qdrant client

client.py

@@ -0,0 +1,47 @@
+from qdrant_client import QdrantClient
+
+
+class HybridClient:
+    DENSE_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+    SPARSE_MODEL = "prithivida/Splade_PP_en_v1"
+
+    def __init__(self):
+        self.qdrant_client = QdrantClient(
+            url="https://e8c7892c-84a5-4b73-9281-27d52258c6d8.europe-west3-0.gcp.cloud.qdrant.io:6333",
+            api_key="B2LX_KVbb7VemiHlxvXxl3ORdz4hxV7xycMRgPcylldtZ1Aakx1rmQ",
+        )
+        self.qdrant_client.set_model(self.DENSE_MODEL)
+        self.qdrant_client.set_sparse_model(self.SPARSE_MODEL)
+
+    def create(self, collection: str):
+        if not self.qdrant_client.collection_exists(collection):
+            self.create_collection(
+                collection_name=collection,
+                vectors_config=self.qdrant_client.get_fastembed_vector_params(),
+                sparse_vectors_config=self.qdrant_client.get_fastembed_sparse_vector_params(),
+            )
+            return collection
+        return None
+
+    def insert(self, collection, chunks):
+        documents = []
+        for chunk in chunks:
+            documents.append(chunk.pop("text"))
+
+        self.qdrant_client.add(
+            collection_name=collection,
+            documents=documents,
+            metadata=chunks,
+            parallel=0,
+        )
+
+    def search(self, collection, text: str, limit: int = 10):
+        search_result = self.qdrant_client.query(
+            collection_name=collection,
+            query_text=text,
+            query_filter=None,
+            limit=limit,
+        )
+        # Select and return metadata
+        # metadata = [hit.metadata for hit in search_result]
+        return search_result