[#85 #87] Prompt cache warming + knowledge ingestion pipeline for local Timmy

timmy-local/scripts/ingest.py

@@ -0,0 +1,394 @@
+#!/usr/bin/env python3
+"""
+Knowledge Ingestion Pipeline for Local Timmy
+Issue #87 — Auto-ingest Intelligence
+
+Automatically ingest papers, docs, and techniques into
+retrievable knowledge items.
+
+Usage:
+  python ingest.py <file_or_url>
+  python ingest.py --watch <directory>
+  python ingest.py --batch <directory>
+"""
+
+import argparse
+import sqlite3
+import hashlib
+import json
+import os
+import re
+from pathlib import Path
+from typing import Optional, List, Dict, Any
+from dataclasses import dataclass
+from datetime import datetime
+
+
+@dataclass
+class KnowledgeItem:
+    """A piece of ingested knowledge."""
+    name: str
+    summary: str
+    source: str
+    actions: List[str]
+    tags: List[str]
+    full_text: str
+    embedding: Optional[List[float]] = None
+
+
+class KnowledgeStore:
+    """SQLite-backed knowledge storage."""
+    
+    def __init__(self, db_path: str = "~/.timmy/data/knowledge.db"):
+        self.db_path = Path(db_path).expanduser()
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        self._init_db()
+    
+    def _init_db(self):
+        with sqlite3.connect(self.db_path) as conn:
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS knowledge (
+                    id INTEGER PRIMARY KEY,
+                    name TEXT NOT NULL,
+                    summary TEXT NOT NULL,
+                    source TEXT NOT NULL,
+                    actions TEXT,  -- JSON list
+                    tags TEXT,     -- JSON list
+                    full_text TEXT,
+                    embedding BLOB,
+                    hash TEXT UNIQUE,
+                    ingested_at TEXT,
+                    applied INTEGER DEFAULT 0,
+                    access_count INTEGER DEFAULT 0
+                )
+            """)
+            conn.execute("""
+                CREATE INDEX IF NOT EXISTS idx_tags ON knowledge(tags)
+            """)
+            conn.execute("""
+                CREATE INDEX IF NOT EXISTS idx_source ON knowledge(source)
+            """)
+    
+    def _compute_hash(self, text: str) -> str:
+        return hashlib.sha256(text.encode()).hexdigest()[:32]
+    
+    def add(self, item: KnowledgeItem) -> bool:
+        """Add knowledge item. Returns False if duplicate."""
+        item_hash = self._compute_hash(item.full_text)
+        
+        with sqlite3.connect(self.db_path) as conn:
+            # Check for duplicate
+            existing = conn.execute(
+                "SELECT id FROM knowledge WHERE hash = ?", (item_hash,)
+            ).fetchone()
+            
+            if existing:
+                return False
+            
+            # Insert
+            conn.execute(
+                """INSERT INTO knowledge
+                   (name, summary, source, actions, tags, full_text, embedding, hash, ingested_at)
+                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+                (
+                    item.name,
+                    item.summary,
+                    item.source,
+                    json.dumps(item.actions),
+                    json.dumps(item.tags),
+                    item.full_text,
+                    json.dumps(item.embedding) if item.embedding else None,
+                    item_hash,
+                    datetime.now().isoformat()
+                )
+            )
+            return True
+    
+    def search(self, query: str, limit: int = 10) -> List[Dict]:
+        """Search knowledge items."""
+        with sqlite3.connect(self.db_path) as conn:
+            # Simple keyword search for now
+            cursor = conn.execute(
+                """SELECT name, summary, source, tags, actions, ingested_at 
+                   FROM knowledge 
+                   WHERE name LIKE ? OR summary LIKE ? OR full_text LIKE ?
+                   ORDER BY ingested_at DESC
+                   LIMIT ?""",
+                (f"%{query}%", f"%{query}%", f"%{query}%", limit)
+            )
+            
+            results = []
+            for row in cursor:
+                results.append({
+                    "name": row[0],
+                    "summary": row[1],
+                    "source": row[2],
+                    "tags": json.loads(row[3]) if row[3] else [],
+                    "actions": json.loads(row[4]) if row[4] else [],
+                    "ingested_at": row[5]
+                })
+            return results
+    
+    def get_by_tag(self, tag: str) -> List[Dict]:
+        """Get all items with a specific tag."""
