Retrieval-augmented generation

As explained in AI Agents in Action

Retrieval augmented generation is a language-model pattern that retrieves relevant external information and incorporates it into generated responses.

Retrieval-augmented generation (RAG) enhances a language model’s prompt with relevant context retrieved from external documents or other stored information. It supports document chat, question answering, reference lookup, and agent knowledge. section 8.1 section 8.2

A typical RAG workflow has two phases:

• Load documents, split them into context chunks, convert the chunks into vector embeddings, and store them in a vector database. section 8.2
• Embed a user query, search for similar stored chunks, and insert the retrieved content into the prompt as additional context. section 8.2
• Send the augmented prompt to the language model, which uses the retrieved context to help formulate an answer. section 8.2

Querying a document collection

A document-chat application can index a PDF or text file, then answer a question by retrieving semantically similar passages. In a LangChain exercise, token-based chunks are stored in Chroma and similarity search retrieves a nursery-rhyme passage even when the query uses different wording, such as asking why the girls are crying. section 8.4.2

Figure 8.2 The two phases of RAG: first, documents must be loaded, transformed, embedded, and stored, and, second, they can be queried using augmented generation.

Use this diagram to distinguish document indexing from query-time augmented generation. section 8.2

How AI Agents in Action covers it

How deep it goes

Explains indexing, chunking, embeddings, vector storage, similarity search, and generation, while deferring more advanced retrieval patterns.

Examples

Demonstrates PDF and text-document chat, LangChain chunking, Chroma retrieval, nursery-rhyme queries, and Nexus knowledge stores.

What it assumes

Assumes basic prompt and agent concepts, while introducing retrieval, document indexing, embeddings, vector databases, and memory from foundational distinctions.

What you can do after

Build RAG workflows that index documents, retrieve semantically similar passages, and connect knowledge stores to supported agents.

How it connects

Connects document indexing, vector embeddings, similarity search, prompt engineering, agent knowledge, and agent memory.

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Related concepts

• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
• hallucinationaddresses · A hallucination is model-generated content that is false, fabricated, or unsupported by relevant evidence but presented as plausible or factual.
• knowledge baseuses · A knowledge base is an organized collection of information that can be accessed or queried to support reference, retrieval, or reasoning.
• information retrievaluses · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
• fine-tuningcontrasts with · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action. The section links point to the parts of the book it draws on.