Building a Full-Stack FAQ RAG Pipeline (Go, Pinecone, Ollama)

#llm #rag #pinecone #fullstack

If you have been experimenting with AI, you probably know that building a Retrieval-Augmented Generation (RAG) pipeline in a Jupyter Notebook is relatively straightforward. But taking that concept and transforming it into a production-ready, full-stack application? That introduces a whole new set of architectural challenges.

I recently built a fully localized, high-performance FAQ RAG Pipeline from scratch to solve this exact gap. In this post, I am sharing the architecture, the tech stack, and the full video breakdown.

The Tech Stack
To ensure high performance, low latency, and zero API costs for inference, I moved away from the standard Python/OpenAI stack and utilized the following tools:

Go (Golang): The backend engine. Go is perfect for this because of its blazing-fast API performance, strong typing, and excellent concurrency model.
Pinecone: The vector database. It handles the storage of our text embeddings and performs highly efficient semantic similarity searches to retrieve the right context.
Ollama: The local inference engine. By running our LLMs locally with Ollama, we ensure complete data privacy and keep our operational costs at absolutely zero.

How the Architecture Works
The application flow solves the core problem of getting an LLM to answer questions strictly based on a provided set of FAQs, preventing hallucinations:

Context Injection: The FAQ data is chunked, converted into vector embeddings, and securely stored inside Pinecone.
User Query: A user submits a question via the frontend, which hits our Go API backend.
Semantic Search: The Go backend embeds the user's question and queries Pinecone to retrieve the most relevant FAQ context.
Local Generation: The retrieved context and the user's original prompt are piped into Ollama, which generates a precise, conversational answer.

Watch the Full Build & Get the Code
If you want to see exactly how these pieces connect—from setting up the vector database logic to writing the Go API endpoints—I recorded a complete full-stack tutorial.