mirror of
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synced 2025-06-24 15:43:54 +00:00
Upgrades the Tongyi LLM and ChatTongyi Model (#14793)
- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi
Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream
generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should
be properly handled when set by environment variables or initialization
parameters.
- Changed the `dashscope` response to incremental output by setting the
parameter `incremental_output`, which eliminates the need for the
prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`,
`_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and
`Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke
the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Shuai Liu
GitHub
parent
6f15cc64b8
commit
4b53440e70
@ -46,6 +46,7 @@ from langchain_community.chat_models.ollama import ChatOllama
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from langchain_community.chat_models.openai import ChatOpenAI
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from langchain_community.chat_models.pai_eas_endpoint import PaiEasChatEndpoint
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from langchain_community.chat_models.promptlayer_openai import PromptLayerChatOpenAI
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from langchain_community.chat_models.tongyi import ChatTongyi
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from langchain_community.chat_models.vertexai import ChatVertexAI
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from langchain_community.chat_models.volcengine_maas import VolcEngineMaasChat
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from langchain_community.chat_models.yandex import ChatYandexGPT
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@ -76,6 +77,7 @@ __all__ = [
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"ChatKonko",
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"PaiEasChatEndpoint",
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"QianfanChatEndpoint",
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"ChatTongyi",
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"ChatFireworks",
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"ChatYandexGPT",
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"ChatBaichuan",
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@ -1,23 +1,25 @@
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from __future__ import annotations
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import asyncio
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import functools
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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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Tuple,
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Type,
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Union,
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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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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.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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@ -25,8 +27,6 @@ from langchain_core.messages import (
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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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@ -36,41 +36,63 @@ 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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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 check_response
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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 dict to a message."""
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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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role = _dict["role"]
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content = _dict["content"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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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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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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return (
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AIMessageChunk(content=content) if is_chunk else AIMessage(content=content)
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)
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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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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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else:
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return ChatMessage(content=_dict["content"], role=role)
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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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if isinstance(message_chunk, HumanMessageChunk):
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return HumanMessage(content=message_chunk.content)
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elif isinstance(message_chunk, AIMessageChunk):
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return AIMessage(content=message_chunk.content)
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elif isinstance(message_chunk, SystemMessageChunk):
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return SystemMessage(content=message_chunk.content)
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elif isinstance(message_chunk, ChatMessageChunk):
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return ChatMessage(role=message_chunk.role, content=message_chunk.content)
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else:
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raise TypeError(f"Got unknown type {message_chunk}")
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def convert_message_to_dict(message: BaseMessage) -> dict:
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@ -83,109 +105,27 @@ def convert_message_to_dict(message: BaseMessage) -> dict:
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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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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 afterwards
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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,
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before_sleep=before_sleep_log(logger, logging.WARNING),
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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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@ -204,10 +144,6 @@ class ChatTongyi(BaseChatModel):
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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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@ -218,10 +154,7 @@ class ChatTongyi(BaseChatModel):
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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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"""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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@ -229,12 +162,6 @@ class ChatTongyi(BaseChatModel):
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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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@ -243,7 +170,9 @@ class ChatTongyi(BaseChatModel):
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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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values["dashscope_api_key"] = get_from_dict_or_env(
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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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@ -264,81 +193,141 @@ class ChatTongyi(BaseChatModel):
