Description
Enhanced semantic search capability for Hailey Assist that leverages natural language processing to deliver highly relevant search results from the existing data set. The system will understand context and intent behind user queries rather than just matching keywords.
Key Components:
- Integration with Agno backend for semantic search processing
- Natural language understanding (NLU) capabilities to interpret user intent
- Contextual relevance scoring algorithm to prioritize results
- User feedback mechanism to improve search accuracy over time
- Query reformulation to handle ambiguous or complex searches
- Support for conversational search queries
- Semantic indexing of existing content
- LLM pass over to further enhance semantic search quality
Benefits:
- Significantly improved search accuracy and relevance
- Reduced time spent searching for information
- Ability to find content without knowing exact keywords
- Support for conversational and question-based queries
- Enhanced user experience through more intuitive interactions
- Decreased training requirements for new users
- Improved productivity across the platform
Example use case:
A compliance manager at a financial services firm needs to find specific regulatory information but isn't familiar with the exact terminology. Instead of searching for precise keywords like "GDPR Article 28 processor requirements," they simply ask Hailey: "What are my obligations when sharing customer data with third-party vendors in Europe?" The semantic search understands the intent, recognizes the relationship to GDPR data processor requirements, and returns highly relevant policy documents, controls, and compliance guidance—even though these exact words weren't used in the query.