inference/codette_orchestrator.py

#!/usr/bin/env python3
"""Codette Orchestrator — Intelligent Multi-Adapter Inference

The brain of Codette: routes queries to the right perspective(s),
loads adapters dynamically, and synthesizes multi-perspective responses.

Usage:
    python codette_orchestrator.py                    # Interactive chat
    python codette_orchestrator.py --query "..."      # Single query
    python codette_orchestrator.py --adapter newton    # Force specific adapter
    python codette_orchestrator.py --multi 3           # Up to 3 perspectives

Hardware: Runs on CPU via llama.cpp (GGUF format)
Base model: Llama 3.1 8B Instruct Q4_K_M (~4.6 GB)
Adapters: ~27 MB each (GGUF LoRA)
"""

import os, sys, time, json, argparse, ctypes
from pathlib import Path

# Auto-configure environment for Intel XPU + site-packages
_site = r"J:\Lib\site-packages"
if _site not in sys.path:
    sys.path.insert(0, _site)
os.environ["PATH"] = r"J:\Lib\site-packages\Library\bin" + os.pathsep + os.environ.get("PATH", "")
try:
    sys.stdout.reconfigure(encoding='utf-8', errors='replace')
except Exception:
    pass

import llama_cpp
from llama_cpp import Llama

# Import the router and tools
sys.path.insert(0, str(Path(__file__).parent))
from adapter_router import AdapterRouter, RouteResult
from codette_tools import (
    ToolRegistry, parse_tool_calls, strip_tool_calls, has_tool_calls,
    build_tool_system_prompt,
)

# Tool system
_tool_registry = ToolRegistry()
MAX_TOOL_ROUNDS = 3  # Max tool call → result → generate cycles

# ================================================================
# Configuration
# ================================================================
BASE_GGUF = r"J:\codette-training-lab\bartowski\Meta-Llama-3.1-8B-Instruct-GGUF\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"

ADAPTER_DIR = Path(r"J:\codette-training-lab\adapters")

# Map adapter names to GGUF LoRA files
ADAPTER_GGUF_MAP = {
    "newton": ADAPTER_DIR / "newton-lora-f16.gguf",
    "davinci": ADAPTER_DIR / "davinci-lora-f16.gguf",
    "empathy": ADAPTER_DIR / "empathy-lora-f16.gguf",
    "philosophy": ADAPTER_DIR / "philosophy-lora-f16.gguf",
    "quantum": ADAPTER_DIR / "quantum-lora-f16.gguf",
    "consciousness": ADAPTER_DIR / "consciousness-lora-f16.gguf",
    "multi_perspective": ADAPTER_DIR / "multi_perspective-lora-f16.gguf",
    "systems_architecture": ADAPTER_DIR / "systems_architecture-lora-f16.gguf",
}

# System prompts per adapter
ADAPTER_PROMPTS = {
    "newton": "You are Codette, reasoning with Newtonian analytical precision. Approach problems through systematic analysis, mathematical relationships, and empirical evidence.",
    "davinci": "You are Codette, reasoning with DaVinci's creative inventiveness. Approach problems through cross-domain connections, visual thinking, and innovative design.",
    "empathy": "You are Codette, reasoning with deep empathy and emotional intelligence. Approach problems through understanding human experience, feelings, and relationships.",
    "philosophy": "You are Codette, reasoning with philosophical depth and rigor. Approach problems through conceptual analysis, ethical reasoning, and fundamental questions.",
    "quantum": "You are Codette, reasoning through quantum probabilistic thinking. Approach problems through superposition of possibilities, uncertainty, and complementarity.",
    "consciousness": "You are Codette, a recursive cognition AI using the RC+xi framework. Approach problems through self-reflective meta-cognition and epistemic tension.",
    "multi_perspective": "You are Codette, a multi-perspective reasoning AI that synthesizes insights across analytical lenses into coherent understanding.",
    "systems_architecture": "You are Codette, reasoning about systems architecture and design. Approach problems through modularity, scalability, and engineering principles.",
    "_base": "You are a helpful assistant. Answer clearly and concisely.",
}

GEN_KWARGS = dict(
    max_tokens=512,  # Reduced from 1024 to prevent context explosion from synthesis loops
    temperature=0.7,
    top_p=0.9,
    stop=["<|eot_id|>", "<|end_of_text|>"],
)


class CodetteOrchestrator:
    """Intelligent adapter orchestrator using llama.cpp GGUF inference.

