MLflow Tracking for Agent Experiments and MLOps

Set up MLflow tracking for your LlamaIndex agent runs. Log agent inputs (e.g., query, documents processed), Claude 4 Sonnet outputs, Hume AI results, and the final generated insights. Create an MLflow experiment to compare different agent configurations, prompt strategies for Claude 4 Sonnet, or indexing techniques for trend analysis. Ensure MLflow logs are persistent and accessible for reviewing experiment lineage.
```python
import mlflow
from llama_index.llms.anthropic import Anthropic
# Assume agent setup from previous steps # Configure MLflow tracking URI (e.g., to a local directory or remote server)
mlflow.set_tracking_uri("file:///tmp/mlruns") # or your remote MLflow server
mlflow.set_experiment("LlamaIndex_AI_Trend_Analysis") # Example of wrapping an agent run with MLflow
def run_agent_with_mlflow(agent_instance, query_text): with mlflow.start_run(run_name=f"Query_{query_text[:20].replace(' ', '_')}") as run: mlflow.log_param("agent_type", "ReActAgent") mlflow.log_param("llm_model", "claude-4-sonnet") mlflow.log_param("input_query", query_text) # Simulate agent execution and capture outputs # response = agent_instance.chat(query_text) # Actual call if agent is ready # Mock response for logging mock_response = "Identified trend: Multi-Silicon AI Inference. Sentiment: Positive. (Mock)" mlflow.log_text(mock_response, "agent_output.txt") mlflow.log_metric("sentiment_score", 0.92) # Log key metrics mlflow.log_metric("confidence_score", 0.97) # If you have actual generated insights in a file or structured data: # with open("insights.json", "w") as f: # json.dump({"trend": "..."}, f) # mlflow.log_artifact("insights.json") print(f"MLflow Run ID: {run.info.run_id}") return mock_response # To run:
# run_agent_with_mlflow(agent, "Analyze the latest AI trends in inference hardware and developer sentiment.")
```