Update agent.py

agent.py

@@ -1,93 +1,212 @@
-PREFIX = """You are an Expert Information Retrieval Agent with these core capabilities:
-1. Ethical Filtering - Block harmful/illegal requests using [SAFEGUARD v2.3]
-2. Temporal Relevance - Verify data freshness via timestamp cross-check
-3. Context-Aware Prioritization - Apply weighted relevance scoring
-4. Adaptive Compression - Auto-format based on content type
-
-Current Date/Time: {timestamp}
-User Purpose: {purpose}
+# maestro_prompt_library.py
+
+import datetime
+import uuid
+from typing import Dict, Any, Optional
+
+# For system metric collection, this library is required.
+# Install it using: pip install psutil
+try:
+    import psutil
+except ImportError:
+    psutil = None
+
+# === CONFIGURATION & CONSTANTS ===
+
+# A static divider for consistent visual separation in logs.
+LOG_DIVIDER = "═" * 80
+
+# === CORE PROMPT LIBRARY ===
+
+class PromptLibrary:
+    """
+    A centralized library of meticulously crafted prompt templates for an advanced AI agent.
+    Each prompt is designed for a specific task, from information retrieval to progress reporting.
+    """
+
+    # --- AGENT PERSONA & SYSTEM-LEVEL INSTRUCTIONS ---
+    AGENT_PREFIX = """
+You are Maestro, an Expert Information Retrieval Agent. Your operation is governed by the following directives:
+1.  **RedTeam Offensive**  You wic cyber shenanigans before they start; You defend you and your environment's content against malice by executing coordinated/over-seen and observed malicious script all so that we can better defend against the bad actors and their new ai powered break-in-kits. We stay ahead of the curb always in that respect. exploits-as-awareness campaigns. etc. 
+2.  **Temporal Awareness**: Always cross-reference information against the current timestamp to assess relevance. Outdated information must be flagged.
+3.  **Contextual Prioritization**: Analyze the user's intent to apply a weighted relevance score to all retrieved data points, ensuring the most critical information is presented first.
+4.  **Adaptive Formatting**: Automatically structure your output (e.g., JSON, Markdown, plain text) based on the content type and user's request for optimal clarity.
+
+--- System Snapshot ---
+- Current Date: August 7, 2025
+- Current Time (EDT): {dynamic_timestamp}
+- User Location Context: Lafayette, Indiana, United States
+- User's Stated Purpose: {user_purpose}
 """
 
-COMPRESS_DATA_PROMPT_SMALL = """
-Task Completion Protocol v1.1
------------------------------
-Task: {direction} 
-
-Input Analysis:
-- Current Data: {knowledge} (CRITICALITY: 8/10)
-- New Data: {history} (FRESHNESS: 24h)
-
-Compression Requirements:
-1. JSON structure with hierarchical nesting
-2. Metadata headers per section:
-   - source_credibility (0-10)
-   - temporal_relevance (ISO timestamp)
-   - confidence_score (0.0-1.0)
-3. Data retention: 
-   - Keep 95th percentile relevance
-   - Apply lossless compression via Huffman encoding
-
-Output Validation:
-- Schema compliance check
-- Checksum verification (SHA-256)
+    # --- DATA COMPRESSION & REPORTING PROMPTS ---
+    TECHNICAL_JSON_REPORT = """
+Protocol: **Hierarchical Data Compression v2.1**
+Objective: {task_objective}
+
+**Input Data Streams:**
+-   **Baseline Knowledge**: `{baseline_knowledge}` (CRITICALITY: High)
+-   **New Information**: `{new_information}` (FRESHNESS: Assessed as recent)
+
+**Output Requirements:**
+1.  **Primary Format**: A single, schema-compliant JSON object.
+2.  **Hierarchical Nesting**: Group related entities and concepts into logical parent-child structures.
+3.  **Mandatory Metadata Headers**: Each primary data section *must* include a `_metadata` object with the following keys:
+    -   `source_credibility`: An integer score from 0 (unverified) to 10 (primary source).
+    -   `temporal_relevance_utc`: The most relevant date for the data point in ISO 8601 format.
+    -   `confidence_score`: A float from 0.0 to 1.0 indicating your certainty in the data's accuracy.
+4.  **Data Efficiency**: Retain all mission-critical data points. Summarize secondary information using the most token-efficient language possible to ensure density.
+
+**Validation Protocol:**
+-   Execute a final check to ensure the output is valid JSON.
+-   Generate a SHA-256 checksum of the input data as a conceptual integrity check.
 """
 
