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Project: RAG Document Q&A System

RAG over company docs: chunking, embedding, retrieval, re-ranking, anchored answers.

Şükrü Yusuf KAYA
14 min read
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RAG pipeline: chunk → embed → retrieve → rerank → answer

RAG'ın 6 Adımı

  1. Ingest: PDF, Markdown, HTML toplanır.
  2. Chunk: 300-1000 token parçalar (overlap 10-20%).
  3. Embed: Embedding modeli ile vektör.
  4. Index: Vector DB.
  5. Retrieve: Sorgu için top-K.
  6. Rerank + answer: Cross-encoder ile yeniden sırala, Claude'la cevapla.
RAG akışı: ingest → chunk → embed → index → retrieve → rerank → answer
RAG pipeline'ı.
python
# Yüksek seviye RAG fonksiyonu
def answer_with_rag(question: str, k: int = 5):
    q_emb = embed(question)
    candidates = vector_db.search(q_emb, top_k=50)
    top = rerank(question, candidates)[:k]
    context = "\n\n".join(f"[{c.id}] {c.text}" for c in top)
    return claude_answer(question, context, citations_required=True)
RAG yüksek seviye — production'da chunk metadata + filter + caching ekle.
Boşluk doldur · text
RAG'ın 6 adımı: ingest, _____ , embed, index, _____ ve rerank+answer. Top-K daraltma için _____ kullanılır. Cevabın her cümlesi _____ ile bağlanır.

Frequently Asked Questions

RAG wins at scale and cost. 1M context isn't practical per request. Hybrid is common: small docs in context, big ones via RAG.

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Connected pillar topics

Pillar topics this article maps to

Pillar Topic

RAG (Retrieval-Augmented Generation) Architecture

RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.