add_note

Create new notes in your knowledge base with automatic chunking and embedding generation.

Parameters

Parameter	Type	Required	Default	Description
`title`	string	Yes	-	Note title (1-500 chars)
`content`	string	Yes	-	Markdown content (1-100000 chars)
`tags`	string[]	No	`[]`	Tags for organization

Example Request

{
  "title": "Project Alpha Requirements",
  "content": "# Project Alpha\n\n## Goals\n\n- Improve search accuracy by 20%\n- Reduce latency to under 100ms\n\n## Technical Requirements\n\n- Use hybrid search with RRF\n- Implement caching layer\n- Add monitoring dashboards",
  "tags": ["project-alpha", "requirements", "technical"]
}

Response

{
  "documentId": "550e8400-e29b-41d4-a716-446655440000",
  "title": "Project Alpha Requirements",
  "chunkCount": 1,
  "tags": ["project-alpha", "requirements", "technical"],
  "createdAt": "2024-11-01T10:00:00Z"
}

Processing Pipeline

When you add a note:

Document Created: Stored in documents table
Content Chunked: Split into ~512 token chunks with overlap
Embeddings Generated: Each chunk embedded via OpenAI/Ollama
Search Index Updated: Full-text search vector created

Use Cases

Knowledge Capture

Save insights from conversations:

{
  "title": "Customer Feedback - October 2024",
  "content": "Key themes from customer calls:\n\n1. Users want faster search\n2. Mobile app requested\n3. Integration with Slack needed",
  "tags": ["customer-feedback", "product"]
}

Meeting Notes

Capture meeting outcomes:

{
  "title": "Sprint Planning - Week 45",
  "content": "## Attendees\n- Team leads\n\n## Decisions\n- Prioritize search improvements\n- Delay mobile app to Q1",
  "tags": ["meetings", "sprint-planning"]
}

Research Notes

Store research findings:

{
  "title": "Hybrid Search Research",
  "content": "## Papers Reviewed\n\n1. RRF paper - key insights...\n2. pgvector benchmarks...",
  "tags": ["research", "search", "technical"]
}

Error Responses

Content too long

{
  "error": "Validation error",
  "message": "Content exceeds maximum length of 100000 characters"
}

Embedding failed

{
  "error": "Embedding failed",
  "message": "OpenAI API rate limit exceeded"
}

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