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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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240
libs/community/langchain_community/llms/pai_eas_endpoint.py
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240
libs/community/langchain_community/llms/pai_eas_endpoint.py
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import json
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import logging
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from typing import Any, Dict, Iterator, List, Mapping, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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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 root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.llms.utils import enforce_stop_tokens
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logger = logging.getLogger(__name__)
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class PaiEasEndpoint(LLM):
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"""Langchain LLM class to help to access eass llm service.
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To use this endpoint, must have a deployed eas chat llm service on PAI AliCloud.
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One can set the environment variable ``eas_service_url`` and ``eas_service_token``.
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The environment variables can set with your eas service url and service token.
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Example:
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.. code-block:: python
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from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint
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eas_chat_endpoint = PaiEasChatEndpoint(
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eas_service_url="your_service_url",
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eas_service_token="your_service_token"
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)
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"""
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"""PAI-EAS Service URL"""
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eas_service_url: str
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"""PAI-EAS Service TOKEN"""
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eas_service_token: str
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"""PAI-EAS Service Infer Params"""
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max_new_tokens: Optional[int] = 512
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temperature: Optional[float] = 0.95
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top_p: Optional[float] = 0.1
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top_k: Optional[int] = 0
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stop_sequences: Optional[List[str]] = None
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"""Enable stream chat mode."""
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streaming: bool = False
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"""Key/value arguments to pass to the model. Reserved for future use"""
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model_kwargs: Optional[dict] = None
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version: Optional[str] = "2.0"
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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["eas_service_url"] = get_from_dict_or_env(
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values, "eas_service_url", "EAS_SERVICE_URL"
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)
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values["eas_service_token"] = get_from_dict_or_env(
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values, "eas_service_token", "EAS_SERVICE_TOKEN"
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)
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return values
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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 "pai_eas_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 Cohere API."""
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return {
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"max_new_tokens": self.max_new_tokens,
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"temperature": self.temperature,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"stop_sequences": [],
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}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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_model_kwargs = self.model_kwargs or {}
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return {
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"eas_service_url": self.eas_service_url,
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"eas_service_token": self.eas_service_token,
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**_model_kwargs,
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}
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def _invocation_params(
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self, stop_sequences: Optional[List[str]], **kwargs: Any
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) -> dict:
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params = self._default_params
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if self.stop_sequences is not None and stop_sequences is not None:
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raise ValueError("`stop` found in both the input and default params.")
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elif self.stop_sequences is not None:
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params["stop"] = self.stop_sequences
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else:
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params["stop"] = stop_sequences
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if self.model_kwargs:
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params.update(self.model_kwargs)
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return {**params, **kwargs}
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@staticmethod
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def _process_response(
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response: Any, stop: Optional[List[str]], version: Optional[str]
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) -> str:
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if version == "1.0":
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text = response
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else:
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text = response["response"]
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if stop:
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text = enforce_stop_tokens(text, stop)
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return "".join(text)
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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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params = self._invocation_params(stop, **kwargs)
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prompt = prompt.strip()
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response = None
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try:
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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, **params):
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completion += chunk.text
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return completion
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else:
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response = self._call_eas(prompt, params)
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_stop = params.get("stop")
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return self._process_response(response, _stop, self.version)
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except Exception as error:
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raise ValueError(f"Error raised by the service: {error}")
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def _call_eas(self, prompt: str = "", params: Dict = {}) -> Any:
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"""Generate text from the eas service."""
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"{self.eas_service_token}",
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}
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if self.version == "1.0":
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body = {
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"input_ids": f"{prompt}",
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}
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else:
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body = {
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"prompt": f"{prompt}",
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}
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# add params to body
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for key, value in params.items():
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body[key] = value
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# make request
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response = requests.post(self.eas_service_url, headers=headers, json=body)
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if response.status_code != 200:
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raise Exception(
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f"Request failed with status code {response.status_code}"
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f" and message {response.text}"
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)
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try:
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return json.loads(response.text)
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except Exception as e:
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if isinstance(e, json.decoder.JSONDecodeError):
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return response.text
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raise e
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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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invocation_params = self._invocation_params(stop, **kwargs)
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headers = {
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"User-Agent": "Test Client",
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"Authorization": f"{self.eas_service_token}",
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}
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if self.version == "1.0":
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pload = {"input_ids": prompt, **invocation_params}
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response = requests.post(
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self.eas_service_url, headers=headers, json=pload, stream=True
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)
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res = GenerationChunk(text=response.text)
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if run_manager:
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run_manager.on_llm_new_token(res.text)
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# yield text, if any
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yield res
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else:
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pload = {"prompt": prompt, "use_stream_chat": "True", **invocation_params}
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response = requests.post(
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self.eas_service_url, headers=headers, json=pload, stream=True
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)
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for chunk in response.iter_lines(
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chunk_size=8192, decode_unicode=False, delimiter=b"\0"
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):
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if chunk:
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data = json.loads(chunk.decode("utf-8"))
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output = data["response"]
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# identify stop sequence in generated text, if any
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stop_seq_found: Optional[str] = None
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for stop_seq in invocation_params["stop"]:
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if stop_seq in output:
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stop_seq_found = stop_seq
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# identify text to yield
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text: Optional[str] = None
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if stop_seq_found:
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text = output[: output.index(stop_seq_found)]
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else:
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text = output
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# yield text, if any
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if text:
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res = GenerationChunk(text=text)
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yield res
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if run_manager:
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run_manager.on_llm_new_token(res.text)
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# break if stop sequence found
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if stop_seq_found:
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break
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