“`html

How to Build a Smart Document Q&A Agent with OpenAI and LangChain (Tutorial)

1. Setting Up Your Development Environment

• Install Python 3.10+ and create a virtual environment with `venv` or `conda`.
• Install required libraries: `openai`, `langchain`, `chromadb`, `pypdf`, and `python-dotenv`.
• Set up your OpenAI API key securely using a `.env` file and `load_dotenv()`.

2. Preparing the Document – Chunking and Embedding

• Load a PDF or text file using LangChain’s document loaders (e.g., `PyPDFLoader`).
• Split the text into overlapping chunks with `RecursiveCharacterTextSplitter` (chunk size ~500, overlap 50).
• Generate embeddings with OpenAI’s `text-embedding-3-small` and store them in a Chroma vector database.

3. Building the Retrieval Chain with LangChain

• Create a retriever from the vector store that fetches the top‑k most relevant chunks (k=3–5).
• Define a prompt template that instructs the LLM to answer strictly from the retrieved context.
• Assemble the chain: `RetrievalQA` or a custom `LLMChain` with the retriever and GPT‑4‑turbo.

4. Implementing a Simple CLI Interface

• Write a `while True` loop that accepts user queries from the command line.
• Pass each query through the retrieval chain and print the generated answer.
• Add a “quit” command to exit gracefully and handle empty inputs.

5. Enhancing Accuracy – Prompt Engineering & Source Citation

• Modify the prompt to force the model to say “I don’t know” when the answer is not in the context.
• Instruct the model to quote exact sentences and return the source page/chunk ID.
• Test with ambiguous queries and tweak chunk overlap or retrieval k‑value to improve recall.

6. Deploying as a Web App with Streamlit

• Build a minimal Streamlit UI with a text input and a “Ask” button.

<