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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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156
libs/community/langchain_community/llms/huggingface_endpoint.py
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156
libs/community/langchain_community/llms/huggingface_endpoint.py
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from typing import Any, Dict, 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.pydantic_v1 import Extra, 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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VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
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class HuggingFaceEndpoint(LLM):
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"""HuggingFace Endpoint models.
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To use, you should have the ``huggingface_hub`` python package installed, and the
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environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
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it as a named parameter to the constructor.
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Only supports `text-generation` and `text2text-generation` for now.
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Example:
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.. code-block:: python
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from langchain_community.llms import HuggingFaceEndpoint
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endpoint_url = (
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"https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
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)
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hf = HuggingFaceEndpoint(
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endpoint_url=endpoint_url,
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huggingfacehub_api_token="my-api-key"
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)
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"""
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endpoint_url: str = ""
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"""Endpoint URL to use."""
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task: Optional[str] = None
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"""Task to call the model with.
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Should be a task that returns `generated_text` or `summary_text`."""
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model_kwargs: Optional[dict] = None
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"""Keyword arguments to pass to the model."""
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huggingfacehub_api_token: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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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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huggingfacehub_api_token = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
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)
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try:
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from huggingface_hub.hf_api import HfApi
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try:
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HfApi(
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endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
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token=huggingfacehub_api_token,
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).whoami()
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except Exception as e:
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raise ValueError(
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"Could not authenticate with huggingface_hub. "
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"Please check your API token."
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) from e
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except ImportError:
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raise ImportError(
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"Could not import huggingface_hub python package. "
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"Please install it with `pip install huggingface_hub`."
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)
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values["huggingfacehub_api_token"] = huggingfacehub_api_token
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return values
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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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**{"endpoint_url": self.endpoint_url, "task": self.task},
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**{"model_kwargs": _model_kwargs},
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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 "huggingface_endpoint"
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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 HuggingFace Hub's inference endpoint.
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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 = hf("Tell me a joke.")
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"""
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_model_kwargs = self.model_kwargs or {}
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# payload samples
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params = {**_model_kwargs, **kwargs}
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parameter_payload = {"inputs": prompt, "parameters": params}
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# HTTP headers for authorization
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headers = {
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"Authorization": f"Bearer {self.huggingfacehub_api_token}",
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"Content-Type": "application/json",
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}
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# send request
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try:
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response = requests.post(
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self.endpoint_url, headers=headers, json=parameter_payload
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)
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except requests.exceptions.RequestException as e: # This is the correct syntax
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raise ValueError(f"Error raised by inference endpoint: {e}")
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generated_text = response.json()
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if "error" in generated_text:
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raise ValueError(
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f"Error raised by inference API: {generated_text['error']}"
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)
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if self.task == "text-generation":
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text = generated_text[0]["generated_text"]
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# Remove prompt if included in generated text.
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if text.startswith(prompt):
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text = text[len(prompt) :]
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elif self.task == "text2text-generation":
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text = generated_text[0]["generated_text"]
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elif self.task == "summarization":
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text = generated_text[0]["summary_text"]
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else:
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raise ValueError(
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f"Got invalid task {self.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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if stop is not None:
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# This is a bit hacky, but I can't figure out a better way to enforce
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# stop tokens when making calls to huggingface_hub.
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text = enforce_stop_tokens(text, stop)
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return text
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