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End-to-end RAG — ingest documents, build vector index, query with retrieval, and evaluate quality
Documents -> [Ingestion Pipeline] -> Vector DB
| |
Chunking Indexing
Embedding
|
[Query Pipeline]
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User Query -> Embed -> Search -> LLM -> Responsedef process_document(file_path):
text = extract_text(file_path)
chunks = chunk_document(text, strategy='semantic')
embeddings = embed_chunks(chunks)
for chunk, embedding in zip(chunks, embeddings):
vector_db.upsert(
values=embedding,
metadata={'text': chunk, 'source': file_path}
)| Metric | Target | What It Measures |
|---|---|---|
| Hit Rate | >90% | Does context contain the answer? |
| MRR | >0.8 | Rank of first relevant result |
| Faithfulness | >95% | LLM stays true to context |
Design a monitoring dashboard for your RAG system. What metrics would you track?