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partners: (langchain-huggingface) Chat Models - Integrate Hugging Face Inference Providers and remove deprecated code (#30733)
Hi there, I'm Célina from 🤗, This PR introduces support for Hugging Face's serverless Inference Providers (documentation [here](https://huggingface.co/docs/inference-providers/index)), allowing users to specify different providers for chat completion and text generation tasks. This PR also removes the usage of `InferenceClient.post()` method in `HuggingFaceEndpoint`, in favor of the task-specific `text_generation` method. `InferenceClient.post()` is deprecated and will be removed in `huggingface_hub v0.31.0`. --- ## Changes made - bumped the minimum required version of the `huggingface-hub` package to ensure compatibility with the latest API usage. - added a `provider` field to `HuggingFaceEndpoint`, enabling users to select the inference provider (e.g., 'cerebras', 'together', 'fireworks-ai'). Defaults to `hf-inference` (HF Inference API). - replaced the deprecated `InferenceClient.post()` call in `HuggingFaceEndpoint` with the task-specific `text_generation` method for future-proofing, `post()` will be removed in huggingface-hub v0.31.0. - updated the `ChatHuggingFace` component: - added async and streaming support. - added support for tool calling. - exposed underlying chat completion parameters for more granular control. - Added integration tests for `ChatHuggingFace` and updated the corresponding unit tests. ✅ All changes are backward compatible. --------- Co-authored-by: ccurme <chester.curme@gmail.com>
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célina
GitHub
@@ -1,5 +1,4 @@
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import inspect
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import json # type: ignore[import-not-found]
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import logging
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import os
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from collections.abc import AsyncIterator, Iterator, Mapping
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@@ -27,7 +26,7 @@ VALID_TASKS = (
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class HuggingFaceEndpoint(LLM):
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"""
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HuggingFace Endpoint.
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Hugging Face Endpoint. This works with any model that supports text generation (i.e. text completion) task.
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To use this class, you should have installed the ``huggingface_hub`` package, and
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the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token,
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@@ -67,6 +66,15 @@ class HuggingFaceEndpoint(LLM):
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)
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print(llm.invoke("What is Deep Learning?"))
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# Basic Example (no streaming) with Mistral-Nemo-Base-2407 model using a third-party provider (Novita).
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llm = HuggingFaceEndpoint(
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repo_id="mistralai/Mistral-Nemo-Base-2407",
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provider="novita",
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max_new_tokens=100,
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do_sample=False,
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huggingfacehub_api_token="my-api-key"
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)
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print(llm.invoke("What is Deep Learning?"))
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""" # noqa: E501
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endpoint_url: Optional[str] = None
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@@ -74,6 +82,11 @@ class HuggingFaceEndpoint(LLM):
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should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
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repo_id: Optional[str] = None
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"""Repo to use. If endpoint_url is not specified then this needs to given"""
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provider: Optional[str] = None
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"""Name of the provider to use for inference with the model specified in `repo_id`.
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e.g. "cerebras". if not specified, Defaults to "auto" i.e. the first of the
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providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
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available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
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huggingfacehub_api_token: Optional[str] = Field(
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default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
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)
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@@ -120,8 +133,7 @@ class HuggingFaceEndpoint(LLM):
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client: Any = None #: :meta private:
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async_client: Any = None #: :meta private:
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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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"""Task to call the model with. Should be a task that returns `generated_text`."""
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model_config = ConfigDict(
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extra="forbid",
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@@ -190,36 +202,22 @@ class HuggingFaceEndpoint(LLM):
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that package is installed and that the API token is valid."""
