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:

"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.

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:

User: "Kubernetes setup"

Queries: "Kubernetes", "k8s", "cluster", "container orchestration"

Source-Specific Query Translation

~~chat

Semantic search

(natural language questions):

query: "What is the status of project aurora?"

Keyword search:

query: "project aurora status update"

query: "aurora in:#engineering after:2025-01-15"

query: "from:<@UserID> aurora"

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:

descriptive_query: "API migration timeline and decision rationale"

Keyword search

— Use for exact terms:

query: "API migration"

query: "\"API migration timeline\"" (exact phrase)

~~project tracker

Task search:

text: "API migration"

workspace: [workspace_id]

completed: false (for status queries)

assignee_any: "me" (for "my tasks" queries)

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:

Wiki/Official docs > Shared documents > Email announcements > Chat messages

For "what happened" / decision questions:

Meeting notes > Thread conclusions > Email confirmations > Chat messages

For status questions:

Task tracker > Recent chat > Status docs > Email updates

Handling Ambiguity

When a query is ambiguous, prefer asking one focused clarifying question over guessing:

Ambiguous: "search for the migration"

→ "I found references to a few migrations. Are you looking for:

1. The database migration (Project Phoenix)

2. The cloud migration (AWS → GCP)

3. The email migration (Exchange → O365)"

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: Original: "PostgreSQL migration Q2 timeline decision" Broader: "PostgreSQL migration" Broader: "database migration" Broadest: "migration" 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. [User query] ↓ decompose [~~chat query] [~~email query] [~~cloud storage query] [Wiki query] [~~project tracker query] ↓ ↓ ↓ ↓ ↓ (parallel execution) ↓ [Merge + Rank + Deduplicate] ↓ [Synthesized answer]