--- license: apache-2.0 metrics: - accuracy ---

Question and Answer based on Anything

Table of Contents

- [What is QAnything](#What-is-QAnything) - [Key features](#Key-features) - [Architecture](#Architecture) - [Getting Started](#getting-started) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Usage](#usage) - [API Document](#API-Document) - [WeChat Group](#WeChat-Group) - [Support](#support) - [License](#license) - [Acknowledgements](#Acknowledgments)

## GitHub [👉This document is not updated in time, it is recommended to follow this link to go to QAnything GitHub to learn more](https://github.com/netease-youdao/QAnything) ## What is QAnything? **Q**uestion and **A**nswer 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

#### 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**.

QAnything uses the retrieval component [BCEmbedding](https://github.com/netease-youdao/BCEmbedding), which is distinguished for its bilingual and crosslingual proficiency. BCEmbedding excels in bridging Chinese and English linguistic gaps, which achieves - **A high performence on Semantic Representation Evaluations in MTEB**; - **A new benchmark in the realm of RAG Evaluations in LlamaIndex**. #### 1st Retrieval（embedding） | Model | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | bge-base-en-v1.5 | 37.14 | 55.06 | 75.45 | 59.73 | 43.05 | 37.74 | 47.20 | | bge-base-zh-v1.5 | 47.60 | 63.72 | 77.40 | 63.38 | 54.85 | 32.56 | 53.60 | | bge-large-en-v1.5 | 37.15 | 54.09 | 75.00 | 59.24 | 42.68 | 37.32 | 46.82 | | bge-large-zh-v1.5 | 47.54 | 64.73 | **79.14** | 64.19 | 55.88 | 33.26 | 54.21 | | jina-embeddings-v2-base-en | 31.58 | 54.28 | 74.84 | 58.42 | 41.16 | 34.67 | 44.29 | | m3e-base | 46.29 | 63.93 | 71.84 | 64.08 | 52.38 | 37.84 | 53.54 | | m3e-large | 34.85 | 59.74 | 67.69 | 60.07 | 48.99 | 31.62 | 46.78 | | ***bce-embedding-base_v1*** | **57.60** | **65.73** | 74.96 | **69.00** | **57.29** | **38.95** | ***59.43*** | - More evaluation details please check [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md)。 #### 2nd Retrieval（rerank） | Model | Reranking | Avg | |:-------------------------------|:--------:|:--------:| | bge-reranker-base | 57.78 | 57.78 | | bge-reranker-large | 59.69 | 59.69 | | ***bce-reranker-base_v1*** | **60.06** | ***60.06*** | - More evaluation details please check [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md) #### RAG Evaluations in LlamaIndex（embedding and rerank）

***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](https://github.com/netease-youdao/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](https://github.com/QwenLM/Qwen) ## Getting Started [👉try QAnything online](https://qanything.ai) ### Prerequisites | **Required item** | **Minimum Requirement** | **Note** | | -------------- | ------------------------- | --------------------------------- | | NVIDIA GPU Memory | >= 16GB | NVIDIA 3090 recommended | | NVIDIA Driver Version | >= 525.105.17 | | | CUDA Version | >= 12.0 | | | docker compose version| >=1.27.4 | [docker compose install](https://docs.docker.com/compose/install/)| ### Installation Please refer to: https://github.com/netease-youdao/QAnything?tab=readme-ov-file#installation ## Usage ### Cross-lingual: Multiple English paper Q&A

### Information extraction

### Various files

### Web Q&A

### API Document If you need to access the API, please refer to the [QAnything API documentation](docs/API.md). ## WeChat Group Welcome to scan the QR code below and join the WeChat group.

## Support Reach out to the maintainer at one of the following places: - [Github issues](https://github.com/netease-youdao/QAnything/issues) - Contact options listed on [this GitHub profile](https://github.com/netease-youdao) ## License `QAnything` is licensed under [Apache 2.0 License](./LICENSE) ## Acknowledgments `QAnything` adopts dependencies from the following: - Thanks to our [BCEmbedding](https://github.com/netease-youdao/BCEmbedding) for the excellent embedding and rerank model. - Thanks to [Qwen](https://github.com/QwenLM/Qwen) for strong base language models. - Thanks to [Triton Inference Server](https://github.com/triton-inference-server/server) for providing great open source inference serving. - Thanks to [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) for highly optimized LLM inference backend. - Thanks to [Langchain](https://github.com/langchain-ai/langchain) for the wonderful llm application framework. - Thanks to [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) for the inspiration provided on local knowledge base Q&A. - Thanks to [Milvus](https://github.com/milvus-io/milvus) for the excellent semantic search library. - Thanks to [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for its ease-to-use OCR library. - Thanks to [Sanic](https://github.com/sanic-org/sanic) for the powerful web service framework.