feat: Add Alateya, Clan, Eonarch agents + fix gateway-router connection
## Agents Added - Alateya: R&D, biotech, innovations - Clan (Spirit): Community spirit agent - Eonarch: Consciousness evolution agent ## Changes - docker-compose.node1.yml: Added tokens for all 3 new agents - gateway-bot/http_api.py: Added configs and webhook endpoints - gateway-bot/clan_prompt.txt: New prompt file - gateway-bot/eonarch_prompt.txt: New prompt file ## Fixes - Fixed ROUTER_URL from :9102 to :8000 (internal container port) - All 9 Telegram agents now working ## Documentation - Created PROJECT-MASTER-INDEX.md - single entry point - Added various status documents and scripts Tokens configured: - Helion, NUTRA, Agromatrix (existing) - Alateya, Clan, Eonarch (new) - Druid, GreenFood, DAARWIZZ (configured)
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scripts/setup_qdrant_collections.py
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scripts/setup_qdrant_collections.py
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#!/usr/bin/env python3
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"""
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Qdrant Vector Collections Setup for Helion Memory v3.0
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Collections:
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1. helion_memory_items - Long-term memory facts (preferences, decisions, lessons)
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2. helion_artifacts - Documents, specs, whitepaper embeddings
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3. helion_messages - Recent message embeddings for context retrieval
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Run: python setup_qdrant_collections.py [--host HOST] [--port PORT]
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"""
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import argparse
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import sys
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as qmodels
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# Cohere embed-multilingual-v3.0 produces 1024-dimensional vectors
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EMBEDDING_DIMENSIONS = 1024
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def setup_collections(host: str = "localhost", port: int = 6333):
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"""Create and configure Qdrant collections for Helion Memory"""
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print(f"🔌 Connecting to Qdrant at {host}:{port}...")
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client = QdrantClient(host=host, port=port)
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# Check connection
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try:
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collections = client.get_collections()
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print(f"✅ Connected. Existing collections: {[c.name for c in collections.collections]}")
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except Exception as e:
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print(f"❌ Failed to connect: {e}")
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sys.exit(1)
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# =========================================================================
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# Collection 1: helion_memory_items
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# =========================================================================
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collection_name = "helion_memory_items"
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print(f"\n📦 Setting up collection: {collection_name}")
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if not client.collection_exists(collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config=qmodels.VectorParams(
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size=EMBEDDING_DIMENSIONS,
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distance=qmodels.Distance.COSINE
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),
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# Optimized for filtering by user and type
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optimizers_config=qmodels.OptimizersConfigDiff(
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indexing_threshold=10000
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),
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# On-disk storage for large collections
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on_disk_payload=True
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)
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print(f" ✅ Created collection: {collection_name}")
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else:
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print(f" ℹ️ Collection already exists: {collection_name}")
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# Create payload indexes for filtering
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print(f" 📇 Creating payload indexes...")
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indexes = [
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("platform_user_id", qmodels.PayloadSchemaType.KEYWORD),
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("type", qmodels.PayloadSchemaType.KEYWORD),
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("category", qmodels.PayloadSchemaType.KEYWORD),
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("visibility", qmodels.PayloadSchemaType.KEYWORD),
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("scope_ref", qmodels.PayloadSchemaType.KEYWORD),
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("confidence", qmodels.PayloadSchemaType.FLOAT),
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("created_at", qmodels.PayloadSchemaType.DATETIME),
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("expires_at", qmodels.PayloadSchemaType.DATETIME),
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("archived", qmodels.PayloadSchemaType.BOOL),
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]
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for field_name, field_type in indexes:
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try:
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client.create_payload_index(
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collection_name=collection_name,
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field_name=field_name,
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field_schema=field_type,
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wait=False
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)
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print(f" ✅ Index: {field_name} ({field_type.value})")
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except Exception as e:
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if "already exists" in str(e).lower():
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print(f" ℹ️ Index exists: {field_name}")
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else:
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print(f" ⚠️ Index {field_name}: {e}")
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# =========================================================================
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# Collection 2: helion_artifacts
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# =========================================================================
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collection_name = "helion_artifacts"
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print(f"\n📦 Setting up collection: {collection_name}")
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if not client.collection_exists(collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config=qmodels.VectorParams(
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size=EMBEDDING_DIMENSIONS,
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distance=qmodels.Distance.COSINE
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),
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on_disk_payload=True
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)
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print(f" ✅ Created collection: {collection_name}")
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else:
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print(f" ℹ️ Collection already exists: {collection_name}")
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# Artifact indexes
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print(f" 📇 Creating payload indexes...")
