> Fetch clean Markdown by appending `.md` to any page URL under https://signalwire.com/docs or requesting it with the HTTP header `Accept: text/markdown`. The root index at https://signalwire.com/docs/llms.txt lists the available documentation indexes.

# Post-prompt normalization

> Read voice and chat post-prompt bodies as one shape, with the summary parsed and the dialogue extracted.

[normalize]: /docs/server-sdks/reference/python/core/post-prompt/normalize-post-prompt

[parse]: /docs/server-sdks/reference/python/core/post-prompt/parse-post-prompt-data

[dialogue]: /docs/server-sdks/reference/python/core/post-prompt/dialogue-turns

[strip]: /docs/server-sdks/reference/python/core/post-prompt/strip-json-fence

[on-summary]: /docs/server-sdks/reference/python/agents/agent-base/on-summary

[on-call-end]: /docs/server-sdks/reference/python/agents/agent-base/on-call-end

One conversation can run over voice and over text chat, and both produce a post-prompt body, but not
the same shape. The `signalwire.core.post_prompt` module absorbs that divergence so your
application sees one artifact regardless of which engine finished the conversation.

| Field              | Voice                                | Chat                                            |
| ------------------ | ------------------------------------ | ----------------------------------------------- |
| `app_name`         | `"swml app"`                         | `"ai_chat"`                                     |
| `conversation_id`  | absent                               | present at top level                            |
| Full log           | `raw_call_log`                       | `raw_messages`                                  |
| Summary arrives as | a `summarize_conversation` tool call | a bare `role: assistant` turn inside `call_log` |
| `post_prompt_data` | parsed object                        | `{"raw": "<fenced JSON string>"}`               |

`conversation_type` is a reliable top-level discriminator on both. The voice engine can also
deliver `post_prompt_data` as `{"parsed": [ {...} ], "raw": "..."}`, an object wrapped in a
list, which passes structural checks and misses every field lookup. The parser unwraps it.

The module doesn't decide what a summary should contain. The schema is whatever your post-prompt
text asked the model to produce, so parsing is schema-agnostic and returns the dict as found.
Nothing here raises: the conversation that produced the body is already over.

```python
from signalwire.core.post_prompt import NormalizedPostPrompt, normalize_post_prompt
```

## Properties

`NormalizedPostPrompt` is a frozen dataclass, one finished conversation leg in a shape that doesn't
vary by engine.

**`medium`** `str` — default: ""

`conversation_type` as reported, such as `"voice"` or `"chat"`. Empty when the engine didn't say.

---

**`conversation_id`** `str | None` — default: None

Present on chat, absent on voice. When `None`, fall back to your own key from `global_data` or
`call_id` rather than treating this as authoritative.

---

**`summary`** `dict[str, Any]` — default: \{}

The parsed `post_prompt_data`, with whatever keys your post-prompt asked for. `{}` when there was
none or it couldn't be parsed. A model that answered in prose instead of JSON yields
`{"summary": "<the prose>"}`.

---

**`dialogue`** `list[dict[str, str]]` — default: \[]

`user` and `assistant` turns only, as `{"role", "content"}` pairs, with tool calls and the chat
engine's summary echo removed.

---

**`call_id`** `str | None` — default: None

The platform call ID, when present.

---

**`raw`** `dict[str, Any]` — default: \{}

The complete request body, untouched.

---

## Functions

#### [normalize\_post\_prompt](/docs/server-sdks/reference/python/core/post-prompt/normalize-post-prompt)

Normalize a post-prompt body from either engine.

#### [parse\_post\_prompt\_data](/docs/server-sdks/reference/python/core/post-prompt/parse-post-prompt-data)

Return post\_prompt\_data as a plain dict, whichever shape it arrived in.

#### [dialogue\_turns](/docs/server-sdks/reference/python/core/post-prompt/dialogue-turns)

Extract the user and assistant turns from a call log.

#### [strip\_json\_fence](/docs/server-sdks/reference/python/core/post-prompt/strip-json-fence)

Unwrap a fenced JSON code block.

## Example

Store every finished leg the same way, whether it came from
[`on_summary()`][on-summary] or [`on_call_end()`][on-call-end]:

```python {11-14}
from signalwire import AgentBase
from signalwire.core.post_prompt import normalize_post_prompt

class DispatchAgent(AgentBase):
    def __init__(self):
        super().__init__(name="dispatch", route="/dispatch")
        self.set_prompt_text("You are Ada, the dispatcher for Bayview Taxi.")
        self.set_post_prompt("Summarize the call as JSON with keys intent and resolved.")

    def on_summary(self, summary, raw_data=None):
        leg = normalize_post_prompt(raw_data)
        if leg.dialogue:
            # Write to your system of record.
            print(leg.medium, leg.conversation_id or leg.call_id, leg.summary)

DispatchAgent().serve()
```

> Read voice and chat post-prompt bodies as one shape, with the summary parsed and the dialogue extracted.

## Docs

- [dialogue_turns](https://signalwire.com/docs/server-sdks/reference/python/core/post-prompt/dialogue-turns.md): Extract the user and assistant turns from a call log, dropping tool traffic and the summary echo.
- [normalize_post_prompt](https://signalwire.com/docs/server-sdks/reference/python/core/post-prompt/normalize-post-prompt.md): Normalize a post-prompt body from the voice or chat engine into one shape.
- [parse_post_prompt_data](https://signalwire.com/docs/server-sdks/reference/python/core/post-prompt/parse-post-prompt-data.md): Return post_prompt_data as a plain dict, whichever of its three shapes arrived.
- [strip_json_fence](https://signalwire.com/docs/server-sdks/reference/python/core/post-prompt/strip-json-fence.md): Unwrap a fenced JSON code block returned verbatim by the chat engine.