在这个例子中，我们将学习如何使用mistralai/Mixtral-8x7B-Instruct-v0.1和Embedchain与Streamlit一起构建一个简单的RAG聊天机器人。
设置
安装Embedchain和Streamlit。
pip install embedchain streamlit
app.py
import os
from embedchain import App
import streamlit as st
with st.sidebar:
huggingface_access_token = st.text_input("Hugging face Token", key="chatbot_api_key", type="password")
"[获取Hugging Face访问令牌](https://huggingface.co/settings/tokens)"
"[查看源代码](https://github.com/embedchain/examples/mistral-streamlit)"
st.title("💬 聊天机器人")
st.caption("🚀 一个由Mistral提供支持的Embedchain应用！")
if "messages" not in st.session_state:
st.session_state.messages = [
{
"role": "assistant",
"content": """
嗨！我是一个聊天机器人。我可以回答问题并学习新事物！\n
问我任何事情，如果你想让我学习新东西，请输入`/add <source>`。\n
我几乎可以学习一切。:)
""",
}
]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("问我任何事情！"):
if not st.session_state.chatbot_api_key:
st.error("请输入你的Hugging Face访问令牌")
st.stop()
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = st.session_state.chatbot_api_key
app = App.from_config(config_path="config.yaml")
if prompt.startswith("/add"):
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({
"role": "user", "content": prompt})
prompt = prompt.replace("/add", "").strip()
with st.chat_message("assistant"):
message_placeholder = st.empty()
message_placeholder.markdown("添加到知识库中...")
app.add(prompt)
message_placeholder.markdown(f"已将{prompt}添加到知识库！")
st.session_state.messages.append({
"role": "assistant", "content": f"已将{prompt}添加到知识库！"})
st.stop()
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({
"role": "user", "content": prompt})
with st.chat_message("assistant"):
msg_placeholder = st.empty()
msg_placeholder.markdown("思考中...")
full_response = ""
for response in app.chat(prompt):
msg_placeholder.empty()
full_response += response
msg_placeholder.markdown(full_response)
st.session_state.messages.append({
"role": "assistant", "content": full_response})
config.yaml
app:
config:
name: 'mistral-streamlit-app'
llm:
provider: huggingface
config:
model: 'mistralai/Mixtral-8x7B-Instruct-v0.1'
temperature: 0.1
max_tokens: 250
top_p: 0.1
stream: true
embedder:
provider: huggingface
config:
model: 'sentence-transformers/all-mpnet-base-v2'
在本地运行
streamlit run app.py
