mirror of
https://github.com/hwchase17/langchain
synced 2024-11-20 03:25:56 +00:00
e0a1278d2b
- **Description:** Be more explicit with the `model_kwargs` and
`encode_kwargs` for `HuggingFaceEmbeddings`.
- **Issue:** -
- **Dependencies:** -
I received some reports by my users that they didn't realise that you
could change the default `batch_size` with `HuggingFaceEmbeddings`,
which may be attributed to how the `model_kwargs` and `encode_kwargs`
don't give much information about what you can specify.
I've added some parameter names & links to the Sentence Transformers
documentation to help clear it up. Let me know if you'd rather have
Markdown/Sphinx-style hyperlinks rather than a "bare URL".
- Tom Aarsen
374 lines
13 KiB
Python
374 lines
13 KiB
Python
from typing import Any, Dict, List, Optional
|
|
|
|
import requests
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, SecretStr
|
|
|
|
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
|
|
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
|
|
DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
|
|
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
|
|
DEFAULT_QUERY_INSTRUCTION = (
|
|
"Represent the question for retrieving supporting documents: "
|
|
)
|
|
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
|
|
"Represent this question for searching relevant passages: "
|
|
)
|
|
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
|
|
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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]
|
|
|
|
|
|
class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
|
|
"""Wrapper around sentence_transformers embedding models.
|
|
|
|
To use, you should have the ``sentence_transformers``
|
|
and ``InstructorEmbedding`` python packages installed.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
|
|
|
|
model_name = "hkunlp/instructor-large"
|
|
model_kwargs = {'device': 'cpu'}
|
|
encode_kwargs = {'normalize_embeddings': True}
|
|
hf = HuggingFaceInstructEmbeddings(
|
|
model_name=model_name,
|
|
model_kwargs=model_kwargs,
|
|
encode_kwargs=encode_kwargs
|
|
)
|
|
"""
|
|
|
|
client: Any #: :meta private:
|
|
model_name: str = DEFAULT_INSTRUCT_MODEL
|
|
"""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 model."""
|
|
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
|
"""Keyword arguments to pass when calling the `encode` method of the model."""
|
|
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
|
|
"""Instruction to use for embedding documents."""
|
|
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
|
|
"""Instruction to use for embedding query."""
|
|
|
|
def __init__(self, **kwargs: Any):
|
|
"""Initialize the sentence_transformer."""
|
|
super().__init__(**kwargs)
|
|
try:
|
|
from InstructorEmbedding import INSTRUCTOR
|
|
|
|
self.client = INSTRUCTOR(
|
|
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
|
|
)
|
|
except ImportError as e:
|
|
raise ImportError("Dependencies for InstructorEmbedding not found.") from e
|
|
|
|
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 instruct model.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
instruction_pairs = [[self.embed_instruction, text] for text in texts]
|
|
embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
|
|
return embeddings.tolist()
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Compute query embeddings using a HuggingFace instruct model.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
instruction_pair = [self.query_instruction, text]
|
|
embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
|
|
return embedding.tolist()
|
|
|
|
|
|
class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
|
|
"""HuggingFace sentence_transformers embedding models.
|
|
|
|
To use, you should have the ``sentence_transformers`` python package installed.
|
|
To use Nomic, make sure the version of ``sentence_transformers`` >= 2.3.0.
|
|
|
|
Bge Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
|
|
|
|
model_name = "BAAI/bge-large-en"
|
|
model_kwargs = {'device': 'cpu'}
|
|
encode_kwargs = {'normalize_embeddings': True}
|
|
hf = HuggingFaceBgeEmbeddings(
|
|
model_name=model_name,
|
|
model_kwargs=model_kwargs,
|
|
encode_kwargs=encode_kwargs
|
|
)
|
|
Nomic Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
|
|
|
|
model_name = "nomic-ai/nomic-embed-text-v1"
|
|
model_kwargs = {
|
|
'device': 'cpu',
|
|
'trust_remote_code':True
|
|
}
|
|
encode_kwargs = {'normalize_embeddings': True}
|
|
hf = HuggingFaceBgeEmbeddings(
|
|
model_name=model_name,
|
|
model_kwargs=model_kwargs,
|
|
encode_kwargs=encode_kwargs,
|
|
query_instruction = "search_query:",
|
|
embed_instruction = "search_document:"
|
|
)
|
|
"""
|
|
|
|
client: Any #: :meta private:
|
|
model_name: str = DEFAULT_BGE_MODEL
|
|
"""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 model."""
|
|
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
|
"""Keyword arguments to pass when calling the `encode` method of the model."""
|
|
query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
|
|
"""Instruction to use for embedding query."""
|
|
embed_instruction: str = ""
|
|
"""Instruction to use for embedding document."""
|
|
|
|
def __init__(self, **kwargs: Any):
|
|
"""Initialize the sentence_transformer."""
|
|
super().__init__(**kwargs)
|
|
try:
|
|
import sentence_transformers
|
|
|
|
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
|
|
)
|
|
if "-zh" in self.model_name:
|
|
self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
|
|
|
|
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.
|
|
"""
|
|
texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
|
|
embeddings = self.client.encode(texts, **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.
|
|
"""
|
|
text = text.replace("\n", " ")
|
|
embedding = self.client.encode(
|
|
self.query_instruction + text, **self.encode_kwargs
|
|
)
|
|
return embedding.tolist()
|
|
|
|
|
|
class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
|
|
"""Embed texts using the HuggingFace API.
|
|
|
|
Requires a HuggingFace Inference API key and a model name.
|
|
"""
|
|
|
|
api_key: SecretStr
|
|
"""Your API key for the HuggingFace Inference API."""
|
|
model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
|
|
"""The name of the model to use for text embeddings."""
|
|
api_url: Optional[str] = None
|
|
"""Custom inference endpoint url. None for using default public url."""
|
|
|
|
@property
|
|
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
|
|
def _headers(self) -> dict:
|
|
return {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Get the embeddings for a list of texts.
|
|
|
|
Args:
|
|
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(
|
|
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]
|