v0.0.1

Author: Boomm-shaka-laka

.env

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+GROQ_API_KEY='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'

.gitgnore

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+.env

ChatBot.py

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+import streamlit as st
+from dotenv import find_dotenv, load_dotenv
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+models = {
+    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
+    "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
+    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
+    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
+    "mixtral-8x7b-32768": {"name": "Mixtral-8x7b-Instruct-v0.1", "tokens": 32768, "developer": "Mistral"},
+}
+
+def load_model(question,model_option):
+    load_dotenv(find_dotenv())  # Load the .env file.
+    chat = ChatGroq(temperature=0, model_name=model_option)
+    system = "You are a helpful assistant."
+    human = "{text}"
+    prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
+    chain = prompt | chat
+    result=chain.invoke({"text":question })
+    return result.content
+
+if __name__ == "__main__":
+    #页面绘制
+    st.header("ChatBot-AI机器人", divider="rainbow", anchor=False)
+    # st.caption("🚀 A streamlit website based by GroqCloud")
+
+    #初始化模型选择
+    if "selected_model" not in st.session_state:
+        st.session_state.selected_model = None
+
+    # 初始化聊天记录
+    if "messages" not in st.session_state:#初始化聊天记录
+        st.session_state["messages"] = list()
+
+    with st.sidebar:
+        #模型选择
+        model_option = st.selectbox(
+        "Choose a model:",
+        options=list(models.keys()),
+        format_func=lambda x: models[x]["name"],
+        index=0  # Default to mixtral
+        )
+        #清除聊天记录
+        st.button("Clear Chat History", on_click=lambda: st.session_state.pop("messages", None))
+        st.text("Gemma适合中文语境")
+        st.text("LLaMA2-70b性能均衡")
+        st.text("LLaMA3-70b精准度高")
+        st.text("LLaMA3-8b速度快")
+        st.text("Mixtral速度快，适合长文本")
+    
+    st.chat_message("assistant").write("你好，我是基于大模型的AI对话机器人，请输入你想询问的问题")
+    # if "messages" not in st.session_state:
+        # st.session_state["messages"] = [
+        #     {"role": "assistant", "content": "你好，我是基于大模型的AI对话机器人，请输入你想询问的问题"}
+        # ]
+    #显示对话过程
+    for msg in st.session_state.messages:
+        st.chat_message(msg["role"]).write(msg["content"])
+
+    #输入问题
+    question = st.chat_input()
+    if question:
+    # print(question)
+        st.session_state.messages.append({"role": "user", "content": question})
+        st.chat_message("user").write(question)
+        response = load_model(question,model_option)
+        st.session_state.messages.append({"role": "assistant", "content": response})
+        st.chat_message("assistant").write(response)
+
+    
+
+
+  

README.md

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+### 基于LLM大模型的AI机器人
+一套基于开源框架、平台的AI语言模型机器人，集成人机对话，信息检索生成，PDF和URL解析对话等功能。
+
+## 工具框架
+Langchain, Streamlit, Oracle Cloud
+
+## 版本更新
+v0.0.1
+1. 构建Streamlit网页基本框架，集成chatBot对话功能
+

