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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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--- |
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<div align="center"> |
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<a href="https://github.com/netease-youdao/QAnything"> |
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<!-- Please provide path to your logo here --> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/SX_dH-aww0WV7Aa7MTdJm.png" alt="Logo" width="800"> |
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</a> |
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<div align="center"> |
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<p align="center" style="font-size: larger;"> |
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<strong>Q</strong>uestion and <strong>A</strong>nswer based on <strong>Anything</strong> |
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</p> |
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<div alin="center"> |
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<p align="center"> |
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<a href="https://github.com/netease-youdao/QAnything">Github</a> | |
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<a href="https://github.com/netease-youdao/QAnything/blob/master/README.md">English</a> | |
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<a href="https://github.com/netease-youdao/QAnything/blob/master/README_zh.md">简体中文</a> |
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</p> |
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</div> |
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<div align="center"> |
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<a href="https://qanything.ai"><img src="https://img.shields.io/badge/try%20online-qanything.ai-purple"></a> |
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<a href="https://read.youdao.com#/home"><img src="https://img.shields.io/badge/try%20online-read.youdao.com-purple"></a> |
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-yellow"></a> |
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<a href="https://github.com/netease-youdao/QAnything/pulls"><img src="https://img.shields.io/badge/PRs-welcome-red"></a> |
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<a href="https://twitter.com/YDopensource"><img src="https://img.shields.io/badge/follow-%40YDOpenSource-1DA1F2?logo=twitter&style={style}"></a> |
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</div> |
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<details close="close"> |
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<summary>Table of Contents</summary> |
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- [What is QAnything](#What-is-QAnything) |
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- [Key features](#Key-features) |
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- [Architecture](#Architecture) |
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- [Getting Started](#getting-started) |
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- [Prerequisites](#prerequisites) |
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- [Installation](#installation) |
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- [Usage](#usage) |
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- [API Document](#API-Document) |
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- [WeChat Group](#WeChat-Group) |
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- [Support](#support) |
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- [License](#license) |
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- [Acknowledgements](#Acknowledgments) |
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</details> |
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## GitHub |
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[👉To QAnything GitHub](https://github.com/netease-youdao/QAnything) |
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## What is QAnything? |
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**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. |
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With `QAnything`, you can simply drop any locally stored file of any format and receive accurate, fast, and reliable answers. |
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Currently supported formats include: **PDF, Word (doc/docx), PPT, Markdown, Eml, TXT, Images (jpg, png, etc.), Web links** and more formats coming soon… |
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### Key features |
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- **Data Security**, supports installation and usage with network cable unplugged throughout the process. |
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- **Cross-language QA support**, freely switch between Chinese and English QA, regardless of the language of the document. |
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- **Supports massive data QA**, two-stage retrieval ranking, solving the degradation problem of large-scale data retrieval; the more data, the better the performance. |
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- **High-performance production-grade system**, directly deployable for enterprise applications. |
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- **User-friendly**, no need for cumbersome configurations, one-click installation and deployment, ready to use. |
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- **Multi knowledge base QA** Support selecting multiple knowledge bases for Q&A |
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### Architecture |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/KDeEwzOcI_EUd1-k860k9.png" width = "700" alt="qanything_system" align=center /> |
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</div> |
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#### Why 2 stage retrieval? |
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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**. |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/lmXhf9sM3q0o7Y-BZsqOW.jpeg" width = "500" alt="two stage retrievaal" align=center /> |
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</div> |
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</br> |
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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 |
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- **A high performence on <a href="https://github.com/netease-youdao/BCEmbedding/tree/master?tab=readme-ov-file#evaluate-semantic-representation-by-mteb" target="_Self">Semantic Representation Evaluations in MTEB</a>**; |
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- **A new benchmark in the realm of <a href="https://github.com/netease-youdao/BCEmbedding/tree/master?tab=readme-ov-file#evaluate-rag-by-llamaindex" target="_Self">RAG Evaluations in LlamaIndex</a>**. |
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#### 1st Retrieval（embedding） |
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| Model | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | Avg | |
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|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
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| bge-base-en-v1.5 | 37.14 | 55.06 | 75.45 | 59.73 | 43.05 | 37.74 | 47.20 | |
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| bge-base-zh-v1.5 | 47.60 | 63.72 | 77.40 | 63.38 | 54.85 | 32.56 | 53.60 | |
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| bge-large-en-v1.5 | 37.15 | 54.09 | 75.00 | 59.24 | 42.68 | 37.32 | 46.82 | |
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| bge-large-zh-v1.5 | 47.54 | 64.73 | **79.14** | 64.19 | 55.88 | 33.26 | 54.21 | |
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| jina-embeddings-v2-base-en | 31.58 | 54.28 | 74.84 | 58.42 | 41.16 | 34.67 | 44.29 | |
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| m3e-base | 46.29 | 63.93 | 71.84 | 64.08 | 52.38 | 37.84 | 53.54 | |
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| m3e-large | 34.85 | 59.74 | 67.69 | 60.07 | 48.99 | 31.62 | 46.78 | |
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| ***bce-embedding-base_v1*** | **57.60** | **65.73** | 74.96 | **69.00** | **57.29** | **38.95** | ***59.43*** | |
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- More evaluation details please check [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md)。 |
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#### 2nd Retrieval（rerank） |
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| Model | Reranking | Avg | |
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|:-------------------------------|:--------:|:--------:| |
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| bge-reranker-base | 57.78 | 57.78 | |
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| bge-reranker-large | 59.69 | 59.69 | |
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| ***bce-reranker-base_v1*** | **60.06** | ***60.06*** | |
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- More evaluation details please check [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md) |
