initial commit
This commit is contained in:
@@ -0,0 +1,493 @@
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import warnings
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from typing import Any, Dict, List, Optional
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import requests
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from langchain_core._api import deprecated, warn_deprecated
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel, ConfigDict, Field, SecretStr
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DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
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DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
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DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
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DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
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DEFAULT_QUERY_INSTRUCTION = (
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"Represent the question for retrieving supporting documents: "
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)
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DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
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"Represent this question for searching relevant passages: "
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)
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DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
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@deprecated(
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since="0.2.2",
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removal="1.0",
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alternative_import="langchain_huggingface.HuggingFaceEmbeddings",
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)
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class HuggingFaceEmbeddings(BaseModel, Embeddings):
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"""HuggingFace sentence_transformers embedding models.
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To use, you should have the ``sentence_transformers`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceEmbeddings
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model_name = "sentence-transformers/all-mpnet-base-v2"
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model_kwargs = {'device': 'cpu'}
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encode_kwargs = {'normalize_embeddings': False}
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hf = HuggingFaceEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_kwargs
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)
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"""
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client: Any = None #: :meta private:
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model_name: str = DEFAULT_MODEL_NAME
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"""Model name to use."""
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cache_folder: Optional[str] = None
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"""Path to store models.
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Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
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`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
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See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
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encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass when calling the `encode` method of the Sentence
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Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`,
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`normalize_embeddings`, and more.
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See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
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multi_process: bool = False
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"""Run encode() on multiple GPUs."""
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show_progress: bool = False
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"""Whether to show a progress bar."""
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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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if "model_name" not in kwargs:
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since = "0.2.16"
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removal = "0.4.0"
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warn_deprecated(
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since=since,
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removal=removal,
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message=f"Default values for {self.__class__.__name__}.model_name"
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+ f" were deprecated in LangChain {since} and will be removed in"
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+ f" {removal}. Explicitly pass a model_name to the"
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+ f" {self.__class__.__name__} constructor instead.",
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)
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try:
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import sentence_transformers
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except ImportError as exc:
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raise ImportError(
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"Could not import sentence_transformers python package. "
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"Please install it with `pip install sentence-transformers`."
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) from exc
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self.client = sentence_transformers.SentenceTransformer(
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self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
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)
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a HuggingFace transformer model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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import sentence_transformers
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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if self.multi_process:
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pool = self.client.start_multi_process_pool()
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embeddings = self.client.encode_multi_process(texts, pool)
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sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
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else:
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embeddings = self.client.encode(
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texts, show_progress_bar=self.show_progress, **self.encode_kwargs
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)
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a HuggingFace transformer model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self.embed_documents([text])[0]
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@deprecated(
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since="0.2.2",
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removal="1.0",
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alternative_import="langchain_huggingface.HuggingFaceEmbeddings",
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)
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class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
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"""Wrapper around sentence_transformers embedding models.
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To use, you should have the ``sentence_transformers``
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and ``InstructorEmbedding`` python packages installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceInstructEmbeddings
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model_name = "hkunlp/instructor-large"
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model_kwargs = {'device': 'cpu'}
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encode_kwargs = {'normalize_embeddings': True}
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hf = HuggingFaceInstructEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_kwargs
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)
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"""
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client: Any = None #: :meta private:
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model_name: str = DEFAULT_INSTRUCT_MODEL
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"""Model name to use."""
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cache_folder: Optional[str] = None
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"""Path to store models.
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Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass to the model."""
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encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass when calling the `encode` method of the model."""
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embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
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"""Instruction to use for embedding documents."""
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query_instruction: str = DEFAULT_QUERY_INSTRUCTION
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"""Instruction to use for embedding query."""
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show_progress: bool = False
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"""Whether to show a progress bar."""
