mirror of
https://github.com/hwchase17/langchain.git
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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
This commit is contained in:
Bagatur
GitHub
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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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AsyncIterator,
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Dict,
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Iterator,
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List,
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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 LLM
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from langchain_core.outputs import GenerationChunk
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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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logger = logging.getLogger(__name__)
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class QianfanLLMEndpoint(LLM):
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"""Baidu Qianfan hosted open source or customized models.
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To use, you should have the ``qianfan`` python package installed, and
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the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with
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your API key and Secret Key.
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ak, sk are required parameters which you could get from
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https://cloud.baidu.com/product/wenxinworkshop
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Example:
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.. code-block:: python
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from langchain_community.llms import QianfanLLMEndpoint
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qianfan_model = QianfanLLMEndpoint(model="ERNIE-Bot",
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endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
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"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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client: Any
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qianfan_ak: Optional[str] = None
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qianfan_sk: Optional[str] = None
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streaming: Optional[bool] = False
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"""Whether to stream the results or not."""
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model: str = "ERNIE-Bot-turbo"
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"""Model name.
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you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
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preset models are mapping to an endpoint.
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`model` will be ignored if `endpoint` is set
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"""
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endpoint: Optional[str] = None
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"""Endpoint of the Qianfan LLM, required if custom model used."""
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request_timeout: Optional[int] = 60
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"""request timeout for chat http requests"""
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top_p: Optional[float] = 0.8
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temperature: Optional[float] = 0.95
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penalty_score: Optional[float] = 1
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"""Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
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In the case of other model, passing these params will not affect the result.
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"""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values["qianfan_ak"] = get_from_dict_or_env(
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values,
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"qianfan_ak",
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"QIANFAN_AK",
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)
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values["qianfan_sk"] = get_from_dict_or_env(
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values,
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"qianfan_sk",
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"QIANFAN_SK",
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)
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params = {
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"ak": values["qianfan_ak"],
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"sk": values["qianfan_sk"],
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"model": values["model"],
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}
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if values["endpoint"] is not None and values["endpoint"] != "":
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params["endpoint"] = values["endpoint"]
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try:
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import qianfan
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values["client"] = qianfan.Completion(**params)
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except ImportError:
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raise ImportError(
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"qianfan package not found, please install it with "
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"`pip install qianfan`"
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)
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return values
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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return {
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**{"endpoint": self.endpoint, "model": self.model},
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**super()._identifying_params,
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}
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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 "baidu-qianfan-endpoint"
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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 Qianfan API."""
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normal_params = {
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"model": self.model,
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"endpoint": self.endpoint,
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"stream": self.streaming,
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"request_timeout": self.request_timeout,
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"top_p": self.top_p,
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"temperature": self.temperature,
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"penalty_score": self.penalty_score,
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}
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return {**normal_params, **self.model_kwargs}
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def _convert_prompt_msg_params(
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self,
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prompt: str,
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**kwargs: Any,
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) -> dict:
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if "streaming" in kwargs:
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kwargs["stream"] = kwargs.pop("streaming")
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return {
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**{"prompt": prompt, "model": self.model},
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**self._default_params,
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**kwargs,
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}
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def _call(
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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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) -> str:
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"""Call out to an qianfan models endpoint for each generation with a prompt.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = qianfan_model("Tell me a joke.")
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"""
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if self.streaming:
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completion = ""
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for chunk in self._stream(prompt, stop, run_manager, **kwargs):
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completion += chunk.text
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return completion
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params = self._convert_prompt_msg_params(prompt, **kwargs)
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response_payload = self.client.do(**params)
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return response_payload["result"]
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async def _acall(
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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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) -> str:
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if self.streaming:
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completion = ""
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async for chunk in self._astream(prompt, stop, run_manager, **kwargs):
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completion += chunk.text
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return completion
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params = self._convert_prompt_msg_params(prompt, **kwargs)
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response_payload = await self.client.ado(**params)
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return response_payload["result"]
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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 = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
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for res in self.client.do(**params):
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if res:
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chunk = GenerationChunk(text=res["result"])
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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)
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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 = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
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async for res in await self.client.ado(**params):
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if res:
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chunk = GenerationChunk(text=res["result"])
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(chunk.text)
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