48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""
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Embedding client wrapper for OpenAI-compatible embedding APIs.
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"""
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from __future__ import annotations
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from typing import List, Optional
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from openai import OpenAI
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class EmbeddingClient:
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"""Thin wrapper around OpenAI-compatible embedding endpoints."""
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def __init__(
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self,
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api_key: Optional[str],
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base_url: str,
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model: str,
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batch_size: int = 32,
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):
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if not base_url:
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raise ValueError("Embedding base_url 未配置")
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if not model:
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raise ValueError("Embedding model 未配置")
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self.api_key = api_key or 'ollama'
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self.base_url = base_url
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self.model = model
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self.batch_size = max(1, int(batch_size))
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self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
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def embed_texts(self, texts: List[str]) -> List[List[float]]:
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"""Embed texts in batches while preserving input order."""
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if not texts:
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return []
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embeddings: List[List[float]] = []
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normalized_inputs = [str(text or ' ').strip() or ' ' for text in texts]
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for start in range(0, len(normalized_inputs), self.batch_size):
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batch = normalized_inputs[start:start + self.batch_size]
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response = self.client.embeddings.create(model=self.model, input=batch)
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data = sorted(response.data, key=lambda item: item.index)
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embeddings.extend(item.embedding for item in data)
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return embeddings
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