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

# Agent Search

> A guide to use advanced search capability in Hymalaia.

Agent Search is Hymalaia advanced knowledge retrieval system that enables answering complex, multi-faceted questions by intelligently decomposing queries, searching across multiple contexts, and synthesizing comprehensive answers.

Unlike traditional search, Agent Search approaches questions like a knowledgeable colleague would:

1. **Decompose and disambiguate**
2. **Analyze narrow, well-defined sub-questions**
3. **Synthesize and present comprehensive context-rich answers**

> 💡 *Example:* When comparing two products (e.g. Car A vs. Car B), Agent Search will independently explore both, then compare them to form a rich, contextual answer.

***

## Key Features

* **Intelligent Query Decomposition**\
  Breaks complex questions into precise sub-questions

* **Parallel Search Processing**\
  Executes multiple analysis threads simultaneously

* **Answer Validation**\
  Refines and validates responses for accuracy and completeness

***

## Configuration

### Basic Setup

To enable Agent Search in your Hymalaia deployment:

1. Update to the latest version of Hymalaia
2. Configure knowledge source connections
3. Set up LLM provider credentials
4. Enable the **Agent** toggle in the chat interface (with a search-capable assistant)

***

### Advanced Configuration

#### Best Practices & Suggestions

* Don’t hesitate to ask **complex or multi-layered questions**.
* Try **comparative queries** like:
  > *“What’s the difference between Solution A and B?”*\
  > Agent Search will separately analyze A and B before comparing.
* Ask **ambiguous questions** such as:
  > *“What are the guiding principles for X?”*\
  > The system will use context to clarify what "guiding principles" refers to.
* Even **simple questions** may benefit from deeper, contextualized answers.
* **Click on sub-question analyses** — they may provide interesting insights individually.

> ⚠️ It is recommended to assign a **faster/cheaper LLM model** as your *Fast Model*, since Agent Search performs many parallel queries.

***

## Common Issues and Solutions

| Issue                          | Solution                                                                                     |
| ------------------------------ | -------------------------------------------------------------------------------------------- |
| **Langgraph/Langchain errors** | Ensure server uses Python 3.11 and installs libraries from `backend/requirements.txt`.       |
| **Rate limits**                | Agent Search may hit rate limits due to parallel queries. Use a provider with higher limits. |
| **Timeouts**                   | Timeout thresholds are enforced to avoid blocking. Contact support if these are too strict.  |
| **High token usage**           | Expect significantly more input/output tokens than with Basic Search.                        |

***

## Summary

Agent Search offers a powerful way to surface deeper insights, especially when working with ambiguous or multi-faceted questions. For best performance:

* Use optimized LLM configurations
* Expect and account for higher token usage
* Experiment with your queries to see how well the system synthesizes knowledge

> 💬 Reach out to us on Slack or Discord if you're experiencing issues or want help fine-tuning your setup.