참고소스 수정본

참고/instructor-main/instructor/providers/xai/client.py

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+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING, cast, overload
+import json
+
+from instructor.dsl.iterable import IterableBase
+from instructor.dsl.partial import PartialBase
+from instructor.dsl.simple_type import AdapterBase
+
+from instructor.utils.core import prepare_response_model
+from pydantic import BaseModel
+
+import instructor
+from .utils import _convert_messages
+
+
+def _raise_xai_sdk_missing() -> None:
+    from ...core.exceptions import ConfigurationError
+
+    raise ConfigurationError(
+        "The xAI provider needs the optional dependency `xai-sdk`. "
+        'Install it with `uv pip install "instructor[xai]"` (or `pip install "instructor[xai]"`). '
+        "Note: xai-sdk requires Python 3.10+."
+    ) from None
+
+
+def _get_model_schema(response_model: Any) -> dict[str, Any]:
+    """
+    Safely get JSON schema from a response model.
+
+    Handles both regular models and wrapped types by checking for the
+    model_json_schema method with hasattr.
+
+    Args:
+        response_model: The response model (may be regular or wrapped)
+
+    Returns:
+        The JSON schema dictionary
+    """
+    if hasattr(response_model, "model_json_schema") and callable(
+        response_model.model_json_schema
+    ):
+        schema_method = response_model.model_json_schema
+        return schema_method()
+    return {}
+
+
+def _get_model_name(response_model: Any) -> str:
+    """
+    Safely get the name of a response model.
+
+    Args:
+        response_model: The response model
+
+    Returns:
+        The model name or 'Model' as fallback
+    """
+    return getattr(response_model, "__name__", "Model")
+
+
+def _finalize_parsed_response(parsed: Any, raw_response: Any) -> Any:
+    if isinstance(parsed, BaseModel):
+        parsed._raw_response = raw_response
+    if isinstance(parsed, IterableBase):
+        return [task for task in parsed.tasks]
+    if isinstance(parsed, AdapterBase):
+        return parsed.content
+    return parsed
+
+
+if TYPE_CHECKING:
+    from xai_sdk.sync.client import Client as SyncClient
+    from xai_sdk.aio.client import Client as AsyncClient
+    from xai_sdk import chat as xchat
+else:
+    try:
+        from xai_sdk.sync.client import Client as SyncClient
+        from xai_sdk.aio.client import Client as AsyncClient
+        from xai_sdk import chat as xchat
+    except ImportError:
+        SyncClient = None
+        AsyncClient = None
+        xchat = None
+
+
+@overload
+def from_xai(
+    client: SyncClient,
+    mode: instructor.Mode = instructor.Mode.XAI_JSON,
+    **kwargs: Any,
+) -> instructor.Instructor: ...
+
+
+@overload
+def from_xai(
+    client: AsyncClient,
+    mode: instructor.Mode = instructor.Mode.XAI_JSON,
+    **kwargs: Any,
+) -> instructor.AsyncInstructor: ...
+
+
+def from_xai(
+    client: SyncClient | AsyncClient,
+    mode: instructor.Mode = instructor.Mode.XAI_JSON,
+    **kwargs: Any,
+) -> instructor.Instructor | instructor.AsyncInstructor:
+    if SyncClient is None or AsyncClient is None or xchat is None:
+        _raise_xai_sdk_missing()
+
+    valid_modes = {instructor.Mode.XAI_JSON, instructor.Mode.XAI_TOOLS}
+
+    if mode not in valid_modes:
+        from ...core.exceptions import ModeError
+
+        raise ModeError(
+            mode=str(mode), provider="xAI", valid_modes=[str(m) for m in valid_modes]
+        )
+
+    if not isinstance(client, (SyncClient, AsyncClient)):
+        from ...core.exceptions import ClientError
+
+        raise ClientError(
+            "Client must be an instance of xai_sdk.sync.client.Client or xai_sdk.aio.client.Client. "
+            f"Got: {type(client).__name__}"
+        )
+
+    async def acreate(
+        response_model: type[BaseModel] | None,
+        messages: list[dict[str, Any]],
+        strict: bool = True,
+        **call_kwargs: Any,
+    ):
+        x_messages = _convert_messages(messages)
+        model = call_kwargs.pop("model")
+        # Remove instructor-specific kwargs that xAI doesn't support
+        call_kwargs.pop("max_retries", None)
+        call_kwargs.pop("validation_context", None)
+        call_kwargs.pop("context", None)
+        call_kwargs.pop("hooks", None)
+        is_stream = call_kwargs.pop("stream", False)
+
+        chat = client.chat.create(model=model, messages=x_messages, **call_kwargs)
+
+        if response_model is None:
+            resp = await chat.sample()  # type: ignore[misc]
+            return resp
+
+        assert response_model is not None
+
+        prepared_model = response_model
+        if mode == instructor.Mode.XAI_TOOLS or is_stream:
+            prepared_model = prepare_response_model(response_model)
+        assert prepared_model is not None
+
+        if mode == instructor.Mode.XAI_JSON:
+            if is_stream:
+                # code from xai_sdk.chat.parse
+                chat.proto.response_format.CopyFrom(
+                    xchat.chat_pb2.ResponseFormat(
+                        format_type=xchat.chat_pb2.FormatType.FORMAT_TYPE_JSON_SCHEMA,
+                        schema=json.dumps(_get_model_schema(prepared_model)),
+                    )
+                )
+                json_chunks = (chunk.content async for _, chunk in chat.stream())  # type: ignore[misc]
