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48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
from abc import ABC
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from typing import List
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from loguru import logger
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from pydantic import BaseModel, Field
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from experiencemaker.schema.vector_node import VectorNode
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class BaseEmbeddingModel(BaseModel, ABC):
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model_name: str = Field(default=..., description="model name")
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dimensions: int = Field(default=..., description="dimensions")
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max_retries: int = Field(default=3, description="max retries")
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raise_exception: bool = Field(default=True, description="raise exception")
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def _get_embeddings(self, input_text: str | List[str]):
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raise NotImplementedError
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def get_embeddings(self, input_text: str | List[str]):
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for i in range(self.max_retries):
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try:
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return self._get_embeddings(input_text)
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except Exception as e:
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logger.exception(f"embedding model name={self.model_name} encounter error with e={e.args}")
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if i == self.max_retries - 1 and self.raise_exception:
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raise e
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return None
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def get_node_embeddings(self, nodes: VectorNode | List[VectorNode]):
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if isinstance(nodes, VectorNode):
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nodes.vector = self.get_embeddings(nodes.content)
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return nodes
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elif isinstance(nodes, list):
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max_batch_size = 10 # text-embedding-v4 batch size should not be larger than 10
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embeddings = [emb for i in range(0, len(nodes), max_batch_size) for emb in
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self.get_embeddings(input_text=[node.content for node in nodes[i:i + max_batch_size]])]
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if len(embeddings) != len(nodes):
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logger.warning(f"embeddings.size={len(embeddings)} <> nodes.size={len(nodes)}")
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else:
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for node, embedding in zip(nodes, embeddings):
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node.vector = embedding
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return nodes
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else:
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raise RuntimeError(f"unsupported type={type(nodes)}")
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