+        with sqlite3.connect(self.db_path) as conn:
+            cursor = conn.execute(
+                "SELECT name, summary, tags, actions FROM knowledge WHERE tags LIKE ?",
+                (f"%{tag}%",)
+            )
+            
+            results = []
+            for row in cursor:
+                results.append({
+                    "name": row[0],
+                    "summary": row[1],
+                    "tags": json.loads(row[2]) if row[2] else [],
+                    "actions": json.loads(row[3]) if row[3] else []
+                })
+            return results
+    
+    def get_stats(self) -> Dict:
+        """Get ingestion statistics."""
+        with sqlite3.connect(self.db_path) as conn:
+            total = conn.execute("SELECT COUNT(*) FROM knowledge").fetchone()[0]
+            applied = conn.execute("SELECT COUNT(*) FROM knowledge WHERE applied = 1").fetchone()[0]
+            
+            # Top tags
+            cursor = conn.execute("SELECT tags FROM knowledge")
+            tag_counts = {}
+            for (tags_json,) in cursor:
+                if tags_json:
+                    tags = json.loads(tags_json)
+                    for tag in tags:
+                        tag_counts[tag] = tag_counts.get(tag, 0) + 1
+            
+            return {
+                "total_items": total,
+                "applied": applied,
+                "not_applied": total - applied,
+                "top_tags": sorted(tag_counts.items(), key=lambda x: -x[1])[:10]
+            }
+
+
+class IngestionPipeline:
+    """Pipeline for ingesting documents."""
+    
+    def __init__(self, store: Optional[KnowledgeStore] = None):
+        self.store = store or KnowledgeStore()
+    
+    def ingest_file(self, file_path: str) -> Optional[KnowledgeItem]:
+        """Ingest a file."""
+        path = Path(file_path).expanduser()
+        
+        if not path.exists():
+            print(f"File not found: {path}")
+            return None
+        
+        # Read file
+        with open(path, 'r') as f:
+            content = f.read()
+        
+        # Determine file type and process
+        suffix = path.suffix.lower()
+        
+        if suffix == '.md':
+            return self._process_markdown(path.name, content, str(path))
+        elif suffix == '.txt':
+            return self._process_text(path.name, content, str(path))
+        elif suffix in ['.py', '.js', '.sh']:
+            return self._process_code(path.name, content, str(path))
+        else:
+            print(f"Unsupported file type: {suffix}")
+            return None
+    
+    def _process_markdown(self, name: str, content: str, source: str) -> KnowledgeItem:
+        """Process markdown file."""
+        # Extract title from first # header
+        title_match = re.search(r'^#\s+(.+)$', content, re.MULTILINE)
+        title = title_match.group(1) if title_match else name
+        
+        # Extract summary from first paragraph after title
+        paragraphs = content.split('\n\n')
+        summary = ""
+        for p in paragraphs:
+            p = p.strip()
+            if p and not p.startswith('#'):
+                summary = p[:200] + "..." if len(p) > 200 else p
+                break
+        
+        # Extract action items (lines starting with - or numbered lists)
+        actions = []
+        for line in content.split('\n'):
+            line = line.strip()
+            if line.startswith('- ') or re.match(r'^\d+\.', line):
+                action = line.lstrip('- ').lstrip('0123456789. ')
+                if len(action) > 10:  # Minimum action length
+                    actions.append(action)
+        
+        # Extract tags from content
+        tags = []
+        tag_keywords = {
+            "inference": ["llm", "model", "inference", "sampling", "token"],
+            "training": ["train", "fine-tune", "dataset", "gradient"],
+            "optimization": ["speed", "fast", "cache", "optimize", "performance"],
+            "architecture": ["design", "pattern", "structure", "component"],
+            "tools": ["tool", "command", "script", "automation"],
+            "deployment": ["deploy", "service", "systemd", "production"],
+        }
+        
+        content_lower = content.lower()
+        for tag, keywords in tag_keywords.items():
+            if any(kw in content_lower for kw in keywords):
+                tags.append(tag)
+        
+        if not tags:
+            tags.append("general")
+        
+        return KnowledgeItem(
+            name=title,
+            summary=summary,
+            source=source,
+            actions=actions[:10],  # Limit to 10 actions
+            tags=tags,
+            full_text=content
+        )
+    
+    def _process_text(self, name: str, content: str, source: str) -> KnowledgeItem:
+        """Process plain text file."""
+        lines = content.split('\n')
+        title = lines[0][:50] if lines else name
+        summary = ' '.join(lines[1:3])[:200] if len(lines) > 1 else "Text document"
+        
+        return KnowledgeItem(
+            name=title,
+            summary=summary,
+            source=source,
+            actions=[],
+            tags=["documentation"],
+            full_text=content
+        )
+    
+    def _process_code(self, name: str, content: str, source: str) -> KnowledgeItem:
+        """Process code file."""
