年翻更新

1、qa回复新增在范围内多条匹配随机命中；
2、gpt问答补充当前时间；
3、命中qa的回复，标示为采纳；
4、新增执行python main.py start命令可自启动；
5、优化CLI机制；
6、优化运行成功后的操作提醒；
7、删掉langchain nlp mudule，修复langchain新旧版包兼容问题。

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

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 <msg> \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')
 
     
 

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

llm/nlp_langchain.py

@@ -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。"
-    
-
-

main.py

@@ -77,12 +77,15 @@ def console_listener():
         if args[0] == 'help':
             util.log(1, 'in <msg> \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()

requirements.txt

@@ -27,5 +27,4 @@ psutil
 langchain
 langchain_openai
 langgraph
-langchain-community 
 bs4