How to build an internal knowledge base using private RAG

A "private RAG" system is the same retrieval-augmented-generation pattern used by any RAG app, with one constraint: the documents, the embedding model, and ideally the LLM itself never leave your infrastructure. That constraint mostly affects which components you choose (local embedding models and vector stores instead of hosted APIs) — the retrieval architecture itself is standard: hybrid search plus reranking beats plain vector search on most real document sets.

Step-by-Step Implementation

1. Parse Input Documents: Extract text records and split them into chunks using smart recursive division rules.

2. Execute Index Queries: Search dense and sparse tables to collect candidate matches.

3. Apply Reranking Model: Sort results using a cross-encoder to select the most relevant chunks for the context window.

# Implementation of a hybrid retrieval and reranking loop
def hybrid_retrieve(query, lexical_db, vector_db, top_n=10):
    # 1. Retrieve lexical keyword hits (e.g. BM25)
    lexical_hits = lexical_db.search(query, limit=top_n)
    # 2. Retrieve dense vector hits
    vector_hits = vector_db.search(query, limit=top_n)
    
    # 3. Combine results and remove duplicates
    combined = {c.id: c for c in (lexical_hits + vector_hits)}.values()
    
    # 4. Rerank matches based on contextual similarity
    # Mock cross-encoder ranking scores
    ranked = sorted(combined, key=lambda x: x.score, reverse=True)
    return ranked[:3]

The reranker step is what makes this "outstanding" rather than "moderate" in practice — vector search alone is good at finding chunks that are topically similar, but a cross-encoder reranker actually reads the query and each candidate chunk together, which is why it catches relevance that pure cosine-distance search misses.