> For the complete documentation index, see [llms.txt](https://docs.hyper-space.io/hyperspace-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hyper-space.io/hyperspace-docs/flows/setting-up/building-and-running-queries.md).

# Building and Running Queries

The following describes how to create a native Python syntax query that returns the identifiers of documents that have the strongest match to your search criteria. These identifiers can then be used to retrieve the corresponding matching documents.

In addition to Python syntax, Hyperspace also supports [Elasticsearch DSL syntax](https://docs.hyper-space.io/hyperspace-docs/flows/es-dsl-query-interface).

A query can define one of the following types of search –

* [**Classic (Lexical) Search**](https://docs.hyper-space.io/hyperspace-docs/flows/setting-up/building-and-running-queries/building-a-lexical-classic-search-query) - Based on keyword and term matching
* [**Vector Search**](https://docs.hyper-space.io/hyperspace-docs/flows/setting-up/building-and-running-queries/building-a-vector-search-query) - Based on vector similarity
* [**Hybrid Search**](https://docs.hyper-space.io/hyperspace-docs/flows/setting-up/building-and-running-queries/building-a-hybrid-search-query) Returns the combined similarity match results of both the Classic and the Vector Search.

{% hint style="info" %}
Classic search can be used to improve the results of vector search, by filtering based on metadata fields.
{% endhint %}


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.hyper-space.io/hyperspace-docs/flows/setting-up/building-and-running-queries.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.