Merge pull request 'feat: Gen AI Evolution Phases 7-9 — Memory Compression, Multilingual Expansion, and Code Refactoring' (#45) from feat/gen-ai-evolution-phases-7-9 into timmy-custom

agent/evolution/code_refactorer.py

@@ -0,0 +1,60 @@
+"""Phase 9: Codebase-Wide Refactoring & Optimization.
+
+Performs a "Deep Audit" of the codebase to identify bottlenecks and vulnerabilities.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class CodeRefactorer:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def audit_codebase(self, file_contents: Dict[str, str]) -> Dict[str, Any]:
+        """Performs a deep audit of the provided codebase files."""
+        logger.info(f"Auditing {len(file_contents)} files for refactoring and optimization.")
+        
+        # Combine file contents for context
+        context = "\n".join([f"--- {path} ---\n{content}" for path, content in file_contents.items()])
+        
+        prompt = f"""
+Codebase Context:
+{context}
+
+Please perform a 'Deep Audit' of this codebase.
+Identify:
+1. Performance bottlenecks (e.g., inefficient loops, redundant API calls).
+2. Security vulnerabilities (e.g., hardcoded keys, PII leaks, insecure defaults).
+3. Architectural debt (e.g., tight coupling, lack of modularity).
+
+Generate a set of 'Refactoring Patches' to address these issues.
+
+Format the output as JSON:
+{{
+  "audit_report": "...",
+  "vulnerabilities": [...],
+  "performance_issues": [...],
+  "patches": [
+    {{
+      "file": "...",
+      "description": "...",
+      "original_code": "...",
+      "replacement_code": "..."
+    }}
+  ]
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Code Refactorer. Your goal is to make the codebase as efficient, secure, and sovereign as possible.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        audit_data = json.loads(result["text"])
+        return audit_data

agent/evolution/memory_compressor.py

@@ -0,0 +1,49 @@
+"""Phase 7: Long-Context Memory Compression.
+
+Compresses years of session transcripts into a hierarchical, searchable "Life Log".
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from agent.symbolic_memory import SymbolicMemory
+
+logger = logging.getLogger(__name__)
+
+class MemoryCompressor:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.symbolic = SymbolicMemory()
+
+    def compress_transcripts(self, transcripts: str) -> Dict[str, Any]:
+        """Compresses massive transcripts into a hierarchical memory map."""
+        logger.info("Compressing transcripts into hierarchical memory map.")
+        
+        prompt = f"""
+The following are session transcripts spanning a long period:
+{transcripts}
+
+Please perform a deep, recursive summarization of these transcripts.
+Identify key themes, major decisions, evolving preferences, and significant events.
+Create a hierarchical 'Life Log' map and extract high-fidelity symbolic triples for the Knowledge Graph.
+
+Format the output as JSON:
+{{
+  "summary": "...",
+  "hierarchy": {{...}},
+  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}]
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Memory Compressor. Your goal is to turn massive context into structured, searchable wisdom.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        memory_data = json.loads(result["text"])
+        self.symbolic.ingest_text(json.dumps(memory_data["triples"]))
+        logger.info(f"Ingested {len(memory_data['triples'])} new memory triples.")
+        return memory_data

agent/evolution/multilingual_expander.py

@@ -0,0 +1,46 @@
+"""Phase 8: Multilingual Sovereign Expansion.
+
+Fine-tunes for high-fidelity reasoning in 50+ languages to ensure sovereignty is global.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class MultilingualExpander:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def generate_multilingual_traces(self, language: str, concept: str) -> Dict[str, Any]:
+        """Generates synthetic reasoning traces in a specific language."""
+        logger.info(f"Generating multilingual traces for {language} on concept: {concept}")
+        
+        prompt = f"""
+Concept: {concept}
+Language: {language}
+
+Please generate a high-fidelity reasoning trace in {language} that explores the concept of {concept} within Timmy's sovereign framework.
+Focus on translating the core principles of SOUL.md (sovereignty, service, honesty) accurately into the cultural and linguistic context of {language}.
+
+Format the output as JSON:
+{{
+  "language": "{language}",
+  "concept": "{concept}",
+  "reasoning_trace": "...",
+  "cultural_nuances": "...",
+  "translation_verification": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction=f"You are Timmy's Multilingual Expander. Ensure the message of sovereignty is accurately translated into {language}.",
+            response_mime_type="application/json",
+            thinking=True
+        )
+        
+        trace_data = json.loads(result["text"])
+        return trace_data