jembedding更新为支持离线加载模型

apps/jembedding/app.py

@@ -1,7 +1,6 @@
 from fastapi import FastAPI
-from api import router
 from settings import settings
-
+from api import router
 
 app = FastAPI(
     title="JEmbedding",

apps/jembedding/service.py

@@ -1,27 +1,52 @@
 import asyncio
 from typing import List, Iterable, AsyncGenerator, Optional
-from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
 
 class JEmbeddingService:
-    def __init__(self, model_name: str = "Qwen/Qwen3-Embedding-0.6B"):
+    def __init__(self, model_path: str = "Qwen/Qwen3-Embedding-0.6B"):
-        self.model_name = model_name
+        self.model_path = model_path
-        self.model: Optional[SentenceTransformer] = None
+        self.tokenizer = None
+        self.model = None
         self._load_model()
 
     def _load_model(self) -> None:
-        self.model = SentenceTransformer(self.model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(self.model_path, trust_remote_code=True)
+        self.model.eval()
 
     async def embed(self, texts: List[str]) -> List[List[float]]:
         if not isinstance(texts, list) or any(not isinstance(t, str) for t in texts):
             raise ValueError("texts必须是字符串列表")
+        
+        def encode_texts():
+            embeddings = []
+            for text in texts:
+                # Tokenize
+                inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+                
+                # Get embeddings from last hidden state
+                with torch.no_grad():
+                    outputs = self.model(**inputs, output_hidden_states=True)
+                    embedding = outputs.hidden_states[-1].mean(dim=1).squeeze()
+                    embeddings.append(embedding.tolist())
+            return embeddings
+        
         loop = asyncio.get_running_loop()
-        embeddings = await loop.run_in_executor(None, self.model.encode, texts)
+        return await loop.run_in_executor(None, encode_texts)
-        return [vec.tolist() if hasattr(vec, 'tolist') else vec for vec in embeddings]
 
     async def similarity(self, embeddings_a: List[List[float]], embeddings_b: List[List[float]]):
+        def compute_similarity():
+            import torch.nn.functional as F
+            # Convert to tensors
+            emb_a = torch.tensor(embeddings_a)
+            emb_b = torch.tensor(embeddings_b)
+            # Compute cosine similarity
+            return F.cosine_similarity(emb_a.unsqueeze(1), emb_b.unsqueeze(0), dim=2).tolist()
+        
         loop = asyncio.get_running_loop()
-        return await loop.run_in_executor(None, self.model.similarity, embeddings_a, embeddings_b)
+        return await loop.run_in_executor(None, compute_similarity)
 
     async def process_batch(self, items: Iterable[str]) -> AsyncGenerator[dict, None]:
         texts: List[str] = []