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

# Playground

> Test your namespace with agentic search in the dashboard

The playground lets you test your namespace from the dashboard without writing code. The chat playground answers questions with [agentic search](/search-and-retrieval/agentic-search): the model searches your documents itself, deciding what to look for and when it has enough context to answer. The playground also includes a search tab for running queries directly.

## How it works

When you send a message, the model runs a tool-calling loop of up to 20 steps with two tools:

| Tool     | Description                                                                                                                                                                                                                                   |
| :------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `search` | Searches the namespace with a query the model writes, in `semantic` or `keyword` mode. Keyword search is available on Turbopuffer-backed namespaces (the default for managed namespaces).                                                     |
| `expand` | Fetches roughly 10 surrounding chunks (5 before, 5 after by position in the document) when a retrieved chunk is cut off or needs nearby context. Available on Turbopuffer-backed namespaces; not yet supported on Pinecone-backed namespaces. |

The chat shows the progress of each search and expand call as the model works, then streams the final answer.

## Modes

The chat playground has two modes:

| Mode                   | Behavior                                                                                                                                     |
| :--------------------- | :------------------------------------------------------------------------------------------------------------------------------------------- |
| **Accurate** (default) | Each semantic search fetches Top K chunks (default 30) and reranks them down to the Rerank Limit (default 10) with the configured re-ranker. |
| **Fast**               | Skips reranking. Each search returns the Rerank Limit (default 10) chunks directly.                                                          |

## Citations

Answers cite sources with inline pills resolved from the retrieved chunks. Click a pill to view the source text and its metadata.

## Parameters

Open the parameters dialog to tune retrieval and generation:

| Parameter         | Description                                                                   |
| :---------------- | :---------------------------------------------------------------------------- |
| **Top K**         | Number of chunks fetched per semantic search, before reranking (default 30)   |
| **Rerank Limit**  | Number of chunks the model sees per search (default 10)                       |
| **System Prompt** | Instructions for the assistant. Defaults to a prompt tuned for agentic search |
| **Re-ranker**     | Model used to rerank results in Accurate mode                                 |
| **Temperature**   | Sampling temperature. Has no effect on reasoning models like GPT-5.5          |

Use the model picker to change the language model. GPT-5.5 is the default.

## Next steps

* [Agentic Search](/search-and-retrieval/agentic-search) — Build the same tool-calling loop in your own app
* [Search](/search-and-retrieval/search) — Query your namespace through the API
* [Hosting UI](/production/hosting-ui) — Share a prebuilt chat interface with your users