# prepare_query_for_vector_search

> **Function** in `langchain_mongodb`

📖 [View in docs](https://reference.langchain.com/python/langchain-mongodb/utils/prepare_query_for_vector_search)

Prepare a query for vector search based on the embedding type.

This function checks if the embedding is an AutoEmbeddings instance.
If it is, the query is returned as-is (string) for server-side embedding.
Otherwise, the query is embedded using the embedding model's embed_query method.

## Signature

```python
prepare_query_for_vector_search(
    query: str,
    embedding: Any,
) -> tuple[str | list[float], bool]
```

## Parameters

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `query` | `str` | Yes | The search query string. |
| `embedding` | `Any` | Yes | The embedding model instance (either AutoEmbeddings or a standard Embeddings). |

## Returns

`str | list[float]`

A tuple containing:

---

[View source on GitHub](https://github.com/langchain-ai/langchain-mongodb/blob/edee54e78705190a78087e13013412a39d71a651/libs/langchain-mongodb/langchain_mongodb/utils.py#L199)