Search indexes, retrievers and Data Libraries

Search index

A search index chunks and embeds text from one DMO. For Case on Subject and Description, Salesforce's Trailhead shows Case_chunk (the text chunks) and Case_index (the index) objects, and about 20 minutes to Ready.

Setting	Options (from Salesforce's guide)
Search type	Vector (semantic) or Hybrid (semantic + keyword). Prefer hybrid for business data: account names, SKUs and order numbers need exact keyword hits
Fields	The text fields to chunk. Include the fields the answer must quote
Chunking	Section-aware chunking; max tokens (1,200 in the guide); overlap; fields to prepend to each chunk (for example Title)
Embedding	Salesforce Embedding V2 Small (SFR-v2-small). Smaller chunks need a model with a smaller supported sequence length

Structured golden records need text. An index embeds text fields, and a golden record is mostly IDs and numbers. The pipeline therefore writes one narrative text field per golden record, such as "Acme Corp: 14 orders, lifetime revenue 120,400, last order 2026-09-12, top products …". It builds that text from the unified DMO and the revenue insight into a custom DMO, and indexes that field (patterns/golden-record-rag-pipeline-for-revenue.md). The hub's reading: that's the documented index mechanism applied to structured data. Salesforce's guides index text-rich objects such as Case and web pages.

API: GET, POST, PATCH and DELETE on /services/data/v66.0/ssot/search-index[/{name}], per the community plugin. Responses carry runtimeStatus, searchType and sourceDmoDeveloperName. Capture a working index's JSON with GET, and replay it.

Retriever

An individual retriever queries one search index on one DMO.

• Configure: filters (all documents, or field filters), the number of results (20 in Salesforce's example), and the fields to return. Those are DirectAttributes from the DMO and RelatedAttributes such as the chunk.
• Save it, then activate it. An inactive retriever can't be used.
• Build it in Agentforce Studio (Build → Data → Retrievers). The Connect API exposes retrievers at /ssot/machine-learning/retrievers for reads. Creating one by API isn't publicly documented; the supported way to move it between orgs is a data kit's AI Retriever component (reference/data360/automation-and-data-kits.md).

Ground a prompt template

Salesforce's Trailhead uses a Knowledge Answers template:

1. Replace its placeholder with Insert Resource → Retrievers → your retriever.
2. Set search text to {!$Input:Query}, output fields to the chunk, and a number of results.
3. Test in Preview with a retriever ID, and check the resolved prompt for chunks.

Calling the template from an agent is a prompt:// action (reference/agentforce/agent-script-actions-and-targets.md).

Give an agent the retriever

Salesforce's documented path for Agent Script is an Agentforce Data Library that wraps the retriever:

sf agent adl create --source-type retriever --retriever-id <active retriever ID> \
  --name "Revenue Golden Records" --developer-name Revenue_Golden_Records --target-org dev --json
sf agent adl get --library-id <libraryId> --target-org dev --json      # status READY, retrieverId set

A RETRIEVER library is ready at once, with no indexing. Then add the knowledge: block and the action to the agent:

knowledge:
    rag_feature_config_id: "ARFPC_1JDXX0000000001AAA"
    citations_enabled: True
    citations_url: ""

• rag_feature_config_id is ARFPC_ + the library ID, not the retriever ID.
• Subagents call the standard AnswerQuestionsWithKnowledge action (target: "standardInvocableAction://streamKnowledgeSearch"), whose inputs default from @knowledge.*. The full action block, copied from Salesforce's sf-skills, is in guides/data360/ground-a-revenue-agent.md.
• The linter also accepts a retriever:// action target, but its inputs aren't documented. Use the Data Library.

Access the agent needs:

• The Einstein Agent User must hold a Data 360 permission set or licence. sf-skills names DataCloudUser, GenieUserEnhancedSecurity or the GenieDataPlatformStarterPsl licence, depending on the org.
• The permission set's data space scope must include the index's data space.
• Without these, every answer is empty.

Failures

Symptom	Cause → fix
Index stuck before Ready	Building takes time (about 20 minutes in Salesforce's example). Poll runtimeStatus, and check the source DMO has rows
Exact names and numbers not found	A vector-only index. Use hybrid
Answers cite the wrong account	Chunks lack the account name. Prepend the name field to every chunk
INVALID_REQUEST_STATE: retriever not active / retriever not found / retrieverId is required (sf agent adl create)	Activate the retriever, check the ID (prefix 1Cx or 0pm, per sf-skills), and pass --retriever-id
unresolved reference @knowledge.rag_feature_config_id	The knowledge: block is missing or misplaced
Agent always says it has no answer (knowledgeSummary empty)	The agent user lacks Data 360 access or data space scope
ADL_UNSUPPORTED_SOURCE_TYPE	File operations aren't allowed on a RETRIEVER library