Passage Retrieval (CPR) implementation overview

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Developer System Administrator

This article outlines the steps required to implement Passage Retrieval (CPR).

Important

Passage Retrieval (CPR) is a paid product extension. Contact Coveo Sales or your Account Manager to add CPR to your organization license.

Step 1: Choose the content to use

The content that you choose for CPR will be used as the raw data from which passages are retrieved.

See Passage Retrieval (CPR) content requirements and best practices.

Note

A Passage Retrieval (CPR) implementation must include both a CPR model and a Semantic Encoder (SE) model. Both the CPR and SE models must be configured to use the same content.

Step 2: Create a CPR model

A CPR model retrieves passages based on the content specified in the model settings. The CPR model then uses the embedding vector space to find the most relevant passages (chunks) that your LLM application will use to generate the output.

See Create and manage Passage Retrieval (CPR) models.

Step 3: Associate the CPR model with a query pipeline

When a CPR model is associated with your LLM-powered application’s query pipeline, it’s used to retrieve relevant passages for submitted queries.

Note

This is only required if you’re using the CPR model with the Passage Retrieval API to feed your custom RAG system LLM application with relevant passages. It’s not required when using the CPR model with the Coveo Search Agent in a Coveo-powered search interface.

See Associate a Passage Retrieval (CPR) model with a query pipeline.

Important

The CPR model must be associated with the same query pipeline as the Semantic Encoder (SE) model.

Step 4: Create a Semantic Encoder (SE) model

An SE model adds vector-based search capabilities during first-stage content retrieval for CPR. An SE model ensures that CPR always uses the most relevant content from which to retrieve passages.

See Create and manage Semantic Encoder (SE) models.

Note

A Passage Retrieval (CPR) implementation must include both a CPR model and a Semantic Encoder (SE) model. Both the CPR and SE models must be configured to use the same content.

Step 5: Associate the SE model with a query pipeline

An SE model adds vector-based search capabilities to a query pipeline.

See Associate a Semantic Encoder (SE) model with a query pipeline.

Important

The SE model must be associated with the same query pipeline that’s used by the CPR model.

Step 6: Integrate the PR API

Use the Passage Retrieval API to retrieve the most relevant passages that were identified by the CPR model to enhance the generated output of your LLM application.

Note

This is only required if you’re using the CPR model with the Passage Retrieval API to feed your custom RAG system LLM application with relevant passages. It’s not required when using the CPR model with the Coveo Search Agent in a Coveo-powered search interface.
Important

Coveo doesn’t manage the creation of the prompt that’s sent to the LLM for output generation, or the component that displays the output.