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636 lines
22 KiB
Python
636 lines
22 KiB
Python
from __future__ import annotations
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import asyncio
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import functools
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import json
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import logging
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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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Mapping,
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Optional,
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Sequence,
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Type,
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Union,
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cast,
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)
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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 BaseChatModel
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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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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessage,
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ToolMessageChunk,
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)
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from langchain_core.output_parsers.openai_tools import (
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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 (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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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
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from langchain_core.tools import BaseTool
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from requests.exceptions import HTTPError
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain_community.llms.tongyi import (
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agenerate_with_last_element_mark,
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check_response,
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generate_with_last_element_mark,
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)
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logger = logging.getLogger(__name__)
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def convert_dict_to_message(
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_dict: Mapping[str, Any], is_chunk: bool = False
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) -> Union[BaseMessage, BaseMessageChunk]:
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"""Convert a dict to a message."""
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role = _dict["role"]
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content = _dict["content"]
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if role == "user":
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return (
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HumanMessageChunk(content=content)
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if is_chunk
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else HumanMessage(content=content)
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)
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elif role == "assistant":
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tool_calls = []
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invalid_tool_calls = []
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if "tool_calls" in _dict:
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additional_kwargs = {"tool_calls": _dict["tool_calls"]}
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for index, value in enumerate(_dict["tool_calls"]):
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if is_chunk:
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try:
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tool_calls.append(
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{
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"name": value["function"].get("name"),
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"args": value["function"].get("arguments"),
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"id": value.get("id"),
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# Tongyi does not respond with index,
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# use index in the list instead
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"index": index,
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}
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)
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except KeyError:
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pass
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else:
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try:
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parsed_tool = parse_tool_call(value, return_id=True)
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if parsed_tool:
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tool_calls.append(parsed_tool)
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except Exception as e:
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invalid_tool_calls.append(make_invalid_tool_call(value, str(e)))
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else:
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additional_kwargs = {}
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return (
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AIMessageChunk(
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content=content,
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additional_kwargs=additional_kwargs,
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tool_call_chunks=tool_calls, # type: ignore[arg-type]
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id=_dict.get("id"),
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)
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if is_chunk
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else AIMessage(
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content=content,
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additional_kwargs=additional_kwargs,
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tool_calls=tool_calls, # type: ignore[arg-type]
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invalid_tool_calls=invalid_tool_calls,
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)
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)
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elif role == "system":
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return (
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SystemMessageChunk(content=content)
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if is_chunk
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else SystemMessage(content=content)
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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 (
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ToolMessageChunk(
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content=_dict.get("content", ""),
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tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
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additional_kwargs=additional_kwargs,
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)
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if is_chunk
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else ToolMessage(
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content=_dict.get("content", ""),
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tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
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additional_kwargs=additional_kwargs,
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)
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)
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else:
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return (
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ChatMessageChunk(role=role, content=content)
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if is_chunk
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else ChatMessage(role=role, content=content)
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)
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def convert_message_chunk_to_message(message_chunk: BaseMessageChunk) -> BaseMessage:
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"""Convert a message chunk to a message.
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Args:
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chunk: Message chunk to convert.
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Returns:
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Message.
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"""
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if not isinstance(message_chunk, BaseMessageChunk):
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return message_chunk
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# chunk classes always have the equivalent non-chunk class as their first parent
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ignore_keys = ["type"]
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if isinstance(message_chunk, AIMessageChunk):
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ignore_keys.append("tool_call_chunks")
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return message_chunk.__class__.__mro__[1](
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**{k: v for k, v in message_chunk.__dict__.items() if k not in ignore_keys}
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)
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def convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a message to a dict."""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, ToolMessage):
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message_dict = {
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"role": "tool",
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"tool_call_id": message.tool_call_id,
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"content": message.content,
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"name": message.name,
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}
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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 _create_retry_decorator(llm: ChatTongyi) -> Callable[[Any], Any]:
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min_seconds = 1
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max_seconds = 4
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterward
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(retry_if_exception_type(HTTPError)),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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class ChatTongyi(BaseChatModel):
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"""Alibaba Tongyi Qwen chat models API.
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To use, you should have the ``dashscope`` python package installed,
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and set env ``DASHSCOPE_API_KEY`` with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatTongyi
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Tongyi_chat = ChatTongyi()
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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 {"dashscope_api_key": "DASHSCOPE_API_KEY"}
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client: Any #: :meta private:
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model_name: str = Field(default="qwen-turbo", alias="model")
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"""Model name to use.
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callable multimodal model:
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- qwen-vl-v1
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- qwen-vl-chat-v1
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- qwen-audio-turbo
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- qwen-vl-plus
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- qwen-vl-max
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"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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top_p: float = 0.8
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"""Total probability mass of tokens to consider at each step."""
