| name | knowledge-summarize |
| description | Generate a TL;DR summary of a specific document or learning unit in the Knowledge base. Pulls chunks from pgvector and synthesizes via Claude Haiku. Use when the user wants a quick overview ('summary of lesson 5', 'TL;DR of this PDF', 'explain document X in one paragraph'). |
knowledge-summarize
Group: Consumption. Generate TL;DR of a document or unit using indexed chunks.
When to trigger
- "Summary of lesson 5"
- "TL;DR of this PDF"
- "Explain document X"
- "Summary of module Y"
Arguments
| Name | Type | Required | Description |
|---|
document_id | str | one of two | Document UUID |
unit_id | str | one of two | Unit UUID (aggregates all docs) |
connection | str | no | Defaults to first ready |
max_tokens | int | no | Limit (default 500) |
Workflow
Step 1 — Fetch chunks
from dashboard.backend.sdk_client import evo
if document_id:
doc = evo.get(f"/api/knowledge/v1/documents/{document_id}",
headers={"X-Knowledge-Connection": connection})
chunks = doc["chunks"]
title = doc["title"]
elif unit_id:
docs = evo.get(f"/api/knowledge/v1/documents?unit_id={unit_id}",
headers={"X-Knowledge-Connection": connection})
chunks = []
for d in docs:
full = evo.get(f"/api/knowledge/v1/documents/{d['id']}",
headers={"X-Knowledge-Connection": connection})
chunks.extend(full["chunks"])
title = f"Unit {unit_id} ({len(docs)} documents)"
Step 2 — Concatenate + truncate
Concatenate chunk.content separated by \n\n. If total > 40k chars: sample first/middle/last third.
Step 3 — LLM call
Model: claude-haiku-4-5-20251001.
Prompt:
Summarize the document in structured markdown. Max {max_tokens} tokens.
## {title}
**TL;DR (1 paragraph):** ...
**Key points:**
- ...
- ...
**Target audience / when to use:** (optional)
### Document
{concatenated_chunks}
Step 4 — Render
Return summary + footer Based on {N} chunks from {M} documents.
Actionable failures
- Neither
document_id nor unit_id passed → "Pass one of the two (mutually exclusive)."
- Not found → "Not found. Use
knowledge-browse to list."
ANTHROPIC_API_KEY missing → "Set ANTHROPIC_API_KEY in .env."
- Doc status != ready → "Not indexed (status={status}). Re-upload the document or wait for ingestion to complete."