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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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libs/community/langchain_community/chat_models/tongyi.py
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404
libs/community/langchain_community/chat_models/tongyi.py
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from __future__ import annotations
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import logging
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from typing import (
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Any,
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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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Tuple,
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Type,
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)
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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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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SystemMessage,
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SystemMessageChunk,
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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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GenerationChunk,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from requests.exceptions import HTTPError
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from tenacity import (
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RetryCallState,
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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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logger = logging.getLogger(__name__)
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def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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content = _dict.get("content", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def convert_message_to_dict(message: BaseMessage) -> 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 "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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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, FunctionMessage):
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message_dict = {
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"role": "function",
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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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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def _stream_response_to_generation_chunk(
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stream_response: Dict[str, Any],
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length: int,
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) -> GenerationChunk:
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"""Convert a stream response to a generation chunk.
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As the low level API implement is different from openai and other llm.
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Stream response of Tongyi is not split into chunks, but all data generated before.
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For example, the answer 'Hi Pickle Rick! How can I assist you today?'
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Other llm will stream answer:
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'Hi Pickle',
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' Rick!',
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' How can I assist you today?'.
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Tongyi answer:
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'Hi Pickle',
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'Hi Pickle Rick!',
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'Hi Pickle Rick! How can I assist you today?'.
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As the GenerationChunk is implemented with chunks. Only return full_text[length:]
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for new chunk.
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"""
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full_text = stream_response["output"]["text"]
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text = full_text[length:]
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finish_reason = stream_response["output"].get("finish_reason", None)
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return GenerationChunk(
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text=text,
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generation_info=dict(
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finish_reason=finish_reason,
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),
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)
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def _create_retry_decorator(
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llm: ChatTongyi,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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) -> Callable[[Any], Any]:
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def _before_sleep(retry_state: RetryCallState) -> None:
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if run_manager:
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run_manager.on_retry(retry_state)
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return None
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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 afterwards
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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,
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)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any],
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default_class: Type[BaseMessageChunk],
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length: int,
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) -> BaseMessageChunk:
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role = _dict.get("role")
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full_content = _dict.get("content") or ""
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content = full_content[length:]
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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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 Tongyi
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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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@property
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def lc_serializable(self) -> bool:
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return True
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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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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[str] = None
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"""Dashscope api key provide by alicloud."""
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n: int = 1
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"""How many completions to generate for each prompt."""
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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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prefix_messages: List = Field(default_factory=list)
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"""Series of messages for Chat input."""
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result_format: str = Field(default="message")
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"""Return result format"""
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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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get_from_dict_or_env(values, "dashscope_api_key", "DASHSCOPE_API_KEY")
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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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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 OpenAI 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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"stream": self.streaming,
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"n": self.n,
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"result_format": self.result_format,
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**self.model_kwargs,
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}
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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**_kwargs: Any) -> Any:
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resp = self.client.call(**_kwargs)
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if resp.status_code == 200:
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return resp
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elif resp.status_code in [400, 401]:
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raise ValueError(
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f"status_code: {resp.status_code} \n "
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f"code: {resp.code} \n message: {resp.message}"
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)
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else:
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raise HTTPError(
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f"HTTP error occurred: status_code: {resp.status_code} \n "
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f"code: {resp.code} \n message: {resp.message}",
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response=resp,
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)
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return _completion_with_retry(**kwargs)
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def stream_completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _stream_completion_with_retry(**_kwargs: Any) -> Any:
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return self.client.call(**_kwargs)
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return _stream_completion_with_retry(**kwargs)
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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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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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if not messages:
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raise ValueError("No messages provided.")
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message_dicts, params = self._create_message_dicts(messages, stop)
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if message_dicts[-1]["role"] != "user":
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raise ValueError("Last message should be user message.")
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params = {**params, **kwargs}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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# Mark current chunk total length
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length = 0
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default_chunk_class = AIMessageChunk
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for chunk in self.stream_completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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):
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if len(chunk["output"]["choices"]) == 0:
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continue
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choice = chunk["output"]["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["message"], default_chunk_class, length
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)
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finish_reason = choice.get("finish_reason")
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generation_info = (
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dict(finish_reason=finish_reason) if finish_reason is not None else None
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)
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
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yield chunk
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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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length = len(choice["message"]["content"])
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = self._client_params()
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# Ensure `stop` is a list of strings
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the openai client."""
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creds: Dict[str, Any] = {
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"api_key": self.dashscope_api_key,
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}
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return {**self._default_params, **creds}
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["output"]["choices"]:
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message = convert_dict_to_message(res["message"])
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.get("finish_reason")),
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)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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return ChatResult(generations=generations, llm_output=llm_output)
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