83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
"""
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嵌入模型包装器，用于 llama.cpp 服务。
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"""
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import os
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from .config import LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
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import httpx
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from typing import List, Optional
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from langchain_core.embeddings import Embeddings
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class LlamaCppEmbedder:
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"""通过 OpenAI 兼容 API 封装 llama.cpp 嵌入服务。"""
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def __init__(
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self,
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base_url: Optional[str] = None,
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api_key: Optional[str] = None,
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model: str = "Qwen3-Embedding-0.6B-Q8_0",
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):
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self.base_url = base_url or LLAMACPP_EMBEDDING_URL
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self.api_key = api_key or LLAMACPP_API_KEY
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self.model = model
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def as_langchain_embeddings(self) -> Embeddings:
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"""创建 LangChain 兼容的嵌入实例。"""
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return _LlamaCppLangchainAdapter(self)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""嵌入一批文档。"""
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return self._call_embedding_api(texts)
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def embed_query(self, text: str) -> List[float]:
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"""嵌入单个查询。"""
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return self._call_embedding_api([text])[0]
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def get_embedding_dimension(self) -> int:
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"""通过嵌入测试字符串获取嵌入维度。"""
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test_embedding = self.embed_query("test")
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return len(test_embedding)
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def _call_embedding_api(self, texts: List[str]) -> List[List[float]]:
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"""直接调用 llama.cpp 嵌入 API。"""
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base = self.base_url.rstrip("/")
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if not base.endswith("/v1"):
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base = base + "/v1"
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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payload = {
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"input": texts,
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"model": self.model,
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}
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with httpx.Client(timeout=120) as client:
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response = client.post(
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f"{base}/embeddings",
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headers=headers,
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json=payload,
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)
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response.raise_for_status()
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data = response.json()
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if isinstance(data, list):
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return [item["embedding"] for item in data]
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elif isinstance(data, dict) and "data" in data:
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return [item["embedding"] for item in sorted(data["data"], key=lambda x: x["index"])]
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else:
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raise ValueError(f"未知的嵌入 API 响应格式: {data}")
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class _LlamaCppLangchainAdapter(Embeddings):
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"""将 LlamaCppEmbedder 适配为 LangChain Embeddings 接口。"""
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def __init__(self, embedder: LlamaCppEmbedder):
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self._embedder = embedder
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return self._embedder.embed_documents(texts)
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def embed_query(self, text: str) -> List[float]:
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return self._embedder.embed_query(text) |