diff --git a/scripts/plugin_mgt.py b/scripts/plugin_mgt.py new file mode 100644 index 00000000..5a89102a --- /dev/null +++ b/scripts/plugin_mgt.py @@ -0,0 +1,68 @@ +import argparse +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) + +from injector import Injector + +from taskweaver.code_interpreter.code_generator.plugin_selection import PluginSelector +from taskweaver.config.config_mgt import AppConfigSource +from taskweaver.logging import LoggingModule +from taskweaver.memory.plugin import PluginModule + +parser = argparse.ArgumentParser() +parser.add_argument( + "--project_dir", + type=str, + default=os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "..", + "project", + ), + help="The project directory for the TaskWeaver.", +) +parser.add_argument("--refresh", action="store_true", help="Refresh plugin embeddings.") +parser.add_argument("--show", action="store_true", help="Show plugin information.") + +args = parser.parse_args() + + +class PluginManager: + def __init__(self): + app_injector = Injector([LoggingModule, PluginModule]) + app_config = AppConfigSource( + config_file_path=os.path.join( + args.project_dir, + "taskweaver_config.json", + ), + app_base_path=args.project_dir, + ) + app_injector.binder.bind(AppConfigSource, to=app_config) + self.plugin_selector = app_injector.create_object(PluginSelector) + + def refresh(self): + self.plugin_selector.refresh() + print("Plugin embeddings refreshed.") + + def show(self): + plugin_list = self.plugin_selector.available_plugins + if len(plugin_list) == 0: + print("No available plugins.") + return + for p in plugin_list: + print(f"* Plugin Name: {p.name}") + print(f"* Plugin Description: {p.spec.description}") + print(f"* Plugin Embedding dim: {len(p.meta_data.embedding)}") + print(f"* Plugin Embedding model: {p.meta_data.embedding_model}") + print(f"* Plugin Args: {p.spec.args}") + print(f"* Plugin Returns: {p.spec.returns}") + print(f"_________________________________") + + +if __name__ == "__main__": + plugin_manager = PluginManager() + if args.refresh: + plugin_manager.refresh() + if args.show: + plugin_manager.show() diff --git a/taskweaver/code_interpreter/code_generator/code_generator.py b/taskweaver/code_interpreter/code_generator/code_generator.py index c11d15e7..3ba274de 100644 --- a/taskweaver/code_interpreter/code_generator/code_generator.py +++ b/taskweaver/code_interpreter/code_generator/code_generator.py @@ -90,8 +90,8 @@ def __init__( if self.config.enable_auto_plugin_selection: self.plugin_selector = PluginSelector(plugin_registry, self.llm_api) - self.plugin_selector.generate_plugin_embeddings() - logger.info("Plugin embeddings generated") + self.plugin_selector.load_plugin_embeddings() + logger.info("Plugin embeddings loaded") self.selected_plugin_pool = SelectedPluginPool() def configure_verification( @@ -272,10 +272,10 @@ def compose_conversation( def select_plugins_for_prompt( self, - user_query: str, + query: str, ) -> List[PluginEntry]: selected_plugins = self.plugin_selector.plugin_select( - user_query, + query, self.config.auto_plugin_selection_topk, ) self.selected_plugin_pool.add_selected_plugins(selected_plugins) @@ -300,10 +300,10 @@ def reply( ) # obtain the user query from the last round - user_query = rounds[-1].user_query + query = rounds[-1].post_list[-1].message if self.config.enable_auto_plugin_selection: - self.plugin_pool = self.select_plugins_for_prompt(user_query) + self.plugin_pool = self.select_plugins_for_prompt(query) prompt = self.compose_prompt(rounds, self.plugin_pool) diff --git a/taskweaver/code_interpreter/code_generator/code_generator_plugin_only.py b/taskweaver/code_interpreter/code_generator/code_generator_plugin_only.py index 533627c1..1044ad6b 100644 --- a/taskweaver/code_interpreter/code_generator/code_generator_plugin_only.py +++ b/taskweaver/code_interpreter/code_generator/code_generator_plugin_only.py @@ -62,8 +62,8 @@ def __init__( if self.config.enable_auto_plugin_selection: self.plugin_selector = PluginSelector(plugin_registry, self.llm_api) - self.plugin_selector.generate_plugin_embeddings() - logger.info("Plugin embeddings generated") + self.plugin_selector.load_plugin_embeddings() + logger.info("Plugin embeddings loaded") self.selected_plugin_pool = SelectedPluginPool() def select_plugins_for_prompt( diff --git a/taskweaver/code_interpreter/code_generator/plugin_selection.py b/taskweaver/code_interpreter/code_generator/plugin_selection.py index 0e52a7f4..99ee23dd 100644 --- a/taskweaver/code_interpreter/code_generator/plugin_selection.py +++ b/taskweaver/code_interpreter/code_generator/plugin_selection.py @@ -1,3 +1,4 @@ +import os from typing import Dict, List import numpy as np @@ -6,6 +7,7 @@ from taskweaver.llm import LLMApi from taskweaver.memory.plugin import PluginEntry, PluginRegistry +from taskweaver.utils import generate_md5_hash, write_yaml class SelectedPluginPool: @@ -70,13 +72,60 @@ def __init__( self.llm_api = llm_api self.plugin_embedding_dict: Dict[str, List[float]] = {} - def generate_plugin_embeddings(self): - plugin_intro_text_list: List[str] = [] - for p in self.available_plugins: - plugin_intro_text_list.append(p.name + ": " + p.spec.description) - plugin_embeddings = self.llm_api.get_embedding_list(plugin_intro_text_list) - for i, p in enumerate(self.available_plugins): - self.plugin_embedding_dict[p.name] = plugin_embeddings[i] + self.exception_message_for_refresh = ( + "Please cd to the `script` directory and " + "run `python -m plugin_mgt --refresh` to refresh the plugin embedding." + ) + + self.meta_file_dir = os.path.join(os.path.dirname(plugin_registry.file_glob), ".meta") + if not os.path.exists(self.meta_file_dir): + os.makedirs(self.meta_file_dir) + + def refresh(self): + plugins_to_embedded = [] + for idx, p in enumerate(self.available_plugins): + if ( + len(p.meta_data.embedding) > 0 + and p.meta_data.embedding_model == self.llm_api.embedding_service.config.embedding_model + and p.meta_data.md5hash == generate_md5_hash(p.spec.name + p.spec.description) + ): + continue + else: + plugins_to_embedded.append((idx, p.name + ": " + p.spec.description)) + + if len(plugins_to_embedded) == 0: + print("All plugins are up-to-date.") + return + + plugin_embeddings = self.llm_api.get_embedding_list([text for idx, text in plugins_to_embedded]) + + for i, embedding in enumerate(plugin_embeddings): + p = self.available_plugins[plugins_to_embedded[i][0]] + p.meta_data.embedding = embedding + p.meta_data.embedding_model = self.llm_api.embedding_service.config.embedding_model + p.meta_data.md5hash = generate_md5_hash(p.spec.name + p.spec.description) + write_yaml(p.meta_data.path, p.meta_data.to_dict()) + + def load_plugin_embeddings(self): + for idx, p in enumerate(self.available_plugins): + # check if the plugin has embedding + assert len(p.meta_data.embedding) > 0, ( + f"Plugin {p.name} has no embedding. " + self.exception_message_for_refresh + ) + # check if the plugin is using the same embedding model as the current session + assert p.meta_data.embedding_model == self.llm_api.embedding_service.config.embedding_model, ( + f"Plugin {p.name} is using embedding model {p.meta_data.embedding_model}, " + f"which is different from the one used by current session" + f" ({self.llm_api.embedding_service.config.embedding_model}). " + f"Please use the same embedding model or refresh the plugin embedding." + + self.exception_message_for_refresh + ) + # check if the plugin has been modified + assert p.meta_data.md5hash == generate_md5_hash(p.spec.name + p.spec.description), ( + f"Plugin {p.name} has been