---
slug: /query-interfaces-of-api
---

# query - vector query

The `query()` method is used to perform vector similarity search to find the most similar documents to the query vector.

:::info

This interface is only available when using the Client. For more information about the Client, see [Client](../50.client.md).

:::

## Prerequisites

* You have installed pyseekdb. For more information about how to install pyseekdb, see [Get Started](../../10.pyseekdb-sdk/10.pyseekdb-sdk-get-started.md).

* You have connected to the database. For more information about how to connect to the database, see [Client](../50.client.md).

* You have created a collection and inserted data. For more information about how to create a collection and insert data, see [create_collection - Create a collection](../200.collection/100.create-collection-of-api.md) and [add - Insert data](../300.dml/200.add-data-of-api.md).

## Request parameters

```python
query()
```

|Parameter|Value type|Required|Description|Example value|
|---|---|---|---|---|
|`query_embeddings`|List[float] or List[List[float]] |Yes|A single vector or a list of vectors for batch queries; if provided, it will be used directly (ignoring `embedding_function`); if not provided, `query_text` must be provided, and the `collection` must have an `embedding_function`|[1.0, 2.0, 3.0]|
|`query_texts`|str or List[str]|No|A single text or a list of texts for query; if provided, it will be used directly (ignoring `embedding_function`); if not provided, `documents` must be provided, and the `collection` must have an `embedding_function`|["my query text"]|
|`n_results`|int|Yes|The number of similar results to return, default is 10|3|
|`where`|dict |No|Metadata filter conditions.|`{"category": {"$eq": "AI"}}`|
|`where_document`|dict|No|Document filter conditions.|`{"$contains": "machine"}`|
|`include`|List[str]|No|List of fields to include: `["documents", "metadatas", "embeddings"]`|["documents", "metadatas", "embeddings"]|

:::info

The `embedding_function` used is associated with the collection (set during `create_collection()` or `get_collection()`). You cannot override it for each operation.

:::

## Request example

```python
import pyseekdb

# Create a client
client = pyseekdb.Client()

collection = client.get_collection("my_collection")
collection1 = client.get_collection("my_collection1")

# Basic vector similarity query (embedding_function not used)
results = collection.query(
    query_embeddings=[1.0, 2.0, 3.0],
    n_results=3
)

# Iterate over results
for i in range(len(results["ids"][0])):
    print(f"ID: {results['ids'][0][i]}, Distance: {results['distances'][0][i]}")
    if results.get("documents"):
        print(f"Document: {results['documents'][0][i]}")
    if results.get("metadatas"):
        print(f"Metadata: {results['metadatas'][0][i]}")

# Query by texts - vectors auto-generated by embedding_function
# Requires: collection must have embedding_function set
results = collection1.query(
    query_texts=["my query text"],
    n_results=10
)
# The collection's embedding_function will automatically convert query_texts to query_embeddings

# Query by multiple texts (batch query)
results = collection1.query(
    query_texts=["query text 1", "query text 2"],
    n_results=5
)
# Returns dict with lists of lists, one list per query text
for i in range(len(results["ids"])):
    print(f"Query {i}: {len(results['ids'][i])} results")

# Query with metadata filter (using query_texts)
results = collection1.query(
    query_texts=["AI research"],
    where={"category": {"$eq": "AI"}},
    n_results=5
)

# Query with comparison operator (using query_texts)
results = collection1.query(
    query_texts=["machine learning"],
    where={"score": {"$gte": 90}},
    n_results=5
)

# Query with document filter (using query_texts)
results = collection1.query(
    query_texts=["neural networks"],
    where_document={"$contains": "machine learning"},
    n_results=5
)

# Query with combined filters (using query_texts)
results = collection1.query(
    query_texts=["AI research"],
    where={"category": {"$eq": "AI"}, "score": {"$gte": 90}},
    where_document={"$contains": "machine"},
    n_results=5
)

# Query with multiple vectors (batch query)
results = collection.query(
    query_embeddings=[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]],
    n_results=2
)
# Returns dict with lists of lists, one list per query vector
for i in range(len(results["ids"])):
    print(f"Query {i}: {len(results['ids'][i])} results")

# Query with specific fields
results = collection.query(
    query_embeddings=[1.0, 2.0, 3.0],
    include=["documents", "metadatas", "embeddings"],
    n_results=3
)
```

## Return parameters

|Parameter|Value type|Required|Description|Example value|
|---|---|---|---|---|
|`ids`|List[List[str]] |Yes|The IDs to add or modify. It can be a single ID or an array of IDs.|item1|
|`embeddings`|[List[List[List[float]]]]|No|The vectors; if provided, it will be used directly (ignoring `embedding_function`), if not provided, `documents` can be provided to generate vectors automatically.|[0.1, 0.2, 0.3]|
|`documents`|[List[List[Dict]]]|No|The documents. If `vectors` are not provided, `documents` will be converted to vectors using the `embedding_function` of the collection.| "Document text"|
|`metadatas`|[List[List[Dict]]]|No|The metadata.|`{"category": "AI"}`|
|`distances`|[List[List[Dict]]]|No| |`{"category": "AI"}`|

## Return example

```python
ID: vec1, Distance: 0.0
Document: None
Metadata: {}
ID: vec2, Distance: 0.025368153802923787
Document: None
Metadata: {}
Query 0: 4 results
Query 1: 4 results
Query 0: 2 results
Query 1: 2 results
```

## Related operations

* [get - Retrieve](300.get-interfaces-of-api.md)
* [Hybrid search](400.hybrid-search-of-api.md)
* [Operators](500.filter-operators-of-api.md)
