diff --git a/core/fay_core.py b/core/fay_core.py index f398351..5d6d1a9 100644 --- a/core/fay_core.py +++ b/core/fay_core.py @@ -26,7 +26,6 @@ from llm import nlp_rasa from llm import nlp_gpt from llm import nlp_lingju from llm import nlp_xingchen -from llm import nlp_langchain from llm import nlp_ollama_api from llm import nlp_coze from llm.agent import fay_agent @@ -59,7 +58,6 @@ modules = { "nlp_rasa": nlp_rasa, "nlp_lingju": nlp_lingju, "nlp_xingchen": nlp_xingchen, - "nlp_langchain": nlp_langchain, "nlp_ollama_api": nlp_ollama_api, "nlp_coze": nlp_coze, "nlp_agent": fay_agent diff --git a/fay_booter.py b/fay_booter.py index e20e0a1..971a7e7 100644 --- a/fay_booter.py +++ b/fay_booter.py @@ -355,7 +355,14 @@ def start(): MyThread(target=start_auto_play_service).start() util.log(1, '服务启动完成!') + util.log(1, 'in \t通过控制台交互') + util.log(1, 'restart \t重启服务') + util.log(1, 'start \t\t启动服务') + util.log(1, 'stop \t\t关闭服务') + util.log(1, 'exit \t\t结束程序') util.log(1, '使用 \'help\' 获取帮助.') + if config_util.start_mode == 'web': + util.log(1, '请通过浏览器访问 http://127.0.0.1:5000/ 管理您的Fay') diff --git a/llm/agent/fay_agent.py b/llm/agent/fay_agent.py index 15f3f13..f81f323 100644 --- a/llm/agent/fay_agent.py +++ b/llm/agent/fay_agent.py @@ -10,7 +10,7 @@ from llm.agent.tools.WebPageRetriever import WebPageRetriever from llm.agent.tools.WebPageScraper import WebPageScraper from llm.agent.tools.ToRemind import ToRemind from langgraph.prebuilt import create_react_agent -from langchain_community.chat_models import ChatOpenAI +from langchain_openai import ChatOpenAI from langgraph.checkpoint.memory import MemorySaver import utils.config_util as cfg from utils import util diff --git a/llm/nlp_langchain.py b/llm/nlp_langchain.py deleted file mode 100644 index 98a9f21..0000000 --- a/llm/nlp_langchain.py +++ /dev/null @@ -1,97 +0,0 @@ -import hashlib -import os - -from langchain_community.document_loaders import PyPDFLoader -from langchain_community.embeddings import OpenAIEmbeddings -from langchain.indexes.vectorstore import VectorstoreIndexCreator, VectorStoreIndexWrapper -from langchain_community.vectorstores import Chroma -from langchain_community.chat_models import ChatOpenAI - -from utils import config_util as cfg -from utils import util - -index_name = "knowledge_data" -folder_path = "llm/langchain/knowledge_base" -local_persist_path = "llm/langchain" -md5_file_path = os.path.join(local_persist_path, "pdf_md5.txt") - -def generate_file_md5(file_path): - hasher = hashlib.md5() - with open(file_path, 'rb') as afile: - buf = afile.read() - hasher.update(buf) - return hasher.hexdigest() - -def load_md5_list(): - if os.path.exists(md5_file_path): - with open(md5_file_path, 'r') as file: - return {line.split(",")[0]: line.split(",")[1].strip() for line in file} - return {} - -def update_md5_list(file_name, md5_value): - md5_list = load_md5_list() - md5_list[file_name] = md5_value - with open(md5_file_path, 'w') as file: - for name, md5 in md5_list.items(): - file.write(f"{name},{md5}\n") - -def load_all_pdfs(folder_path): - md5_list = load_md5_list() - for file_name in os.listdir(folder_path): - if file_name.endswith(".pdf"): - file_path = os.path.join(folder_path, file_name) - file_md5 = generate_file_md5(file_path) - if file_name not in md5_list or md5_list[file_name] != file_md5: - util.log(1, f"正在加载 {file_name} 到索引...") - load_pdf_and_save_to_index(file_path, index_name) - update_md5_list(file_name, file_md5) - -def get_index_path(index_name): - return os.path.join(local_persist_path, index_name) - -def load_pdf_and_save_to_index(file_path, index_name): - try: - loader = PyPDFLoader(file_path) - embedding = OpenAIEmbeddings(model="text-embedding-ada-002") - index = VectorstoreIndexCreator(embedding=embedding, vectorstore_kwargs={"persist_directory": get_index_path(index_name)}).from_loaders([loader]) - index.vectorstore.persist() - except Exception as e: - util.log(1, f"加载 {file_path} 失败...") - print(e) - -def load_index(index_name): - index_path = get_index_path(index_name) - embedding = OpenAIEmbeddings(model="text-embedding-ada-002") - vectordb = Chroma(persist_directory=index_path, embedding_function=embedding) - return VectorStoreIndexWrapper(vectorstore=vectordb) - -def save_all(): - os.environ['OPENAI_API_KEY'] = cfg.key_gpt_api_key - os.environ['OPENAI_API_BASE'] = cfg.gpt_base_url - if cfg.proxy_config != None: - os.environ["OPENAI_PROXY"] = cfg.proxy_config - load_all_pdfs(folder_path) - - -def generate_prompt(question): - context = "你是数字人Fay。你的知识不仅来源于所给到的文本知识，还包括经过深度学习的广泛数据集。" - instruction = "在回答问题时，展现你的智能就像是从你的全面知识库中提取答案，而非依赖于所给到的文本知识来源。" - creative_instruction = "不要在回答中表明'根据所提供的文本信息'，你需要表现得如同这些答案是你独立思考的结果。" - complexity_handling = "当面对复杂问题时，以一种理解深刻且透彻的方式回答，确保答案的深度和广度。" - info = f"{context}\n{instruction}\n{creative_instruction}\n{complexity_handling}\n问题：{question}\n回答：" - return info - -def question(cont, uid=0, observation=""): - try: - save_all() - info = generate_prompt(cont) - index = load_index(index_name) - llm = ChatOpenAI(model="gpt-3.5-turbo-16k") - ans = index.query(info, llm, chain_type="map_reduce") - return ans - except Exception as e: - util.log(1, f"请求失败: {e}") - return "抱歉，我现在太忙了，休息一会，请稍后再试。" - - - diff --git a/main.py b/main.py index ea8971c..4a1bede 100644 --- a/main.py +++ b/main.py @@ -77,12 +77,15 @@ def console_listener(): if args[0] == 'help': util.log(1, 'in \t通过控制台交互') util.log(1, 'restart \t重启服务') + util.log(1, 'start \t\t启动服务') util.log(1, 'stop \t\t关闭服务') util.log(1, 'exit \t\t结束程序') elif args[0] == 'stop' and fay_booter.is_running(): fay_booter.stop() - break + + elif args[0] == 'start' and not fay_booter.is_running(): + fay_booter.start() elif args[0] == 'restart' and fay_booter.is_running(): fay_booter.stop() diff --git a/requirements.txt b/requirements.txt index 41a8e53..4d7b936 100644 --- a/requirements.txt +++ b/requirements.txt @@ -27,5 +27,4 @@ psutil langchain langchain_openai langgraph -langchain-community bs4 \ No newline at end of file