base on Chatbot for documentation, that allows you to chat with your data. Privately deployable, provides AI knowledge sharing and integrates knowledge into your AI workflow <h1 align="center"> DocsGPT 🦖 </h1> <p align="center"> <strong>Open-Source Documentation Assistant</strong> </p> <p align="left"> <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers. Say goodbye to time-consuming manual searches, and let <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance. </p> <div align="center"> <a href="https://github.com/arc53/DocsGPT"></a> <a href="https://github.com/arc53/DocsGPT"></a> <a href="https://github.com/arc53/DocsGPT/blob/main/LICENSE"></a> <a href="https://discord.gg/n5BX8dh8rU"></a> <a href="https://twitter.com/docsgptai"></a> </div> ### Production Support / Help for Companies: We're eager to provide personalized assistance when deploying your DocsGPT to a live environment. [Book a Meeting :wave:](https://cal.com/arc53/docsgpt-demo-b2b)⁠ [Send Email :email:](mailto:[email protected]?subject=DocsGPT%20support%2Fsolutions) <img src="https://github.com/user-attachments/assets/9a1f21de-7a15-4e42-9424-70d22ba5a913" alt="video-example-of-docs-gpt" width="1000" height="500"> ## Roadmap You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT! ## Our Open-Source Models Optimized for DocsGPT: | Name | Base Model | Requirements (or similar) | | --------------------------------------------------------------------- | ----------- | ------------------------- | | [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral) | Mistral-7b | 1xA10G gpu | | [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b) | llama-2-14b | 2xA10 gpu's | | [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b | 8xA10G gpu's | If you don't have enough resources to run it, you can use bitsnbytes to quantize. ## End to End AI Framework for Information Retrieval  ## Useful Links - :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/) - :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU) - :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/) - :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md) - :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.cloud/Guides/How-to-train-on-other-documentation) - :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.cloud/Guides/How-to-use-different-LLM) ## Project Structure - Application - Flask app (main application). - Extensions - Chrome extension. - Scripts - Script that creates similarity search index for other libraries. - Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>. ## QuickStart > [!Note] > Make sure you have [Docker](https://docs.docker.com/engine/install/) installed On Mac OS or Linux, write: `./setup.sh` It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API. Otherwise, refer to this Guide for Windows: 1. Download and open this repository with `git clone https://github.com/arc53/DocsGPT.git` 2. Create a `.env` file in your root directory and set the env variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not. It should look like this inside: ``` LLM_NAME=[docsgpt or openai or others] VITE_API_STREAMING=true API_KEY=[if LLM_NAME is openai] ``` See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files. 3. Run [./run-with-docker-compose.sh](https://github.com/arc53/DocsGPT/blob/main/run-with-docker-compose.sh). 4. Navigate to http://localhost:5173/. To stop, just run `Ctrl + C`. ## Development Environments ### Spin up Mongo and Redis For development, only two containers are used from [docker-compose.yaml](https://github.com/arc53/DocsGPT/blob/main/docker-compose.yaml) (by deleting all services except for Redis and Mongo). See file [docker-compose-dev.yaml](./docker-compose-dev.yaml). Run ``` docker compose -f docker-compose-dev.yaml build docker compose -f docker-compose-dev.yaml up -d ``` ### Run the Backend > [!Note] > Make sure you have Python 3.12 installed. 1. Export required environment variables or prepare a `.env` file in the project folder: - Copy [.env-template](https://github.com/arc53/DocsGPT/blob/main/application/.env-template) and create `.env`. (check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.) 2. (optional) Create a Python virtual environment: You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments. a) On Mac OS and Linux ```commandline python -m venv venv . venv/bin/activate ``` b) On Windows ```commandline python -m venv venv venv/Scripts/activate ``` 3. Download embedding model and save it in the `model/` folder: You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder. ```commandline wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip unzip mpnet-base-v2.zip -d model rm mpnet-base-v2.zip ``` 4. Install dependencies for the backend: ```commandline pip install -r application/requirements.txt ``` 5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`. 6. Start worker with `celery -A application.app.celery worker -l INFO`. ### Start Frontend > [!Note] > Make sure you have Node version 16 or higher. 1. Navigate to the [/frontend](https://github.com/arc53/DocsGPT/tree/main/frontend) folder. 2. Install the required packages `husky` and `vite` (ignore if already installed). ```commandline npm install husky -g npm install vite -g ``` 3. Install dependencies by running `npm install --include=dev`. 4. Run the app using `npm run dev`. ## Contributing Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information about how to get involved. We welcome issues, questions, and pull requests. ## Code Of Conduct We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing. ## Many Thanks To Our Contributors⚡ <a href="https://github.com/arc53/DocsGPT/graphs/contributors" alt="View Contributors"> <img src="https://contrib.rocks/image?repo=arc53/DocsGPT" alt="Contributors" /> </a> ## License The source code license is [MIT](https://opensource.org/license/mit/), as described in the [LICENSE](LICENSE) file. <p>This project is supported by:</p> <p> <a href="https://www.digitalocean.com/?utm_medium=opensource&utm_source=DocsGPT"> <img src="https://opensource.nyc3.cdn.digitaloceanspaces.com/attribution/assets/SVG/DO_Logo_horizontal_blue.svg" width="201px"> </a> </p> ", Assign "at most 3 tags" to the expected json: {"id":"2889","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"