Step 2: Configure Data 360 and Intelligent Context

Now that you’ve planned your content strategy, set up the infrastructure to process and retrieve your visual data. Upload your files to Agentforce Data Library, configure Intelligent Context to parse and index the content, and create a retriever that your agent uses at run time. These three components work together to transform your raw visual content into searchable, grounded knowledge that powers your agent’s responses.

Create a Data Library and upload your unstructured data files. Data libraries transform large sets of information from documents, web content, or text fields into searchable, structured data. They enhance your Agentforce agent’s accuracy by connecting it to your trusted data sources. Learn more about data libraries.

To experiment with Agentforce Data Library, you can use this sample PDF instructions for deck building.

  1. Go to Setup.
  2. In the quick find box, find and select Agentforce Data Library.
  3. Click New Library.
  4. Enter a descriptive name and API name.
  5. Save your changes.
  6. For Data Type, select Files.
  7. Select the relevant data space.
  8. Upload your unstructured data files and click Done.

Intelligent Context is an AI-powered workspace where you interact with prompts to process unstructured data and create search index configurations tailored to your business context. Set up intelligent context with a new search configuration, and test it.

Learn more about Intelligent Context in Data 360.

To experiment with Intelligent Context, you can use this sample PDF instructions for deck building.

  1. From the App Launcher, find and select Data Cloud.
  2. Go to the Process Content tab and open Intelligent Context.
  3. Click New Configuration.
  4. Enter the relevant data space.
  5. Enter a descriptive name and API name.
  6. Save your changes.
  7. Upload up to 5 files on which you want to test your search configuration.
  8. Click Done and wait a minute for the file to be ready for preview.
  9. Click Set up my configuration using smart defaults and wait for chunks generation.
  10. Click Modify to go to Edit Configuration.
  11. Configure:
    • Set Parsing Options: Select LLM-based Parsing. This setting extracts all text, images, and other visual elements using an LLM. Learn more about LLM-based parsing.
    • Set Preprocessing Options: Select the default No preprocessing, and turn off Image Processing.
  12. Apply your changes and wait for chunks generation.
  13. Click Publish.
  14. Select the relevant unstructured data model object (UDMO) that Agentforce data library creates for you: find the data library name with ADL prefix, for example, ADL_deck.
  15. Click Next.
  16. Click Publish.
  17. Test your search index configuration by asking questions in the agent chat window or compare the generated chunks with your source document. Each generated chunk contains a confidence score, a source file reference, and a citation. Iterate it until you get good results. Learn more about testing and iterating an Intelligent Context configuration.

Test your search index configuration by asking questions in the Agentforce pane on the right. Iterate it until you get good results.

Create an individual retriever to fetch relevant data from your search index at run time. When you configure a retriever, you select its search index, define filters to narrow the search scope, and specify which fields to return to your agent. Filters focus the search on the most relevant data, while the returned fields provide the context your prompt needs to generate accurate responses. Each time you edit and save a retriever, the system creates a new version. You can activate one version at a time.

  1. Go to Setup.
  2. On the quick find box, find and select Agentforce Agents.
  3. Launch the new Agentforce builder.
  4. From the Build menu on the left, select Data and then select Retrievers.
  5. Click New Retriever.
  6. For retriever type, select Individual Retriever and click Next.
  7. Select Data Cloud as the data source for the new retriever.
  8. Define Retrieval Details:
    • Under Select the data space where the data resides, select default.
    • Under Select a data model object, select the data model that Agentforce Data Library creates for you. Find the data library name with ADL prefix, for example ADL_deck.
    • Under Data model object’s search index configuration, select the search index you created in step 2 – Set Up Intelligent Context.
    • Click Next.
  9. Define filters to narrow the search focus to more relevant data. For the purposes of this example, select All Documents and click Next.
  10. Under Fields to Return, configure which information to return by specifying one or more fields that ground the prompt. Select only fields that augment the prompt for your use case and help make prompt responses more accurate and relevant.
    • Enter a unique, descriptive Field Label.
    • For Field Name, select Related Attributes, then select the chunk that Agentforce Data Library creates for you, for example, ADL_deck chunk. Then under Attributes, select Chunk.
    • Click Add Field to add more fields that give the best responses for your use case. You can try different combinations of fields to maximize the quality of your responses. Field examples: Chunk Sequence Number, Data Source, or Record ID. See more examples in DIY Storefront use case.
    • Click Next.
  11. Save your changes and then click Activate to activate your individual retriever.

Step 3: Build Agentforce Agent: Finalize the setup to activate your grounded agent.