> ## 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.retrieve_utils.query_vector_db

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

#### query\_vector\_db

```python theme={null}
query_vector_db(
    query_texts: list[str],
    n_results: int = 10,
    client: API = None,
    db_path: str = 'tmp/chromadb.db',
    collection_name: str = 'all-my-documents',
    search_string: str = '',
    embedding_model: str = 'all-MiniLM-L6-v2',
    embedding_function: Callable = None
) -> QueryResult
```

Query a vector db. We support chromadb compatible APIs, it's not required if you prepared your own vector db
and query function.<br />

<b>Parameters:</b>

| Name                 | Description                                                                                                                                                                                                                                                                                                                                                            |
| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `query_texts`        | the list of strings which will be used to query the vector db.<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 chromadb compatible client.<br /><br />Default is None, a chromadb client will be used.<br /><br />**Type:** API<br /><br />**Default:** None                                                                                                                                                                                                                      |
| `db_path`            | the path to the vector db.<br /><br />Default is "tmp/chromadb.db".<br /><br />The default was `/tmp/chromadb.db` for version `=0.2.24`.<br /><br />**Type:** str<br /><br />**Default:** 'tmp/chromadb.db'                                                                                                                                                            |
| `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 />Only docs that contain an exact match of this string will be retrieved.<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:** 'all-MiniLM-L6-v2'                                                                                                                                                               |
| `embedding_function` | the embedding function to use.<br /><br />Default is None, SentenceTransformer with the given `embedding_model` will be used.<br /><br />If you want to use OpenAI, Cohere, HuggingFace or other embedding functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.<br /><br />**Type:** Callable<br /><br />**Default:** None |

<b>Returns:</b>

| Type        | Description                                                                                                                                                                                                                                                    |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| QueryResult | The query result. The format is: `python class QueryResult(TypedDict): ids: List[IDs] embeddings: Optional[List[List[Embedding]]] documents: Optional[List[List[Document]]] metadatas: Optional[List[List[Metadata]]] distances: Optional[List[List[float]]] ` |

<br />