+                # response_model is guaranteed to be a type[BaseModel] at this point due to earlier assertion
+                rm = cast(type[BaseModel], prepared_model)
+                if issubclass(rm, IterableBase):
+                    return rm.tasks_from_chunks_async(json_chunks)  # type: ignore
+                elif issubclass(rm, PartialBase):
+                    return rm.model_from_chunks_async(json_chunks)  # type: ignore
+                else:
+                    raise ValueError(
+                        f"Unsupported response model type for streaming: {_get_model_name(response_model)}"
+                    )
+            else:
+                raw, parsed = await chat.parse(response_model)  # type: ignore[misc]
+                parsed._raw_response = raw
+                return parsed
+        else:
+            tool_obj = xchat.tool(
+                name=_get_model_name(prepared_model),
+                description=prepared_model.__doc__ or "",
+                parameters=_get_model_schema(prepared_model),
+            )
+            chat.proto.tools.append(tool_obj)  # type: ignore[arg-type]
+            tool_name = tool_obj.function.name  # type: ignore[attr-defined]
+            chat.proto.tool_choice.CopyFrom(xchat.required_tool(tool_name))
+            if is_stream:
+                stream_iter = chat.stream()  # type: ignore[misc]
+                args = (
+                    resp.tool_calls[0].function.arguments  # type: ignore[index,attr-defined]
+                    async for resp, _ in stream_iter  # type: ignore[assignment]
+                    if resp.tool_calls and resp.finish_reason == "REASON_INVALID"  # type: ignore[attr-defined]
+                )
+                rm = cast(type[BaseModel], prepared_model)
+                if issubclass(rm, IterableBase):
+                    return rm.tasks_from_chunks_async(args)  # type: ignore
+                elif issubclass(rm, PartialBase):
+                    return rm.model_from_chunks_async(args)  # type: ignore
+                else:
+                    raise ValueError(
+                        f"Unsupported response model type for streaming: {_get_model_name(response_model)}"
+                    )
+            else:
+                resp = await chat.sample()  # type: ignore[misc]
+                if not resp.tool_calls:  # type: ignore[attr-defined]
+                    # If no tool calls, try to extract from text content
+                    from ...processing.function_calls import _validate_model_from_json
+                    from ...utils import extract_json_from_codeblock
+
+                    # Try to extract JSON from text content
+                    text_content: str = ""
+                    if hasattr(resp, "text") and resp.text:  # type: ignore[attr-defined]
+                        text_content = str(resp.text)  # type: ignore[attr-defined]
+                    elif hasattr(resp, "content") and resp.content:  # type: ignore[attr-defined]
+                        content = resp.content  # type: ignore[attr-defined]
+                        if isinstance(content, str):
+                            text_content = content
+                        elif isinstance(content, list) and content:
+                            text_content = str(content[0])
+
+                    if text_content:
+                        json_str = extract_json_from_codeblock(text_content)
+                        model_for_validation = cast(type[Any], prepared_model)
+                        parsed = _validate_model_from_json(
+                            model_for_validation, json_str, None, strict
+                        )
+                        return _finalize_parsed_response(parsed, resp)
+
+                    raise ValueError(
+                        f"No tool calls returned from xAI and no text content available. "
+                        f"Response: {resp}"
+                    )
+
+                args = resp.tool_calls[0].function.arguments  # type: ignore[index,attr-defined]
+                from ...processing.function_calls import _validate_model_from_json
+
+                model_for_validation = cast(type[Any], prepared_model)
+                parsed = _validate_model_from_json(
+                    model_for_validation, args, None, strict
+                )
+                return _finalize_parsed_response(parsed, resp)
+
+    def create(
+        response_model: type[BaseModel] | None,
+        messages: list[dict[str, Any]],
+        strict: bool = True,
+        **call_kwargs: Any,
+    ):
+        x_messages = _convert_messages(messages)
+        model = call_kwargs.pop("model")
+        # Remove instructor-specific kwargs that xAI doesn't support
+        call_kwargs.pop("max_retries", None)
+        call_kwargs.pop("validation_context", None)
+        call_kwargs.pop("context", None)
+        call_kwargs.pop("hooks", None)
+        # Check if streaming is requested
+        is_stream = call_kwargs.pop("stream", False)
+
+        chat = client.chat.create(model=model, messages=x_messages, **call_kwargs)
+
+        if response_model is None:
+            resp = chat.sample()  # type: ignore[misc]
+            return resp
+
+        assert response_model is not None
+
+        prepared_model = response_model
+        if mode == instructor.Mode.XAI_TOOLS or is_stream:
+            prepared_model = prepare_response_model(response_model)
