> ## Documentation Index
> Fetch the complete documentation index at: https://upstash-vector.mintlify.site/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.
* `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.
* `top_k`: The number of top matching vectors to retrieve.

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.
* `vector`: The vector itself (included only if `include_vector` is set to `True`).

## Query Example

```python
from upstash_vector import Index
import random

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)]

# Set parameters for the query
include_metadata = True
include_vector = False
top_k = 5

# Execute the query
query_result = index.query(
    vector=query_vector,
    include_metadata=include_metadata,
    include_vector=include_vector,
    top_k=top_k
)

# 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()
```