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Author SHA1 Message Date
Jason Woltje
12abdfe81d feat(#93): implement agent spawn via federation 
Implements FED-010: Agent Spawn via Federation feature that enables
spawning and managing Claude agents on remote federated Mosaic Stack
instances via COMMAND message type.

Features:
- Federation agent command types (spawn, status, kill)
- FederationAgentService for handling agent operations
- Integration with orchestrator's agent spawner/lifecycle services
- API endpoints for spawning, querying status, and killing agents
- Full command routing through federation COMMAND infrastructure
- Comprehensive test coverage (12/12 tests passing)

Architecture:
- Hub → Spoke: Spawn agents on remote instances
- Command flow: FederationController → FederationAgentService →
  CommandService → Remote Orchestrator
- Response handling: Remote orchestrator returns agent status/results
- Security: Connection validation, signature verification

Files created:
- apps/api/src/federation/types/federation-agent.types.ts
- apps/api/src/federation/federation-agent.service.ts
- apps/api/src/federation/federation-agent.service.spec.ts

Files modified:
- apps/api/src/federation/command.service.ts (agent command routing)
- apps/api/src/federation/federation.controller.ts (agent endpoints)
- apps/api/src/federation/federation.module.ts (service registration)
- apps/orchestrator/src/api/agents/agents.controller.ts (status endpoint)
- apps/orchestrator/src/api/agents/agents.module.ts (lifecycle integration)

Testing:
- 12/12 tests passing for FederationAgentService
- All command service tests passing
- TypeScript compilation successful
- Linting passed

Refs #93

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
 2026-02-03 14:37:06 -06:00
Jason Woltje
3ec2059470 feat: add semantic search with pgvector (closes #68, #69, #70)
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Issues resolved:
- #68: pgvector Setup
  * Added pgvector vector index migration for knowledge_embeddings
  * Vector index uses HNSW algorithm with cosine distance
  * Optimized for 1536-dimension OpenAI embeddings

- #69: Embedding Generation Pipeline
  * Created EmbeddingService with OpenAI integration
  * Automatic embedding generation on entry create/update
  * Batch processing endpoint for existing entries
  * Async generation to avoid blocking API responses
  * Content preparation with title weighting

- #70: Semantic Search API
  * POST /api/knowledge/search/semantic - pure vector search
  * POST /api/knowledge/search/hybrid - RRF combined search
  * POST /api/knowledge/embeddings/batch - batch generation
  * Comprehensive test coverage
  * Full documentation in docs/SEMANTIC_SEARCH.md

Technical details:
- Uses OpenAI text-embedding-3-small model (1536 dims)
- HNSW index for O(log n) similarity search
- Reciprocal Rank Fusion for hybrid search
- Graceful degradation when OpenAI not configured
- Async embedding generation for performance

Configuration:
- Added OPENAI_API_KEY to .env.example
- Optional feature - disabled if API key not set
- Falls back to keyword search in hybrid mode
 2026-01-30 15:19:13 -06:00