modified. " + self.exception_message_for_refresh + ) + + self.plugin_embedding_dict[p.name] = p.meta_data.embedding def plugin_select(self, user_query: str, top_k: int = 5) -> List[PluginEntry]: user_query_embedding = np.array(self.llm_api.get_embedding(user_query)) diff --git a/taskweaver/memory/plugin.py b/taskweaver/memory/plugin.py index 97b44ae5..fd07213c 100644 --- a/taskweaver/memory/plugin.py +++ b/taskweaver/memory/plugin.py @@ -10,6 +10,34 @@ from taskweaver.utils import read_yaml, validate_yaml +@dataclass +class PluginMetaData: + name: str + embedding: List[float] = field(default_factory=list) + embedding_model: Optional[str] = None + path: Optional[str] = None + md5hash: Optional[str] = None + + @staticmethod + def from_dict(d: Dict[str, Any]): + return PluginMetaData( + name=d["name"], + embedding=d["embedding"] if "embedding" in d else [], + embedding_model=d["embedding_model"] if "embedding_model" in d else None, + path=d["path"] if "path" in d else None, + md5hash=d["md5hash"] if "md5hash" in d else None, + ) + + def to_dict(self): + return { + "name": self.name, + "embedding": self.embedding, + "embedding_model": self.embedding_model, + "path": self.path, + "md5hash": self.md5hash, + } + + @dataclass class PluginParameter: """PluginParameter is the data structure for plugin parameters (including arguments and return values.)""" @@ -41,6 +69,14 @@ def line(cnt: str): return "\n".join(lines) + def to_dict(self): + return { + "name": self.name, + "type": self.type, + "required": self.required, + "description": self.description, + } + @dataclass class PluginSpec: @@ -50,7 +86,6 @@ class PluginSpec: description: str = "" args: List[PluginParameter] = field(default_factory=list) returns: List[PluginParameter] = field(default_factory=list) - embedding: List[float] = field(default_factory=list) @staticmethod def from_dict(d: Dict[str, Any]): @@ -59,9 +94,16 @@ def from_dict(d: Dict[str, Any]): description=d["description"], args=[PluginParameter.from_dict(p) for p in d["parameters"]], returns=[PluginParameter.from_dict(p) for p in d["returns"]], - embedding=[], ) + def to_dict(self): + return { + "name": self.name, + "description": self.description, + "parameters": [p.to_dict() for p in self.args], + "returns": [p.to_dict() for p in self.returns], + } + def format_prompt(self) -> str: def normalize_type(t: str) -> str: if t.lower() == "string": @@ -120,14 +162,22 @@ class PluginEntry: config: Dict[str, Any] required: bool enabled: bool = True + meta_data: Optional[PluginMetaData] = None @staticmethod def from_yaml_file(path: str) -> Optional["PluginEntry"]: content = read_yaml(path) - return PluginEntry.from_yaml_content(content) + yaml_file_name = os.path.basename(path) + meta_file_path = os.path.join(os.path.dirname(path), ".meta", f"meta_{yaml_file_name}") + if os.path.exists(meta_file_path): + meta_data = PluginMetaData.from_dict(read_yaml(meta_file_path)) + meta_data.path = meta_file_path + else: + meta_data = PluginMetaData(name=os.path.splitext(yaml_file_name)[0], path=meta_file_path) + return PluginEntry.from_yaml_content(content, meta_data) @staticmethod - def from_yaml_content(content: Dict) -> Optional["PluginEntry"]: + def from_yaml_content(content: Dict, meta_data: Optional[PluginMetaData] = None) -> Optional["PluginEntry"]: do_validate = False valid_state = False if do_validate: @@ -142,6 +192,7 @@ def from_yaml_content(content: Dict) -> Optional["PluginEntry"]: required=content.get("required", False), enabled=content.get("enabled", True), plugin_only=content.get("plugin_only", False), + meta_data=meta_data, ) return None @@ -156,6 +207,7 @@ def to_dict(self): "config": self.config, "required": self.required, "enabled": self.enabled, + "plugin_only": self.plugin_only, } def format_function_calling(self) -> Dict: diff --git