Initial commit

app.py

@@ -1,93 +1,88 @@
 import streamlit as st
-from llama_index import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.legacy.callbacks import CallbackManager
 from llama_index.llms.openai_like import OpenAILike
 
-# Callback manager
+import os
+
+
+# 下载模型
+os.system('huggingface-cli download --resume-download sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 --local-dir /root/model/sentence-transformer')
+# Create an instance of CallbackManager
 callback_manager = CallbackManager()
 
-# API 信息
-api_base_url = "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
+api_base_url =  "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
 model = "internlm2.5-latest"
-api_key = "eyJ0eXBlIjoiSldUIiwiYWxnIjoiSFM1MTIifQ.eyJqdGkiOiI4MDAwNzM0OCIsInJvbCI6IlJPTEVfUkVHSVNURVIiLCJpc3MiOiJPcGVuWExhYiIsImlhdCI6MTczNzczMjUwMCwiY2xpZW50SWQiOiJlYm1ydm9kNnlvMG5semFlazF5cCIsInBob25lIjoiMTk4MjEyMTUyNzEiLCJ1dWlkIjoiZjc2MDM3NTctMTU2Yy00MTM3LWE1YmEtNTk3MDljODRiNDRkIiwiZW1haWwiOiIiLCJleHAiOjE3NTMyODQ1MDB9.4zD9ixAv1JdP_AJLQhNW3tCgzCGquW6eFcbV0XNqmqCZ0pL5A4hIPVA0zeFleg-n04O1IsyIZZ0rmkATZ1V6_A"  # 替换为你的真实 API Key
+api_key = "eyJ0eXBlIjoiSldUIiwiYWxnIjoiSFM1MTIifQ.eyJqdGkiOiI4MDAwNzM0OCIsInJvbCI6IlJPTEVfUkVHSVNURVIiLCJpc3MiOiJPcGVuWExhYiIsImlhdCI6MTczNzczMjUwMCwiY2xpZW50SWQiOiJlYm1ydm9kNnlvMG5semFlazF5cCIsInBob25lIjoiMTk4MjEyMTUyNzEiLCJ1dWlkIjoiZjc2MDM3NTctMTU2Yy00MTM3LWE1YmEtNTk3MDljODRiNDRkIiwiZW1haWwiOiIiLCJleHAiOjE3NTMyODQ1MDB9.4zD9ixAv1JdP_AJLQhNW3tCgzCGquW6eFcbV0XNqmqCZ0pL5A4hIPVA0zeFleg-n04O1IsyIZZ0rmkATZ1V6_A"
+
+# api_base_url =  "https://api.siliconflow.cn/v1"
+# model = "internlm/internlm2_5-7b-chat"
+# api_key = "请填写 API Key"
 
-# 初始化 LLM
-llm = OpenAILike(
-    model=model,
-    api_base=api_base_url,
-    api_key=api_key,
-    is_chat_model=True,
-    callback_manager=callback_manager,
-)
+llm =OpenAILike(model=model, api_base=api_base_url, api_key=api_key, is_chat_model=True,callback_manager=callback_manager)
 
-# Streamlit 页面配置
-st.set_page_config(page_title="Llama Index Demo", page_icon="🦜🔗")
-st.title("Llama Index Demo")
+
+
+st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")
+st.title("llama_index_demo")
 
 # 初始化模型
 @st.cache_resource
 def init_models():
-    try:
-        # 使用 Hugging Face Hub 的公开模型
-        embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        Settings.embed_model = embed_model
+    embed_model = HuggingFaceEmbedding(
+        model_name="/root/model/sentence-transformer"
+    )
+    Settings.embed_model = embed_model
 
-        # 初始化 LLM
-        Settings.llm = llm
+    #用初始化llm
+    Settings.llm = llm
 
-        # 加载文档数据（可以替换为你自己的数据路径或逻辑）
-        documents = SimpleDirectoryReader("./data").load_data()
-        index = VectorStoreIndex.from_documents(documents)
+    documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
+    index = VectorStoreIndex.from_documents(documents)
+    query_engine = index.as_query_engine()
 
-        # 创建查询引擎
-        query_engine = index.as_query_engine()
-        return query_engine
-    except Exception as e:
-        st.error(f"模型初始化失败：{e}")
-        return None
+    return query_engine
 
 # 检查是否需要初始化模型
 if 'query_engine' not in st.session_state:
     st.session_state['query_engine'] = init_models()
 
-# 模型查询函数
 def greet2(question):
-    query_engine = st.session_state['query_engine']
-    if query_engine:
-        response = query_engine.query(question)
-        return response.response  # 确保返回的内容是字符串
-    else:
-        return "模型初始化失败，请检查环境配置。"
-
-# 清空聊天记录
-def clear_chat_history():
-    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]
+    response = st.session_state['query_engine'].query(question)
+    return response
 
-# 初始化聊天记录
-if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]
+      
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]    
 
-# 显示聊天记录
+    # Display or clear chat messages
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.write(message["content"])
 
-# 清空聊天记录按钮
+def clear_chat_history():
+    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]
+
 st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
 
-# 用户输入处理
-if prompt := st.chat_input("请输入您的问题："):
+# Function for generating LLaMA2 response
+def generate_llama_index_response(prompt_input):
+    return greet2(prompt_input)
+
+# User-provided prompt
+if prompt := st.chat_input():
     st.session_state.messages.append({"role": "user", "content": prompt})
     with st.chat_message("user"):
         st.write(prompt)
 
-    # Assistant 响应
+# Gegenerate_llama_index_response last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
     with st.chat_message("assistant"):
-        with st.spinner("思考中..."):
-            response = greet2(prompt)  # 调用查询函数
-            if response:
-                st.write(response)
-                st.session_state.messages.append({"role": "assistant", "content": response})
-            else:
-                st.error("未能生成响应，请检查模型状态。")
+        with st.spinner("Thinking..."):
+            response = generate_llama_index_response(prompt)
+            placeholder = st.empty()
+            placeholder.markdown(response)
+    message = {"role": "assistant", "content": response}
+    st.session_state.messages.append(message)