Question and Answer based on Anything (QAnything) is a local knowledge base question-answering system designed to support a wide range of file formats and databases, allowing for offline installation and use.
With QAnything, you can simply drop any locally stored file of any format and receive accurate, fast, and reliable answers.
Currently supported formats include: PDF, Word (doc/docx), PPT, Markdown, Eml, TXT, Images (jpg, png, etc.), Web links and more formats coming soon…
Key features
Data Security, supports installation and usage with network cable unplugged throughout the process.
Cross-language QA support, freely switch between Chinese and English QA, regardless of the language of the document.
Supports massive data QA, two-stage retrieval ranking, solving the degradation problem of large-scale data retrieval; the more data, the better the performance.
High-performance production-grade system, directly deployable for enterprise applications.
User-friendly, no need for cumbersome configurations, one-click installation and deployment, ready to use.
Multi knowledge base QA Support selecting multiple knowledge bases for Q&A
Architecture
qanything_system
Why 2 stage retrieval?
In scenarios with a large volume of knowledge base data, the advantages of a two-stage approach are very clear. If only a first-stage embedding retrieval is used, there will be a problem of retrieval degradation as the data volume increases, as indicated by the green line in the following graph. However, after the second-stage reranking, there can be a stable increase in accuracy, the more data, the better the performance.
two stage retrievaal
QAnything uses the retrieval component BCEmbedding, which is distinguished for its bilingual and crosslingual proficiency. BCEmbedding excels in bridging Chinese and English linguistic gaps, which achieves
RAG Evaluations in LlamaIndex（embedding and rerank）
rag evaluation in llamaindex
NOTE:
In WithoutReranker setting, our bce-embedding-base_v1 outperforms all the other embedding models.
With fixing the embedding model, our bce-reranker-base_v1 achieves the best performence.
The combination of bce-embedding-base_v1 and bce-reranker-base_v1 is SOTA.
If you want to use embedding and rerank separately, please refer to BCEmbedding
LLM
The open source version of QAnything is based on QwenLM and has been fine-tuned on a large number of professional question-answering datasets. It greatly enhances the ability of question-answering.
If you need to use it for commercial purposes, please follow the license of QwenLM. For more details, please refer to: QwenLM
