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
This commit is contained in:
Apple
9
services/memory-orchestrator/backends/__init__.py
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9
services/memory-orchestrator/backends/__init__.py
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from .short_term_pg import ShortTermBackend
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from .vector_store_pg import VectorStoreBackend
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from .kb_filesystem import KnowledgeBaseBackend
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__all__ = ['ShortTermBackend', 'VectorStoreBackend', 'KnowledgeBaseBackend']
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75
services/memory-orchestrator/backends/kb_filesystem.py
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75
services/memory-orchestrator/backends/kb_filesystem.py
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import os
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import json
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from typing import Optional
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from models import MemoryItem
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from datetime import datetime
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class KnowledgeBaseBackend:
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"""
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Long-term knowledge base (filesystem)
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Phase 3: Stub implementation
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Stores docs, roadmaps, and structured knowledge
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"""
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def __init__(self, kb_path: str = "/data/kb"):
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self.kb_path = kb_path
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async def initialize(self):
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"""Create KB directory"""
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if not os.path.exists(self.kb_path):
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try:
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os.makedirs(self.kb_path, exist_ok=True)
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print(f"✅ KB directory created: {self.kb_path}")
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except Exception as e:
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print(f"⚠️ Failed to create KB directory: {e}")
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print(" Using in-memory stub")
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async def query(
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self,
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agent_id: str,
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query_text: str,
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limit: int = 5
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) -> list[MemoryItem]:
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"""
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Query knowledge base
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Phase 3: Returns stub/empty results
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Phase 4: Implement proper KB indexing and search
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"""
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# Stub implementation for Phase 3
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print(f"ℹ️ KB query (stub): {query_text[:50]}...")
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# Return empty results for now
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# In Phase 4, this would:
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# 1. Index docs/roadmaps with embeddings
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# 2. Perform semantic search
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# 3. Return relevant knowledge chunks
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return []
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async def store(
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self,
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agent_id: str,
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microdao_id: str,
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kind: str,
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content: dict,
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metadata: Optional[dict] = None
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) -> str:
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"""
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Store knowledge base entry
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Phase 3: Stub implementation
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"""
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# Stub for Phase 3
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entry_id = f"kb-{datetime.now().timestamp()}"
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print(f"ℹ️ KB store (stub): {entry_id}")
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# In Phase 4, would write to filesystem or DB
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# with proper indexing
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return entry_id
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109
services/memory-orchestrator/backends/short_term_pg.py
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109
services/memory-orchestrator/backends/short_term_pg.py
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import asyncpg
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import json
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from datetime import datetime
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from uuid import uuid4
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from typing import Optional
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from models import MemoryItem
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class ShortTermBackend:
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"""
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Short-term memory backend (PostgreSQL)
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Stores recent conversations and events for quick retrieval
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"""
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def __init__(self, pool: asyncpg.Pool):
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self.pool = pool
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async def initialize(self):
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"""Create tables if not exist"""
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async with self.pool.acquire() as conn:
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS agent_memories_short (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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agent_id TEXT NOT NULL,
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microdao_id TEXT NOT NULL,
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channel_id TEXT,
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kind TEXT NOT NULL,
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content JSONB NOT NULL,
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX IF NOT EXISTS idx_short_agent_time
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ON agent_memories_short (agent_id, created_at DESC);
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CREATE INDEX IF NOT EXISTS idx_short_microdao
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ON agent_memories_short (microdao_id);
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""")
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print("✅ Short-term memory table initialized")
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async def store(
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self,
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agent_id: str,
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microdao_id: str,
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kind: str,
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content: dict,
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channel_id: Optional[str] = None,
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metadata: Optional[dict] = None
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) -> str:
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"""Store a memory entry"""
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memory_id = str(uuid4())
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async with self.pool.acquire() as conn:
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await conn.execute(
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"""
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INSERT INTO agent_memories_short
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(id, agent_id, microdao_id, channel_id, kind, content, metadata, created_at)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8)
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""",
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memory_id, agent_id, microdao_id, channel_id, kind,
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json.dumps(content), json.dumps(metadata or {}), datetime.now()
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)
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return memory_id
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async def query(
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self,
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agent_id: str,
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limit: int = 10,
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kind_filter: Optional[list[str]] = None
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) -> list[MemoryItem]:
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"""Query recent memories (simple time-based retrieval)"""
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query = """
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SELECT id, kind, content, metadata, created_at
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FROM agent_memories_short
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WHERE agent_id = $1
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"""
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params = [agent_id]
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if kind_filter:
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query += f" AND kind = ANY($2)"
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params.append(kind_filter)
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query += " ORDER BY created_at DESC LIMIT $" + str(len(params) + 1)
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params.append(limit)
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async with self.pool.acquire() as conn:
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rows = await conn.fetch(query, *params)
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items = []
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for row in rows:
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content_dict = row['content']
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# Convert content dict to string for MemoryItem
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content_str = json.dumps(content_dict) if isinstance(content_dict, dict) else str(content_dict)
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items.append(MemoryItem(
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id=str(row['id']),
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kind=row['kind'],
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score=1.0, # Time-based, no relevance score
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content=content_str,
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meta=row['metadata'] or {},
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created_at=row['created_at']