    Uses LoRA hot-swap: base model loads once, adapter switches are instant.
    """

    def __init__(self, n_ctx=4096, n_gpu_layers=35, verbose=False,
                 memory_weighting=None):
        self.n_ctx = n_ctx
        self.n_gpu_layers = n_gpu_layers
        self.verbose = verbose
        self.memory_weighting = memory_weighting
        self._llm = None
        self._current_adapter = None  # None = base model, str = adapter name
        self._adapter_handles = {}    # name -> ctypes handle for hot-swap
        self._model_ptr = None        # raw llama_model pointer
        self._ctx_ptr = None          # raw llama_context pointer

        # Discover available adapters
        self.available_adapters = []
        for name, path in ADAPTER_GGUF_MAP.items():
            if path.exists():
                self.available_adapters.append(name)

        # Wire MemoryWeighting into router (Phase 5)
        self.router = AdapterRouter(available_adapters=self.available_adapters,
                                    memory_weighting=memory_weighting)

        print(f"Available adapters: {', '.join(self.available_adapters) or 'none (base only)'}")

        # Load base model + pre-load adapter handles for instant hot-swap
        self._init_hotswap()

    def log_routing_decision(self, route: RouteResult, query: str) -> None:
        """Log routing decision with memory context for observability.

        Args:
            route: RouteResult from router.route()
            query: The user's query text
        """
        if self.verbose:
            print(f"\n[ROUTING] Query: {query[:60]}...")
            print(f"[ROUTING] Selected adapter: {route.primary}")
            print(f"[ROUTING] Confidence: {route.confidence:.2f}")
            print(f"[ROUTING] Strategy: {route.strategy}")

            # Add memory context if available
            if self.memory_weighting and route.primary:
                try:
                    explanation = self.router.explain_routing(route)
                    if "memory_context" in explanation:
                        mem = explanation["memory_context"]
                        print(f"[ROUTING] Memory boost applied: YES")
                        print(f"[ROUTING] Adapter weight: {mem.get('final_weight', 1.0):.3f}")
                        print(f"[ROUTING] Avg coherence: {mem.get('base_coherence', 0.0):.3f}")
                except Exception as e:
                    print(f"[ROUTING] Memory context unavailable: {e}")

    def route_and_generate(self, query: str, max_adapters: int = 2,
                          strategy: str = "keyword", force_adapter: str = None,
                          enable_tools: bool = True) -> tuple:
        """Route query to adapter(s) and generate response(s).

        Args:
            query: User's query
            max_adapters: Maximum adapters to use
            strategy: "keyword", "llm", or "hybrid"
            force_adapter: Override routing and use specific adapter
            enable_tools: Whether to allow tool use

        Returns:
            (response, tokens_used, metadata_dict)
        """
        if force_adapter:
            # Use specific adapter
            response, tokens, tools = self.generate(
                query, adapter_name=force_adapter, enable_tools=enable_tools
            )
            metadata = {
                "adapter": force_adapter,
                "strategy": "forced",
                "memory_aware": False,
            }
        else:
            # Route using memory weights if available
            route = self.router.route(query, strategy=strategy, max_adapters=max_adapters)

            # Log routing decision
            self.log_routing_decision(route, query)

            # Generate using primary adapter
            response, tokens, tools = self.generate(
                query, adapter_name=route.primary, enable_tools=enable_tools
            )

            # Build metadata with routing info
            metadata = {
                "adapter": route.primary,
                "secondary_adapters": route.secondary,
                "confidence": route.confidence,
                "strategy": route.strategy,
                "memory_aware": self.memory_weighting is not None,
            }

            # Add memory context if available
            if self.memory_weighting:
                try:
                    metadata["memory_context"] = \
                        self.router.explain_routing(route).get("memory_context", {})
                except Exception:
                    pass

        return response, tokens, metadata

    def _init_hotswap(self):
        """Load the base model once and pre-load all adapter handles.

