mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
4c132b4cc6
After upgrading langchain-community to 0.0.22, it's not possible to use
openai from the community package with streaming=True
```
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_community/chat_models/openai.py", line 434, in _generate
return generate_from_stream(stream_iter)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 65, in generate_from_stream
for chunk in stream:
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_community/chat_models/openai.py", line 418, in _stream
run_manager.on_llm_new_token(chunk.text, chunk=cg_chunk)
^^^^^^^^^^
AttributeError: 'AIMessageChunk' object has no attribute 'text'
```
Fix regression of https://github.com/langchain-ai/langchain/pull/17907
**Twitter handle:** @nicoloboschi
688 lines
26 KiB
Python
688 lines
26 KiB
Python
"""OpenAI chat wrapper."""
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from __future__ import annotations
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import logging
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import os
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import sys
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from typing import (
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TYPE_CHECKING,
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Sequence,
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Tuple,
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Type,
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Union,
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)
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.messages import (
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessageChunk,
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FunctionMessageChunk,
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HumanMessageChunk,
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SystemMessageChunk,
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ToolMessageChunk,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
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from langchain_core.runnables import Runnable
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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from langchain_community.adapters.openai import (
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convert_dict_to_message,
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convert_message_to_dict,
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)
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from langchain_community.utils.openai import is_openai_v1
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if TYPE_CHECKING:
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import tiktoken
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logger = logging.getLogger(__name__)
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def _import_tiktoken() -> Any:
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try:
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import tiktoken
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except ImportError:
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raise ValueError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate get_token_ids. "
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"Please install it with `pip install tiktoken`."
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)
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return tiktoken
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def _create_retry_decorator(
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llm: ChatOpenAI,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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import openai
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errors = [
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openai.error.Timeout,
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openai.error.APIError,
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openai.error.APIConnectionError,
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openai.error.RateLimitError,
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openai.error.ServiceUnavailableError,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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async def acompletion_with_retry(
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llm: ChatOpenAI,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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if is_openai_v1():
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return await llm.async_client.create(**kwargs)
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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# Use OpenAI's async api https://github.com/openai/openai-python#async-api
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return await llm.client.acreate(**kwargs)
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return await _completion_with_retry(**kwargs)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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additional_kwargs: Dict = {}
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if _dict.get("function_call"):
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function_call = dict(_dict["function_call"])
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if "name" in function_call and function_call["name"] is None:
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function_call["name"] = ""
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additional_kwargs["function_call"] = function_call
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if _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = _dict["tool_calls"]
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role == "tool" or default_class == ToolMessageChunk:
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return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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@deprecated(
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since="0.0.10", removal="0.2.0", alternative_import="langchain_openai.ChatOpenAI"
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)
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class ChatOpenAI(BaseChatModel):
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"""`OpenAI` Chat large language models API.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``OPENAI_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatOpenAI
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openai = ChatOpenAI(model_name="gpt-3.5-turbo")
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"""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"openai_api_key": "OPENAI_API_KEY"}
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "openai"]
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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attributes: Dict[str, Any] = {}
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if self.openai_organization:
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attributes["openai_organization"] = self.openai_organization
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if self.openai_api_base:
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attributes["openai_api_base"] = self.openai_api_base
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if self.openai_proxy:
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attributes["openai_proxy"] = self.openai_proxy
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return attributes
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return True
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="gpt-3.5-turbo", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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# When updating this to use a SecretStr
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# Check for classes that derive from this class (as some of them
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# may assume openai_api_key is a str)
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openai_api_key: Optional[str] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
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openai_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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openai_organization: Optional[str] = Field(default=None, alias="organization")
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"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
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# to support explicit proxy for OpenAI
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openai_proxy: Optional[str] = None
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request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
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None."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Union[Any, None] = None
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"""Optional httpx.Client."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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values["openai_api_key"] = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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# Check OPENAI_ORGANIZATION for backwards compatibility.
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values["openai_organization"] = (
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values["openai_organization"]
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or os.getenv("OPENAI_ORG_ID")
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or os.getenv("OPENAI_ORGANIZATION")
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)
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|
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
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"OPENAI_API_BASE"
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)
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values["openai_proxy"] = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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|
)
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try:
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import openai
|
|
|
|
except ImportError:
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|
raise ImportError(
|
|
"Could not import openai python package. "
|
|
"Please install it with `pip install openai`."
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|
)
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|
|
if is_openai_v1():
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client_params = {
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|
"api_key": values["openai_api_key"],
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|
"organization": values["openai_organization"],
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|
"base_url": values["openai_api_base"],
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|
"timeout": values["request_timeout"],
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|
"max_retries": values["max_retries"],
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|
"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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"http_client": values["http_client"],
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}
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if not values.get("client"):
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values["client"] = openai.OpenAI(**client_params).chat.completions
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if not values.get("async_client"):
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values["async_client"] = openai.AsyncOpenAI(
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**client_params
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).chat.completions
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elif not values.get("client"):
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values["client"] = openai.ChatCompletion
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|
else:
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pass
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return values
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|
|
@property
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def _default_params(self) -> Dict[str, Any]:
|
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"""Get the default parameters for calling OpenAI API."""
