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:
- Decompose and disambiguate
- Analyze narrow, well-defined sub-questions
- 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:
- Update to the latest version of Hymalaia
- Configure knowledge source connections
- Set up LLM provider credentials
- 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.
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:
- Decompose and disambiguate
- Analyze narrow, well-defined sub-questions
- 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:
- Update to the latest version of Hymalaia
- Configure knowledge source connections
- Set up LLM provider credentials
- 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.