Building a Production RAG Pipeline

End-to-end RAG — ingest documents, build vector index, query with retrieval, and evaluate quality

Building a Production RAG Pipeline

The Architecture

Documents -> [Ingestion Pipeline] -> Vector DB
              |                          |
           Chunking                  Indexing
           Embedding
                                       |
                                [Query Pipeline]
                                       |
                            User Query -> Embed -> Search -> LLM -> Response

Ingestion Pipeline

python

def 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}
        )

Key Metrics

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

Your Turn!

Design a monitoring dashboard for your RAG system. What metrics would you track?

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📤 Output

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