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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
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