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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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"""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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"stream": self.streaming,
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"n": self.n,
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"result_format": self.result_format,
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"api_key": self.dashscope_api_key,
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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(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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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, run_manager=run_manager)
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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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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 check_response(resp)
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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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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, run_manager=run_manager)
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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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return self.client.call(**_kwargs)
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responses = self.client.call(**_kwargs)
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for resp in responses:
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yield check_response(resp)
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return _stream_completion_with_retry(**kwargs)
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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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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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generations = []
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if self.streaming:
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generation: 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 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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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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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(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
generations.append(self._chunk_to_generation(generation))
|
||||
else:
|
||||
params: Dict[str, Any] = self._invocation_params(
|
||||
messages=messages, stop=stop, **kwargs
|
||||
)
|
||||
resp = await asyncio.get_running_loop().run_in_executor(
|
||||
None,
|
||||
functools.partial(
|
||||
self.completion_with_retry, **{"run_manager": run_manager, **params}
|
||||
),
|
||||
)
|
||||
generations.append(
|
||||
ChatGeneration(**self._chat_generation_from_qwen_resp(resp))
|
||||
)
|
||||
return ChatResult(
|
||||
generations=generations,
|
||||
llm_output={
|
||||
"model_name": self.model_name,
|
||||
},
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
@ -347,62 +336,83 @@ class ChatTongyi(BaseChatModel):
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
# Mark current chunk total length
|
||||
length = 0
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in self.stream_completion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
):
|
||||
if len(chunk["output"]["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["output"]["choices"][0]
|
||||
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["message"], default_chunk_class, length
|
||||
params: Dict[str, Any] = self._invocation_params(
|
||||
messages=messages, stop=stop, stream=True, **kwargs
|
||||
)
|
||||
for stream_resp in self.stream_completion_with_retry(**params):
|
||||
chunk = ChatGenerationChunk(
|
||||
**self._chat_generation_from_qwen_resp(stream_resp, is_chunk=True)
|
||||
)
|
||||
finish_reason = choice.get("finish_reason")
|
||||
generation_info = (
|
||||
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
||||
)
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
||||
length = len(choice["message"]["content"])
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._client_params()
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
params: Dict[str, Any] = self._invocation_params(
|
||||
messages=messages, stop=stop, stream=True, **kwargs
|
||||
)
|
||||
async for stream_resp in self.astream_completion_with_retry(**params):
|
||||
chunk = ChatGenerationChunk(
|
||||
**self._chat_generation_from_qwen_resp(stream_resp, is_chunk=True)
|
||||
)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
||||
|
||||
# Ensure `stop` is a list of strings
|
||||
def _invocation_params(
|
||||
self, messages: List[BaseMessage], stop: Any, **kwargs: Any
|
||||
) -> Dict[str, Any]:
|
||||
params = {**self._default_params, **kwargs}
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
if params.get("stream"):
|
||||
params["incremental_output"] = True
|
||||
|
||||
message_dicts = [convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _client_params(self) -> Dict[str, Any]:
|
||||
"""Get the parameters used for the openai client."""
|
||||
creds: Dict[str, Any] = {
|
||||
"api_key": self.dashscope_api_key,
|
||||
}
|
||||
return {**self._default_params, **creds}
|
||||
# According to the docs, the last message should be a `user` message
|
||||
if message_dicts[-1]["role"] != "user":
|
||||
raise ValueError("Last message should be user message.")
|
||||
# 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 or system_message_indices[0] != 0:
|
||||
raise ValueError("System message can only be the first message.")
|
||||
|
||||
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
||||
generations = []
|
||||
for res in response["output"]["choices"]:
|
||||
message = convert_dict_to_message(res["message"])
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=dict(finish_reason=res.get("finish_reason")),
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
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
|
||||
) -> Dict[str, Any]:
|
||||
choice = resp["output"]["choices"][0]
|
||||
message = convert_dict_to_message(choice["message"], is_chunk=is_chunk)
|
||||
return dict(
|
||||
message=message,
|
||||
generation_info=dict(
|
||||
finish_reason=choice["finish_reason"],
|
||||
request_id=resp["request_id"],
|
||||
token_usage=dict(resp["usage"]),
|
||||
),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _chunk_to_generation(chunk: ChatGenerationChunk) -> ChatGeneration:
|
||||
return ChatGeneration(
|
||||
message=convert_message_chunk_to_message(chunk.message),
|
||||
generation_info=chunk.generation_info,
|
||||
)
|
||||
|
||||
@ -1,11 +1,25 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import functools
|
||||
import logging
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Callable,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Mapping,
|
||||
Optional,
|
||||
)
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.outputs import Generation, LLMResult
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models.llms import BaseLLM
|
||||
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env
|
||||
from requests.exceptions import HTTPError
|
||||
@ -24,7 +38,7 @@ def _create_retry_decorator(llm: Tongyi) -> Callable[[Any], Any]:
|
||||
min_seconds = 1
|
||||
max_seconds = 4
|
||||
# Wait 2^x * 1 second between each retry starting with
|
||||
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
|
||||
# 4 seconds, then up to 10 seconds, then 10 seconds afterward
|
||||
return retry(
|
||||
reraise=True,
|
||||
stop=stop_after_attempt(llm.max_retries),
|
||||
@ -34,6 +48,23 @@ def _create_retry_decorator(llm: Tongyi) -> Callable[[Any], Any]:
|
||||
)
|
||||
|
||||
|
||||
def check_response(resp: Any) -> Any:
|
||||
"""Check the response from the completion call."""