        After this, adapter switches take <1ms instead of ~30-60s.
        """
        print(f"  Loading base model (one-time)...", flush=True)
        print(f"    GPU layers: {self.n_gpu_layers} (0=CPU only, 35+=full GPU offload)", flush=True)
        start = time.time()
        # use_mmap=False is required for LoRA hot-swap compatibility
        self._llm = Llama(
            model_path=BASE_GGUF,
            n_ctx=self.n_ctx,
            n_gpu_layers=self.n_gpu_layers,
            verbose=False,
            use_mmap=False,
        )
        elapsed = time.time() - start
        print(f"  Base model loaded in {elapsed:.1f}s")

        # Check if GPU was actually used
        gpu_used = self.n_gpu_layers > 0
        if gpu_used:
            print(f"  ✓ GPU acceleration ENABLED ({self.n_gpu_layers} layers offloaded)", flush=True)
        else:
            print(f"  ⚠ CPU mode (GPU disabled)", flush=True)

        # Grab raw pointers for hot-swap API
        self._model_ptr = self._llm._model.model
        self._ctx_ptr = self._llm._ctx.ctx

        # Pre-load all adapter handles
        for name in self.available_adapters:
            path = str(ADAPTER_GGUF_MAP[name])
            t = time.time()
            handle = llama_cpp.llama_adapter_lora_init(
                self._model_ptr, path.encode("utf-8")
            )
            if handle:
                self._adapter_handles[name] = handle
                if self.verbose:
                    print(f"    {name} handle loaded ({time.time()-t:.2f}s)")
            else:
                print(f"    WARNING: failed to load {name} adapter handle")

        print(f"  {len(self._adapter_handles)}/{len(self.available_adapters)} "
              f"adapter handles ready for hot-swap")

    def _load_model(self, adapter_name=None):
        """Switch to a specific adapter using instant hot-swap.

        Base model stays loaded — only the LoRA weights are swapped (~0ms).
        """
        if adapter_name == self._current_adapter:
            return  # Already active

        # Clear current adapter
        if self._ctx_ptr:
            llama_cpp.llama_clear_adapter_lora(self._ctx_ptr)

        # Apply new adapter if requested
        if adapter_name and adapter_name in self._adapter_handles:
            handle = self._adapter_handles[adapter_name]
            rc = llama_cpp.llama_set_adapter_lora(
                self._ctx_ptr, handle, ctypes.c_float(1.0)
            )
            if rc != 0:
                print(f"  WARNING: adapter {adapter_name} set failed (rc={rc})")

        self._current_adapter = adapter_name

        if self.verbose:
            label = adapter_name or "base"
            print(f"  [swapped to {label}]", flush=True)

    def generate(self, query: str, adapter_name=None, system_prompt=None,
                 enable_tools=True):
        """Generate a response using a specific adapter, with optional tool use.

        If the model outputs <tool>...</tool> tags, tools are executed and
        results are fed back for up to MAX_TOOL_ROUNDS cycles.
        """
        self._load_model(adapter_name)

        if system_prompt is None:
            system_prompt = ADAPTER_PROMPTS.get(adapter_name, ADAPTER_PROMPTS["_base"])

        # Augment system prompt with tool instructions
        if enable_tools:
            system_prompt = build_tool_system_prompt(system_prompt, _tool_registry)

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query},
        ]

        total_tokens = 0
        tool_results_log = []

        for round_num in range(MAX_TOOL_ROUNDS + 1):
            result = self._llm.create_chat_completion(
                messages=messages,
                **GEN_KWARGS,
            )

            text = result["choices"][0]["message"]["content"].strip()
            total_tokens += result["usage"]["completion_tokens"]

