059b6cee6d
1、修复服务器ip配置，配置页没替换问题； 2、修复开启状态偶尔没对齐问题； 3、修复关闭时关闭按钮停留在关闭中问题； 4、修复星座读取错误问题； 5、修复刷新重复提醒开启问题； 6、新增是否进行语音合成的选择； 7、文字沟通接口加入“观察描述”； 8、聊天记录时间改为毫秒级； 9、补充数字人和远程音频的连接状态显示； 10、修复备注填写无法保存问题。
98 lines
3.9 KiB
Python
98 lines
3.9 KiB
Python
import hashlib
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import os
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from langchain.document_loaders import PyPDFLoader
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.indexes.vectorstore import VectorstoreIndexCreator, VectorStoreIndexWrapper
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from langchain.vectorstores.chroma import Chroma
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from langchain.chat_models import ChatOpenAI
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from utils import config_util as cfg
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from utils import util
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index_name = "knowledge_data"
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folder_path = "llm/langchain/knowledge_base"
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local_persist_path = "llm/langchain"
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md5_file_path = os.path.join(local_persist_path, "pdf_md5.txt")
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def generate_file_md5(file_path):
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hasher = hashlib.md5()
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with open(file_path, 'rb') as afile:
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buf = afile.read()
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hasher.update(buf)
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return hasher.hexdigest()
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def load_md5_list():
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if os.path.exists(md5_file_path):
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with open(md5_file_path, 'r') as file:
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return {line.split(",")[0]: line.split(",")[1].strip() for line in file}
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return {}
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def update_md5_list(file_name, md5_value):
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md5_list = load_md5_list()
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md5_list[file_name] = md5_value
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with open(md5_file_path, 'w') as file:
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for name, md5 in md5_list.items():
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file.write(f"{name},{md5}\n")
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def load_all_pdfs(folder_path):
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md5_list = load_md5_list()
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for file_name in os.listdir(folder_path):
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if file_name.endswith(".pdf"):
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file_path = os.path.join(folder_path, file_name)
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file_md5 = generate_file_md5(file_path)
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if file_name not in md5_list or md5_list[file_name] != file_md5:
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util.log(1, f"正在加载 {file_name} 到索引...")
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load_pdf_and_save_to_index(file_path, index_name)
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update_md5_list(file_name, file_md5)
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def get_index_path(index_name):
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return os.path.join(local_persist_path, index_name)
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def load_pdf_and_save_to_index(file_path, index_name):
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try:
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loader = PyPDFLoader(file_path)
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embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
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index = VectorstoreIndexCreator(embedding=embedding, vectorstore_kwargs={"persist_directory": get_index_path(index_name)}).from_loaders([loader])
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index.vectorstore.persist()
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except Exception as e:
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util.log(1, f"加载 {file_path} 失败...")
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print(e)
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def load_index(index_name):
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index_path = get_index_path(index_name)
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embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
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vectordb = Chroma(persist_directory=index_path, embedding_function=embedding)
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return VectorStoreIndexWrapper(vectorstore=vectordb)
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def save_all():
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os.environ['OPENAI_API_KEY'] = cfg.key_gpt_api_key
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os.environ['OPENAI_API_BASE'] = cfg.gpt_base_url
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if cfg.proxy_config != None:
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os.environ["OPENAI_PROXY"] = cfg.proxy_config
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load_all_pdfs(folder_path)
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def generate_prompt(question):
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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。"
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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。"
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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。"
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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。"
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info = f"{context}\n{instruction}\n{creative_instruction}\n{complexity_handling}\n问题：{question}\n回答："
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return info
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def question(cont, uid=0, observation=""):
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try:
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save_all()
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info = generate_prompt(cont)
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index = load_index(index_name)
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llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
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ans = index.query(info, llm, chain_type="map_reduce")
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return ans
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except Exception as e:
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util.log(1, f"请求失败: {e}")
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return "抱歉，我现在太忙了，休息一会，请稍后再试。"
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