a/taskweaver/utils/__init__.py b/taskweaver/utils/__init__.py index 5dc97e7d..49a582a6 100644 --- a/taskweaver/utils/__init__.py +++ b/taskweaver/utils/__init__.py @@ -5,6 +5,7 @@ import os import secrets from datetime import datetime +from hashlib import md5 from typing import Any, Dict @@ -24,6 +25,16 @@ def read_yaml(path: str) -> Dict[str, Any]: raise ValueError(f"Yaml loading failed due to: {e}") +def write_yaml(path: str, content: Dict[str, Any]): + import yaml + + try: + with open(path, "w") as file: + yaml.safe_dump(content, file, sort_keys=False) + except Exception as e: + raise ValueError(f"Yaml writing failed due to: {e}") + + def validate_yaml(content: Any, schema: str) -> bool: import jsonschema @@ -58,3 +69,7 @@ def json_dumps(obj: Any) -> str: def json_dump(obj: Any, fp: Any): json.dump(obj, fp, cls=EnhancedJSONEncoder) + + +def generate_md5_hash(content: str) -> str: + return md5(content.encode()).hexdigest() diff --git a/tests/unit_tests/data/plugins/meta_anomaly_detection.yaml b/tests/unit_tests/data/plugins/meta_anomaly_detection.yaml new file mode 100644 index 00000000..6bc80a2a --- /dev/null +++ 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2.3479702984746022e-34 +- 0.004175903275609016 +- 0.031230423599481583 +- 0.024655306711792946 +- -0.047157905995845795 +- 0.00853145681321621 +- -0.00806446373462677 +- 0.013154953718185425 +- -0.030740724876523018 +- -0.017885657027363777 +- -0.005409740377217531 +- -0.014177471399307251 +embedding_model: all-mpnet-base-v2 +path: D:\python_project\TaskWeaver\tests\unit_tests\data\plugins\meta_sql_pull_data.yaml +md5hash: 3c6fd6e7af0b3872a2cdbfed75b49c0f diff --git a/tests/unit_tests/test_plugin_selector.py b/tests/unit_tests/test_plugin_selector.py index fd41a758..c4523c61 100644 --- a/tests/unit_tests/test_plugin_selector.py +++ b/tests/unit_tests/test_plugin_selector.py @@ -24,7 +24,8 @@ def test_plugin_selector(): ) app_injector.binder.bind(AppConfigSource, to=app_config) plugin_selector = app_injector.get(PluginSelector) - plugin_selector.generate_plugin_embeddings() + plugin_selector.refresh() + plugin_selector.load_plugin_embeddings() query1 = "detect abnormal data points in ./data.csv." selected_plugins = plugin_selector.plugin_select(query1, top_k=3) diff --git a/website/docs/customization/plugin/plugin_selection.md b/website/docs/customization/plugin/plugin_selection.md index 9d5dda0a..a4b127c6 100644 --- a/website/docs/customization/plugin/plugin_selection.md +++ b/website/docs/customization/plugin/plugin_selection.md @@ -31,6 +31,28 @@ During the subsequent automatic plugin selection phase, newly chosen plugins are - `code_generator.enable_auto_plugin_selection`: Whether to enable auto plugin selection. The default value is `false`. - `code_generator.auto_plugin_selection_topk`: The number of auto selected plugins in each round. The default value is `3`. + +## Auto Plugin Selection Preparation + +Before using the auto plugin selection mechanism, we need to run the following command to generate the plugin meta files with embeddings. + +```bash +cd scripts +python -m plugin_mgt --refresh +``` +After that, you can find the `.meta` directory is generated in the `plugins` folder. +Then you can start a new TaskWeaver session with the auto plugin selection mechanism enabled. +Code Generator will automatically load the plugin meta files with embeddings. + +🎈Plugin meta files will be treated as invalid if: + - The plugin embedding vector is not generated. + - The plugin is modified. + - The plugin embedding model is changed. + +In this case, you cannot start the TaskWeaver and you need to run the above command again to refresh the plugin meta files. + +```bash + ## Auto Plugin Selection Example We show the auto plugin selection mechanism in the following example.