Introduction
In this article, we will be building a GenAi app using the deepseek open-source model, which is capable of generating code and other stuff based on our requirements. In building this app, we will be using open-source tools like Langchain (a framework for AI workflows), Ollama, and DeepSeek-R1. For the code for this article, you can check out my GitHub.
Why are we using DeepSeek-R1, Langchain, and Ollama?
- DeepSeek-R1: A highly capable open-source LLM excelling in reasoning, coding, and creative tasks.
- Langchain: Simplifies chaining AI components (models, prompts, databases) into cohesive workflows.
- Ollama: It lets you run LLMs like DeepSeek-R1, Llama3.3, and Phi-4 locally on your machine, even without a GPU.
Step 1. Download Ollama from the below link
Step 2. Now Download the DeepSeek-R1 Model with Ollama
ollama run deepseek-r1:1.5b
Step 3. Install the below library to run the model
pip install streamlit
pip install langchain_core
pip install langchain_community
pip install langchain_ollama
Now let's start writing code step-wise:
1. LLM Initialization
llm_engine = ChatOllama(
model=selected_model,
base_url="http://localhost:11434",
temperature=0.3,
streaming=True
)
- ChatOllama: A client to interact with the local LLM hosted at localhost:11434.
- selected_model: The LLM you’ve chosen (e.g., DeepSeek).
- temperature=0.3: Controls randomness (0 = deterministic, 1 = creative).
- streaming=True: Enables real-time response streaming.
2. System Prompt Configuration
system_prompt = SystemMessagePromptTemplate.from_template(
"You are an expert AI coding assistant..."
)
- Defines the initial behavior of the AI (concise, correct, and with debugging tips).
- Acts like giving context to the model before the conversation starts.
3. Session State Management
if "message_log" not in st.session_state:
st.session_state.message_log = [{"role": "ai", "content": "Hi! I'm DeepSeek..."}]
- st.session_state: Maintains chat history across interactions.
- The first message is a greeting from the AI if no prior chat exists.
4. Display Chat History
def display_chat_history():
for message in st.session_state.message_log:
with st.chat_message(message["role"]):
st.markdown(message["content"])
- Iterates through all past messages (both user and AI).
- Displays them with appropriate roles (user or AI) using st.chat_message().
5. Prompt Chain Building
def build_prompt_chain():
prompt_sequence = [system_prompt]
for msg in st.session_state.message_log:
if msg["role"] == "user":
prompt_sequence.append(HumanMessagePromptTemplate.from_template(msg["content"]))
elif msg["role"] == "ai":
prompt_sequence.append(AIMessagePromptTemplate.from_template(msg["content"]))
return ChatPromptTemplate.from_messages(prompt_sequence)
- Initialization: The function starts by adding a system_prompt to the prompt sequence to set the AI's behavior.
- Conversation History Processing: It loops through st.session_state.message_log, adding user messages with HumanMessagePromptTemplate and AI responses with AIMessagePromptTemplate.
- Prompt Chain Creation: Finally, it returns a ChatPromptTemplate containing the system prompt and full conversation history for context-aware AI responses.
6. Generating AI Response with Streaming
def generate_response():
prompt_chain = build_prompt_chain()
processing_pipeline = prompt_chain | llm_engine | StrOutputParser()
return processing_pipeline.stream({})
- Chains together: Prompt, LLM, and Output Parser
- stream(): Streams the response chunk-by-chunk, enabling real-time typing effects.
