feat: ✨ remove api key from advanced and update HuggingFace components (#3397)
* feat: ✨ remove api key from advanced * refactor: 🎨 improve naming and descriptions * fix: 🐛 Fix hf api component * [autofix.ci] apply automated fixes * feat: 🎨 Add default values * [autofix.ci] apply automated fixes * fix: 🐛 fix hf api component * [autofix.ci] apply automated fixes * feat: 🔥 remove hugging face embeddings (local) * refactor: Removed HuggingFaceEmbeddingsComponent from __init__.py --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
This commit is contained in:
Rodrigo Nader
GitHub
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commit
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4 changed files with 25 additions and 64 deletions
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@ -1,36 +0,0 @@
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from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Embeddings
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from langflow.io import BoolInput, DictInput, MessageTextInput, Output
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class HuggingFaceEmbeddingsComponent(LCModelComponent):
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display_name = "Hugging Face Embeddings"
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description = "Generate embeddings using HuggingFace models."
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documentation = (
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"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
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)
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icon = "HuggingFace"
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name = "HuggingFaceEmbeddings"
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inputs = [
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MessageTextInput(name="cache_folder", display_name="Cache Folder", advanced=True),
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DictInput(name="encode_kwargs", display_name="Encode Kwargs", advanced=True),
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DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
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MessageTextInput(name="model_name", display_name="Model Name", value="sentence-transformers/all-mpnet-base-v2"),
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BoolInput(name="multi_process", display_name="Multi Process", advanced=True),
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]
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outputs = [
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Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
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]
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def build_embeddings(self) -> Embeddings:
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return HuggingFaceEmbeddings(
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cache_folder=self.cache_folder,
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encode_kwargs=self.encode_kwargs,
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model_kwargs=self.model_kwargs,
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model_name=self.model_name,
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multi_process=self.multi_process,
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)
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@ -7,14 +7,14 @@ from langflow.io import MessageTextInput, Output, SecretStrInput
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class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
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display_name = "Hugging Face API Embeddings"
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display_name = "HuggingFace Embeddings"
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description = "Generate embeddings using Hugging Face Inference API models."
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documentation = "https://github.com/huggingface/text-embeddings-inference"
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icon = "HuggingFace"
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name = "HuggingFaceInferenceAPIEmbeddings"
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inputs = [
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SecretStrInput(name="api_key", display_name="API Key", advanced=True),
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SecretStrInput(name="api_key", display_name="API Key"),
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MessageTextInput(name="api_url", display_name="API URL", advanced=True, value="http://localhost:8080"),
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MessageTextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
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]
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@ -3,7 +3,6 @@ from .AmazonBedrockEmbeddings import AmazonBedrockEmbeddingsComponent
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from .AstraVectorize import AstraVectorizeComponent
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from .AzureOpenAIEmbeddings import AzureOpenAIEmbeddingsComponent
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from .CohereEmbeddings import CohereEmbeddingsComponent
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from .HuggingFaceEmbeddings import HuggingFaceEmbeddingsComponent
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from .HuggingFaceInferenceAPIEmbeddings import HuggingFaceInferenceAPIEmbeddingsComponent
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from .OllamaEmbeddings import OllamaEmbeddingsComponent
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from .OpenAIEmbeddings import OpenAIEmbeddingsComponent
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@ -15,7 +14,6 @@ __all__ = [
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"AstraVectorizeComponent",
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"AzureOpenAIEmbeddingsComponent",
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"CohereEmbeddingsComponent",
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"HuggingFaceEmbeddingsComponent",
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"HuggingFaceInferenceAPIEmbeddingsComponent",
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"OllamaEmbeddingsComponent",
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"OpenAIEmbeddingsComponent",
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@ -1,6 +1,4 @@
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from tenacity import retry, stop_after_attempt, wait_fixed
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from langchain_community.chat_models.huggingface import ChatHuggingFace
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from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
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from langflow.base.models.model import LCModelComponent
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@ -9,53 +7,54 @@ from langflow.io import DictInput, DropdownInput, SecretStrInput, StrInput, IntI
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class HuggingFaceEndpointsComponent(LCModelComponent):
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display_name: str = "Hugging Face API"
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display_name: str = "HuggingFace"
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description: str = "Generate text using Hugging Face Inference APIs."
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icon = "HuggingFace"
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name = "HuggingFaceModel"
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inputs = LCModelComponent._base_inputs + [
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SecretStrInput(name="endpoint_url", display_name="Endpoint URL", password=True),
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StrInput(
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name="model_id",
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display_name="Model Id",
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info="Id field of endpoint_url response.",
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display_name="Model ID",
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value="openai-community/gpt2",
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),
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DropdownInput(
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name="task",
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display_name="Task",
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options=["text2text-generation", "text-generation", "summarization"],
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options=["text2text-generation", "text-generation", "summarization", "translation"],
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value="text-generation",
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),
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SecretStrInput(name="huggingfacehub_api_token", display_name="API token", password=True),
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SecretStrInput(name="huggingfacehub_api_token", display_name="API Token", password=True),
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DictInput(name="model_kwargs", display_name="Model Keyword Arguments", advanced=True),
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IntInput(name="retry_attempts", display_name="Retry Attempts", value=1),
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IntInput(name="retry_attempts", display_name="Retry Attempts", value=1, advanced=True),
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]
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def create_huggingface_endpoint(self, endpoint_url, task, huggingfacehub_api_token, model_kwargs):
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@retry(stop=stop_after_attempt(self.retry_attempts), wait=wait_fixed(2))
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def create_huggingface_endpoint(
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self, model_id: str, task: str, huggingfacehub_api_token: str, model_kwargs: dict
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) -> HuggingFaceEndpoint:
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retry_attempts = self.retry_attempts # Access the retry attempts input
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endpoint_url = f"https://api-inference.huggingface.co/models/{model_id}"
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@retry(stop=stop_after_attempt(retry_attempts), wait=wait_fixed(2))
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def _attempt_create():
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try:
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return HuggingFaceEndpoint( # type: ignore
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endpoint_url=endpoint_url,
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task=task,
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huggingfacehub_api_token=huggingfacehub_api_token,
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model_kwargs=model_kwargs,
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)
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except Exception as e:
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raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
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return HuggingFaceEndpoint(
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endpoint_url=endpoint_url,
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task=task,
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huggingfacehub_api_token=huggingfacehub_api_token,
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model_kwargs=model_kwargs,
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)
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return _attempt_create()
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def build_model(self) -> LanguageModel: # type: ignore[type-var]
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endpoint_url = self.endpoint_url
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model_id = self.model_id
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task = self.task
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huggingfacehub_api_token = self.huggingfacehub_api_token
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model_kwargs = self.model_kwargs or {}
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try:
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llm = self.create_huggingface_endpoint(endpoint_url, task, huggingfacehub_api_token, model_kwargs)
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llm = self.create_huggingface_endpoint(model_id, task, huggingfacehub_api_token, model_kwargs)
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except Exception as e:
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raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
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output = ChatHuggingFace(llm=llm, model_id=self.model_id)
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return output # type: ignore
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return llm
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