Content Action Steps

For common fields, string substitutions, metadata filters, caching, and execution order, see the Agent Steps Overview.

Retrieval Step

Searches a knowledge base using semantic similarity (vector search) to find the most relevant content for a given query. This is the foundation of RAG (Retrieval-Augmented Generation) workflows.

Fields

Reranker Models

Rerankers improve result quality by re-scoring the initial retrieval results using a cross-encoder model. The retrieval first fetches top_n results, then the reranker re-scores them and returns the top top_k.

Output Format

When content_type is application/json (default), the output is a JSON object with a matches array of matching documents. Each object in matches contains:

Example JSON output:

{
  "matches": [
    {
      "content_title": "Getting Started Guide",
      "source_type": "website",
      "source": "Documentation Site",
      "content_url": "https://app.seclai.com/app/abc123/contents/def456?start=0&end=1200",
      "external_url": "https://docs.example.com/getting-started",
      "content": "Welcome to the getting started guide...",
      "chunk_start": 0,
      "chunk_end": 1200,
      "content_publish_date": "2026-01-15T10:30:00Z"
    }
  ]
}

external_url is only present for website and rss_feed source types. For custom_index and other source types, the field is omitted.

When content_type is text/plain, the output is the concatenated text of matching documents, suitable for direct use in prompts.

Use Case Examples

Basic semantic search:

• Knowledge base: Product documentation
• Query: {{agent.input}}
• Top N: 10
• Content type: text/plain

The step searches for content semantically similar to the user's input and returns the top 10 matches as plain text.

Filtered retrieval with reranking:

• Knowledge base: News articles
• Query: {{agent.input}}
• Filter: {"category": {"$eq": "{{metadata.category}}"}}
• Top N: 50
• Reranker: Cohere v3.5
• Top K: 10
• Added after: {{metadata.start_date}}

First retrieves 50 candidates matching the category filter added after the start date, then reranks them to select the 10 most relevant.

Time-bounded retrieval:

• Knowledge base: RSS feed content
• Query: Latest news about AI
• Added after: 2026-02-10
• Added before: 2026-02-17
• Top N: 20

Retrieves only content added within the specified date range.

AI Assistant

The retrieval step includes an AI assistant that can generate a complete configuration from a natural-language description. Click the AI Assistant button to open the assistant modal.

The assistant can set all retrieval fields:

• Knowledge base — selects the most appropriate knowledge base when none is chosen, or suggests switching when another KB better matches the request
• Query — writes query templates using substitution variables like {{input}} and {{step.<id>.output}}
• Top N / Top K — sets retrieval and reranking counts based on use case
• Reranker model — recommends a reranker when high relevance or multilingual content is needed
• Time range — sets added_after / added_before filters using date templates
• Metadata filter — builds MongoDB-style filter objects using known metadata fields from the knowledge base
• Content type — selects the output format (JSON, plain text, HTML, XML)

The assistant is aware of the available knowledge bases (their names, descriptions, source counts, and detected metadata fields), the available reranker models, and the full agent workflow context. When no knowledge base is selected, the assistant will recommend one based on your request.

Write Metadata Step

Writes a value to the content's metadata by key. This enables agents to persist structured information — such as AI-generated classifications, tags, or scores — directly on the content record so that it can be used for retrieval filtering and gate conditions in future agent runs. For larger information like summaries, consider using Write Content Attachment and Read Content Attachment steps instead.

Fields

Behavior

• If the value is valid JSON, it is stored as a parsed JSON value (object, array, number, boolean, or null). Otherwise it is stored as a plain string.
• The total serialized metadata payload must not exceed 10 KB. Performance may degrade with payloads above 2 KB.
• Metadata written by this step is merged with content version metadata when content is retrieved. The content metadata takes precedence over content version metadata for duplicate keys.
• The step requires a source_connection_content_version_id in the run metadata — this is automatically provided when the agent is triggered by a content event (content_added, content_updated, or content_added_or_updated).

Use Case Examples

Persist an AI-generated category for retrieval filtering:

Step 1: Insight — Classify the content into a category
Step 2: Write Metadata (metadata_key: "category")

A subsequent retrieval step can filter by {"category": {"$eq": "technology"}} to narrow results.

Store a sentiment score for gate conditions:

Step 1: Prompt Call — "Rate the sentiment of this content as positive, negative, or neutral"
Step 2: Write Metadata (metadata_key: "sentiment")

A gate step in a later agent can use metadata.sentiment with $eq to route content conditionally.

Write a JSON summary object:

Step 1: Insight — Extract key topics and summary as JSON
Step 2: Write Metadata (metadata_key: "analysis", content: "{{step.extract-insight.output}}")

The JSON object is stored as structured metadata, and individual fields can be accessed in filters.

Write Content Attachment Step

Writes an attachment to content. The step input (or explicit content) is stored as a file-backed attachment under a specified key. This is ideal for persisting large outputs — such as full translations, analysis reports, or transformed content — that are too large for metadata but should be associated with the content record.

