BGE vs PageIndex
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A toolkit of open embedding and reranking models for search and retrieval-augmented generation (RAG).
Open source · MIT
Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search.
The short answer
Choose BGE if you need…
- Developers building semantic search over their own documents
- Teams adding retrieval-augmented generation (RAG) to an LLM app
- Researchers comparing or fine-tuning embedding and reranker models
- Multilingual projects that need one embedding model for many languages
Choose PageIndex if you need…
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
Side-by-side
| BGE | PageIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.3k | 40k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 10 days ago | 8 days ago |
What each one is
BGE
FlagEmbedding (the BGE project) provides text embedding models that turn words and passages into numeric vectors, so software can find text with similar meaning. It also includes reranker models that re-order search results, and tools for fine-tuning these models. People use it to build semantic search and to supply relevant documents to large language models in RAG systems.
PageIndex
PageIndex is a Python library that builds a hierarchical tree index of a document, similar to a table of contents, and then uses an LLM to reason through that tree to find the relevant sections. It is aimed at long, complex professional documents such as financial reports, legal filings, and manuals, where the README says vector similarity search often returns the wrong passages. It does not use a vector database or split documents into chunks.
Best for
BGE
- Developers building semantic search over their own documents
- Teams adding retrieval-augmented generation (RAG) to an LLM app
- Researchers comparing or fine-tuning embedding and reranker models
- Multilingual projects that need one embedding model for many languages
PageIndex
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
Strengths
BGE
- MIT licensed, so the code can be used commercially according to the README
- Broad set of models, including multilingual and multimodal (BGE-VL) variants
- Documentation and tutorials are maintained alongside the code
PageIndex
- The README describes retrieval with explicit, traceable references rather than opaque similarity scores
- No vector database or chunking step is required, according to the README
- Released under the MIT license with a published PyPI package
Not ideal for
BGE
- People who want a finished search app without writing code, since this is a toolkit of models and scripts
- Projects that cannot run machine-learning models locally or pay for hosted inference, since the models need compute
- Anyone who needs guaranteed stable APIs, as the README lists many research projects and frequent new releases
PageIndex
- People who need a fast, very cheap search over huge collections, since the README says each query relies on LLM reasoning over the tree and cost depends on the chosen model
- Users who want a no-code tool; the README centers on a Python SDK and API
- Short or simple documents, where the README's positioning suggests ordinary retrieval is enough
Trade-offs
BGE
- Many components are research projects with varying levels of documentation
- Larger models need substantial GPU or memory resources
- The README is long and spread across many sub-projects, which takes time to navigate
PageIndex
- Each query calls an LLM, so answer cost and speed depend on the model you choose; the README reports benchmark figures but your results will vary
- The benchmarks and cost numbers are published by the project itself and rely on its own test setup
- The README is truncated and does not fully document limits or edge cases
Getting started
BGE
Clone the repository
git clone https://github.com/FlagOpen/FlagEmbeddingFrequently asked questions
What is the difference between BGE and PageIndex?
BGE: A toolkit of open embedding and reranking models for search and retrieval-augmented generation (RAG). PageIndex: Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search. BGE suits developers building semantic search over their own documents, while PageIndex suits developers building question-answering over long pdfs such as annual reports or manuals.
Are BGE and PageIndex free?
BGE: Free and open source. PageIndex: Free and open source.
Is BGE or PageIndex more popular?
PageIndex has more GitHub stars (40k) than BGE (12.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to BGE and PageIndex?
Other options people compare them with include LlamaIndex, LightRAG, LEANN, turbovec, LangChain.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.