mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-28 11:55:21 +00:00
af1f723ada
ChatOpenAI supports a kwarg `stream_options` which can take values
`{"include_usage": True}` and `{"include_usage": False}`.
Setting include_usage to True adds a message chunk to the end of the
stream with usage_metadata populated. In this case the final chunk no
longer includes `"finish_reason"` in the `response_metadata`. This is
the current default and is not yet released. Because this could be
disruptive to workflows, here we remove this default. The default will
now be consistent with OpenAI's API (see parameter
[here](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stream_options)).
Examples:
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
for chunk in llm.stream("hi"):
print(chunk)
```
```
content='' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='Hello' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='!' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='' response_metadata={'finish_reason': 'stop'} id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
```
```python
for chunk in llm.stream("hi", stream_options={"include_usage": True}):
print(chunk)
```
```
content='' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='Hello' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='!' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='' response_metadata={'finish_reason': 'stop'} id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='' id='run-39ab349b-f954-464d-af6e-72a0927daa27' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}
```
```python
llm = ChatOpenAI().bind(stream_options={"include_usage": True})
for chunk in llm.stream("hi"):
print(chunk)
```
```
content='' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='Hello' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='!' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='' response_metadata={'finish_reason': 'stop'} id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}
```
1192 lines
49 KiB
Python
1192 lines
49 KiB
Python
"""OpenAI chat wrapper."""
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from __future__ import annotations
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import json
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import logging
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import os
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import sys
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from operator import itemgetter
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from typing import (
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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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|
Literal,
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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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TypedDict,
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TypeVar,
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Union,
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cast,
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overload,
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)
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import openai
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import tiktoken
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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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LangSmithParams,
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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.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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InvalidToolCall,
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SystemMessage,
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SystemMessageChunk,
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ToolCall,
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ToolMessage,
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ToolMessageChunk,
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)
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from langchain_core.messages.ai import UsageMetadata
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from langchain_core.output_parsers import (
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JsonOutputParser,
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PydanticOutputParser,
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)
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from langchain_core.output_parsers.base import OutputParserLike
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from langchain_core.output_parsers.openai_tools import (
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JsonOutputKeyToolsParser,
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PydanticToolsParser,
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make_invalid_tool_call,
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parse_tool_call,
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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, SecretStr, root_validator
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.tools import BaseTool
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from langchain_core.utils import (
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convert_to_secret_str,
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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_core.utils.function_calling import (
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convert_to_openai_function,
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convert_to_openai_tool,
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)
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from langchain_core.utils.utils import build_extra_kwargs
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logger = logging.getLogger(__name__)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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"""Convert a dictionary to a LangChain message.
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Args:
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_dict: The dictionary.
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Returns:
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The LangChain message.
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"""
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role = _dict.get("role")
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name = _dict.get("name")
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id_ = _dict.get("id")
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if role == "user":
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return HumanMessage(content=_dict.get("content", ""), id=id_, name=name)
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") or ""
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additional_kwargs: Dict = {}
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if function_call := _dict.get("function_call"):
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additional_kwargs["function_call"] = dict(function_call)
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tool_calls = []
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invalid_tool_calls = []
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if raw_tool_calls := _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = raw_tool_calls
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for raw_tool_call in raw_tool_calls:
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try:
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tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
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except Exception as e:
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invalid_tool_calls.append(
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make_invalid_tool_call(raw_tool_call, str(e))
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)
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return AIMessage(
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content=content,
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additional_kwargs=additional_kwargs,
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name=name,
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id=id_,
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tool_calls=tool_calls,
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invalid_tool_calls=invalid_tool_calls,
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)
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elif role == "system":
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return SystemMessage(content=_dict.get("content", ""), name=name, id=id_)
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elif role == "function":
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return FunctionMessage(
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content=_dict.get("content", ""), name=cast(str, _dict.get("name")), id=id_
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)
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elif role == "tool":
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additional_kwargs = {}
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if "name" in _dict:
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additional_kwargs["name"] = _dict["name"]
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return ToolMessage(
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content=_dict.get("content", ""),
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tool_call_id=cast(str, _dict.get("tool_call_id")),
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additional_kwargs=additional_kwargs,
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name=name,
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id=id_,
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)
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else:
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return ChatMessage(content=_dict.get("content", ""), role=role, id=id_)
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def _format_message_content(content: Any) -> Any:
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"""Format message content."""
