Query decomposition and multi-source search orchestration. Breaks natural language questions into targeted searches per source, translates queries into source-specific syntax, ranks results by relevance, and handles ambiguity and fallback strategies.

Search Strategy If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). The core intelligence behind enterprise search. Transforms a single natural language question into parallel, source specific searches and produces ranked, deduplicated results. The Goal Turn this: Into targeted searches across every connected source: Then synthesize the results into a single coherent answer. Query Decomposition Step 1: Identify Query Type Classify the user's question to determine search strategy: Query Type Example Strategy Decision "What did we decide about X?" Prioritize conversations (~~chat, email), look for conclusion signals Status "What's the status of Project Y?" Prioritize recent activity, task trackers, status updates Document "Where's the spec for Z?" Prioritize Drive, wiki, shared docs Person "Who's working on X?" Search task assignments, message authors, doc collaborators Factual "What's our policy on X?" Prioritize wiki, official docs, then confirmatory conversations Temporal "When did X happen?" Search with broad date range, look for timestamps Exploratory "What do we know about X?" Broad search across all sources, synthesize Step 2: Extract Search Components From the query, extract: Keywords : Core terms that must appear in results Entities : People, projects, teams, tools (use memory system if available) Intent signals : Decision words, status words, temporal markers Constraints : Time ranges, source hints, author filters Negations : Things to exclude Step 3: Generate Sub Queries Per Source For each available source, create one or more targeted queries: Prefer semantic search for: Conceptual questions ("What do we think about...") Questions where exact keywords are unknown Exploratory queries Prefer keyword search for: Known terms, project names, acronyms Exact phrases the user quoted Filter heavy queries (from:, in:, after:) Generate multiple query variants when the topic might be referred to differently: Source Specific Query Translation ~~chat Semantic search (natural language questions): Keyword search: Filter mapping: Enterprise filter ~~chat syntax from:sarah from:sarah or from:<@USERID in:engineering in:engineering after:2025 01 01 after:2025 01 01 before:2025 02 01 before:2025 02 01 type:thread is:thread type:file has:file ~~knowledge base (Wiki) Semantic search — Use for conceptual queries: Keyword search — Use for exact terms: ~~project tracker Task search: Filter mapping: Enterprise filter ~~project tracker parameter from:sarah assignee any or created by any after:2025 01 01 modified on after: "2025 01 01" type:milestone resource subtype: "milestone" Result Ranking Relevance Scoring Score each result on these factors (weighted by query type): Factor Weight (Decision) Weight (Status) Weight (Document) Weight (Factual) Keyword match 0.3 0.2 0.4 0.3 Freshness 0.3 0.4 0.2 0.1 Authority 0.2 0.1 0.3 0.4 Completeness 0.2 0.3 0.1 0.2 Authority Hierarchy Depends on query type: For factual/policy questions: For "what happened" / decision questions: For status questions: Handling Ambiguity When a query is ambiguous, prefer asking one focused clarifying question over guessing: Only ask for clarification when: There are genuinely distinct interpretations that would produce very different results The ambiguity would significantly affect which sources to search Do NOT ask for clarification when: The query is clear enough to produce useful results Minor ambiguity can be resolved by returning results from multiple interpretations Fallback Strategies When a source is unavailable or returns no results: 1. Source unavailable : Skip it, search remaining sources, note the gap 2. No results from a source : Try broader query terms, remove date filters, try alternate keywords 3. All sources return nothing : Suggest query modifications to the user 4. Rate limited : Note the limitation, return results from other sources, suggest retrying later Query Broadening If initial queries return too few results: Remove constraints in this order: 1. Date filters (search all time) 2. Source/location filters 3. Less important keywords 4. Keep only core entity/topic terms Parallel Execution Always execute searches across sources in parallel, never sequentially. The total search time should be roughly equal to the slowest single source, not the sum of all sources.

search-strategy