+        assert prepared_model is not None
+
+        if mode == instructor.Mode.XAI_JSON:
+            if is_stream:
+                # code from xai_sdk.chat.parse
+                chat.proto.response_format.CopyFrom(
+                    xchat.chat_pb2.ResponseFormat(
+                        format_type=xchat.chat_pb2.FormatType.FORMAT_TYPE_JSON_SCHEMA,
+                        schema=json.dumps(_get_model_schema(prepared_model)),
+                    )
+                )
+                json_chunks = (chunk.content for _, chunk in chat.stream())  # type: ignore[misc]
+                rm = cast(type[BaseModel], prepared_model)
+                if issubclass(rm, IterableBase):
+                    return rm.tasks_from_chunks(json_chunks)
+                elif issubclass(rm, PartialBase):
+                    return rm.model_from_chunks(json_chunks)
+                else:
+                    raise ValueError(
+                        f"Unsupported response model type for streaming: {_get_model_name(response_model)}"
+                    )
+            else:
+                raw, parsed = chat.parse(response_model)  # type: ignore[misc]
+                parsed._raw_response = raw
+                return parsed
+        else:
+            tool_obj = xchat.tool(
+                name=_get_model_name(prepared_model),
+                description=prepared_model.__doc__ or "",
+                parameters=_get_model_schema(prepared_model),
+            )
+            chat.proto.tools.append(tool_obj)  # type: ignore[arg-type]
+            tool_name = tool_obj.function.name  # type: ignore[attr-defined]
+            chat.proto.tool_choice.CopyFrom(xchat.required_tool(tool_name))
+            if is_stream:
+                stream_iter = chat.stream()  # type: ignore[misc]
+                for resp, _ in stream_iter:  # type: ignore[assignment]
+                    # For xAI, tool_calls are returned at the end of the response.
+                    # Effectively, it is not a streaming response.
+                    # See: https://docs.x.ai/docs/guides/function-calling
+                    if resp.tool_calls:  # type: ignore[attr-defined]
+                        args = resp.tool_calls[0].function.arguments  # type: ignore[index,attr-defined]
+                        rm = cast(type[BaseModel], prepared_model)
+                        if issubclass(rm, IterableBase):
+                            return rm.tasks_from_chunks(args)
+                        elif issubclass(rm, PartialBase):
+                            return rm.model_from_chunks(args)
+                        else:
+                            raise ValueError(
+                                f"Unsupported response model type for streaming: {_get_model_name(response_model)}"
+                            )
+            else:
+                resp = chat.sample()  # type: ignore[misc]
+                if not resp.tool_calls:  # type: ignore[attr-defined]
+                    # If no tool calls, try to extract from text content
+                    from ...processing.function_calls import _validate_model_from_json
+                    from ...utils import extract_json_from_codeblock
+
+                    # Try to extract JSON from text content
+                    text_content: str = ""
+                    if hasattr(resp, "text") and resp.text:  # type: ignore[attr-defined]
+                        text_content = str(resp.text)  # type: ignore[attr-defined]
+                    elif hasattr(resp, "content") and resp.content:  # type: ignore[attr-defined]
+                        content = resp.content  # type: ignore[attr-defined]
+                        if isinstance(content, str):
+                            text_content = content
+                        elif isinstance(content, list) and content:
+                            text_content = str(content[0])
+
+                    if text_content:
+                        json_str = extract_json_from_codeblock(text_content)
+                        model_for_validation = cast(type[Any], prepared_model)
+                        parsed = _validate_model_from_json(
+                            model_for_validation, json_str, None, strict
+                        )
+                        return _finalize_parsed_response(parsed, resp)
+
+                    raise ValueError(
+                        f"No tool calls returned from xAI and no text content available. "
+                        f"Response: {resp}"
+                    )
+
+                args = resp.tool_calls[0].function.arguments  # type: ignore[index,attr-defined]
+                from ...processing.function_calls import _validate_model_from_json
+
+                model_for_validation = cast(type[Any], prepared_model)
+                parsed = _validate_model_from_json(
+                    model_for_validation, args, None, strict
+                )
+                return _finalize_parsed_response(parsed, resp)
+
+    if isinstance(client, AsyncClient):
+        return instructor.AsyncInstructor(
+            client=client,
+            create=acreate,
+            provider=instructor.Provider.XAI,
+            mode=mode,
+            **kwargs,
+        )
+    else:
+        return instructor.Instructor(
+            client=client,
+            create=create,
+            provider=instructor.Provider.XAI,
+            mode=mode,
+            **kwargs,
+        )