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