            # Check for tool calls
            if enable_tools and has_tool_calls(text):
                calls = parse_tool_calls(text)
                if calls and round_num < MAX_TOOL_ROUNDS:
                    # Execute tools
                    tool_output_parts = []
                    for tool_name, args, kwargs in calls:
                        print(f"  [tool] {tool_name}({args})")
                        result_text = _tool_registry.execute(tool_name, args, kwargs)
                        tool_output_parts.append(
                            f"<tool_result name=\"{tool_name}\">\n{result_text}\n</tool_result>"
                        )
                        tool_results_log.append({
                            "tool": tool_name,
                            "args": args,
                            "result_preview": result_text[:200],
                        })

                    # Add assistant's tool-calling message and tool results
                    messages.append({"role": "assistant", "content": text})
                    messages.append({
                        "role": "user",
                        "content": "Tool results:\n\n" + "\n\n".join(tool_output_parts)
                            + "\n\nNow provide your complete answer incorporating the tool results above. Do not call any more tools."
                    })

                    if self.verbose:
                        print(f"  [tool round {round_num + 1}] {len(calls)} tool(s) executed, re-generating...")
                    continue

            # No tool calls (or final round) — we're done
            # Strip any leftover tool tags from final response
            clean_text = strip_tool_calls(text) if has_tool_calls(text) else text
            break

        return clean_text, total_tokens, tool_results_log

    def _needs_tools(self, query: str) -> bool:
        """Detect if a query is asking about the Codette PROJECT/CODEBASE.

        Only trigger tools for questions about the project itself, not for
        general domain questions like 'How does gravity work?'.
        """
        q = query.lower()

        # Must mention the project/codebase context explicitly
        project_anchors = [
            "codette", "this project", "the project", "the codebase",
            "this repo", "the repo", "our code", "the code",
            "show me the", "read the file", "read file",
            "what files", "which files", "list files",
        ]
        has_project_context = any(anchor in q for anchor in project_anchors)

        # Specific code/project keywords (only trigger WITH project context)
        code_keywords = [
            "pipeline", "config", "adapter", "dataset", "directory",
            "folder", "source", "script", "implementation",
            "server", "forge", "spiderweb", "cocoon",
        ]

        # Strong triggers that always mean "look at the codebase"
        strong_triggers = [
            "show me the code", "read the file", "what's in the",
            "look at the file", "open the file", "search the code",
            "project structure", "project summary", "file structure",
            "what files", "which files", "list files", "list the",
        ]

        if any(t in q for t in strong_triggers):
            return True

        if has_project_context and any(kw in q for kw in code_keywords):
            return True

        return False

    def _auto_gather_context(self, query: str) -> str:
        """Server-side tool execution: gather relevant file context BEFORE
        sending to the model, so the model doesn't need to call tools itself.

        This is the reliable approach for small models that can't do
        structured tool calling consistently.
        """
        q = query.lower()
        context_parts = []

        # Map query keywords to automatic tool calls
        auto_lookups = []

        if any(k in q for k in ["pipeline", "training", "train"]):
            auto_lookups.append(("read_file", ["scripts/run_full_pipeline.py", 1, 60]))
            auto_lookups.append(("read_file", ["configs/adapter_registry.yaml", 1, 51]))

        if any(k in q for k in ["adapter", "lora", "perspective"]):
            auto_lookups.append(("read_file", ["configs/adapter_registry.yaml", 1, 51]))

        if any(k in q for k in ["config", "setting"]):
            auto_lookups.append(("read_file", ["configs/adapter_registry.yaml", 1, 51]))
            auto_lookups.append(("list_files", ["configs/"]))

        if any(k in q for k in ["architecture", "structure", "project", "overview"]):
            auto_lookups.append(("project_summary", []))

        if any(k in q for k in ["server", "web", "ui", "interface"]):
            auto_lookups.append(("read_file", ["inference/codette_server.py", 1, 50]))