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if content and isinstance(content, list):
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# Remove unexpected block types
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formatted_content = []
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for block in content:
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if (
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isinstance(block, dict)
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and "type" in block
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and block["type"] == "tool_use"
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):
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continue
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else:
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formatted_content.append(block)
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else:
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formatted_content = content
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return formatted_content
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a LangChain message to a dictionary.
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Args:
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message: The LangChain message.
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Returns:
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The dictionary.
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"""
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message_dict: Dict[str, Any] = {
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"content": _format_message_content(message.content),
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}
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if (name := message.name or message.additional_kwargs.get("name")) is not None:
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message_dict["name"] = name
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# populate role and additional message data
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if isinstance(message, ChatMessage):
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message_dict["role"] = message.role
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elif isinstance(message, HumanMessage):
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message_dict["role"] = "user"
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elif isinstance(message, AIMessage):
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message_dict["role"] = "assistant"
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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if message.tool_calls or message.invalid_tool_calls:
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message_dict["tool_calls"] = [
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_lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
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] + [
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_lc_invalid_tool_call_to_openai_tool_call(tc)
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for tc in message.invalid_tool_calls
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]
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elif "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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tool_call_supported_props = {"id", "type", "function"}
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message_dict["tool_calls"] = [
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{k: v for k, v in tool_call.items() if k in tool_call_supported_props}
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for tool_call in message_dict["tool_calls"]
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]
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else:
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pass
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# If tool calls present, content null value should be None not empty string.
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if "function_call" in message_dict or "tool_calls" in message_dict:
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message_dict["content"] = message_dict["content"] or None
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elif isinstance(message, SystemMessage):
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message_dict["role"] = "system"
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elif isinstance(message, FunctionMessage):
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message_dict["role"] = "function"
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elif isinstance(message, ToolMessage):
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message_dict["role"] = "tool"
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message_dict["tool_call_id"] = message.tool_call_id
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supported_props = {"content", "role", "tool_call_id"}
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message_dict = {k: v for k, v in message_dict.items() if k in supported_props}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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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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id_ = _dict.get("id")
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role = cast(str, _dict.get("role"))
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content = cast(str, _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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tool_call_chunks = []
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if raw_tool_calls := _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = raw_tool_calls
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try:
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tool_call_chunks = [
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{
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"name": rtc["function"].get("name"),
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"args": rtc["function"].get("arguments"),
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"id": rtc.get("id"),
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"index": rtc["index"],
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}
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for rtc in raw_tool_calls
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]
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except KeyError:
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pass
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content, id=id_)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(
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content=content,
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additional_kwargs=additional_kwargs,
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id=id_,
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tool_call_chunks=tool_call_chunks,
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)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content, id=id_)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"], id=id_)
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elif role == "tool" or default_class == ToolMessageChunk:
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return ToolMessageChunk(
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content=content, tool_call_id=_dict["tool_call_id"], id=id_
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)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role, id=id_)
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else:
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return default_class(content=content, id=id_) # type: ignore
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class _FunctionCall(TypedDict):
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name: str
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_BM = TypeVar("_BM", bound=BaseModel)
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_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]]
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_DictOrPydantic = Union[Dict, _BM]
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class _AllReturnType(TypedDict):
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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class BaseChatOpenAI(BaseChatModel):
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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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openai_api_key: Optional[SecretStr] = 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."""
|
|
# 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(
|
|
default=None, alias="timeout"
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|
)
|
|
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
|
|
None."""
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max_retries: int = 2
|
|
"""Maximum number of retries to make when generating."""
|
|
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
|
|
"""Maximum number of tokens to generate."""
|
|
tiktoken_model_name: Optional[str] = None
|
|
"""The model name to pass to tiktoken when using this class.
|
|
Tiktoken is used to count the number of tokens in documents to constrain
|
|
them to be under a certain limit. By default, when set to None, this will
|
|
be the same as the embedding model name. However, there are some cases
|
|
where you may want to use this Embedding class with a model name not
|
|
supported by tiktoken. This can include when using Azure embeddings or
|
|
when using one of the many model providers that expose an OpenAI-like
|
|
API but with different models. In those cases, in order to avoid erroring
|
|
when tiktoken is called, you can specify a model name to use here."""
|
|
default_headers: Union[Mapping[str, str], None] = None
|
|
default_query: Union[Mapping[str, object], None] = None
|
|
# Configure a custom httpx client. See the
|
|
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
|
|
http_client: Union[Any, None] = None
|
|
"""Optional httpx.Client. Only used for sync invocations. Must specify
|
|
http_async_client as well if you'd like a custom client for async invocations.
|
|
"""
|
|
http_async_client: Union[Any, None] = None
|
|
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
|
|
http_client as well if you'd like a custom client for sync invocations."""
