Knowledge Base

Brand-Aware Content Generator with RAG

A RAG content platform that embeds your company's own documents into Qdrant, retrieves the most relevant context by semantic search, and feeds it to an LLM so every generated social post or article is grounded in your real messaging.

What This Builds

This recipe builds a content generator that writes in your company’s voice instead of generic AI prose. Users upload company documents (links, DOCX, TXT, MD); the backend chunks them, embeds them, and stores the vectors in Qdrant. When someone asks for a social post, article, or demo concept, the system runs a semantic search over Qdrant, retrieves the most relevant context, and passes it to the LLM as grounding. The result is on-brand content backed by retrieval rather than hallucination.

The Stack

  • Qdrant Cloud stores the document embeddings and powers semantic similarity search to fetch relevant context at generation time. The free tier covers a starter project.
  • Nebius | Token Factory (formerly AI Studio) serves both the embedding model and the generation model with high throughput.
  • Llama 3.3 70B Instruct generates the final content from the retrieved context.
  • Qwen3 Embedding turns document chunks and queries into vectors.

Step-by-Step Outline

  1. Get a Nebius AI Studio API key and a Qdrant Cloud API key.
  2. Build a document service that ingests links/DOCX/TXT/MD and splits them into manageable chunks.
  3. Embed each chunk with the Qwen3-Embedding model via the AI Studio API.
  4. Upsert the vectors into a Qdrant collection so they are searchable.
  5. On a content request, embed the query, run a semantic search in Qdrant, and pull the top-matching chunks as context.
  6. Pass the retrieved context to Llama-3.3-70B-Instruct to generate the social post, article, or demo concept.
  7. Track generations and performance in a dashboard (history + analytics) so the team can compare outputs over time.

Why This Shape Works

Brand-aware content is fundamentally a retrieval problem, not a prompting trick. By making the company’s own documents the knowledge base and grounding every generation in semantically retrieved chunks, the model stays anchored to real messaging instead of inventing claims. Qdrant keeps retrieval fast and scalable as the corpus grows, and the same index can later serve other tools (assistants, search) as a shared memory.

Source

Nebius, “Building a RAG-powered content generation platform with Nebius AI and Qdrant”: https://nebius.com/blog/posts/building-a-rag-powered-content-generation-platform