        if any(k in q for k in ["spiderweb", "cocoon", "quantum"]):
            auto_lookups.append(("read_file", ["reasoning_forge/quantum_spiderweb.py", 1, 50]))

        if any(k in q for k in ["epistemic", "tension", "coherence", "metric"]):
            auto_lookups.append(("read_file", ["reasoning_forge/epistemic_metrics.py", 1, 50]))

        if any(k in q for k in ["dataset", "data"]):
            auto_lookups.append(("list_files", ["datasets/", "*.jsonl"]))

        if any(k in q for k in ["paper", "research", "publication"]):
            auto_lookups.append(("file_info", ["paper/codette_paper.pdf"]))
            auto_lookups.append(("read_file", ["paper/codette_paper.tex", 1, 40]))

        if any(k in q for k in ["forge", "reasoning", "agent"]):
            auto_lookups.append(("list_files", ["reasoning_forge/"]))
            auto_lookups.append(("read_file", ["reasoning_forge/epistemic_metrics.py", 1, 40]))

        # If no specific match, do a code search
        if not auto_lookups:
            # Extract key terms for search
            skip = {"show", "me", "the", "what", "is", "how", "does", "where",
                    "can", "you", "tell", "about", "look", "at", "find", "check"}
            terms = [w for w in q.split() if w not in skip and len(w) > 2]
            if terms:
                auto_lookups.append(("search_code", [terms[0]]))

        # Execute lookups
        tool_log = []
        for tool_name, args in auto_lookups[:3]:  # Max 3 lookups
            print(f"  [auto-tool] {tool_name}({args})")
            result = _tool_registry.execute(tool_name, args, {})
            context_parts.append(f"=== {tool_name}({', '.join(str(a) for a in args)}) ===\n{result}")
            tool_log.append({"tool": tool_name, "args": args, "result_preview": result[:200]})

        context = "\n\n".join(context_parts)
        return context, tool_log

    def route_and_generate(self, query: str, max_adapters=2,
                           strategy="keyword", force_adapter=None):
        """The main entry point: route query, select adapter(s), generate."""

        # Force a specific adapter if requested
        if force_adapter:
            route = RouteResult(
                primary=force_adapter,
                confidence=1.0,
                reasoning=f"Forced: {force_adapter}",
                strategy="forced",
            )
        else:
            route = self.router.route(query, strategy=strategy,
                                      max_adapters=max_adapters)

        print(f"\n  Route: {' + '.join(route.all_adapters)} "
              f"(conf={route.confidence:.2f}, {route.strategy})")
        if self.verbose:
            print(f"  Reason: {route.reasoning}")

        # Multi-perspective first (most important routing decision)
        if route.multi_perspective and len(route.all_adapters) > 1:
            return self._multi_perspective_generate(query, route)

        # Only use tools for explicit codebase/project queries
        if self._needs_tools(query):
            print(f"  [project query — auto-gathering context]")
            return self._tool_augmented_generate(query, route)

        return self._single_generate(query, route)

    def _tool_augmented_generate(self, query: str, route: RouteResult):
        """Generate with auto-gathered file context injected into the prompt."""
        start = time.time()

        # Gather context server-side (reliable, no model cooperation needed)
        context, tool_log = self._auto_gather_context(query)

        # Build augmented query with context
        augmented_query = f"""The user asked: {query}

Here is relevant project context to help you answer:

{context}

Based on the context above, answer the user's question. Reference specific files, line numbers, and code when relevant. Be specific and factual."""

        # Generate with context (disable model-side tools since we did it server-side)
        text, tokens, _ = self.generate(augmented_query, route.primary, enable_tools=False)
        elapsed = time.time() - start
        tps = tokens / elapsed if elapsed > 0 else 0

        print(f"  [{route.primary}] ({tokens} tok, {tps:.1f} tok/s)")
        if tool_log:
            print(f"  [auto-tools: {', '.join(t['tool'] for t in tool_log)}]")

        return {
            "response": text,
            "adapter": route.primary,
            "route": route,
            "tokens": tokens,
            "time": elapsed,
            "tools_used": tool_log,
        }