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
allow_population_by_field_name = True
|
|
|
|
@root_validator(pre=True)
|
|
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Build extra kwargs from additional params that were passed in."""
|
|
all_required_field_names = get_pydantic_field_names(cls)
|
|
extra = values.get("model_kwargs", {})
|
|
values["model_kwargs"] = build_extra_kwargs(
|
|
extra, values, all_required_field_names
|
|
)
|
|
return values
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
if values["n"] < 1:
|
|
raise ValueError("n must be at least 1.")
|
|
if values["n"] > 1 and values["streaming"]:
|
|
raise ValueError("n must be 1 when streaming.")
|
|
|
|
values["openai_api_key"] = convert_to_secret_str(
|
|
get_from_dict_or_env(values, "openai_api_key", "OPENAI_API_KEY")
|
|
)
|
|
# Check OPENAI_ORGANIZATION for backwards compatibility.
|
|
values["openai_organization"] = (
|
|
values["openai_organization"]
|
|
or os.getenv("OPENAI_ORG_ID")
|
|
or os.getenv("OPENAI_ORGANIZATION")
|
|
)
|
|
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
|
|
"OPENAI_API_BASE"
|
|
)
|
|
values["openai_proxy"] = get_from_dict_or_env(
|
|
values,
|
|
"openai_proxy",
|
|
"OPENAI_PROXY",
|
|
default="",
|
|
)
|
|
|
|
client_params = {
|
|
"api_key": (
|
|
values["openai_api_key"].get_secret_value()
|
|
if values["openai_api_key"]
|
|
else None
|
|
),
|
|
"organization": values["openai_organization"],
|
|
"base_url": values["openai_api_base"],
|
|
"timeout": values["request_timeout"],
|
|
"max_retries": values["max_retries"],
|
|
"default_headers": values["default_headers"],
|
|
"default_query": values["default_query"],
|
|
}
|
|
|
|
openai_proxy = values["openai_proxy"]
|
|
if not values.get("client"):
|
|
if openai_proxy and not values["http_client"]:
|
|
try:
|
|
import httpx
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import httpx python package. "
|
|
"Please install it with `pip install httpx`."
|
|
) from e
|
|
values["http_client"] = httpx.Client(proxy=openai_proxy)
|
|
sync_specific = {"http_client": values["http_client"]}
|
|
values["client"] = openai.OpenAI(
|
|
**client_params, **sync_specific
|
|
).chat.completions
|
|
if not values.get("async_client"):
|
|
if openai_proxy and not values["http_async_client"]:
|
|
try:
|
|
import httpx
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import httpx python package. "
|
|
"Please install it with `pip install httpx`."
|
|
) from e
|
|
values["http_async_client"] = httpx.AsyncClient(proxy=openai_proxy)
|
|
async_specific = {"http_client": values["http_async_client"]}
|
|
values["async_client"] = openai.AsyncOpenAI(
|
|
**client_params, **async_specific
|
|
).chat.completions
|
|
return values
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling OpenAI API."""
