mirror of
https://github.com/hwchase17/langchain.git
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bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
import importlib
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import importlib.metadata
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from typing import Any, Dict, List, Literal, Optional
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import numpy as np
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.utils import pre_init
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MIN_VERSION = "0.2.0"
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class FastEmbedEmbeddings(BaseModel, Embeddings):
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"""Qdrant FastEmbedding models.
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FastEmbed is a lightweight, fast, Python library built for embedding generation.
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See more documentation at:
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* https://github.com/qdrant/fastembed/
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* https://qdrant.github.io/fastembed/
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To use this class, you must install the `fastembed` Python package.
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`pip install fastembed`
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Example:
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from langchain_community.embeddings import FastEmbedEmbeddings
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fastembed = FastEmbedEmbeddings()
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"""
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model_name: str = "BAAI/bge-small-en-v1.5"
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"""Name of the FastEmbedding model to use
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Defaults to "BAAI/bge-small-en-v1.5"
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Find the list of supported models at
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https://qdrant.github.io/fastembed/examples/Supported_Models/
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"""
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max_length: int = 512
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"""The maximum number of tokens. Defaults to 512.
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Unknown behavior for values > 512.
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"""
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cache_dir: Optional[str]
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"""The path to the cache directory.
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Defaults to `local_cache` in the parent directory
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"""
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threads: Optional[int]
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"""The number of threads single onnxruntime session can use.
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Defaults to None
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"""
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doc_embed_type: Literal["default", "passage"] = "default"
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"""Type of embedding to use for documents
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The available options are: "default" and "passage"
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"""
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batch_size: int = 256
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"""Batch size for encoding. Higher values will use more memory, but be faster.
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Defaults to 256.
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"""
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parallel: Optional[int] = None
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"""If `>1`, parallel encoding is used, recommended for encoding of large datasets.
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If `0`, use all available cores.
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If `None`, don't use data-parallel processing, use default onnxruntime threading.
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Defaults to `None`.
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"""
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_model: Any # : :meta private:
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class Config:
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extra = "allow"
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@pre_init
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that FastEmbed has been installed."""
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model_name = values.get("model_name")
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max_length = values.get("max_length")
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cache_dir = values.get("cache_dir")
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threads = values.get("threads")
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try:
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fastembed = importlib.import_module("fastembed")
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except ModuleNotFoundError:
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raise ImportError(
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"Could not import 'fastembed' Python package. "
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"Please install it with `pip install fastembed`."
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)
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if importlib.metadata.version("fastembed") < MIN_VERSION:
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raise ImportError(
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'FastEmbedEmbeddings requires `pip install -U "fastembed>=0.2.0"`.'
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)
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values["_model"] = fastembed.TextEmbedding(
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model_name=model_name,
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max_length=max_length,
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cache_dir=cache_dir,
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threads=threads,
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)
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Generate embeddings for documents using FastEmbed.
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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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embeddings: List[np.ndarray]
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if self.doc_embed_type == "passage":
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embeddings = self._model.passage_embed(
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texts, batch_size=self.batch_size, parallel=self.parallel
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)
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else:
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embeddings = self._model.embed(
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texts, batch_size=self.batch_size, parallel=self.parallel
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)
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return [e.tolist() for e in embeddings]
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def embed_query(self, text: str) -> List[float]:
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"""Generate query embeddings using FastEmbed.
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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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query_embeddings: np.ndarray = next(
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self._model.query_embed(
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text, batch_size=self.batch_size, parallel=self.parallel
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)
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)
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return query_embeddings.tolist()
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