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huggingface: init package (#21097)
First Pr for the langchain_huggingface partner Package - Moved some of the hugging face related class from `community` to the new `partner package` Still needed : - Documentation - Tests - Support for the new apply_chat_template in `ChatHuggingFace` - Confirm choice of class to support for embeddings witht he sentence-transformer team. cc : @efriis --------- Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Jofthomas
GitHub
17
libs/partners/huggingface/langchain_huggingface/__init__.py
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17
libs/partners/huggingface/langchain_huggingface/__init__.py
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@@ -0,0 +1,17 @@
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from langchain_huggingface.chat_models import ChatHuggingFace
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from langchain_huggingface.embeddings import (
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HuggingFaceEmbeddings,
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HuggingFaceEndpointEmbeddings,
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)
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from langchain_huggingface.llms import (
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HuggingFaceEndpoint,
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HuggingFacePipeline,
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)
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__all__ = [
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"ChatHuggingFace",
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"HuggingFaceEndpointEmbeddings",
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"HuggingFaceEmbeddings",
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"HuggingFaceEndpoint",
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"HuggingFacePipeline",
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]
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@@ -0,0 +1,15 @@
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from langchain_huggingface.chat_models.huggingface import (
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TGI_MESSAGE,
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TGI_RESPONSE,
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ChatHuggingFace,
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_convert_message_to_chat_message,
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_convert_TGI_message_to_LC_message,
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)
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__all__ = [
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"ChatHuggingFace",
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"_convert_message_to_chat_message",
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"_convert_TGI_message_to_LC_message",
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"TGI_MESSAGE",
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"TGI_RESPONSE",
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]
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@@ -0,0 +1,350 @@
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"""Hugging Face Chat Wrapper."""
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from dataclasses import dataclass
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Optional,
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Sequence,
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Type,
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Union,
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cast,
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)
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from langchain_community.llms.huggingface_hub import HuggingFaceHub
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from langchain_community.llms.huggingface_text_gen_inference import (
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HuggingFaceTextGenInference,
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)
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from langchain_core.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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ToolMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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from langchain_core.runnables import Runnable
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from langchain_core.tools import BaseTool
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
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DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful, and honest assistant."""
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@dataclass
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class TGI_RESPONSE:
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choices: List[Any]
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usage: Dict
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@dataclass
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class TGI_MESSAGE:
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role: str
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content: str
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tool_calls: List[Dict]
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def _convert_message_to_chat_message(
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message: BaseMessage,
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) -> Dict:
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if isinstance(message, ChatMessage):
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return dict(role=message.role, content=message.content)
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elif isinstance(message, HumanMessage):
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return dict(role="user", content=message.content)
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elif isinstance(message, AIMessage):
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if "tool_calls" in message.additional_kwargs:
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tool_calls = [
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{
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"function": {
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"],
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}
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}
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for tc in message.additional_kwargs["tool_calls"]
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]
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else:
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tool_calls = None
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return {
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"role": "assistant",
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"content": message.content,
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"tool_calls": tool_calls,
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}
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elif isinstance(message, SystemMessage):
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return dict(role="system", content=message.content)
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elif isinstance(message, ToolMessage):
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return {
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"role": "tool",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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def _convert_TGI_message_to_LC_message(
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_message: TGI_MESSAGE,
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) -> BaseMessage:
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role = _message.role
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assert role == "assistant", f"Expected role to be 'assistant', got {role}"
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content = cast(str, _message.content)
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if content is None:
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content = ""
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additional_kwargs: Dict = {}
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if tool_calls := _message.tool_calls:
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if "arguments" in tool_calls[0]["function"]:
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functions_string = str(tool_calls[0]["function"].pop("arguments"))
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corrected_functions = functions_string.replace("'", '"')
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tool_calls[0]["function"]["arguments"] = corrected_functions
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additional_kwargs["tool_calls"] = tool_calls
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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class ChatHuggingFace(BaseChatModel):
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"""
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Wrapper for using Hugging Face LLM's as ChatModels.
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Works with `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`,
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and `HuggingFaceHub` LLMs.
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Upon instantiating this class, the model_id is resolved from the url
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provided to the LLM, and the appropriate tokenizer is loaded from
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the HuggingFace Hub.
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Adapted from: https://python.langchain.com/docs/integrations/chat/llama2_chat
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"""
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llm: Any
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"""LLM, must be of type HuggingFaceTextGenInference, HuggingFaceEndpoint, or
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HuggingFaceHub."""
