Introduction & Project Setup

Welcome to this hands-on LangChain course! We'll build a fully functional AI Interview Coach that can:

• Conduct technical interviews in multiple styles
• Remember the conversation context
• Provide structured feedback and scoring
• Generate questions from job descriptions
• Dynamically adjust difficulty based on performance

What You'll Learn

Project Architecture

interview-coach/
├── main.py              # Entry point
├── chains/
│   ├── interviewer.py   # Interview chain
│   └── evaluator.py     # Feedback chain
├── memory/
│   └── conversation.py  # Memory management
├── rag/
│   ├── loader.py        # Document loaders
│   └── retriever.py     # Vector store
├── agents/
│   └── coach.py         # Main agent
└── data/
    └── job_descriptions/ # Sample JDs

Setup

1. Create Virtual Environment

mkdir interview-coach && cd interview-coach
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

2. Install Dependencies

pip install langchain langchain-openai langchain-community
pip install chromadb tiktoken python-dotenv
pip install pypdf docx2txt  # For document loading

3. Configure API Keys

Create a .env file:

OPENAI_API_KEY=sk-your-key-here
# Optional: For other providers
ANTHROPIC_API_KEY=sk-ant-your-key

4. Verify Installation

# test_setup.py
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI

load_dotenv()

llm = ChatOpenAI(model="gpt-4o-mini")
response = llm.invoke("Say 'Setup complete!' if you can hear me.")
print(response.content)

Run it:

python test_setup.py
# Output: Setup complete!

The Application We're Building

Here's a preview of what the final interview coach looks like:

from interview_coach import InterviewCoach

# Initialize with a job description
coach = InterviewCoach(
    job_description="Senior Python Developer at TechCorp...",
    interview_type="technical",
    difficulty="adaptive"
)

# Start the interview
coach.start()

# Interactive loop
while not coach.is_complete:
    question = coach.ask_question()
    print(f"Interviewer: {question}")

    answer = input("You: ")
    feedback = coach.evaluate_answer(answer)

    print(f"[Score: {feedback.score}/10]")

# Get final report
report = coach.generate_report()
print(report.summary)
print(report.strengths)
print(report.areas_to_improve)

Key LangChain Concepts

Before we start coding, let's understand the core concepts:

Chains

Chains combine prompts, LLMs, and output parsers into reusable pipelines.

prompt | llm | output_parser

Memory

Memory persists information across conversation turns.

ConversationBufferMemory()  # Stores all messages
ConversationSummaryMemory() # Stores summaries

RAG (Retrieval Augmented Generation)

Load documents → Split into chunks → Create embeddings → Store in vector DB → Retrieve relevant chunks → Pass to LLM

Agents

Agents dynamically decide which tools to use based on the input.

agent = create_react_agent(llm, tools, prompt)

Ready?

In the next lesson, we'll create our first interview chain that can ask questions and process responses.

Let's build!