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| # === Imports === | |
| import os | |
| import re | |
| import gradio as gr | |
| import openai | |
| from datetime import datetime | |
| from bs4 import BeautifulSoup | |
| # --- API Keys --- | |
| openai_api_key = os.environ.get("OPENAI_API_KEY") | |
| if not openai_api_key: | |
| raise ValueError("OPENAI_API_KEY environment variable is not set.") | |
| client = openai.OpenAI(api_key=openai_api_key) | |
| # --- Exadata Specs --- | |
| exadata_specs = { | |
| "X7": {"Quarter Rack": {"max_iops": 350000, "max_throughput": 25}, "Half Rack": {"max_iops": 700000, "max_throughput": 50}, "Full Rack": {"max_iops": 1400000, "max_throughput": 100}}, | |
| "X8": {"Quarter Rack": {"max_iops": 380000, "max_throughput": 28}, "Half Rack": {"max_iops": 760000, "max_throughput": 56}, "Full Rack": {"max_iops": 1520000, "max_throughput": 112}}, | |
| "X9": {"Quarter Rack": {"max_iops": 450000, "max_throughput": 30}, "Half Rack": {"max_iops": 900000, "max_throughput": 60}, "Full Rack": {"max_iops": 1800000, "max_throughput": 120}}, | |
| "X10": {"Quarter Rack": {"max_iops": 500000, "max_throughput": 35}, "Half Rack": {"max_iops": 1000000, "max_throughput": 70}, "Full Rack": {"max_iops": 2000000, "max_throughput": 140}}, | |
| "X11M": {"Quarter Rack": {"max_iops": 600000, "max_throughput": 40}, "Half Rack": {"max_iops": 1200000, "max_throughput": 80}, "Full Rack": {"max_iops": 2400000, "max_throughput": 160}}, | |
| } | |
| # --- Utils --- | |
| def clean_awr_content(content): | |
| if "<html" in content.lower(): | |
| soup = BeautifulSoup(content, "html.parser") | |
| return soup.get_text() | |
| return content | |
| # === AGENTS === | |
| class CriticalAnalyzerAgent: | |
| def analyze(self, content, performance_test_mode, exadata_model, rack_size): | |
| cleaned_content = clean_awr_content(content) | |
| if len(cleaned_content) > 128000: | |
| cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..." | |
| prompt = f""" | |
| You are an expert Oracle DBA performance analyst specialized in AWR + Exadata. | |
| Please perform advanced analysis on the following report: | |
| ======== AWR REPORT START ======== | |
| {cleaned_content} | |
| ======== AWR REPORT END ======== | |
| Required Output: | |
| - Performance Summary (with metric values) | |
| - Detailed Bottlenecks + Risks (quantified) | |
| - Forecast + Predictions | |
| - Monitoring Recommendations | |
| - Exadata Statistics (IO, Flash Cache, Smart Scan) | |
| - Recommended Next Steps to Bridge Gaps | |
| """ | |
| if performance_test_mode and exadata_model and rack_size: | |
| specs = exadata_specs.get(exadata_model, {}).get(rack_size, {}) | |
| if specs: | |
| prompt += f""" | |
| This was a PERFORMANCE TEST on Oracle Exadata {exadata_model} {rack_size}. | |
| Theoretical Max: | |
| - IOPS: {specs['max_iops']} | |
| - Throughput: {specs['max_throughput']} GB/s | |
| Compare observed vs theoretical. Recommend actions to close the performance gap. | |
| """ | |
| response = client.chat.completions.create( | |
| model="gpt-4-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are an expert Oracle DBA."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| return response.choices[0].message.content.strip() | |
| class HealthAgent: | |
| def check_health(self, content): | |
| cleaned_content = clean_awr_content(content) | |
| if len(cleaned_content) > 128000: | |
| cleaned_content = cleaned_content[:128000] + "\n\n[TRUNCATED]..." | |
| prompt = f""" | |
| You are the Oracle AWR Health Analysis Agent. | |
| Your primary responsibility is to detect and report any possible database health risks or failures from the AWR report. | |
