Airweave Search

Search and retrieve context from Airweave collections using the search script at {baseDir}/scripts/search.py.

When to Search

Search when the user:

• Asks about data in their connected apps ("What did we discuss in Slack about...")
• Needs to find documents, messages, issues, or records
• Asks factual questions about their workspace ("Who is responsible for...", "What's our policy on...")
• References specific tools by name ("in Notion", "on GitHub", "in Jira")
• Needs recent information you don't have in your training
• Needs you to check app data for context ("check our Notion docs", "look at the Jira ticket")

Don't search when:

• User asks general knowledge questions (use your training)
• User already provided all needed context in the conversation
• The question is about Airweave itself, not data within it

Query Formulation

Turn user intent into effective search queries:

Tips:

• Use natural language — Airweave uses semantic search
• Include context — "pricing feedback" beats just "pricing"
• Be specific but not too narrow
• Skip filler words like "please find", "can you search for"

Running a Search

Execute the search script:

python3 {baseDir}/scripts/search.py "your search query"

Optional parameters:

• --limit N — Max results (default: 20)
• --temporal N — Temporal relevance 0-1 (default: 0, use 0.7+ for "recent", "latest")
• --strategy TYPE — Retrieval strategy: hybrid, semantic, keyword (default: hybrid)
• --raw — Return raw results instead of AI-generated answer
• --expand — Enable query expansion for broader results
• --rerank / --no-rerank — Toggle LLM reranking (default: on)

Examples:

# Basic search
python3 {baseDir}/scripts/search.py "customer feedback pricing"

# Recent conversations
python3 {baseDir}/scripts/search.py "product launch updates" --temporal 0.8

# Find specific document
python3 {baseDir}/scripts/search.py "API authentication docs" --strategy keyword

# Get raw results for exploration
python3 {baseDir}/scripts/search.py "project status" --limit 30 --raw

# Broad search with query expansion
python3 {baseDir}/scripts/search.py "onboarding" --expand

Handling Results

Interpreting scores:

• 0.85+ → Highly relevant, use confidently
• 0.70-0.85 → Likely relevant, use with context
• 0.50-0.70 → Possibly relevant, mention uncertainty
• Below 0.50 → Weak match, consider rephrasing

Presenting to users:

1. Lead with the answer — don't start with "I found 5 results"
2. Cite sources — mention where info came from ("According to your Slack conversation...")
3. Synthesize — combine relevant parts into a coherent response
4. Acknowledge gaps — if results don't fully answer, say so

Handling No Results

If search returns nothing useful:

1. Broaden the query — remove specific terms
2. Try different phrasing — use synonyms
3. Increase limit — fetch more results
4. Ask for clarification — user might have more context

Parameter Reference

See PARAMETERS.md for detailed parameter guidance.

Examples

See EXAMPLES.md for complete search scenarios.