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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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if "model_name" not in kwargs:
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since = "0.2.16"
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removal = "0.4.0"
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warn_deprecated(
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since=since,
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removal=removal,
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message=f"Default values for {self.__class__.__name__}.model_name"
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+ f" were deprecated in LangChain {since} and will be removed in"
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+ f" {removal}. Explicitly pass a model_name to the"
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+ f" {self.__class__.__name__} constructor instead.",
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)
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try:
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from InstructorEmbedding import INSTRUCTOR
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self.client = INSTRUCTOR(
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self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
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)
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except ImportError as e:
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raise ImportError("Dependencies for InstructorEmbedding not found.") from e
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if "show_progress_bar" in self.encode_kwargs:
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warn_deprecated(
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since="0.2.5",
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removal="1.0",
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name="encode_kwargs['show_progress_bar']",
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alternative=f"the show_progress method on {self.__class__.__name__}",
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)
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if self.show_progress:
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warnings.warn(
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"Both encode_kwargs['show_progress_bar'] and show_progress are set;"
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"encode_kwargs['show_progress_bar'] takes precedence"
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)
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self.show_progress = self.encode_kwargs.pop("show_progress_bar")
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a HuggingFace instruct model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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instruction_pairs = [[self.embed_instruction, text] for text in texts]
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embeddings = self.client.encode(
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instruction_pairs,
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show_progress_bar=self.show_progress,
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**self.encode_kwargs,
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)
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a HuggingFace instruct model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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instruction_pair = [self.query_instruction, text]
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embedding = self.client.encode(
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[instruction_pair],
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show_progress_bar=self.show_progress,
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**self.encode_kwargs,
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)[0]
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return embedding.tolist()
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@deprecated(
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since="0.2.2",
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removal="1.0",
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alternative_import="langchain_huggingface.HuggingFaceEmbeddings",
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)
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class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
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"""HuggingFace sentence_transformers embedding models.
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To use, you should have the ``sentence_transformers`` python package installed.
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To use Nomic, make sure the version of ``sentence_transformers`` >= 2.3.0.
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Bge Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceBgeEmbeddings
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model_name = "BAAI/bge-large-en-v1.5"
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model_kwargs = {'device': 'cpu'}
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encode_kwargs = {'normalize_embeddings': True}
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hf = HuggingFaceBgeEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_kwargs
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)
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Nomic Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceBgeEmbeddings
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model_name = "nomic-ai/nomic-embed-text-v1"
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model_kwargs = {
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'device': 'cpu',
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'trust_remote_code':True
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}
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encode_kwargs = {'normalize_embeddings': True}
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hf = HuggingFaceBgeEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_kwargs,
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query_instruction = "search_query:",
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embed_instruction = "search_document:"
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)
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"""
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client: Any = None #: :meta private:
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model_name: str = DEFAULT_BGE_MODEL
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"""Model name to use."""
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cache_folder: Optional[str] = None
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"""Path to store models.
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Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass to the model."""
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encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass when calling the `encode` method of the model."""
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query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
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"""Instruction to use for embedding query."""
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embed_instruction: str = ""
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"""Instruction to use for embedding document."""
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show_progress: bool = False
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"""Whether to show a progress bar."""
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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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if "model_name" not in kwargs:
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since = "0.2.5"
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removal = "0.4.0"
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warn_deprecated(
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since=since,
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removal=removal,
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message=f"Default values for {self.__class__.__name__}.model_name"
|
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+ f" were deprecated in LangChain {since} and will be removed in"
|
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+ f" {removal}. Explicitly pass a model_name to the"
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+ f" {self.__class__.__name__} constructor instead.",
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)
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try:
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import sentence_transformers
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except ImportError as exc:
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raise ImportError(
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"Could not import sentence_transformers python package. "
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"Please install it with `pip install sentence-transformers`."
