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Advanced Retrieval

📚 Agentic RAG⏱ 10 min⭐ 80 BASE XP⌨ HANDS-ON LAB

Beyond Basic Vector Search

Simple vector search fails when concepts are spread out or use completely different vocabulary. Production systems use Hybrid Search.

  • Dense Search (Embeddings): Matches semantic meaning (e.g., "puppy" matches "dog").
  • Sparse Search (BM25/Keyword): Matches exact keywords (e.g., "CVE-2023-4521").

Reranking

Always fetch more documents than you need (e.g., top 20), then use a dedicated Reranker model (like Cohere Rerank) to resort them. The reranker is much more accurate but too slow to run on millions of documents, so it's used as a second pass.

⌨ HANDS-ON LABRun Hybrid BM25 & Dense Reciprocal Rank Fusion (RRF)
⭐ +150 XP

Merge sparse lexical search (BM25) and dense semantic vector search scores using Reciprocal Rank Fusion (RRF).

1Execute RRF algorithm combining sparse and dense rankings.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
QUERY 1 // 1
What is the benefit of a Reranker in a RAG pipeline?
It generates the final answer
It compresses text into embeddings
It acts as a highly accurate second-pass sorter for documents initially fetched by a vector search
It translates text to other languages