demo.py

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+import streamlit as st
+from typing import Generator
+from groq import Groq
+
+st.set_page_config(page_icon="💬", layout="wide",
+                   page_title="Groq Goes Brrrrrrrr...")
+
+
+def icon(emoji: str):
+    """Shows an emoji as a Notion-style page icon."""
+    st.write(
+        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
+        unsafe_allow_html=True,
+    )
+
+
+icon("🏎️")
+
+st.subheader("Groq Chat Streamlit App", divider="rainbow", anchor=False)
+
+client = Groq(
+    api_key='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',
+)
+
+# Initialize chat history and selected model
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = None
+
+# Define model details
+models = {
+    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
+    "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
+    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
+    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
+    "mixtral-8x7b-32768": {"name": "Mixtral-8x7b-Instruct-v0.1", "tokens": 32768, "developer": "Mistral"},
+}
+
+# Layout for model selection and max_tokens slider
+col1, col2 = st.columns(2)
+
+with col1:
+    model_option = st.selectbox(
+        "Choose a model:",
+        options=list(models.keys()),
+        format_func=lambda x: models[x]["name"],
+        index=4  # Default to mixtral
+    )
+
+# Detect model change and clear chat history if model has changed
+if st.session_state.selected_model != model_option:
+    st.session_state.messages = []
+    st.session_state.selected_model = model_option
+
+max_tokens_range = models[model_option]["tokens"]
+
+with col2:
+    # Adjust max_tokens slider dynamically based on the selected model
+    max_tokens = st.slider(
+        "Max Tokens:",
+        min_value=512,  # Minimum value to allow some flexibility
+        max_value=max_tokens_range,
+        # Default value or max allowed if less
+        value=min(32768, max_tokens_range),
+        step=512,
+        help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {max_tokens_range}"
+    )
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    avatar = '🤖' if message["role"] == "assistant" else '👨‍💻'
+    with st.chat_message(message["role"], avatar=avatar):
+        st.markdown(message["content"])
+
+
+def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
+    """Yield chat response content from the Groq API response."""
+    for chunk in chat_completion:
+        if chunk.choices[0].delta.content:
+            yield chunk.choices[0].delta.content
+
+
+if prompt := st.chat_input("Enter your prompt here..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("user", avatar='👨‍💻'):
+        st.markdown(prompt)
+
+    # Fetch response from Groq API
+    try:
+        chat_completion = client.chat.completions.create(
+            model=model_option,
+            messages=[
+                {
+                    "role": m["role"],
+                    "content": m["content"]
+                }
+                for m in st.session_state.messages
+            ],
+            max_tokens=max_tokens,
+            stream=True
+        )
+
+        # Use the generator function with st.write_stream
+        with st.chat_message("assistant", avatar="🤖"):
+            chat_responses_generator = generate_chat_responses(chat_completion)
+            full_response = st.write_stream(chat_responses_generator)
+    except Exception as e:
+        st.error(e, icon="🚨")
+
+    # Append the full response to session_state.messages
+    if isinstance(full_response, str):
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response})
+    else:
+        # Handle the case where full_response is not a string
+        combined_response = "\n".join(str(item) for item in full_response)
+        st.session_state.messages.append(
+            {"role": "assistant", "content": combined_response})

pages/RetrievalBot.py

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+import streamlit as st
+
+
+st.subheader("基于URL检索-AI机器人", divider="gray", anchor=False)
+# if "messages" not in st.session_state:
+#     st.session_state["messages"] = [{"role": "assistant", "content": ""}]

pages/SummaryBot.py

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+import streamlit as st
+
+
+st.subheader("概要-AI机器人", divider="gray", anchor=False)
+# if "messages" not in st.session_state:
+#     st.session_state["messages"] = [{"role": "assistant", "content": ""}]

pages/pdfBot.py

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+import streamlit as st
+
+
+st.subheader("基于PDF检索-AI机器人", divider="gray", anchor=False)
+# if "messages" not in st.session_state:
+#     st.session_state["messages"] = [{"role": "assistant", "content": ""}]

rag.py

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+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from dotenv import find_dotenv, load_dotenv
+
+
+class Chatbot:
+    def __init__(self):
+        load_dotenv(find_dotenv())  # Load the .env file.
+        self.llm = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+
+
+
+
+
+if __name__ == "__main__":
+    # chatbot = Chatbot()
+    load_dotenv(find_dotenv())  # Load the .env file.
+    chat = ChatGroq(temperature=0, model_name="gemma-7b-it")
+    system = "You are a helpful assistant."
+    human = "{text}"
+    prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
+
+    chain = prompt | chat
+    result=chain.invoke({"text": "你知道铭传大学吗."})
+    print(result)
+    

requirements.txt

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+streamlit
+langchain
+langchain-groq
+python-dotenv