Working with Retrievers in Gen AI Builder

What is a Retriever

A Retriever in Gen AI Builder is a specialized Griptape Structure that defines how your AI Assistants retrieve information from Knowledge Bases. Retrievers enable precise, customizable information retrieval — the core of Retrieval-Augmented Generation (RAG) workflows.

At query time, your Assistant uses its configured Retriever(s) to:

• Search one or more Knowledge Bases
• Select the most relevant results ("chunks") based on semantic similarity and other filters
• Feed that context into the Large Language Model (LLM) to generate an informed response

In short: A Retriever connects user queries → Knowledge Base content → AI answers.

For a deep dive, see Retrievers explained. For step-by-step configuration, see Create a Retriever.

Why use Retrievers

• Ground AI responses: Retrievers ensure that Assistants provide factually grounded answers based on your organization’s Knowledge Bases.
• Support explainability: Retrieved content can be referenced in the Assistant’s responses, enabling transparent and auditable AI.
• Control search behavior: You can configure which Knowledge Bases are queried, control token limits, and supply advanced metadata.
• Enable multi-source RAG: Retrievers can target multiple Knowledge Bases simultaneously — useful for complex Assistants with broad knowledge scope.

When to use Retrievers

• Whenever you are building an AI Assistant that needs to answer questions based on internal content.
• Whenever your content is stored in Knowledge Bases and you want dynamic, query-time retrieval.
• When you want your AI application to support semantic search and not rely solely on the LLM’s internal training.

Examples:

• Support chatbots answering questions from product documentation
• Financial advisors surfacing policies and legal disclaimers on demand
• Sales tools providing quick access to internal pricing documents and competitive intelligence

How do Retrievers work

At runtime, the process is:

1. User asks a question.
2. Assistant forwards the query to its assigned Retriever(s).
3. Retriever runs semantic + optional structured search across configured Knowledge Bases.
4. Retriever returns the most relevant results.
5. Assistant passes this context to the LLM.
6. LLM generates a response incorporating the retrieved information.

Under the hood: A Retriever is implemented as a Griptape Structure, giving you flexibility in how it performs retrieval.

See Griptape concepts for more on Structures.

Key components of a Retriever

• Knowledge Bases targeted: Determines the scope of retrieval.
• Max Tokens: Controls how much content is retrieved per query.
• Metadata: Optional structured data for advanced configuration or tagging.
• Associated Assistants: Controls which Assistants can use this Retriever.

Getting started

Pre-requisites

• One or more Knowledge Bases created and populated in Gen AI Builder.

First steps

• Create a Retriever and configure it to target your Knowledge Bases.
• Associate the Retriever with an AI Assistant.
• Test the Assistant to verify that retrieved content is correctly influencing responses.

See Create a Retriever for the full step-by-step guide.

Related topics

• Retrievers explained
• Create a Retriever
• Knowledge Bases explained
• Structures explained
• Retrieval Augmented Generation (RAG)
• Hybrid Manager: Using Gen AI Builder