from fastapi import Body, Request from fastapi.responses import StreamingResponse from typing import List from loguru import logger import importlib import copy import json from configs.model_config import ( llm_model_dict, LLM_MODEL, PROMPT_TEMPLATE, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD) from dev_opsgpt.tools import ( toLangchainTools, TOOL_DICT, TOOL_SETS ) from dev_opsgpt.connector.phase import BasePhase from dev_opsgpt.connector.agents import BaseAgent, ReactAgent from dev_opsgpt.connector.chains import BaseChain from dev_opsgpt.connector.connector_schema import ( Message, load_phase_configs, load_chain_configs, load_role_configs ) from dev_opsgpt.connector.shcema import Memory from dev_opsgpt.chat.utils import History, wrap_done from dev_opsgpt.connector.configs import PHASE_CONFIGS, AGETN_CONFIGS, CHAIN_CONFIGS PHASE_MODULE = importlib.import_module("dev_opsgpt.connector.phase") class AgentChat: def __init__( self, engine_name: str = "", top_k: int = 1, stream: bool = False, ) -> None: self.top_k = top_k self.stream = stream def chat( self, query: str = Body(..., description="用户输入", examples=["hello"]), phase_name: str = Body(..., description="执行场景名称", examples=["chatPhase"]), chain_name: str = Body(..., description="执行链的名称", examples=["chatChain"]), history: List[History] = Body( [], description="历史对话", examples=[[{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"}]] ), doc_engine_name: str = Body(..., description="知识库名称", examples=["samples"]), search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]), code_engine_name: str = Body(..., description="代码引擎名称", examples=["samples"]), top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"), score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1), stream: bool = Body(False, description="流式输出"), local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"), choose_tools: List[str] = Body([], description="选择tool的集合"), do_search: bool = Body(False, description="是否进行搜索"), do_doc_retrieval: bool = Body(False, description="是否进行知识库检索"), do_code_retrieval: bool = Body(False, description="是否执行代码检索"), do_tool_retrieval: bool = Body(False, description="是否执行工具检索"), custom_phase_configs: dict = Body({}, description="自定义phase配置"), custom_chain_configs: dict = Body({}, description="自定义chain配置"), custom_role_configs: dict = Body({}, description="自定义role配置"), history_node_list: List = Body([], description="代码历史相关节点"), isDetaild: bool = Body([], description="是否输出完整的agent相关内容"), **kargs ) -> Message: # update configs phase_configs, chain_configs, agent_configs = self.update_configs( custom_phase_configs, custom_chain_configs, custom_role_configs) # choose tools tools = toLangchainTools([TOOL_DICT[i] for i in choose_tools if i in TOOL_DICT]) input_message = Message( role_content=query, role_type="human", role_name="user", input_query=query, phase_name=phase_name, chain_name=chain_name, do_search=do_search, do_doc_retrieval=do_doc_retrieval, do_code_retrieval=do_code_retrieval, do_tool_retrieval=do_tool_retrieval, doc_engine_name=doc_engine_name, search_engine_name=search_engine_name, code_engine_name=code_engine_name, score_threshold=score_threshold, top_k=top_k, history_node_list=history_node_list, tools=tools ) # history memory mangemant history = Memory([ Message(role_name=i["role"], role_type=i["role"], role_content=i["content"]) for i in history ]) # start to execute phase_class = getattr(PHASE_MODULE, phase_configs[input_message.phase_name]["phase_type"]) phase = phase_class(input_message.phase_name, task = input_message.task, phase_config = phase_configs, chain_config = chain_configs, role_config = agent_configs, do_summary=phase_configs[input_message.phase_name]["do_summary"], do_code_retrieval=input_message.do_code_retrieval, do_doc_retrieval=input_message.do_doc_retrieval, do_search=input_message.do_search, ) output_message, local_memory = phase.step(input_message, history) # logger.debug(f"local_memory: {local_memory.to_str_messages(content_key='step_content')}") # return { # "answer": output_message.role_content, # "db_docs": output_message.db_docs, # "search_docs": output_message.search_docs, # "code_docs": output_message.code_docs, # "figures": output_message.figures # } def chat_iterator(message: Message, local_memory: Memory, isDetaild=False): result = { "answer": "", "db_docs": [str(doc) for doc in message.db_docs], "search_docs": [str(doc) for doc in message.search_docs], "code_docs": [str(doc) for doc in message.code_docs], "related_nodes": [doc.get_related_node() for idx, doc in enumerate(message.code_docs) if idx==0], "figures": message.figures } related_nodes, has_nodes = [], [ ] for nodes in result["related_nodes"]: for node in nodes: if node not in has_nodes: related_nodes.append(node) result["related_nodes"] = related_nodes # logger.debug(f"{result['figures'].keys()}") message_str = local_memory.to_str_messages(content_key='step_content') if isDetaild else message.role_content if self.stream: for token in message_str: result["answer"] = token yield json.dumps(result, ensure_ascii=False) else: for token in message_str: result["answer"] += token yield json.dumps(result, ensure_ascii=False) return StreamingResponse(chat_iterator(output_message, local_memory, isDetaild), media_type="text/event-stream") def _chat(self, ): pass def update_configs(self, custom_phase_configs, custom_chain_configs, custom_role_configs): '''update phase/chain/agent configs''' phase_configs = copy.deepcopy(PHASE_CONFIGS) phase_configs.update(custom_phase_configs) chain_configs = copy.deepcopy(CHAIN_CONFIGS) chain_configs.update(custom_chain_configs) agent_configs = copy.deepcopy(AGETN_CONFIGS) agent_configs.update(custom_role_configs) # phase_configs = load_phase_configs(new_phase_configs) # chian_configs = load_chain_configs(new_chain_configs) # agent_configs = load_role_configs(new_agent_configs) return phase_configs, chain_configs, agent_configs