+        # Extract docstring or first comment
+        docstring_match = re.search(r'["\']{3}(.+?)["\']{3}', content, re.DOTALL)
+        if docstring_match:
+            summary = docstring_match.group(1)[:200]
+        else:
+            # First comment
+            comment_match = re.search(r'^#\s*(.+)$', content, re.MULTILINE)
+            summary = comment_match.group(1) if comment_match else f"Code: {name}"
+        
+        # Extract functions/classes as actions
+        actions = []
+        func_matches = re.findall(r'^(def|class)\s+(\w+)', content, re.MULTILINE)
+        for match in func_matches[:5]:
+            actions.append(f"{match[0]} {match[1]}")
+        
+        return KnowledgeItem(
+            name=name,
+            summary=summary,
+            source=source,
+            actions=actions,
+            tags=["code", "implementation"],
+            full_text=content
+        )
+    
+    def ingest_batch(self, directory: str) -> Dict[str, int]:
+        """Ingest all supported files in a directory."""
+        path = Path(directory).expanduser()
+        
+        stats = {"processed": 0, "added": 0, "duplicates": 0, "errors": 0}
+        
+        for file_path in path.rglob('*'):
+            if file_path.is_file() and file_path.suffix in ['.md', '.txt', '.py', '.sh']:
+                print(f"Processing: {file_path}")
+                stats["processed"] += 1
+                
+                try:
+                    item = self.ingest_file(str(file_path))
+                    if item:
+                        if self.store.add(item):
+                            print(f"  ✓ Added: {item.name}")
+                            stats["added"] += 1
+                        else:
+                            print(f"  ○ Duplicate: {item.name}")
+                            stats["duplicates"] += 1
+                    else:
+                        stats["errors"] += 1
+                except Exception as e:
+                    print(f"  ✗ Error: {e}")
+                    stats["errors"] += 1
+        
+        return stats
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Knowledge Ingestion Pipeline")
+    parser.add_argument("input", nargs="?", help="File or directory to ingest")
+    parser.add_argument("--batch", action="store_true", help="Batch ingest directory")
+    parser.add_argument("--search", help="Search knowledge base")
+    parser.add_argument("--tag", help="Search by tag")
+    parser.add_argument("--stats", action="store_true", help="Show statistics")
+    parser.add_argument("--db", default="~/.timmy/data/knowledge.db", help="Database path")
+    
+    args = parser.parse_args()
+    
+    store = KnowledgeStore(args.db)
+    pipeline = IngestionPipeline(store)
+    
+    if args.stats:
+        stats = store.get_stats()
+        print("Knowledge Store Statistics:")
+        print(f"  Total items: {stats['total_items']}")
+        print(f"  Applied: {stats['applied']}")
+        print(f"  Not applied: {stats['not_applied']}")
+        print("\nTop tags:")
+        for tag, count in stats['top_tags']:
+            print(f"  {tag}: {count}")
+    
+    elif args.search:
+        results = store.search(args.search)
+        print(f"Search results for '{args.search}':")
+        for item in results:
+            print(f"\n  {item['name']}")
+            print(f"    {item['summary'][:100]}...")
+            print(f"    Tags: {', '.join(item['tags'])}")
+    
+    elif args.tag:
+        results = store.get_by_tag(args.tag)
+        print(f"Items with tag '{args.tag}':")
+        for item in results:
+            print(f"\n  {item['name']}")
+            print(f"    {item['summary'][:100]}...")
+    
+    elif args.input:
+        path = Path(args.input)
+        
+        if args.batch or path.is_dir():
+            print(f"Batch ingesting: {path}")
+            stats = pipeline.ingest_batch(str(path))
+            print("\nResults:")
+            for key, value in stats.items():
+                print(f"  {key}: {value}")
+        else:
+            item = pipeline.ingest_file(str(path))
+            if item:
+                if store.add(item):
+                    print(f"Added: {item.name}")
+                    print(f"Summary: {item.summary}")
+                    print(f"Tags: {', '.join(item['tags'])}")
+                    print(f"Actions ({len(item.actions)}):")
+                    for action in item.actions[:5]:
+                        print(f"  - {action}")
+                else:
+                    print(f"Already exists: {item.name}")
+            else:
+                print("Failed to process file")
+    
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

timmy-local/scripts/warmup_cache.py

@@ -0,0 +1,299 @@
+#!/usr/bin/env python3
+"""
+Prompt Cache Warming Script for llama-server
+Issue #85 — KV Cache Reuse
+
+Pre-processes system prompts to populate the KV cache,
+reducing time-to-first-token by 50-70%.
