search-strategy

Multi-source search decomposes a natural language query into parallel, source-specific sub-queries. It applies semantic and keyword search strategies to map the query to each connected data source, retrieving relevant information. Results are then ranked by relevance and deduplicated across sources.

Enterprise search workflows handle ambiguity by applying query decomposition to map natural language questions to specific sources. When data is missing, the search strategy employs fallback mechanisms to ensure the workflow still returns the most relevant information available across connected data sources.

Ranking and deduplicating search results from different sources requires a multi-source orchestration strategy. After decomposing the query into parallel searches, the system aggregates the retrieved information, ranks results by relevance, and removes duplicate entries to provide a unified answer.

Yes, query decomposition maps semantic searches to specific data sources by breaking down a natural language query. It applies both semantic and keyword search strategies to target each connected source individually, enabling parallel searches that retrieve relevant information based on the source's structure.

Multi-source search orchestration works for researching topics across chat, docs, and issue trackers by transforming natural language questions into parallel, source-specific searches. It retrieves relevant information from these multiple connected sources and ranks the results to obtain a unified answer.