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artifact_indexes = [
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("artifact_id", qmodels.PayloadSchemaType.KEYWORD),
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("project_id", qmodels.PayloadSchemaType.KEYWORD),
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("source", qmodels.PayloadSchemaType.KEYWORD),
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("source_type", qmodels.PayloadSchemaType.KEYWORD), # whitepaper, spec, landing, faq
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("language", qmodels.PayloadSchemaType.KEYWORD),
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("version", qmodels.PayloadSchemaType.KEYWORD),
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("chunk_index", qmodels.PayloadSchemaType.INTEGER),
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("created_at", qmodels.PayloadSchemaType.DATETIME),
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]
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for field_name, field_type in artifact_indexes:
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try:
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client.create_payload_index(
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collection_name=collection_name,
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field_name=field_name,
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field_schema=field_type,
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wait=False
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)
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print(f" ✅ Index: {field_name} ({field_type.value})")
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except Exception as e:
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if "already exists" in str(e).lower():
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print(f" ℹ️ Index exists: {field_name}")
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else:
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print(f" ⚠️ Index {field_name}: {e}")
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# =========================================================================
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# Collection 3: helion_messages (for recent context retrieval)
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# =========================================================================
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collection_name = "helion_messages"
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print(f"\n📦 Setting up collection: {collection_name}")
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if not client.collection_exists(collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config=qmodels.VectorParams(
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size=EMBEDDING_DIMENSIONS,
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distance=qmodels.Distance.COSINE
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),
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# Faster retrieval for recent messages
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optimizers_config=qmodels.OptimizersConfigDiff(
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indexing_threshold=5000
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),
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on_disk_payload=True
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)
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print(f" ✅ Created collection: {collection_name}")
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else:
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print(f" ℹ️ Collection already exists: {collection_name}")
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# Message indexes
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print(f" 📇 Creating payload indexes...")
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message_indexes = [
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("conversation_id", qmodels.PayloadSchemaType.KEYWORD),
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("platform_user_id", qmodels.PayloadSchemaType.KEYWORD),
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("channel", qmodels.PayloadSchemaType.KEYWORD),
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("chat_id", qmodels.PayloadSchemaType.KEYWORD),
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("role", qmodels.PayloadSchemaType.KEYWORD), # user, assistant, system
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("timestamp", qmodels.PayloadSchemaType.DATETIME),
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]
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for field_name, field_type in message_indexes:
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try:
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client.create_payload_index(
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collection_name=collection_name,
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field_name=field_name,
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field_schema=field_type,
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wait=False
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)
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print(f" ✅ Index: {field_name} ({field_type.value})")
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except Exception as e:
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if "already exists" in str(e).lower():
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print(f" ℹ️ Index exists: {field_name}")
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else:
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print(f" ⚠️ Index {field_name}: {e}")
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# =========================================================================
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# Summary
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# =========================================================================
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print("\n" + "=" * 60)
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print("📊 Qdrant Collections Summary")
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print("=" * 60)
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for coll in client.get_collections().collections:
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info = client.get_collection(coll.name)
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print(f"\n{coll.name}:")
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print(f" Points: {info.points_count}")
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print(f" Vectors: {info.vectors_count}")
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print(f" Status: {info.status}")
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print("\n✅ Qdrant setup complete!")
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return True
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def main():
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parser = argparse.ArgumentParser(description="Setup Qdrant collections for Helion Memory")
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parser.add_argument("--host", default="localhost", help="Qdrant host")
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parser.add_argument("--port", type=int, default=6333, help="Qdrant port")
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args = parser.parse_args()
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setup_collections(args.host, args.port)
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if __name__ == "__main__":
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main()
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