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system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
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tokenizer: Any = None
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model_id: Optional[str] = None
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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from transformers import AutoTokenizer # type: ignore[import]
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self._resolve_model_id()
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self.tokenizer = (
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AutoTokenizer.from_pretrained(self.model_id)
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if self.tokenizer is None
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else self.tokenizer
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)
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@root_validator()
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def validate_llm(cls, values: dict) -> dict:
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if not isinstance(
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values["llm"],
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(HuggingFaceHub, HuggingFaceTextGenInference, HuggingFaceEndpoint),
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):
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raise TypeError(
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"Expected llm to be one of HuggingFaceTextGenInference, "
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f"HuggingFaceEndpoint, HuggingFaceHub, received {type(values['llm'])}"
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)
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return values
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def _create_chat_result(self, response: TGI_RESPONSE) -> ChatResult:
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generations = []
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finish_reason = response.choices[0].finish_reason
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gen = ChatGeneration(
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message=_convert_TGI_message_to_LC_message(response.choices[0].message),
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generation_info={"finish_reason": finish_reason},
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)
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generations.append(gen)
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token_usage = response.usage
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model_object = self.llm.inference_server_url
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llm_output = {"token_usage": token_usage, "model": model_object}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _generate(
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self,
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messages: List[BaseMessage],
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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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) -> ChatResult:
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if isinstance(self.llm, HuggingFaceTextGenInference):
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message_dicts = self._create_message_dicts(messages, stop)
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answer = self.llm.client.chat(messages=message_dicts, **kwargs)
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return self._create_chat_result(answer)
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elif isinstance(self.llm, HuggingFaceEndpoint):
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message_dicts = self._create_message_dicts(messages, stop)
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answer = self.llm.client.chat_completion(messages=message_dicts, **kwargs)
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return self._create_chat_result(answer)
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else:
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llm_input = self._to_chat_prompt(messages)
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llm_result = self.llm._generate(
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prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
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)
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return self._to_chat_result(llm_result)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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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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) -> ChatResult:
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if isinstance(self.llm, HuggingFaceTextGenInference):
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message_dicts = self._create_message_dicts(messages, stop)
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answer = await self.llm.async_client.chat(messages=message_dicts, **kwargs)
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return self._create_chat_result(answer)
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else:
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llm_input = self._to_chat_prompt(messages)
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llm_result = await self.llm._agenerate(
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prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
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)
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return self._to_chat_result(llm_result)
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def _to_chat_prompt(
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self,
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messages: List[BaseMessage],
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) -> str:
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"""Convert a list of messages into a prompt format expected by wrapped LLM."""
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if not messages:
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raise ValueError("At least one HumanMessage must be provided!")
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if not isinstance(messages[-1], HumanMessage):
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raise ValueError("Last message must be a HumanMessage!")
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messages_dicts = [self._to_chatml_format(m) for m in messages]
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return self.tokenizer.apply_chat_template(
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messages_dicts, tokenize=False, add_generation_prompt=True
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)
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def _to_chatml_format(self, message: BaseMessage) -> dict:
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"""Convert LangChain message to ChatML format."""
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if isinstance(message, SystemMessage):
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role = "system"
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elif isinstance(message, AIMessage):
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role = "assistant"
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elif isinstance(message, HumanMessage):
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role = "user"
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else:
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raise ValueError(f"Unknown message type: {type(message)}")
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return {"role": role, "content": message.content}
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@staticmethod
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def _to_chat_result(llm_result: LLMResult) -> ChatResult:
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chat_generations = []
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for g in llm_result.generations[0]:
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chat_generation = ChatGeneration(
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message=AIMessage(content=g.text), generation_info=g.generation_info
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)
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chat_generations.append(chat_generation)
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return ChatResult(
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generations=chat_generations, llm_output=llm_result.llm_output
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)
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def _resolve_model_id(self) -> None:
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"""Resolve the model_id from the LLM's inference_server_url"""
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from huggingface_hub import list_inference_endpoints # type: ignore[import]
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available_endpoints = list_inference_endpoints("*")
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if isinstance(self.llm, HuggingFaceHub) or (
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hasattr(self.llm, "repo_id") and self.llm.repo_id
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):
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self.model_id = self.llm.repo_id
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return
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elif isinstance(self.llm, HuggingFaceTextGenInference):
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endpoint_url: Optional[str] = self.llm.inference_server_url
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else:
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endpoint_url = self.llm.endpoint_url
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for endpoint in available_endpoints:
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if endpoint.url == endpoint_url:
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self.model_id = endpoint.repository
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if not self.model_id:
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raise ValueError(
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"Failed to resolve model_id:"
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f"Could not find model id for inference server: {endpoint_url}"
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"Make sure that your Hugging Face token has access to the endpoint."
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)
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def bind_tools(
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self,
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tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
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*,
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tool_choice: Optional[Union[dict, str, Literal["auto", "none"], bool]] = None,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, BaseMessage]:
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"""Bind tool-like objects to this chat model.
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Assumes model is compatible with OpenAI tool-calling API.
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Args:
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tools: A list of tool definitions to bind to this chat model.
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Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
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models, callables, and BaseTools will be automatically converted to
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their schema dictionary representation.
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tool_choice: Which tool to require the model to call.
