feat(ml2): feedback_loop, hypertuner, heavy_predictor

agents/core-logic/feedback_loop.py

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+# agents/core-logic/feedback_loop.py
+
+import requests
+from google.cloud import bigquery
+
+# Configuration
+TELEMETRY_URL = "https://osvauco-agent-357036551735.europe-west1.run.app/telemetry/history"
+BIGQUERY_PROJECT = "propane-will-491900-m5"
+BIGQUERY_DATASET = "osvauco_logs"
+BIGQUERY_TABLE = "cloud_run_logs"
+
+def collect_and_store_telemetry():
+    """
+    Collects telemetry data from the specified endpoint and stores it in BigQuery.
+    """
+    try:
+        # 1. Collect telemetry data
+        response = requests.get(TELEMETRY_URL)
+        response.raise_for_status()  # Raise an exception for bad status codes
+        telemetry_data = response.json()
+
+        # 2. Store data in BigQuery
+        client = bigquery.Client(project=BIGQUERY_PROJECT)
+        table_id = f"{BIGQUERY_PROJECT}.{BIGQUERY_DATASET}.{BIGQUERY_TABLE}"
+
+        # Assuming telemetry_data is a list of dicts matching the table schema
+        errors = client.insert_rows_json(table_id, telemetry_data)
+        if errors == []:
+            print(f"Successfully inserted {len(telemetry_data)} rows into {table_id}")
+        else:
+            print(f"Encountered errors while inserting rows: {errors}")
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error collecting telemetry data: {e}")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    collect_and_store_telemetry()

agents/core-logic/heavy_predictor.py

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+# agents/core-logic/heavy_predictor.py
+
+from google.cloud import bigquery
+
+# Configuration
+BIGQUERY_PROJECT = "propane-will-491900-m5"
+BIGQUERY_DATASET = "osvauco_logs"
+BIGQUERY_TABLE = "cloud_run_logs"
+TELEMETRY_CALLS_THRESHOLD = 500
+
+def check_and_trigger_heavy_mode():
+    """
+    Checks the number of telemetry calls and triggers heavy mode if the threshold is exceeded.
+    """
+    try:
+        client = bigquery.Client(project=BIGQUERY_PROJECT)
+        table_id = f"{BIGQUERY_PROJECT}.{BIGQUERY_DATASET}.{BIGQUERY_TABLE}"
+
+        # 1. Query the number of telemetry calls from BigQuery
+        query = f"""
+            SELECT COUNT(*) as total_calls
+            FROM `{table_id}`
+            WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
+        """
+        query_job = client.query(query)
+        results = query_job.result()
+
+        for row in results:
+            total_calls = row.total_calls
+            print(f"Total telemetry calls in the last 24 hours: {total_calls}")
+
+            # 2. Trigger heavy mode if the threshold is exceeded
+            if total_calls > TELEMETRY_CALLS_THRESHOLD:
+                print("Telemetry calls threshold exceeded. Triggering heavy mode.")
+                # Placeholder for logic to trigger heavy mode
+                # This could involve scaling up resources, switching to a more powerful model, etc.
+                pass
+            else:
+                print("Telemetry calls are within acceptable limits.")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    check_and_trigger_heavy_mode()

agents/core-logic/hypertuner.py

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+# agents/core-logic/hypertuner.py
+
+from google.cloud import bigquery
+
+# Configuration
+BIGQUERY_PROJECT = "propane-will-491900-m5"
+BIGQUERY_DATASET = "osvauco_logs"
+BIGQUERY_TABLE = "cloud_run_logs"
+RESPONSE_TIME_THRESHOLD = 1000  # in milliseconds
+
+def adjust_prompt_parameters():
+    """
+    Reads telemetry data from BigQuery, analyzes response times,
+    and adjusts prompt parameters accordingly.
+    """
+    try:
+        client = bigquery.Client(project=BIGQUERY_PROJECT)
+        table_id = f"{BIGQUERY_PROJECT}.{BIGQUERY_DATASET}.{BIGQUERY_TABLE}"
+
+        # 1. Query response time data from BigQuery
+        query = f"""
+            SELECT AVG(latency_ms) as avg_latency
+            FROM `{table_id}`
+            WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR)
+        """
+        query_job = client.query(query)
+        results = query_job.result()
+
+        for row in results:
+            avg_latency = row.avg_latency
+            print(f"Average response time in the last hour: {avg_latency} ms")
+
+            # 2. Adjust prompt parameters based on response time
+            if avg_latency > RESPONSE_TIME_THRESHOLD:
+                print("Response time is high. Adjusting prompt parameters to reduce complexity.")
+                # Placeholder for logic to adjust prompt parameters
+                # For example, reduce max_tokens, use a simpler model, etc.
+                pass
+            else:
+                print("Response time is within acceptable limits.")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    adjust_prompt_parameters()