Add REDIS session
This commit is contained in:
Samuele E. Locatelli
+69
-29
@@ -1,21 +1,55 @@
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####
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#### Streamlit Streaming using LM Studio as OpenAI Standin
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#### run with `streamlit run app.py`
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# !pip install pypdf langchain langchain_openai
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#### Streamlit Streaming using LM Studio + Redis for multi-user context
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#### run with: streamlit run app.py
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#### Requires: pip install pypdf langchain langchain_openai redis
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import streamlit as st
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import redis
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import json
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import uuid
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_openai import ChatOpenAI
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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# app config
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st.set_page_config(page_title="Egalware Chatbot", page_icon="🤖")
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st.title("Egalware's Streaming Chatbot")
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# ---------------------
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# Redis connection
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# ---------------------
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# Adjust host/port as needed
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r = redis.Redis(host="localhost", port=6379, db=1, decode_responses=True)
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# ---------------------
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# Helpers for session & history
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# ---------------------
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def get_session_id():
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"""Ensure each user gets a unique, persistent session ID."""
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if "session_id" not in st.session_state:
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st.session_state.session_id = str(uuid.uuid4())
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return st.session_state.session_id
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def load_history(session_id):
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"""Retrieve chat history for a given session from Redis."""
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raw = r.get(f"chatbot:history:{session_id}")
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if raw:
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messages = json.loads(raw)
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return [AIMessage(content=m["content"]) if m["type"] == "ai"
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else HumanMessage(content=m["content"])
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for m in messages]
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return [AIMessage(content="Hello, I am EgalWare's current ChatBot. How can I help you? (puoi fare domande in italiano, ma in inglese funziona meglio...)")]
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def save_history(session_id, history):
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"""Save chat history back to Redis."""
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messages = []
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for m in history:
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messages.append({"type": "ai" if isinstance(m, AIMessage) else "human", "content": m.content})
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r.set(f"chatbot:history:{session_id}", json.dumps(messages))
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# Optional TTL so old sessions expire:
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r.expire(f"chatbot:history:{session_id}", 60 * 60 * 24 * 7) # 7 days
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# ---------------------
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# LLM interaction
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# ---------------------
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def get_response(user_query, chat_history):
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template = """
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You are a helpful assistant. Answer the following questions considering the history of the conversation:
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@@ -23,44 +57,50 @@ def get_response(user_query, chat_history):
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User question: {user_question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# Using LM Studio Local Inference Server
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llm = ChatOpenAI(base_url="http://10.74.83.100:1234/v1",api_key="lm-studio", model="qwen/qwen3-4b-2507")
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llm = ChatOpenAI(
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base_url="http://10.74.83.100:1234/v1",
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api_key="lm-studio",
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model="qwen/qwen3-4b-2507"
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)
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chain = prompt | llm | StrOutputParser()
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return chain.stream({
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"chat_history": chat_history,
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"user_question": user_query,
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})
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# session state
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = [
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AIMessage(content="Hello, I am a bot. How can I help you?"),
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]
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# ---------------------
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# Streamlit app setup
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# ---------------------
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st.set_page_config(page_title="Egalware Chatbot", page_icon="🤖")
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st.title("Egalware's Chatbot")
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# conversation
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# Identify user & load history from Redis
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session_id = get_session_id()
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st.session_state.chat_history = load_history(session_id)
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# Render conversation
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for message in st.session_state.chat_history:
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if isinstance(message, AIMessage):
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with st.chat_message("AI"):
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st.write(message.content)
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elif isinstance(message, HumanMessage):
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with st.chat_message("Human"):
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st.write(message.content)
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role = "AI" if isinstance(message, AIMessage) else "Human"
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with st.chat_message(role):
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st.write(message.content)
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# user input
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# Handle new input
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user_query = st.chat_input("Type your message here...")
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if user_query is not None and user_query != "":
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if user_query:
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st.session_state.chat_history.append(HumanMessage(content=user_query))
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with st.chat_message("Human"):
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st.markdown(user_query)
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with st.chat_message("AI"):
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response = st.write_stream(get_response(user_query, st.session_state.chat_history))
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response_text = st.write_stream(get_response(user_query, st.session_state.chat_history))
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st.session_state.chat_history.append(AIMessage(content=response_text))
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# Save updated history to Redis
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save_history(session_id, st.session_state.chat_history)
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st.session_state.chat_history.append(AIMessage(content=response))
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@@ -0,0 +1,66 @@
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####
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#### Streamlit Streaming using LM Studio as OpenAI Standin
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#### run with `streamlit run app.py`
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# !pip install pypdf langchain langchain_openai
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import streamlit as st
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_openai import ChatOpenAI
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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# app config
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st.set_page_config(page_title="Egalware Chatbot", page_icon="🤖")
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st.title("Egalware's Chatbot")
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def get_response(user_query, chat_history):
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template = """
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You are a helpful assistant. Answer the following questions considering the history of the conversation:
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Chat history: {chat_history}
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User question: {user_question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# Using LM Studio Local Inference Server
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llm = ChatOpenAI(base_url="http://10.74.83.100:1234/v1",api_key="lm-studio", model="qwen/qwen3-4b-2507")
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chain = prompt | llm | StrOutputParser()
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return chain.stream({
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"chat_history": chat_history,
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"user_question": user_query,
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})
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# session state
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = [
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AIMessage(content="Hello, I am EgalWare's current ChatBot. How can I help you? (puoi fare domande in italiano, ma in inglese funziona meglio...)"),
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]
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# conversation
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for message in st.session_state.chat_history:
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if isinstance(message, AIMessage):
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with st.chat_message("AI"):
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st.write(message.content)
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elif isinstance(message, HumanMessage):
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with st.chat_message("Human"):
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st.write(message.content)
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# user input
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user_query = st.chat_input("Type your message here...")
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if user_query is not None and user_query != "":
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st.session_state.chat_history.append(HumanMessage(content=user_query))
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with st.chat_message("Human"):
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st.markdown(user_query)
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with st.chat_message("AI"):
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response = st.write_stream(get_response(user_query, st.session_state.chat_history))
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st.session_state.chat_history.append(AIMessage(content=response))
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