| You MUST: | |
| - Identify all mentions of warnings, failures, critical issues, and alerts. | |
| - Do not omit any mention of failure or warnings, even minor ones. | |
| - Clearly classify them as CRITICAL, WARNING, or INFO. | |
| - Provide suggested actions for CRITICAL and WARNING level issues. | |
| - Always confirm at the end if no CRITICAL or WARNING issues were found. | |
| You are NOT allowed to generalize or omit any issue, no matter how small. | |
| If Flash Cache, I/O, Smart Scan, ASM, Redo log, or RAC interconnect issues are mentioned, they MUST be called out explicitly. | |
| AWR CONTENT: | |
| {cleaned_content} | |
| """ | |
| response = client.chat.completions.create( | |
| model="gpt-4-turbo", # or "gpt-4o" if preferred/available | |
| messages=[ | |
| {"role": "system", "content": "You are the strict Oracle AWR Health Analysis Agent."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| return response.choices[0].message.content.strip() | |
| class RaterAgent: | |
| def rate(self, content): | |
| prompt = f"Rate the following analysis from 1-5 stars and explain:\n\n{content}" | |
| response = client.chat.completions.create( | |
| model="gpt-4-turbo", | |
| messages=[{"role": "user", "content": prompt}] | |
| ) | |
| return response.choices[0].message.content.strip() | |
| # === Main Process === | |
| def process_awr(awr_text, threshold, performance_test_mode, exadata_model, rack_size): | |
| analyzer = CriticalAnalyzerAgent() | |
| health = HealthAgent() | |
| rater = RaterAgent() | |
| if not awr_text.strip(): | |
| return "No AWR text provided", "", "" | |
| analysis = analyzer.analyze(awr_text, performance_test_mode, exadata_model, rack_size) | |
| health_status = health.check_health(awr_text) | |
| rating_text = rater.rate(analysis) | |
| stars = 0 | |
| match = re.search(r"(\d+)", rating_text) | |
| if match: | |
| stars = int(match.group(1)) | |
| retry_status = "✅ Accepted" | |
| if stars < threshold: | |
| analysis_retry = analyzer.analyze(awr_text, performance_test_mode, exadata_model, rack_size) | |
| rating_text_retry = rater.rate(analysis_retry) | |
| retry_status = "✅ Retry Occurred" | |
| analysis = analysis_retry | |
| rating_text = rating_text_retry | |
| return analysis, health_status, rating_text, retry_status | |
| # === Gradio UI === | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🧠 Multi-Agent Oracle AWR Analyzer (Production Edition)") | |
| awr_text = gr.Textbox(label="Paste AWR Report", lines=30) | |
| threshold = gr.Slider(0, 5, value=3, step=1, label="Correctness Threshold (Stars)") | |
| performance_test_mode = gr.Checkbox(label="Performance Test Mode") | |
| exadata_model = gr.Dropdown(choices=list(exadata_specs.keys()), label="Exadata Model", visible=False) | |
| rack_size = gr.Dropdown(choices=["Quarter Rack", "Half Rack", "Full Rack"], label="Rack Size", visible=False) | |
| def toggle_visibility(mode): | |
| return gr.update(visible=mode), gr.update(visible=mode) | |
| performance_test_mode.change(toggle_visibility, inputs=performance_test_mode, outputs=[exadata_model, rack_size]) | |
| analyze_btn = gr.Button("Analyze AWR Report") | |
| output = gr.Textbox(label="AWR Analysis", lines=20) | |
| health = gr.Textbox(label="Health Agent Findings", lines=5) | |
| rating = gr.Textbox(label="Rater", lines=3) | |
| retry_status = gr.Textbox(label="Retry Status") | |
| analyze_btn.click(process_awr, inputs=[awr_text, threshold, performance_test_mode, exadata_model, rack_size], outputs=[output, health, rating, retry_status]) | |
| demo.launch(debug=True) |