Download code/db_upload.py from Qdrant/google-landmark-geo: direct link, hf CLI and curl.
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https://huggingface.co/datasets/Qdrant/google-landmark-geo/resolve/main/code/db_upload.py
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hf download hf://datasets/Qdrant/google-landmark-geo/code/db_upload.py
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curl -L -o db_upload.py https://huggingface.co/datasets/Qdrant/google-landmark-geo/resolve/main/code/db_upload.py
1.02 kB
| from qdrant_client import QdrantClient | |
| from qdrant_client.models import Distance, VectorParams | |
| class DBUpload: | |
| def __init__(self, vector_size: int, collection_name: str): | |
| self.client = QdrantClient(host="localhost", port=6333) | |
| self.vector_size = vector_size | |
| self.collection_name = collection_name | |
| self.client.recreate_collection( | |
| collection_name=collection_name, | |
| vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE), | |
| ) | |
| def upsert_vectors(self, ids, vectors, payloads): | |
| self.client.upload_collection( | |
| collection_name=self.collection_name, | |
| ids=ids, | |
| vectors=vectors, | |
| payload=payloads | |
| ) | |
| def query_vector(self, vector): | |
| print(type(vector)) | |
| vector = vector[0] | |
| search_result = self.client.search( | |
| collection_name=self.collection_name, | |
| query_vector=vector, | |
| limit=5 | |
| ) | |
| return search_result | |