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Must be the name of the single provided function or
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"auto" to automatically determine which function to call
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(if any), or a dict of the form:
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{"type": "function", "function": {"name": <<tool_name>>}}.
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**kwargs: Any additional parameters to pass to the
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:class:`~langchain.runnable.Runnable` constructor.
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"""
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formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
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if tool_choice is not None and tool_choice:
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if len(formatted_tools) != 1:
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raise ValueError(
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"When specifying `tool_choice`, you must provide exactly one "
|
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f"tool. Received {len(formatted_tools)} tools."
|
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)
|
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if isinstance(tool_choice, str):
|
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if tool_choice not in ("auto", "none"):
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tool_choice = {
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"type": "function",
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"function": {"name": tool_choice},
|
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}
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elif isinstance(tool_choice, bool):
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tool_choice = formatted_tools[0]
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elif isinstance(tool_choice, dict):
|
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if (
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formatted_tools[0]["function"]["name"]
|
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!= tool_choice["function"]["name"]
|
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):
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raise ValueError(
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f"Tool choice {tool_choice} was specified, but the only "
|
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f"provided tool was {formatted_tools[0]['function']['name']}."
|
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)
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else:
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raise ValueError(
|
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f"Unrecognized tool_choice type. Expected str, bool or dict. "
|
||||
f"Received: {tool_choice}"
|
||||
)
|
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kwargs["tool_choice"] = tool_choice
|
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return super().bind(tools=formatted_tools, **kwargs)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
|
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) -> List[Dict[Any, Any]]:
|
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message_dicts = [_convert_message_to_chat_message(m) for m in messages]
|
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return message_dicts
|
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|
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@property
|
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def _llm_type(self) -> str:
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return "huggingface-chat-wrapper"
|
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@@ -0,0 +1,9 @@
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from langchain_huggingface.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from langchain_huggingface.embeddings.huggingface_endpoint import (
|
||||
HuggingFaceEndpointEmbeddings,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"HuggingFaceEmbeddings",
|
||||
"HuggingFaceEndpointEmbeddings",
|
||||
]
|
||||
@@ -0,0 +1,102 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra, Field
|
||||
|
||||
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
|
||||
|
||||
|
||||
class HuggingFaceEmbeddings(BaseModel, Embeddings):
|
||||
"""HuggingFace sentence_transformers embedding models.
|
||||
|
||||
To use, you should have the ``sentence_transformers`` python package installed.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.embeddings import HuggingFaceEmbeddings
|
||||
|
||||
model_name = "sentence-transformers/all-mpnet-base-v2"
|
||||
model_kwargs = {'device': 'cpu'}
|
||||
encode_kwargs = {'normalize_embeddings': False}
|
||||
hf = HuggingFaceEmbeddings(
|
||||
model_name=model_name,
|
||||
model_kwargs=model_kwargs,
|
||||
encode_kwargs=encode_kwargs
|
||||
)
|
||||
"""
|
||||
|
||||
client: Any #: :meta private:
|
||||
model_name: str = DEFAULT_MODEL_NAME
|
||||
"""Model name to use."""
|
||||
cache_folder: Optional[str] = None
|
||||
"""Path to store models.
|
||||
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
|
||||
`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
|
||||
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
|
||||
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Keyword arguments to pass when calling the `encode` method of the Sentence
|
||||
Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`,
|
||||
`normalize_embeddings`, and more.
|
||||
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
|
||||
multi_process: bool = False
|
||||
"""Run encode() on multiple GPUs."""
|
||||
show_progress: bool = False
|
||||
"""Whether to show a progress bar."""
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
"""Initialize the sentence_transformer."""
|
||||
super().__init__(**kwargs)
|
||||
try:
|
||||
import sentence_transformers # type: ignore[import]
|
||||
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"Could not import sentence_transformers python package. "
|
||||
"Please install it with `pip install sentence-transformers`."
|
||||
) from exc
|
||||
|
||||
self.client = sentence_transformers.SentenceTransformer(
|
||||
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
|
||||
)
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Compute doc embeddings using a HuggingFace transformer model.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
import sentence_transformers # type: ignore[import]
|
||||
|
||||
texts = list(map(lambda x: x.replace("\n", " "), texts))
|
||||
if self.multi_process:
|
||||
pool = self.client.start_multi_process_pool()
|
||||
embeddings = self.client.encode_multi_process(texts, pool)
|
||||
sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
|
||||
else:
|
||||
embeddings = self.client.encode(
|
||||
texts, show_progress_bar=self.show_progress, **self.encode_kwargs
|
||||
)
|
||||
|
||||
return embeddings.tolist()
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Compute query embeddings using a HuggingFace transformer model.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
return self.embed_documents([text])[0]
|
||||
@@ -0,0 +1,151 @@
|
||||
import json
|
||||
import os
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
|
||||
|
||||
DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
|
||||
VALID_TASKS = ("feature-extraction",)
|
||||
|
||||
|
||||
class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
|
||||
"""HuggingFaceHub embedding models.
