mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-11 10:06:05 +00:00
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),
}
```
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
import importlib.util
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from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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class SpacyEmbeddings(BaseModel, Embeddings):
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"""Embeddings by spaCy models.
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Attributes:
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model_name (str): Name of a spaCy model.
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nlp (Any): The spaCy model loaded into memory.
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Methods:
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embed_documents(texts: List[str]) -> List[List[float]]:
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Generates embeddings for a list of documents.
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embed_query(text: str) -> List[float]:
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Generates an embedding for a single piece of text.
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"""
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model_name: str = "en_core_web_sm"
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nlp: Optional[Any] = None
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class Config:
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extra = "forbid"
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""
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Validates that the spaCy package and the model are installed.
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Args:
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values (Dict): The values provided to the class constructor.
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Returns:
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The validated values.
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Raises:
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ValueError: If the spaCy package or the
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model are not installed.
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"""
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if values.get("model_name") is None:
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values["model_name"] = "en_core_web_sm"
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model_name = values.get("model_name")
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# Check if the spaCy package is installed
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if importlib.util.find_spec("spacy") is None:
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raise ValueError(
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"SpaCy package not found. "
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"Please install it with `pip install spacy`."
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)
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try:
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# Try to load the spaCy model
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import spacy
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values["nlp"] = spacy.load(model_name)
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except OSError:
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# If the model is not found, raise a ValueError
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raise ValueError(
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f"SpaCy model '{model_name}' not found. "
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f"Please install it with"
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f" `python -m spacy download {model_name}`"
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"or provide a valid spaCy model name."
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)
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return values # Return the validated values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Generates embeddings for a list of documents.
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Args:
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texts (List[str]): The documents to generate embeddings for.
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Returns:
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A list of embeddings, one for each document.
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"""
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return [self.nlp(text).vector.tolist() for text in texts] # type: ignore[misc]
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def embed_query(self, text: str) -> List[float]:
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"""
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Generates an embedding for a single piece of text.
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Args:
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text (str): The text to generate an embedding for.
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Returns:
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The embedding for the text.
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"""
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return self.nlp(text).vector.tolist() # type: ignore[misc]
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async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Asynchronously generates embeddings for a list of documents.
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This method is not implemented and raises a NotImplementedError.
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Args:
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texts (List[str]): The documents to generate embeddings for.
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Raises:
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NotImplementedError: This method is not implemented.
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"""
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raise NotImplementedError("Asynchronous embedding generation is not supported.")
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async def aembed_query(self, text: str) -> List[float]:
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"""
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Asynchronously generates an embedding for a single piece of text.
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This method is not implemented and raises a NotImplementedError.
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Args:
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text (str): The text to generate an embedding for.
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Raises:
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NotImplementedError: This method is not implemented.
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"""
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raise NotImplementedError("Asynchronous embedding generation is not supported.")
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