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))
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return items
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185
services/memory-orchestrator/backends/vector_store_pg.py
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185
services/memory-orchestrator/backends/vector_store_pg.py
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@@ -0,0 +1,185 @@
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import asyncpg
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import json
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from datetime import datetime
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from uuid import uuid4
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from typing import Optional
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from models import MemoryItem
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from embedding_client import EmbeddingClient
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class VectorStoreBackend:
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"""
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Mid-term memory backend with vector search (PostgreSQL + pgvector)
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For Phase 3: Uses simple stub if pgvector not available
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"""
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def __init__(self, pool: asyncpg.Pool, embedding_client: EmbeddingClient):
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self.pool = pool
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self.embedding_client = embedding_client
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self.pgvector_available = False
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async def initialize(self):
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"""Create tables if not exist"""
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async with self.pool.acquire() as conn:
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# Try to enable pgvector extension
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try:
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await conn.execute("CREATE EXTENSION IF NOT EXISTS vector;")
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self.pgvector_available = True
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print("✅ pgvector extension enabled")
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except Exception as e:
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print(f"⚠️ pgvector not available: {e}")
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print(" Will use fallback (simple text search)")
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# Create table (with or without vector column)
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if self.pgvector_available:
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS agent_memories_vector (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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agent_id TEXT NOT NULL,
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microdao_id TEXT NOT NULL,
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channel_id TEXT,
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kind TEXT NOT NULL,
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content TEXT NOT NULL,
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content_json JSONB,
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embedding vector(1024),
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX IF NOT EXISTS idx_vector_agent
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ON agent_memories_vector (agent_id);
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CREATE INDEX IF NOT EXISTS idx_vector_embedding
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ON agent_memories_vector USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = 100);
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""")
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else:
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# Fallback table without vector column
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS agent_memories_vector (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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agent_id TEXT NOT NULL,
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microdao_id TEXT NOT NULL,
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channel_id TEXT,
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kind TEXT NOT NULL,
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content TEXT NOT NULL,
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content_json JSONB,
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX IF NOT EXISTS idx_vector_agent
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ON agent_memories_vector (agent_id);
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""")
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print("✅ Vector memory table initialized")
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async def store(
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self,
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agent_id: str,
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microdao_id: str,
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kind: str,
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content: dict,
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channel_id: Optional[str] = None,
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metadata: Optional[dict] = None
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) -> str:
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"""Store a memory with embedding"""
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memory_id = str(uuid4())
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# Convert content to text for embedding
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content_text = json.dumps(content)
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# Generate embedding
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embedding = await self.embedding_client.embed(content_text)
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async with self.pool.acquire() as conn:
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if self.pgvector_available:
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await conn.execute(
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"""
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INSERT INTO agent_memories_vector
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(id, agent_id, microdao_id, channel_id, kind, content, content_json, embedding, metadata)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8::vector, $9)
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""",
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memory_id, agent_id, microdao_id, channel_id, kind,
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content_text, json.dumps(content), embedding, json.dumps(metadata or {})
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)
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else:
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# Fallback without embedding
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await conn.execute(
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"""
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INSERT INTO agent_memories_vector
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(id, agent_id, microdao_id, channel_id, kind, content, content_json, metadata)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8)
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""",
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memory_id, agent_id, microdao_id, channel_id, kind,
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content_text, json.dumps(content), json.dumps(metadata or {})
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)
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return memory_id
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async def query(
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self,
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agent_id: str,
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query_text: str,
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limit: int = 5,
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kind_filter: Optional[list[str]] = None
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) -> list[MemoryItem]:
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"""Query memories by semantic similarity"""
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if self.pgvector_available:
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# Vector search
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query_embedding = await self.embedding_client.embed(query_text)
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query_sql = """
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SELECT id, kind, content, metadata, created_at,
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1 - (embedding <=> $2::vector) as score
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FROM agent_memories_vector
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WHERE agent_id = $1
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"""
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params = [agent_id, query_embedding]
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if kind_filter:
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query_sql += f" AND kind = ANY($3)"
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params.append(kind_filter)
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query_sql += f" ORDER BY embedding <=> $2::vector LIMIT ${len(params) + 1}"
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params.append(limit)
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async with self.pool.acquire() as conn:
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rows = await conn.fetch(query_sql, *params)
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else:
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# Fallback: simple text search (ILIKE)
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query_sql = """
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SELECT id, kind, content, metadata, created_at, 0.5 as score
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FROM agent_memories_vector
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WHERE agent_id = $1 AND content ILIKE $2
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"""
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params = [agent_id, f"%{query_text}%"]
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if kind_filter:
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query_sql += f" AND kind = ANY($3)"
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params.append(kind_filter)
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query_sql += f" ORDER BY created_at DESC LIMIT ${len(params) + 1}"
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params.append(limit)
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async with self.pool.acquire() as conn:
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rows = await conn.fetch(query_sql, *params)
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items = []
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for row in rows:
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items.append(MemoryItem(
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id=str(row['id']),
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kind=row['kind'],
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score=float(row['score']),
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content=row['content'],
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meta=row['metadata'] or {},
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created_at=row['created_at']
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))
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return items
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