|
||||
|
||||
To use, you should have the ``huggingface_hub`` python package installed, and the
|
||||
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
|
||||
it as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.embeddings import HuggingFaceEndpointEmbeddings
|
||||
model = "sentence-transformers/all-mpnet-base-v2"
|
||||
hf = HuggingFaceEndpointEmbeddings(
|
||||
model=model,
|
||||
task="feature-extraction",
|
||||
huggingfacehub_api_token="my-api-key",
|
||||
)
|
||||
"""
|
||||
|
||||
client: Any #: :meta private:
|
||||
async_client: Any #: :meta private:
|
||||
model: Optional[str] = None
|
||||
"""Model name to use."""
|
||||
repo_id: Optional[str] = None
|
||||
"""Huggingfacehub repository id, for backward compatibility."""
|
||||
task: Optional[str] = "feature-extraction"
|
||||
"""Task to call the model with."""
|
||||
model_kwargs: Optional[dict] = None
|
||||
"""Keyword arguments to pass to the model."""
|
||||
|
||||
huggingfacehub_api_token: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
huggingfacehub_api_token = values["huggingfacehub_api_token"] or os.getenv(
|
||||
"HUGGINGFACEHUB_API_TOKEN"
|
||||
)
|
||||
|
||||
try:
|
||||
from huggingface_hub import ( # type: ignore[import]
|
||||
AsyncInferenceClient,
|
||||
InferenceClient,
|
||||
)
|
||||
|
||||
if values["model"]:
|
||||
values["repo_id"] = values["model"]
|
||||
elif values["repo_id"]:
|
||||
values["model"] = values["repo_id"]
|
||||
else:
|
||||
values["model"] = DEFAULT_MODEL
|
||||
values["repo_id"] = DEFAULT_MODEL
|
||||
|
||||
client = InferenceClient(
|
||||
model=values["model"],
|
||||
token=huggingfacehub_api_token,
|
||||
)
|
||||
|
||||
async_client = AsyncInferenceClient(
|
||||
model=values["model"],
|
||||
token=huggingfacehub_api_token,
|
||||
)
|
||||
|
||||
if values["task"] not in VALID_TASKS:
|
||||
raise ValueError(
|
||||
f"Got invalid task {values['task']}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
values["client"] = client
|
||||
values["async_client"] = async_client
|
||||
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please install it with `pip install huggingface_hub`."
|
||||
)
|
||||
return values
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# replace newlines, which can negatively affect performance.
|
||||
texts = [text.replace("\n", " ") for text in texts]
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
responses = self.client.post(
|
||||
json={"inputs": texts, "parameters": _model_kwargs}, task=self.task
|
||||
)
|
||||
return json.loads(responses.decode())
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Async Call to HuggingFaceHub's embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
# replace newlines, which can negatively affect performance.
|
||||
texts = [text.replace("\n", " ") for text in texts]
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
responses = await self.async_client.post(
|
||||
json={"inputs": texts, "parameters": _model_kwargs}, task=self.task
|
||||
)
|
||||
return json.loads(responses.decode())
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Call out to HuggingFaceHub's embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
response = self.embed_documents([text])[0]
|
||||
return response
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""Async Call to HuggingFaceHub's embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
response = (await self.aembed_documents([text]))[0]
|
||||
return response
|
||||
@@ -0,0 +1,7 @@
|
||||
from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
|
||||
__all__ = [
|
||||
"HuggingFaceEndpoint",
|
||||
"HuggingFacePipeline",
|
||||
]
|
||||
@@ -0,0 +1,372 @@
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
|
||||
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.outputs import GenerationChunk
|
||||
from langchain_core.pydantic_v1 import Extra, Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
VALID_TASKS = (
|
||||
"text2text-generation",
|
||||
"text-generation",
|
||||
"summarization",
|
||||
"conversational",
|
||||
)
|
||||
|
||||
|
||||
class HuggingFaceEndpoint(LLM):
|
||||
"""
|
||||
HuggingFace Endpoint.
|
||||
|
||||
To use this class, you should have installed the ``huggingface_hub`` package, and
|
||||
the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token,
|
||||
or given as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
# Basic Example (no streaming)
|
||||
llm = HuggingFaceEndpoint(
|
||||
endpoint_url="http://localhost:8010/",
|
||||
max_new_tokens=512,
|
||||
top_k=10,
|
||||
top_p=0.95,
|
||||
typical_p=0.95,
|
||||
temperature=0.01,
|
||||
repetition_penalty=1.03,
|
||||
huggingfacehub_api_token="my-api-key"
|
||||
)
|
||||
print(llm.invoke("What is Deep Learning?"))
