Merge branch 'master' into main

app.py

@@ -0,0 +1,83 @@
+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 = ""
+
+# api_base_url =  "https://api.siliconflow.cn/v1"
+# model = "internlm/internlm2_5-7b-chat"
+# api_key = "请填写 API Key"
+
+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("/root/llamaindex_demo/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)

llamaindex_RAG.py

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+import os 
+os.environ['NLTK_DATA'] = '/root/nltk_data'
+
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from llama_index.core.settings import 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 = ""
+
+# api_base_url =  "https://api.siliconflow.cn/v1"
+# model = "internlm/internlm2_5-7b-chat"
+# api_key = "请填写 API Key"
+
+
+
+llm =OpenAILike(model=model, api_base=api_base_url, api_key=api_key, is_chat_model=True,callback_manager=callback_manager)
+
+
+#初始化一个HuggingFaceEmbedding对象，用于将文本转换为向量表示
+embed_model = HuggingFaceEmbedding(
+#指定了一个预训练的sentence-transformer模型的路径
+    model_name="/root/model/sentence-transformer"
+)
+#将创建的嵌入模型赋值给全局设置的embed_model属性，
+#这样在后续的索引构建过程中就会使用这个模型。
+Settings.embed_model = embed_model
+
+#初始化llm
+Settings.llm = llm
+
+#从指定目录读取所有文档，并加载数据到内存中
+documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
+#创建一个VectorStoreIndex，并使用之前加载的文档来构建索引。
+# 此索引将文档转换为向量，并存储这些向量以便于快速检索。
+index = VectorStoreIndex.from_documents(documents)
+# 创建一个查询引擎，这个引擎可以接收查询并返回相关文档的响应。
+query_engine = index.as_query_engine()
+response = query_engine.query("xtuner是什么?")
+
+print(response)

test_internlm.py

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+from openai import OpenAI
+
+base_url = "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
+api_key = ""
+model="internlm2.5-latest"
+
+# base_url = "https://api.siliconflow.cn/v1"
+# api_key = "sk-请填写准确的 token！"
+# model="internlm/internlm2_5-7b-chat"
+
+client = OpenAI(
+    api_key=api_key , 
+    base_url=base_url,
+)
+
+chat_rsp = client.chat.completions.create(
+    model=model,
+    messages=[{"role": "user", "content": "xtuner是什么？"}],
+)
+
+for choice in chat_rsp.choices:
+    print(choice.message.content)