Documentation Index
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Method
To retrieve vectors from the index based on specific criteria, you can use the query method, which accepts the following parameters:
vector: The reference vector for similarity comparison.sparse_vector: The sparse vector value to query.data: A string for text-based queries (mutually exclusive with vector).include_metadata: A boolean flag indicating whether to include metadata in the query results.include_vector: A boolean flag indicating whether to include vectors in the query results.include_data: A boolean flag indicating whether to include data in the query results.top_k: The number of top matching vectors to retrieve.filter: Metadata filtering of the vector is used to query your data based on the filters and narrow down the query results.namespace: The namespace to use. When not specified, the default namespace is used.weighting_strategy: Weighting strategy to be used for sparse vectors.fusion_algorithm: Fusion algorithm to use while fusing scores from hybrid vectors.query_mode: Query mode for hybrid indexes with Upstash-hosted embedding models.
As response, the object has the following fields:
id: The identifier associated with the matching vector.metadata: Additional information or attributes linked to the matching vector.score: A measure of similarity indicating how closely the vector matches the query vector. The score is normalized to the range [0, 1], where 1 indicates a perfect match.vector: The vector itself (included only if include_vector is set to True).sparse_vector: The sparse vector itself (included only if include_vector is set to True).data: Additional unstructured information linked to the matching vector.
Query Example
import random
from upstash_vector import Index
index = Index.from_env()
# Generate a random vector for similarity comparison
dimension = 128 # Adjust based on your index's dimension
query_vector = [random.random() for _ in range(dimension)]
# Execute the query
query_result = index.query(
vector=query_vector,
include_metadata=True,
include_data=True,
include_vectors=False,
top_k=5,
filter="genre = 'fantasy' and title = 'Lord of the Rings'",
)
# Print the query result
for result in query_result:
print("Score:", result.score)
print("ID:", result.id)
print("Vector:", result.vector)
print("Metadata:", result.metadata)
print("Data:", result.data)
Batch Query Method
It is also possible to perform a batch of queries in a single
call to eliminate round trips to server.
Batch Query Example
import random
from upstash_vector import Index
index = Index.from_env()
# Generate a random vector for similarity comparison
dimension = 128 # Adjust based on your index's dimension
query_vectors = [[random.random() for _ in range(dimension)] for _ in range(2)]
# Execute the query
query_results = index.query_many(
queries=[
{
"vector": query_vectors[0],
"include_metadata": True,
"include_data": True,
"include_vectors": False,
"top_k": 5,
"filter": "genre = 'fantasy' and title = 'Lord of the Rings'",
},
{
"vector": query_vectors[1],
"include_metadata": False,
"include_data": False,
"include_vectors": True,
"top_k": 3,
"filter": "genre = 'drama'",
},
]
)
for i, query_result in enumerate(query_results):
print(f"Query-{i} result:")
# Print the query result
for result in query_result:
print("Score:", result.score)
print("ID:", result.id)
print("Vector:", result.vector)
print("Metadata:", result.metadata)
print("Data:", result.data)
index.query(..., namespace="ns")