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params = {
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"model": self.model_name,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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if self.max_tokens is not None:
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params["max_tokens"] = self.max_tokens
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|
if self.request_timeout is not None and not is_openai_v1():
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|
params["request_timeout"] = self.request_timeout
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|
return params
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|
|
|
def completion_with_retry(
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|
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
|
|
) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
if is_openai_v1():
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return self.client.create(**kwargs)
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return self.client.create(**kwargs)
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return _completion_with_retry(**kwargs)
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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overall_token_usage: dict = {}
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system_fingerprint = None
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for output in llm_outputs:
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if output is None:
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# Happens in streaming
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continue
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token_usage = output["token_usage"]
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if token_usage is not None:
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for k, v in token_usage.items():
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if k in overall_token_usage:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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if system_fingerprint is None:
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system_fingerprint = output.get("system_fingerprint")
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combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
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if system_fingerprint:
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combined["system_fingerprint"] = system_fingerprint
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return combined
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|
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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for chunk in self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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finish_reason = choice.get("finish_reason")
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generation_info = (
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dict(finish_reason=finish_reason) if finish_reason is not None else None
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)
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default_chunk_class = chunk.__class__
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cg_chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info
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)
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if run_manager:
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run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk)
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yield cg_chunk
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|
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
stream: Optional[bool] = None,
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|
**kwargs: Any,
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|
) -> ChatResult:
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|
should_stream = stream if stream is not None else self.streaming
|
|
if should_stream:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
|
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return generate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {
|
|
**params,
|
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**({"stream": stream} if stream is not None else {}),
|
|
**kwargs,
|
|
}
|
|
response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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|
)
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return self._create_chat_result(response)
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|
|
def _create_message_dicts(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
|
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
|
params = self._client_params
|
|
if stop is not None:
|
|
if "stop" in params:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
params["stop"] = stop
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|
message_dicts = [convert_message_to_dict(m) for m in messages]
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|
return message_dicts, params
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|
|
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
|
|
generations = []
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|
if not isinstance(response, dict):
|
|
response = response.dict()
|
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for res in response["choices"]:
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message = convert_dict_to_message(res["message"])
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|
generation_info = dict(finish_reason=res.get("finish_reason"))
|
|
if "logprobs" in res:
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generation_info["logprobs"] = res["logprobs"]
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gen = ChatGeneration(
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message=message,
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generation_info=generation_info,
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)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {
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|
"token_usage": token_usage,
|
|
"model_name": self.model_name,
|
|
"system_fingerprint": response.get("system_fingerprint", ""),
|
|
}
|
|
return ChatResult(generations=generations, llm_output=llm_output)
|
|
|
|
async def _astream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
|
|
default_chunk_class = AIMessageChunk
|
|
async for chunk in await acompletion_with_retry(
|
|
self, messages=message_dicts, run_manager=run_manager, **params
|
|
):
|
|
if not isinstance(chunk, dict):
|
|
chunk = chunk.dict()
|
|
if len(chunk["choices"]) == 0:
|
|
continue
|
|
choice = chunk["choices"][0]
|
|
chunk = _convert_delta_to_message_chunk(
|
|
choice["delta"], default_chunk_class
|
|
)
|
|
finish_reason = choice.get("finish_reason")
|
|
generation_info = (
|
|
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
|
)
|
|
default_chunk_class = chunk.__class__
|
|
cg_chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info
|
|
)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(token=cg_chunk.text, chunk=cg_chunk)
|
|
yield cg_chunk
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
stream: Optional[bool] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
should_stream = stream if stream is not None else self.streaming
|
|
if should_stream:
|
|
stream_iter = self._astream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return await agenerate_from_stream(stream_iter)
|
|
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {
|
|
**params,
|
|
**({"stream": stream} if stream is not None else {}),
|
|
**kwargs,
|
|
}
|
|
response = await acompletion_with_retry(
|
|
self, messages=message_dicts, run_manager=run_manager, **params
|
|
)
|
|
return self._create_chat_result(response)
|
|
|
|
@property
|
|
def _identifying_params(self) -> Dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {**{"model_name": self.model_name}, **self._default_params}
|
|
|
|
@property
|
|
def _client_params(self) -> Dict[str, Any]:
|
|
"""Get the parameters used for the openai client."""
