Building a RAG Search Engine: Data Cleaning, Chunking, and Embeddings

The author details the process of building Dalil, a RAG search engine for Russian religious documents, highlighting the challenges encountered beyond basic setup. Initial steps involved data cleaning and conversion to JSONL for efficient processing, followed by a structured chunking approach using existing record fields (title/question and answer/footnote) instead of arbitrary splits. For indexing, OpenAI's text-embedding-3-large model was used to generate 3,072-dimensional vectors, stored in Qdrant, with deterministic IDs assigned to each chunk. AI

IMPACT Details the practical challenges and solutions in implementing RAG systems for specialized document search.

RANK_REASON The item describes the technical implementation of a specific tool (a RAG search engine) rather than a new model release or significant industry event.

Read on dev.to — LLM tag →

• Dalil
• GitHub
• OpenAI
• Python
• Qdrant
• retrieval-augmented generation
• text-embedding-3-large

• product
• infra

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