STORM Retrieval

    Advancing Data Structuring and Chunking Technologies
    With a high-completeness proprietary RAG pipeline, STORM RAG maximizes both performance and quality, while enabling even non-experts to refine the model directly through user feedback.

    KEY POINT

    Self-Improving RAG

    Automates fine-tuning using feedback, enabling continuous improvement in retrieval quality through simple user response evaluations.

    Optimized Retrieval Architecture for Large Document Sets

    Multi-stage filtering efficiently navigates large document collections, delivering precise answers even within complex datasets.

    Improved Accuracy through Hybrid Search

    Combines lexical search for exact keyword matching with semantic search for contextual similarity, ensuring accurate and comprehensive results.

    RAG PERFORMANCE

    By optimizing each engine in the tech stack and designing a high-efficiency vector search logic, we have significantly enhanced the performance and quality of our RAG system

    performance

    1. Compared the search quality of <O Corp. Embedding + Re-ranker> and <STORM Embedding + Re-ranker + Search Logic> using RECALL@K.

    2. Compared the search quality of <O Corp. Embedding> and <STORM Embedding> using RECALL@K.

    3. After retrieving chunks using the <O Corp. embedding model>, compared the re-ranking performance between <O Corp. Re-ranker> and <STORM Re-ranker> using RECALL@K.

    CORE TECHNOLOGY

    Optimized Chunking Strategy

    Optimized Chunking Strategy

    Enhances contextual understanding by preserving document-level flow and leveraging multi-scale chunking.

    • LATE CHUNKING: Maintains global document context
    • MULTI-SCALE CHUNKING: Utilizes chunks of multiple sizes simultaneously
    • CONTEXTUAL CHUNKING: Adds local context to each chunk
    Query Expansion

    Query Expansion

    nterprets the intent behind user questions and enriches the query with synonyms and semantically related expressions to improve retrieval accuracy and coverage.

    • Analyzes diverse cues in the query and explores multiple retrieval paths
    • Automatically expands to include semantically related terms and synonyms
    • Reformulates queries based on prior conversation context
    • Interprets and adjusts for temporal expressions like “last year” or “yesterday”
    Hybrid Search

    Hybrid Search

    Maximizes search accuracy through LEXICAL + SEMANTIC retrieval strategies.

    • Exact keyword matching (LEXICAL)
    • Semantic similarity search (SEMANTIC)
    • Combined and optimized hybrid result ranking
    Post-Processing

    Post-Processing

    Optimizes final results through multi-reranking and chunk clustering.

    • Re-ranks outputs based on semantic similarity
    • Improving LLM response quality by grouping related chunks to enhance context
    • Merges overlapping and adjacent information to generate more refined inputs, enabling high-quality LLM responses