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#### RAG Evaluations in LlamaIndex（embedding and rerank） |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/vs-dxcGpjzVQileTd1JOO.jpeg" width = "800" alt="rag evaluation in llamaindex" align=center /> |
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</div> |
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***NOTE:*** |
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- In `WithoutReranker` setting, our `bce-embedding-base_v1` outperforms all the other embedding models. |
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- With fixing the embedding model, our `bce-reranker-base_v1` achieves the best performence. |
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- **The combination of `bce-embedding-base_v1` and `bce-reranker-base_v1` is SOTA**. |
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- If you want to use embedding and rerank separately, please refer to [BCEmbedding](https://github.com/netease-youdao/BCEmbedding) |
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#### LLM |
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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. |
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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) |
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## Getting Started |
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[👉try QAnything online](https://qanything.ai) |
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### Prerequisites |
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| **Required item** | **Minimum Requirement** | **Note** | |
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| -------------- | ------------------------- | --------------------------------- | |
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| NVIDIA GPU Memory | >= 16GB | NVIDIA 3090 recommended | |
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| NVIDIA Driver Version | >= 525.105.17 | | |
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| CUDA Version | >= 12.0 | | |
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| docker compose version| >=1.27.4 | [docker compose install](https://docs.docker.com/compose/install/)| |
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### Installation |
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#### step1: pull qanything repository |
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``` |
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git clone https://github.com/netease-youdao/QAnything.git |
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``` |
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#### step2: download the model and unzip it to the root directory of the current project. |
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``` |
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cd QAnything |
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git lfs install |
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git clone https://www.modelscope.cn/netease-youdao/qanything_models.git |
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unzip qanything_models/models.zip # in root directory of the current project |
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``` |
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#### step3: change config |
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in the Windows system |
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``` |
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vim docker-compose-windows.yaml # change CUDA_VISIBLE_DEVICES to your gpu device id |
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``` |
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in the Linux system |
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``` |
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vim docker-compose-linux.yaml # change CUDA_VISIBLE_DEVICES to your gpu device id |
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``` |
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#### step4: start server |
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in the Windows system |
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``` |
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docker-compose -f docker-compose-windows.yaml up -d |
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``` |
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in the Linux system |
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``` |
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docker-compose -f docker-compose-linux.yaml up -d |
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``` |
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After successful installation, you can experience the application by entering the following addresses in your web browser. |
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- Frontend address: http://{your_host}:5052/qanything |
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- API address: http://{your_host}:5052/api/ |
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For detailed API documentation, please refer to [QAnything API 文档](docs/API.md) |
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## Usage |
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### Cross-lingual: Multiple English paper Q&A |
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<div align="center"> |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/Iz-Tp5DCb1cpwjGZKei05.mp4" width = "800" alt="multi English paper" align=center></video> |
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</div> |
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### Information extraction |
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<div align="center"> |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/KBXAReIo_fYyV9hsQXCm_.mp4" width = "800" alt="information extraction" align=center></video> |
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</div> |
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### Various files |
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<div align="center"> |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/KoX1g-lWJEtZpiD2ItOnw.mp4" width = "800" alt="various files" align=center></video> |
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</div> |
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### Web Q&A |
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<div align="center"> |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/_Uijrg055LlChJykjnC37.mp4" width = "800" alt="web qa" align=center></video> |
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</div> |
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### API Document |
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If you need to access the API, please refer to the [QAnything API documentation](docs/API.md). |
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## WeChat Group |
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Welcome to scan the QR code below and join the WeChat group. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63aa90473453852ef5458cd3/7rfhUkkgwIJwSu903ZZvo.jpeg" width="20%" height="auto"> |
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## Support |
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Reach out to the maintainer at one of the following places: |
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- [Github issues](https://github.com/netease-youdao/QAnything/issues) |
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- Contact options listed on [this GitHub profile](https://github.com/netease-youdao) |
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## License |
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`QAnything` is licensed under [Apache 2.0 License](./LICENSE) |
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## Acknowledgments |
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`QAnything` adopts dependencies from the following: |
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- Thanks to our [BCEmbedding](https://github.com/netease-youdao/BCEmbedding) for the excellent embedding and rerank model. |
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- Thanks to [Qwen](https://github.com/QwenLM/Qwen) for strong base language models. |
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- Thanks to [Triton Inference Server](https://github.com/triton-inference-server/server) for providing great open source inference serving. |
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- Thanks to [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) for highly optimized LLM inference backend. |
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- Thanks to [Langchain](https://github.com/langchain-ai/langchain) for the wonderful llm application framework. |
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- Thanks to [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) for the inspiration provided on local knowledge base Q&A. |
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- Thanks to [Milvus](https://github.com/milvus-io/milvus) for the excellent semantic search library. |
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- Thanks to [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for its ease-to-use OCR library. |
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- Thanks to [Sanic](https://github.com/sanic-org/sanic) for the powerful web service framework. |