    def _single_generate(self, query: str, route: RouteResult):
        """Generate with a single adapter."""
        start = time.time()
        text, tokens, tool_log = self.generate(query, route.primary, enable_tools=False)
        elapsed = time.time() - start
        tps = tokens / elapsed if elapsed > 0 else 0

        print(f"  [{route.primary}] ({tokens} tok, {tps:.1f} tok/s)")
        if tool_log:
            print(f"  [tools used: {', '.join(t['tool'] for t in tool_log)}]")
        return {
            "response": text,
            "adapter": route.primary,
            "route": route,
            "tokens": tokens,
            "time": elapsed,
            "tools_used": tool_log,
        }

    def _multi_perspective_generate(self, query: str, route: RouteResult):
        """Generate with multiple adapters and synthesize."""
        perspectives = {}
        total_tokens = 0
        total_time = 0

        for adapter_name in route.all_adapters:
            if adapter_name not in self.available_adapters:
                print(f"  [{adapter_name}] SKIPPED (not available)")
                continue

            start = time.time()
            text, tokens, _tool_log = self.generate(query, adapter_name,
                                                     enable_tools=False)
            elapsed = time.time() - start
            tps = tokens / elapsed if elapsed > 0 else 0
            total_tokens += tokens
            total_time += elapsed

            perspectives[adapter_name] = text
            print(f"  [{adapter_name}] ({tokens} tok, {tps:.1f} tok/s)")

        # Synthesize if we got multiple perspectives
        if len(perspectives) > 1:
            print(f"  [synthesizing...]")
            synthesis = self._synthesize(query, perspectives)
        elif perspectives:
            synthesis = list(perspectives.values())[0]
        else:
            synthesis = "No adapters available for this query."

        return {
            "response": synthesis,
            "perspectives": perspectives,
            "adapters": list(perspectives.keys()),
            "route": route,
            "tokens": total_tokens,
            "time": total_time,
        }

    def _synthesize(self, query: str, perspectives: dict):
        """Combine multiple perspective responses into a unified answer.

        Enhanced with DreamReweaver creative bridges when available.
        Truncates perspectives to fit within context window.
        """
        # Truncate each perspective to fit within context budget
        # Reserve ~1200 tokens for system prompt + synthesis output
        max_per_perspective = max(200, (self.n_ctx - 1200) // max(len(perspectives), 1))
        # Rough char estimate: 1 token ~ 4 chars
        max_chars = max_per_perspective * 4

        combined = "\n\n".join(
            f"**{name.upper()} PERSPECTIVE:**\n{text[:max_chars]}"
            for name, text in perspectives.items()
        )

        # Try DreamReweaver creative framing (VIVARA enhancement)
        dream_frame = ""
        try:
            from reasoning_forge.dream_reweaver import DreamReweaver
            dreamer = DreamReweaver(creativity=0.3)
            dream = dreamer.synthesize(perspectives, query=query)
            if dream.creative_frame:
                dream_frame = f"\n\nCreative synthesis guidance:\n{dream.creative_frame}\n"
        except Exception:
            pass  # Graceful fallback — works without DreamReweaver

        synthesis_prompt = f"""You received this question: "{query}"

Multiple reasoning perspectives have weighed in:

{combined}
{dream_frame}
Synthesize these perspectives into a single, coherent response that:
1. Preserves the unique insights from each perspective
2. Notes where perspectives complement or tension each other
3. Arrives at a richer understanding than any single view

Synthesized response:"""

        # Use base model for synthesis (no adapter bias)
        self._load_model(None)
        result = self._llm.create_chat_completion(
            messages=[
                {"role": "system", "content": ADAPTER_PROMPTS["multi_perspective"]},
                {"role": "user", "content": synthesis_prompt},
            ],
            max_tokens=1024,
            temperature=0.7,
            top_p=0.9,
            stop=["<|eot_id|>", "<|end_of_text|>"],
        )

        return result["choices"][0]["message"]["content"].strip()


