vllm.reasoning.cohere_command_reasoning_parser ¶

def _has_effective_tools(
    tools: str | list[Any] | None,
) -> TypeGuard[str | list[Any]]:
    """
    True when ``tools`` contains at least one tool definition to convert.

    ``ResponsesRequest`` defaults ``tools`` to ``[]``; ``ChatCompletionRequest``
    uses ``None``. Both mean "no tools" here. Strings (e.g. a JSON blob) are
    treated as effective only when non-blank.
    """
    if tools is None:
        return False
    if isinstance(tools, str):
        return bool(tools.strip())
    return len(tools) > 0

def _maybe_parse_json_dict(value: Any) -> dict | None:
    """If value is a JSON string, parse to dict; otherwise require dict."""
    if isinstance(value, dict):
        return value
    if isinstance(value, str):
        try:
            parsed = json.loads(value)
        except (TypeError, json.JSONDecodeError):
            return None
        return parsed if isinstance(parsed, dict) else None
    return None

def _schema_dict_from_chat_response_format(
    rf: AnyResponseFormat | dict | None,
) -> dict | None:
    """JSON schema dict from Chat Completions ``request.response_format`` only."""
    if rf is None:
        return None
    rf_type = _response_format_type(rf)
    if rf_type == "json_object":
        return {"type": "object"}
    if rf_type != "json_schema":
        return None
    js_wr = (
        rf.get("json_schema")
        if isinstance(rf, dict)
        else getattr(rf, "json_schema", None)
    )
    return _schema_from_json_schema_field(js_wr)

def _schema_dict_from_structured_outputs(
    so: StructuredOutputsParams | None,
) -> dict | None:
    """Schema dict from ``structured_outputs`` (``json`` / ``json_object``).

    Same unwrapping as ``json_schema``. ``json`` is expected to be ``str`` or
    ``dict`` (enforced by ``StructuredOutputsParams`` / request models); other
    types raise ``ValueError`` only if a caller bypasses that validation.
    """
    if so is None:
        return None
    if so.json_object:
        return {"type": "object"}
    raw: Any = so.json
    if raw is None:
        return None

    if hasattr(raw, "model_dump"):
        out = _schema_from_json_schema_field(raw)
        if out is None:
            raise ValueError(
                "structured_outputs.json model has no extractable JSON Schema."
            )
        return out

    if isinstance(raw, str):
        if not raw.strip():
            raise ValueError("structured_outputs.json cannot be empty.")
        try:
            raw = json.loads(raw)
        except json.JSONDecodeError as e:
            raise ValueError("structured_outputs.json must be valid JSON.") from e
        if not isinstance(raw, dict):
            raise ValueError("structured_outputs.json must decode to a JSON object.")

    if isinstance(raw, Mapping):
        body = raw if isinstance(raw, dict) else dict(raw)
        return _schema_from_json_schema_field(body) or body

    raise ValueError(
        f"structured_outputs.json has unsupported type {type(raw).__name__}."
    )

def _schema_from_json_schema_field(js_wr: Any) -> dict | None:
    """
    Extract the JSON Schema object from Chat Completions ``json_schema`` payload.

    Accepts:
    - ``JsonSchemaResponseFormat`` (Pydantic) with ``schema`` / ``json_schema`` field
    - dict in OpenAI shape ``{"name": ..., "schema": {...}}``
    - dict with ``json_schema`` key holding either the schema or a nested wrapper
    - dict that is already a JSON Schema document (some clients omit the wrapper)
    - JSON strings for any of the above
    """
    if js_wr is None:
        return None

    parsed_wr = _maybe_parse_json_dict(js_wr)
    if parsed_wr is not None:
        js_wr = parsed_wr

    if hasattr(js_wr, "model_dump"):
        for by_alias in (True, False):
            try:
                data = js_wr.model_dump(by_alias=by_alias, exclude_none=False)
            except TypeError:
                data = js_wr.model_dump(by_alias=by_alias)
            out = _unwrap_nested_schema(data.get("schema") or data.get("json_schema"))
            if out is not None:
                return out
        inner_attr = getattr(js_wr, "json_schema", None)
        return inner_attr if isinstance(inner_attr, dict) else None

    if isinstance(js_wr, dict):
        for key in ("schema", "json_schema"):
            out = _unwrap_nested_schema(js_wr.get(key))
            if out is not None:
                return out
        return js_wr

    return None

def _tool_definitions_to_schema_list(
    tools: str | list[Any],
) -> list[CohereNormalizedTool]:
    """
    Build the list of ``CohereNormalizedTool`` dicts expected by
    ``collect_tool_schema``.