|
||||
|
||||
# Streaming response example
|
||||
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
|
||||
|
||||
callbacks = [StreamingStdOutCallbackHandler()]
|
||||
llm = HuggingFaceEndpoint(
|
||||
endpoint_url="http://localhost:8010/",
|
||||
max_new_tokens=512,
|
||||
top_k=10,
|
||||
top_p=0.95,
|
||||
typical_p=0.95,
|
||||
temperature=0.01,
|
||||
repetition_penalty=1.03,
|
||||
callbacks=callbacks,
|
||||
streaming=True,
|
||||
huggingfacehub_api_token="my-api-key"
|
||||
)
|
||||
print(llm.invoke("What is Deep Learning?"))
|
||||
|
||||
""" # noqa: E501
|
||||
|
||||
endpoint_url: Optional[str] = None
|
||||
"""Endpoint URL to use."""
|
||||
repo_id: Optional[str] = None
|
||||
"""Repo to use."""
|
||||
huggingfacehub_api_token: Optional[str] = None
|
||||
max_new_tokens: int = 512
|
||||
"""Maximum number of generated tokens"""
|
||||
top_k: Optional[int] = None
|
||||
"""The number of highest probability vocabulary tokens to keep for
|
||||
top-k-filtering."""
|
||||
top_p: Optional[float] = 0.95
|
||||
"""If set to < 1, only the smallest set of most probable tokens with probabilities
|
||||
that add up to `top_p` or higher are kept for generation."""
|
||||
typical_p: Optional[float] = 0.95
|
||||
"""Typical Decoding mass. See [Typical Decoding for Natural Language
|
||||
Generation](https://arxiv.org/abs/2202.00666) for more information."""
|
||||
temperature: Optional[float] = 0.8
|
||||
"""The value used to module the logits distribution."""
|
||||
repetition_penalty: Optional[float] = None
|
||||
"""The parameter for repetition penalty. 1.0 means no penalty.
|
||||
See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details."""
|
||||
return_full_text: bool = False
|
||||
"""Whether to prepend the prompt to the generated text"""
|
||||
truncate: Optional[int] = None
|
||||
"""Truncate inputs tokens to the given size"""
|
||||
stop_sequences: List[str] = Field(default_factory=list)
|
||||
"""Stop generating tokens if a member of `stop_sequences` is generated"""
|
||||
seed: Optional[int] = None
|
||||
"""Random sampling seed"""
|
||||
inference_server_url: str = ""
|
||||
"""text-generation-inference instance base url"""
|
||||
timeout: int = 120
|
||||
"""Timeout in seconds"""
|
||||
streaming: bool = False
|
||||
"""Whether to generate a stream of tokens asynchronously"""
|
||||
do_sample: bool = False
|
||||
"""Activate logits sampling"""
|
||||
watermark: bool = False
|
||||
"""Watermarking with [A Watermark for Large Language Models]
|
||||
(https://arxiv.org/abs/2301.10226)"""
|
||||
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any text-generation-inference server parameters not explicitly specified"""
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
"""Holds any model parameters valid for `call` not explicitly specified"""
|
||||
model: str
|
||||
client: Any
|
||||
async_client: Any
|
||||
task: Optional[str] = None
|
||||
"""Task to call the model with.
|
||||
Should be a task that returns `generated_text` or `summary_text`."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from additional params that were passed in."""
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
extra = values.get("model_kwargs", {})
|
||||
for field_name in list(values):
|
||||
if field_name in extra:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
logger.warning(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please make sure that {field_name} is what you intended."""
|
||||
)
|
||||
extra[field_name] = values.pop(field_name)
|
||||
|
||||
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
values["model_kwargs"] = extra
|
||||
if "endpoint_url" not in values and "repo_id" not in values:
|
||||
raise ValueError(
|
||||
"Please specify an `endpoint_url` or `repo_id` for the model."
|
||||
)
|
||||
if "endpoint_url" in values and "repo_id" in values:
|
||||
raise ValueError(
|
||||
"Please specify either an `endpoint_url` OR a `repo_id`, not both."
|
||||
)
|
||||
values["model"] = values.get("endpoint_url") or values.get("repo_id")
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that package is installed and that the API token is valid."""
|
||||
try:
|
||||
from huggingface_hub import login # type: ignore[import]
|
||||
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please install it with `pip install huggingface_hub`."
|
||||
)
|
||||
try:
|
||||
huggingfacehub_api_token = get_from_dict_or_env(
|
||||
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
|
||||
)
|
||||
login(token=huggingfacehub_api_token)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
"Could not authenticate with huggingface_hub. "
|
||||
"Please check your API token."
|
||||
) from e
|
||||
|
||||
from huggingface_hub import AsyncInferenceClient, InferenceClient
|
||||
|
||||
values["client"] = InferenceClient(
|
||||
model=values["model"],
|
||||
timeout=values["timeout"],
|
||||
token=huggingfacehub_api_token,
|
||||
**values["server_kwargs"],
|
||||
)
|
||||
values["async_client"] = AsyncInferenceClient(
|
||||
model=values["model"],
|
||||
timeout=values["timeout"],
|
||||
token=huggingfacehub_api_token,
|
||||
**values["server_kwargs"],
|
||||
)
|
||||
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling text generation inference API."""