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try:
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from huggingface_hub import login # type: ignore[import]
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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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huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
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"HF_TOKEN"
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)
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if huggingfacehub_api_token is not None:
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try:
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login(token=huggingfacehub_api_token)
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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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from huggingface_hub import AsyncInferenceClient, InferenceClient
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from huggingface_hub import ( # type: ignore[import]
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AsyncInferenceClient, # type: ignore[import]
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InferenceClient, # type: ignore[import]
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)
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# Instantiate clients with supported kwargs
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sync_supported_kwargs = set(inspect.signature(InferenceClient).parameters)
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self.client = InferenceClient(
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model=self.model,
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timeout=self.timeout,
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token=huggingfacehub_api_token,
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api_key=huggingfacehub_api_token,
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provider=self.provider, # type: ignore[arg-type]
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**{
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key: value
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for key, value in self.server_kwargs.items()
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@@ -231,14 +229,14 @@ class HuggingFaceEndpoint(LLM):
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self.async_client = AsyncInferenceClient(
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model=self.model,
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timeout=self.timeout,
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token=huggingfacehub_api_token,
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api_key=huggingfacehub_api_token,
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provider=self.provider, # type: ignore[arg-type]
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**{
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key: value
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for key, value in self.server_kwargs.items()
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if key in async_supported_kwargs
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},
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)
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ignored_kwargs = (
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set(self.server_kwargs.keys())
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- sync_supported_kwargs
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@@ -264,7 +262,7 @@ class HuggingFaceEndpoint(LLM):
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"repetition_penalty": self.repetition_penalty,
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"return_full_text": self.return_full_text,
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"truncate": self.truncate,
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"stop_sequences": self.stop_sequences,
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"stop": self.stop_sequences,
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"seed": self.seed,
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"do_sample": self.do_sample,
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"watermark": self.watermark,
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@@ -276,7 +274,11 @@ class HuggingFaceEndpoint(LLM):
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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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**{
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"endpoint_url": self.endpoint_url,
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"task": self.task,
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"provider": self.provider,
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},
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**{"model_kwargs": _model_kwargs},
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}
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@@ -289,7 +291,7 @@ class HuggingFaceEndpoint(LLM):
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self, runtime_stop: Optional[list[str]], **kwargs: Any
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) -> dict[str, Any]:
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params = {**self._default_params, **kwargs}
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params["stop_sequences"] = params["stop_sequences"] + (runtime_stop or [])
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params["stop"] = params["stop"] + (runtime_stop or [])
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return params
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def _call(
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@@ -307,19 +309,15 @@ class HuggingFaceEndpoint(LLM):
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completion += chunk.text
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return completion
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else:
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invocation_params["stop"] = invocation_params[
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"stop_sequences"
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] # porting 'stop_sequences' into the 'stop' argument
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response = self.client.post(
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json={"inputs": prompt, "parameters": invocation_params},
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stream=False,
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task=self.task,
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response_text = self.client.text_generation(
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prompt=prompt,
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model=self.model,
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**invocation_params,
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)
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response_text = json.loads(response.decode())[0]["generated_text"]
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# Maybe the generation has stopped at one of the stop sequences:
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# then we remove this stop sequence from the end of the generated text
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for stop_seq in invocation_params["stop_sequences"]:
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for stop_seq in invocation_params["stop"]:
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if response_text[-len(stop_seq) :] == stop_seq:
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response_text = response_text[: -len(stop_seq)]
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return response_text
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@@ -340,17 +338,16 @@ class HuggingFaceEndpoint(LLM):
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completion += chunk.text
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return completion
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else:
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invocation_params["stop"] = invocation_params["stop_sequences"]
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response = await self.async_client.post(
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json={"inputs": prompt, "parameters": invocation_params},
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response_text = await self.async_client.text_generation(
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prompt=prompt,
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**invocation_params,
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model=self.model,
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stream=False,
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task=self.task,
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)
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response_text = json.loads(response.decode())[0]["generated_text"]
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# Maybe the generation has stopped at one of the stop sequences:
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# then remove this stop sequence from the end of the generated text
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for stop_seq in invocation_params["stop_sequences"]:
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for stop_seq in invocation_params["stop"]:
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if response_text[-len(stop_seq) :] == stop_seq:
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response_text = response_text[: -len(stop_seq)]
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return response_text
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@@ -369,7 +366,7 @@ class HuggingFaceEndpoint(LLM):
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):
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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_sequences"]:
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for stop_seq in invocation_params["stop"]:
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if stop_seq in response:
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stop_seq_found = stop_seq
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@@ -405,7 +402,7 @@ class HuggingFaceEndpoint(LLM):
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):
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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_sequences"]:
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for stop_seq in invocation_params["stop"]:
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if stop_seq in response:
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stop_seq_found = stop_seq
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