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64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""Wrapper around sentence transformer embedding models."""
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.embeddings.base import Embeddings
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class SentenceTransformerEmbeddings(BaseModel, Embeddings):
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embedding_function: Any #: :meta private:
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model: Optional[str] = Field("all-MiniLM-L6-v2", alias="model")
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"""Transformer model to use."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that sentence_transformers library is installed."""
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model = values["model"]
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try:
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from sentence_transformers import SentenceTransformer
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values["embedding_function"] = SentenceTransformer(model)
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except ImportError:
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raise ModuleNotFoundError(
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"Could not import sentence_transformers library. "
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"Please install the sentence_transformers library to "
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"use this embedding model: pip install sentence_transformers"
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)
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except Exception:
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raise NameError(f"Could not load SentenceTransformer model {model}.")
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using the SentenceTransformer 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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embeddings = self.embedding_function.encode(
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texts, convert_to_numpy=True
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).tolist()
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return [list(map(float, e)) for e in embeddings]
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using the SentenceTransformer model.
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Args:
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text: The text to embed.
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Returns:
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Embedding for the text.
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"""
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return self.embed_documents([text])[0]
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