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Leverage the power of Retrieval-Augmented Generation (RAG) to search through your meeting content with unprecedented accuracy and context awareness. Find exactly what you need with natural language queries.

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What is RAG?

RAG (Retrieval-Augmented Generation) is an AI architecture that enhances traditional search by combining:

Information Retrieval

Precise Content Finding
  • Semantic search across all meeting data
  • Vector-based similarity matching
  • Context-aware content retrieval
  • Real-time index updates

Language Generation

Intelligent Response Creation
  • Natural language understanding
  • Context-aware answer generation
  • Multi-document synthesis
  • Conversational search interface

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How RAG Works in Optiverse

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The RAG Pipeline

1

Content Ingestion

Data Processing
  • Meeting transcripts are processed in real-time
  • Content is segmented into meaningful chunks
  • Metadata is extracted and indexed
  • Vector embeddings are generated
2

Query Processing

Search Understanding
  • User query is analyzed for intent
  • Key entities and concepts are identified
  • Search parameters are optimized
  • Semantic similarity is calculated
3

Retrieval

Content Discovery
  • Relevant content chunks are identified
  • Similarity scores are calculated
  • Context windows are assembled
  • Source attribution is maintained
4

Generation

Response Creation
  • Retrieved content is synthesized
  • Natural language responses are generated
  • Source citations are included
  • Follow-up suggestions are provided

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Technical Architecture

Embedding StorageVector Embeddings:
  • High-dimensional representations of meeting content
  • Semantic similarity preservation
  • Efficient similarity search
  • Real-time updates and indexing
Index Structure:
Meeting Content → Text Chunks → Vector Embeddings → Index

Example:
"We decided to increase the marketing budget by 20%"
↓
[0.123, -0.456, 0.789, ..., 0.321] (768-dimensional vector)
↓
Indexed with metadata: {meeting_id, timestamp, speakers, etc.}

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Advanced Search Capabilities

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Semantic Understanding

Beyond Keywords: RAG search understands meaning and context, not just keyword matches. This enables more natural and precise search experiences.

Text Search

Transcript Analysis
  • Full-text search across transcripts
  • Semantic meaning extraction
  • Context-aware matching
  • Sentiment analysis integration

Speaker Search

Person-Centric Queries
  • Individual contribution tracking
  • Role-based search filtering
  • Expertise identification
  • Communication pattern analysis

Temporal Search

Time-Based Discovery
  • Chronological event tracking
  • Trend analysis over time
  • Meeting series connections
  • Historical context retrieval

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Complex Query Handling

Compound QueriesQuestion Decomposition:
Complex Query: "What decisions were made about the mobile app launch timeline, and who was assigned to handle the marketing campaign?"

Decomposition:
1. Find decisions about mobile app launch timeline
2. Find assignments related to marketing campaign
3. Connect both topics to provide comprehensive answer

Response Structure:
- Timeline decisions with context
- Marketing assignments with details
- Connections between the two topics

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Performance Optimization

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Search Speed Enhancement

1

Index Optimization

Efficient Storage
  • Hierarchical vector indexing
  • Compressed embedding storage
  • Parallel processing capabilities
  • Cache optimization strategies
2

Query Optimization

Smart Processing
  • Query preprocessing and optimization
  • Intelligent result caching
  • Predictive prefetching
  • Load balancing across servers
3

Result Ranking

Relevance Optimization
  • Machine learning-based ranking
  • User behavior analysis
  • Personalization algorithms
  • Continuous improvement loops

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Quality Assurance

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Advanced Features

User Preferences

Customized Experience
  • Search history analysis
  • Personal relevance scoring
  • Preferred information types
  • Individual workflow patterns

Team Context

Collaborative Intelligence
  • Team-specific terminology
  • Project context awareness
  • Role-based result filtering
  • Organizational knowledge mapping

