requirements.txt

@@ -16,5 +16,3 @@ google-generativeai
 sentence-transformers
 # Graph database for relationship mapping
 neo4j
-qdrant-client
-sentence-transformers

scripts/init_athena_db.py

@@ -1,159 +0,0 @@
-
-import os
-import re
-from typing import List, Dict
-import logging
-from dotenv import load_dotenv
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
-from sentence_transformers import SentenceTransformer
-
-# Setup logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-# Load environment variables
-load_dotenv()
-
-# Configuration
-AGENT_CATALOG_PATH = os.path.join(os.path.dirname(__file__), '../docs/AGENT_CATALOG.md')
-QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
-QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
-COLLECTION_NAME = "routing_index"
-EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2" # Defines the vector size as 384
-VECTOR_SIZE = 384
-
-def read_agent_catalog(file_path: str) -> List[Dict]:
-    """Reads the agent catalog and extracts agent information."""
-    if not os.path.exists(file_path):
-        logger.error(f"Agent catalog not found at {file_path}")
-        return []
-
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    agents = []
-    # Regex to find agent blocks
-    # Looking for ### Agent Name ... - **Crews:** Crew Name
-    agent_blocks = re.split(r'### ', content)[1:] # Split and skip header
-
-    for block in agent_blocks:
-        lines = block.strip().split('\n')
-        name = lines[0].strip()
-        
-        description = ""
-        crew = ""
-        
-        for line in lines:
-            if line.startswith("- **Papel:**"):
-                role = line.split(":", 1)[1].strip()
-                description += f"Papel: {role}. "
-            elif line.startswith("- **Especialidade:**"):
-                specialty = line.split(":", 1)[1].strip()
-                description += f"Especialidade: {specialty}. "
-            elif line.startswith("- **Crews:**"):
-                crew = line.split(":", 1)[1].strip()
-        
-        if name and description and crew:
-            agents.append({
-                "name": name,
-                "description": description,
-                "crew": crew
-            })
-            logger.info(f"Found agent: {name} (Crew: {crew})")
-
-    return agents
-
-def get_embeddings(texts: List[str]) -> List[List[float]]:
-    """Generates embeddings for a list of texts."""
-    logger.info(f"Generating embeddings using model {EMBEDDING_MODEL_NAME}...")
-    model = SentenceTransformer(EMBEDDING_MODEL_NAME)
-    embeddings = model.encode(texts)
-    return embeddings.tolist()
-
-def init_qdrant_collection(client: QdrantClient, collection_name: str, vector_size: int):
-    """Creates the Qdrant collection if it does not exist."""
-    collections = client.get_collections().collections
-    exists = any(c.name == collection_name for c in collections)
-
-    if not exists:
-        logger.info(f"Creating collection '{collection_name}' with vector size {vector_size}...")
-        client.create_collection(
-            collection_name=collection_name,
-            vectors_config=models.VectorParams(
-                size=vector_size,
-                distance=models.Distance.COSINE
-            )
-        )
-    else:
-        logger.info(f"Collection '{collection_name}' already exists.")
-
-def populate_collection(client: QdrantClient, collection_name: str, agents: List[Dict]):
-    """Populates the collection with agent embeddings."""
-    if not agents:
-        logger.warning("No agents to index.")
-        return
-
-    descriptions = [agent["description"] for agent in agents]
-    embeddings = get_embeddings(descriptions)
-    
-    points = []
-    for i, agent in enumerate(agents):
-        # We process crew string to maybe pick the first one if multiple?
-        # For now, let's keep the raw string, or split if it's comma separated
-        # The instruction says payload: {"target_crew": "NomeDaCrew"}
-        # If an agent belongs to multiple crews, we might want to create multiple points or just string match
-        # Let's keep it simple for now as per instructions.
-        
-        target_crew = agent["crew"]
-        
-        points.append(models.PointStruct(
-            id=i + 1, # Simple integer ID
-            vector=embeddings[i],
-            payload={
-                "agent_name": agent["name"],
-                "target_crew": target_crew,
-                "description": agent["description"]
-            }
-        ))
-    
-    logger.info(f"Upserting {len(points)} points into '{collection_name}'...")
-    client.upsert(
-        collection_name=collection_name,
-        points=points
-    )
-    logger.info("Indexing complete.")
-
-def main():
-    logger.info("Starting Athena DB Initialization...")
-    
-    # 1. Read Catalog
-    agents = read_agent_catalog(AGENT_CATALOG_PATH)
-    if not agents:
-        logger.error("Failed to extract agents from catalog.")
-        return
-
-    # 2. Connect to Qdrant
-    try:
-        client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT)
-        # Check connection likely by getting collections
-        client.get_collections()
-        logger.info(f"Connected to Qdrant at {QDRANT_HOST}:{QDRANT_PORT}")
-    except Exception as e:
-        logger.error(f"Failed to connect to Qdrant: {e}")
-        # For the sake of this task execution without running Qdrant, we might fail here.
-        # But the task is to CREATE the script.
-        # If the user has Qdrant running, it will work. 
-        # If not, the script is still valid.
-        return
-
-    # 3. Init Collection
-    init_qdrant_collection(client, COLLECTION_NAME, VECTOR_SIZE)
-
-    # 4. Populate
-    populate_collection(client, COLLECTION_NAME, agents)
-    
-    logger.info("Athena DB Initialization finished successfully.")
-
-if __name__ == "__main__":
-    main()