search-strategy

# 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:
```
"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 f