mirror of
https://github.com/arc53/DocsGPT.git
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d6c49bdbf0
- Add 104 comprehensive tests for agent system - Integrate agent tests into CI/CD pipeline - Standardize tests with @pytest.mark.unit markers - Fix cross-platform path compatibility - Clean up unused imports and dependencies
416 lines
16 KiB
Python
416 lines
16 KiB
Python
import logging
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import uuid
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from abc import ABC, abstractmethod
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from typing import Dict, Generator, List, Optional
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from bson.objectid import ObjectId
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from application.agents.tools.tool_action_parser import ToolActionParser
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from application.agents.tools.tool_manager import ToolManager
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from application.core.mongo_db import MongoDB
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from application.core.settings import settings
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from application.llm.handlers.handler_creator import LLMHandlerCreator
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from application.llm.llm_creator import LLMCreator
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from application.logging import build_stack_data, log_activity, LogContext
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from application.retriever.base import BaseRetriever
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logger = logging.getLogger(__name__)
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class BaseAgent(ABC):
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def __init__(
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self,
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endpoint: str,
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llm_name: str,
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gpt_model: str,
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api_key: str,
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user_api_key: Optional[str] = None,
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prompt: str = "",
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chat_history: Optional[List[Dict]] = None,
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decoded_token: Optional[Dict] = None,
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attachments: Optional[List[Dict]] = None,
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json_schema: Optional[Dict] = None,
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):
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self.endpoint = endpoint
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self.llm_name = llm_name
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self.gpt_model = gpt_model
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self.api_key = api_key
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self.user_api_key = user_api_key
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self.prompt = prompt
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self.decoded_token = decoded_token or {}
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self.user: str = self.decoded_token.get("sub")
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self.tool_config: Dict = {}
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self.tools: List[Dict] = []
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self.tool_calls: List[Dict] = []
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self.chat_history: List[Dict] = chat_history if chat_history is not None else []
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self.llm = LLMCreator.create_llm(
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llm_name,
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api_key=api_key,
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user_api_key=user_api_key,
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decoded_token=decoded_token,
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)
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self.llm_handler = LLMHandlerCreator.create_handler(
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llm_name if llm_name else "default"
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)
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self.attachments = attachments or []
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self.json_schema = json_schema
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@log_activity()
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def gen(
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self, query: str, retriever: BaseRetriever, log_context: LogContext = None
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) -> Generator[Dict, None, None]:
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yield from self._gen_inner(query, retriever, log_context)
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@abstractmethod
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def _gen_inner(
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self, query: str, retriever: BaseRetriever, log_context: LogContext
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) -> Generator[Dict, None, None]:
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pass
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def _get_tools(self, api_key: str = None) -> Dict[str, Dict]:
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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agents_collection = db["agents"]
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tools_collection = db["user_tools"]
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agent_data = agents_collection.find_one({"key": api_key or self.user_api_key})
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tool_ids = agent_data.get("tools", []) if agent_data else []
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tools = (
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tools_collection.find(
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{"_id": {"$in": [ObjectId(tool_id) for tool_id in tool_ids]}}
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)
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if tool_ids
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else []
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)
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tools = list(tools)
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tools_by_id = {str(tool["_id"]): tool for tool in tools} if tools else {}
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return tools_by_id
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def _get_user_tools(self, user="local"):
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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user_tools_collection = db["user_tools"]
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user_tools = user_tools_collection.find({"user": user, "status": True})
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user_tools = list(user_tools)
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return {str(i): tool for i, tool in enumerate(user_tools)}
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def _build_tool_parameters(self, action):
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params = {"type": "object", "properties": {}, "required": []}
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for param_type in ["query_params", "headers", "body", "parameters"]:
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if param_type in action and action[param_type].get("properties"):
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for k, v in action[param_type]["properties"].items():
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if v.get("filled_by_llm", True):
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params["properties"][k] = {
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key: value
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for key, value in v.items()
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if key != "filled_by_llm" and key != "value"
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}
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params["required"].append(k)
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return params
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def _prepare_tools(self, tools_dict):
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self.tools = [
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{
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"type": "function",
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"function": {
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"name": f"{action['name']}_{tool_id}",
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"description": action["description"],
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"parameters": self._build_tool_parameters(action),
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},
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}
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for tool_id, tool in tools_dict.items()
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if (
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(tool["name"] == "api_tool" and "actions" in tool.get("config", {}))
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or (tool["name"] != "api_tool" and "actions" in tool)
