ability to query external knowledge bases (Vector DBs, Search Indices) to ground their answers in fact and access proprietary data.

How does Knowledge Retrieval (RAG) relate to Database Querying / Search Index Integration (Elasticsearch)?

Fetching data from storage to populate a view or answer a request. However, there is a key divergence: RAG involves semantic search (vector embeddings) rather than keyword match. It creates a prompt context, not just a data object. The "Schema" is the semantic meaning of the data chunks.

What are the production trade-offs of Knowledge Retrieval (RAG)?

You must evaluate the Retrieval (Did we get the right doc?) and the Generation (Did the LLM use the doc correctly?). This is known as "RAG Triad" evaluation. Vector search + LLM generation is slower than a DB lookup. Latency optimization requires careful indexing and caching strategies.