|
||||
return {
|
||||
"max_new_tokens": self.max_new_tokens,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
"typical_p": self.typical_p,
|
||||
"temperature": self.temperature,
|
||||
"repetition_penalty": self.repetition_penalty,
|
||||
"return_full_text": self.return_full_text,
|
||||
"truncate": self.truncate,
|
||||
"stop_sequences": self.stop_sequences,
|
||||
"seed": self.seed,
|
||||
"do_sample": self.do_sample,
|
||||
"watermark": self.watermark,
|
||||
**self.model_kwargs,
|
||||
}
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
return {
|
||||
**{"endpoint_url": self.endpoint_url, "task": self.task},
|
||||
**{"model_kwargs": _model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "huggingface_endpoint"
|
||||
|
||||
def _invocation_params(
|
||||
self, runtime_stop: Optional[List[str]], **kwargs: Any
|
||||
) -> Dict[str, Any]:
|
||||
params = {**self._default_params, **kwargs}
|
||||
params["stop_sequences"] = params["stop_sequences"] + (runtime_stop or [])
|
||||
return params
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to HuggingFace Hub's inference endpoint."""
|
||||
invocation_params = self._invocation_params(stop, **kwargs)
|
||||
if self.streaming:
|
||||
completion = ""
|
||||
for chunk in self._stream(prompt, stop, run_manager, **invocation_params):
|
||||
completion += chunk.text
|
||||
return completion
|
||||
else:
|
||||
invocation_params["stop"] = invocation_params[
|
||||
"stop_sequences"
|
||||
] # porting 'stop_sequences' into the 'stop' argument
|
||||
response = self.client.post(
|
||||
json={"inputs": prompt, "parameters": invocation_params},
|
||||
stream=False,
|
||||
task=self.task,
|
||||
)
|
||||
response_text = json.loads(response.decode())[0]["generated_text"]
|
||||
|
||||
# Maybe the generation has stopped at one of the stop sequences:
|
||||
# then we remove this stop sequence from the end of the generated text
|
||||
for stop_seq in invocation_params["stop_sequences"]:
|
||||
if response_text[-len(stop_seq) :] == stop_seq:
|
||||
response_text = response_text[: -len(stop_seq)]
|
||||
return response_text
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
invocation_params = self._invocation_params(stop, **kwargs)
|
||||
if self.streaming:
|
||||
completion = ""
|
||||
async for chunk in self._astream(
|
||||
prompt, stop, run_manager, **invocation_params
|
||||
):
|
||||
completion += chunk.text
|
||||
return completion
|
||||
else:
|
||||
invocation_params["stop"] = invocation_params["stop_sequences"]
|
||||
response = await self.async_client.post(
|
||||
json={"inputs": prompt, "parameters": invocation_params},
|
||||
stream=False,
|
||||
task=self.task,
|
||||
)
|
||||
response_text = json.loads(response.decode())[0]["generated_text"]
|
||||
|
||||
# Maybe the generation has stopped at one of the stop sequences:
|
||||
# then remove this stop sequence from the end of the generated text
|
||||
for stop_seq in invocation_params["stop_sequences"]:
|
||||
if response_text[-len(stop_seq) :] == stop_seq:
|
||||
response_text = response_text[: -len(stop_seq)]
|
||||
return response_text
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
invocation_params = self._invocation_params(stop, **kwargs)
|
||||
|
||||
for response in self.client.text_generation(
|
||||
prompt, **invocation_params, stream=True
|
||||
):
|
||||
# identify stop sequence in generated text, if any
|
||||
stop_seq_found: Optional[str] = None
|
||||
for stop_seq in invocation_params["stop_sequences"]:
|
||||
if stop_seq in response:
|
||||
stop_seq_found = stop_seq
|
||||
|
||||
# identify text to yield
|
||||
text: Optional[str] = None
|
||||
if stop_seq_found:
|
||||
text = response[: response.index(stop_seq_found)]
|
||||
else:
|
||||
text = response
|
||||
|
||||
# yield text, if any
|
||||
if text:
|
||||
chunk = GenerationChunk(text=text)
|
||||
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text)
|
||||
yield chunk
|
||||
|
||||
# break if stop sequence found
|
||||
if stop_seq_found:
|
||||
break
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
invocation_params = self._invocation_params(stop, **kwargs)
|
||||
async for response in await self.async_client.text_generation(
|
||||
prompt, **invocation_params, stream=True
|
||||
):
|
||||
# identify stop sequence in generated text, if any
|
||||
stop_seq_found: Optional[str] = None
|
||||
for stop_seq in invocation_params["stop_sequences"]:
|
||||
if stop_seq in response:
|
||||
stop_seq_found = stop_seq
|
||||
|
||||
# identify text to yield
|
||||
text: Optional[str] = None
|
||||
if stop_seq_found:
|
||||
text = response[: response.index(stop_seq_found)]
|
||||
else:
|
||||
text = response
|
||||
|
||||
# yield text, if any
|
||||
if text:
|
||||
chunk = GenerationChunk(text=text)
|
||||
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(chunk.text)
|
||||
yield chunk
|
||||
|
||||
# break if stop sequence found
|
||||
if stop_seq_found:
|
||||
break
|
||||
@@ -0,0 +1,299 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
import logging
|
||||
from typing import Any, List, Mapping, Optional
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.language_models.llms import BaseLLM
|
||||
from langchain_core.outputs import Generation, LLMResult
|
||||
from langchain_core.pydantic_v1 import Extra
|
||||
|
||||
DEFAULT_MODEL_ID = "gpt2"
|
||||
DEFAULT_TASK = "text-generation"
|
||||
VALID_TASKS = (
|
||||
"text2text-generation",
|
||||
"text-generation",
|
||||
"summarization",
|
||||
"translation",
|
||||
)
|
||||
DEFAULT_BATCH_SIZE = 4
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HuggingFacePipeline(BaseLLM):
|
||||
"""HuggingFace Pipeline API.