|
||||
if resp.status_code == 200:
|
||||
return resp
|
||||
elif resp.status_code in [400, 401]:
|
||||
raise ValueError(
|
||||
f"status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}"
|
||||
)
|
||||
else:
|
||||
raise HTTPError(
|
||||
f"HTTP error occurred: status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}",
|
||||
response=resp,
|
||||
)
|
||||
|
||||
|
||||
def generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
|
||||
"""Use tenacity to retry the completion call."""
|
||||
retry_decorator = _create_retry_decorator(llm)
|
||||
@ -41,19 +72,7 @@ def generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
|
||||
@retry_decorator
|
||||
def _generate_with_retry(**_kwargs: Any) -> Any:
|
||||
resp = llm.client.call(**_kwargs)
|
||||
if resp.status_code == 200:
|
||||
return resp
|
||||
elif resp.status_code in [400, 401]:
|
||||
raise ValueError(
|
||||
f"status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}"
|
||||
)
|
||||
else:
|
||||
raise HTTPError(
|
||||
f"HTTP error occurred: status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}",
|
||||
response=resp,
|
||||
)
|
||||
return check_response(resp)
|
||||
|
||||
return _generate_with_retry(**kwargs)
|
||||
|
||||
@ -64,28 +83,44 @@ def stream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
|
||||
|
||||
@retry_decorator
|
||||
def _stream_generate_with_retry(**_kwargs: Any) -> Any:
|
||||
stream_resps = []
|
||||
resps = llm.client.call(**_kwargs)
|
||||
for resp in resps:
|
||||
if resp.status_code == 200:
|
||||
stream_resps.append(resp)
|
||||
elif resp.status_code in [400, 401]:
|
||||
raise ValueError(
|
||||
f"status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}"
|
||||
)
|
||||
else:
|
||||
raise HTTPError(
|
||||
f"HTTP error occurred: status_code: {resp.status_code} \n "
|
||||
f"code: {resp.code} \n message: {resp.message}",
|
||||
response=resp,
|
||||
)
|
||||
return stream_resps
|
||||
responses = llm.client.call(**_kwargs)
|
||||
for resp in responses:
|
||||
yield check_response(resp)
|
||||
|
||||
return _stream_generate_with_retry(**kwargs)
|
||||
|
||||
|
||||
class Tongyi(LLM):
|
||||
async def astream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
|
||||
"""Because the dashscope SDK doesn't provide an async API,
|
||||
we wrap `stream_generate_with_retry` with an async generator."""
|
||||
|
||||
class _AioTongyiGenerator:
|
||||
def __init__(self, _llm: Tongyi, **_kwargs: Any):
|
||||
self.generator = stream_generate_with_retry(_llm, **_kwargs)
|
||||
|
||||
def __aiter__(self) -> AsyncIterator[Any]:
|
||||
return self
|
||||
|
||||
async def __anext__(self) -> Any:
|
||||
value = await asyncio.get_running_loop().run_in_executor(
|
||||
None, self._safe_next
|
||||
)
|
||||
if value is not None:
|
||||
return value
|
||||
else:
|
||||
raise StopAsyncIteration
|
||||
|
||||
def _safe_next(self) -> Any:
|
||||
try:
|
||||
return next(self.generator)
|
||||
except StopIteration:
|
||||
return None
|
||||
|
||||
async for chunk in _AioTongyiGenerator(llm, **kwargs):
|
||||
yield chunk
|
||||
|
||||
|
||||
class Tongyi(BaseLLM):
|
||||
"""Tongyi Qwen large language models.
