/dm:search-knowledge

Purpose

Semantic search across all stored brand knowledge in the vector database and knowledge graph. Answers questions like "What worked for email in Q4?", "What are our brand voice guidelines?", "Show me learnings about audience X", or "What did we learn about competitor Y's pricing?" Returns relevant entries ranked by similarity with full provenance context, so agents and users can make decisions informed by everything the brand has ever learned — not just what they remember from the current session. Searches all connected memory layers simultaneously: vector DB for semantic similarity, knowledge graph for entity relationships, and local index for un-synced recent entries.

Input Required

The user must provide (or will be prompted for):

• Search query: Natural language question or topic — e.g., "What email subject line patterns drove the highest open rates?", "What are our compliance restrictions for the EU market?", "Show me everything we know about competitor X", or "What campaign strategies worked for audience millennials in Q4?" The query is embedded and matched semantically, so exact wording does not need to match stored content
• Content type filter (optional): Narrow results to a specific type — guideline, campaign-learning, competitive-intel, performance-insight, or brand-asset. Omit to search all types. Multiple types can be specified as a comma-separated list
• Date range filter (optional): Restrict results to a time window — e.g., "last 90 days", "Q4 2025", "2025-01-01 to 2025-06-30", or "this year". Useful for recency-sensitive queries where older knowledge may be stale or superseded
• Max results (optional): Number of results to return — default 10, maximum 50. Use higher limits for comprehensive research queries and knowledge audits, lower limits for quick factual lookups
• Tags filter (optional): Further narrow by specific tags — e.g., "email", "paid-social", "audience-millennials", "black-friday". Combines with content type and date range as AND filters for precise retrieval
• Priority filter (optional): Filter by knowledge priority — high for proactively surfaced insights, normal for standard entries, or all (default). Use high when you need only the most impactful learnings
• Include expired (optional): Whether to include knowledge entries past their expiration date — default false. Set to true for historical research where stale knowledge still has archival value
• Search mode (optional): semantic (default — natural language similarity), exact (keyword match for precise terms like campaign names or metric values), or hybrid (combines both with weighted scoring)