|
||||
|
||||
To use, you should have the ``transformers`` python package installed.
|
||||
|
||||
Only supports `text-generation`, `text2text-generation`, `summarization` and
|
||||
`translation` for now.
|
||||
|
||||
Example using from_model_id:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.llms import HuggingFacePipeline
|
||||
hf = HuggingFacePipeline.from_model_id(
|
||||
model_id="gpt2",
|
||||
task="text-generation",
|
||||
pipeline_kwargs={"max_new_tokens": 10},
|
||||
)
|
||||
Example passing pipeline in directly:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.llms import HuggingFacePipeline
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
|
||||
model_id = "gpt2"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
pipe = pipeline(
|
||||
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
|
||||
)
|
||||
hf = HuggingFacePipeline(pipeline=pipe)
|
||||
"""
|
||||
|
||||
pipeline: Any #: :meta private:
|
||||
model_id: str = DEFAULT_MODEL_ID
|
||||
"""Model name to use."""
|
||||
model_kwargs: Optional[dict] = None
|
||||
"""Keyword arguments passed to the model."""
|
||||
pipeline_kwargs: Optional[dict] = None
|
||||
"""Keyword arguments passed to the pipeline."""
|
||||
batch_size: int = DEFAULT_BATCH_SIZE
|
||||
"""Batch size to use when passing multiple documents to generate."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@classmethod
|
||||
def from_model_id(
|
||||
cls,
|
||||
model_id: str,
|
||||
task: str,
|
||||
backend: str = "default",
|
||||
device: Optional[int] = -1,
|
||||
device_map: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
pipeline_kwargs: Optional[dict] = None,
|
||||
batch_size: int = DEFAULT_BATCH_SIZE,
|
||||
**kwargs: Any,
|
||||
) -> HuggingFacePipeline:
|
||||
"""Construct the pipeline object from model_id and task."""
|
||||
try:
|
||||
from transformers import ( # type: ignore[import]
|
||||
AutoModelForCausalLM,
|
||||
AutoModelForSeq2SeqLM,
|
||||
AutoTokenizer,
|
||||
)
|
||||
from transformers import pipeline as hf_pipeline # type: ignore[import]
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import transformers python package. "
|
||||
"Please install it with `pip install transformers`."
|
||||
)
|
||||
|
||||
_model_kwargs = model_kwargs or {}
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
|
||||
|
||||
try:
|
||||
if task == "text-generation":
|
||||
if backend == "openvino":
|
||||
try:
|
||||
from optimum.intel.openvino import ( # type: ignore[import]
|
||||
OVModelForCausalLM,
|
||||
)
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import optimum-intel python package. "
|
||||
"Please install it with: "
|
||||
"pip install 'optimum[openvino,nncf]' "
|
||||
)
|
||||
try:
|
||||
# use local model
|
||||
model = OVModelForCausalLM.from_pretrained(
|
||||
model_id, **_model_kwargs
|
||||
)
|
||||
|
||||
except Exception:
|
||||
# use remote model
|
||||
model = OVModelForCausalLM.from_pretrained(
|
||||
model_id, export=True, **_model_kwargs
|
||||
)
|
||||
else:
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id, **_model_kwargs
|
||||
)
|
||||
elif task in ("text2text-generation", "summarization", "translation"):
|
||||
if backend == "openvino":
|
||||
try:
|
||||
from optimum.intel.openvino import OVModelForSeq2SeqLM
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import optimum-intel python package. "
|
||||
"Please install it with: "
|
||||
"pip install 'optimum[openvino,nncf]' "
|
||||
)
|
||||
try:
|
||||
# use local model
|
||||
model = OVModelForSeq2SeqLM.from_pretrained(
|
||||
model_id, **_model_kwargs
|
||||
)
|
||||
|
||||
except Exception:
|
||||
# use remote model
|
||||
model = OVModelForSeq2SeqLM.from_pretrained(
|
||||
model_id, export=True, **_model_kwargs
|
||||
)
|
||||
else:
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained(
|
||||
model_id, **_model_kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Got invalid task {task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
except ImportError as e:
|
||||
raise ValueError(
|
||||
f"Could not load the {task} model due to missing dependencies."