|
||||
|
||||
To use, you should have the ``dashscope`` python package installed, and the
|
||||
@ -96,17 +131,13 @@ class Tongyi(LLM):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.llms import Tongyi
|
||||
Tongyi = tongyi()
|
||||
tongyi = tongyi()
|
||||
"""
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"dashscope_api_key": "DASHSCOPE_API_KEY"}
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
return False
|
||||
|
||||
client: Any #: :meta private:
|
||||
model_name: str = "qwen-plus"
|
||||
|
||||
@ -117,10 +148,7 @@ class Tongyi(LLM):
|
||||
"""Total probability mass of tokens to consider at each step."""
|
||||
|
||||
dashscope_api_key: Optional[str] = None
|
||||
"""Dashscope api key provide by alicloud."""
|
||||
|
||||
n: int = 1
|
||||
"""How many completions to generate for each prompt."""
|
||||
"""Dashscope api key provide by Alibaba Cloud."""
|
||||
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results or not."""
|
||||
@ -128,9 +156,6 @@ class Tongyi(LLM):
|
||||
max_retries: int = 10
|
||||
"""Maximum number of retries to make when generating."""
|
||||
|
||||
prefix_messages: List = Field(default_factory=list)
|
||||
"""Series of messages for Chat input."""
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
@ -139,7 +164,9 @@ class Tongyi(LLM):
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
get_from_dict_or_env(values, "dashscope_api_key", "DASHSCOPE_API_KEY")
|
||||
values["dashscope_api_key"] = get_from_dict_or_env(
|
||||
values, "dashscope_api_key", "DASHSCOPE_API_KEY"
|
||||
)
|
||||
try:
|
||||
import dashscope
|
||||
except ImportError:
|
||||
@ -160,45 +187,18 @@ class Tongyi(LLM):
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling OpenAI API."""
|
||||
"""Get the default parameters for calling Tongyi Qwen API."""
|
||||
normal_params = {
|
||||
"model": self.model_name,
|
||||
"top_p": self.top_p,
|
||||
"api_key": self.dashscope_api_key,
|
||||
}
|
||||
|
||||
return {**normal_params, **self.model_kwargs}
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to Tongyi's generate endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = tongyi("Tell me a joke.")
|
||||
"""
|
||||
params: Dict[str, Any] = {
|
||||
**{"model": self.model_name},
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
completion = generate_with_retry(
|
||||
self,
|
||||
prompt=prompt,
|
||||
**params,
|
||||
)
|
||||
return completion["output"]["text"]
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
return {"model_name": self.model_name, **super()._identifying_params}
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
@ -208,70 +208,136 @@ class Tongyi(LLM):
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
params: Dict[str, Any] = {
|
||||
**{"model": self.model_name},
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
if self.streaming:
|
||||
if len(prompts) > 1:
|
||||
raise ValueError("Cannot stream results with multiple prompts.")
|
||||
params["stream"] = True
|
||||
temp = ""
|
||||
for stream_resp in stream_generate_with_retry(
|
||||
self, prompt=prompts[0], **params
|
||||
):
|
||||
if run_manager:
|
||||
stream_resp_text = stream_resp["output"]["text"]
|
||||
stream_resp_text = stream_resp_text.replace(temp, "")
|
||||
# Ali Cloud's streaming transmission interface, each return content
|
||||
# will contain the output
|
||||
# of the previous round(as of September 20, 2023, future updates to
|
||||
# the Alibaba Cloud API may vary)
|
||||
run_manager.on_llm_new_token(stream_resp_text)
|
||||
# The implementation of streaming transmission primarily relies on
|
||||
# the "on_llm_new_token" method
|
||||
# of the streaming callback.
|
||||
temp = stream_resp["output"]["text"]
|
||||
|
||||
generations.append(
|
||||
[
|
||||
Generation(
|
||||
text=stream_resp["output"]["text"],
|
||||
generation_info=dict(
|
||||
finish_reason=stream_resp["output"]["finish_reason"],
|
||||
),
|
||||
)
|
||||
]
|
||||
)
|
||||
generations.reverse()