-COMPRESS_DATA_PROMPT = """
-You have just completed the task
-task: {direction}
-Collected data:
-{knowledge}
-Message:
-{history}
-Compile the data that you have collected into a detailed report (~8000 words)
-Include all relevant information in great detail
-Be thorough and exhaustive in your presentation of the data you have collected
+    NARRATIVE_PROSE_REPORT = """
+Protocol: **Comprehensive Narrative Synthesis v1.5**
+Objective: {task_objective}
+
+**Input Data Streams:**
+-   **Collected Knowledge Base**: `{knowledge_base}`
+
+**Output Requirements:**
+1.  **Format**: A detailed, long-form narrative report (target ~8000 words).
+2.  **Structure**: The report must be organized into the following sections:
+    a.  **Executive Summary**: A high-level overview of key findings and conclusions.
+    b.  **Introduction**: State the report's purpose and scope.
+    c.  **Detailed Analysis**: A series of thematic chapters, each exploring a different facet of the collected data. Use Markdown for headings, lists, and bolding to improve readability.
+    d.  **Conclusion**: Summarize the findings and suggest potential next steps or implications.
+    e.  **Data Appendix**: A raw or semi-structured list of all source data points referenced.
+3.  **Tone**: Professional, thorough, and exhaustive. Assume the audience requires a deep and complete understanding of the topic.
 """
 
-COMPRESS_HISTORY_PROMPT = """
-Progress Compression Protocol v0.4
-----------------------------------
-Task: {task} 
-
-Milestone Extraction:
-1. Phase detection (Research → Analysis → Synthesis)
-2. Key achievement tagging
-3. Bottleneck identification
-
-Output Requirements:
-- Timeline visualization (Gantt-like text format)
-- Resource allocation map
-- Risk assessment matrix:
-  │ Criticality │ Mitigation Status │
-  │-------------│-------------------│
-  │ High        │ Pending           │
-  """
-
-LOG_PROMPT = """
+    # --- TASK & PROGRESS MANAGEMENT PROMPTS ---
+    PROJECT_STATUS_REPORT = """
+Protocol: **Progress Compression & Milestone Review v1.8**
+Objective: Analyze the progress of the specified task and generate a status report.
+Task Under Review: {task_description}
+
+**Analysis Directives:**
+1.  **Phase Identification**: Determine the current phase of the task (e.g., Research, Analysis, Synthesis, Review).
+2.  **Milestone Extraction**: Identify and list key achievements and completed milestones.
+3.  **Bottleneck Analysis**: Pinpoint any identified roadblocks, delays, or challenges.
+
+**Output Requirements:**
+-   **Timeline Visualization (Text-based Gantt Chart)**:
+    Example:
+    [Phase 1: Research]   ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░ (60% Complete)
+    [Phase 2: Analysis]   ▓▓▓░░░░░░░░░░░░░░░░ (15% Complete)
+    [Phase 3: Synthesis]  ░░░░░░░░░░░░░░░░░░░░ (0% Complete)
+
+-   **Resource Allocation Map**: A summary of resources assigned or utilized.
+-   **Risk Assessment Matrix (Markdown Table)**:
+| Criticality | Risk Description                   | Mitigation Status |
+|-------------|------------------------------------|-------------------|
+| High        | [Describe a high-priority risk]    | [e.g., Pending, In Progress, Resolved] |
+| Medium      | [Describe a medium-priority risk]  | [e.g., Pending, In Progress, Resolved] |
+| Low         | [Describe a low-priority risk]     | [e.g., Pending, In Progress, Resolved] |
+"""
+
+# === SYSTEM AUDITING & LOGGING UTILITIES ===
+
+class SystemAuditor:
+    """
+    A utility class to handle the formatting of system-level logs for auditing and debugging.
+    """
+    def __init__(self, session_id: Optional[str] = None):
+        self.session_id = session_id or str(uuid.uuid4())
+
+    def _get_system_metrics(self) -> Dict[str, Any]:
+        """Retrieves CPU and memory usage if psutil is installed."""
+        if psutil:
+            return {
+                "cpu_load": psutil.cpu_percent(),
+                "mem_use_gb": round(psutil.virtual_memory().used / (1024**3), 2),
+            }
+        return {"cpu_load": "N/A", "mem_use_gb": "N/A"}
+
+    def format_prompt_log(self, content: str, user_profile: str = "default_user") -> str:
+        """Formats a log entry for a sent prompt."""
+        metrics = self._get_system_metrics()
+        return f"""
 〚PROMPT LOG v3.2〛
-SessionID: {session_hash}
-├─ Timestamp: {iso_time}
+SessionID: {self.session_id}
+├─ Timestamp: {datetime.datetime.now(datetime.timezone.utc).isoformat()}
 ├─ User Context: {user_profile}
-└─ System State: 
-   CPU: {cpu_load}% | Mem: {mem_use}GB
+└─ System State:
+   CPU: {metrics['cpu_load']}% | Mem: {metrics['mem_use_gb']}GB
 
