What You’ll Build
In about two hours, you will have an AI knowledge base that ingests your company documents, wikis, and support articles, then answers natural language questions with accurate, cited responses drawn directly from your content. Users ask questions in plain English, and the system returns answers with clickable source references. The entire stack runs on a dedicated GPU server with no data leaving your infrastructure.
Traditional knowledge bases force users to know the right search terms. RAG-powered systems understand intent and retrieve relevant passages even when the question uses different terminology than the source documents. Self-hosting on open-source LLMs means your proprietary documentation stays private while delivering answers that rival commercial AI assistants.
Architecture Overview
The system uses Retrieval-Augmented Generation across three stages: document ingestion and chunking, semantic retrieval via vector search, and answer generation with citation. Documents upload through a web interface or API, get chunked into overlapping passages, and embed into a vector database using a GPU-accelerated embedding model. At query time, LangChain retrieves the top relevant chunks and feeds them to an LLM served by vLLM for answer synthesis.
The ingestion pipeline handles PDFs, Word documents, HTML pages, Markdown files, and scanned documents via OCR processing. A metadata extraction step tags each chunk with source document, page number, section heading, and last-updated timestamp. The answer generation prompt instructs the model to cite specific sources and decline questions that fall outside the indexed knowledge, reducing hallucination to near zero through RAG hosting best practices.
GPU Requirements
| Knowledge Base Size | Recommended GPU | VRAM | Query Latency |
|---|---|---|---|
| Up to 10,000 documents | RTX 5090 | 24 GB | ~1.5 seconds |
| 10,000 – 100,000 documents | RTX 6000 Pro | 40 GB | ~1.8 seconds |
| 100,000+ documents | RTX 6000 Pro 96 GB | 80 GB | ~2.2 seconds |
The embedding model and the generation model both occupy VRAM. A compact embedding model like BGE-large uses approximately 1.3 GB, leaving the rest for the generation model. For large knowledge bases, the vector database runs on CPU and SSD, so document count scales independently of GPU memory. See our self-hosted LLM guide for sizing details.
Step-by-Step Build
Provision your GPU server and deploy vLLM alongside a vector database like Qdrant or Milvus. Install the embedding model for document vectorisation. Build the ingestion pipeline that watches a document directory or accepts uploads through an API endpoint.
# Document ingestion pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
splitter = RecursiveCharacterTextSplitter(
chunk_size=512, chunk_overlap=64,
separators=["\n\n", "\n", ". ", " "]
)
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
# Query with citation
RAG_PROMPT = """Answer the question using ONLY the provided context.
Cite sources as [Source: document_name, page X].
If the answer is not in the context, say "I don't have information on that."
Context:
{retrieved_chunks}
Question: {user_question}
Answer:"""The frontend can be a simple chat interface or a search bar embedded in your existing intranet. Each response includes the answer text, confidence score, and source citations linking back to original documents. Follow our chatbot server guide for building the conversational interface layer.
Performance and Accuracy
On an RTX 6000 Pro, the system answers queries in 1.5-2 seconds including retrieval and generation. Retrieval accuracy depends heavily on chunking strategy and embedding model quality. With optimised chunk sizes of 512 tokens and BGE-large embeddings, relevant passage retrieval hits 92% recall at top-5. The citation requirement in the generation prompt keeps hallucination rates below 3% on internal benchmarks.
Incremental indexing lets you add new documents without re-embedding the entire corpus. A scheduled crawler can automatically re-index updated pages from Confluence, Notion, or SharePoint. For multi-department deployments, namespace isolation ensures each team only searches their own documents through AI chatbot hosting infrastructure.
Deploy Your Knowledge Base
A self-hosted RAG knowledge base replaces expensive enterprise search products while delivering conversational answers instead of ranked document lists. Your data stays on-premises, updates are instant, and there are no per-query fees. Launch on GigaGPU dedicated GPU hosting and make your company knowledge instantly accessible. Explore more use case guides for additional AI build patterns.
