Open Notebook provides powerful search capabilities to help you find information quickly across your entire knowledge base. This guide covers both traditional search methods and AI-powered question answering.
Open Notebook offers two main search approaches:
- Direct Search - Find specific content using text or vector search
- Ask Your Knowledge Base - Get AI-generated answers based on your content
Text search uses full-text indexing with BM25 ranking to find exact matches and similar terms across your content.
Best for:
- Finding specific keywords, phrases, or terms
- Locating exact quotes or references
- Technical terms and proper nouns
- When you know approximately what you're looking for
Search Coverage:
- Sources: Title, full text content, embedded chunks, and insights
- Notes: Title and content
Features:
- Highlighted search results show matching terms
- BM25 relevance scoring
- Stemming and lowercase matching
- Punctuation and camel case tokenization
Vector search uses semantic embeddings to find conceptually similar content, even when exact keywords don't match.
Best for:
- Finding concepts and ideas
- Discovering related content
- Exploring themes and topics
- When you're not sure of exact terminology
Requirements:
- An embedding model must be configured (see Models Guide)
- Content must be processed with embeddings
Search Coverage:
- Sources: Embedded content chunks and insights
- Notes: Full note content (with embeddings)
Features:
- Cosine similarity scoring
- Configurable minimum similarity threshold (default: 0.2)
- Semantic understanding of content relationships
- Go to the Search tab in the "Ask and Search" page
- Enter your search query
- Select search type (Text or Vector)
- Choose what to search:
- Search Sources: Include imported documents and content
- Search Notes: Include your personal notes
- Click Search
Results are displayed with:
- Relevance/Similarity Score: Higher scores indicate better matches
- Title: Content title or note title
- Content Preview: Matching text excerpt
- Source Link: Click to view the full source or note
- Highlights: Matching terms highlighted in text search
- Use specific keywords for better results
- Try different variations of terms
- Use quotes for exact phrase matching
- Include technical terms and acronyms
- Be specific rather than general
Examples:
machine learning algorithms
"neural network architecture"
API documentation
React hooks
- Use natural language descriptions
- Focus on concepts rather than exact words
- Describe what you're looking for thematically
- Use complete sentences or phrases
Examples:
How to optimize database performance
Strategies for team collaboration
Best practices for code review
User interface design principles
- Search Sources: Include imported documents, PDFs, web pages, etc.
- Search Notes: Include your personal notes and AI-generated content
- Limit: Maximum number of results (default: 100, max: 1000)
- Minimum Score: For vector search, set similarity threshold (0.0 to 1.0)
- Start with vector search for broad concept discovery
- Use text search for specific details
- Cross-reference results between search types
- Begin with broader terms
- Refine based on initial results
- Use discovered keywords for follow-up searches
- Explore related concepts found in results
- Pay attention to similarity/relevance scores
- Look for patterns in top results
- Use result previews to assess relevance
- Follow source links for full context
The Ask feature uses AI to generate comprehensive answers based on your content, combining multiple search queries automatically.
- Query Strategy: AI analyzes your question and generates multiple search queries
- Individual Searches: Each query is processed using vector search
- Individual Answers: AI generates answers for each search result
- Final Answer: All individual answers are combined into a comprehensive response
- Embedding Model: Required for vector search functionality
- Three AI Models:
- Query Strategy Model: Powerful model for search planning (GPT-4, Claude, etc.)
- Individual Answer Model: Can be faster/cheaper model (GPT-4 Mini, etc.)
- Final Answer Model: Powerful model for synthesis (GPT-4, Claude, etc.)
- Go to the Ask Your Knowledge Base tab
- Enter your question in natural language
- Select your AI models for each processing stage
- Click Ask
Recommended: GPT-4, Claude Sonnet, Gemini Pro, Llama 3.2
- Needs strong reasoning for search strategy
- Determines what information to look for
- Critical for answer quality
Recommended: GPT-4 Mini, Gemini Flash, cheaper models
- Processes individual search results
- Can use faster models for efficiency
- Multiple instances run in parallel
Recommended: GPT-4, Claude Sonnet, Gemini Pro
- Synthesizes all information
- Creates coherent final response
- Benefits from strong language capabilities
What are the main benefits of microservices architecture?
How does React handle state management?
What security measures are recommended for APIs?
Compare different database indexing strategies
Analyze the pros and cons of remote work policies
What are the trade-offs between SQL and NoSQL databases?
Summarize the key findings from my research on user experience
What patterns emerge from my project retrospectives?
How do different sources approach machine learning optimization?
- Answers include links to source documents
- Click citations to view original content
- Source attribution for fact-checking
- Transparency in information sources
- Save AI-generated answers as notes
- Select target notebook
- Preserved as "AI" note type
- Maintains question-answer format
- Be specific about what you need
- Provide context when helpful
- Ask follow-up questions to drill down
- Use natural language
Good:
How do the papers in my collection approach neural network optimization?
What are the common themes in my customer feedback notes?
Based on my research, what are the best practices for API design?
Less Effective:
Tell me about AI
What's in my notes?
Help me understand stuff
- Use cheaper models for individual answers
- Reserve powerful models for strategy and final synthesis
- Monitor token usage in model settings
- Consider using local models for frequent queries
- Source Processing: Ensure sources are properly imported and processed
- Note Organization: Well-structured notes improve search results
- Embedding Coverage: Verify content has embeddings for vector search
- Progressive Refinement: Start broad, then narrow down
- Mixed Approach: Combine text and vector search
- Result Evaluation: Review search scores and relevance
- Embedding Model: Choose appropriate model for your use case
- Index Health: Ensure search indices are properly maintained
- Content Volume: Balance between comprehensive coverage and search speed
- Direct Saving: Save useful search results as notes
- Answer Preservation: Save AI-generated answers for reference
- Notebook Organization: Organize saved searches by topic
- Research Phase: Use search to gather relevant information
- Analysis Phase: Ask targeted questions about findings
- Synthesis Phase: Combine insights into new notes
- Review Phase: Search for related content and updates
- Use search results to inform chat conversations
- Ask follow-up questions based on search findings
- Reference search results in chat for context
Problem: Vector search option not showing Solution: Configure an embedding model in the Models section
Problem: Search returns irrelevant results Solutions:
- Try different keywords or phrases
- Switch between text and vector search
- Check search filters (sources/notes)
- Verify content has been properly processed
Problem: Ask feature shows errors Solutions:
- Ensure embedding model is configured
- Verify all three AI models are selected
- Check model API keys and settings
- Confirm content has embeddings
Problem: Search takes too long Solutions:
- Reduce search limit
- Use more specific queries
- Check system resources
- Consider content volume optimization
If you encounter issues:
- Check the Troubleshooting Guide
- Verify model configurations
- Review search query syntax
- Check system requirements
- Review relevance scores to understand match quality
- Use highlighted excerpts to verify result accuracy
- Follow source links for full context
- Use Ask feature for processing multiple related questions
- Save answers as notes for systematic knowledge building
- Create question templates for consistent analysis
- Combine search with transformation features
- Use search results as input for AI analysis
- Create knowledge maps from search patterns
Open Notebook's search capabilities provide both precision and discovery tools for your knowledge base. By combining traditional text search with modern vector search and AI-powered question answering, you can efficiently find information and generate insights from your content.
Remember to:
- Choose the right search type for your needs
- Configure appropriate AI models for Ask feature
- Save valuable results as notes
- Use iterative search strategies for best results
- Leverage both search types for comprehensive coverage
The search system grows more valuable as you add more content and develop better search strategies tailored to your specific knowledge domains.