|
|
params = {
|
|
"model": self.model_name,
|
|
"stream": self.streaming,
|
|
"n": self.n,
|
|
"temperature": self.temperature,
|
|
**self.model_kwargs,
|
|
}
|
|
if self.max_tokens is not None:
|
|
params["max_tokens"] = self.max_tokens
|
|
return params
|
|
|
|
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
|
overall_token_usage: dict = {}
|
|
system_fingerprint = None
|
|
for output in llm_outputs:
|
|
if output is None:
|
|
# Happens in streaming
|
|
continue
|
|
token_usage = output["token_usage"]
|
|
if token_usage is not None:
|
|
for k, v in token_usage.items():
|
|
if k in overall_token_usage:
|
|
overall_token_usage[k] += v
|
|
else:
|
|
overall_token_usage[k] = v
|
|
if system_fingerprint is None:
|
|
system_fingerprint = output.get("system_fingerprint")
|
|
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
|
|
if system_fingerprint:
|
|
combined["system_fingerprint"] = system_fingerprint
|
|
return combined
|
|
|
|
def _stream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
|
|
default_chunk_class = AIMessageChunk
|
|
with self.client.create(messages=message_dicts, **params) as response:
|
|
for chunk in response:
|
|
if not isinstance(chunk, dict):
|
|
chunk = chunk.model_dump()
|
|
if len(chunk["choices"]) == 0:
|
|
if token_usage := chunk.get("usage"):
|
|
usage_metadata = UsageMetadata(
|
|
input_tokens=token_usage.get("prompt_tokens", 0),
|
|
output_tokens=token_usage.get("completion_tokens", 0),
|
|
total_tokens=token_usage.get("total_tokens", 0),
|
|
)
|
|
chunk = ChatGenerationChunk(
|
|
message=default_chunk_class(
|
|
content="", usage_metadata=usage_metadata
|
|
)
|
|
)
|
|
else:
|
|
continue
|
|
else:
|
|
choice = chunk["choices"][0]
|
|
if choice["delta"] is None:
|
|
continue
|
|
chunk = _convert_delta_to_message_chunk(
|
|
choice["delta"], default_chunk_class
|
|
)
|
|
generation_info = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
default_chunk_class = chunk.__class__
|
|
chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info or None
|
|
)
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(
|
|
chunk.text, chunk=chunk, logprobs=logprobs
|
|
)
|
|
yield chunk
|
|
|
|
def _generate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
stream_iter = self._stream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return generate_from_stream(stream_iter)
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs}
|
|
response = self.client.create(messages=message_dicts, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
def _create_message_dicts(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
|
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
|
params = self._default_params
|
|
if stop is not None:
|
|
if "stop" in params:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
params["stop"] = stop
|
|
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
|
return message_dicts, params
|
|
|
|
def _create_chat_result(
|
|
self, response: Union[dict, openai.BaseModel]
|
|
) -> ChatResult:
|
|
generations = []
|
|
if not isinstance(response, dict):
|
|
response = response.model_dump()
|
|
|
|
# Sometimes the AI Model calling will get error, we should raise it.
|
|
# Otherwise, the next code 'choices.extend(response["choices"])'
|
|
# will throw a "TypeError: 'NoneType' object is not iterable" error
|
|
# to mask the true error. Because 'response["choices"]' is None.
|
|
if response.get("error"):
|
|
raise ValueError(response.get("error"))
|
|
|
|
token_usage = response.get("usage", {})
|
|
for res in response["choices"]:
|
|
message = _convert_dict_to_message(res["message"])
|
|
if token_usage and isinstance(message, AIMessage):
|
|
message.usage_metadata = {
|
|
"input_tokens": token_usage.get("prompt_tokens", 0),
|
|
"output_tokens": token_usage.get("completion_tokens", 0),
|
|
"total_tokens": token_usage.get("total_tokens", 0),
|
|
}
|
|
generation_info = dict(finish_reason=res.get("finish_reason"))
|
|
if "logprobs" in res:
|
|
generation_info["logprobs"] = res["logprobs"]
|
|
gen = ChatGeneration(
|
|
message=message,
|
|
generation_info=generation_info,
|
|
)
|
|
generations.append(gen)
|
|
llm_output = {
|
|
"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
|
|
response = await self.async_client.create(messages=message_dicts, **params)
|
|
async with response:
|
|
async for chunk in response:
|
|
if not isinstance(chunk, dict):
|
|
chunk = chunk.model_dump()
|
|
if len(chunk["choices"]) == 0:
|
|
if token_usage := chunk.get("usage"):
|
|
usage_metadata = UsageMetadata(
|
|
input_tokens=token_usage.get("prompt_tokens", 0),
|
|
output_tokens=token_usage.get("completion_tokens", 0),
|
|
total_tokens=token_usage.get("total_tokens", 0),
|
|
)
|
|
chunk = ChatGenerationChunk(
|
|
message=default_chunk_class(
|
|
content="", usage_metadata=usage_metadata
|
|
)
|
|
)
|
|
else:
|
|
continue
|
|
else:
|
|
choice = chunk["choices"][0]
|
|
if choice["delta"] is None:
|
|
continue
|
|
chunk = _convert_delta_to_message_chunk(
|
|
choice["delta"], default_chunk_class
|
|
)
|
|
generation_info = {}
|
|