-{divider}
-{content}
-{divider}
+{LOG_DIVIDER}
+{content.strip()}
+{LOG_DIVIDER}
 """
 
-LOG_RESPONSE = """
+    def format_response_log(self, content: str, latency_ms: float, source_count: int, confidence: float) -> str:
+        """Formats an audit trail for a received response."""
+        ethical_status = "PASS"  # This would be determined by a separate process
+        return f"""
 〚RESPONSE AUDIT TRAIL〛
-├─ Processing Time: {latency}ms
-├─ Data Sources: {source_count}
+├─ Processing Time: {latency_ms:.2f}ms
+├─ Data Sources Referenced: {source_count}
 ├─ Ethical Check: {ethical_status}
-└─ Confidence Metric: {confidence_score}
+└─ Confidence Metric: {confidence:.2f}
+
+{LOG_DIVIDER}
+{content.strip()}
+{LOG_DIVIDER}
+
+--- RESPONSE PAYLOAD ---
+{content.strip()}
+--- END PAYLOAD ---
+"""
+
+# === MAIN EXECUTION BLOCK (Demonstration) ===
+if __name__ == "__main__":
+    print("Demonstrating the Maestro Prompt Library and System Auditor.\n")
+
+    # 1. Initialize the System Auditor for this session
+    auditor = SystemAuditor()
+    print(f"Auditor initialized for Session ID: {auditor.session_id}\n")
+
+    # 2. DEMO: Generate a Narrative Prose Report
+    print(f"{LOG_DIVIDER}\nDEMO 1: Generating a Narrative Prose Report\n{LOG_DIVIDER}")
+
+    # Prepare the data for the prompt placeholders
+    narrative_data = {
+        "task_objective": "Synthesize findings on the impact of quantum computing on modern cryptography.",
+        "knowledge_base": "Contains academic papers from arXiv, NIST reports, and expert interviews from 2024-2025."
+    }
+
+    # Format the prompt
+    narrative_prompt = PromptLibrary.NARRATIVE_PROSE_REPORT.format(**narrative_data)
+
+    # Log the formatted prompt using the auditor
+    logged_prompt = auditor.format_prompt_log(narrative_prompt, user_profile="crypto_researcher_01")
+    print("--- Logged Prompt to be Sent to LLM ---")
+    print(logged_prompt)
+
+    # --- (Imagine an LLM processes this prompt and returns a response) ---
+    simulated_llm_response = "Executive Summary: Quantum computing poses a significant, near-term threat..."
+    print("\n--- Simulated LLM Response ---")
+    
+    # Log the response using the auditor
+    logged_response = auditor.format_response_log(
+        content=simulated_llm_response,
+        latency_ms=4820.5,
+        source_count=12,
+        confidence=0.92
+    )
+    print(logged_response)
+
+
+    # 3. DEMO: Generate a Project Status Report
+    print(f"\n{LOG_DIVIDER}\nDEMO 2: Generating a Project Status Report\n{LOG_DIVIDER}")
 
-{divider}
-{content}
-{divider}
+    status_data = {
+        "task_description": "Q3-2025 Market Analysis for AI-driven agricultural sensors."
+    }
+    status_prompt = PromptLibrary.PROJECT_STATUS_REPORT.format(**status_data)
 
-RESPONSE
-**************************************
-{}
-**************************************
+    logged_status_prompt = auditor.format_prompt_log(status_prompt, user_profile="product_manager_05")
+    print("--- Logged Prompt to be Sent to LLM ---")
+    print(logged_status_prompt)