mirror of
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04e2611fea
Thank you for contributing to LangChain!
- [ ] **HuggingFaceInferenceAPIEmbeddings**: "Additional Headers"
- Where: langchain, community, embeddings. huggingface.py.
- Community: add additional headers when needed by custom HuggingFace
TEI embedding endpoints. HuggingFaceInferenceAPIEmbeddings"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Adding the `additional_headers` to be passed to
requests library if needed
- **Dependencies:** none
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. Tested with locally available TEI endpoints with and without
`additional_headers`
2. Example Usage
```python
embeddings=HuggingFaceInferenceAPIEmbeddings(
api_key=MY_CUSTOM_API_KEY,
api_url=MY_CUSTOM_TEI_URL,
additional_headers={
"Content-Type": "application/json"
}
)
```
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Massimiliano Pronesti <massimiliano.pronesti@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
379 lines
13 KiB
Python
379 lines
13 KiB
Python
from typing import Any, Dict, List, Optional
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import requests
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, 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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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 #: :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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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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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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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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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 #: :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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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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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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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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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(instruction_pairs, **self.encode_kwargs)
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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([instruction_pair], **self.encode_kwargs)[0]
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return embedding.tolist()
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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"
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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 #: :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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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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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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if "-zh" in self.model_name:
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self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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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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texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
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embeddings = self.client.encode(texts, **self.encode_kwargs)
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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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text = text.replace("\n", " ")
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embedding = self.client.encode(
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self.query_instruction + text, **self.encode_kwargs
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)
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return embedding.tolist()
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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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"""
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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] = {}
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"""Pass additional headers to the requests library if needed."""
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@property
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def _api_url(self) -> str:
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return self.api_url or self._default_api_url
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@property
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def _default_api_url(self) -> str:
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return (
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"https://api-inference.huggingface.co"
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"/pipeline"
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"/feature-extraction"
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f"/{self.model_name}"
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)
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@property
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def _headers(self) -> dict:
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return {
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"Authorization": f"Bearer {self.api_key.get_secret_value()}",
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**self.additional_headers,
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}
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Get the embeddings for a list of texts.
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Args:
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texts (Documents): A list of texts to get embeddings for.
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Returns:
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Embedded texts as List[List[float]], where each inner List[float]
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corresponds to a single input text.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
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hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
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api_key="your_api_key",
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model_name="sentence-transformers/all-MiniLM-l6-v2"
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)
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texts = ["Hello, world!", "How are you?"]
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hf_embeddings.embed_documents(texts)
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""" # noqa: E501
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response = requests.post(
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self._api_url,
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headers=self._headers,
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json={
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"inputs": texts,
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"options": {"wait_for_model": True, "use_cache": True},
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},
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)
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return response.json()
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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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