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dashscope_api_key: Optional[SecretStr] = Field(None, alias="api_key")
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"""Dashscope api key provide by Alibaba Cloud."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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max_retries: int = 10
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"""Maximum number of retries to make when generating."""
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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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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "tongyi"
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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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values["dashscope_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "dashscope_api_key", "DASHSCOPE_API_KEY")
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)
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try:
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import dashscope
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except ImportError:
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raise ImportError(
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"Could not import dashscope python package. "
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"Please install it with `pip install dashscope --upgrade`."
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)
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dashscope_multimodal_models = [
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"qwen-vl-v1",
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"qwen-vl-chat-v1",
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"qwen-audio-turbo",
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"qwen-vl-plus",
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"qwen-vl-max",
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]
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if (
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values["model_name"] in dashscope_multimodal_models
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or "vl" in values["model_name"]
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):
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try:
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values["client"] = dashscope.MultiModalConversation
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except AttributeError:
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raise ValueError(
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"`dashscope` has no `MultiModalConversation` attribute, this is "
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"likely due to an old version of the dashscope package. Try "
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"upgrading it with `pip install --upgrade dashscope`."
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)
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else:
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try:
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values["client"] = dashscope.Generation
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except AttributeError:
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raise ValueError(
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"`dashscope` has no `Generation` attribute, this is likely "
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"due to an old version of the dashscope package. Try upgrading it "
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"with `pip install --upgrade dashscope`."
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)
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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 Tongyi Qwen API."""
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return {
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"model": self.model_name,
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"top_p": self.top_p,
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"api_key": cast(SecretStr, self.dashscope_api_key).get_secret_value(),
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"result_format": "message",
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**self.model_kwargs,
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}
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def completion_with_retry(self, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self)
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@retry_decorator
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def _completion_with_retry(**_kwargs: Any) -> Any:
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resp = self.client.call(**_kwargs)
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return check_response(resp)
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return _completion_with_retry(**kwargs)
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def stream_completion_with_retry(self, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self)
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@retry_decorator
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def _stream_completion_with_retry(**_kwargs: Any) -> Any:
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responses = self.client.call(**_kwargs)
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prev_resp = None
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for resp in responses:
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# If we are streaming without `incremental_output = True`,
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# we need to calculate the delta response manually
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if _kwargs.get("stream") and not _kwargs.get(
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"incremental_output", False
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):
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if prev_resp is None:
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delta_resp = resp
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else:
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delta_resp = self.subtract_client_response(resp, prev_resp)
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prev_resp = resp
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yield check_response(delta_resp)
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else:
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yield check_response(resp)
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return _stream_completion_with_retry(**kwargs)
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def subtract_client_response(self, resp: Any, prev_resp: Any) -> Any:
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"""Subtract prev response from curr response.
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Useful when streaming without `incremental_output = True`
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"""
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resp_copy = json.loads(json.dumps(resp))
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choice = resp_copy["output"]["choices"][0]
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message = choice["message"]
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prev_resp_copy = json.loads(json.dumps(prev_resp))
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prev_choice = prev_resp_copy["output"]["choices"][0]
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prev_message = prev_choice["message"]
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message["content"] = message["content"].replace(prev_message["content"], "")
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if message.get("tool_calls"):
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for index, tool_call in enumerate(message["tool_calls"]):
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function = tool_call["function"]
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if prev_message.get("tool_calls"):
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prev_function = prev_message["tool_calls"][index]["function"]
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function["name"] = function["name"].replace(
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prev_function["name"], ""
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)
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function["arguments"] = function["arguments"].replace(
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prev_function["arguments"], ""
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)
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return resp_copy
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async def astream_completion_with_retry(self, **kwargs: Any) -> Any:
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"""Because the dashscope SDK doesn't provide an async API,
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we wrap `stream_generate_with_retry` with an async generator."""
