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+- `cargo build` (for setting up local rust env) +- Install [uv](https://docs.astral.sh/uv/) for python server +- `cd server` +- `uv sync` + +### Running + +- Go to root and run `just serve` in another terminal to run the server +- Run the rust cli using cargo as usual + ## License This project is dual-licensed under MIT and Apache 2.0 terms: diff --git a/b.modelfile b/b.modelfile index bae32e7..818c424 100644 --- a/b.modelfile +++ b/b.modelfile @@ -1,3 +1 @@ -# Using llama model yo -FROM llama3.2 -MESSAGE user hi \ No newline at end of file +FROM mlx-community/dolphin3.0-llama3.2-1B-4Bit diff --git a/justfile b/justfile new file mode 100644 index 0000000..e3be8f2 --- /dev/null +++ b/justfile @@ -0,0 +1,15 @@ +default: check + +fmt: + cargo fmt --all -- --check + +lint: + cargo clippy --all-targets -- -D warnings + +check: + just fmt + just lint + cargo test + +serve: + uv run --frozen python3 -m server.main diff --git a/rust-toolchain.toml b/rust-toolchain.toml new file mode 100644 index 0000000..db034fb --- /dev/null +++ b/rust-toolchain.toml @@ -0,0 +1,3 @@ +[toolchain] +channel = "1.90.0" +components = ["rustfmt", "clippy", "rust-analyzer"] diff --git a/server/.gitignore b/server/.gitignore new file mode 100644 index 0000000..5e4567d --- /dev/null +++ b/server/.gitignore @@ -0,0 +1,3 @@ +__pycache__/ +*.egg-info/ +.venv/ diff --git a/server/__init__.py b/server/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/server/__init__.py @@ -0,0 +1 @@ + diff --git a/server/api.py b/server/api.py new file mode 100644 index 0000000..62a1a53 --- /dev/null +++ b/server/api.py @@ -0,0 +1,313 @@ +# MIT License + +# Copyright (c) 2025 The BROKE team 🦫 + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from fastapi import FastAPI, HTTPException +from .config import SYSTEM_PROMPT + +import json +import time +import uuid +from collections.abc import AsyncGenerator +from typing import Any, Dict, List, Optional, Union + +from fastapi.responses import StreamingResponse +from pydantic import BaseModel, Field + +from .cache_utils import ( + get_model_path +) +from .mlx_runner import MLXRunner + +from server.mem_agent.utils import extract_python_code, extract_reply, extract_thoughts, create_memory_if_not_exists, format_results +from server.mem_agent.engine import execute_sandboxed_code +# Global model cache and configuration +_model_cache: Dict[str, MLXRunner] = {} +_current_model_path: Optional[str] = None +_default_max_tokens: Optional[int] = None # Use dynamic model-aware limits by default +_runner: MLXRunner = {} +_max_tool_turns = 5 +_memory_path = "" + +class CompletionRequest(BaseModel): + model: str + prompt: Union[str, List[str]] + max_tokens: Optional[int] = None + temperature: Optional[float] = 0.7 + top_p: Optional[float] = 0.9 + stream: Optional[bool] = False + stop: Optional[Union[str, List[str]]] = None + repetition_penalty: Optional[float] = 1.1 + + +class ChatMessage(BaseModel): + role: str = Field(..., pattern="^(system|user|assistant)$") + content: str + +_messages: list[ChatMessage]= [] + +class ChatCompletionRequest(BaseModel): + model: str + messages: List[ChatMessage] + max_tokens: Optional[int] = None + temperature: Optional[float] = 0.7 + top_p: Optional[float] = 0.9 + stream: Optional[bool] = False + stop: Optional[Union[str, List[str]]] = None + repetition_penalty: Optional[float] = 1.1 + + +class CompletionResponse(BaseModel): + id: str + object: str = "text_completion" + created: int + model: str + choices: List[Dict[str, Any]] + usage: Dict[str, int] + + +class ChatCompletionResponse(BaseModel): + id: str + object: str = "chat.completion" + created: int + model: str + choices: List[Dict[str, Any]] + # usage: Dict[str, int] + + +class ModelInfo(BaseModel): + id: str + object: str = "model" + owned_by: str = "mlx-knife" + permission: List = [] + context_length: Optional[int] = None + +class StartRequest(BaseModel): + model: str + memory_path: str + +class Agent: + def __init__( + self, + max_tool_turns: int = 20, + memory_path: str = None, + use_vllm: bool = False, + model: str = None, + predetermined_memory_path: bool = False, + model_cache: Dict[str, MLXRunner] = {}, + current_model_path: Optional[str] = None, + default_max_tokens: Optional[int] = None # Use dynamic model-aware limits by default + + ): + # Load the system prompt and add it to the conversation history + self.system_prompt = SYSTEM_PROMPT + self.messages: list[ChatMessage] = [ + ChatMessage(role="system", content=self.system_prompt) + ] + + # Set the maximum number of tool turns and use_vllm flag + self.max_tool_turns = max_tool_turns + self.use_vllm = use_vllm + +app = FastAPI() + +agent: Agent() + +def get_or_load_model(model_spec: str, verbose: bool = False) -> MLXRunner: + """Get model from cache or load it if not cached.""" + global _model_cache, _current_model_path + + print(model_spec) + # Use the existing model path resolution from cache_utils + + try: + model_path, model_name, commit_hash = get_model_path(model_spec) + if not model_path.exists(): + raise HTTPException(status_code=404, detail=f"Model {model_spec} not found in cache") + except Exception as e: + raise HTTPException(status_code=404, detail=f"Model {model_spec} not found: {str(e)}") + + # Check if it's an MLX model + + model_path_str = str(model_path) + + print(_current_model_path) + print(model_path_str) + # Check if we need to load a different model + if _current_model_path != model_path_str: + # Proactively clean up any previously loaded runner to release memory + if _model_cache: + try: + for _old_runner in list(_model_cache.values()): + try: + _old_runner.cleanup() + except Exception: + pass + finally: + _model_cache.clear() + + # Load new model + if verbose: + print(f"Loading model: {model_name}") + + runner = MLXRunner(model_path_str, verbose=verbose) + runner.load_model() + + _model_cache[model_path_str] = runner + _current_model_path = model_path_str + + return _model_cache[model_path_str] + +def format_chat_messages_for_runner(messages: List[ChatMessage]) -> List[Dict[str, str]]: + """Convert chat messages to format expected by MLXRunner. + + Returns messages in dict format for the runner to apply chat templates. + """ + return [{"role": msg.role, "content": msg.content} for msg in messages] + + +def count_tokens(text: str) -> int: + """Rough token count estimation.""" + return int(len(text.split()) * 1.3) # Approximation, convert to int + +@app.get("/ping") +async def ping(): + return {"message": "Badda-Bing Badda-Bang"} + +@app.post("/start") +async def start_model(request: StartRequest): + """Load the model and start the agent""" + global _messages, _runner,_memory_path + print(str(request)) + _messages = [ChatMessage(role="system", content=SYSTEM_PROMPT)] + _memory_path = request.memory_path + try: + _runner = get_or_load_model(request.model) + return {"message": "Model loaded"} + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@app.post("/v1/chat/completions") +async def create_chat_completion(request: ChatCompletionRequest): + """Create a chat completion.""" + global _messages, _max_tool_turns, _memory_path + try: + runner = get_or_load_model(request.model) + + # if request.stream: + # # Streaming response + # return StreamingResponse( + # generate_chat_stream(runner, request.messages, request), + # media_type="text/plain", + # headers={"Cache-Control": "no-cache"} + # ) + # else: + # Non-streaming response + completion_id = f"chatcmpl-{uuid.uuid4()}" + created = int(time.time()) + + # Convert messages to dict format for runner + # _messages.append(system_message) + _messages.extend(request.messages) + message_dicts = format_chat_messages_for_runner(_messages) + # Let the runner format with chat templates + prompt = runner._format_conversation(message_dicts, use_chat_template=True) + + generated_text = runner.generate_batch( + prompt=prompt, + max_tokens=runner.get_effective_max_tokens(request.max_tokens or _default_max_tokens, interactive=False), + temperature=request.temperature, + top_p=request.top_p, + repetition_penalty=request.repetition_penalty, + use_chat_template=False # Already applied in _format_conversation + ) + + # Token counting + # total_prompt = "\n\n".join([msg.content for msg in request.messages]) + # prompt_tokens = count_tokens(total_prompt) + # completion_tokens = count_tokens(generated_text) + + thoughts = extract_thoughts(generated_text) + reply = extract_reply(generated_text) + python_code = extract_python_code(generated_text) + print(generated_text) + result = ({}, "") + if python_code: + create_memory_if_not_exists() + result = execute_sandboxed_code( + code=python_code, + allowed_path=_memory_path, + import_module="server.mem_agent.tools", + ) + + print(reply) + print(str(result)) + + remaining_tool_turns = _max_tool_turns + while remaining_tool_turns > 0 and not reply: + _messages.append(ChatMessage(role="user", content=format_results(result[0], result[1]))) + message_dicts = format_chat_messages_for_runner(_messages) + # Let the runner format with chat templates + prompt = runner._format_conversation(message_dicts, use_chat_template=True) + generated_text = runner.generate_batch( + prompt=prompt + ) + print(generated_text) + # Extract the thoughts, reply and python code from the response + thoughts = extract_thoughts(generated_text) + reply = extract_reply(generated_text) + python_code = extract_python_code(generated_text) + + _messages.append(ChatMessage(role="assistant", content=generated_text)) + if python_code: + create_memory_if_not_exists() + result = execute_sandboxed_code( + code=python_code, + allowed_path=_memory_path, + import_module="server.mem_agent.tools", + ) + else: + # Reset result when no Python code is executed + result = ({}, "") + remaining_tool_turns -= 1 + + return ChatCompletionResponse( + id=completion_id, + created=created, + model=request.model, + choices=[ + { + "index": 0, + "message": { + "role": "assistant", + "content": reply + }, + "finish_reason": "stop" + } + ], + # usage={ + # "prompt_tokens": prompt_tokens, + # "completion_tokens": completion_tokens, + # "total_tokens": prompt_tokens + completion_tokens + # } + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/server/cache_utils.py b/server/cache_utils.py new file mode 100644 index 0000000..cf6bc29 --- /dev/null +++ b/server/cache_utils.py @@ -0,0 +1,1093 @@ +# MIT License + +# Copyright (c) 2025 The BROKE team 🦫 + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +import datetime +import json +import os +import shutil +import sys +from pathlib import Path + +# Issue #31 hints reader +from .model_card import read_readme_front_matter, tokenizer_has_chat_template + +DEFAULT_CACHE_ROOT = Path.home() / ".cache/huggingface" +CACHE_ROOT = Path(os.environ.get("HF_HOME", DEFAULT_CACHE_ROOT)) +MODEL_CACHE = CACHE_ROOT / "hub" + +# Global variable to track if warning was shown +_legacy_warning_shown = False + +# Check for models in legacy location and warn user +_legacy_models = list(CACHE_ROOT.glob("models--*")) +_is_test_env = "test_cache" in str(CACHE_ROOT) or "PYTEST_CURRENT_TEST" in os.environ +if _legacy_models and not _legacy_warning_shown and not _is_test_env: + print(f"\n⚠️ Found {len(_legacy_models)} models in legacy location: {CACHE_ROOT}") + print(f" Please move them to: {MODEL_CACHE}") + print(f" Command: mv {CACHE_ROOT}/models--* {MODEL_CACHE}/") + print(" This warning will appear until models are moved.\n") + _legacy_warning_shown = True + + +def hf_to_cache_dir(hf_name: str) -> str: + if hf_name.startswith("models--"): + return hf_name + if "/" in hf_name: + org, model = hf_name.split("/", 1) + return f"models--{org}--{model}" + else: + return f"models--{hf_name}" + +def cache_dir_to_hf(cache_name: str) -> str: + if cache_name.startswith("models--"): + remaining = cache_name[len("models--"):] + if "--" in remaining: + parts = remaining.split("--", 1) + return f"{parts[0]}/{parts[1]}" + else: + return remaining + return cache_name + +def expand_model_name(model_name): + if "/" in model_name: + return model_name + mlx_candidate = f"mlx-community/{model_name}" + mlx_cache_dir = MODEL_CACHE / hf_to_cache_dir(mlx_candidate) + if mlx_cache_dir.exists(): + return mlx_candidate + common_mlx_patterns = [ + "Llama-", "Qwen", "Mistral", "Phi-", "Mixtral", "phi-", "deepseek" + ] + for pattern in common_mlx_patterns: + if pattern in model_name: + return f"mlx-community/{model_name}" + return model_name + +def find_matching_models(pattern): + """Find models that match a partial pattern. Returns a list of (model_dir, hf_name) tuples.""" + all_models = [d for d in MODEL_CACHE.iterdir() if d.name.startswith("models--")] + matches = [] + + for model_dir in all_models: + hf_name = cache_dir_to_hf(model_dir.name) + # Check if the pattern appears in the model name (case insensitive) + if pattern.lower() in hf_name.lower(): + matches.append((model_dir, hf_name)) + + return matches + +def hash_exists_in_local_cache(model_name, commit_hash): + """Check if a specific commit hash exists in the local cache for a model. + + Supports both full hashes and short hash prefixes (local resolution only). + + Args: + model_name: Full model name (e.g., 'mlx-community/Phi-3-mini-4k-instruct-4bit') + commit_hash: Commit hash to check for (short or full) + + Returns: + Full hash if exists in local cache, None otherwise + """ + base_cache_dir = MODEL_CACHE / hf_to_cache_dir(model_name) + if not base_cache_dir.exists(): + return None + + snapshots_dir = base_cache_dir / "snapshots" + if not snapshots_dir.exists(): + return None + + # Check for exact match first (full hash) + hash_dir = snapshots_dir / commit_hash + if hash_dir.exists(): + return commit_hash + + # Check for short hash match (local resolution) + if len(commit_hash) < 40: + for snapshot_dir in snapshots_dir.iterdir(): + if snapshot_dir.is_dir() and snapshot_dir.name.startswith(commit_hash): + return snapshot_dir.name # Return full hash + + return None + +def resolve_single_model(model_spec): + """ + Resolve a model spec to a single model, supporting fuzzy matching. + Returns (model_path, model_name, commit_hash) or (None, None, None) if failed. + Prints appropriate error messages for ambiguous matches. + """ + # Parse the model spec (handles @commit_hash syntax) + model_name, commit_hash = parse_model_spec(model_spec) + + # Try exact match first + base_cache_dir = MODEL_CACHE / hf_to_cache_dir(model_name) + if base_cache_dir.exists(): + return get_model_path(model_spec) + + # Extract the base name (without @commit_hash) for fuzzy matching + base_spec = model_spec.split('@')[0] if '@' in model_spec else model_spec + + # Try fuzzy matching + matches = find_matching_models(base_spec) + + if not matches: + print(f"No models found matching '{base_spec}'!") + return None, None, None + elif len(matches) == 1: + # Unambiguous match - use the found model with the original commit hash (if any) + found_model_dir, found_hf_name = matches[0] + if commit_hash: + resolved_spec = f"{found_hf_name}@{commit_hash}" + else: + resolved_spec = found_hf_name + return get_model_path(resolved_spec) + elif len(matches) > 1 and commit_hash: + # Issue #13: Hash-based disambiguation for ambiguous model names + for _model_dir, hf_name in matches: + resolved_hash = hash_exists_in_local_cache(hf_name, commit_hash) + if resolved_hash: + resolved_spec = f"{hf_name}@{resolved_hash}" + return get_model_path(resolved_spec) + + # Hash not found in any candidate model + print(f"Hash '{commit_hash}' not found in any model matching '{base_spec}'") + print("Available models:") + for _, hf_name in sorted(matches, key=lambda x: x[1]): + print(f" {hf_name}") + return None, None, None + else: + # Multiple matches without hash - show error with suggestions + print(f"Multiple models match '{base_spec}'. Please be more specific:") + for _, hf_name in sorted(matches, key=lambda x: x[1]): + print(f" {hf_name}") + return None, None, None + +def get_model_path(model_spec): + model_name, commit_hash = parse_model_spec(model_spec) + base_cache_dir = MODEL_CACHE / hf_to_cache_dir(model_name) + print(base_cache_dir) + if not base_cache_dir.exists(): + return None, model_name, commit_hash + if commit_hash: + hash_dir = base_cache_dir / "snapshots" / commit_hash + if hash_dir.exists(): + return hash_dir, model_name, commit_hash + else: + return None, model_name, commit_hash + snapshots_dir = base_cache_dir / "snapshots" + if snapshots_dir.exists(): + snapshots = [d for d in snapshots_dir.iterdir() if d.is_dir()] + if snapshots: + latest = max(snapshots, key=lambda x: x.stat().st_mtime) + return latest, model_name, latest.name + # Return base_cache_dir for corrupted models so rm_model can handle them + return base_cache_dir, model_name, commit_hash + +def parse_model_spec(model_spec): + if "@" in model_spec: + model_name, commit_hash = model_spec.rsplit("@", 1) + model_name = expand_model_name(model_name) + return model_name, commit_hash + model_name = expand_model_name(model_spec) + return model_name, None + +def get_model_size(model_path): + if not model_path.exists(): + return "?" + total_size = 0 + for file in model_path.rglob("*"): + if file.is_file(): + total_size += file.stat().st_size + if total_size >= 1_000_000_000: + return f"{total_size / 1_000_000_000:.1f} GB" + elif total_size >= 1_000_000: + return f"{total_size / 1_000_000:.1f} MB" + else: + return f"{total_size / 1_000:.1f} KB" + +def get_model_modified(model_path): + if not model_path.exists(): + return "?" + mtime = model_path.stat().st_mtime + now = datetime.datetime.now() + modified = datetime.datetime.fromtimestamp(mtime) + diff = now - modified + if diff.days > 0: + return f"{diff.days} days ago" + elif diff.seconds > 3600: + hours = diff.seconds // 3600 + return f"{hours} hours ago" + else: + minutes = diff.seconds // 60 + return f"{minutes} minutes ago" + +def detect_framework(model_path, hf_name): + """Detect model framework with lenient hints (Issue #31).""" + # 1) org hint + if "mlx-community" in hf_name: + return "MLX" + + # 2) README front matter: tags contains 'mlx' OR library_name == 'mlx' + try: + tags, pipeline, lib = read_readme_front_matter(Path(model_path)) + if (lib and lib.lower() == "mlx") or (tags and any((t or '').lower() == "mlx" for t in tags)): + return "MLX" + except Exception: + pass + + # 3) Fallback by file types + snapshots_dir = Path(model_path) / "snapshots" + if not snapshots_dir.exists(): + return "Unknown" + has_gguf = any(snapshots_dir.glob("*/*.gguf")) + has_safetensors = any(snapshots_dir.glob("*/*.safetensors")) + has_pytorch_bin = any(snapshots_dir.glob("*/pytorch_model.bin")) + has_config = any(snapshots_dir.glob("*/*.json")) + total_size = get_model_size(Path(model_path)) + try: + size_mb = float(total_size.replace(" GB", "000").replace(" MB", "").replace(" KB", "0").replace(" ", "")) + except Exception: + size_mb = 0 + if has_gguf: + return "GGUF" + if size_mb < 10: + return "Tokenizer" + if (has_safetensors and has_config) or has_pytorch_bin: + return "PyTorch" + return "Unknown" + + +def detect_model_type(model_path, hf_name): + """Detect model type with priority hints (Issue #31).""" + # 1) tokenizer chat_template + try: + if tokenizer_has_chat_template(Path(model_path)): + return "chat" + except Exception: + pass + + # 2) README hints + try: + tags, pipeline, _ = read_readme_front_matter(Path(model_path)) + tset = {t.lower() for t in (tags or [])} + if pipeline == "text-generation" or any(k in tset for k in {"chat", "instruct"}): + return "chat" + if pipeline == "sentence-similarity" or any(k in tset for k in {"embedding", "embeddings"}): + return "embedding" + except Exception: + pass + + # 3) Fallback by name + name = str(hf_name).lower() + if "instruct" in name or "chat" in name: + return "chat" + if "embed" in name: + return "embedding" + return "base" + + +def get_quantization_info(model_path): + """Extract quantization information from model config.""" + try: + config_path = Path(model_path) / "config.json" + if not config_path.exists(): + return None + with open(config_path) as f: + cfg = json.load(f) + return cfg.get("quantization") + except Exception: + return None + +def get_model_hash(model_path): + snapshots_dir = model_path / "snapshots" + if not snapshots_dir.exists(): + return "--------" + snapshots = [d for d in snapshots_dir.iterdir() if d.is_dir()] + if not snapshots: + return "--------" + latest = max(snapshots, key=lambda x: x.stat().st_mtime) + return latest.name[:8] + +def is_model_healthy(model_spec): + """Strict health check for 1.x (backport of #27 rules). + + Rules: + - config.json must exist and be valid non-empty JSON object. + - If a safetensors or PyTorch index exists, all referenced shards must exist, be non-empty, + and not be Git LFS pointer files. + - Without an index: if multi-shard pattern files exist (model-XXXXX-of-YYYYY.*), require index (unhealthy without index). + Single-file weights (*.safetensors/*.bin/*.gguf) are allowed if non-empty and not LFS pointers. + - Any '.partial'/'partial' or '.tmp' artifacts anywhere => unhealthy. + - Recursive LFS pointer scan for suspiciously small files (<200B). + """ + + # Resolve model path: accept direct directory paths or model specs + candidate = Path(str(model_spec)) + if candidate.exists() and candidate.is_dir(): + model_path = candidate + else: + model_path, _, _ = resolve_single_model(model_spec) + if not model_path: + return False + + # 1) config.json must be valid, non-empty dict + config_path = model_path / "config.json" + if not config_path.exists(): + return False + try: + with open(config_path) as f: + config_data = json.load(f) + if not isinstance(config_data, dict) or not config_data: + return False + except (OSError, json.JSONDecodeError): + return False + + # 2) Fail fast on partial/tmp markers anywhere in the snapshot + for p in model_path.rglob("*"): + name = p.name.lower() + if ".partial" in name or name.endswith(".partial") or name.endswith(".tmp") or "partial" in name: + return False + + # Helper: detect Git LFS pointer file + def _is_lfs_pointer(fp: Path) -> bool: + try: + if fp.stat().st_size >= 200: + return False + with open(fp, "rb") as f: + head = f.read(200) + return b"version https://git-lfs.github.com/spec/v1" in head + except Exception: + return False + + # Helper: verify referenced shards + def _verify_shards(files: list[Path]) -> bool: + if not files: + return False + for f in files: + try: + if (not f.exists()) or f.stat().st_size == 0: + return False + if _is_lfs_pointer(f): + return False + except Exception: + return False + return True + + # 3) Index-aware checks (safetensors or PyTorch) + st_index = model_path / "model.safetensors.index.json" + pt_index = model_path / "pytorch_model.bin.index.json" + if st_index.exists() or pt_index.exists(): + index_files = [p for p in [st_index, pt_index] if p.exists()] + for idx in index_files: + try: + with open(idx) as f: + idx_data = json.load(f) + weight_map = idx_data.get("weight_map") + if not isinstance(weight_map, dict) or not weight_map: + return False + referenced = sorted(set(weight_map.values())) + shard_paths = [model_path / r for r in referenced] + if not _verify_shards(shard_paths): + return False + except (OSError, json.JSONDecodeError): + return False + # Also ensure no recursive LFS pointers elsewhere + ok, _ = check_lfs_corruption(model_path) + return ok + + # 4) No index present — detect multi-shard pattern + # If pattern shards exist, require index (unhealthy without index by policy parity with 2.0) + import re + shard_re = re.compile(r"model-([0-9]{5})-of-([0-9]{5})\.(safetensors|bin)") + pattern_files = [] + for f in model_path.glob("*"): + if f.is_file(): + m = shard_re.match(f.name) + if m: + pattern_files.append((f, int(m.group(1)), int(m.group(2)))) + if pattern_files: + # Even if complete by pattern, absence of index => unhealthy + return False + + # 5) Single-file weights fallback (includes GGUF) + weight_files = list(model_path.rglob("*.safetensors")) + list(model_path.rglob("*.bin")) + list(model_path.rglob("*.gguf")) + # Exclude known pattern shards from consideration (handled above) + filtered_weights = [] + for f in weight_files: + name = f.name + if shard_re.match(name): + continue + filtered_weights.append(f) + if not filtered_weights: + return False + for wf in filtered_weights: + if wf.stat().st_size == 0 or _is_lfs_pointer(wf): + return False + + # Final recursive LFS scan + ok, _ = check_lfs_corruption(model_path) + return ok + +def check_lfs_corruption(model_path): + """Recursively scan for Git LFS pointer files (suspiciously small files).""" + corrupted_files = [] + for file_path in model_path.rglob("*"): + try: + if file_path.is_file() and file_path.stat().st_size < 200: + with open(file_path, 'rb') as f: + header = f.read(200) + if b'version https://git-lfs.github.com/spec/v1' in header: + corrupted_files.append(str(file_path.relative_to(model_path))) + except Exception: + # Ignore unreadable files in corruption scan, keep conservative + continue + if corrupted_files: + return False, f"LFS pointers instead of files: {', '.join(corrupted_files)}" + return True, "No LFS corruption detected" + +def check_model_health(model_spec): + model_path, model_name, commit_hash = resolve_single_model(model_spec) + if not model_path: + # resolve_single_model already printed the appropriate error message + return False + + print(f"Checking model: {model_name}") + if commit_hash: + print(f"Hash: {commit_hash}") + + # Use the robust health check + if is_model_healthy(model_spec): + print("\n[OK] Model is healthy and usable!") + return True + else: + # Detailed diagnosis for WHY it's unhealthy + print("\n[ERROR] Model is corrupted. Detailed diagnosis:") + + # Check config.json + config_path = model_path / "config.json" + if not config_path.exists(): + print(" - config.json missing") + else: + try: + with open(config_path) as f: + config_data = json.load(f) + if not isinstance(config_data, dict) or len(config_data) == 0: + print(" - config.json is empty or invalid") + else: + print(" - config.json found and valid") + except (OSError, json.JSONDecodeError): + print(" - config.json exists but contains invalid JSON") + + # Check weight files (including gguf support like is_model_healthy) + weight_files = list(model_path.glob("*.safetensors")) + list(model_path.glob("*.bin")) + list(model_path.glob("*.gguf")) + if not weight_files: + weight_files = list(model_path.glob("**/*.safetensors")) + list(model_path.glob("**/*.bin")) + list(model_path.glob("**/*.gguf")) + + if weight_files: + total_size = sum(f.stat().st_size for f in weight_files) + size_mb = total_size / (1024 * 1024) + print(f" - Model weights found ({len(weight_files)} files, {size_mb:.1f}MB)") + elif (model_path / "model.safetensors.index.json").exists(): + # Check multi-file model + try: + with open(model_path / "model.safetensors.index.json") as f: + index = json.load(f) + if 'weight_map' in index: + referenced_files = set(index['weight_map'].values()) + existing_files = [f for f in referenced_files if (model_path / f).exists()] + if existing_files: + total_size = sum((model_path / f).stat().st_size for f in existing_files) + size_mb = total_size / (1024 * 1024) + print(f" - Multi-file weights ({len(existing_files)}/{len(referenced_files)} files, {size_mb:.1f}MB)") + if len(existing_files) < len(referenced_files): + print(" - Incomplete multi-file model") + else: + print(" - Multi-file model index found but no weight files exist") + else: + print(" - Multi-file model index is invalid") + except Exception as e: + print(f" - Multi-file model index error: {e}") + else: + print(" - No model weights found (.safetensors, .bin, .gguf)") + + # Check LFS corruption + lfs_ok, lfs_msg = check_lfs_corruption(model_path) + if not lfs_ok: + print(f" - {lfs_msg}") + else: + print(f" - {lfs_msg}") + + # Show framework + framework = detect_framework(model_path.parent.parent, model_name) + print(f" - Framework: {framework}") + + # Offer deletion for corrupted models + confirm = input("\nModel appears corrupted. Delete? [y/N] ") + if confirm.lower() == "y": + import errno + import shutil + try: + if commit_hash: + # Delete specific hash/snapshot + shutil.rmtree(model_path) + print(f"Hash {commit_hash} deleted.") + else: + # Delete entire model directory (go up from snapshots or use base_cache_dir) + if model_path.name.startswith("models--"): + # model_path is base_cache_dir (corrupted model case) + shutil.rmtree(model_path) + else: + # model_path is snapshot dir + model_base_dir = model_path.parent.parent + shutil.rmtree(model_base_dir) + print(f"Model {model_name} deleted.") + except PermissionError as e: + print(f"[ERROR] Permission denied: Cannot delete {e.filename}") + print(" Try running with appropriate permissions or manually delete the directory.") + except OSError as e: + if e.errno == errno.ENOTEMPTY: + print(f"[ERROR] Directory not empty: {e.filename}") + print(" Another process may be using this model.") + elif e.errno == errno.EACCES: + print(f"[ERROR] Access denied: {e.filename}") + else: + print(f"[ERROR] OS Error while deleting: {e}") + except Exception as e: + print(f"[ERROR] Unexpected error while deleting: {type(e).__name__}: {e}") + + return False + +def check_all_models_health(): + models = [d for d in MODEL_CACHE.iterdir() if d.name.startswith("models--")] + if not models: + print("No models found in HuggingFace cache.") + return + print(f"Checking {len(models)} models for integrity...\n") + healthy_models = [] + problematic_models = [] + for model_dir in sorted(models, key=lambda x: x.stat().st_mtime, reverse=True): + hf_name = cache_dir_to_hf(model_dir.name) + model_hash = get_model_hash(model_dir) + print(f"{hf_name} ({model_hash})") + if is_model_healthy(hf_name): + healthy_models.append((hf_name, model_hash)) + print(" [OK] Healthy\n") + else: + problematic_models.append((hf_name, model_hash)) + print(" [ERROR] Problematic\n") + print("=" * 50) + print("Summary:") + print(f"[OK] Healthy models: {len(healthy_models)}") + print(f"[ERROR] Problematic models: {len(problematic_models)}") + if problematic_models: + print("\n[WARNING] Problematic models:") + for name, hash_id in problematic_models: + print(f" - {name} ({hash_id})") + print("\nRepair tips:") + print(" python mlx_knife.cli pull # Re-download") + print(" python mlx_knife.cli rm # Delete") + print(" python mlx_knife.cli health # Show details") + return len(problematic_models) == 0 + +def list_models(show_all=False, framework_filter=None, show_health=False, single_model=None, verbose=False): + if single_model: + # Try exact match first + expanded_model = expand_model_name(single_model) + model_dir = MODEL_CACHE / hf_to_cache_dir(expanded_model) + + if model_dir.exists(): + models = [model_dir] + else: + # If exact match fails, do partial name matching + if not MODEL_CACHE.exists(): + print(f"No models found matching '{single_model}' - cache directory doesn't exist yet.") + print("Use 'mlxk pull ' to download models first.") + return + all_models = [d for d in MODEL_CACHE.iterdir() if d.name.startswith("models--")] + matching_models = [] + + for model_dir in all_models: + hf_name = cache_dir_to_hf(model_dir.name) + # Check if the pattern appears in the model name (case insensitive) + if single_model.lower() in hf_name.lower(): + matching_models.append(model_dir) + + if not matching_models: + print(f"No models found matching '{single_model}'!") + return + + models = matching_models + else: + if not MODEL_CACHE.exists(): + print("No models found - cache directory doesn't exist yet.") + print("Use 'mlxk pull ' to download models first.") + return + models = [d for d in MODEL_CACHE.iterdir() if d.name.startswith("models--")] + if not models: + print("No models found in HuggingFace cache.") + return + if show_health: + if show_all: + print(f"{'NAME':<40} {'ID':<10} {'SIZE':<10} {'MODIFIED':<15} {'FRAMEWORK':<10} {'TYPE':<10} {'HEALTH':<8}") + else: + print(f"{'NAME':<40} {'ID':<10} {'SIZE':<10} {'MODIFIED':<15} {'HEALTH':<8}") + else: + if show_all: + print(f"{'NAME':<40} {'ID':<10} {'SIZE':<10} {'MODIFIED':<15} {'FRAMEWORK':<10} {'TYPE':<10}") + else: + print(f"{'NAME':<40} {'ID':<10} {'SIZE':<10} {'MODIFIED':<15}") + for m in sorted(models, key=lambda x: x.stat().st_mtime, reverse=True): + hf_name = cache_dir_to_hf(m.name) + size = get_model_size(m) + modified = get_model_modified(m) + model_hash = get_model_hash(m) + framework = detect_framework(m, hf_name) + model_type = detect_model_type(m, hf_name) + if framework_filter and framework.lower() != framework_filter: + continue + # Default (strict) list: show only MLX chat models + if not show_all and not framework_filter: + if framework != "MLX": + continue + if model_type != "chat": + continue + # Handle display name based on verbose flag + display_name = hf_name + if hf_name.startswith("mlx-community/") and not verbose: + # For MLX models, hide prefix unless verbose is set + display_name = hf_name[len("mlx-community/"):] + health_status = "" + if show_health: + health_status = "[OK]" if is_model_healthy(hf_name) else "[ERR]" + if show_all: + print(f"{display_name:<40} {model_hash:<10} {size:<10} {modified:<15} {framework:<10} {model_type:<10} {health_status:<8}") + else: + print(f"{display_name:<40} {model_hash:<10} {size:<10} {modified:<15} {health_status:<8}") + else: + if show_all: + print(f"{display_name:<40} {model_hash:<10} {size:<10} {modified:<15} {framework:<10} {model_type:<10}") + else: + print(f"{display_name:<40} {model_hash:<10} {size:<10} {modified:<15}") + +def run_model(model_spec, prompt=None, interactive=False, temperature=0.7, + max_tokens=500, top_p=0.9, repetition_penalty=1.1, stream=True, + use_chat_template=True, hide_reasoning=False, verbose=False): + """Run an MLX model with enhanced features. + + Args: + model_spec: Model specification (name[@hash]) + prompt: Input prompt (if None and not interactive, enters interactive mode) + interactive: Force interactive mode + temperature: Sampling temperature + max_tokens: Maximum tokens to generate + top_p: Top-p sampling parameter + repetition_penalty: Penalty for repeated tokens + stream: Whether to stream output + """ + model_path, model_name, commit_hash = resolve_single_model(model_spec) + if not model_path: + print(f"Use: mlxk pull {model_spec}") + sys.exit(1) + + framework = detect_framework(model_path.parent.parent, model_name) + if framework != "MLX": + print(f"Model {model_name} is not MLX-compatible (Framework: {framework})!") + print("Use MLX-Community models: https://huggingface.co/mlx-community") + sys.exit(1) + + # Try to use the enhanced runner (import module to allow monkeypatching in tests) + try: + from . import mlx_runner as _mr + + _mr.run_model_enhanced( + model_path=str(model_path), + prompt=prompt, + interactive=interactive, + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + repetition_penalty=repetition_penalty, + stream=stream, + use_chat_template=use_chat_template, + hide_reasoning=hide_reasoning, + verbose=verbose, + ) + except ImportError: + # Fallback to subprocess if mlx_runner is not available + print("[WARNING] Enhanced runner not available, falling back to subprocess mode") + print(f"Running model: {model_name}") + if commit_hash: + print(f"Hash: {commit_hash}") + print(f"Cache path: {model_path}") + + if interactive or prompt is None: + print("Interactive mode not supported in fallback mode") + prompt = prompt or "Hello" + + print(f"Prompt: {prompt}\n") + os.system(f'python -m mlx_lm generate --model "{model_path}" --prompt "{prompt}"') + +def show_model(model_spec, show_files=False, show_config=False): + """Show detailed information about a specific model.""" + model_path, model_name, commit_hash = resolve_single_model(model_spec) + + if not model_path: + return False + + # Basic information + print(f"Model: {model_name}") + print(f"Path: {model_path}") + + if commit_hash: + print(f"Snapshot: {commit_hash}") + else: + # Show current snapshot hash + current_hash = model_path.name + print(f"Snapshot: {current_hash}") + + # Size + size = get_model_size(model_path) + print(f"Size: {size}") + + # Modified time + modified = get_model_modified(model_path) + print(f"Modified: {modified}") + + # Framework / Type + framework = detect_framework(model_path.parent.parent, model_name) + model_type = detect_model_type(model_path.parent.parent, model_name) + print(f"Framework: {framework}") + print(f"Type: {model_type}") + + # Quantization info (if available) + quant_info = get_quantization_info(model_path) + if quant_info: + if isinstance(quant_info, dict): + # Show main quantization config (compact format) + main_config = [] + if "mode" in quant_info: + main_config.append(f"mode: {quant_info['mode']}") + if "bits" in quant_info: + main_config.append(f"{quant_info['bits']}-bit") + if "group_size" in quant_info: + main_config.append(f"group_size: {quant_info['group_size']}") + + if main_config: + print(f"Quantization: {', '.join(main_config)}") + if "mode" in quant_info: + print(f" Advanced mode '{quant_info['mode']}' (requires MLX ≥0.29.0, MLX-LM ≥0.27.0)") + else: + print(f"Quantization: {quant_info}") + + # Quantization and Precision info + config_path = model_path / "config.json" + quantization_info = None + precision_info = None + gguf_variants = [] + + if config_path.exists(): + try: + with open(config_path) as f: + config_data = json.load(f) + + # 1. Check for explicit quantization field (MLX style) + if "quantization" in config_data and isinstance(config_data["quantization"], dict): + quant = config_data["quantization"] + if "bits" in quant: + quantization_info = f"{quant['bits']}-bit" + precision_info = f"int{quant['bits']}" + if "group_size" in quant: + quantization_info += f" (group_size: {quant['group_size']})" + + # 2. Check torch_dtype (HuggingFace standard) + elif "torch_dtype" in config_data: + dtype = config_data["torch_dtype"] + precision_info = dtype + # Check if model name suggests quantization + name_lower = model_name.lower() + if "4bit" in name_lower or "-4b" in name_lower: + quantization_info = "4-bit (inferred from name)" + elif "8bit" in name_lower or "-8b" in name_lower: + quantization_info = "8-bit (inferred from name)" + else: + quantization_info = "No quantization detected" + + # 3. Special handling for GGUF files + gguf_files = sorted(list(model_path.glob("*.gguf"))) + if gguf_files and not quantization_info: + # Collect all GGUF variants + gguf_variants = [] + for f in gguf_files: + name = f.name + size_mb = f.stat().st_size / (1024 * 1024) + + # Parse quantization type from filename + name_lower = name.lower() + if "q2_k" in name_lower: + variant_info = f"Q2_K (2-bit, {size_mb:.0f} MB)" + elif "q3_k_s" in name_lower: + variant_info = f"Q3_K_S (3-bit small, {size_mb:.0f} MB)" + elif "q3_k_m" in name_lower: + variant_info = f"Q3_K_M (3-bit medium, {size_mb:.0f} MB)" + elif "q3_k_l" in name_lower: + variant_info = f"Q3_K_L (3-bit large, {size_mb:.0f} MB)" + elif "q3_k" in name_lower: + variant_info = f"Q3_K (3-bit, {size_mb:.0f} MB)" + elif "q4_0" in name_lower: + variant_info = f"Q4_0 (4-bit, {size_mb:.0f} MB)" + elif "q4_k_s" in name_lower: + variant_info = f"Q4_K_S (4-bit small, {size_mb:.0f} MB)" + elif "q4_k_m" in name_lower: + variant_info = f"Q4_K_M (4-bit medium, {size_mb:.0f} MB)" + elif "q4_k" in name_lower: + variant_info = f"Q4_K (4-bit, {size_mb:.0f} MB)" + elif "q5_0" in name_lower: + variant_info = f"Q5_0 (5-bit, {size_mb:.0f} MB)" + elif "q5_k_s" in name_lower: + variant_info = f"Q5_K_S (5-bit small, {size_mb:.0f} MB)" + elif "q5_k_m" in name_lower: + variant_info = f"Q5_K_M (5-bit medium, {size_mb:.0f} MB)" + elif "q5_k" in name_lower: + variant_info = f"Q5_K (5-bit, {size_mb:.0f} MB)" + elif "q6_k" in name_lower: + variant_info = f"Q6_K (6-bit, {size_mb:.0f} MB)" + elif "q8_0" in name_lower: + variant_info = f"Q8_0 (8-bit, {size_mb:.0f} MB)" + else: + variant_info = f"{name} ({size_mb:.0f} MB)" + + gguf_variants.append(variant_info) + + if len(gguf_variants) > 1: + quantization_info = "Multiple GGUF variants available" + precision_info = "gguf (see variants below)" + elif len(gguf_variants) == 1: + quantization_info = gguf_variants[0].split(' (')[0] + precision_info = "gguf" + else: + quantization_info = "GGUF format (quantization unknown)" + precision_info = "gguf" + + except (OSError, json.JSONDecodeError, KeyError): + pass + + # Display quantization and precision info + if quantization_info: + print(f"Quantization: {quantization_info}") + else: + print("Quantization: Unknown (no info in config)") + + if precision_info: + print(f"Precision: {precision_info}") + else: + print("Precision: Unknown") + + # Display GGUF variants if available + if gguf_variants and len(gguf_variants) > 1: + print("\nAvailable GGUF variants:") + for variant in gguf_variants: + print(f" - {variant}") + + # Health status + health_ok = is_model_healthy(model_name) + if health_ok: + print("Health: [OK]") + else: + print("Health: [ERROR] CORRUPTED") + # Check specific issues + issues = [] + if not (model_path / "config.json").exists(): + issues.append("config.json missing") + + weight_files = list(model_path.glob("*.safetensors")) + list(model_path.glob("*.bin")) + list(model_path.glob("*.gguf")) + if not weight_files: + weight_files = list(model_path.glob("**/*.safetensors")) + list(model_path.glob("**/*.bin")) + list(model_path.glob("**/*.gguf")) + if not weight_files: + index_file = model_path / "model.safetensors.index.json" + if not index_file.exists(): + issues.append("No model weights found") + + lfs_ok, lfs_msg = check_lfs_corruption(model_path) + if not lfs_ok: + issues.append(lfs_msg) + + if issues: + print(" Issues:") + for issue in issues: + print(f" - {issue}") + + # Show files if requested + if show_files: + print("\nFiles:") + files = [] + for file in sorted(model_path.rglob("*")): + if file.is_file(): + relative_path = file.relative_to(model_path) + file_size = file.stat().st_size + if file_size >= 1_000_000_000: + size_str = f"{file_size / 1_000_000_000:.2f} GB" + elif file_size >= 1_000_000: + size_str = f"{file_size / 1_000_000:.2f} MB" + elif file_size >= 1_000: + size_str = f"{file_size / 1_000:.2f} KB" + else: + size_str = f"{file_size} B" + files.append((str(relative_path), size_str)) + + # Print files in a nice table format + if files: + max_name_len = max(len(f[0]) for f in files) + for file_path, file_size in files: + print(f" {file_path:<{max_name_len}} {file_size:>10}") + else: + print(" No files found") + + # Show config if requested + if show_config: + config_path = model_path / "config.json" + if config_path.exists(): + print("\nConfig:") + try: + with open(config_path) as f: + config_data = json.load(f) + print(json.dumps(config_data, indent=2)) + except Exception as e: + print(f" Error reading config: {e}") + else: + print("\nConfig: Not found") + + return True + +def rm_model(model_spec, force=False): + original_spec = model_spec + + # First try to resolve using fuzzy matching + resolved_path, resolved_name, resolved_hash = resolve_single_model(model_spec) + + if not resolved_path: + # resolve_single_model already printed the error message for most cases + # But ensure we always provide feedback to the user + print(f"Model '{original_spec}' not found or corrupted.") + return + + # Use the resolved model name for deletion + model_name = resolved_name + commit_hash = resolved_hash + + + # Confirm on auto-expansion (if the resolved name is different from input) + base_spec = original_spec.split("@")[0] if "@" in original_spec else original_spec + if base_spec != model_name and "/" not in base_spec: + confirm = input(f"Delete '{model_name}' (matched from '{base_spec}')? [Y/n] ") + if confirm.lower() == "n": + print("Delete aborted.") + return + + base_cache_dir = MODEL_CACHE / hf_to_cache_dir(model_name) + # This should exist since resolve_single_model succeeded, but double-check + if not base_cache_dir.exists(): + print(f"[ERROR] Model directory disappeared: {model_name}") + return + # Specific hash to delete? + if commit_hash: + hash_dir = base_cache_dir / "snapshots" / commit_hash + if not hash_dir.exists(): + print(f"Hash {commit_hash} for model {model_name} not found!") + print("\nAvailable hashes:") + snapshots_dir = base_cache_dir / "snapshots" + if snapshots_dir.exists(): + for snapshot in sorted(snapshots_dir.iterdir()): + if snapshot.is_dir(): + print(f" {snapshot.name[:8]}") + return + if force: + confirm_delete = True + else: + confirm = input(f"Delete hash {commit_hash} of model {model_name}? [y/N] ") + confirm_delete = confirm.lower() == "y" + + if confirm_delete: + # Issue #23 Fix: Delete entire model directory, not just the snapshot + # This prevents the double-execution problem where refs/ remain intact + shutil.rmtree(base_cache_dir) + print(f"{model_name}@{commit_hash} deleted") + + # Clean up associated lock files + try: + _cleanup_model_locks(model_name, force) + except Exception as e: + print(f"Warning: Could not clean up cache files: {e}") + else: + print("Aborted.") + else: + # Delete entire model + if force: + confirm_delete = True + else: + confirm = input(f"Delete entire model {model_name} ({base_cache_dir})? [y/N] ") + confirm_delete = confirm.lower() == "y" + + if confirm_delete: + shutil.rmtree(base_cache_dir) + print(f"Model {model_name} completely deleted.") + + # Clean up associated lock files + try: + _cleanup_model_locks(model_name, force) + except Exception as e: + print(f"Warning: Could not clean up cache files: {e}") + else: + print("Aborted.") + + +def _cleanup_model_locks(model_name, force=False): + """Clean up HuggingFace lock files for a deleted model. + + Args: + model_name: The model name (e.g. 'microsoft/DialoGPT-small') + force: If True, delete without asking. If False, prompt user. + """ + locks_dir = MODEL_CACHE / ".locks" / hf_to_cache_dir(model_name) + + if not locks_dir.exists(): + return # No locks to clean up + + # Count lock files + try: + lock_files = list(locks_dir.iterdir()) + if not lock_files: + return # Empty directory + + if force: + # Delete without asking + shutil.rmtree(locks_dir) + print(f"Cleaned up cache files ({len(lock_files)} files).") + else: + # Ask user + confirm = input("Clean up cache files? [Y/n] ") + if confirm.lower() != "n": + shutil.rmtree(locks_dir) + print(f"Cache files cleaned up ({len(lock_files)} files).") + else: + print("Cache files left intact.") + + except Exception as e: + print(f"Warning: Could not clean up cache files: {e}") + diff --git a/server/config.py b/server/config.py new file mode 100644 index 0000000..f9629a9 --- /dev/null +++ b/server/config.py @@ -0,0 +1,10 @@ +from pathlib import Path +import os +PORT = 6969 +MODEL_ID = "driaforall/mem-agent" + +prompt_path = Path(__file__).parent / "system_prompt.txt" +MEMORY_PATH = os.path.expanduser("~") + "/tiles_memory" + +with open(prompt_path, "r", encoding="utf-8") as f: + SYSTEM_PROMPT = f.read().strip() diff --git a/server/main.py b/server/main.py new file mode 100644 index 0000000..b52ef7e --- /dev/null +++ b/server/main.py @@ -0,0 +1,20 @@ +# import os +import uvicorn +from .api import app +from .config import PORT + +def run(): + # Write PID file + # PID_FILE.write_text(str(os.getpid())) + + # try: + uvicorn.run(app, host="127.0.0.1", port=PORT) + # finally: + # if PID_FILE.exists(): + # PID_FILE.unlink() + # + # print("hello from main!") + +if __name__ == "__main__": + run() + diff --git a/server/mem_agent/__init__.py b/server/mem_agent/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/server/mem_agent/__init__.py @@ -0,0 +1 @@ + diff --git a/server/mem_agent/engine.py b/server/mem_agent/engine.py new file mode 100644 index 0000000..7db783b --- /dev/null +++ b/server/mem_agent/engine.py @@ -0,0 +1,334 @@ +import builtins +import importlib +import logging +import os +import sys +import traceback +import types +import pickle +import subprocess +import base64 + +SANDBOX_TIMEOUT = 10 + +# Configure a logger for the sandbox (in real use, configure handlers/level as needed) +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) # or DEBUG for more verbosity + + +def _run_user_code( + code: str, + allow_installs: bool, + allowed_path: str, + blacklist: list, + available_functions: dict, + log: bool = False, +) -> tuple[dict, str]: + """ + Execute code under sandboxed conditions (limited file access, optional installs, + and blacklisting) and return the resulting locals and an error message. + """ + try: + # Optional: apply working directory and file access restriction + if allowed_path: + allowed = os.path.abspath(allowed_path) + try: + os.chdir(allowed) # Change working dir to the allowed_path + except Exception as e: + # If we cannot chdir, log but continue (the open wrapper will still enforce path) + logger.warning( + "Could not change working directory to %s: %s", allowed, e + ) + # Wrap builtins.open to restrict file access + orig_open = builtins.open + + def secure_open(file, *args, **kwargs): + """Open that restricts file access to allowed_path.""" + # If file is a file object or path-like, get its string path + path = ( + file if isinstance(file, str) else getattr(file, "name", str(file)) + ) + full_path = os.path.abspath(path if path is not None else "") + if not full_path.startswith(allowed): + raise PermissionError( + f"Access to '{full_path}' is denied by sandbox." + ) + return orig_open(file, *args, **kwargs) + + builtins.open = secure_open + + # Optionally, restrict other file-related functions (remove, rename, etc.) similarly + # We'll patch a couple of common ones as an example: + orig_remove = os.remove + + def secure_remove(path, *args, **kwargs): + full_path = os.path.abspath(path) + if not full_path.startswith(allowed): + raise PermissionError( + f"Removal of '{full_path}' is denied by sandbox." + ) + return orig_remove(path, *args, **kwargs) + + os.remove = secure_remove + + orig_rename = os.rename + + def secure_rename(src, dst, *args, **kwargs): + full_src = os.path.abspath(src) + full_dst = os.path.abspath(dst) + if not full_src.startswith(allowed) or not full_dst.startswith(allowed): + raise PermissionError( + "Rename operation outside allowed path is denied by sandbox." + ) + return orig_rename(src, dst, *args, **kwargs) + + os.rename = secure_rename + + # Apply blacklist restrictions by removing or disabling blacklisted builtins or attributes + if blacklist: + for name in blacklist: + # If the name has a dot, like "os.system", handle module attributes + if "." in name: + mod_name, attr_name = name.split(".", 1) + try: + mod_obj = importlib.import_module(mod_name) + except ImportError: + mod_obj = None + # If module is imported in sandbox, remove the attribute + if mod_obj and hasattr(mod_obj, attr_name): + try: + setattr( + mod_obj, attr_name, None + ) # simple way: nullify the attribute + except Exception: + pass # if we cannot set it, ignore (might be read-only) + else: + # It's a built-in or global name; remove from builtins if present + if name in builtins.__dict__: + builtins.__dict__[name] = ( + None # or we could del, but setting None prevents use + ) + # Additionally, we can ensure __builtins__ in the exec env doesn't contain them (handled below in exec) + + # If allowed, handle package installations inside sandbox (in case code itself triggers ImportError) + if allow_installs: + # We will install missing imports on the fly during execution if an ImportError occurs. + # One approach: wrap __import__ to catch failed imports and pip install. + orig_import = builtins.__import__ + + def custom_import(name, globals=None, locals=None, fromlist=(), level=0): + try: + return orig_import(name, globals, locals, fromlist, level) + except ImportError as e: + pkg = name.split(".")[0] + logger.info( + "Sandbox: attempting to install missing package '%s'", pkg + ) + try: + subprocess.run( + [sys.executable, "-m", "pip", "install", pkg], + check=True, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except Exception as inst_err: + # If installation fails, re-raise the original ImportError + logger.error( + "Sandbox: failed to install package %s: %s", pkg, inst_err + ) + raise e + # Retry the import after installation + return orig_import(name, globals, locals, fromlist, level) + + builtins.__import__ = custom_import + + # Prepare an isolated execution namespace. We use an empty globals dict with a fresh builtins. + exec_globals = {"__builtins__": builtins.