adds qwen for testing

app.py

@@ -20,7 +20,8 @@ ALLOWED_API_KEY = str(API_KEY)
 async def search(q: str, type: int, lt: str | None = None, offset: int = 0):
     query = q.lower()
     xss = nh3.clean(query)
-    data = await doc_searcher.search(text=xss,type=type,law_type=lt,offset=offset)
+    # data = await doc_searcher.search(text=xss,type=type,law_type=lt,offset=offset)
+    data = await doc_searcher.search_temp(text=xss)
     return data
 
 @app.get("/api/suggestions")

doc_searcher.py

@@ -10,6 +10,7 @@ class DocSearcher:
     def __init__(self, collection_name):
         self.collection_name = collection_name
         self.dense_model = SentenceTransformer(DENSE_MODEL,device="cpu",token=HUGGING_FACE_API_KEY)
+        self.model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B",device="cpu")
         self.sparse_model = SparseTextEmbedding(SPARSE_MODEL)
         self.late_interaction_model = LateInteractionTextEmbedding(LATE_INTERACTION_MODEL)
         self.qdrant_client = QdrantClient(QDRANT_URL,api_key=QDRANT_API_KEY,timeout=30)
@@ -93,6 +94,42 @@ class DocSearcher:
 
         data = []
 
+        for hit in search_result:
+            data.append(hit.payload)
+        
+        return data
+    
+    async def search_temp(self, text: str):
+        
+        dense_query = self.model.encode(text).tolist()
+        sparse_query = next(self.sparse_model.query_embed(text))
+
+        prefetch = [
+                models.Prefetch(
+                    query=dense_query,
+                    using=DENSE_MODEL,
+                    limit=100
+                ),
+                # models.Prefetch(
+                #     query=models.SparseVector(**sparse_query.as_object()),
+                #     using=SPARSE_MODEL,
+                #     limit=100
+                # )
+        ]
+            
+        search_result = self.qdrant_client.query_points(
+            collection_name= self.collection_name,
+            query_filter=filter,
+            prefetch=prefetch,
+            query=models.FusionQuery(
+                fusion=models.Fusion.RRF,
+            ),
+            with_payload=True,
+            limit = 10,
+        ).points
+
+        data = []
+
         for hit in search_result:
             data.append(hit.payload)