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class _AioTongyiGenerator:
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def __init__(self, generator: Any):
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self.generator = generator
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def __aiter__(self) -> AsyncIterator[Any]:
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return self
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async def __anext__(self) -> Any:
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value = await asyncio.get_running_loop().run_in_executor(
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None, self._safe_next
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)
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if value is not None:
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return value
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else:
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raise StopAsyncIteration
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def _safe_next(self) -> Any:
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try:
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return next(self.generator)
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except StopIteration:
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return None
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async for chunk in _AioTongyiGenerator(
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generator=self.stream_completion_with_retry(**kwargs)
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):
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yield chunk
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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,
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**kwargs: Any,
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) -> ChatResult:
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generations = []
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if self.streaming:
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generation_chunk: Optional[ChatGenerationChunk] = None
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for chunk in self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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):
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if generation_chunk is None:
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generation_chunk = chunk
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else:
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generation_chunk += chunk
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assert generation_chunk is not None
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generations.append(self._chunk_to_generation(generation_chunk))
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else:
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params: Dict[str, Any] = self._invocation_params(
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messages=messages, stop=stop, **kwargs
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)
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resp = self.completion_with_retry(**params)
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generations.append(
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ChatGeneration(**self._chat_generation_from_qwen_resp(resp))
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)
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return ChatResult(
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generations=generations,
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llm_output={
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"model_name": self.model_name,
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},
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)
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async def _agenerate(
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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[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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generations = []
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if self.streaming:
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generation: Optional[ChatGenerationChunk] = None
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async for chunk in self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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generations.append(self._chunk_to_generation(generation))
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else:
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params: Dict[str, Any] = self._invocation_params(
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messages=messages, stop=stop, **kwargs
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)
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resp = await asyncio.get_running_loop().run_in_executor(
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None,
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functools.partial(self.completion_with_retry, **params),
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)
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generations.append(
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ChatGeneration(**self._chat_generation_from_qwen_resp(resp))
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)
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return ChatResult(
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generations=generations,
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llm_output={
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"model_name": self.model_name,
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},
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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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params: Dict[str, Any] = self._invocation_params(
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messages=messages, stop=stop, stream=True, **kwargs
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)
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for stream_resp, is_last_chunk in generate_with_last_element_mark(
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self.stream_completion_with_retry(**params)
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):
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choice = stream_resp["output"]["choices"][0]
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message = choice["message"]
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|
if (
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choice["finish_reason"] == "null"
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and message["content"] == ""
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and "tool_calls" not in message
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):
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continue
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chunk = ChatGenerationChunk(
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**self._chat_generation_from_qwen_resp(
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stream_resp, is_chunk=True, is_last_chunk=is_last_chunk
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)
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)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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async def _astream(
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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[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
|
|
params: Dict[str, Any] = self._invocation_params(
|
|
messages=messages, stop=stop, stream=True, **kwargs
|
|
)
|
|
async for stream_resp, is_last_chunk in agenerate_with_last_element_mark(
|
|
self.astream_completion_with_retry(**params)
|
|
):
|
|
chunk = ChatGenerationChunk(
|
|
**self._chat_generation_from_qwen_resp(
|
|
stream_resp, is_chunk=True, is_last_chunk=is_last_chunk
|
|
)
|
|
)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
|
yield chunk
|
|
|
|
def _invocation_params(
|
|
self, messages: List[BaseMessage], stop: Any, **kwargs: Any
|
|
) -> Dict[str, Any]:
|
|
params = {**self._default_params, **kwargs}
|
|
if stop is not None:
|
|
params["stop"] = stop
|
|
# According to the Tongyi official docs,
|
|
# `incremental_output` with `tools` is not supported yet
|
|
if params.get("stream") and not params.get("tools"):
|
|
params["incremental_output"] = True
|
|
|
|
message_dicts = [convert_message_to_dict(m) for m in messages]
|
|
|
|
# And the `system` message should be the first message if present
|
|
system_message_indices = [
|
|
i for i, m in enumerate(message_dicts) if m["role"] == "system"
|
|
]
|
|
if len(system_message_indices) == 1 and system_message_indices[0] != 0:
|
|
raise ValueError("System message can only be the first message.")
|
|
elif len(system_message_indices) > 1:
|
|
raise ValueError("There can be only one system message at most.")
|
|
|
|
params["messages"] = message_dicts
|
|
|
|
return params
|
|
|
|
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
|
if llm_outputs[0] is None:
|
|
return {}
|
|
return llm_outputs[0]
|
|
|
|
@staticmethod
|
|
def _chat_generation_from_qwen_resp(
|
|
resp: Any, is_chunk: bool = False, is_last_chunk: bool = True
|
|
) -> Dict[str, Any]:
|
|
# According to the response from dashscope,
|
|
# each chunk's `generation_info` overwrites the previous one.
|
|
# Besides, The `merge_dicts` method,
|
|
# which is used to concatenate `generation_info` in `GenerationChunk`,
|
|
# does not support merging of int type values.
|
|
# Therefore, we adopt the `generation_info` of the last chunk
|
|
# and discard the `generation_info` of the intermediate chunks.
|
|
choice = resp["output"]["choices"][0]
|
|
message = convert_dict_to_message(choice["message"], is_chunk=is_chunk)
|
|
if is_last_chunk:
|
|
return dict(
|
|
message=message,
|
|
generation_info=dict(
|
|
finish_reason=choice["finish_reason"],
|
|
request_id=resp["request_id"],
|
|
token_usage=dict(resp["usage"]),
|
|
),
|
|
)
|
|
else:
|
|
return dict(message=message)
|
|
|
|
@staticmethod
|
|
def _chunk_to_generation(chunk: ChatGenerationChunk) -> ChatGeneration:
|
|
return ChatGeneration(
|
|
message=convert_message_chunk_to_message(chunk.message),
|
|
generation_info=chunk.generation_info,
|
|
)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
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.
|
|
**kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
|
|
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
|
return super().bind(tools=formatted_tools, **kwargs)
|