if finish_reason := choice.get("finish_reason"):
|
|
generation_info["finish_reason"] = finish_reason
|
|
logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
default_chunk_class = chunk.__class__
|
|
chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info or None
|
|
)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
token=chunk.text, chunk=chunk, logprobs=logprobs
|
|
)
|
|
yield chunk
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
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, **kwargs}
|
|
response = await self.async_client.create(messages=message_dicts, **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}
|
|
|
|
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,
|
|
}
|
|
|
|
def _get_ls_params(
|
|
self, stop: Optional[List[str]] = None, **kwargs: Any
|
|
) -> LangSmithParams:
|
|
"""Get standard params for tracing."""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
|
|
ls_params = LangSmithParams(
|
|
ls_provider="openai",
|
|
ls_model_name=self.model_name,
|
|
ls_model_type="chat",
|
|
ls_temperature=params.get("temperature", self.temperature),
|
|
)
|
|
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
|
|
ls_params["ls_max_tokens"] = ls_max_tokens
|
|
if ls_stop := stop or params.get("stop", None):
|
|
ls_params["ls_stop"] = ls_stop
|
|
return ls_params
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "openai-chat"
|
|
|
|
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
|
|
if self.tiktoken_model_name is not None:
|
|
model = self.tiktoken_model_name
|
|
else:
|
|
model = self.model_name
|
|
try:
|
|
encoding = tiktoken.encoding_for_model(model)
|
|
except KeyError:
|
|
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."""
|
|
if self.custom_get_token_ids is not None:
|
|
return self.custom_get_token_ids(text)
|
|
# 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://platform.openai.com/docs/guides/text-generation/managing-tokens"
|
|
" 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, BaseTool]],
|
|
function_call: Optional[
|
|
Union[_FunctionCall, str, Literal["auto", "none"]]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind functions (and other objects) to this chat model.
|
|
|
|
Assumes model is compatible with OpenAI function-calling API.
|
|
|
|
NOTE: Using bind_tools is recommended instead, as the `functions` and
|
|
`function_call` request parameters are officially marked as deprecated by
|
|
OpenAI.
|
|
|
|
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.
|
|
"""
|
|
|
|
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
|
if function_call is not None:
|
|
function_call = (
|
|
{"name": function_call}
|
|
if isinstance(function_call, str)
|
|
and function_call not in ("auto", "none")
|
|
else function_call
|
|
)
|
|
if isinstance(function_call, dict) and len(formatted_functions) != 1:
|
|
raise ValueError(
|
|
"When specifying `function_call`, you must provide exactly one "
|
|
"function."
|
|
)
|
|
if (
|
|
isinstance(function_call, dict)
|
|
and formatted_functions[0]["name"] != function_call["name"]
|
|
):
|
|
raise ValueError(
|
|
f"Function call {function_call} was specified, but the only "
|
|
f"provided function was {formatted_functions[0]['name']}."
|
|
)
|
|
kwargs = {**kwargs, "function_call": function_call}
|
|
return super().bind(
|
|
functions=formatted_functions,
|
|
**kwargs,
|
|
)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
*,
|
|
tool_choice: Optional[
|
|
Union[dict, str, Literal["auto", "none", "required", "any"], bool]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
Assumes model is compatible with OpenAI tool-calling API.
|
|
|
|
Args:
|
|
tools: A list of tool definitions to bind to this chat model.
|
|
Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
|
|
models, callables, and BaseTools will be automatically converted to
|
|
their schema dictionary representation.
|
|
tool_choice: Which tool to require the model to call.
|
|
Options are:
|
|
name of the tool (str): calls corresponding tool;
|
|
"auto": automatically selects a tool (including no tool);
|
|
"none": does not call a tool;
|
|
"any" or "required": force at least one tool to be called;
|
|
True: forces tool call (requires `tools` be length 1);
|
|
False: no effect;
|
|
|
|
or a dict of the form:
|
|
{"type": "function", "function": {"name": <<tool_name>>}}.
|
|
**kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
|
|
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
|
if tool_choice:
|
|
if isinstance(tool_choice, str):
|
|
# tool_choice is a tool/function name
|
|
if tool_choice not in ("auto", "none", "any", "required"):
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {"name": tool_choice},
|
|
}
|
|
# 'any' is not natively supported by OpenAI API.
|
|
# We support 'any' since other models use this instead of 'required'.
|
|
if tool_choice == "any":
|
|
tool_choice = "required"
|
|
elif isinstance(tool_choice, bool):
|
|
if len(tools) > 1:
|
|
raise ValueError(
|
|
"tool_choice=True can only be used when a single tool is "
|
|
f"passed in, received {len(tools)} tools."