|
||||
) from e
|
||||
|
||||
if tokenizer.pad_token is None:
|
||||
tokenizer.pad_token_id = model.config.eos_token_id
|
||||
|
||||
if (
|
||||
(
|
||||
getattr(model, "is_loaded_in_4bit", False)
|
||||
or getattr(model, "is_loaded_in_8bit", False)
|
||||
)
|
||||
and device is not None
|
||||
and backend == "default"
|
||||
):
|
||||
logger.warning(
|
||||
f"Setting the `device` argument to None from {device} to avoid "
|
||||
"the error caused by attempting to move the model that was already "
|
||||
"loaded on the GPU using the Accelerate module to the same or "
|
||||
"another device."
|
||||
)
|
||||
device = None
|
||||
|
||||
if (
|
||||
device is not None
|
||||
and importlib.util.find_spec("torch") is not None
|
||||
and backend == "default"
|
||||
):
|
||||
import torch
|
||||
|
||||
cuda_device_count = torch.cuda.device_count()
|
||||
if device < -1 or (device >= cuda_device_count):
|
||||
raise ValueError(
|
||||
f"Got device=={device}, "
|
||||
f"device is required to be within [-1, {cuda_device_count})"
|
||||
)
|
||||
if device_map is not None and device < 0:
|
||||
device = None
|
||||
if device is not None and device < 0 and cuda_device_count > 0:
|
||||
logger.warning(
|
||||
"Device has %d GPUs available. "
|
||||
"Provide device={deviceId} to `from_model_id` to use available"
|
||||
"GPUs for execution. deviceId is -1 (default) for CPU and "
|
||||
"can be a positive integer associated with CUDA device id.",
|
||||
cuda_device_count,
|
||||
)
|
||||
if device is not None and device_map is not None and backend == "openvino":
|
||||
logger.warning("Please set device for OpenVINO through: " "'model_kwargs'")
|
||||
if "trust_remote_code" in _model_kwargs:
|
||||
_model_kwargs = {
|
||||
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
|
||||
}
|
||||
_pipeline_kwargs = pipeline_kwargs or {}
|
||||
pipeline = hf_pipeline(
|
||||
task=task,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
device=device,
|
||||
device_map=device_map,
|
||||
batch_size=batch_size,
|
||||
model_kwargs=_model_kwargs,
|
||||
**_pipeline_kwargs,
|
||||
)
|
||||
if pipeline.task not in VALID_TASKS:
|
||||
raise ValueError(
|
||||
f"Got invalid task {pipeline.task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
return cls(
|
||||
pipeline=pipeline,
|
||||
model_id=model_id,
|
||||
model_kwargs=_model_kwargs,
|
||||
pipeline_kwargs=_pipeline_kwargs,
|
||||
batch_size=batch_size,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"model_id": self.model_id,
|
||||
"model_kwargs": self.model_kwargs,
|
||||
"pipeline_kwargs": self.pipeline_kwargs,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "huggingface_pipeline"
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
# List to hold all results
|
||||
text_generations: List[str] = []
|
||||
pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
|
||||
|
||||
for i in range(0, len(prompts), self.batch_size):
|
||||
batch_prompts = prompts[i : i + self.batch_size]
|
||||
|
||||
# Process batch of prompts
|
||||
responses = self.pipeline(
|
||||
batch_prompts,
|
||||
**pipeline_kwargs,
|
||||
)
|
||||
|
||||
# Process each response in the batch
|
||||
for j, response in enumerate(responses):
|
||||
if isinstance(response, list):
|
||||
# if model returns multiple generations, pick the top one
|
||||
response = response[0]
|
||||
|
||||
if self.pipeline.task == "text-generation":
|
||||
text = response["generated_text"]
|
||||
elif self.pipeline.task == "text2text-generation":
|
||||
text = response["generated_text"]
|
||||
elif self.pipeline.task == "summarization":
|
||||
text = response["summary_text"]
|
||||
elif self.pipeline.task in "translation":
|
||||
text = response["translation_text"]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Got invalid task {self.pipeline.task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
|
||||
# Append the processed text to results
|
||||
text_generations.append(text)
|
||||
|
||||
return LLMResult(
|
||||
generations=[[Generation(text=text)] for text in text_generations]
|
||||
)
|
||||
Reference in New Issue
Block a user