---
schema_version: "1"
id: 31dd14eddfc4a14615676888716177f1e514816a7bfd1053cb98c1fad70a59bd
title: Retrieving Content with Vector Search
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_search_index_retrieve_content.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_retrieve_content.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: aa315e06d49c3a829623d2490fa3d53f3238484cc0c87d99bee4890903bdc548
description: With vector search and AI agent queries, you can capture queries, search across structured and unstructured data sources, and receive summarized responses that improve efficiency and accuracy.
products:
  - Data 360
release: "264"
---
# Retrieving Content with Vector Search

With vector search and AI agent queries, you can capture queries, search across structured and unstructured data sources, and receive summarized responses that improve efficiency and accuracy.

In these example workflows, a data aware specialist has knowledge articles stored in Salesforce and other PDF files stored externally in an Azure blob store. The data aware specialist wants Salesforce users to get summarized answers from the available content in AI agents using natural language search. The content has been chunked and vectorized in the Data Cloud search index.

## Workflow 1

This example demonstrates how an AI agent and vector search streamline the process of retrieving and summarizing relevant content based on a user query.

![An AI agent query and response from vector search and the LLM using retrievers.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_search_index_retrieve_content_retrievers_v2.png)

1. The user initiates a request through an AI agent.
2. The agent triggers a prompt template.
3. The prompt initiates a retriever, which is sent to Data 360 vector search.
4. Data 360 vector search retrieves matching excerpts from relevant documents sourced from Salesforce knowledge articles and PDFs stored in an Azure blob store.
5. The prompt template is populated and sent to a large language model (LLM) for summarization.
6. The LLM processes and returns a summarized response to the prompt template.
7. This response is then relayed back to the agent.
8. The response is displayed to the user.

## Workflow 2

This example demonstrates how AI agents, Apex invocable actions, Flows, and vector search streamline the process of retrieving and summarizing relevant content based on a user query.

![An AI agent query and response from vector search and the LLM.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_search_index_retrieve_content_v2.png)

1. The user initiates a request through an AI agent.
2. The agent activates a flow.
3. Flow captures and stores the user’s input in a custom object.
4. Flow triggers a prompt template.
5. Prompt Builder retrieves data from the custom object and executes an Apex invocable action.
6. The Apex invocable action engages the Data 360 vector search.
7. Data 360 vector search retrieves matching excerpts from relevant documents sourced from Salesforce knowledge articles and PDFs stored in an Azure blob store.
8. The Apex invocable action receives the matching excerpts from Data 360 and populates the prompt template.
9. The prompt template is sent to a large language model (LLM) for summarization.
10. The LLM processes and returns a summarized response to the prompt template.
11. This response is then relayed back through Flow.
12. Flow sends the response back to the agent.
13. The response is displayed to the user.

#### See Also

- [_Salesforce Help_: Connect Unstructured Data](https://help.salesforce.com/s/articleView?id=data.c360_a_unstructured_data_connect_overview.htm&release=264.0.0&type=5 "Use unstructured data in Data 360 to ground Agentforce agents, generative AI, analytics, and automation use cases with business-specific data that delivers deeper insights for your users and customers.")
- [_Salesforce Help_: Search Index](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_vector_index.htm&release=264.0.0&type=5 "In Data 360, you can create searchable semantic vector embeddings from your unstructured and structured data. Vector search helps your generative AI, automation, and analytics applications understand semantic similarities and context between embeddings.")
- [_Salesforce Help_: Agentforce](https://help.salesforce.com/s/articleView?id=ai.copilot_intro.htm&language=en_US)