---

id: kb_ingest name: Knowledge Base Ingestion version: 1.0.0 category: knowledge description: Ingest and manage documents in the vector knowledge base

tool_configs:

• name: kb_ingest_text description: Ingest plain text content into the knowledge base method: ingest_text args:

  • name: text type: string required: true description: The text content to ingest
  • name: title type: string required: true description: Title/name for the document
  • name: collection type: string required: false description: Collection to add the document to requires_skill_loaded: true

• name: kb_ingest_url description: Fetch and ingest content from a URL into the knowledge base method: ingest_url args:

  • name: url type: string required: true description: The URL to fetch content from
  • name: collection type: string required: false description: Collection to add the document to requires_skill_loaded: true

• name: kb_delete_document description: Delete a document from the knowledge base method: delete_document args:

  • name: document_id type: string required: true description: The ID of the document to delete requires_skill_loaded: true

• name: kb_get_document description: Get information about a specific document method: get_document args:

  • name: document_id type: string required: true description: The ID of the document to retrieve requires_skill_loaded: true

resource_configs:

• name: ingestion_guidelines description: Guidelines for effective knowledge base ingestion content: |

  Ingestion Guidelines

  Text Content

  • Use descriptive titles that help identify the content
  • Organize related content into collections
  • Keep individual documents focused on a single topic
  • Larger documents are automatically chunked for better search

  URL Content

  • Web pages are fetched and converted to plain text
  • JavaScript-heavy pages may not extract well
  • Consider using the text ingestion for better control

  Collections

  • Use collections to organize documents by topic or source
  • Collection names should be lowercase with underscores
  • Examples: "company_policies", "technical_docs", "meeting_notes"

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Knowledge Base Ingestion Skill

You have the ability to add content to the vector knowledge base. This allows you to build a searchable repository of information that can be retrieved later using semantic search.

Capabilities

1. Text Ingestion: Add plain text content directly to the knowledge base. The text is automatically chunked and embedded for semantic search.

2. URL Ingestion: Fetch content from a URL and add it to the knowledge base. Useful for adding web pages, documentation, or online resources.

3. Document Management: Delete documents that are no longer needed, or get information about existing documents.

When to Use This Skill

Use knowledge base ingestion when:

• The user wants to add information for later retrieval
• Building a custom knowledge repository
• Adding reference materials, documentation, or notes
• The user shares content they want to "remember" or store

Ingestion Guidelines

1. Use Descriptive Titles: Choose titles that will help identify the content later. Good titles make it easier to find documents.

2. Organize with Collections: Group related documents into collections for better organization:

   • technical_docs - Technical documentation
   • meeting_notes - Meeting summaries
   • research - Research materials
   • policies - Company policies

3. Chunk Size: Documents are automatically split into smaller chunks for better search relevance. You don't need to worry about document size.

4. Confirm Success: After ingestion, confirm the document was added successfully by reporting the document ID and chunk count.

Example Workflows

Adding Text Content

1. User provides text to remember
2. Call kb_ingest_text with the text and a descriptive title
3. Optionally specify a collection
4. Confirm success with document details

Adding Web Content

1. User provides a URL
2. Call kb_ingest_url with the URL
3. Content is fetched, processed, and stored
4. Confirm success with document details

Removing Content

1. User requests document removal
2. Use kb_list_documents (from KB Search skill) to find the document
3. Call kb_delete_document with the document ID
4. Confirm deletion

Important Notes

• Ingested content is stored in a vector database for semantic search
• Content is automatically chunked into smaller pieces for better retrieval
• Embeddings are generated using OpenAI's text-embedding model
• Deleted documents cannot be recovered