This is a minimal RAG (Retrieval-Augmented Generation) prototype I built to understand how tools like ChromaDB, sentence-transformers, and an LLM work together in a local pipeline.

I’ve used AI tools before, but this was my first time breaking things down at a systems level - no external orchestration frameworks, just raw file parsing, embedding, vector search, and response generation. I wanted a behind-the-scenes look at how RAG works under the hood.

This project also gave me a chance to:

• Practice basic modularity across multiple Python files
• Explore how conversation memory, query rewriting, and LLM prompting fit into a cohesive loop
• Understand why abstraction layers like LangChain exist - not just for convenience, but to handle real architectural complexity that would otherwise get unwieldy at scale

• app.py - Orchestrates communication between the user, retriever, and LLM.
• conversation_memory.py - Maintains conversation history throughout a session (Required since this app uses the OpenAI Chat Completions API not Responses API).
• query_refiner.py - Converts follow-up questions into standalone queries using chat history.
• retrieval.py - Performs semantic search over embedded document chunks in ChromaDB.
• llm_client.py - Initializes the LLM client and formats prompts.
• utils.py - Handles document parsing, chunking, and batching.

I don’t plan to build larger LLM applications entirely from scratch, as doing so would introduce unnecessary complexity. Now that I’ve seen how the parts fit together, I plan to:

• Use frameworks like LangChain or LangGraph to manage multi-step LLM workflows
• Explore agent-style orchestration using state machines or tool calling
• Apply what I’ve learned in more ambitious personal projects, where abstraction is a feature, not a crutch

This project served its purpose: to provide me with a clear mental model of the RAG building blocks, allowing me to move forward with better judgment and more modular design.