> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ag2.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent.query_qdrant

<code class="doc-symbol doc-symbol-heading doc-symbol-function" />

#### query\_qdrant

```python theme={null}
query_qdrant(
    query_texts: list[str],
    n_results: int = 10,
    client: QdrantClient = None,
    collection_name: str = 'all-my-documents',
    search_string: str = '',
    embedding_model: str = 'BAAI/bge-small-en-v1.5',
    qdrant_client_options: dict[str, Any] | None = {}
) -> list[list['QueryResponse']]
```

Perform a similarity search with filters on a Qdrant collection

<b>Parameters:</b>

| Name                    | Description                                                                                                                                                                                                                                                                                                                                                          |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `query_texts`           | the query texts.<br /><br />**Type:** list\[str]                                                                                                                                                                                                                                                                                                                     |
| `n_results`             | the number of results to return.<br /><br />Default is 10.<br /><br />**Type:** int<br /><br />**Default:** 10                                                                                                                                                                                                                                                       |
| `client`                | the QdrantClient instance.<br /><br />A default in-memory client will be instantiated if None.<br /><br />**Type:** QdrantClient<br /><br />**Default:** None                                                                                                                                                                                                        |
| `collection_name`       | the name of the collection.<br /><br />Default is "all-my-documents".<br /><br />**Type:** str<br /><br />**Default:** 'all-my-documents'                                                                                                                                                                                                                            |
| `search_string`         | the search string.<br /><br />Default is "".<br /><br />**Type:** str<br /><br />**Default:** ''                                                                                                                                                                                                                                                                     |
| `embedding_model`       | the embedding model to use.<br /><br />Default is "all-MiniLM-L6-v2".<br /><br />Will be ignored if embedding\_function is not None.<br /><br />**Type:** str<br /><br />**Default:** 'BAAI/bge-small-en-v1.5'                                                                                                                                                       |
| `qdrant_client_options` | (Optional, dict): the options for instantiating the qdrant client.<br /><br />Reference: [https://github.com/qdrant/qdrant-client/blob/master/qdrant\_client/qdrant\_client.py#L36-L58](https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58).<br /><br />**Type:** dict\[str, typing.Any] \| None<br /><br />**Default:** \{} |

<b>Returns:</b>

| Type                          | Description                                                                                                                                                                                                                               |
| ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| list\[list\['QueryResponse']] | list\[list\[QueryResponse]]: the query result. The format is: class QueryResponse(BaseModel, extra="forbid"): # type: ignore id: Union\[str, int] embedding: Optional\[List\[float]] metadata: Dict\[str, Any] document: str score: float |

<br />