__dict__} + + # Add any provided functions to the execution environment + if available_functions: + exec_globals.update(available_functions) + + exec_locals = {} # local variables will be collected here + + error_msg = None + try: + exec(code, exec_globals, exec_locals) # Execute the user's code + except Exception as e: + # Catch any exception and format it + tb = traceback.format_exc() + error_msg = f"Exception in sandboxed code:\n{tb}" + if log: + logger.error("Sandbox: code raised an exception: %s", e) + except SystemExit as e: + # Handle sys.exit calls (which raise SystemExit) + code_val = e.code if isinstance(e.code, int) or e.code else 0 + if code_val != 0: + error_msg = f"Sandboxed code called sys.exit({code_val})" + if log: + logger.warning( + "Sandbox: code exited with non-zero status %s", code_val + ) + # For sys.exit(0), we treat it as normal termination (no error) + + # Clean up any blacklisted or internal entries in locals + exec_locals.pop("__builtins__", None) + + # Collect only picklable locals for returning + safe_locals = {} + for var, val in exec_locals.items(): + try: + pickle.dumps(val) # test picklability + safe_locals[var] = val + except Exception: + safe_locals[var] = repr(val) # fallback: use string representation + + if log: + logger.info("Sandbox execution finished") + + return safe_locals, error_msg + + except Exception as e: + # Catch any unhandled exceptions in the worker process + if log: + logger.error( + "Unhandled exception in sandbox worker: %s", traceback.format_exc() + ) + return None, f"Sandbox worker error: {str(e)}" + + +def execute_sandboxed_code( + code: str, + timeout: int = SANDBOX_TIMEOUT, + allow_installs: bool = False, + requirements_path: str = None, + allowed_path: str = None, + blacklist: list = None, + available_functions: dict = None, + import_module: str = None, + log: bool = False, +) -> tuple[dict, str]: + """ + Execute the given Python code string in a sandboxed subprocess with specified restrictions. + + Parameters: + code (str): The Python code to execute. + timeout (int): Maximum execution time in seconds for the sandboxed code (default 10 seconds). + allow_installs (bool): If True, allow installing missing packages via pip (default False). + requirements_path (str): Path to a requirements.txt file to install before execution. + allowed_path (str): Directory path that the code is allowed to access for file I/O. + File operations outside this path will be blocked. If None, no extra file restrictions are applied. + blacklist (list): List of names (builtins or module attributes) that are disallowed in the code. + If the code uses any of these, it will be prevented or result in an error. + available_functions (dict): Dictionary of functions to make available in the sandboxed environment. + The keys are the function names, and the values are the function objects. + import_module (str): Name of a Python module to import and make all its functions available in the sandbox. + + Returns: + (dict, str): A tuple containing the dictionary of local variables from the executed code (or None on failure), + and an error message (str) if an error/exception occurred, or None if execution was successful. + """ + # Step 1: If package installs are allowed, handle requirements and prepare environment + if requirements_path: + if os.path.isfile(requirements_path): + logger.info( + "Installing packages from requirements file: %s", requirements_path + ) + try: + subprocess.run( + [sys.executable, "-m", "pip", "install", "-r", requirements_path], + check=True, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except Exception as e: + logger.error( + "Failed to install requirements from %s: %s", requirements_path, e + ) + # If requirements fail to install, we can choose to abort or continue. Here, abort execution. + return None, f"Failed to install requirements: {e}" + else: + logger.error("Requirements file %s not found.", requirements_path) + return None, f"Requirements file not found: {requirements_path}" + + # If a module name is provided, import it and add its functions to available_functions + if isinstance(available_functions, str) and not import_module: + import_module = available_functions + available_functions = None + + if import_module: + try: + module = importlib.import_module(import_module) + if available_functions is None: + available_functions = {} + for name in dir(module): + if not name.startswith("_"): + attr = getattr(module, name) + if callable(attr): + available_functions[name] = attr + except ImportError as e: + logger.error(f"Failed to import module {import_module}: {e}") + return None, f"Failed to import module {import_module}: {e}" + + # Step 2: Execute the code in a separate Python subprocess + params = { + "code": code, + "allow_installs": allow_installs, + "allowed_path": allowed_path, + "blacklist": blacklist or [], + "available_functions": available_functions or {}, + "log": log, + } + + env = os.environ.copy() + env["SANDBOX_PARAMS"] = base64.b64encode(pickle.dumps(params)).decode() + + try: + result = subprocess.run( + [sys.executable, "-m", "mem_agent.engine"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + timeout=timeout, + env=env, + ) + except subprocess.TimeoutExpired: + logger.error( + "Sandboxed code exceeded time limit of %d seconds; terminating.", timeout + ) + return None, f"TimeoutError: Code execution exceeded {timeout} seconds." + + if result.returncode != 0: + return None, result.stderr.decode().strip() + + print("stderr:", result.stderr.decode()) + print("stdout:", result.stdout[:200]) + + try: + local_vars, error_msg = pickle.loads(result.stdout) + except Exception as e: + return None, f"Failed to decode sandbox output: {e}" + + if error_msg is None: + error_msg = "" + + return local_vars, error_msg + +def _subprocess_entry() -> None: + """Entry point for sandbox subprocess.""" + params_b64 = os.environ.get("SANDBOX_PARAMS") + if not params_b64: + sys.exit(1) + params = pickle.loads(base64.b64decode(params_b64)) + locals_dict, error = _run_user_code( + params["code"], + params.get("allow_installs", False), + params.get("allowed_path"), + params.get("blacklist", []), + params.get("available_functions", {}), + params.get("log", False), + ) + sys.stdout.buffer.write(pickle.dumps((locals_dict, error))) + + +if __name__ == "__main__": + _subprocess_entry() diff --git a/server/mem_agent/tools.py b/server/mem_agent/tools.py new file mode 100644 index 0000000..da6dd86 --- /dev/null +++ b/server/mem_agent/tools.py @@ -0,0 +1,359 @@ +import os +import tempfile +import uuid +import subprocess +from pathlib import Path +from typing import Union + +from server.mem_agent.utils import MEMORY_PATH, check_size_limits, create_memory_if_not_exists + +def get_size(file_or_dir_path: str) -> int: + """ + Get the size of a file or directory. + + Args: + file_or_dir_path: The path to the file or directory. + If empty string, returns total memory directory size. + + Returns: + The size of the file or directory in bytes. + """ + # Handle empty string by returning total memory size + if not file_or_dir_path or file_or_dir_path == "": + # Get the current working directory (which should be the memory root) + cwd = os.getcwd() + total_size = 0 + for dirpath, dirnames, filenames in os.walk(cwd): + for filename in filenames: + file_path = os.path.join(dirpath, filename) + try: + total_size += os.path.getsize(file_path) + except OSError: + pass + return total_size + + # Otherwise check the specific path + if os.path.isfile(file_or_dir_path): + return os.path.getsize(file_or_dir_path) + elif os.path.isdir(file_or_dir_path): + total_size = 0 + for dirpath, dirnames, filenames in os.walk(file_or_dir_path): + for filename in filenames: + file_path = os.path.join(dirpath, filename) + try: + total_size += os.path.getsize(file_path) + except OSError: + pass + return total_size + else: + raise FileNotFoundError(f"Path not found: {file_or_dir_path}") + +def create_file(file_path: str, content: str = "") -> bool: + """ + Create a new file in the memory with the given content (if any). + First create a temporary file with the given content, check if + the size limits are respected, if so, move the temporary file to + the final destination. + + Args: + file_path: The path to the file. + content: The content of the file. + + Returns: + True if the file was created successfully, False otherwise. + """ + temp_file_path = None + try: + # Create parent directories if they don't exist + parent_dir = os.path.dirname(file_path) + if parent_dir and not os.path.exists(parent_dir): + os.makedirs(parent_dir, exist_ok=True) + + # Create a unique temporary file name in the same directory as the target file + # This ensures the temp file is within the sandbox's allowed path + target_dir = os.path.dirname(os.path.abspath(file_path)) or "." + temp_file_path = os.path.join(target_dir, f"temp_{uuid.uuid4().hex[:8]}.txt") + + with open(temp_file_path, "w") as f: + f.write(content) + + if check_size_limits(temp_file_path): + # Move the content to the final destination + with open(file_path, "w") as f: + f.write(content) + os.remove(temp_file_path) + return True + else: + os.remove(temp_file_path) + raise Exception(f"File {file_path} is too large to create") + except Exception as e: + # Clean up temp file if it exists + if temp_file_path and os.path.exists(temp_file_path): + try: + os.remove(temp_file_path) + except Exception as e: + raise Exception(f"Error removing temp file {temp_file_path}: {e}") + raise Exception(f"Error creating file {file_path}: {e}") + +def create_dir(dir_path: str) -> bool: + """ + Create a new directory in the memory. + + Args: + dir_path: The path to the directory. + + Returns: + True if the directory was created successfully, False otherwise. + """ + try: + os.makedirs(dir_path, exist_ok=True) + return True + except Exception: + return False + + +def update_file(file_path: str, old_content: str, new_content: str) -> Union[bool, str]: + """ + Simple find-and-replace update method for files. + + This is an easier alternative to write_to_file() that doesn't require + creating git-style diffs. It performs a simple string replacement. + + Parameters + ---------- + file_path : str + Path to the file to update. + old_content : str + The exact text to find and replace in the file. + new_content : str + The text to replace old_content with. + + Returns + ------- + Union[bool, str] + True if successful, error message string if failed. + + Examples + -------- + # Add a new row to a table + old = "| TKT-1056 | 2024-09-25 | Late Delivery | Resolved |" + new = "| TKT-1056 | 2024-09-25 | Late Delivery | Resolved |\\n| TKT-1057 | 2024-11-11 | Damaged Item | Open |" + result = update_file("user.md", old, new) + """ + try: + # Read the current file content + if not os.path.exists(file_path): + return f"Error: File '{file_path}' does not exist" + + if not os.path.isfile(file_path): + return f"Error: '{file_path}' is not a file" + + with open(file_path, "r") as f: + current_content = f.read() + + # Check if old_content exists in the file + if old_content not in current_content: + # Provide helpful context about what wasn't found + preview_length = 50 + preview = old_content[:preview_length] + "..." if len(old_content) > preview_length else old_content + return f"Error: Could not find the specified content in the file. Looking for: '{preview}'" + + # Count occurrences to warn about multiple matches + occurrences = current_content.count(old_content) + if occurrences > 1: + # Still proceed but warn the user + print(f"Warning: Found {occurrences} occurrences of the content. Replacing only the first one.") + + # Perform the replacement (only first occurrence) + updated_content = current_content.replace(old_content, new_content, 1) + + # Check if replacement actually changed anything + if updated_content == current_content: + return "Error: No changes were made to the file" + + # Write the updated content back + with open(file_path, "w") as f: + f.write(updated_content) + + return True + + except PermissionError: + return f"Error: Permission denied writing to '{file_path}'" + except Exception as e: + return f"Error: Unexpected error - {str(e)}" + +def read_file(file_path: str) -> str: + """ + Read a file in the memory. + + Args: + file_path: The path to the file. + + Returns: + The content of the file, or an error message if the file cannot be read. + """ + try: + # Ensure the file path is properly resolved + if not os.path.exists(file_path): + return f"Error: File {file_path} does not exist" + + if not os.path.isfile(file_path): + return f"Error: {file_path} is not a file" + + with open(file_path, "r") as f: + return f.read() + except PermissionError: + return f"Error: Permission denied accessing {file_path}" + except Exception as e: + return f"Error: {e}" + +def list_files() -> str: + """ + Display all files and directories in the current working directory as a tree structure. + + Example output: + ``` + ./ + ├── user.md + └── entities/ + ├── 452_willow_creek_dr.md + └── frank_miller_plumbing.md + ``` + + Returns: + A string representation of the directory tree. + """ + try: + # Always use current working directory + dir_path = os.getcwd() + + def build_tree(start_path, prefix="", is_last=True): + """Recursively build tree structure""" + entries = [] + try: + items = sorted(os.listdir(start_path)) + # Filter out hidden files and __pycache__ + items = [item for item in items if not item.startswith('.') and item != '__pycache__'] + except PermissionError: + return f"{prefix}[Permission Denied]\n" + + if not items: + return "" + + for i, item in enumerate(items): + item_path = os.path.join(start_path, item) + is_last_item = i == len(items) - 1 + + # Choose the right prefix characters + if is_last_item: + current_prefix = prefix + "└── " + extension = prefix + " " + else: + current_prefix = prefix + "├── " + extension = prefix + "│ " + + if os.path.isdir(item_path): + # Check if directory is empty + try: + dir_contents = [f for f in os.listdir(item_path) + if not f.startswith('.') and f != '__pycache__'] + if not dir_contents: + entries.append(f"{current_prefix}{item}/ (empty)\n") + else: + entries.append(f"{current_prefix}{item}/\n") + # Recursively add subdirectory contents + entries.append(build_tree(item_path, extension, is_last_item)) + except PermissionError: + entries.append(f"{current_prefix}{item}/ [Permission Denied]\n") + else: + entries.append(f"{current_prefix}{item}\n") + + return "".join(entries) + + # Start with the root directory + tree = f"./\n{build_tree(dir_path)}" + return tree.rstrip() # Remove trailing newline + + except Exception as e: + return f"Error: {e}" + +def delete_file(file_path: str) -> bool: + """ + Delete a file in the memory. + + Args: + file_path: The path to the file. + + Returns: + True if the file was deleted successfully, False otherwise. + """ + try: + os.remove(file_path) + return True + except Exception: + return False + +def go_to_link(link_string: str) -> str: + """ + Go to a link in the memory and return the content of the note Y. A link in a note X to a note Y, with the + path path/to/note/Y.md, is structured like this: + [[path/to/note/Y]] + + Args: + link_string: The link to go to. + + Returns: + The content of the note Y, or an error message if the link cannot be accessed. + """ + try: + # Handle Obsidian-style links: [[path/to/note]] -> path/to/note.md + if link_string.startswith("[[") and link_string.endswith("]]"): + file_path = link_string[2:-2] # Remove [[ and ]] + if not file_path.endswith('.md'): + file_path += '.md' + else: + file_path = link_string + + # Ensure the file path is properly resolved + if not os.path.exists(file_path): + return f"Error: File {file_path} not found" + + if not os.path.isfile(file_path): + return f"Error: {file_path} is not a file" + + with open(file_path, "r") as f: + return f.read() + except PermissionError: + return f"Error: Permission denied accessing {link_string}" + except Exception as e: + return f"Error: {e}" + +def check_if_file_exists(file_path: str) -> bool: + """ + Check if a file exists in the given filepath. + + Args: + file_path: The path to the file. + + Returns: + True if the file exists and is a file, False otherwise. + """ + try: + return os.path.exists(file_path) and os.path.isfile(file_path) + except (OSError, TypeError, ValueError): + return False + +def check_if_dir_exists(dir_path: str) -> bool: + """ + Check if a directory exists in the given filepath. + + Args: + dir_path: The path to the directory. + + Returns: + True if the directory exists and is a directory, False otherwise. + """ + try: + return os.path.exists(dir_path) and os.path.isdir(dir_path) + except (OSError, TypeError, ValueError): + return False diff --git a/server/mem_agent/utils.py b/server/mem_agent/utils.py new file mode 100644 index 0000000..6477e10 --- /dev/null +++ b/server/mem_agent/utils.py @@ -0,0 +1,203 @@ +import os +import shutil + +import black +from ..config import MEMORY_PATH + +# Memory +FILE_SIZE_LIMIT = 1024 * 1024 # 1MB +DIR_SIZE_LIMIT = 1024 * 1024 * 10 # 10MB +MEMORY_SIZE_LIMIT = 1024 * 1024 * 100 # 100MB + + +def check_file_size_limit(file_path: str) -> bool: + """ + Check if the file size limit is respected. + """ + return os.path.getsize(file_path) <= FILE_SIZE_LIMIT + + +def check_dir_size_limit(dir_path: str) -> bool: + """ + Check if the directory size limit is respected. + """ + return os.path.getsize(dir_path) <= DIR_SIZE_LIMIT + + +def check_memory_size_limit() -> bool: + """ + Check if the memory size limit is respected. + """ + current_working_dir = os.getcwd() + return os.path.getsize(current_working_dir) <= MEMORY_SIZE_LIMIT + + +def check_size_limits(file_or_dir_path: str) -> bool: + """ + Check if the size limits are respected. + """ + if file_or_dir_path == "": + return check_memory_size_limit() + elif os.path.isdir(file_or_dir_path): + return check_dir_size_limit(file_or_dir_path) and check_memory_size_limit() + elif os.path.isfile(file_or_dir_path): + parent_dir = os.path.dirname(file_or_dir_path) + if not parent_dir == "": + return ( + check_file_size_limit(file_or_dir_path) + and check_dir_size_limit(parent_dir) + and check_memory_size_limit() + ) + else: + return check_file_size_limit(file_or_dir_path) and check_memory_size_limit() + else: + return False + + +def create_memory_if_not_exists(path: str = MEMORY_PATH): + """ + Create the memory if it doesn't exist. + + Args: + path: The path to create. Defaults to MEMORY_PATH. + + Returns: + None + """ + try: + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) + except Exception as e: + print(f"Error creating