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initialization.py
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103 lines (68 loc) · 3.36 KB
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from __future__ import annotations
import os
from typing import Dict, List, TYPE_CHECKING
import yaml
from multiagents.llms import llm_registry
from multiagents.agents import agent_registry
from multiagents.environments.base import BaseEnvironment
from multiagents.environments import env_registry
from multiagents.memory import memory_registry
from multiagents.custom_parser import output_parser_registry
from multiagents.tools.api_retrieval import APICaller, register_functions_from_module
import importlib
if TYPE_CHECKING:
from agents import BaseAgent
LANGUAGE = "en"
def load_llm(llm_config: Dict):
llm_type = llm_config.pop("llm_type", "xxxx")
return llm_registry.build(llm_type, **llm_config)
def load_memory(memory_config: Dict):
memory_type = memory_config.pop("memory_type", "chat_history")
return memory_registry.build(memory_type, **memory_config)
def load_tools(tool_config: List[Dict], agent_name):
if len(tool_config) == 0:
return []
caller = APICaller() # adopt tool matching model
# register all the apis from required tools
for tool in tool_config:
api_module = importlib.import_module(f"""multiagents.tools.{tool["tool_name"]}.api""")
register_functions_from_module(api_module, caller, agent_name)
return caller
def load_environment(env_config: Dict) -> BaseEnvironment:
env_type = env_config.pop("env_type", "basic")
return env_registry.build(env_type, **env_config)
def load_agent(agent_config: Dict) -> BaseAgent:
agent_type = agent_config.pop("agent_type", "conversation")
agent = agent_registry.build(agent_type, **agent_config)
return agent
def prepare_task_config(task, args):
"""Read the yaml config of the given task in `tasks` directory."""
task_path = os.path.join(os.path.dirname(__file__), task)
config_path = os.path.join(task_path, args.config_file)
if not os.path.exists(task_path):
raise ValueError(f"Config {task} not found.")
if not os.path.exists(config_path):
raise ValueError(
"You should include the config.yaml file in the task directory"
)
task_config = yaml.safe_load(open(config_path, encoding='utf8'))
global LANGUAGE
LANGUAGE = task_config.get("language", LANGUAGE)
# Build the output parser
parser = output_parser_registry.build(task)
task_config["output_parser"] = parser
for i, agent_configs in enumerate(task_config["agents"]):
agent_configs["memory"] = load_memory(agent_configs.get("memory", {}))
if agent_configs.get("tool_memory", None) is not None:
agent_configs["tool_memory"] = load_memory(agent_configs["tool_memory"])
llm = load_llm(agent_configs.get("llm", "xxxx"))
agent_configs["llm"] = llm
agent_configs["tools"] = load_tools(agent_configs.get("tools", []), agent_configs['name'])
agent_configs["name"] = agent_configs['name']
# 临时给qwen写了个output parser, 不会影响之前的（主要是role assigner的config里有个dict）
if isinstance(agent_configs.get("output_parser", None), str):
agent_configs["output_parser"] = output_parser_registry.build(agent_configs["output_parser"])
else:
agent_configs["output_parser"] = task_config["output_parser"]
agent_configs["language"] = task_config.get("language", "en")
return task_config