|
||||
# In the official implementation of the OpenAI API,
|
||||
# the "generations" parameter passed to LLMResult seems to be a 1*1*1
|
||||
# two-dimensional list
|
||||
# (including in non-streaming mode).
|
||||
# Considering that Alibaba Cloud's streaming transmission
|
||||
# (as of September 20, 2023, future updates to the Alibaba Cloud API may
|
||||
# vary)
|
||||
# includes the output of the previous round in each return,
|
||||
# reversing this "generations" list should suffice
|
||||
# (This is the solution with the least amount of changes to the source code,
|
||||
# while still allowing for convenient modifications in the future,
|
||||
# although it may result in slightly more memory consumption).
|
||||
generation: Optional[GenerationChunk] = None
|
||||
for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
generations.append([self._chunk_to_generation(generation)])
|
||||
else:
|
||||
params: Dict[str, Any] = self._invocation_params(stop=stop, **kwargs)
|
||||
for prompt in prompts:
|
||||
completion = generate_with_retry(
|
||||
self,
|
||||
prompt=prompt,
|
||||
**params,
|
||||
completion = generate_with_retry(self, prompt=prompt, **params)
|
||||
generations.append(
|
||||
[Generation(**self._generation_from_qwen_resp(completion))]
|
||||
)
|
||||
return LLMResult(
|
||||
generations=generations,
|
||||
llm_output={
|
||||
"model_name": self.model_name,
|
||||
},
|
||||
)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
if self.streaming:
|
||||
if len(prompts) > 1:
|
||||
raise ValueError("Cannot stream results with multiple prompts.")
|
||||
generation: Optional[GenerationChunk] = None
|
||||
async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
generations.append([self._chunk_to_generation(generation)])
|
||||
else:
|
||||
params: Dict[str, Any] = self._invocation_params(stop=stop, **kwargs)
|
||||
for prompt in prompts:
|
||||
completion = await asyncio.get_running_loop().run_in_executor(
|
||||
None,
|
||||
functools.partial(
|
||||
generate_with_retry, **{"llm": self, "prompt": prompt, **params}
|
||||
),
|
||||
)
|
||||
generations.append(
|
||||
[
|
||||
Generation(
|
||||
text=completion["output"]["text"],
|
||||
generation_info=dict(
|
||||
finish_reason=completion["output"]["finish_reason"],
|
||||
),
|
||||
)
|
||||
]
|
||||
[Generation(**self._generation_from_qwen_resp(completion))]
|
||||
)
|
||||
return LLMResult(generations=generations)
|
||||
return LLMResult(
|
||||
generations=generations,
|
||||
llm_output={
|
||||
"model_name": self.model_name,
|
||||
},
|
||||
)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
params: Dict[str, Any] = self._invocation_params(
|
||||
stop=stop, stream=True, **kwargs
|
||||
)
|
||||
for stream_resp in stream_generate_with_retry(self, prompt=prompt, **params):
|
||||
chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
params: Dict[str, Any] = self._invocation_params(
|
||||
stop=stop, stream=True, **kwargs
|
||||
)
|
||||
async for stream_resp in astream_generate_with_retry(
|
||||
self, prompt=prompt, **params
|
||||
):
|
||||
chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
def _invocation_params(self, stop: Any, **kwargs: Any) -> Dict[str, Any]:
|
||||
params = {
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
if stop is not None:
|
||||
params["stop"] = stop
|
||||
if params.get("stream"):
|
||||
params["incremental_output"] = True
|
||||
return params
|
||||
|
||||
@staticmethod
|
||||
def _generation_from_qwen_resp(resp: Any) -> Dict[str, Any]:
|
||||
return dict(
|
||||
text=resp["output"]["text"],
|
||||
generation_info=dict(
|
||||
finish_reason=resp["output"]["finish_reason"],
|
||||
request_id=resp["request_id"],
|
||||
token_usage=dict(resp["usage"]),
|
||||
),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _chunk_to_generation(chunk: GenerationChunk) -> Generation:
|
||||
return Generation(
|
||||
text=chunk.text,
|
||||
generation_info=chunk.generation_info,
|
||||
)
|
||||
|
||||
@ -26,6 +26,7 @@ EXPECTED_ALL = [
|
||||
"ChatKonko",
|
||||
"PaiEasChatEndpoint",
|
||||
"QianfanChatEndpoint",
|
||||
"ChatTongyi",
|
||||
"ChatFireworks",
|
||||
"ChatYandexGPT",
|
||||
"ChatBaichuan",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user