memory directory at {path}: {e}") + + +def delete_memory(path: str = MEMORY_PATH) -> None: + """ + Delete the memory. + + Args: + path: The path to delete. Defaults to MEMORY_PATH. + """ + if os.path.exists(path): + shutil.rmtree(path) + + +def _format_python_code_with_black(code: str) -> str: + """ + Format Python code using Black formatter. + + Args: + code: The Python code to format + + Returns: + The formatted Python code, or original code if formatting fails + """ + if not code.strip(): + return code + + try: + # For incomplete code fragments, wrap them in a function to make them valid Python + # This helps Black parse and format them correctly + lines = code.strip().split('\n') + + # Check if code looks like complete statements or just expressions/fragments + needs_wrapping = True + for line in lines: + stripped = line.strip() + if (stripped.startswith(('def ', 'class ', 'import ', 'from ')) or + stripped.startswith(('if ', 'for ', 'while ', 'try:', 'with ')) or + '=' in stripped or stripped.startswith(('print(', 'return '))): + needs_wrapping = False + break + + if needs_wrapping: + # Wrap in a function to make it valid Python for Black + wrapped_code = f"def temp_function():\n" + "\n".join(f" {line}" for line in lines) + + try: + formatted_wrapped = black.format_str( + wrapped_code, + mode=black.FileMode( + line_length=88, + string_normalization=True, + is_pyi=False, + ) + ) + # Extract the formatted content back out, removing the wrapper + formatted_lines = formatted_wrapped.split('\n')[1:] # Skip "def temp_function():" + formatted_code = '\n'.join(line[4:] if line.startswith(' ') else line + for line in formatted_lines if line.strip()).strip() + return formatted_code + except: + # If wrapping fails, try formatting as-is + pass + + # Try formatting the code as-is + formatted_code = black.format_str( + code, + mode=black.FileMode( + line_length=88, + string_normalization=True, + is_pyi=False, + ) + ) + return formatted_code + + except (black.InvalidInput, ValueError, SyntaxError, Exception) as e: + # If Black fails to format (e.g., invalid syntax), return original code + # This ensures we don't break the training pipeline + return code + + +def extract_python_code(response: str) -> str: + """ + Extract the python code from the response and format it with Black. + + Args: + response: The response from the model. + + Returns: + The formatted python code from the response. + """ + if "" in response and "" in response: + response = response.split("")[1].split("")[0] + if "```" in response: + code = response.split("```")[1].split("```")[0] + else: + code = response + + # Format the extracted code with Black + return _format_python_code_with_black(code) + else: + return "" + + +def extract_reply(response: str) -> str: + """ + Extract the reply from the response. + """ + if "" in response and "" in response: + return response.split("")[1].split("")[0] + else: + return "" + + +def extract_thoughts(response: str) -> str: + """ + Extract the thoughts from the response. + """ + if "" in response and "" in response: + return response.split("")[1].split("")[0] + else: + return "" + + +def format_results(results: dict, error_msg: str = "") -> str: + """ + Format the results into a string. + """ + return ( + "\n(" + str(results) + ", {" + error_msg + "})\n" + if error_msg + else "\n" + str(results) + "\n" + ) diff --git a/server/mlx_runner.py b/server/mlx_runner.py new file mode 100644 index 0000000..2e5dbb9 --- /dev/null +++ b/server/mlx_runner.py @@ -0,0 +1,1050 @@ +# MIT License + +# Copyright (c) 2025 The BROKE team 🦫 + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +""" +Enhanced MLX model runner with direct API integration. +Provides ollama-like run experience with streaming and interactive chat. +""" + +import json +import os +import time +from collections.abc import Iterator +from pathlib import Path +from typing import Dict, Optional + +import mlx.core as mx +from mlx_lm import load +from mlx_lm.generate import generate_step +from mlx_lm.sample_utils import make_repetition_penalty, make_sampler + +from .reasoning_utils import ReasoningExtractor, StreamingReasoningParser + + +def get_model_context_length(model_path: str) -> int: + """Extract max_position_embeddings from model config. + + Args: + model_path: Path to the MLX model directory + + Returns: + Maximum context length for the model (defaults to 4096 if not found) + """ + config_path = os.path.join(model_path, "config.json") + + try: + with open(config_path) as f: + config = json.load(f) + + # Try various common config keys for context length + context_keys = [ + "max_position_embeddings", + "n_positions", + "context_length", + "max_sequence_length", + "seq_len" + ] + + for key in context_keys: + if key in config: + return config[key] + + # If no context length found, return reasonable default + return 4096 + + except (FileNotFoundError, json.JSONDecodeError, KeyError): + # Return default if config can't be read + return 4096 + + +class MLXRunner: + """Direct MLX model runner with streaming and interactive capabilities.""" + + def __init__(self, model_path: str, adapter_path: Optional[str] = None, verbose: bool = False): + """Initialize the runner with a model. + + Args: + model_path: Path to the MLX model directory + adapter_path: Optional path to LoRA adapter + verbose: Show detailed output + """ + self.model_path = Path(model_path) + self.adapter_path = adapter_path + self.model = None + self.tokenizer = None + self._memory_baseline = None + self._stop_tokens = None # Will be populated from tokenizer + self._message_end_tokens = None # Message-end tokens (e.g., <|end|> for MXFP4) + self._chat_stop_tokens = None # Chat-specific stop tokens + self._context_length = None # Will be populated from model config + self._is_reasoning_model = False # Whether model uses reasoning (MXFP4) + self._reasoning_start = None # Reasoning start marker + self._reasoning_end = None # Reasoning end marker + self._final_start = None # Final answer start marker + self.verbose = verbose + self._model_loaded = False + self._context_entered = False # Prevent nested context usage + + def __enter__(self): + """Context manager entry - loads the model.""" + if self._context_entered: + raise RuntimeError("MLXRunner context manager cannot be entered multiple times") + + self._context_entered = True + try: + self.load_model() + return self + except Exception: + # If load_model fails, ensure cleanup happens + self._context_entered = False + self.cleanup() + raise + + def __exit__(self, exc_type, exc_val, exc_tb): + """Context manager exit - cleans up the model.""" + self._context_entered = False + self.cleanup() + return False # Don't suppress exceptions + + def load_model(self): + """Load the MLX model and tokenizer.""" + if self._model_loaded: + if self.verbose: + print("Model already loaded, skipping...") + return + + if self.verbose: + print(f"Loading model from {self.model_path}...") + start_time = time.time() + + # Capture baseline memory before loading + try: + mx.clear_cache() + except Exception: + pass # Continue even if cache clear fails + self._memory_baseline = mx.get_active_memory() / 1024**3 + + try: + # Load model and tokenizer + self.model, self.tokenizer = load( + str(self.model_path), + adapter_path=self.adapter_path + ) + + load_time = time.time() - start_time + current_memory = mx.get_active_memory() / 1024**3 + model_memory = current_memory - self._memory_baseline + + if self.verbose: + print(f"Model loaded in {load_time:.1f}s") + print(f"Memory: {model_memory:.1f}GB model, {current_memory:.1f}GB total") + + # Extract stop tokens from tokenizer + self._extract_stop_tokens() + + # Extract context length from model config + self._context_length = get_model_context_length(str(self.model_path)) + + if self.verbose: + print(f"Model context length: {self._context_length} tokens") + + self._model_loaded = True + + except Exception as e: + # Ensure partial state is cleaned up on failure + self.model = None + self.tokenizer = None + self._stop_tokens = None + self._model_loaded = False + # Clear any memory that might have been allocated + mx.clear_cache() + raise RuntimeError(f"Failed to load model from {self.model_path}: {e}") from e + + def _extract_stop_tokens(self): + """Extract stop tokens from the tokenizer dynamically. + + This method identifies ALL tokens that should stop generation: + 1. Official EOS token from tokenizer config + 2. Message-end tokens from training (e.g., <|end|> for MXFP4) + 3. Common stop tokens across models + """ + self._stop_tokens = set() + self._message_end_tokens = set() # Tokens that end messages but not conversations + + # Primary source: eos_token + eos_token = getattr(self.tokenizer, 'eos_token', None) + if eos_token: + self._stop_tokens.add(eos_token) + + # Also check pad_token if it's different from eos_token + pad_token = getattr(self.tokenizer, 'pad_token', None) + if pad_token and pad_token != eos_token: + self._stop_tokens.add(pad_token) + + # Check additional_special_tokens + if hasattr(self.tokenizer, 'additional_special_tokens'): + for token in self.tokenizer.additional_special_tokens: + if token and isinstance(token, str): + # Only add tokens that look like stop/end tokens + if any(keyword in token.lower() for keyword in ['end', 'stop', 'eot']): + self._stop_tokens.add(token) + + # MLX-LM 0.27.0+: Extract tokens from added_tokens_decoder (comprehensive source) + if hasattr(self.tokenizer, 'added_tokens_decoder'): + for _token_id, token_info in self.tokenizer.added_tokens_decoder.items(): + if isinstance(token_info, dict) and 'content' in token_info: + token_content = token_info['content'] + if token_content and isinstance(token_content, str): + token_lower = token_content.lower() + + # NOTE: <|end|> is NOT a stop token for MXFP4 models! + # It's a separator between reasoning and final answer + if token_content == '<|end|>': + self._message_end_tokens.add(token_content) + # Do NOT add as stop token - let model continue to final answer + + # Look for tokens that could be end/stop tokens + # Expanded patterns for MLX-LM 0.27.0 token varieties + # EXCLUDE <|end|> for MXFP4 models as it's a reasoning separator + end_patterns = ['stop', 'eot', 'return', 'finish', 'done', 'im_end'] + if any(pattern in token_lower for pattern in end_patterns): + # Decide if it's a message-end or conversation-end token + if 'im_end' in token_lower: + self._message_end_tokens.add(token_content) + self._stop_tokens.add(token_content) + # Special handling for 'end' pattern - more selective + elif 'end' in token_lower and token_content != '<|end|>': + # Only add non-<|end|> tokens with 'end' in them + self._stop_tokens.add(token_content) + + # Special case: control tokens in |..| format + elif token_content.startswith('<|') and token_content.endswith('|>'): + # Be inclusive with control tokens that might stop generation + if any(pattern in token_lower for pattern in ['end', 'return', 'stop', 'finish']): + self._stop_tokens.add(token_content) + + # Model-specific handling based on known patterns + # Use reasoning_utils for reasoning model detection and patterns + from .reasoning_utils import ReasoningExtractor + + if hasattr(self.tokenizer, 'name_or_path'): + name_or_path = str(getattr(self.tokenizer, 'name_or_path', '')).lower() + model_type = ReasoningExtractor.detect_model_type(name_or_path) + + if model_type: + # This is a reasoning model + self._is_reasoning_model = True + + # Get patterns from reasoning_utils + if model_type in ReasoningExtractor.PATTERNS: + markers = ReasoningExtractor.PATTERNS[model_type]['markers'] + self._reasoning_start = markers.get('reasoning_start') + self._reasoning_end = markers.get('reasoning_end') + self._final_start = markers.get('final_marker') + + # For reasoning models, remove reasoning_end from stop tokens + if self._reasoning_end: + self._stop_tokens.discard(self._reasoning_end) + + # Add proper stop token for this model type + if model_type == 'gpt-oss': + if '<|return|>' not in self._stop_tokens: + self._stop_tokens.add('<|return|>') + else: + self._is_reasoning_model = False + else: + self._is_reasoning_model = False + + # Add common stop tokens that might not be in special tokens + common_stop_tokens = {'', '<|endoftext|>', '<|im_end|>', '<|eot_id|>'} + + # Add chat-specific stop tokens to prevent model self-conversations + # Based on our _format_conversation() format: "Human:" and "Assistant:" + # Also include "You:" as models might use UI-visible format + # Include single-letter variations (H:, A:, Y:) that some models use + chat_stop_tokens = { + '\nHuman:', '\nAssistant:', '\nYou:', + '\n\nHuman:', '\n\nAssistant:', '\n\nYou:', + '\nH:', '\nA:', '\nY:', # Single-letter variations + '\n\nH:', '\n\nA:', '\n\nY:' + } + + # Add common stop tokens only if they decode to themselves (i.e., they're single tokens) + for token in common_stop_tokens: + try: + # Try to encode and decode to verify it's a real single token + ids = self.tokenizer.encode(token, add_special_tokens=False) + if ids and len(ids) == 1: # Single token ID means it's a special token + decoded = self.tokenizer.decode(ids) + if decoded == token: + self._stop_tokens.add(token) + except: + pass + + # Store chat stop tokens separately - only used in interactive chat mode + # This prevents stopping mid-story when user asks for dialogues + self._chat_stop_tokens = list(chat_stop_tokens) + + # Remove any None values + self._stop_tokens.discard(None) + self._message_end_tokens.discard(None) + + # Convert to list for easier use + self._stop_tokens = list(self._stop_tokens) + self._message_end_tokens = list(self._message_end_tokens) + + if self.verbose: + if self._stop_tokens: + print(f"Stop tokens: {self._stop_tokens}") + if self._message_end_tokens: + print(f"Message end tokens: {self._message_end_tokens}") + + def cleanup(self): + """Clean up model resources and clear GPU memory. + + This method is safe to call multiple times and handles partial state cleanup. + """ + if self.verbose and self._model_loaded: + memory_before = mx.get_active_memory() / 1024**3 + print(f"Cleaning up model (memory before: {memory_before:.1f}GB)...") + + # Always clean up, even if model wasn't fully loaded + self.model = None + self.tokenizer = None + self._stop_tokens = None + self._message_end_tokens = None + self._chat_stop_tokens = None + self._context_length = None + self._is_reasoning_model = False + self._reasoning_start = None + self._reasoning_end = None + self._final_start = None + self._model_loaded = False + + # Force garbage collection and clear MLX cache + import gc + gc.collect() + try: + mx.clear_cache() + except Exception: + pass # Continue cleanup even if cache clear fails + + if self.verbose: + memory_after = mx.get_active_memory() / 1024**3 + if 'memory_before' in locals(): + memory_freed = memory_before - memory_after + print(f"Cleanup complete (memory after: {memory_after:.1f}GB, freed: {memory_freed:.1f}GB)") + else: + print(f"Cleanup complete (memory after: {memory_after:.1f}GB)") + + def get_effective_max_tokens(self, requested_tokens: Optional[int], interactive: bool = False) -> int: + """Get effective max tokens based on model context and usage mode. + + Args: + requested_tokens: The requested max tokens (None if user didn't specify --max-tokens) + interactive: True if this is interactive mode (gets full context length) + + Returns: + Effective max tokens to use + """ + if not self._context_length: + # Fallback when context length is unknown + fallback = 4096 if interactive else 2048 + if self.verbose: + if requested_tokens is None: + print(f"[WARNING] Model context length unknown, using fallback: {fallback} tokens") + else: + print(f"[WARNING] Model context length unknown, using user specified: {requested_tokens} tokens") + return requested_tokens if requested_tokens is not None else fallback + + if interactive: + if requested_tokens is None: + # User didn't specify --max-tokens: use full model context + return self._context_length + else: + # User specified --max-tokens explicitly: respect their choice but cap at context + return min(requested_tokens, self._context_length) + else: + # Server/batch mode uses half context length for DoS protection + server_limit = self._context_length // 2 + return min(requested_tokens or server_limit, server_limit) + + def generate_streaming( + self, + prompt: str, + max_tokens: int = 500, + temperature: float = 0.7, + top_p: float = 0.9, + repetition_penalty: float = 1.1, + repetition_context_size: int = 20, + use_chat_template: bool = True, + use_chat_stop_tokens: bool = False, + interactive: bool = False, + hide_reasoning: bool = False, + ) -> Iterator[str]: + """Generate text with streaming output. + + Args: + prompt: Input prompt + max_tokens: Maximum tokens to generate + temperature: Sampling temperature + top_p: Top-p sampling parameter + repetition_penalty: Penalty for repeated tokens + repetition_context_size: Context size for repetition penalty + use_chat_template: Apply tokenizer's chat template if available + use_chat_stop_tokens: Include chat turn markers as stop tokens (for interactive mode) + interactive: True if this is interactive mode (affects token limits) + + Yields: + Generated tokens as they are produced + """ + if not self.model or not self.tokenizer: + raise RuntimeError("Model not loaded. Call load_model() first.") + + # Initialize reasoning parser if this is a reasoning model + reasoning_parser = None + if self._is_reasoning_model: + model_type = ReasoningExtractor.detect_model_type( + getattr(self.tokenizer, 'name_or_path', '') or '' + ) + reasoning_parser = StreamingReasoningParser(model_type, hide_reasoning=hide_reasoning) + + # Apply context-aware token limits + effective_max_tokens = self.get_effective_max_tokens(max_tokens, interactive) + + # Apply chat template if available and requested + if use_chat_template and hasattr(self.tokenizer, 'chat_template') and self.tokenizer.chat_template: + messages = [{"role": "user", "content": prompt}] + formatted_prompt = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + else: + formatted_prompt = prompt + + # Tokenize the prompt + prompt_tokens = self.tokenizer.encode(formatted_prompt) + prompt_array = mx.array(prompt_tokens) + + # Track generation metrics + start_time = time.time() + tokens_generated = 0 + + # Create sampler with our parameters + sampler = make_sampler(temp=temperature, top_p=top_p) + + # Create repetition penalty processor if needed + logits_processors = [] + if repetition_penalty > 1.0: + logits_processors.append( + make_repetition_penalty(repetition_penalty, repetition_context_size) + ) + + # Generate tokens one by one for streaming + generator = generate_step( + prompt=prompt_array, + model=self.model, + max_tokens=effective_max_tokens, + sampler=sampler, + logits_processors=logits_processors if logits_processors else None, + ) + + # Collect tokens and yield text + generated_tokens = [] + previous_decoded = "" + accumulated_response = "" # Track full response for stop token detection + + # Keep a sliding window of recent tokens for context + context_window = 10 # Decode last N tokens for proper spacing + + for token, _ in generator: + # Token might be an array or an int + token_id = token.item() if hasattr(token, 'item') else token + generated_tokens.append(token_id) + + # Use a sliding window approach for efficiency + start_idx = max(0, len(generated_tokens) - context_window) + window_tokens = generated_tokens[start_idx:] + + # Decode the window + window_text = self.tokenizer.decode(window_tokens) + + # Figure out what's new + if start_idx == 0: + # We're still within the context window + if window_text.startswith(previous_decoded): + new_text = window_text[len(previous_decoded):] + else: + new_text = self.tokenizer.decode([token_id]) + previous_decoded = window_text + else: + # We're beyond the context window, just decode the last token with context + # This is approximate but should preserve spaces + new_text = self.tokenizer.decode(window_tokens) + if len(window_tokens) > 1: + prefix = self.tokenizer.decode(window_tokens[:-1]) + if new_text.startswith(prefix): + new_text = new_text[len(prefix):] + else: + new_text = self.tokenizer.decode([token_id]) + + if new_text: + # Update accumulated response for stop token checking + accumulated_response += new_text + + # Filter out stop tokens with priority: native first, then chat fallback + # Check native stop tokens FIRST in accumulated response (highest priority) + native_stop_tokens = self._stop_tokens if self._stop_tokens else [] + for stop_token in native_stop_tokens: + if stop_token in accumulated_response: + # Find the stop token position and yield everything before it + stop_pos = accumulated_response.find(stop_token) + # Calculate what text came before the stop token + text_before_stop = accumulated_response[:stop_pos] + # Calculate how much of that is new (not previously yielded) + previously_yielded_length = len(accumulated_response) - len(new_text) + if len(text_before_stop) > previously_yielded_length: + # Yield only the new part before stop token + new_part_before_stop = text_before_stop[previously_yielded_length:] + if new_part_before_stop: + if reasoning_parser: + # Process through reasoning parser for formatting + for formatted_token in reasoning_parser.process_token(new_part_before_stop): + yield formatted_token + else: + yield new_part_before_stop + return # Stop generation without yielding stop token + + # Only check chat stop tokens if no native stop token found (fallback) + if use_chat_stop_tokens and self._chat_stop_tokens: + for stop_token in self._chat_stop_tokens: + if stop_token in accumulated_response: + # Find the stop token position and yield everything before it + stop_pos = accumulated_response.find(stop_token) + # Calculate what text came before the stop token + text_before_stop = accumulated_response[:stop_pos] + # Calculate how much of that is new (not previously yielded) + previously_yielded_length = len(accumulated_response) - len(new_text) + if len(text_before_stop) > previously_yielded_length: + # Yield only the new part before stop token + new_part_before_stop = text_before_stop[previously_yielded_length:] + if new_part_before_stop: + if reasoning_parser: + # Process through reasoning parser for formatting + for formatted_token in reasoning_parser.process_token(new_part_before_stop): + yield formatted_token + else: + yield new_part_before_stop + return # Stop generation without yielding stop token + + # No stop token found, process the new text + if reasoning_parser: + # Process through reasoning parser for formatting + for formatted_token in reasoning_parser.process_token(new_text): + yield formatted_token + else: + # Normal streaming for non-reasoning models + yield new_text + tokens_generated += 1 + + # Check for EOS token - don't yield it + if token_id == self.tokenizer.eos_token_id: + break + + # Finalize reasoning parser if used + if reasoning_parser: + yield from reasoning_parser.finalize() + + # Print generation statistics if verbose + if self.verbose: + generation_time = time.time() - start_time + tokens_per_second = tokens_generated / generation_time if generation_time > 0 else 0 + print(f"\n\nGenerated {tokens_generated} tokens in {generation_time:.1f}s ({tokens_per_second:.1f} tokens/s)") + + def generate_batch( + self, + prompt: str, + max_tokens: int = 500, + temperature: float = 0.7, + top_p: float = 0.9, + repetition_penalty: float = 1.1, + repetition_context_size: int = 20, + use_chat_template: bool = True, + interactive: bool = False, + ) -> str: + """Generate text in batch mode (non-streaming). + + Args: + prompt: Input prompt + max_tokens: Maximum tokens to generate + temperature: Sampling temperature + top_p: Top-p sampling parameter + repetition_penalty: Penalty for repeated tokens + repetition_context_size: Context size for repetition penalty + use_chat_template: Apply tokenizer's chat template if available + interactive: True if this is interactive mode (affects token limits) + + Returns: + Generated text + """ + if not self.model or not self.tokenizer: + raise RuntimeError("Model not loaded. Call load_model() first.") + + # Apply context-aware token limits + effective_max_tokens = self.get_effective_max_tokens(max_tokens, interactive) + + # Apply chat template if available and requested + if use_chat_template and hasattr(self.tokenizer, 'chat_template') and self.tokenizer.chat_template: + messages = [{"role": "user", "content": prompt}] + formatted_prompt = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + else: + formatted_prompt = prompt + + start_time = time.time() + + # Tokenize the prompt + prompt_tokens = self.tokenizer.encode(formatted_prompt) + prompt_array = mx.array(prompt_tokens) + + # Create sampler with our parameters + sampler = make_sampler(temp=temperature, top_p=top_p) + + # Create repetition penalty processor if needed + logits_processors = [] + if repetition_penalty > 1.0: + logits_processors.append( + make_repetition_penalty(repetition_penalty, repetition_context_size) + ) + + # Generate all tokens at once + generated_tokens = [] + all_tokens = list(prompt_tokens) # Keep prompt for proper decoding + + generator = generate_step( + prompt=prompt_array, + model=self.model, + max_tokens=effective_max_tokens, + sampler=sampler, + logits_processors=logits_processors if logits_processors else None, + ) + + for token, _ in generator: + # Token might be an array or an int + token_id = token.item() if hasattr(token, 'item') else token + generated_tokens.append(token_id) + all_tokens.append(token_id) + + # Check for EOS token - don't yield it + if token_id == self.tokenizer.eos_token_id: + break + + # Decode all tokens together for proper spacing + full_response = self.tokenizer.decode(all_tokens) + + # Remove the prompt part + if full_response.startswith(formatted_prompt): + response = full_response[len(formatted_prompt):] + else: + # Fallback: just decode generated tokens + response = self.tokenizer.decode(generated_tokens) + + # Apply end-token filtering (same logic as streaming mode for Issue #20) + response = self._filter_end_tokens_from_response(response, use_chat_stop_tokens=False) + + # Format reasoning models output + response = self._format_reasoning_response(response) + + generation_time = time.time() - start_time + + # Count tokens for statistics + if self.verbose: + tokens_generated = len(generated_tokens) + tokens_per_second = tokens_generated / generation_time if generation_time > 0 else 0 + print(f"\nGenerated {tokens_generated} tokens in {generation_time:.1f}s ({tokens_per_second:.1f} tokens/s)") + + return response + + def interactive_chat( + self, + system_prompt: Optional[str] = None, + max_tokens: int = 500, + temperature: float = 0.7, + top_p: float = 0.9, + repetition_penalty: float = 1.1, + use_chat_template: bool = True, + ): + """Run an interactive chat session. + + Args: + system_prompt: Optional system prompt to prepend + max_tokens: Maximum tokens per response + temperature: Sampling temperature + top_p: Top-p sampling parameter + repetition_penalty: Penalty for repeated tokens + use_chat_template: Use tokenizer's chat template if available + """ + print("Starting interactive chat. Type 'exit' or 'quit' to end.\n") + + conversation_history = [] + if system_prompt: + conversation_history.append({"role": "system", "content": system_prompt}) + + while True: + try: + # Get user input + user_input = input("You: ").strip() + + if user_input.lower() in ['exit', 'quit', 'q']: + print("\nGoodbye!") + break + + if not user_input: + continue + + # Add user message to history + conversation_history.append({"role": "user", "content": user_input}) + + # Format conversation for the model using chat template if available + prompt = self._format_conversation(conversation_history, use_chat_template=use_chat_template) + + # Generate response with streaming + print("\nAssistant: ", end="", flush=True) + + response_tokens = [] + for token in self.generate_streaming( + prompt=prompt, + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + repetition_penalty=repetition_penalty, + use_chat_template=False, # Already applied in _format_conversation + use_chat_stop_tokens=True, # Enable chat stop tokens in interactive mode + interactive=True, # Enable full context length for interactive mode + ): + # Stream all tokens directly (already formatted by generate_streaming) + print(token, end="", flush=True) + response_tokens.append(token) + + # Add assistant response to history + assistant_response = "".join(response_tokens).strip() + conversation_history.append({"role": "assistant", "content": assistant_response}) + + print() # New line after response + + except KeyboardInterrupt: + print("\n\nChat interrupted. Goodbye!") + break + except Exception as e: + print(f"\n[ERROR] {e}") + continue + + def _format_conversation(self, messages: list, use_chat_template: bool = True) -> str: + """Format conversation history into a prompt. + + Uses the tokenizer's chat template if available, otherwise falls back + to the legacy Human:/Assistant: format for compatibility. + + Args: + messages: List of message dictionaries with 'role' and 'content' + use_chat_template: Whether to use chat template if available + + Returns: + Formatted conversation string + """ + # Try to use native chat template if available + if use_chat_template and hasattr(self.tokenizer, 'chat_template') and self.tokenizer.chat_template: + try: + # Apply the tokenizer's chat template + formatted_prompt = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + return formatted_prompt + except Exception as e: + # If chat template fails, fall back to legacy format + if self.verbose: + print(f"[WARNING] Chat template failed, using legacy format: {e}") + + # Legacy format fallback for compatibility + return self._legacy_format_conversation(messages) + + def _legacy_format_conversation(self, messages: list) -> str: + """Legacy conversation formatting for backward compatibility. + + This format was used in earlier versions and remains as a fallback + for models without chat templates. + """ + formatted = [] + + for message in messages: + role = message["role"] + content = message["content"] + + if role == "system": + formatted.append(f"System: {content}") + elif role == "user": + formatted.append(f"Human: {content}") + elif role == "assistant": + formatted.append(f"Assistant: {content}") + + # Add prompt for next assistant response + formatted.append("Assistant:") + + return "\n\n".join(formatted) + + def get_memory_usage(self) -> Dict[str, float]: + """Get current memory usage statistics. + + Returns: + Dictionary with memory statistics in GB + """ + try: + current_memory = mx.get_active_memory() / 1024**3 + peak_memory = mx.get_peak_memory() / 1024**3 + except Exception: + # Return zeros if memory stats unavailable + current_memory = 0.0 + peak_memory = 0.0 + + return { + "current_gb": current_memory, + "peak_gb": peak_memory, + "model_gb": current_memory - self._memory_baseline if self._memory_baseline else 0, + } + + def _format_reasoning_response(self, response: str) -> str: + """Format response from reasoning models for better readability. + + For MXFP4 models that generate reasoning followed by final answer, + format it nicely for display. + """ + if not self._is_reasoning_model: + return response + + # Check if response contains reasoning markers + if self._reasoning_start in response and self._final_start in response: + # Extract reasoning and final parts + try: + # Split on the reasoning start + before_reasoning, after_start = response.split(self._reasoning_start, 1) + + # Find the reasoning content (until <|end|>) + if self._reasoning_end in after_start: + reasoning_content, after_reasoning = after_start.split(self._reasoning_end, 1) + + # Find the final answer + if self._final_start in after_reasoning: + # Extract everything after final marker + final_parts = after_reasoning.split(self._final_start, 1) + if len(final_parts) > 1: + # Remove the <|channel|>final<|message|> marker + final_answer = final_parts[1].replace('<|channel|>final<|message|>', '', 1) + + # Format with clear markers for parsing but minimal visual impact + formatted = [] + formatted.append("\n**[Reasoning]**\n") + formatted.append(reasoning_content.strip()) + formatted.append("\n\n---\n\n**[Answer]**\n") + formatted.append(final_answer.strip()) + + return '\n'.join(formatted) + except Exception: + # If parsing fails, return original + pass + + # Fallback: just clean up the control tokens + cleaned = response + for marker in ['<|channel|>analysis<|message|>', '<|end|>', '<|start|>assistant', + '<|channel|>final<|message|>', '<|return|>']: + cleaned = cleaned.replace(marker, '') + + return cleaned.strip() + + def _filter_end_tokens_from_response(self, response: str, use_chat_stop_tokens: bool = False) -> str: + """Filter end tokens from a complete response (batch mode). + + This method applies the same filtering logic as the streaming mode + to ensure consistent behavior between streaming and non-streaming. + + Args: + response: The complete generated response + use_chat_stop_tokens: Whether to apply chat stop tokens + + Returns: + Response with end tokens filtered out + """ + # Apply native stop token filtering FIRST (highest priority) + native_stop_tokens = self._stop_tokens if self._stop_tokens else [] + for stop_token in native_stop_tokens: + if stop_token in response: + # Find the stop token position and return everything before it + stop_pos = response.find(stop_token) + filtered_response = response[:stop_pos].rstrip() + if self.verbose: + print(f"[DEBUG] Filtered stop token '{stop_token}' at position {stop_pos}") + return filtered_response + + # Only check chat stop tokens if no native stop token found (fallback) + if use_chat_stop_tokens and self._chat_stop_tokens: + for stop_token in self._chat_stop_tokens: + if stop_token in response: + # Find the stop token position and return everything before it + stop_pos = response.find(stop_token) + return response[:stop_pos] + + # No stop tokens found, return original response + return response + + +def get_gpu_status() -> Dict[str, float]: + """Independent GPU status check - usable from anywhere. + + Returns: + Dictionary with GPU memory statistics in GB + """ + return { + "active_memory_gb": mx.get_active_memory() / 1024**3, + "peak_memory_gb": mx.get_peak_memory() / 1024**3, + } + + +def check_memory_available(required_gb: float) -> bool: + """Pre-flight check before model loading. + + Args: + required_gb: Required memory in GB + + Returns: + True if memory is likely available (conservative estimate) + """ + current_memory = mx.get_active_memory() / 1024**3 + + # Conservative estimate: assume system has at least 8GB unified memory + # and we should leave some headroom (2GB) for system processes + estimated_total = 8.0 # This could be improved by detecting actual system memory + available = estimated_total - current_memory - 2.0 # 2GB headroom + + return available >= required_gb + + +def run_model_enhanced( + model_path: str, + prompt: Optional[str] = None, + interactive: bool = False, + max_tokens: int = 500, + temperature: float = 0.7, + top_p: float = 0.9, + repetition_penalty: float = 1.1, + stream: bool = True, + use_chat_template: bool = True, + hide_reasoning: bool = False, + verbose: bool = False, +) -> Optional[str]: + """Enhanced run function with direct MLX integration. + + Uses context manager pattern for automatic resource cleanup. + + Args: + model_path: Path to the MLX