Add application file

README.md

@@ -10,4 +10,18 @@ pinned: false
 short_description: Streamlit+LlamaIndex+浦语API
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 使用前准备
+1. 克隆项目后，需要准备以下数据文件：
+   - 在 `data/` 目录下放入您的知识库文件夹
+   - 在 `nltk_data/` 目录下放入 NLTK 数据文件
+
+2. 数据文件获取方式：
+   - data/: [说明如何获取或准备知识库文件]
+   - nltk_data/: 运行以下命令下载
+    git clone https://gitee.com/yzy0612/nltk_data.git  --branch gh-pages
+    cd nltk_data
+    mv packages/*  ./
+    cd tokenizers
+    unzip punkt.zip
+    cd ../taggers
+    unzip averaged_perceptron_tagger.zip

app.py

@@ -0,0 +1,86 @@
+import os
+
+os.environ['NLTK_DATA'] = './nltk_data'
+
+import streamlit as st
+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
+
+# Create an instance of CallbackManager
+
+callback_manager = CallbackManager()
+
+api_base_url =  "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
+model = "internlm2.5-latest"
+api_key = "eyJ0eXBlIjoiSldUIiwiYWxnIjoiSFM1MTIifQ.eyJqdGkiOiI1MDE3MzQyMiIsInJvbCI6IlJPTEVfUkVHSVNURVIiLCJpc3MiOiJPcGVuWExhYiIsImlhdCI6MTczMTkxNjc5NiwiY2xpZW50SWQiOiJlYm1ydm9kNnlvMG5semFlazF5cCIsInBob25lIjoiMTM2MjA2MTQyMTAiLCJ1dWlkIjoiNGJkYzBjMTktMmU3YS00ODdiLWE3MTYtY2I3YzJlYmIwZjIzIiwiZW1haWwiOiIiLCJleHAiOjE3NDc0Njg3OTZ9.XzwqEiksNVBHmaLpHV5jk-gC8Iwl-itTUvslIxGWJ-sPSdgJhQSsusGH_EeocQiEeEjvsCLgO7IM4yNfq_f9Vg"
+
+
+
+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")
+
+# 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
+
+    #用初始化llm
+    Settings.llm = llm
+
+    documents = SimpleDirectoryReader("./data").load_data()
+    index = VectorStoreIndex.from_documents(documents)
+    query_engine = index.as_query_engine()
+
+    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()
+
+def greet2(question):
+    response = st.session_state['query_engine'].query(question)
+    return response
+
+      
+# 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？"}]    
+
+    # Display or clear chat messages
+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？"}]
+
+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():
+    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":
+    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)