# text.summarize

Ollama-backed faithful summarisation. Sends source text to a
configured LLM endpoint with a fidelity-over-creativity system
prompt and emits the summary plus audit-grade
model-provenance fields.

## Capability

- `text.summarize@0.1.0`

## Inputs

| Name        | Type | Description                                                                  |
| ----------- | ---- | ---------------------------------------------------------------------------- |
| `text`      | text | Source text to summarise.                                                    |
| `style`     | text | Optional style hint (e.g. `one paragraph`, `three bullet points`, `a tweet`). Default: `one paragraph`. |
| `language`  | text | Optional output-language hint (e.g. `German`, `ja-JP`). Empty = same as source. |
| `endpoint`  | text | Ollama-shaped `/api/chat` endpoint URL.                                      |
| `model`     | text | Model identifier the endpoint serves.                                        |
| `api_key`   | text | Optional bearer token for cloud-hosted endpoints.                            |

## Outputs

| Name             | Type | Description                                                  |
| ---------------- | ---- | ------------------------------------------------------------ |
| `summary`        | text | The summary.                                                 |
| `style`          | text | Echo of the input style.                                     |
| `language`       | text | Echo of the input language.                                  |
| `model_endpoint` | text | URL the summary was generated against.                       |
| `model_name`     | text | Model identifier as supplied to the LLM API.                 |
| `model_digest`   | text | SHA-256 digest of the served Ollama model (when reachable).  |

## Fidelity over creativity

The system prompt is tuned so the summary is derivable from
the source — no speculation, no implied context, no
"creative" rephrasing that drifts. Suitable for compliance
flows where a hallucinated summary is worse than a verbose
one. The model still has the freedom the operator's chosen
LLM gives it; this module is the prompt + audit harness, not
a fine-tuned model.

## Permissions

```yaml
permissions:
  - "net: localhost"
  - "net: 127.0.0.1"
  - "net: api.openai.com"
  - "net: api.anthropic.com"
```

Same shape as `text.translate` — local Ollama by default,
opt-in cloud via operator policy.

## Limits in v0.1.0

- Single-shot. Very long source texts (>32k tokens depending
  on the model) need an upstream chunker; v0.1.0 does not yet
  do automatic chunked-summarise + reduce.
- No structured output. `summary` is free-form text. JSON-
  formatted summaries (e.g. `{ key_points: [], conclusions:
  [] }`) are a v0.2.0 candidate.

## Example flow

```yaml
name: extract-summarise
inputs:
  document: bytes
steps:
  - id: extract
    use: text.extract@^0
    with:
      document: $inputs.document
  - id: summarise
    use: text.summarize@^0
    with:
      text: $extract.extracted.pages[*].text
      style: "three bullet points"
      endpoint: "http://localhost:11434/api/chat"
      model: "qwen2.5:14b"
outputs:
  summary: $summarise.summary
  audit_model: $summarise.model_digest
```

## Build

```bash
cargo build --release --target wasm32-wasip2
# Output: target/wasm32-wasip2/release/text_summarize.wasm
```