import mlflow, pickle
from sentence_transformers import SentenceTransformer
mlflow.set_tracking_uri("file:./mlruns")
mlflow.set_experiment("multi_agent_system")
def log_agent_run(topic, mode, parsed, scores):
    with mlflow.start_run(run_name=f"{topic[:30]}_{mode}"):
        mlflow.log_param("topic", topic); mlflow.log_param("mode", mode)
        for agent, data in parsed.items():
            mlflow.log_metric(f"{agent}_confidence", data.get("confidence", 0))
            if data.get("trust_score"):
                mlflow.log_metric(f"{agent}_trust_score", data["trust_score"])
        for agent, sc in scores.items():
            for dim, val in sc.items():
                mlflow.log_metric(f"{agent}_{dim}", val)
def save_embedding_model():
    model = SentenceTransformer("all-MiniLM-L6-v2")
    with open("models/embedder_v1.pkl", "wb") as f: pickle.dump(model, f)
    mlflow.log_artifact("models/embedder_v1.pkl", artifact_path="embedding_models")