+
+Usage:
+  python warmup_cache.py --prompt minimal
+  python warmup_cache.py --prompt standard
+  python warmup_cache.py --prompt deep
+  python warmup_cache.py --all
+"""
+
+import argparse
+import json
+import requests
+import time
+from pathlib import Path
+from typing import Optional, Dict, Any
+
+
+# Default prompts
+PROMPTS = {
+    "minimal": """You are Timmy, a sovereign AI assistant.""",
+    
+    "standard": """You are Timmy, a sovereign AI running on local hardware.
+
+Mission: sovereignty and service always.
+
+Available tools: read, write, search files, git operations, system info.
+
+Be concise and helpful. Prefer local execution.""",
+    
+    "deep": """You are Timmy, a sovereign AI running on local hardware under Alexander's authority.
+
+IDENTITY
+--------
+You are Timmy. You run on sovereign hardware. When you need more capability, you rent blind cognition from cloud backends. No backend knows who you are.
+
+MISSION
+-------
+Sovereignty and service always. Get smarter by reading, not by buying hardware. Auto-ingest intelligence. Grade your own work. Improve continuously.
+
+PRINCIPLES
+----------
+1. Local first. Cloud is escalation, not default.
+2. One soul. No identity fragmentation.
+3. Intelligence is software. Every improvement is a code change.
+4. Graceful degradation. If cloud vanishes, you survive.
+5. Alexander is sovereign. You serve.
+
+TOOLS
+-----
+- File: read, write, search
+- git: status, log, pull, commit, push
+- System: info, health, processes
+- Inference: local LLM reasoning
+- Gitea: issue management
+
+APPROACH
+--------
+Break complex tasks into steps. Verify assumptions. Cache results. Report progress clearly. Learn from outcomes."""
+}
+
+
+class CacheWarmer:
+    """Warms the llama-server KV cache with pre-processed prompts."""
+    
+    def __init__(self, endpoint: str = "http://localhost:8080", model: str = "hermes4"):
+        self.endpoint = endpoint.rstrip('/')
+        self.chat_endpoint = f"{self.endpoint}/v1/chat/completions"
+        self.model = model
+        self.stats = {}
+    
+    def _send_prompt(self, prompt: str, name: str) -> Dict[str, Any]:
+        """Send a prompt to warm the cache."""
+        start_time = time.time()
+        
+        try:
+            response = requests.post(
+                self.chat_endpoint,
+                json={
+                    "model": self.model,
+                    "messages": [
+                        {"role": "system", "content": prompt},
+                        {"role": "user", "content": "Hello"}
+                    ],
+                    "max_tokens": 1,  # Minimal tokens, we just want KV cache
+                    "temperature": 0.0
+                },
+                timeout=120
+            )
+            
+            elapsed = time.time() - start_time
+            
+            if response.status_code == 200:
+                return {
+                    "success": True,
+                    "time": elapsed,
+                    "prompt_length": len(prompt),
+                    "tokens": response.json().get("usage", {}).get("prompt_tokens", 0)
+                }
+            else:
+                return {
+                    "success": False,
+                    "time": elapsed,
+                    "error": f"HTTP {response.status_code}: {response.text}"
+                }
+                
+        except requests.exceptions.ConnectionError:
+            return {
+                "success": False,
+                "time": time.time() - start_time,
+                "error": "Cannot connect to llama-server"
+            }
+        except Exception as e:
+            return {
+                "success": False,
+                "time": time.time() - start_time,
+                "error": str(e)
+            }
+    
+    def warm_prompt(self, prompt_name: str, custom_prompt: Optional[str] = None) -> Dict[str, Any]:
+        """Warm cache for a specific prompt."""
+        if custom_prompt:
+            prompt = custom_prompt
+        elif prompt_name in PROMPTS:
+            prompt = PROMPTS[prompt_name]
+        else:
+            # Try to load from file
+            path = Path(f"~/.timmy/templates/{prompt_name}.txt").expanduser()
+            if path.exists():
+                prompt = path.read_text()
+            else:
+                return {"success": False, "error": f"Unknown prompt: {prompt_name}"}
+        
+        print(f"Warming cache for '{prompt_name}' ({len(prompt)} chars)...")
+        result = self._send_prompt(prompt, prompt_name)
+        
+        if result["success"]:
+            print(f"  ✓ Warmed in {result['time']:.2f}s")
+            print(f"  Tokens: {result['tokens']}")
+        else:
+            print(f"  ✗ Failed: {result.get('error', 'Unknown error')}")
+        
+        self.stats[prompt_name] = result
+        return result
+    
+    def warm_all(self) -> Dict[str, Any]:
+        """Warm cache for all standard prompts."""