|
|
)
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {"name": formatted_tools[0]["function"]["name"]},
|
|
}
|
|
elif isinstance(tool_choice, dict):
|
|
tool_names = [
|
|
formatted_tool["function"]["name"]
|
|
for formatted_tool in formatted_tools
|
|
]
|
|
if not any(
|
|
tool_name == tool_choice["function"]["name"]
|
|
for tool_name in tool_names
|
|
):
|
|
raise ValueError(
|
|
f"Tool choice {tool_choice} was specified, but the only "
|
|
f"provided tools were {tool_names}."
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unrecognized tool_choice type. Expected str, bool or dict. "
|
|
f"Received: {tool_choice}"
|
|
)
|
|
kwargs["tool_choice"] = tool_choice
|
|
return super().bind(tools=formatted_tools, **kwargs)
|
|
|
|
@overload
|
|
def with_structured_output(
|
|
self,
|
|
schema: Optional[_DictOrPydanticClass] = None,
|
|
*,
|
|
method: Literal["function_calling", "json_mode"] = "function_calling",
|
|
include_raw: Literal[True] = True,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, _AllReturnType]:
|
|
...
|
|
|
|
@overload
|
|
def with_structured_output(
|
|
self,
|
|
schema: Optional[_DictOrPydanticClass] = None,
|
|
*,
|
|
method: Literal["function_calling", "json_mode"] = "function_calling",
|
|
include_raw: Literal[False] = False,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
|
|
...
|
|
|
|
def with_structured_output(
|
|
self,
|
|
schema: Optional[_DictOrPydanticClass] = None,
|
|
*,
|
|
method: Literal["function_calling", "json_mode"] = "function_calling",
|
|
include_raw: bool = False,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
|
|
"""Model wrapper that returns outputs formatted to match the given schema.
|
|
|
|
Args:
|
|
schema: The output schema as a dict or a Pydantic class. If a Pydantic class
|
|
then the model output will be an object of that class. If a dict then
|
|
the model output will be a dict. With a Pydantic class the returned
|
|
attributes will be validated, whereas with a dict they will not be. If
|
|
`method` is "function_calling" and `schema` is a dict, then the dict
|
|
must match the OpenAI function-calling spec or be a valid JSON schema
|
|
with top level 'title' and 'description' keys specified.
|
|
method: The method for steering model generation, either "function_calling"
|
|
or "json_mode". If "function_calling" then the schema will be converted
|
|
to an OpenAI function and the returned model will make use of the
|
|
function-calling API. If "json_mode" then OpenAI's JSON mode will be
|
|
used. Note that if using "json_mode" then you must include instructions
|
|
for formatting the output into the desired schema into the model call.
|
|
include_raw: If False then only the parsed structured output is returned. If
|
|
an error occurs during model output parsing it will be raised. If True
|
|
then both the raw model response (a BaseMessage) and the parsed model
|
|
response will be returned. If an error occurs during output parsing it
|
|
will be caught and returned as well. The final output is always a dict
|
|
with keys "raw", "parsed", and "parsing_error".
|
|
|
|
Returns:
|
|
A Runnable that takes any ChatModel input and returns as output:
|
|
|
|
If include_raw is True then a dict with keys:
|
|
raw: BaseMessage
|
|
parsed: Optional[_DictOrPydantic]
|
|
parsing_error: Optional[BaseException]
|
|
|
|
If include_raw is False then just _DictOrPydantic is returned,
|
|
where _DictOrPydantic depends on the schema:
|
|
|
|
If schema is a Pydantic class then _DictOrPydantic is the Pydantic
|
|
class.
|
|
|
|
If schema is a dict then _DictOrPydantic is a dict.
|
|
|
|
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
|
|
.. code-block:: python
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
|
|
# -> AnswerWithJustification(
|
|
# answer='They weigh the same',
|
|
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
|
|
# )
|
|
|
|
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
|
|
.. code-block:: python
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
# -> {
|
|
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
|
|
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: Function-calling, dict schema (method="function_calling", include_raw=False):
|
|
.. code-block:: python
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.utils.function_calling import convert_to_openai_tool
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
dict_schema = convert_to_openai_tool(AnswerWithJustification)
|
|
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
|
|
structured_llm = llm.with_structured_output(dict_schema)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
# -> {
|
|
# 'answer': 'They weigh the same',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
|
# }
|
|
|
|
Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
|
|
.. code-block::
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
|
|
structured_llm = llm.with_structured_output(
|
|
AnswerWithJustification,
|
|
method="json_mode",
|
|
include_raw=True
|
|
)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
|
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
|
|
.. code-block::
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
|
|
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
|
|
|
|
structured_llm.invoke(
|
|
"Answer the following question. "
|
|
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
|
"What's heavier a pound of bricks or a pound of feathers?"