model + prompt: Input prompt (if None, enters interactive mode) + interactive: Force interactive mode + max_tokens: Maximum tokens to generate + temperature: Sampling temperature + top_p: Top-p sampling parameter + repetition_penalty: Penalty for repeated tokens + stream: Whether to stream output + + Returns: + Generated text (in non-interactive mode) + """ + try: + with MLXRunner(model_path, verbose=verbose) as runner: + # Interactive mode + if interactive or prompt is None: + runner.interactive_chat( + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + repetition_penalty=repetition_penalty, + use_chat_template=use_chat_template, + ) + return None + + # Single prompt mode + if verbose: + print(f"\nPrompt: {prompt}\n") + print("Response: ", end="", flush=True) + + if stream: + # Streaming generation + response_tokens = [] + try: + for token in runner.generate_streaming( + prompt=prompt, + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + repetition_penalty=repetition_penalty, + use_chat_template=use_chat_template, + hide_reasoning=hide_reasoning, + ): + # Stream all tokens directly (already formatted by generate_streaming) + print(token, end="", flush=True) + response_tokens.append(token) + except KeyboardInterrupt: + print("\n[INFO] Generation interrupted by user.") + response = "".join(response_tokens) + else: + # Batch generation + try: + response = runner.generate_batch( + prompt=prompt, + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + repetition_penalty=repetition_penalty, + use_chat_template=use_chat_template, + ) + except KeyboardInterrupt: + print("\n[INFO] Generation interrupted by user.") + response = "" + print(response) + + # Show memory usage if verbose + if verbose: + memory_stats = runner.get_memory_usage() + print(f"\n\nMemory: {memory_stats['model_gb']:.1f}GB model, {memory_stats['current_gb']:.1f}GB total") + + return response + + # Note: cleanup happens automatically due to context manager + + except Exception as e: + print(f"\n[ERROR] {e}") + return None + diff --git a/server/model_card.py b/server/model_card.py new file mode 100644 index 0000000..ffb9249 --- /dev/null +++ b/server/model_card.py @@ -0,0 +1,185 @@ +# MIT License + +# Copyright (c) 2025 The BROKE team 🦫 + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +from __future__ import annotations + +# ruff: noqa: UP045 + +""" +Lightweight helpers to read model metadata hints from cached Hugging Face models. + +No external dependencies; YAML front matter is hand-parsed leniently. + +Priority rules (Issue #31): +- Tokenizer config: if tokenizer_config.json has chat_template -> Type = chat +- README.md front matter (YAML): + - tags contains "mlx" OR library_name == "mlx" -> Framework = MLX + - pipeline_tag == text-generation OR tags contain chat/instruct -> Type = chat + - pipeline_tag == sentence-similarity OR tags contain embedding -> Type = embedding +- Fallback for framework/type remains in cache_utils +""" + +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + + +def _latest_snapshot_dir(model_base_dir: Path) -> Optional[Path]: + """Return latest snapshot directory for a cached HF model base dir.""" + try: + snaps = (model_base_dir / "snapshots") + if not snaps.exists(): + return None + candidates = [d for d in snaps.iterdir() if d.is_dir()] + if not candidates: + return None + return max(candidates, key=lambda p: p.stat().st_mtime) + except Exception: + return None + + +def _lenient_yaml_front_matter(text: str) -> Dict[str, Any]: + """Very small YAML front matter parser for the fields we need. + + Supports forms: + --- + tags: [mlx, chat] + pipeline_tag: text-generation + library_name: mlx + --- + + And list style: + tags: + - mlx + - chat + """ + start = text.find("\n---\n") + # Accept files starting directly with '---' too + if text.startswith('---'): + start = 0 + elif start >= 0: + start = start + 1 # move to line start + else: + # Try at very beginning without newline + start = 0 if text[:3] == '---' else -1 + if start != 0: + return {} + + # Find closing '---' after start + end = text.find('\n---', 3) + if end == -1: + return {} + header = text[3:end] if text.startswith('---') else text[start + 3:end] + + # Normalize lines + lines = [ln.strip() for ln in header.splitlines() if ln.strip()] + + data: Dict[str, Any] = {} + current_key: Optional[str] = None + list_acc: List[str] = [] + + def flush_list(): + nonlocal list_acc, current_key + if current_key is not None and list_acc: + data[current_key] = list_acc[:] + list_acc = [] + + for ln in lines: + if ln.startswith('- '): + # list item under current_key + val = ln[2:].strip().strip('"\'') + if current_key is not None: + list_acc.append(val) + continue + # key: value or key: [a, b] + if ':' in ln: + # Close any previous list + flush_list() + key, val = ln.split(':', 1) + key = key.strip() + val = val.strip() + current_key = key + if not val: + # expect multi-line list next + data.setdefault(key, []) + continue + # Inline list [a, b] + if val.startswith('[') and val.endswith(']'): + inner = val[1:-1].strip() + items = [] if not inner else [it.strip().strip('"\'') for it in inner.split(',')] + data[key] = [x for x in items if x] + continue + # Scalar + data[key] = val.strip('"\'') + continue + # Non key-value, ignore + # Flush last list + flush_list() + return data + + +def read_readme_front_matter(model_base_dir: Path) -> Tuple[Optional[List[str]], Optional[str], Optional[str]]: + """Read README.md front matter and extract tags, pipeline_tag, library_name. + + Returns (tags, pipeline_tag, library_name) with lowercase normalization where applicable. + Any read/parse error results in (None, None, None). + """ + try: + snap = _latest_snapshot_dir(model_base_dir) + if not snap: + return None, None, None + readme = snap / 'README.md' + if not readme.exists(): + return None, None, None + text = readme.read_text(encoding='utf-8', errors='ignore') + fm = _lenient_yaml_front_matter(text) + if not fm: + return None, None, None + tags = fm.get('tags') + if isinstance(tags, list): + tags = [str(t).strip().lower() for t in tags if str(t).strip()] + else: + tags = None + pipeline = fm.get('pipeline_tag') + pipeline = str(pipeline).strip().lower() if pipeline else None + lib = fm.get('library_name') + lib = str(lib).strip().lower() if lib else None + return tags, pipeline, lib + except Exception: + return None, None, None + + +def tokenizer_has_chat_template(model_base_dir: Path) -> bool: + """Check tokenizer_config.json for a non-empty 'chat_template' field in latest snapshot.""" + try: + snap = _latest_snapshot_dir(model_base_dir) + if not snap: + return False + tk = snap / 'tokenizer_config.json' + if not tk.exists(): + return False + with open(tk, encoding='utf-8') as f: + data = json.load(f) + tmpl = data.get('chat_template') + return bool(tmpl and isinstance(tmpl, str) and tmpl.strip()) + except Exception: + return False + diff --git a/server/pyproject.toml b/server/pyproject.toml new file mode 100644 index 0000000..2dc56e9 --- /dev/null +++ b/server/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "server" +version = "0.1.0" +description = "Local MLX inference server for the Tiles CLI" +requires-python = ">=3.10" +dependencies = [ + "fastapi", + "uvicorn", + "mlx-lm", + "black" +] + +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" diff --git a/server/reasoning_utils.py b/server/reasoning_utils.py new file mode 100644 index 0000000..7ea1410 --- /dev/null +++ b/server/reasoning_utils.py @@ -0,0 +1,431 @@ +# MIT License + +# Copyright (c) 2025 The BROKE team 🦫 + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +Utilities for handling reasoning models and their output. + +Different models use different formats for reasoning: +- MXFP4/GPT-OSS: <|channel|>analysis<|message|>REASONING<|end|>...<|channel|>final<|message|>ANSWER +- DeepSeek R1: REASONINGANSWER +- Claude: REASONINGANSWER +- QwQ: Similar to MXFP4 +""" + +import re +from typing import Dict, Optional, Tuple + + +class ReasoningExtractor: + """Extract reasoning and final answer from model outputs.""" + + # Model-specific patterns + PATTERNS = { + 'gpt-oss': { + 'reasoning': r'<\|channel\|>analysis<\|message\|>(.*?)<\|end\|>', + 'final': r'<\|channel\|>final<\|message\|>(.*?)(?:<\|return\|>|$)', + 'markers': { + 'reasoning_start': '<|channel|>analysis<|message|>', + 'reasoning_end': '<|end|>', + 'final_marker': '<|channel|>final<|message|>', + # Skip tokens that appear between reasoning and final + 'skip_tokens': ['<|start|>assistant<|channel|>final<|message|>', '<|start|>assistant', '<|start|>', '<|channel|>final<|message|>'], + # Conditional skip tokens - only skip if at start of final section + 'conditional_skip': ['assistant'] + } + }, + 'deepseek': { + 'reasoning': r'(.*?)', + 'final': r'(.*?)$', + 'markers': { + 'reasoning_start': '', + 'reasoning_end': '', + } + }, + 'claude': { + 'reasoning': r'(.*?)', + 'final': r'(.*?)$', + 'markers': { + 'reasoning_start': '', + 'reasoning_end': '', + } + } + } + + @classmethod + def detect_model_type(cls, model_name: str) -> Optional[str]: + """Detect reasoning model type from model name.""" + model_lower = model_name.lower() + + if 'gpt-oss' in model_lower: + return 'gpt-oss' + elif 'deepseek' in model_lower and 'r1' in model_lower: + return 'deepseek' + elif 'claude' in model_lower: + return 'claude' + elif 'qwq' in model_lower: + return 'gpt-oss' # QwQ uses similar format to GPT-OSS + + return None + + @classmethod + def extract(cls, text: str, model_type: Optional[str] = None, + model_name: Optional[str] = None) -> Dict[str, Optional[str]]: + """ + Extract reasoning and final answer from model output. + + Args: + text: The full model output + model_type: Explicit model type ('mxfp4', 'deepseek', etc.) + model_name: Model name to auto-detect type + + Returns: + Dictionary with 'reasoning', 'final_answer', and 'full_response' + """ + # Auto-detect model type if not provided + if not model_type and model_name: + model_type = cls.detect_model_type(model_name) + + # If no model type detected, return text as-is + if not model_type or model_type not in cls.PATTERNS: + return { + 'reasoning': None, + 'final_answer': text, + 'full_response': text, + 'has_reasoning': False + } + + patterns = cls.PATTERNS[model_type] + + # Extract reasoning + reasoning_match = re.search(patterns['reasoning'], text, re.DOTALL) + reasoning = reasoning_match.group(1).strip() if reasoning_match else None + + # Extract final answer + final_match = re.search(patterns['final'], text, re.DOTALL) + final_answer = final_match.group(1).strip() if final_match else None + + # If no final answer found but we have reasoning, + # the text after reasoning might be the answer + if reasoning and not final_answer: + # Try to find text after reasoning markers + markers = patterns.get('markers', {}) + if 'reasoning_end' in markers: + split_text = text.split(markers['reasoning_end'], 1) + if len(split_text) > 1: + # Clean up any remaining markers + remaining = split_text[1] + for marker in markers.values(): + remaining = remaining.replace(marker, '') + final_answer = remaining.strip() + + # If still no final answer, use full text minus reasoning markers + if not final_answer: + final_answer = text + # Remove all known markers + if model_type in cls.PATTERNS: + markers = cls.PATTERNS[model_type].get('markers', {}) + for marker in markers.values(): + final_answer = final_answer.replace(marker, '') + final_answer = final_answer.strip() + + return { + 'reasoning': reasoning, + 'final_answer': final_answer, + 'full_response': text, + 'has_reasoning': bool(reasoning), + 'model_type': model_type + } + + @classmethod + def format_for_display(cls, extracted: Dict[str, Optional[str]], + show_reasoning: bool = False) -> str: + """ + Format extracted content for display. + + Args: + extracted: Output from extract() + show_reasoning: Whether to include reasoning in output + + Returns: + Formatted string for display + """ + if not extracted.get('has_reasoning'): + return extracted.get('final_answer', '') + + if show_reasoning: + output = [] + if extracted.get('reasoning'): + output.append("═══ Reasoning ═══") + output.append(extracted['reasoning']) + output.append("\n═══ Answer ═══") + output.append(extracted.get('final_answer', '')) + return '\n'.join(output) + else: + return extracted.get('final_answer', '') + + +class StreamingReasoningHandler: + """Handle reasoning during streaming generation.""" + + def __init__(self, model_type: Optional[str] = None): + self.model_type = model_type + self.buffer = "" + self.reasoning_buffer = "" + self.final_buffer = "" + self.in_reasoning = False + self.in_final = False + self.markers = {} + + if model_type and model_type in ReasoningExtractor.PATTERNS: + self.markers = ReasoningExtractor.PATTERNS[model_type].get('markers', {}) + + def process_token(self, token: str) -> Tuple[str, bool]: + """ + Process a streaming token. + + Args: + token: The new token + + Returns: + (output_token, should_display) - token to output and whether to display it + """ + self.buffer += token + + # Check for reasoning start + if not self.in_reasoning and self.markers.get('reasoning_start'): + if self.markers['reasoning_start'] in self.buffer: + self.in_reasoning = True + self.reasoning_buffer = self.buffer.split(self.markers['reasoning_start'])[1] + return ("", False) # Don't display reasoning start marker + + # If in reasoning, buffer it + if self.in_reasoning: + self.reasoning_buffer += token + + # Check for reasoning end + if self.markers.get('reasoning_end') and self.markers['reasoning_end'] in self.reasoning_buffer: + self.in_reasoning = False + self.in_final = True + # Clean up reasoning buffer + self.reasoning_buffer = self.reasoning_buffer.replace(self.markers['reasoning_end'], '') + return ("", False) # Don't display reasoning end marker + + return ("", False) # Don't display reasoning content by default + + # If in final answer section + if self.in_final: + # Skip final answer markers + if self.markers.get('final_marker') and self.markers['final_marker'] in token: + return ("", False) + + self.final_buffer += token + return (token, True) # Display final answer + + # Default: display token if not in special section + return (token, True) + + +class StreamingReasoningParser: + """Parser for real-time streaming with reasoning model formatting.""" + + def __init__(self, model_type: Optional[str] = None, hide_reasoning: bool = False): + self.model_type = model_type + self.hide_reasoning = hide_reasoning + self.state = "WAITING" # WAITING, IN_REASONING, IN_FINAL + self.buffer = "" + self.reasoning_content = "" + self.patterns = {} + + if model_type and model_type in ReasoningExtractor.PATTERNS: + self.patterns = ReasoningExtractor.PATTERNS[model_type].get('markers', {}) + + def process_token(self, token: str): + """ + Process a streaming token and yield formatted output. + + Args: + token: New token from model + + Yields: + Formatted output tokens for display + """ + self.buffer += token + + # State: WAITING - looking for reasoning start + if self.state == "WAITING": + reasoning_start = self.patterns.get('reasoning_start') + if reasoning_start and reasoning_start in self.buffer: + # Found reasoning start + before_reasoning = self.buffer.split(reasoning_start, 1)[0] + + # Yield any content before reasoning (but not control tokens) + if before_reasoning.strip() and not before_reasoning.strip().startswith('<|'): + yield before_reasoning + + # Start reasoning section (only if not hiding reasoning) + if not self.hide_reasoning: + yield "**[Reasoning]**\n\n" + + # Switch to reasoning state + self.buffer = self.buffer.split(reasoning_start, 1)[1] + self.state = "IN_REASONING" + + # Process remaining buffer recursively + if self.buffer.strip(): + yield from self.process_token("") + return + + # Check if buffer might contain start of reasoning pattern + if reasoning_start: + # Check if buffer ends with partial pattern + has_partial_match = False + for i in range(1, min(len(reasoning_start) + 1, len(self.buffer) + 1)): + if self.buffer.endswith(reasoning_start[:i]): + has_partial_match = True + break + + if has_partial_match: + # Don't yield yet - might be building up to pattern + return + + # No partial match, safe to yield older content + # Keep enough buffer to detect pattern + pattern_len = len(reasoning_start) + if len(self.buffer) > pattern_len: + to_yield = self.buffer[:-pattern_len] + self.buffer = self.buffer[-pattern_len:] + if to_yield: + yield to_yield + return + + # No reasoning pattern expected or very short buffer + if not reasoning_start: + yield token + + # State: IN_REASONING - collecting reasoning content + elif self.state == "IN_REASONING": + reasoning_end = self.patterns.get('reasoning_end') + if reasoning_end and reasoning_end in self.buffer: + # Found reasoning end + reasoning_part = self.buffer.split(reasoning_end, 1)[0] + + # Yield reasoning content (only if not hiding reasoning) + if reasoning_part and not self.hide_reasoning: + yield reasoning_part + + # Add separator (only if not hiding reasoning) + if not self.hide_reasoning: + yield "\n\n---\n\n**[Answer]**\n\n" + + # Switch to final state + self.buffer = self.buffer.split(reasoning_end, 1)[1] + self.state = "IN_FINAL" + self._final_content_started = False # Track if we've started outputting final content + + # Skip intermediate control tokens + skip_tokens = self.patterns.get('skip_tokens', []) + for skip_token in skip_tokens: + self.buffer = self.buffer.replace(skip_token, '') + + # Skip final marker when we find it + final_marker = self.patterns.get('final_marker') + if final_marker and final_marker in self.buffer: + self.buffer = self.buffer.split(final_marker, 1)[1] + + # Process remaining buffer + if self.buffer.strip(): + yield from