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Continuous Learning

User-Driven ImprovementLearning Mechanisms:
  • Search result ratings
  • Click-through analysis
  • Query refinement patterns
  • User correction feedback
Improvement Cycles:
Feedback Collection → Analysis → Model Updates → Testing → Deployment

Example:
User indicates search result was not relevant
↓
Algorithm analyzes query-result mismatch
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Ranking model is updated
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Similar queries get better results

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Analytics and Insights

1

Search Analytics

Usage Intelligence
  • Query pattern analysis
  • Popular search topics
  • User behavior insights
  • Performance metrics
2

Content Insights

Knowledge Discovery
  • Frequently referenced topics
  • Knowledge gap identification
  • Expertise mapping
  • Content utilization patterns
3

Predictive Analytics

Future Insights
  • Trending topics prediction
  • Information need forecasting
  • Proactive content suggestions
  • Strategic decision support

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Integration Capabilities

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API Access

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Embedding Integration

Custom Applications

Developer Integration
  • Embed search in custom apps
  • White-label search interfaces
  • API-first architecture
  • Flexible response formats

Third-Party Tools

External Integrations
  • Slack search commands
  • Microsoft Teams integration
  • Notion database search
  • Custom dashboard widgets

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Best Practices

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Query Optimization

Search Efficiency: Well-crafted queries not only return better results but also perform faster and consume fewer resources.
1

Specific Queries

Precision Over Breadth
❌ Vague: "meetings about stuff"
✅ Specific: "Q4 budget planning decisions in marketing meetings"

❌ Too broad: "what happened yesterday"
✅ Focused: "action items from yesterday's product review meeting"
2

Context Inclusion

Provide Relevant Context
❌ Ambiguous: "What did John say?"
✅ Contextual: "What did John say about the API integration timeline in the engineering standup?"

❌ Unclear: "budget issues"
✅ Specific: "budget concerns raised during Q4 planning for the mobile app project"
3

Filter Utilization

Narrow Search Scope
  • Use date ranges for time-sensitive queries
  • Apply speaker filters for person-specific searches
  • Utilize meeting type filters for context
  • Employ project tags for focused results

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Advanced Search Techniques

Progressive SearchRefinement Process:
  1. Start with broad query
  2. Analyze initial results
  3. Identify relevant themes
  4. Refine query with specific terms
  5. Apply appropriate filters
Example Progression:
Query 1: "project delays"
→ Review results, identify specific projects

Query 2: "mobile app project delays in Q3"
→ Narrow to specific causes

Query 3: "mobile app API integration delays September"
→ Refined, specific results

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Troubleshooting

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Common Issues

Possible Causes:
  • Query too vague or ambiguous
  • Missing relevant context
  • Incorrect filters applied
  • Content not yet indexed
Solutions:
  • Use more specific search terms
  • Include relevant context and names
  • Review and adjust filters
  • Wait for recent content indexing
Possible Causes:
  • Complex query processing
  • Large result set generation
  • Network connectivity issues
  • Server load limitations
Solutions:
  • Simplify complex queries
  • Apply filters to reduce scope
  • Check internet connection
  • Try searching during off-peak hours
Possible Causes:
  • Content not properly indexed
  • Privacy/permission restrictions
  • Meeting processing incomplete
  • Search scope too narrow
Solutions:
  • Verify meeting was processed
  • Check access permissions
  • Expand search criteria
  • Contact support for indexing issues

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Performance Optimization

Query Optimization

Faster Searches
  • Use specific keywords
  • Apply relevant filters
  • Limit result count
  • Cache frequent queries

Result Refinement

Better Accuracy
  • Provide clear context
  • Use proper terminology
  • Include relevant details
  • Iterate on queries

System Efficiency

Resource Management
  • Monitor search patterns
  • Optimize index structure
  • Update content regularly
  • Maintain system health
RAG Evolution: RAG technology continuously improves through usage patterns, feedback, and advances in AI research. Your search experience will become more accurate and efficient over time.
Next Steps: