How to Build a Custom AI Assistant Using LangChain and GPT‑4

1. Prerequisites and Environment Setup

• Install Python 3.10+, pip, and a virtual environment (venv or conda).
• Set up your OpenAI API key securely using environment variables or a .env file.
• Install core dependencies: langchain, openai, python-dotenv, and streamlit (for UI).

2. Understanding LangChain’s Core Components

• Learn the role of models (LLMs), prompts, chains, and memory – the four pillars of LangChain.
• Explore how prompt templates allow dynamic, reusable instructions for the LLM.
• Understand the difference between simple sequential chains and more complex routing chains.

3. Creating the Assistant’s Core Logic

• Write a chain that takes user input, formats a system prompt, and calls GPT‑4 via LangChain’s ChatOpenAI.
• Add a fallback mechanism to handle API errors or rate limits gracefully.
• Implement a simple “persona” (e.g., a helpful coding tutor) using a system message template.

4. Adding Conversation Memory and Context

• Integrate ConversationBufferMemory to retain chat history across turns.
• Use ConversationSummaryMemory for longer sessions to avoid token limits.
• Test memory persistence by asking follow-up questions that reference earlier answers.

5. Building a Web Interface with Streamlit

• Create a simple chat UI using st.chat_input and st.chat_message components.
• Wire the LangChain chain to the UI and display streaming responses with st.write_stream.

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