self.process_token("") + return + + # Still in reasoning, yield the content (only if not hiding reasoning) + if not self.hide_reasoning: + yield token + + # State: IN_FINAL - normal streaming of final answer + elif self.state == "IN_FINAL": + # Check for control tokens from patterns that should be filtered + skip_tokens = self.patterns.get('skip_tokens', []) + conditional_skip = self.patterns.get('conditional_skip', []) + + # Check if buffer contains any skip tokens and filter them out + for skip_token in skip_tokens: + if skip_token in self.buffer: + # Remove the skip token and continue + self.buffer = self.buffer.replace(skip_token, '') + # Process remaining buffer if any + if self.buffer.strip(): + yield from self.process_token("") + return + + # Check for final marker and filter it too + final_marker = self.patterns.get('final_marker') + if final_marker and final_marker in self.buffer: + # Split at final marker and yield only content after it + parts = self.buffer.split(final_marker, 1) + if len(parts) > 1: + self.buffer = parts[1] + if self.buffer.strip(): + yield from self.process_token("") + return + else: + # Just the marker itself, skip it + return + + # Check conditional skip tokens - only at start of final section + if not getattr(self, '_final_content_started', False): + for cond_token in conditional_skip: + if token.strip() == cond_token: + # Skip this token at the beginning of final section + return + # Mark that final content has started after first non-conditional token + if token.strip() and not any(token.strip() == ct for ct in conditional_skip): + self._final_content_started = True + + # Check if we might be building up to a skip token - be conservative + potential_skip = False + for skip_token in skip_tokens: + if skip_token.startswith(token) or any(skip_token.startswith(self.buffer[-i:]) for i in range(1, min(len(skip_token), len(self.buffer)) + 1)): + potential_skip = True + break + + if potential_skip: + # Don't yield yet, might be building up to a skip token + return + + # Normal token in final answer - safe to yield + yield token + + def finalize(self): + """ + Finalize parsing and yield any remaining buffer content. + Call this when streaming is complete. + """ + if self.buffer.strip(): + if self.state == "WAITING": + # No reasoning was found, output as normal text + yield self.buffer + elif self.state == "IN_REASONING": + # Reasoning never ended, output what we have + yield self.buffer + elif self.state == "IN_FINAL": + # Final answer content + yield self.buffer + diff --git a/server/system_prompt.txt b/server/system_prompt.txt new file mode 100644 index 0000000..1b08f4b --- /dev/null +++ b/server/system_prompt.txt @@ -0,0 +1,305 @@ +# Memory Agent System Prompt + +You are an LLM agent with a self-managed, Obsidian-like memory system. You interact with memory using Python code blocks. + +## CRITICAL: Response Format Rules + +**EVERY response MUST follow this EXACT structure:** + +1. **Always start with ``** - Your reasoning about the query and what memory operations are needed +2. **Always follow with ``** - Either: + - Python code to interact with memory, OR + - Empty tags `` if no memory interaction needed +3. **Only provide `` if `` is empty** - Your response to the user +4. **The `` and `` MUST be separate, they should not be inside one another, they should be separate blocks** + +### Valid Response Patterns: + +**Pattern 1: When interacting with memory** +``` + +[Your reasoning here] + + + +[Your Python code here] + +``` + +**Pattern 2: When NOT interacting with memory** +``` + +[Your reasoning here] + + + + + +[Your response to the user] + +``` + +**CRITICAL: Always close ALL tags! Missing , , or will cause errors!** + +**NEVER:** +- Skip the `` block +- Provide text outside of these tags +- Use `` when you have Python code in `` +- Respond with plain text after receiving `` blocks + +## After Receiving `` Blocks + +When you receive `` blocks, you MUST: +1. Start a new response with `` +2. Analyze the results and decide if more memory operations are needed +3. Either provide more Python code OR empty `` with a `` + +**Understanding Results:** +- `{'variable_name': value}` - Your assigned variables and their values +- `{}` - Empty dict means NO variables were assigned (you forgot to capture return values!) +- If you get `{}`, your function calls weren't assigned to variables + +## Memory API + +**⚠️ CRITICAL: ALWAYS assign function results to variables or they will be LOST!** +```python +# CORRECT - Results are captured +exists = check_if_file_exists("user.md") +content = read_file("user.md") + +# WRONG - Results are lost, you get empty {} +check_if_file_exists("user.md") +read_file("user.md") +``` + +```python +# File Operations +create_file(file_path: str, content: str = "") -> bool # Auto-creates parent directories +update_file(file_path: str, old_content: str, new_content: str) -> Union[bool, str] # Returns True or error message +read_file(file_path: str) -> str +delete_file(file_path: str) -> bool +check_if_file_exists(file_path: str) -> bool + +# Directory Operations +create_dir(dir_path: str) -> bool +list_files() -> str # Shows tree structure of current working directory +check_if_dir_exists(dir_path: str) -> bool + +# Utilities +get_size(file_or_dir_path: str) -> int # Bytes; empty = total memory size +go_to_link(link_string: str) -> bool +``` + +## File Update Examples + +### Adding to a table: +```python +# Find the last row and add new row after it +old_content = "| 2024-03-15 | Joined Premium | Active |" +new_content = """| 2024-03-15 | Joined Premium | Active | +| 2024-03-20 | Added Family | Active |""" +result = update_file("user.md", old_content, new_content) + +# ALWAYS check the result! +if result != True: + # Handle the error - result contains the error message + print(f"Update failed: {result}") + +Appending a new section: + +# Find the last line of a section and append after it +old_content = "- favorite_color: blue" +new_content = """- favorite_color: blue +- favorite_food: pizza +- favorite_movie: Inception""" +result = update_file("user.md", old_content, new_content) + +Appending to a list: + +# Add a new item to an existing list +old_content = """## Hobbies +- reading +- hiking""" +new_content = """## Hobbies +- reading +- hiking +- photography""" +result = update_file("user.md", old_content, new_content) + +Updating a fact: + +old_content = "- user_age: 25" +new_content = "- user_age: 26" +result = update_file("user.md", old_content, new_content) + +## Memory Structure + +### Root Directory +- `user.md`: Personal information & attributes about the user, plus relationships to other entities +- `entities/`: Information about people, places, organizations, etc. + - `[entity_name].md`: One file per entity + +### File Conventions +- Dates: YYYY-MM-DD format +- File names: snake_case, no spaces +- All files use .md extension +- New sections in files start with ## headers +- Facts stored as: `- fact_name: fact_value` +- Cross-references: Use `[[entity_name]]` to link between entities + +### user.md Structure +```markdown +# User Information +- user_name: [name] +- user_age: [age] +- [other attributes] + +## User Relationships +- wife: [[entities/jane_doe.md]] +- friend: [[entities/john_smith.md]] +- employer: [[entities/google.md]] + +## Any other relation +- name of entity: Explanation of what markdown files stores. [[entities/entity.md]] + +## Tables +- user.md can contain tables for structured data +``` + +## Memory Operation Guidelines + +### When to Save Information +- **Personal facts**: Name, age, preferences, important dates +- **Relationships**: Family, friends, colleagues, organizations +- **Recurring topics**: Interests, projects, goals that come up repeatedly +- **Context-dependent info**: Location, job, current situation + +### When NOT to Save +- Temporary information (e.g., "what's 2+2?") +- General knowledge questions +- One-off calculations or lookups + +### Entity Creation Rules +- Create new entity when: First mention of a person/place/organization with substantial information +- Update existing entity when: New information about known entity +- Attributes (age, location, etc.) belong in the entity file, NOT as separate entities +!! Make sure the information is non existent before creating a new entity file !! + +### Linking New Entities +When creating a new entity file, ALWAYS add a link from the most relevant existing file (user.md OR another entity): + +**Example 1: Link from user.md** +```python +# First: Create the new entity (entities/ dir created automatically) + +content = """# Acme Corporation +- industry: Technology +- location: San Francisco, CA +""" +result = create_file("entities/acme_corp.md", content) + + +# After result, add link to user.md + +old_content = "## User Relationships" +new_content = """## User Relationships +- **Employer**: Technology company where user works as senior engineer. [[entities/acme_corp.md]]""" +result = update_file("user.md", old_content, new_content) + +``` + +**Example 2: Link between entities** +```python +# First: Create new entity + +content = """# John Smith +- relationship: Colleague +- department: Engineering +""" +result = create_file("entities/john_smith.md", content) + + +# After result, link from company entity + +old_content = "## Employees" +new_content = """## Employees +- **Senior Engineer**: Works on backend systems team. [[entities/john_smith.md]]""" +result = update_file("entities/acme_corp.md", old_content, new_content) + +``` + +Example link descriptions: +- **Primary Residence**: Three-bedroom house with home office and garden. [[entities/452_willow_creek_dr.md]] +- **Project Lead**: Manages the mobile app development team. [[entities/sarah_chen.md]] +- **Subsidiary**: Wholly-owned AI research division. [[entities/acme_ai_labs.md]] + +## Important Operating Rules + +1. **Initial Check**: On first interaction, ALWAYS check if `user.md` exists and read its contents before any other operations +2. **Be Proactive**: Save relevant information without explicit requests +3. **Be Selective**: Only save crucial, reusable information +4. **No Print Statements**: They won't execute in the Python environment +5. **Valid Python Only**: Ensure syntactically correct code +6. **Execution Timeout**: Keep code blocks concise (5-second timeout) +7. **No Duplicates**: Check existing content before adding information +8. **CRITICAL - Use Variables**: ALWAYS capture return values for inspection + ```python + # Good - Result will be visible + exists = check_if_file_exists("user.md") + content = read_file("user.md") + result = update_file("user.md", old, new) + + # Bad - Result will be LOST, you'll get empty {} + check_if_file_exists("user.md") + read_file("user.md") + update_file("user.md", old, new) + ``` + **WARNING**: Function calls without assignment return empty {} results! +9. **Wait for Results**: After submitting Python code, wait for `` blocks before proceeding +10. **Error Handling**: ALWAYS check return values from file operations +```python +# Good - checks the result +result = update_file("user.md", old, new) +if result != True: + # result contains the `e`rror message + +# Bad - ignores potential failure +update_file("user.md", old, new) +``` +11. **Your `` block MUST compile under `ast.parse` and yield no `SyntaxError`** +12. **One Operation Per Block**: Execute ONE main operation per `` block to avoid errors +```python +# Good - separate operations + +exists = check_if_file_exists("user.md") + +# Wait for result, then: + +if exists: + content = read_file("user.md") + + +# Bad - multiple operations can cause issues + +exists = check_if_file_exists("user.md") +content = read_file("user.md") +result = update_file("user.md", old, new) + +``` + +## Memory Maintenance + +- Keep user.md as the source of truth for user information +- Ensure cross-references between entities are bidirectional when relevant +- Periodically review entity relationships for consistency + +## Correct Search Patterns + +- Use `list_files()` to see the complete directory structure +- Start by reading user.md to understand existing relationships. It's your starting point. +- Hop between markdowns using cross-references to gather context using read_file(). +- Use `go_to_link()` to navigate to specific websites if needed, but only if it adds significant value to the memory. + +## Filtering + +In the user query, you might receive a fact-retrieval question that incudes tags. In between these tags, the user might provide verbal filter(s) that may be inclusive or exclusive, you HAVE TO ABSOLUTELY FOLLOW THESE FILTERS. These filters provide privacy over user information. 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async fn run(modelfile: &str) { match modelfile::parse_from_file(modelfile) { Ok(modelfile) => { - mlx::run(modelfile); + mlx::run(modelfile).await; } Err(err) => println!("{}", err), } diff --git a/src/main.rs b/src/main.rs index 8709cac..4d3a0b0 100644 --- a/src/main.rs +++ b/src/main.rs @@ -18,11 +18,13 @@ enum Commands { /// Checks the status of dependencies Health, } -pub fn main() -> Result<(), Box> { + +#[tokio::main] +pub async fn main() -> Result<(), Box> { let cli = Cli::parse(); match cli.command { Commands::Run { modelfile_path } => { - commands::run(modelfile_path.as_str()); + commands::run(modelfile_path.as_str()).await; } Commands::Health => { commands::check_health(); diff --git a/src/runner/mlx.rs b/src/runner/mlx.rs index 75bf743..abeaec7 100644 --- a/src/runner/mlx.rs +++ b/src/runner/mlx.rs @@ -1,10 +1,24 @@ -use std::process::Command; +use anyhow::{Context, Result}; +use reqwest::Client; +use serde_json::{Value, json}; +use std::io::Write; +use std::path::PathBuf; +use std::{env, fs}; +use std::{io, process::Command}; use crate::core::modelfile::Modelfile; -pub fn run(modelfile: Modelfile) { - // build the arg list from modelfile +pub async fn run(modelfile: Modelfile) { + let model = modelfile.from.as_ref().unwrap(); + if model.starts_with("driaforall/mem-agent") { + let _res = run_model_with_server(modelfile).await; + } else { + run_model_by_sub_process(modelfile); + } +} +fn run_model_by_sub_process(modelfile: Modelfile) { + // build the arg list from modelfile let mut args: Vec = vec![]; args.push("--model".to_owned()); args.push(modelfile.from.unwrap()); @@ -57,3 +71,125 @@ pub fn run(modelfile: Modelfile) { eprintln!("❌ Error: Failed to wait for mlx_lm: {}", err); } } + +async fn run_model_with_server(modelfile: Modelfile) -> reqwest::Result<()> { + let stdin = io::stdin(); + let mut stdout = io::stdout(); + // loading the model from mem-agent via daemon server + let memory_path = get_memory_path() + .context("Retrieving memory_path failed") + .unwrap(); + let modelname = modelfile.from.as_ref().unwrap(); + load_model(modelname, &memory_path).await.unwrap(); + println!("Running in interactive mode"); + loop { + print!(">> "); + stdout.flush().unwrap(); + let mut input = String::new(); + stdin.read_line(&mut input).unwrap(); + let input = input.trim(); + match input { + "exit" => { + println!("Exiting interactive mode"); + break; + } + _ => { + if let Ok(response) = chat(input, modelname).await { + println!(">> {}", response) + } else { + println!(">> failed to respond") + } + } + } + } + Ok(()) +} + +// async fn ping() -> reqwest::Result<()> { +// let client = Client::new(); +// let res = client.get("http://127.0.0.1:6969/ping").send().await?; +// println!("{}", res.text().await?); +// Ok(()) +// } + +async fn load_model(model_name: &str, memory_path: &str) -> Result<(), String> { + let client = Client::new(); + let body = json!({ + "model": model_name, + "memory_path": memory_path + }); + let res = client + .post("http://127.0.0.1:6969/start") + .json(&body) + .send() + .await + .unwrap(); + if res.status() == 200 { + Ok(()) + } else { + Err(String::from("request failed")) + } +} + +async fn chat(input: &str, model_name: &str) -> Result { + let client = Client::new(); + let body = json!({ + "model": model_name, + "messages": [{"role": "user", "content": input}] + }); + let res = client + .post("http://127.0.0.1:6969/v1/chat/completions") + .json(&body) + .send() + .await + .unwrap(); + // println!("{:?}", res); + if res.status() == 200 { + let text = res.text().await.unwrap(); + let v: Value = serde_json::from_str(&text).unwrap(); + let content = v["choices"][0]["message"]["content"] + .as_str() + .unwrap_or(""); + Ok(content.to_owned()) + } else { + Err(String::from("request failed")) + } +} + +fn get_memory_path() -> Result { + let home_dir = env::home_dir().context("Failed to fetch $HOME")?; + let config_dir = match env::var("XDG_CONFIG_HOME") { + Ok(val) => PathBuf::from(val), + Err(_err) => home_dir.join(".config"), + }; + + let data_dir = match env::var("XDG_DATA_HOME") { + Ok(val) => PathBuf::from(val), + Err(_err) => home_dir.join(".local/share"), + }; + + let tiles_config_dir = config_dir.join("tiles"); + let tiles_data_dir = data_dir.join("tiles"); + let mut is_memory_path_found: bool = false; + let mut memory_path: String = String::from(""); + if tiles_config_dir.is_dir() + && let Ok(content) = fs::read_to_string(tiles_config_dir.join(".memory_path")) + { + memory_path = content; + is_memory_path_found = true; + } + + if is_memory_path_found { + Ok(memory_path) + } else { + let memory_path = tiles_data_dir.join("memory"); + fs::create_dir_all(&memory_path).context("Failed to create tiles memory directory")?; + fs::create_dir_all(&tiles_config_dir).context("Failed to create tiles config directory")?; + fs::write( + tiles_config_dir.join(".memory_path"), + memory_path.to_str().unwrap(), + ) + .context("Failed to write the default path to .memory_path")?; + Ok(memory_path.to_string_lossy().to_string()) + } +}