|
|
)
|
|
# -> {
|
|
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
|
# 'parsed': {
|
|
# 'answer': 'They are both the same weight.',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
|
|
# },
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
|
|
""" # noqa: E501
|
|
if kwargs:
|
|
raise ValueError(f"Received unsupported arguments {kwargs}")
|
|
is_pydantic_schema = _is_pydantic_class(schema)
|
|
if method == "function_calling":
|
|
if schema is None:
|
|
raise ValueError(
|
|
"schema must be specified when method is 'function_calling'. "
|
|
"Received None."
|
|
)
|
|
llm = self.bind_tools([schema], tool_choice=True)
|
|
if is_pydantic_schema:
|
|
output_parser: OutputParserLike = PydanticToolsParser(
|
|
tools=[schema], first_tool_only=True
|
|
)
|
|
else:
|
|
key_name = convert_to_openai_tool(schema)["function"]["name"]
|
|
output_parser = JsonOutputKeyToolsParser(
|
|
key_name=key_name, first_tool_only=True
|
|
)
|
|
elif method == "json_mode":
|
|
llm = self.bind(response_format={"type": "json_object"})
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema)
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unrecognized method argument. Expected one of 'function_calling' or "
|
|
f"'json_format'. Received: '{method}'"
|
|
)
|
|
|
|
if include_raw:
|
|
parser_assign = RunnablePassthrough.assign(
|
|
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
|
)
|
|
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
|
parser_with_fallback = parser_assign.with_fallbacks(
|
|
[parser_none], exception_key="parsing_error"
|
|
)
|
|
return RunnableMap(raw=llm) | parser_with_fallback
|
|
else:
|
|
return llm | output_parser
|
|
|
|
|
|
class ChatOpenAI(BaseChatOpenAI):
|
|
"""`OpenAI` Chat large language models API.
|
|
|
|
To use, you should have the environment variable ``OPENAI_API_KEY``
|
|
set with your API key, or pass it as a named parameter to the constructor.
|
|
|
|
Any parameters that are valid to be passed to the openai.create call can be passed
|
|
in, even if not explicitly saved on this class.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
|
|
model = ChatOpenAI(model="gpt-3.5-turbo")
|
|
"""
|
|
|
|
@property
|
|
def lc_secrets(self) -> Dict[str, str]:
|
|
return {"openai_api_key": "OPENAI_API_KEY"}
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "chat_models", "openai"]
|
|
|
|
@property
|
|
def lc_attributes(self) -> Dict[str, Any]:
|
|
attributes: Dict[str, Any] = {}
|
|
|
|
if self.openai_organization:
|
|
attributes["openai_organization"] = self.openai_organization
|
|
|
|
if self.openai_api_base:
|
|
attributes["openai_api_base"] = self.openai_api_base
|
|
|
|
if self.openai_proxy:
|
|
attributes["openai_proxy"] = self.openai_proxy
|
|
|
|
return attributes
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
"""Return whether this model can be serialized by Langchain."""
|
|
return True
|
|
|
|
|
|
def _is_pydantic_class(obj: Any) -> bool:
|
|
return isinstance(obj, type) and issubclass(obj, BaseModel)
|
|
|
|
|
|
def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
|
|
return {
|
|
"type": "function",
|
|
"id": tool_call["id"],
|
|
"function": {
|
|
"name": tool_call["name"],
|
|
"arguments": json.dumps(tool_call["args"]),
|
|
},
|
|
}
|
|
|
|
|
|
def _lc_invalid_tool_call_to_openai_tool_call(
|
|
invalid_tool_call: InvalidToolCall,
|
|
) -> dict:
|
|
return {
|
|
"type": "function",
|
|
"id": invalid_tool_call["id"],
|
|
"function": {
|
|
"name": invalid_tool_call["name"],
|
|
"arguments": invalid_tool_call["args"],
|
|
},
|
|
}
|