> ## Documentation Index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Query

## 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.

<Tip>If you wanna learn more about filtering check: [Metadata Filtering](/vector/features/filtering)</Tip>

## Query Example

```python theme={"system"}
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

```python theme={"system"}
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)
```

Also, you can specify a namespace to operate on. When no namespace
is provided, the default namespace will be used.

```python theme={"system"}
index.query(..., namespace="ns")
```