    Accepts:
    - JSON string
    - list of dicts with top-level ``name`` / ``parameters``
    - list of Chat Completions-style ``{"type": "function", "function": {...}}``
    - list of Pydantic models with ``model_dump()``
    """
    if isinstance(tools, str):
        try:
            parsed = json.loads(tools)
        except json.JSONDecodeError:
            return []
        if not isinstance(parsed, list):
            return []
    else:
        parsed = list(tools)

    out: list[CohereNormalizedTool] = []
    for raw in parsed:
        t = raw.model_dump() if hasattr(raw, "model_dump") else raw
        if not isinstance(t, dict):
            continue
        # Unwrap Chat Completions' ``{"type": "function", "function": {...}}``
        # shape; otherwise take the dict as-is.
        if t.get("type") == "function" and isinstance(t.get("function"), dict):
            t = t["function"]
        name = t.get("name")
        if not isinstance(name, str):
            continue
        params = t.get("parameters")
        out.append(
            CohereNormalizedTool(
                name=name,
                parameters=params if isinstance(params, dict) else {},
            )
        )
    return out

def _unwrap_nested_schema(candidate: Any) -> dict | None:
    """Return ``candidate`` as a dict, unwrapping a nested ``schema`` if present.

    Returns ``None`` if ``candidate`` is not (and cannot be parsed into) a dict.
    """
    cand = _maybe_parse_json_dict(candidate)
    if not isinstance(cand, dict):
        return None
    nested = cand.get("schema")
    return nested if isinstance(nested, dict) else cand

def collect_tool_schema(tool_schema: list[CohereNormalizedTool]) -> str:
    """Build an xgrammar EBNF grammar that matches a JSON array of tool calls.

    The grammar shape is architecture-independent; callers are responsible for
    wrapping it in the correct structural tag (see ``CohereTagStyle.tools``).
    """
    tool_dictionary: dict[str, str] = {}
    for tool in tool_schema:
        tool_name = tool["name"]
        tool_parameters = json.dumps(tool["parameters"])
        json_schema = f"""{{
                        "type": "object",
                        "properties": {{
                            "tool_call_id": {{
                                "type": "string",
                                "pattern": "^[0-9]+$"
                            }},
                            "tool_name": {{
                                "type": "string",
                                "const": "{tool_name}"
                            }},
                            "parameters": {tool_parameters}
                            }}
                            }}"""
        tool_grammar = str(xgr.Grammar.from_json_schema(json_schema))
        for match in re.findall(r"\b(\w+)\s*::=", tool_grammar):
            tool_grammar = re.sub(
                rf"\b{re.escape(match)}\b", tool_name + match, tool_grammar
            )
        tool_dictionary[tool_name] = f"{tool_name} ::= {tool_name}root\n{tool_grammar}"
    # Emitted grammar shape:
    #   root  ::= tools
    #   tools ::= ws "[" ws tool ws ("," ws tool)* ws "]" ws
    #   ws    ::= (" " | "\t" | "\n")*
    #   tool  ::= <tool_a> | <tool_b> | ...         (one alternative per input)
    #   <tool_x>     ::= <tool_x>root               (per-tool xgrammar rules)
    #   <tool_x>root ::= ...                        (from xgr.Grammar.from_json_schema)
    tool_alternatives = "tool ::= " + " | ".join(tool_dictionary.keys())
    tool_rules = "\n    ".join(tool_dictionary.values())
    grammar = f"""root ::= tools
    tools ::= ws "[" ws tool ws ("," ws tool)*  ws "]" ws
    ws    ::= (" " | "\\t" | "\\n")*
    {tool_alternatives}
    {tool_rules}
    """
    return grammar

def convert_schema_to_structural_tags(
    schema: dict | None = None,
    tools: str | list[Any] | None = None,
    model_architecture: str | None = None,
) -> str | None:
    """
    Returns a response_format string accepted by xgrammar's structural tag format.
    Uses the canonical shape: {"type": "structural_tag", "format": {...}} with
    format.type "triggered_tags" and tag content type "json_schema" or "grammar".

    Callers that are not on an engine path (e.g. the reasoning parser) must pass
    ``model_architecture`` explicitly.
    """
    if model_architecture is None or model_architecture not in MODEL_TO_TAG_STYLE:
        return None
    style = MODEL_TO_TAG_STYLE[model_architecture]

    tags: list[dict] = []
    triggers: list[str] = []

    def _add_tag(tag: CohereTagRegistry, content: dict) -> None:
        tags.append({"begin": tag.trigger, "content": content, "end": tag.end})
        triggers.append(tag.trigger)

    if schema is not None:
        # One structural tag per JSON wrapper (e.g. MOE: response + text).
        # Same for schema-only and "tools plus JSON mode" (North: schema when
        # the model does not call tools).
        for jt in style.json_tags:
            _add_tag(jt, {"type": "json_schema", "json_schema": schema})

    if _has_effective_tools(tools):
        # ``tools`` may be a JSON string (poseidon / RESPONSE_FORMAT_TOOL_DEFINITIONS)
        # or a list (Chat Completions ``request.tools`` as Pydantic models or dicts).
        tool_schema_list = _tool_definitions_to_schema_list(tools)
        if not tool_schema_list:
            raise ValueError(
                "No valid tool definitions could be parsed from the request for "
                "structural tag conversion."
            )
        tool_grammar = collect_tool_schema(tool_schema_list)
        _add_tag(style.tools, {"type": "grammar", "grammar": tool_grammar})

    if not tags:
        return None
    return json.dumps(
        {
            "type": "structural_tag",
            "format": {
                "type": "triggered_tags",
                "triggers": triggers,
                "tags": tags,
            },
        }
    )