Search & RAG Use Cases

Build production search and retrieval systems with hybrid search, vector embeddings, and semantic retrieval using Beluga AI’s RAG pipeline. These use cases demonstrate Beluga AI’s default hybrid search approach (Vector + BM25 + RRF fusion), advanced retrieval strategies (CRAG, HyDE, multi-strategy), and the composable RAG pipeline with pluggable loaders, splitters, embedders, and vector stores.

Use Case	Description
Enterprise RAG Knowledge Base	Build a production RAG pipeline with hybrid search, multi-source ingestion, and semantic retrieval.
Semantic Search and Recommendations	Build intelligent search and recommendation engines with vector embeddings and similarity search.
Cross-lingual Document Retrieval	Search documents in any language and retrieve relevant results across all languages.
Internal Search Everything Bot	Build a unified search bot that queries across all internal systems using REST and MCP APIs.
RAG for Large Code Repositories	Build efficient code search with code-aware text splitting that respects function boundaries.
Enterprise Knowledge Q&A	Build scalable knowledge search with semantic retrieval across millions of documents.
RAG Strategies	A practical comparison of RAG retrieval strategies to select the best approach.
Regulatory Policy Search	Hybrid semantic search for regulatory policy discovery with zero compliance violations.
Scientific Paper Processing	Process scientific papers with academic-aware text splitting preserving equations and citations.
Recommendation Engine	Build semantic product recommendations with vector similarity search.
Cloud Document Sync	Maintain real-time RAG knowledge bases by syncing documents from cloud storage.
Semantic Image Search	Build intelligent image search with natural language queries using multimodal embeddings.