feat: Add presence heartbeat for Matrix online status
- matrix-gateway: POST /internal/matrix/presence/online endpoint - usePresenceHeartbeat hook with activity tracking - Auto away after 5 min inactivity - Offline on page close/visibility change - Integrated in MatrixChatRoom component
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Apple
244
services/memory-orchestrator/main.py
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244
services/memory-orchestrator/main.py
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"""
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DAARION Memory Orchestrator Service
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Port: 7008
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Unified memory API for agent intelligence (short/mid/long-term)
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"""
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import os
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import asyncpg
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from fastapi import FastAPI, HTTPException, Header
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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from models import (
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MemoryQueryRequest, MemoryQueryResponse,
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MemoryStoreRequest, MemoryStoreResponse,
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MemorySummarizeRequest, MemorySummarizeResponse
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)
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from embedding_client import EmbeddingClient
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from backends import ShortTermBackend, VectorStoreBackend, KnowledgeBaseBackend
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# ============================================================================
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# Global State
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# ============================================================================
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db_pool: asyncpg.Pool | None = None
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embedding_client: EmbeddingClient | None = None
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short_term: ShortTermBackend | None = None
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mid_term: VectorStoreBackend | None = None
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long_term: KnowledgeBaseBackend | None = None
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# ============================================================================
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# App Setup
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# ============================================================================
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Startup and shutdown"""
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global db_pool, embedding_client, short_term, mid_term, long_term
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# Startup
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print("🚀 Starting Memory Orchestrator service...")
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# Database connection
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database_url = os.getenv("DATABASE_URL", "postgresql://postgres:postgres@localhost:5432/daarion")
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db_pool = await asyncpg.create_pool(database_url, min_size=2, max_size=10)
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print("✅ Database pool created")
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# Embedding client
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embedding_endpoint = os.getenv("EMBEDDING_ENDPOINT", "http://localhost:8001/embed")
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embedding_client = EmbeddingClient(endpoint=embedding_endpoint)
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print("✅ Embedding client initialized")
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# Initialize backends
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short_term = ShortTermBackend(db_pool)
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await short_term.initialize()
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mid_term = VectorStoreBackend(db_pool, embedding_client)
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await mid_term.initialize()
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long_term = KnowledgeBaseBackend()
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await long_term.initialize()
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print("✅ Memory Orchestrator ready")
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yield
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# Shutdown
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print("🛑 Shutting down Memory Orchestrator...")
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if db_pool:
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await db_pool.close()
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if embedding_client:
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await embedding_client.close()
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app = FastAPI(
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title="DAARION Memory Orchestrator",
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version="1.0.0",
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description="Unified memory API for agent intelligence",
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lifespan=lifespan
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# ============================================================================
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# API Endpoints
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# ============================================================================
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@app.post("/internal/agent-memory/query", response_model=MemoryQueryResponse)
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async def query_memory(
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request: MemoryQueryRequest,
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x_internal_secret: str = Header(None, alias="X-Internal-Secret")
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):
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"""
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Query agent memory (semantic search)
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Searches across short-term, mid-term, and long-term memory
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"""
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# Simple auth check
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expected_secret = os.getenv("MEMORY_ORCHESTRATOR_SECRET", "dev-secret-token")
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if x_internal_secret != expected_secret:
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raise HTTPException(401, "Invalid or missing X-Internal-Secret header")
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all_items = []
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try:
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# Query mid-term (vector search) - primary source
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mid_items = await mid_term.query(
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agent_id=request.agent_id,
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query_text=request.query,
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limit=request.limit,
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kind_filter=request.kind_filter
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)
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all_items.extend(mid_items)
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# If not enough results, query short-term (recent context)
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if len(all_items) < request.limit:
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short_items = await short_term.query(
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agent_id=request.agent_id,
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limit=request.limit - len(all_items),
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kind_filter=request.kind_filter
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)
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all_items.extend(short_items)
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# Query long-term (knowledge base) - optional
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if len(all_items) < request.limit:
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kb_items = await long_term.query(
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agent_id=request.agent_id,
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query_text=request.query,
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limit=request.limit - len(all_items)
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)
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all_items.extend(kb_items)
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# Sort by score and limit
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all_items.sort(key=lambda x: x.score, reverse=True)
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all_items = all_items[:request.limit]
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return MemoryQueryResponse(
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items=all_items,
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total=len(all_items),
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query=request.query
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)
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except Exception as e:
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raise HTTPException(500, f"Memory query failed: {str(e)}")
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@app.post("/internal/agent-memory/store", response_model=MemoryStoreResponse)
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async def store_memory(
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request: MemoryStoreRequest,
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x_internal_secret: str = Header(None, alias="X-Internal-Secret")
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):
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"""
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Store a new memory entry
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Stores in both short-term (quick access) and mid-term (vector search)
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"""
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expected_secret = os.getenv("MEMORY_ORCHESTRATOR_SECRET", "dev-secret-token")
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if x_internal_secret != expected_secret:
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raise HTTPException(401, "Invalid or missing X-Internal-Secret header")
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try:
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# Store in short-term (always)
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memory_id = await short_term.store(
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agent_id=request.agent_id,
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microdao_id=request.microdao_id,
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kind=request.kind,
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content=request.content,
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channel_id=request.channel_id,
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metadata=request.metadata
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)
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# Store in mid-term (with embedding) for conversations
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if request.kind in ["conversation", "task", "dao-event"]:
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await mid_term.store(
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agent_id=request.agent_id,
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microdao_id=request.microdao_id,
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kind=request.kind,
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content=request.content,
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channel_id=request.channel_id,
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metadata=request.metadata
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)
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return MemoryStoreResponse(ok=True, id=memory_id)
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except Exception as e:
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raise HTTPException(500, f"Memory store failed: {str(e)}")
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@app.post("/internal/agent-memory/summarize", response_model=MemorySummarizeResponse)
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async def summarize_memory(
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request: MemorySummarizeRequest,
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x_internal_secret: str = Header(None, alias="X-Internal-Secret")
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):
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"""
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Summarize recent memories (stub for Phase 3)
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Phase 4: Will use LLM to generate summaries
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"""
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expected_secret = os.getenv("MEMORY_ORCHESTRATOR_SECRET", "dev-secret-token")
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if x_internal_secret != expected_secret:
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raise HTTPException(401, "Invalid or missing X-Internal-Secret header")
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# Stub implementation for Phase 3
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# Phase 4: Call LLM Proxy to generate summary
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try:
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items = await short_term.query(
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agent_id=request.agent_id,
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limit=request.limit
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)
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summary = f"Recent activity summary for {request.agent_id}: {len(items)} memories retrieved. [Summary generation coming in Phase 4]"
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return MemorySummarizeResponse(
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summary=summary,
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items_processed=len(items)
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)
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except Exception as e:
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raise HTTPException(500, f"Memory summarize failed: {str(e)}")
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@app.get("/health")
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async def health():
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"""Health check"""
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return {
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"status": "ok",
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"service": "memory-orchestrator",
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"database": "connected" if db_pool else "disconnected",
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"embedding": "ready" if embedding_client else "not ready"
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}
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# ============================================================================
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# Run
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# ============================================================================
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=7008)
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