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)
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for action in (
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tool["config"]["actions"].values()
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if tool["name"] == "api_tool"
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else tool["actions"]
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)
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if action.get("active", True)
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]
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def _execute_tool_action(self, tools_dict, call):
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parser = ToolActionParser(self.llm.__class__.__name__)
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tool_id, action_name, call_args = parser.parse_args(call)
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call_id = getattr(call, "id", None) or str(uuid.uuid4())
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# Check if parsing failed
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if tool_id is None or action_name is None:
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error_message = f"Error: Failed to parse LLM tool call. Tool name: {getattr(call, 'name', 'unknown')}"
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logger.error(error_message)
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tool_call_data = {
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"tool_name": "unknown",
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"call_id": call_id,
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"action_name": getattr(call, "name", "unknown"),
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"arguments": call_args or {},
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"result": f"Failed to parse tool call. Invalid tool name format: {getattr(call, 'name', 'unknown')}",
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
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self.tool_calls.append(tool_call_data)
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return "Failed to parse tool call.", call_id
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# Check if tool_id exists in available tools
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if tool_id not in tools_dict:
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error_message = f"Error: Tool ID '{tool_id}' extracted from LLM call not found in available tools_dict. Available IDs: {list(tools_dict.keys())}"
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logger.error(error_message)
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# Return error result
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tool_call_data = {
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"tool_name": "unknown",
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"call_id": call_id,
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"action_name": f"{action_name}_{tool_id}",
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"arguments": call_args,
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"result": f"Tool with ID {tool_id} not found. Available tools: {list(tools_dict.keys())}",
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
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self.tool_calls.append(tool_call_data)
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return f"Tool with ID {tool_id} not found.", call_id
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tool_call_data = {
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"tool_name": tools_dict[tool_id]["name"],
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"call_id": call_id,
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"action_name": f"{action_name}_{tool_id}",
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"arguments": call_args,
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "pending"}}
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tool_data = tools_dict[tool_id]
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action_data = (
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tool_data["config"]["actions"][action_name]
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if tool_data["name"] == "api_tool"
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else next(
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action
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for action in tool_data["actions"]
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if action["name"] == action_name
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)
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)
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query_params, headers, body, parameters = {}, {}, {}, {}
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param_types = {
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"query_params": query_params,
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"headers": headers,
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"body": body,
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"parameters": parameters,
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}
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for param_type, target_dict in param_types.items():
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if param_type in action_data and action_data[param_type].get("properties"):
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for param, details in action_data[param_type]["properties"].items():
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if param not in call_args and "value" in details:
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target_dict[param] = details["value"]
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for param, value in call_args.items():
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for param_type, target_dict in param_types.items():
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if param_type in action_data and param in action_data[param_type].get(
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"properties", {}
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):
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target_dict[param] = value
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tm = ToolManager(config={})
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# Prepare tool_config and add tool_id for memory tools
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if tool_data["name"] == "api_tool":
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tool_config = {
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"url": tool_data["config"]["actions"][action_name]["url"],
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"method": tool_data["config"]["actions"][action_name]["method"],
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"headers": headers,
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"query_params": query_params,
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}
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else:
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tool_config = tool_data["config"].copy() if tool_data["config"] else {}
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# Add tool_id from MongoDB _id for tools that need instance isolation (like memory tool)
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# Use MongoDB _id if available, otherwise fall back to enumerated tool_id
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tool_config["tool_id"] = str(tool_data.get("_id", tool_id))
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tool = tm.load_tool(
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tool_data["name"],
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tool_config=tool_config,
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user_id=self.user, # Pass user ID for MCP tools credential decryption
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)
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if tool_data["name"] == "api_tool":
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print(
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f"Executing api: {action_name} with query_params: {query_params}, headers: {headers}, body: {body}"
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)
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result = tool.execute_action(action_name, **body)
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else:
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print(f"Executing tool: {action_name} with args: {call_args}")
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result = tool.execute_action(action_name, **parameters)
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tool_call_data["result"] = (
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f"{str(result)[:50]}..." if len(str(result)) > 50 else result
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)
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "completed"}}
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self.tool_calls.append(tool_call_data)
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return result, call_id
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def _get_truncated_tool_calls(self):
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return [
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{
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**tool_call,
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"result": (
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f"{str(tool_call['result'])[:50]}..."