Fields

Behavior

• Attachments are stored in file storage and linked to the content version.
• Unlike metadata (which has a 10 KB limit), attachments can store large content.
• When indexed is enabled, the attachment's text is chunked and embedded into the knowledge base's vector store, making it searchable via retrieval steps. The retrieval step's include_attachments field controls whether indexed attachments appear in results.
• The step requires a source_connection_content_version_id in the run metadata.

Use Case Examples

Store a full translation alongside original content:

Step 1: Prompt Call — Translate the content to Spanish
Step 2: Write Content Attachment (attachment_key: "translation_es", content_type: text/plain)

Persist an indexed analysis that's searchable:

Step 1: Insight — Generate a detailed analysis report
Step 2: Write Content Attachment (attachment_key: "analysis", indexed: true)

The analysis is now searchable via retrieval, appearing alongside the original content in results.

Store structured data as a JSON attachment:

Step 1: Insight — Extract entities and relationships as JSON
Step 2: Write Content Attachment (attachment_key: "entities", content_type: application/json)

Load Content Step

Loads the full text body of a source document (content version) and returns it as the step output. For content-triggered agents, the triggering content is used automatically unless an explicit content version is selected.

Fields

Behavior

• Returns the full text body of the content version as the step output.
• For content-triggered agents, the triggering content version is used when content_version_id is not specified.
• When an explicit content_version_id is provided, that content version is loaded regardless of trigger context.
• The step requires a source_connection_content_version_id in the run metadata (provided automatically for content-triggered agents).

Use Case Examples

Load and summarize article content:

Step 1: Load Content
Step 2: Prompt Call — Summarize the following article: {{input}}

Load content for multi-step analysis:

Step 1: Load Content
Step 2: Insight — Analyze the content for key themes and sentiment
Step 3: Extract JSON — Parse the structured analysis

Load Content Attachment Step

Loads a previously written attachment from content by key and returns its text content as the step output. Use this to recall persisted content — such as a previous analysis, translation, or extracted data — for use in subsequent processing steps.

Fields

Behavior

• Returns the text content of the attachment as the step output.
• If the attachment does not exist for the given key, the step output is empty.
• The step requires a source_connection_content_version_id in the run metadata.

Use Case Examples

Load a previous translation for comparison:

Step 1: Load Content Attachment (attachment_key: "translation_es")
Step 2: Prompt Call — Compare this translation with the original content

Build on a previous analysis:

Step 1: Load Content Attachment (attachment_key: "analysis")
Step 2: Insight — Using the previous analysis, identify any changes

Load Content Step vs Load Content Attachment vs Prompt-Call Tools

These three approaches all load document content, but serve different purposes:

The key distinction: the Load Content step runs unconditionally as part of the pipeline, while the load_content prompt-call tool is invoked by the AI model on demand during a Prompt Call step. If you always need the document, use the step. If the model should decide whether to fetch it, enable the tool.

Combining Metadata and Attachments

Metadata and attachments serve complementary purposes:

Example: Insight-driven content enrichment pipeline:

Step 1: Insight — Classify content and extract summary
Step 2: Extract JSON — Parse the JSON output
  Step 3: Write Metadata (metadata_key: "category", content: "{{step.extract-json.output.category}}")
  Step 4: Write Metadata (metadata_key: "sentiment", content: "{{step.extract-json.output.sentiment}}")
  Step 5: Write Content Attachment (attachment_key: "detailed_summary", indexed: true)

This pipeline: classifies content (filterable via metadata), records sentiment (usable in gate conditions), and persists a searchable detailed summary (retrievable via attachment indexing).

Publish Content Step

Publishes content to a Content Store source connection. The content goes through the standard indexing pipeline and becomes searchable via retrieval. Use this to let agents create or update knowledge base content — for example, storing generated summaries, reports, or transformed data.

Fields

Behavior

• Only Content Store sources are supported as targets. Other source types (RSS feeds, websites) will be rejected.
• In auto mode, every run creates a new content item with a unique identifier.
• In custom mode, publishing with the same identifier updates the existing item (creating a new version) or skips it, depending on the if_exists setting.
• The suppress_triggers flag defaults to true to prevent infinite loops when a content-triggered agent publishes back to a monitored source. Only disable this if you are certain the target is not monitored by content triggers.
• Works with any agent type. For content-triggered agents, the source connection and content version can be inherited from the trigger context.

Use Case Examples

Generate and store article summaries:

Step 1: Load Content
Step 2: Prompt Call — Summarize the article
Step 3: Publish Content (source_connection_id: "<custom-index-id>", title: "Summary: {{metadata.title}}")

Overwrite a daily report:

Step 1: Retrieval — Search for today's data
Step 2: Prompt Call — Generate daily report
Step 3: Publish Content (content_identifier_mode: custom, content_identifier: "daily-report", if_exists: new_version)

Content-triggered enrichment (publish back to same source):

Step 1: Load Content
Step 2: Insight — Analyze and enrich the content
Step 3: Publish Content (suppress_triggers: true)

Related pages:

• Content Sources — Configure the sources that feed your knowledge bases
• Contents — Inspect, replace, and delete indexed content items via API
• Knowledge Bases — Organize sources into searchable collections

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