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

python
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."