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if len(str(tool_call["result"])) > 50
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else tool_call["result"]
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),
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"status": "completed",
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}
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for tool_call in self.tool_calls
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]
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def _build_messages(
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self,
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system_prompt: str,
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query: str,
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retrieved_data: List[Dict],
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) -> List[Dict]:
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docs_with_filenames = []
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for doc in retrieved_data:
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filename = doc.get("filename") or doc.get("title") or doc.get("source")
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if filename:
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chunk_header = str(filename)
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docs_with_filenames.append(f"{chunk_header}\n{doc['text']}")
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else:
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docs_with_filenames.append(doc["text"])
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docs_together = "\n\n".join(docs_with_filenames)
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p_chat_combine = system_prompt.replace("{summaries}", docs_together)
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messages_combine = [{"role": "system", "content": p_chat_combine}]
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for i in self.chat_history:
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if "prompt" in i and "response" in i:
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messages_combine.append({"role": "user", "content": i["prompt"]})
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messages_combine.append({"role": "assistant", "content": i["response"]})
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if "tool_calls" in i:
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for tool_call in i["tool_calls"]:
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call_id = tool_call.get("call_id") or str(uuid.uuid4())
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function_call_dict = {
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"function_call": {
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"name": tool_call.get("action_name"),
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"args": tool_call.get("arguments"),
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"call_id": call_id,
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}
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}
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function_response_dict = {
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"function_response": {
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"name": tool_call.get("action_name"),
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"response": {"result": tool_call.get("result")},
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"call_id": call_id,
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}
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}
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messages_combine.append(
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{"role": "assistant", "content": [function_call_dict]}
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)
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messages_combine.append(
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{"role": "tool", "content": [function_response_dict]}
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)
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messages_combine.append({"role": "user", "content": query})
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return messages_combine
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def _retriever_search(
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self,
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retriever: BaseRetriever,
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query: str,
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log_context: Optional[LogContext] = None,
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) -> List[Dict]:
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retrieved_data = retriever.search(query)
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if log_context:
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data = build_stack_data(retriever, exclude_attributes=["llm"])
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log_context.stacks.append({"component": "retriever", "data": data})
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return retrieved_data
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def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
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gen_kwargs = {"model": self.gpt_model, "messages": messages}
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if (
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hasattr(self.llm, "_supports_tools")
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and self.llm._supports_tools
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and self.tools
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):
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gen_kwargs["tools"] = self.tools
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if (
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self.json_schema
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and hasattr(self.llm, "_supports_structured_output")
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and self.llm._supports_structured_output()
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):
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structured_format = self.llm.prepare_structured_output_format(
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self.json_schema
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)
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if structured_format:
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if self.llm_name == "openai":
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gen_kwargs["response_format"] = structured_format
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elif self.llm_name == "google":
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gen_kwargs["response_schema"] = structured_format
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resp = self.llm.gen_stream(**gen_kwargs)
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if log_context:
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data = build_stack_data(self.llm, exclude_attributes=["client"])
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log_context.stacks.append({"component": "llm", "data": data})
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return resp
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def _llm_handler(
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self,
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resp,
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tools_dict: Dict,
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messages: List[Dict],
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log_context: Optional[LogContext] = None,
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attachments: Optional[List[Dict]] = None,
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):
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resp = self.llm_handler.process_message_flow(
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self, resp, tools_dict, messages, attachments, True
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)
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if log_context:
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data = build_stack_data(self.llm_handler, exclude_attributes=["tool_calls"])
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log_context.stacks.append({"component": "llm_handler", "data": data})
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return resp
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def _handle_response(self, response, tools_dict, messages, log_context):
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is_structured_output = (
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self.json_schema is not None
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and hasattr(self.llm, "_supports_structured_output")
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and self.llm._supports_structured_output()
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)
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if isinstance(response, str):
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answer_data = {"answer": response}
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if is_structured_output:
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answer_data["structured"] = True
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answer_data["schema"] = self.json_schema
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yield answer_data
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return
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if hasattr(response, "message") and getattr(response.message, "content", None):
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answer_data = {"answer": response.message.content}
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if is_structured_output:
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answer_data["structured"] = True
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answer_data["schema"] = self.json_schema
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yield answer_data
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return
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processed_response_gen = self._llm_handler(
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response, tools_dict, messages, log_context, self.attachments
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)
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for event in processed_response_gen:
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if isinstance(event, str):
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answer_data = {"answer": event}
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if is_structured_output:
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answer_data["structured"] = True
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answer_data["schema"] = self.json_schema
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yield answer_data
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elif hasattr(event, "message") and getattr(event.message, "content", None):
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answer_data = {"answer": event.message.content}
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if is_structured_output:
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answer_data["structured"] = True
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answer_data["schema"] = self.json_schema
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yield answer_data
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elif isinstance(event, dict) and "type" in event:
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yield event
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