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Knowledge Retrieval

Retrieve relevant information from your knowledge base during task execution.

How It Works

Xagent uses RAG (Retrieval-Augmented Generation) to enhance responses with knowledge from your knowledge base:
  1. Understand Request - Xagent analyzes your question
  2. Search Knowledge Base - Finds relevant documents
  3. Retrieve Context - Extracts matching content
  4. Generate Response - Combines knowledge with reasoning

Using Knowledge in Tasks

Search Documents in Task

Natural Language Queries

In regular tasks, simply describe what you want to know in natural language: Examples:
Xagent will:
  • Automatically search relevant knowledge bases
  • Find matching documents
  • Extract relevant information
  • Provide answers based on retrieved content

When Knowledge is Used

Xagent automatically uses knowledge base when:
  • Your question references specific information
  • You ask to search or look up documentation
  • You mention knowledge base, documents, or manuals
  • You ask for company policies, procedures, or guidelines

Using Knowledge in Agents

Configuring Agent Knowledge

When building an agent, you can specify which knowledge bases to use:
  1. Go to Build page
  2. Create or edit an agent
  3. In the Knowledge Bases section:
    • Select one or more knowledge bases
    • Agent will only search these knowledge bases
    • Useful for domain-specific agents
Example:
  • Customer Support Agent - Attach product documentation and FAQ knowledge bases
  • HR Assistant - Attach employee handbook and policy knowledge bases
  • Technical Support Agent - Attach troubleshooting and API documentation

Agent Behavior

When an agent has knowledge bases configured:
  • Agent automatically searches when questions match the domain
  • Only searches specified knowledge bases
  • Provides answers based on retrieved knowledge
  • Cites sources when available

Search Options

Search Types

Hybrid Search (Default)
  • Combines dense (vector) and sparse (keyword) search
  • Best balance of relevance and coverage
  • Recommended for most use cases
Dense Search
  • Pure vector similarity search
  • Better for semantic understanding
  • Good for conceptual queries
Sparse Search
  • Pure keyword matching
  • Better for exact terms
  • Good for specific phrases or names

Search Parameters

Top K (Default: 5)
  • Maximum number of results per knowledge base
  • Higher values = more results, slower
  • Adjust based on your needs
Min Score (Default: 0.3)
  • Minimum relevance threshold (0.0 - 1.0)
  • Filters out low-quality matches
  • Higher = stricter filtering

Search Results

What You Get

When Xagent searches the knowledge base, results include: Content
  • Relevant text passages from documents
  • Document source and name
  • Section or page reference
Metadata
  • Relevance score
  • Knowledge base name
  • Document information
Citations
  • Source document name
  • Page or section reference
  • Link to original document (if available)

Interpreting Results

High Score (0.7+)
  • Very relevant to your query
  • Directly addresses the question
  • Primary source for answer
Medium Score (0.4-0.7)
  • Somewhat relevant
  • Contains related information
  • May need additional context
Low Score (0.3-0.4)
  • Loosely related
  • General background information
  • Use with caution

Tips for Better Retrieval

For Users

Be Specific
Use Domain Language
Mention Knowledge Base

For Agent Builders

Select Relevant Knowledge Bases
  • Only attach knowledge bases the agent needs
  • Too many = slower, less accurate
  • Group related documents together
Organize by Topic
  • Separate knowledge bases by domain
  • Product docs vs. policies vs. procedures
  • Makes results more relevant
Keep Knowledge Bases Updated
  • Remove outdated documents
  • Add new information regularly
  • Re-upload when content changes significantly

For Knowledge Base Managers

Quality Content
  • Well-formatted documents process better
  • Clear structure and headings
  • Remove duplicates and outdated content
Appropriate Chunking
  • Smaller chunks = more precise results
  • Larger chunks = more context
  • Adjust overlap for your use case
Regular Maintenance
  • Monitor search quality
  • Update content regularly
  • Remove failed or duplicate documents

Troubleshooting

No Results Found

Possible reasons:
  • Knowledge base doesn’t contain relevant information
  • Search terms don’t match document content
  • Min score threshold too high
  • Wrong knowledge base selected
Solutions:
  • Try different search terms
  • Check knowledge base has relevant documents
  • Lower min score threshold
  • Search broader domain

Irrelevant Results

Possible reasons:
  • Documents too generic
  • Chunk size too large
  • Knowledge base contains unrelated content
  • Search query too vague
Solutions:
  • Be more specific in your query
  • Filter knowledge base content
  • Adjust chunk size
  • Use domain-specific terminology
Possible reasons:
  • Too many knowledge bases
  • Large knowledge base size
  • High top_k value
  • Slow embedding model
Solutions:
  • Reduce number of knowledge bases
  • Lower top_k value
  • Use faster embedding model
  • Clean up knowledge base

Agent Not Using Knowledge

Check:
  • Knowledge bases are attached to agent
  • Agent has knowledge tool category enabled
  • Query matches knowledge base domain
  • Knowledge base contains relevant content
Verify:
  • Test agent with knowledge-specific questions
  • Check agent configuration in Build page
  • Confirm knowledge bases are published

Next Steps