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) from exc
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extra_model_kwargs = [
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"torch_dtype",
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"attn_implementation",
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"provider",
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"file_name",
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"export",
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]
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extra_model_kwargs_dict = {
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k: self.model_kwargs.pop(k)
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for k in extra_model_kwargs
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if k in self.model_kwargs
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}
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self.client = sentence_transformers.SentenceTransformer(
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self.model_name,
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cache_folder=self.cache_folder,
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**self.model_kwargs,
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model_kwargs=extra_model_kwargs_dict,
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)
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if "-zh" in self.model_name:
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self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
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if "show_progress_bar" in self.encode_kwargs:
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warn_deprecated(
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since="0.2.5",
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removal="1.0",
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name="encode_kwargs['show_progress_bar']",
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alternative=f"the show_progress method on {self.__class__.__name__}",
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)
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if self.show_progress:
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warnings.warn(
|
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"Both encode_kwargs['show_progress_bar'] and show_progress are set;"
|
||||
"encode_kwargs['show_progress_bar'] takes precedence"
|
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)
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self.show_progress = self.encode_kwargs.pop("show_progress_bar")
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
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"""Compute doc embeddings using a HuggingFace transformer model.
|
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|
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Args:
|
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texts: The list of texts to embed.
|
||||
|
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Returns:
|
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List of embeddings, one for each text.
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"""
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texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
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embeddings = self.client.encode(
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texts, show_progress_bar=self.show_progress, **self.encode_kwargs
|
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)
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return embeddings.tolist()
|
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|
||||
def embed_query(self, text: str) -> List[float]:
|
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"""Compute query embeddings using a HuggingFace transformer model.
|
||||
|
||||
Args:
|
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text: The text to embed.
|
||||
|
||||
Returns:
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Embeddings for the text.
|
||||
"""
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text = text.replace("\n", " ")
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embedding = self.client.encode(
|
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self.query_instruction + text,
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show_progress_bar=self.show_progress,
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**self.encode_kwargs,
|
||||
)
|
||||
return embedding.tolist()
|
||||
|
||||
|
||||
@deprecated(
|
||||
since="0.2.2",
|
||||
removal="1.0",
|
||||
alternative_import="langchain_huggingface.HuggingFaceEndpointEmbeddings",
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||||
)
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class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
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"""Embed texts using the HuggingFace API.
|
||||
|
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Requires a HuggingFace Inference API key and a model name.
|
||||
"""
|
||||
|
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api_key: SecretStr
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||||
"""Your API key for the HuggingFace Inference API."""
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model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
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||||
"""The name of the model to use for text embeddings."""
|
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api_url: Optional[str] = None
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"""Custom inference endpoint url. None for using default public url."""
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additional_headers: Dict[str, str] = {}
|
||||
"""Pass additional headers to the requests library if needed."""
|
||||
|
||||
model_config = ConfigDict(extra="forbid", protected_namespaces=())
|
||||
|
||||
@property
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||||
def _api_url(self) -> str:
|
||||
return self.api_url or self._default_api_url
|
||||
|
||||
@property
|
||||
def _default_api_url(self) -> str:
|
||||
return (
|
||||
"https://api-inference.huggingface.co"
|
||||
"/pipeline"
|
||||
"/feature-extraction"
|
||||
f"/{self.model_name}"
|
||||
)
|
||||
|
||||
@property
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||||
def _headers(self) -> dict:
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||||
return {
|
||||
"Authorization": f"Bearer {self.api_key.get_secret_value()}",
|
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**self.additional_headers,
|
||||
}
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Get the embeddings for a list of texts.
|
||||
|
||||
Args:
|
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texts (Documents): A list of texts to get embeddings for.
|
||||
|
||||
Returns:
|
||||
Embedded texts as List[List[float]], where each inner List[float]
|
||||
corresponds to a single input text.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.embeddings import (
|
||||
HuggingFaceInferenceAPIEmbeddings,
|
||||
)
|
||||
|
||||
hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
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||||
api_key="your_api_key",
|
||||
model_name="sentence-transformers/all-MiniLM-l6-v2"
|
||||
)
|
||||
texts = ["Hello, world!", "How are you?"]
|
||||
hf_embeddings.embed_documents(texts)
|
||||
""" # noqa: E501
|
||||
response = requests.post(
|
||||
self._api_url,
|
||||
headers=self._headers,
|
||||
json={
|
||||
"inputs": texts,
|
||||
"options": {"wait_for_model": True, "use_cache": True},
|
||||
},
|
||||
)
|
||||
return response.json()
|
||||
|
||||
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]
|
||||
Reference in New Issue
Block a user