Building a Self-Hosted RAG Pipeline: From Document to Answer

A RAG pipeline (retrieval-augmented generation) is how you make a language model answer from your own documents (contracts, policies, manuals, a knowledge base) rather than from the general knowledge it was trained on. The idea is that a model alone does not know your company's data, and stuffing all of it into the question is impossible because it is larger than the context window. The solution is to first search your documents for the passages relevant to the question, then pass them to the model along with the question so it composes the answer from them. This runs through five steps: chunking documents into small pieces, converting each piece into a numeric vector (embedding) that represents its meaning, storing the vectors in a vector database, retrieving the closest pieces to the user's question at query time, then generating the answer with a language model grounded in those pieces. The major benefit of self-hosting is that your sensitive data never leaves your server, so it is not sent to any external AI service that might retain or process it outside the Kingdom. In practice, a medium knowledge base (thousands of pages) runs smoothly on a server with 8 to 16GB of RAM combining a quantized model through Ollama and a vector database like pgvector. The practical rule is to start small with a fully open stack on a single cloud server, and test answer quality on real questions before scaling. This guide explains the five RAG components in detail, why self-hosting protects your data, a practical ready stack, and the right server size for each stage.

The Five Components of a RAG Pipeline

Understanding the five steps shows where quality is built and where it is wasted. Let us go through them in order from the raw document to the final answer.

1. Chunking

Each document is split into small, semantically coherent pieces, usually a few hundred words each with slight overlap between them. This step matters more than it seems, because pieces that are too large mix several ideas and weaken retrieval, and pieces that are too small lose context. Good chunking based on logical boundaries (paragraphs, headings) gives far better results than blind splitting by a fixed character count.

2. Embeddings

Each piece is passed through an embedding model that turns it into a list of numbers representing its meaning in a mathematical space, so pieces close in meaning sit close together. This is what enables search by meaning rather than by literal word, so a question like "how do I cancel my subscription" finds a passage about "stopping the service" even if the words differ. Open embedding models run locally through Ollama with no data sent outside.

3. Storing in a Vector Database

The vectors are kept in a database built for fast search between them, like pgvector on top of PostgreSQL or a dedicated database like Qdrant. This database finds the closest vectors to the question's vector in fractions of a second even among millions of pieces, through approximate indexes like HNSW.

4. Retrieval

When a user's question arrives, it is itself turned into a vector, then the closest pieces to it are retrieved from the database (five to ten pieces, for example). The quality of this step determines the quality of the whole answer, because the model will not answer correctly if the right pieces never reach it in the first place.

5. Generation

The retrieved pieces are passed with the question to the language model, with an instruction to answer only from these pieces, so it composes a precise answer grounded in your documents and can cite the source. This greatly reduces hallucination because the model leans on real text rather than its memory.

Why Self-Hosting Protects Your Data

When you use an external AI service to build RAG, you send your documents, possibly the most sensitive ones, to third-party servers outside your control and possibly outside the Kingdom. For a contract, an HR policy, or customer data, this is a real risk to confidentiality and compliance. Self-hosting flips the equation: the model, the vector database, and the documents are all on your server, so not a single byte leaves. This gives you three advantages: full data sovereignty, easier compliance with Saudi data residency requirements, and a fixed cost with no per-call fees. Government bodies and companies with custom systems in particular find this solves a dilemma of wanting to benefit from AI without leaking their secrets. Building it on a server inside Riyadh with around 8ms response time combines privacy and speed. See AI hosting for a quick start.

A Practical Ready Stack

You do not need complex tools to start. A fully open stack is enough: Ollama to run the generation model and the embedding model locally, PostgreSQL with the pgvector extension as the vector database, and an orchestration tool that links the steps like n8n (no code) or a small script in your language. The latter receives documents, chunks them, generates their vectors, and stores them, then at query time retrieves and generates. n8n is an excellent choice for teams that prefer visual wiring, and it is self-hosted too, so everything stays inside your server. The fastest start is to run an AI server on CLOUD with an Open WebUI and Ollama template preinstalled, then add pgvector and the orchestration tool, and reach your first answer from your documents in a short time.

Table: The Right Server Size for Each Stage

Knowledge base size	Model	Suggested RAM	CLOUD plan
Trial (hundreds of pages)	7B quantized	8GB	Business 8GB (11,500 SAR)
Medium (thousands of pages)	7B to 13B quantized	16GB	Enterprise 16GB (18,000 SAR)
Large (tens of thousands)	13B quantized	16 + RAM add-on	Enterprise + 2GB add-on (600 SAR)

Note: the vector database itself is relatively light on memory compared to the model, so the model is usually the biggest RAM consumer. Resource add-ons on CLOUD are instant, so you start with the Business plan and grow to Enterprise as your base grows, with no rebuild. See cloud server and plans and pricing.

Common Mistakes That Weaken Answer Quality

What disappoints results most is not the model but the steps around it. Bad chunking (pieces too large that mix ideas or too small that lose context) ruins retrieval from the start. Retrieving too few pieces starves the model of enough context, and too many distracts it and fills the context window with noise. Neglecting the quality of the embedding model makes semantic search weak so unrelated pieces arrive. Finally, not giving the model an explicit instruction to rely on the pieces only makes it mix your documents with its general knowledge and hallucinate. The practical rule is to test each step on its own with real questions, and improve chunking and retrieval first before blaming the model, since the problem is usually before it, not in it.

A Practical Summary

Start with an open stack on a single cloud server: Ollama for a quantized model and an embedding model, pgvector for storage, and an orchestration tool linking the five steps. Prepare a small knowledge base first and test answer quality on real questions from your team, and improve chunking and retrieval before scaling. Match the server size to your base from the table: Business 8GB for the trial, Enterprise 16GB for the medium base. Most important, everything stays inside your server in Riyadh, so you get an assistant that answers from your company's knowledge alone, respects your data confidentiality, and runs at a fixed cost. Start today with one document and one question, since the first correct answer from your documents proves the idea works, and the rest is gradual expansion.