LlamaIndex RAG & Knowledge Systems
Transform enterprise PDFs, relational SQL databases, spreadsheets, and APIs into citation-backed AI reasoning engines using LlamaIndex’s state-of-the-art ingestion and query pipelines.
parser = HierarchicalNodeParser.from_defaults(
chunk_sizes=[2048, 512, 128]
)
retriever = AutoMergingRetriever(
storage_context=storage_context,
similarity_top_k=12
)
reranker = CohereRerank(top_n=4)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
node_postprocessors=[reranker]
)
Beyond Basic Vector Search: Accurate Retrieval
Basic RAG fails on complex tables, dense financial reports, and multi-hop queries. LlamaIndex solves retrieval fragmentation.
Hierarchical & Sentence Window Chunking
Break documents into parent/child hierarchies so smaller semantic chunks are retrieved while parent context is preserved for accurate synthesis.
Knowledge Graph + Vector Fusion
Combine Graph RAG entity relationships with dense embeddings to enable complex multi-hop question answering across enterprise datasets.
Cross-Encoder Re-Ranking
Filter top-k semantic matches through Cohere or BGE re-rankers to discard irrelevant noise and deliver precision context to the LLM.
Supercharge Your Enterprise Knowledge Base
Connect LlamaIndex to your Snowflake, PostgreSQL, Notion, or SharePoint clusters with Webnext’s production engineering team.
