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          Create a Vector Search Index for Contract Documents for Manual Contract Search Set Up

          Create a Vector Search Index for Contract Documents for Manual Contract Search Set Up

          Use the Search Index Builder to create a vector search index on Contract Document Version. This index powers AI-driven contract search using semantic matching and document filtering.

          Required Editions

          Available in: Lightning Experience
          Available in: Professional, Enterprise, Unlimited, and Developer editions.
          User Permissions Needed
          To create vector search index:

          Data Cloud Architect [Home Org]

          OR

          Data Cloud One [Companion Org]

          AND

          CLM Admin User

          AND

          Contracts AI User

          Prerequisite:Configure Security for PDF Previews

          1. In Data Cloud, click the Search Index tab, and click New.
          2. Click New, then select Advanced Setup.
            Search Index Builder opens in a new tab.
          3. Make sure the search type is set to Vector Search.
          4. Under Data Model Object Label, select Contract Document Version, then click Next.
          5. Click Manage Fields, and then select Contract Document Version Status.
          6. Save your changes.
          7. Click Include File Attachments, and then select Content Document Version.
          8. Save your changes.
          9. In the File Extension list, click the Delete icon for all extensions except pdf.
          10. Click Next.
          11. Make sure E5 Large V2 Embedding Model is selected for Embedding Model.
          12. Click Next.
          13. Under Direct Fields, expand Contract Document Version.
          14. Drag Contract, Contract Document Version Id, and Contract Document Version Status into the Selected fields section.
          15. Click Next.
          16. Save your changes.
          Note
          Note Contract Document Version chunk and Contract Document Version index data lake objects are created automatically. Segmented content is stored in the chunk object, and numeric relevance scores are stored in the index object.
           
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