RAG Knowledge Pipeline & Vector Store

SyncCrawl processes raw web data to generate RAG (Retrieval-Augmented Generation) knowledge bases for enterprise generative AI applications.

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End-to-End Knowledge Pipeline

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Core Processing Stages

1. Boilerplate Stripping & Structural Extraction

Raw webpages contain headers, footers, ad scripts, and navigation elements that can dilute retrieval accuracy.

• Density-Based Text Extraction: Identifies primary content using DOM text-density analysis.
• Metadata Preservation: Enriches each chunk with canonical URL, crawl timestamp, document title, and category taxonomy for verified citations.

2. Context-Preserving Semantic Chunking

• Structural Boundary Detection: Splits content along headings (<h1>–<h6>), paragraphs (<p>), tables (<table>), and list tags.
• Sliding Window Overlap: Maintains token overlaps (e.g., 100–200 tokens) between adjacent chunks to preserve context across boundaries.

3. Enterprise Embedding Models

Standard integration with LangChain4j allows modular selection of embedding models:

Category	Recommended Models	Environment
On-Premise (Air-Gapped)	BGE-M3, KoSimCSE, Snowflake-Arctic	Isolated on-premise GPU environments
Cloud API	OpenAI text-embedding-3-large, Cohere v3	Global multilingual applications
Edge / CPU	all-MiniLM-L6-v2, ONNX Runtime	Resource-constrained edge environments

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Supported Vector Databases

SyncCrawl provides drivers for standard vector databases:

// LangChain4j Vector DB Configuration (Spring Boot Bean)
@Bean
public EmbeddingStore<TextSegment> embeddingStore(PgVectorProperties properties) {
    return PgVectorEmbeddingStore.builder()
            .host(properties.getHost())
            .port(properties.getPort())
            .database(properties.getDatabase())
            .user(properties.getUsername())
            .password(properties.getPassword())
            .table("synccrawl_knowledge_vectors")
            .dimension(1024) // BGE-M3 standard
            .build();
}

• PostgreSQL (pgvector): Unified relational and vector storage without requiring a dedicated vector database.
• Milvus / Qdrant: Distributed clustering capable of managing large vector indices with fast search.
• Elasticsearch / OpenSearch: Hybrid search combining BM25 keyword matching with dense vector embeddings.

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Synchronization & Lifecycle Governance

• Incremental Change Sync: Recalculates embeddings when page content hash changes.
• TTL-Based Expiration: Purges outdated items from vector stores based on configurable TTL policies.
• Domain Trust Weighting: Configures search weighting between official sources and general web targets.