mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
1a55e950aa
**Description:** #18040 forces `fastembed>2.0`, and this causes dependency conflicts with the new `unstructured` package (different `onnxruntime`). There may be other dependency conflicts.. The only way to use `langchain-community>=0.0.28` is rollback to `unstructured 0.10.X`. But new `unstructured` contains many fixes. This PR allows to use both `fastembed` `v1` and `v2`. How to reproduce: `pyproject.toml`: ```toml [tool.poetry] name = "depstest" version = "0.0.0" description = "test" authors = ["<dev@example.org>"] [tool.poetry.dependencies] python = ">=3.10,<3.12" langchain-community = "^0.0.28" fastembed = "^0.2.0" unstructured = {extras = ["pdf"], version = "^0.12"} ``` ```bash $ poetry lock ``` Co-authored-by: Sergey Kozlov <sergey.kozlov@ludditelabs.io>
120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
from typing import Any, Dict, List, Literal, Optional
|
|
|
|
import numpy as np
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
|
|
|
|
|
|
class FastEmbedEmbeddings(BaseModel, Embeddings):
|
|
"""Qdrant FastEmbedding models.
|
|
FastEmbed is a lightweight, fast, Python library built for embedding generation.
|
|
See more documentation at:
|
|
* https://github.com/qdrant/fastembed/
|
|
* https://qdrant.github.io/fastembed/
|
|
|
|
To use this class, you must install the `fastembed` Python package.
|
|
|
|
`pip install fastembed`
|
|
Example:
|
|
from langchain_community.embeddings import FastEmbedEmbeddings
|
|
fastembed = FastEmbedEmbeddings()
|
|
"""
|
|
|
|
model_name: str = "BAAI/bge-small-en-v1.5"
|
|
"""Name of the FastEmbedding model to use
|
|
Defaults to "BAAI/bge-small-en-v1.5"
|
|
Find the list of supported models at
|
|
https://qdrant.github.io/fastembed/examples/Supported_Models/
|
|
"""
|
|
|
|
max_length: int = 512
|
|
"""The maximum number of tokens. Defaults to 512.
|
|
Unknown behavior for values > 512.
|
|
"""
|
|
|
|
cache_dir: Optional[str]
|
|
"""The path to the cache directory.
|
|
Defaults to `local_cache` in the parent directory
|
|
"""
|
|
|
|
threads: Optional[int]
|
|
"""The number of threads single onnxruntime session can use.
|
|
Defaults to None
|
|
"""
|
|
|
|
doc_embed_type: Literal["default", "passage"] = "default"
|
|
"""Type of embedding to use for documents
|
|
The available options are: "default" and "passage"
|
|
"""
|
|
|
|
_model: Any # : :meta private:
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that FastEmbed has been installed."""
|
|
model_name = values.get("model_name")
|
|
max_length = values.get("max_length")
|
|
cache_dir = values.get("cache_dir")
|
|
threads = values.get("threads")
|
|
|
|
try:
|
|
# >= v0.2.0
|
|
from fastembed import TextEmbedding
|
|
|
|
values["_model"] = TextEmbedding(
|
|
model_name=model_name,
|
|
max_length=max_length,
|
|
cache_dir=cache_dir,
|
|
threads=threads,
|
|
)
|
|
except ImportError as ie:
|
|
try:
|
|
# < v0.2.0
|
|
from fastembed.embedding import FlagEmbedding
|
|
|
|
values["_model"] = FlagEmbedding(
|
|
model_name=model_name,
|
|
max_length=max_length,
|
|
cache_dir=cache_dir,
|
|
threads=threads,
|
|
)
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import 'fastembed' Python package. "
|
|
"Please install it with `pip install fastembed`."
|
|
) from ie
|
|
return values
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Generate embeddings for documents using FastEmbed.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
embeddings: List[np.ndarray]
|
|
if self.doc_embed_type == "passage":
|
|
embeddings = self._model.passage_embed(texts)
|
|
else:
|
|
embeddings = self._model.embed(texts)
|
|
return [e.tolist() for e in embeddings]
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Generate query embeddings using FastEmbed.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
query_embeddings: np.ndarray = next(self._model.query_embed(text))
|
|
return query_embeddings.tolist()
|