Search_Mode_Guide
Purpose
Use this guide to choose search_mode (keyword vs semantic) for GovTribe Search_* requests. Use it when:
a request needs exact lookup by identifiers, codes, titles, or quoted phrases.
a request is conceptual and needs meaning-based retrieval.
the user asks for aggregations, counts, top-N lists, distributions, or trends.
a request mixes strict identifiers with exploratory context and needs a tie-break decision.
Decision Checklist
Parameter Contract
Omit
search_modeto use the default keyword behavior.Always include
query.Keep mode and query style aligned:
keyword: exact tokens, identifiers, and literal overlap.semantic: plain-language intent and conceptual similarity.
Aggregations Support
Aggregations and rollups should use the default keyword behavior.
Aggregation signals include totals, counts, top-N lists, breakdowns by field, distributions, and time-series trends.
Set
search_mode: "semantic"only when you intentionally want semantic retrieval; it should not be the default for aggregation-driven asks.
Keyword Mode
What it does:
prioritizes lexical and phrase overlap with typo tolerance.
Strengths:
best for exact identifiers, codes, titles, quoted phrases, and short specific lookups.
supports aggregation workflows.
Limitations:
weaker for broad conceptual asks where synonym coverage drives relevance.
Choose
keywordwhen:a query includes identifier-like tokens (for example solicitation IDs, UEI/CAGE, NAICS/PSC).
a query includes quoted text or exact title/name intent.
the request is a direct lookup or navigation to a specific item.
the query is short and specific (roughly 1-3 salient tokens).
Semantic Mode
What it does:
prioritizes conceptual similarity and meaning over strict token overlap.
Strengths:
best for exploratory requests such as “similar to,” “alternatives,” and “ways to.”
handles synonym-heavy and wording-variable topics.
Limitations:
does not reliably support aggregation-heavy workflows.
can underweight strict identifier precision.
Choose
semanticwhen:the request is conceptual, exploratory, or intent-driven.
the query reads like a question or guidance ask.
the topic is broad/ambiguous and depends on synonym matching.
One-Screen Decision Checklist
Choose
keywordif any are true:the query includes quoted text.
the query includes identifier-like tokens or strict codes.
the intent is direct lookup.
the user asks for aggregations or rollups.
Choose
semanticif any are true:the request asks for similar, related, or alternative results.
the query is conceptual and meaning-driven.
the query has no unique identifiers and would benefit from synonym matching.
Tie-breakers:
unique identifier plus conceptual context: choose
keywordand keep the ID explicit.no unique tokens and broad concept: choose
semantic.strict phrase plus “similar to”: choose
semanticunless a unique ID is present.
Examples
Default Keyword-Behavior Examples
Exact solicitation lookup: Tool: Search_Federal_Contract_Opportunities
Quoted phrase and entity lookup: Tool: Search_Federal_Contract_Opportunities
Aggregation-driven request with structured filters: Tool: Search_Federal_Contract_Awards
Semantic Mode Examples
Conceptual similarity request: Tool: Search_Federal_Contract_Opportunities
Alternatives-oriented discovery request: Tool: Search_Federal_Contract_Opportunities
Broad intent without unique identifiers: Tool: Search_Federal_Contract_Opportunities
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