Initial commit

app.py

@@ -1,83 +1,93 @@
 import streamlit as st
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index 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
 
-# Create an instance of CallbackManager
+# Callback manager
 callback_manager = CallbackManager()
 
-api_base_url =  "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
+# API 信息
+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"
+api_key = "eyJ0eXBlIjoiSldUIiwiYWxnIjoiSFM1MTIifQ.eyJqdGkiOiI4MDAwNzM0OCIsInJvbCI6IlJPTEVfUkVHSVNURVIiLCJpc3MiOiJPcGVuWExhYiIsImlhdCI6MTczNzczMjUwMCwiY2xpZW50SWQiOiJlYm1ydm9kNnlvMG5semFlazF5cCIsInBob25lIjoiMTk4MjEyMTUyNzEiLCJ1dWlkIjoiZjc2MDM3NTctMTU2Yy00MTM3LWE1YmEtNTk3MDljODRiNDRkIiwiZW1haWwiOiIiLCJleHAiOjE3NTMyODQ1MDB9.4zD9ixAv1JdP_AJLQhNW3tCgzCGquW6eFcbV0XNqmqCZ0pL5A4hIPVA0zeFleg-n04O1IsyIZZ0rmkATZ1V6_A"  # 替换为你的真实 API Key
 
-# 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)
-
-
-
-st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")
-st.title("llama_index_demo")
+# Streamlit 页面配置
+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():
-    embed_model = HuggingFaceEmbedding(
-        model_name="/root/model/sentence-transformer"
-    )
-    Settings.embed_model = embed_model
+    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
 
-    #用初始化llm
-    Settings.llm = llm
+        # 初始化 LLM
+        Settings.llm = llm
 
-    documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
-    index = VectorStoreIndex.from_documents(documents)
-    query_engine = index.as_query_engine()
+        # 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)
 
-    return 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
 
 # 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):
-    response = st.session_state['query_engine'].query(question)
-    return response
+    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？"}]
 
-      
-# 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？"}]    
+# 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？"}]
 
-    # Display or clear chat messages
+# 显示聊天记录
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.write(message["content"])
 
-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？"}]
-
+# 清空聊天记录按钮
 st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
 
-# Function for generating LLaMA2 response
-def generate_llama_index_response(prompt_input):
-    return greet2(prompt_input)
-
-# User-provided prompt
-if prompt := st.chat_input():
+# 用户输入处理
+if prompt := st.chat_input("请输入您的问题："):
     st.session_state.messages.append({"role": "user", "content": prompt})
     with st.chat_message("user"):
         st.write(prompt)
 
-# Gegenerate_llama_index_response last message is not from assistant
-if st.session_state.messages[-1]["role"] != "assistant":
+    # Assistant 响应
     with st.chat_message("assistant"):
-        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)
+        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。")