knowledge-summarize

# 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."