Search Strategy
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.
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:
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:
~~knowledge base (Wiki)
Semantic search — Use for conceptual queries:
Keyword search — Use for exact terms:
~~project tracker
Task search:
Filter mapping:
Result Ranking
Relevance Scoring
Score each result on these factors (weighted by query type):
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:
- Source unavailable: Skip it, search remaining sources, note the gap
- No results from a source: Try broader query terms, remove date filters, try alternate keywords
- All sources return nothing: Suggest query modifications to the user
- 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:
- Date filters (search all time)
- Source/location filters
- Less important keywords
- 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.