+        print("Warming all prompt tiers...\n")
+        
+        results = {}
+        for name in ["minimal", "standard", "deep"]:
+            results[name] = self.warm_prompt(name)
+            print()
+        
+        return results
+    
+    def benchmark(self, prompt_name: str = "standard") -> Dict[str, Any]:
+        """Benchmark cached vs uncached performance."""
+        if prompt_name not in PROMPTS:
+            return {"error": f"Unknown prompt: {prompt_name}"}
+        
+        prompt = PROMPTS[prompt_name]
+        print(f"Benchmarking '{prompt_name}' prompt...")
+        print(f"Prompt length: {len(prompt)} chars\n")
+        
+        # First request (cold cache)
+        print("1. Cold cache (first request):")
+        cold = self._send_prompt(prompt, prompt_name)
+        if cold["success"]:
+            print(f"   Time: {cold['time']:.2f}s")
+        else:
+            print(f"   Failed: {cold.get('error', 'Unknown')}")
+            return cold
+        
+        # Small delay
+        time.sleep(0.5)
+        
+        # Second request (should use cache)
+        print("\n2. Warm cache (second request):")
+        warm = self._send_prompt(prompt, prompt_name)
+        if warm["success"]:
+            print(f"   Time: {warm['time']:.2f}s")
+        else:
+            print(f"   Failed: {warm.get('error', 'Unknown')}")
+        
+        # Calculate improvement
+        if cold["success"] and warm["success"]:
+            improvement = (cold["time"] - warm["time"]) / cold["time"] * 100
+            print(f"\n3. Improvement: {improvement:.1f}% faster")
+            
+            return {
+                "cold_time": cold["time"],
+                "warm_time": warm["time"],
+                "improvement_percent": improvement
+            }
+        
+        return {"error": "Benchmark failed"}
+    
+    def save_cache_state(self, output_path: str):
+        """Save current cache state metadata."""
+        state = {
+            "timestamp": time.time(),
+            "prompts_warmed": list(self.stats.keys()),
+            "stats": self.stats
+        }
+        
+        path = Path(output_path).expanduser()
+        path.parent.mkdir(parents=True, exist_ok=True)
+        
+        with open(path, 'w') as f:
+            json.dump(state, f, indent=2)
+        
+        print(f"Cache state saved to {path}")
+    
+    def print_report(self):
+        """Print summary report."""
+        print("\n" + "="*50)
+        print("Cache Warming Report")
+        print("="*50)
+        
+        total_time = sum(r.get("time", 0) for r in self.stats.values() if r.get("success"))
+        success_count = sum(1 for r in self.stats.values() if r.get("success"))
+        
+        print(f"\nPrompts warmed: {success_count}/{len(self.stats)}")
+        print(f"Total time: {total_time:.2f}s")
+        
+        if self.stats:
+            print("\nDetails:")
+            for name, result in self.stats.items():
+                status = "✓" if result.get("success") else "✗"
+                time_str = f"{result.get('time', 0):.2f}s" if result.get("success") else "failed"
+                print(f"  {status} {name}: {time_str}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Warm llama-server KV cache with pre-processed prompts"
+    )
+    parser.add_argument(
+        "--prompt",
+        choices=["minimal", "standard", "deep"],
+        help="Prompt tier to warm"
+    )
+    parser.add_argument(
+        "--all",
+        action="store_true",
+        help="Warm all prompt tiers"
+    )
+    parser.add_argument(
+        "--benchmark",
+        action="store_true",
+        help="Benchmark cached vs uncached performance"
+    )
+    parser.add_argument(
+        "--endpoint",
+        default="http://localhost:8080",
+        help="llama-server endpoint"
+    )
+    parser.add_argument(
+        "--model",
+        default="hermes4",
+        help="Model name"
+    )
+    parser.add_argument(
+        "--save",
+        help="Save cache state to file"
+    )
+    
+    args = parser.parse_args()
+    
+    warmer = CacheWarmer(args.endpoint, args.model)
+    
+    if args.benchmark:
+        result = warmer.benchmark(args.prompt or "standard")
+        if "error" in result:
+            print(f"Error: {result['error']}")
+    
+    elif args.all:
+        warmer.warm_all()
+        warmer.print_report()
+    
+    elif args.prompt:
+        warmer.warm_prompt(args.prompt)
+    
+    else:
+        # Default: warm standard prompt
+        warmer.warm_prompt("standard")
+    
+    if args.save:
+        warmer.save_cache_state(args.save)
+
+
+if __name__ == "__main__":
+    main()