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.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
The core intelligence behind enterprise search. Transforms a single natural language question into parallel, source-specific searches and produces ranked, deduplicated results.
Turn this:
"What did we decide about the API migration timeline?"
Into targeted searches across every connected source:
~~chat: "API migration timeline decision" (semantic) + "API migration" in:#engineering after:2025-01-01
~~knowledge base: semantic search "API migration timeline decision"
~~project tracker: text search "API migration" in relevant workspace
Then synthesize the results into a single coherent answer.
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 |
From the query, extract:
For each available source, create one or more targeted queries:
Prefer semantic search for:
Prefer keyword search for:
Generate multiple query variants when the topic might be referred to differently:
User: "Kubernetes setup"
Queries: "Kubernetes", "k8s", "cluster", "container orchestration"
Semantic search (natural language questions):