mirror of
https://github.com/hwchase17/langchain.git
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844955d6e1
Assigning missed defaults in various classes. Most clients were being
assigned during the `model_validator(mode="before")` step, so this
change should amount to a no-op in those cases.
---
This PR was autogenerated using gritql
```shell
grit apply 'class_definition(name=$C, $body, superclasses=$S) where {
$C <: ! "Config", // Does not work in this scope, but works after class_definition
$body <: block($statements),
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
or {
$y <: `Field($z)`,
$x <: "model_config"
}
},
// And has either Any or Optional fields without a default
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
$t <: or {
r"Optional.*",
r"Any",
r"Union[None, .*]",
r"Union[.*, None, .*]",
r"Union[.*, None]",
},
$y <: ., // Match empty node
$t => `$t = None`,
},
}
' --language python .
```
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from typing import Any, List, Optional
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel, ConfigDict
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class ModelScopeEmbeddings(BaseModel, Embeddings):
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"""ModelScopeHub embedding models.
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To use, you should have the ``modelscope`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import ModelScopeEmbeddings
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model_id = "damo/nlp_corom_sentence-embedding_english-base"
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embed = ModelScopeEmbeddings(model_id=model_id, model_revision="v1.0.0")
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"""
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embed: Any = None
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model_id: str = "damo/nlp_corom_sentence-embedding_english-base"
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"""Model name to use."""
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model_revision: Optional[str] = None
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def __init__(self, **kwargs: Any):
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"""Initialize the modelscope"""
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super().__init__(**kwargs)
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try:
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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except ImportError as e:
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raise ImportError(
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"Could not import some python packages."
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"Please install it with `pip install modelscope`."
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) from e
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self.embed = pipeline(
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Tasks.sentence_embedding,
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model=self.model_id,
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model_revision=self.model_revision,
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)
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a modelscope embedding 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 = list(map(lambda x: x.replace("\n", " "), texts))
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inputs = {"source_sentence": texts}
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embeddings = self.embed(input=inputs)["text_embedding"]
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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 modelscope embedding 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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inputs = {"source_sentence": [text]}
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embedding = self.embed(input=inputs)["text_embedding"][0]
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return embedding.tolist()
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