|
|
openai_creds: Dict[str, Any] = {
|
|
"model": self.model_name,
|
|
}
|
|
if not is_openai_v1():
|
|
openai_creds.update(
|
|
{
|
|
"api_key": self.openai_api_key,
|
|
"api_base": self.openai_api_base,
|
|
"organization": self.openai_organization,
|
|
}
|
|
)
|
|
if self.openai_proxy:
|
|
import openai
|
|
|
|
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
|
|
return {**self._default_params, **openai_creds}
|
|
|
|
def _get_invocation_params(
|
|
self, stop: Optional[List[str]] = None, **kwargs: Any
|
|
) -> Dict[str, Any]:
|
|
"""Get the parameters used to invoke the model."""
|
|
return {
|
|
"model": self.model_name,
|
|
**super()._get_invocation_params(stop=stop),
|
|
**self._default_params,
|
|
**kwargs,
|
|
}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "openai-chat"
|
|
|
|
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
|
|
tiktoken_ = _import_tiktoken()
|
|
if self.tiktoken_model_name is not None:
|
|
model = self.tiktoken_model_name
|
|
else:
|
|
model = self.model_name
|
|
if model == "gpt-3.5-turbo":
|
|
# gpt-3.5-turbo may change over time.
|
|
# Returning num tokens assuming gpt-3.5-turbo-0301.
|
|
model = "gpt-3.5-turbo-0301"
|
|
elif model == "gpt-4":
|
|
# gpt-4 may change over time.
|
|
# Returning num tokens assuming gpt-4-0314.
|
|
model = "gpt-4-0314"
|
|
# Returns the number of tokens used by a list of messages.
|
|
try:
|
|
encoding = tiktoken_.encoding_for_model(model)
|
|
except KeyError:
|
|
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
|
model = "cl100k_base"
|
|
encoding = tiktoken_.get_encoding(model)
|
|
return model, encoding
|
|
|
|
def get_token_ids(self, text: str) -> List[int]:
|
|
"""Get the tokens present in the text with tiktoken package."""
|
|
# tiktoken NOT supported for Python 3.7 or below
|
|
if sys.version_info[1] <= 7:
|
|
return super().get_token_ids(text)
|
|
_, encoding_model = self._get_encoding_model()
|
|
return encoding_model.encode(text)
|
|
|
|
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
|
|
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
|
|
|
|
Official documentation: https://github.com/openai/openai-cookbook/blob/
|
|
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
|
|
if sys.version_info[1] <= 7:
|
|
return super().get_num_tokens_from_messages(messages)
|
|
model, encoding = self._get_encoding_model()
|
|
if model.startswith("gpt-3.5-turbo-0301"):
|
|
# every message follows <im_start>{role/name}\n{content}<im_end>\n
|
|
tokens_per_message = 4
|
|
# if there's a name, the role is omitted
|
|
tokens_per_name = -1
|
|
elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
|
|
tokens_per_message = 3
|
|
tokens_per_name = 1
|
|
else:
|
|
raise NotImplementedError(
|
|
f"get_num_tokens_from_messages() is not presently implemented "
|
|
f"for model {model}."
|
|
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
|
|
"information on how messages are converted to tokens."
|
|
)
|
|
num_tokens = 0
|
|
messages_dict = [convert_message_to_dict(m) for m in messages]
|
|
for message in messages_dict:
|
|
num_tokens += tokens_per_message
|
|
for key, value in message.items():
|
|
# Cast str(value) in case the message value is not a string
|
|
# This occurs with function messages
|
|
num_tokens += len(encoding.encode(str(value)))
|
|
if key == "name":
|
|
num_tokens += tokens_per_name
|
|
# every reply is primed with <im_start>assistant
|
|
num_tokens += 3
|
|
return num_tokens
|
|
|
|
def bind_functions(
|
|
self,
|
|
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
|
|
function_call: Optional[str] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind functions (and other objects) to this chat model.
|
|
|
|
Args:
|
|
functions: A list of function definitions to bind to this chat model.
|
|
Can be a dictionary, pydantic model, or callable. Pydantic
|
|
models and callables will be automatically converted to
|
|
their schema dictionary representation.
|
|
function_call: Which function to require the model to call.
|
|
Must be the name of the single provided function or
|
|
"auto" to automatically determine which function to call
|
|
(if any).
|
|
kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
from langchain.chains.openai_functions.base import convert_to_openai_function
|
|
|
|
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
|
if function_call is not None:
|
|
if len(formatted_functions) != 1:
|
|
raise ValueError(
|
|
"When specifying `function_call`, you must provide exactly one "
|
|
"function."
|
|
)
|
|
if formatted_functions[0]["name"] != function_call:
|
|
raise ValueError(
|
|
f"Function call {function_call} was specified, but the only "
|
|
f"provided function was {formatted_functions[0]['name']}."
|
|
)
|
|
function_call_ = {"name": function_call}
|
|
kwargs = {**kwargs, "function_call": function_call_}
|
|
return super().bind(
|
|
functions=formatted_functions,
|
|
**kwargs,
|
|
)
|