mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
4b53440e70
- **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>
344 lines
11 KiB
Python
344 lines
11 KiB
Python
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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)
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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.llms import BaseLLM
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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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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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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logger = logging.getLogger(__name__)
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def _create_retry_decorator(llm: Tongyi) -> 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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def check_response(resp: Any) -> Any:
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"""Check the response from the completion call."""
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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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def generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _generate_with_retry(**_kwargs: Any) -> Any:
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resp = llm.client.call(**_kwargs)
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return check_response(resp)
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return _generate_with_retry(**kwargs)
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def stream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _stream_generate_with_retry(**_kwargs: Any) -> Any:
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responses = llm.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_generate_with_retry(**kwargs)
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async def astream_generate_with_retry(llm: Tongyi, **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, _llm: Tongyi, **_kwargs: Any):
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self.generator = stream_generate_with_retry(_llm, **_kwargs)
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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(llm, **kwargs):
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yield chunk
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class Tongyi(BaseLLM):
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"""Tongyi Qwen large language models.
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To use, you should have the ``dashscope`` python package installed, and the
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environment variable ``DASHSCOPE_API_KEY`` set 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.llms import Tongyi
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tongyi = tongyi()
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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 = "qwen-plus"
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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 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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@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"] = 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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raise ImportError(
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"Could not import dashscope python package. "
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"Please install it with `pip install dashscope`."
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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 Tongyi Qwen API."""
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normal_params = {
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"model": self.model_name,
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"top_p": self.top_p,
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"api_key": self.dashscope_api_key,
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}
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return {**normal_params, **self.model_kwargs}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {"model_name": self.model_name, **super()._identifying_params}
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def _generate(
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self,
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prompts: List[str],
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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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) -> LLMResult:
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generations = []
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if self.streaming:
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if len(prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
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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(stop=stop, **kwargs)
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for prompt in prompts:
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completion = generate_with_retry(self, prompt=prompt, **params)
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generations.append(
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[Generation(**self._generation_from_qwen_resp(completion))]
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)
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return LLMResult(
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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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prompts: List[str],
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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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) -> LLMResult:
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generations = []
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if self.streaming:
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if len(prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
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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(stop=stop, **kwargs)
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for prompt in prompts:
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completion = await asyncio.get_running_loop().run_in_executor(
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None,
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functools.partial(
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generate_with_retry, **{"llm": self, "prompt": prompt, **params}
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),
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)
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generations.append(
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[Generation(**self._generation_from_qwen_resp(completion))]
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)
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return LLMResult(
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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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prompt: str,
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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[GenerationChunk]:
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params: Dict[str, Any] = self._invocation_params(
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stop=stop, stream=True, **kwargs
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)
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for stream_resp in stream_generate_with_retry(self, prompt=prompt, **params):
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chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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)
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async def _astream(
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self,
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prompt: str,
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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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) -> AsyncIterator[GenerationChunk]:
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params: Dict[str, Any] = self._invocation_params(
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stop=stop, stream=True, **kwargs
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)
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async for stream_resp in astream_generate_with_retry(
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self, prompt=prompt, **params
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):
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chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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)
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def _invocation_params(self, stop: Any, **kwargs: Any) -> Dict[str, Any]:
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params = {
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**self._default_params,
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**kwargs,
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}
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if stop is not None:
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params["stop"] = stop
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if params.get("stream"):
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params["incremental_output"] = True
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return params
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@staticmethod
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def _generation_from_qwen_resp(resp: Any) -> Dict[str, Any]:
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return dict(
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text=resp["output"]["text"],
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generation_info=dict(
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finish_reason=resp["output"]["finish_reason"],
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request_id=resp["request_id"],
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token_usage=dict(resp["usage"]),
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),
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)
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@staticmethod
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def _chunk_to_generation(chunk: GenerationChunk) -> Generation:
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return Generation(
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text=chunk.text,
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generation_info=chunk.generation_info,
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)
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