Technical Interview Chatbot

How I Created a Specialized Technical Interview Chatbot

Creating a chatbot for technical interviews might seem complex, but with the right tools, it can be done efficiently and simply. In this article, I'll tell you how I developed a chatbot using LangChain, the Groq API, and Streamlit. Additionally, I deployed it on Streamlit Cloud so anyone could interact with it.

The Objective The purpose of this chatbot is to simulate a technical interviewer. It is designed to:

Ask relevant technical questions based on the user's experience.
Provide feedback on responses.
Continue the conversation with deeper or different questions.

To achieve this, I combined key technologies that allow integrating an advanced language model, managing conversation history, and presenting a user-friendly interface.

Tools and Technologies

LangChain: A framework that facilitates language model integration and message history management.
Groq API: I used Groq's llama-3.1-70b-versatile model, known for handling complex and contextualized conversations.
Streamlit: To create an interactive web interface.
Streamlit Cloud: For easy chatbot deployment.

Code Structure

1. Initial Setup

Libraries were imported and API keys configured from .env or Streamlit secrets to ensure protected, production-ready application..

from dotenv import load_dotenv

load_dotenv()

GROQ_API_KEY = st.secrets.get("GROQ_API_KEY")

Additionally, the initial page was set up with st.set_page_configto customize the title and icon.

2. Conversation Memory

StreamlitChatMessageHistory was used to manage user and model message history, enabling coherent conversation continuation.

msgs = StreamlitChatMessageHistory(key="langchain_messages")

if len(msgs.messages) == 0:

msgs.add_ai_message("Hola soy tu entrevistador hoy, cuéntame un poco sobre tu experiencia con la tecnología")

This allows the chatbot to remember previous messages and continue the conversation coherently.

3. Prompt and Model

The prompt defined the model's behavior as a technical interview expert. The llama-3.1-70b-versatile Groq model was used to ensure advanced, contextualized responses.

prompt = ChatPromptTemplate.from_messages(

[

("system", "Eres un experto en tecnología, y entrevistas técnicas para ingenieros y programadores. Tu misión es ir haciendo preguntas relevantes, ofrecer feedback y continuar con más preguntas."),

MessagesPlaceholder(variable_name="history"),

("human", "{question}"),

]

)

The Groq llama-3.1-70b-versatile model is called using ChatGroq, ensuring advanced and contextualized responses.

4. LangChain Integration

RunnableWithMessageHistory connected message flow with the model, maintaining conversation coherence.

chain_with_history = RunnableWithMessageHistory(

chain,

lambda session_id: msgs,

input_messages_key="question",

history_messages_key="history",

)

5. User Interface

Streamlit presented message history and an input box for new questions.

if prompt := st.chat_input():

st.chat_message("human").write(prompt)

response = chain_with_history.invoke({"question": prompt})

st.chat_message("ai").write(response.content)

A PDF export functionality was added for interview performance analysis..

if export_as_pdf:

pdf = FPDF()

pdf.add_page()

pdf.set_font("Arial", size=12)

for msg in msgs.messages:

pdf.multi_cell(0, 10, f"{msg.type}: {msg.content}")

html = create_download_link(pdf.output(dest="S").encode("latin-1"), "tech-interview")

st.markdown(html, unsafe_allow_html=True)

Deployment

I deployed the application on Streamlit Cloud. This allows anyone to access the chatbot without complex configurations.

Conclusion

With tools like LangChain and the Groq API, creating a specialized chatbot is more accessible than ever. This project is useful for its ability to personalize interviews based on user responses. If you want to build a similar chatbot, this code can be an excellent starting point!

Technical Interview Chatbot

Conclusion

With tools like LangChain and the Groq API, creating a specialized chatbot is more accessible than ever. This project is useful for its ability to personalize interviews based on user responses. If you want to build a similar chatbot, this code can be an excellent starting point!

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