34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
from langchain.embeddings.base import Embeddings
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from typing import List, Dict, Any
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import requests
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import os
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embedding_host = os.getenv("EMBEDDING_HOST", "http://183.111.96.67:30136")
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class WeaviateCustomEmbeddings(Embeddings):
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"""커스텀 엔드포인트를 사용하는 임베딩 모델"""
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def __init__(self):
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self.api_url = embedding_host.rstrip("/")
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print(self.api_url)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""문서 리스트를 임베딩합니다."""
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embeddings = []
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for text in texts:
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embeddings.append(self.embed_query(text))
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""쿼리 텍스트를 임베딩합니다."""
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query_text = text.question if hasattr(text, 'question') else str(text)
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response = requests.post(
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f"{self.api_url}/vectors",
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json={"text": query_text}
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)
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if response.status_code != 200:
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raise ValueError(f"임베딩 API 호출 실패: {self.api_url}/vectors, {query_text}, {response.status_code}, {response.text}")
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return response.json()["vector"]
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