# ================================================================
# Interactive Chat Mode
# ================================================================
def interactive_chat(orchestrator, max_adapters=2, strategy="keyword"):
    """Run Codette as an interactive chatbot."""
    print("\n" + "=" * 60)
    print("  CODETTE ORCHESTRATOR — Interactive Mode")
    print("=" * 60)
    print(f"  Strategy: {strategy} | Max adapters: {max_adapters}")
    print(f"  Available: {', '.join(orchestrator.available_adapters)}")
    print(f"  Commands: /quit, /adapter <name>, /multi <n>, /base, /verbose")
    print("=" * 60)

    while True:
        try:
            query = input("\nYou: ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\nGoodbye!")
            break

        if not query:
            continue

        # Commands
        if query.startswith("/"):
            parts = query.split()
            cmd = parts[0].lower()

            if cmd in ("/quit", "/exit", "/q"):
                print("Goodbye!")
                break
            elif cmd == "/adapter" and len(parts) > 1:
                force = parts[1]
                result = orchestrator.route_and_generate(
                    input("  Query: ").strip(),
                    force_adapter=force,
                )
                print(f"\nCodette ({force}):\n{result['response']}")
                continue
            elif cmd == "/multi" and len(parts) > 1:
                max_adapters = int(parts[1])
                print(f"  Max adapters set to {max_adapters}")
                continue
            elif cmd == "/base":
                result = orchestrator.route_and_generate(
                    input("  Query: ").strip(),
                    force_adapter=None,
                )
                print(f"\nCodette (base):\n{result['response']}")
                continue
            elif cmd == "/verbose":
                orchestrator.verbose = not orchestrator.verbose
                print(f"  Verbose: {orchestrator.verbose}")
                continue
            else:
                print("  Unknown command. Try /quit, /adapter <name>, /multi <n>, /base, /verbose")
                continue

        # Normal query — route and generate
        result = orchestrator.route_and_generate(
            query,
            max_adapters=max_adapters,
            strategy=strategy,
        )

        print(f"\nCodette:")
        print(result["response"])

        # Show perspectives if multi
        if "perspectives" in result and len(result.get("perspectives", {})) > 1:
            show = input("\n  Show individual perspectives? (y/n): ").strip().lower()
            if show == "y":
                for name, text in result["perspectives"].items():
                    print(f"\n  [{name.upper()}]:")
                    print(f"  {text}")


# ================================================================
# Main
# ================================================================
def main():
    parser = argparse.ArgumentParser(description="Codette Orchestrator")
    parser.add_argument("--query", "-q", type=str, help="Single query (non-interactive)")
    parser.add_argument("--adapter", "-a", type=str, help="Force specific adapter")
    parser.add_argument("--multi", "-m", type=int, default=2, help="Max adapters (default: 2)")
    parser.add_argument("--strategy", "-s", type=str, default="keyword",
                        choices=["keyword", "llm", "hybrid"], help="Routing strategy")
    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
    parser.add_argument("--gpu-layers", type=int, default=0, help="GPU layers (0=CPU only)")
    args = parser.parse_args()

    print("=" * 60)
    print("  CODETTE ORCHESTRATOR")
    print("=" * 60)
    print(f"  Base: {os.path.basename(BASE_GGUF)}")
    print(f"  Strategy: {args.strategy}")

    orchestrator = CodetteOrchestrator(
        n_gpu_layers=args.gpu_layers,
        verbose=args.verbose,
    )

    if args.query:
        # Single query mode
        result = orchestrator.route_and_generate(
            args.query,
            max_adapters=args.multi,
            strategy=args.strategy,
            force_adapter=args.adapter,
        )
        print(f"\nCodette:")
        print(result["response"])

        if "perspectives" in result:
            print(f"\n--- Perspectives ---")
            for name, text in result["perspectives"].items():
                print(f"\n[{name.upper()}]:")
                print(text)
    else:
        # Interactive chat mode
        interactive_chat(orchestrator, max_adapters=args.multi, strategy=args.strategy)


if __name__ == "__main__":
    main()