supavecOpen-source RAG-as-a-service (the Carbon.ai alternative): upload any data source, get vector search and a chat API in minutes — Supabase-based, multi-tenant with RLS, streaming responses.
Why switchBoth answer questions over your documents; supavec is vector-DB RAG-as-a-service (embed + similarity search), PageIndex is vectorless — a reasoning tree index with no chunking or embeddings.
Full comparison → LlamaIndexData framework for connecting custom data sources to LLMs — ingestion, indexing, retrieval.
Why switchBoth index documents for LLM retrieval; llamaindex is the general vector/index data framework, PageIndex is a reasoning-tree approach purpose-built for long documents.
Full comparison → contextgemDeclarative LLM extraction from documents: describe Aspects and Concepts in plain language, get structured values back with paragraph- or sentence-level references and built-in justifications.
Why switchBoth reject chunk-and-embed for long documents, then diverge on what replaces it. PageIndex builds a hierarchical tree the model navigates to reach the right section; ContextGem skips retrieval entirely and sends the whole document into a long context window. Navigate versus extract in place.
Full comparison → turbovecRust vector index with Python bindings built on Google's TurboQuant: no training step, online ingest, hand-written SIMD kernels — 10M x 1536 vectors in ~4 GB, with allowlist-filtered search.
Why switchOpposite bets on retrieval. PageIndex removes vectors entirely and has the model reason over a tree index; turbovec makes vectors cheap enough that keeping them is the easy call. Choose by whether your corpus rewards similarity or structure.
Full comparison → ChromaOpen-source embedding database for building AI apps with retrieval.
Why switchPageIndex is explicitly 'no vector DB' — it replaces embedding-similarity retrieval (chroma's job) with LLM reasoning over a document tree.
Full comparison →