Build an AI-Powered Research Assistant: A Step-by-Step Tutorial Using LangChain & Streamlit

1. Setting Up Your Development Environment

• Install Python 3.10+, create a virtual environment, and install core dependencies: langchain, streamlit, openai, tiktoken, and python-dotenv.
• Set up your OpenAI API key securely using a .env file and load it with python-dotenv — never hardcode secrets.
• Verify the setup by writing a quick “Hello World” script that calls the OpenAI Chat Completion API and prints a response.

2. Structuring the Research Assistant Logic

• Define the assistant’s core workflow: accept a user query, search for relevant context (simulated or real), and generate a cited, concise answer.
• Create a modular Python class ResearchAssistant with methods for retrieve_context(), build_prompt(), and generate_answer().
• Implement a simple fallback mechanism: if no context is found, the assistant gracefully informs the user instead of hallucinating.

3. Building the Retrieval Layer with LangChain

• Use langchain.document_loaders to load sample research PDFs or web articles, then split them into chunks with RecursiveCharacterTextSplitter.
• Create a vector store using Chroma or FAISS with OpenAI embeddings — store the chunks and their metadata.
• Implement a retrieve() function that takes the user query, performs a similarity search on the vector store, and returns the top-3 most relevant chunks.

4. Crafting the Prompt Template for Accurate Answers

• Design a system prompt that instructs the assistant to answer strictly from the provided context and cite sources by document name and chunk index.
• Use LangChain’s ChatPromptTemplate to dynamically inject the retrieved context and user question into the message structure.

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