Below is the combined code in below code. I have added streamlit and html&css code to see the the app on the UI.
import streamlit as st
from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import (
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
AIMessagePromptTemplate,
ChatPromptTemplate
)
import warnings
import sys
# Custom CSS styling for the app
st.markdown("""
<style>
/* Existing styles */
.main {
background-color: #1a1a1a;
color: #ffffff;
}
.sidebar .sidebar-content {
background-color: #2d2d2d;
}
.stTextInput textarea {
color: #ffffff !important;
}
/* Add these new styles for select box */
.stSelectbox div[data-baseweb="select"] {
color: white !important;
background-color: #3d3d3d !important;
}
.stSelectbox svg {
fill: white !important;
}
.stSelectbox option {
background-color: #2d2d2d !important;
color: white !important;
}
/* For dropdown menu items */
div[role="listbox"] div {
background-color: #2d2d2d !important;
color: white !important;
}
</style>
""", unsafe_allow_html=True)
st.title("🧠 DeepSeek Code Companion build by Lokendra")
st.caption("🚀 Your AI Pair Programmer with Debugging Superpowers")
# Sidebar configuration
with st.sidebar:
st.header("⚙️ Configuration")
selected_model = st.selectbox(
"Choose Model",
["deepseek-r1:1.5b", "deepseek-r1:3b", "deepseek-r1:7b", "deepseek-r1:8b"],
index=0
)
st.divider()
st.markdown("### Model Capabilities")
st.markdown("""
- 🐍 Code genaration
- 🐞 Debugging Assistant
- 📝 Documentation
""")
st.divider()
st.markdown("### 🔗 Resources")
st.markdown("""
- [Ollama Documentation](https://ollama.ai/)
- [LangChain Guides](https://python.langchain.com/)
- [DeepSeek GitHub](https://github.com/deepseek-ai)
""")
# main code logic starts from hare
# Initialize chat engine with selected model(Deepseek)
llm_engine = ChatOllama(
model=selected_model,
base_url="http://localhost:11434",
temperature=0.3,
streaming=True
)
# System prompt configuration
system_prompt = SystemMessagePromptTemplate.from_template(
"You are an expert AI coding assistant. Provide concise, correct solutions "
"with strategic print statements for debugging. Always respond in English."
)
# Session state management
if "message_log" not in st.session_state:
st.session_state.message_log = [{
"role": "ai",
"content": "Hi! I'm DeepSeek. How can I help you code today? 💻"
}]
# Display chat messages
def display_chat_history():
for message in st.session_state.message_log:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Build the prompt chain
def build_prompt_chain():
prompt_sequence = [system_prompt]
for msg in st.session_state.message_log:
if msg["role"] == "user":
prompt_sequence.append(HumanMessagePromptTemplate.from_template(msg["content"]))
elif msg["role"] == "ai":
prompt_sequence.append(AIMessagePromptTemplate.from_template(msg["content"]))
return ChatPromptTemplate.from_messages(prompt_sequence)
# Generate AI response with streaming
def generate_response():
prompt_chain = build_prompt_chain()
processing_pipeline = prompt_chain | llm_engine | StrOutputParser()
return processing_pipeline.stream({})
# Display chat history
display_chat_history()
# Handle user input
user_query = st.chat_input("Type your coding question here...")
if user_query:
# Add user message to history
st.session_state.message_log.append({"role": "user", "content": user_query})
# Display user message immediately
with st.chat_message("user"):
st.markdown(user_query)
# Create placeholder for assistant response
with st.chat_message("ai"):
response_placeholder = st.empty()
response_placeholder.markdown('<div class="typing-indicator">Generating<span class="dot"></span><span class="dot"></span><span class="dot"></span></div>', unsafe_allow_html=True)
# Generate and stream response
full_response = ""
for chunk in generate_response():
full_response += chunk
response_placeholder.markdown(full_response + "▌")
# Update final response
response_placeholder.markdown(full_response)
# Add assistant response to history
st.session_state.message_log.append({"role": "ai", "content": full_response})
To run the above code, you have to use the below command:
streamlit run app.py
Now you are ready to ask any type of question from your genAi application without using the internet like below:
![Localhost]()
Conclusion
By using DeepSeek-R1, Langchain, and Ollama, we've built a powerful Gen AI application capable of generating intelligent responses. This architecture can be extended to support complex workflows, real-time data processing, and advanced NLP tasks.