{"id":16955010,"url":"https://github.com/praneethravuri/open-llms","last_synced_at":"2026-02-05T08:32:37.028Z","repository":{"id":240666768,"uuid":"803258338","full_name":"praneethravuri/open-llms","owner":"praneethravuri","description":"A full-stack application that allows you to chat with open-source language models in a ChatGPT-like interface","archived":false,"fork":false,"pushed_at":"2024-12-21T05:40:33.000Z","size":313,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-11T21:40:57.198Z","etag":null,"topics":["ai","axios","chatbot","chatgpt","fastapi","huggingface","llm","llm-aggregator","llms","machine-learning","nextjs","nlp","python","react","tailwindcss","transformers","typescript"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/praneethravuri.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-20T11:36:27.000Z","updated_at":"2024-12-21T05:40:37.000Z","dependencies_parsed_at":"2024-05-21T05:23:46.932Z","dependency_job_id":"127a26f7-c365-48c6-94ba-78f1d864f4e2","html_url":"https://github.com/praneethravuri/open-llms","commit_stats":null,"previous_names":["praneethravuri/llm-aggregator","praneethravuri/open-llms"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/praneethravuri/open-llms","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praneethravuri%2Fopen-llms","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praneethravuri%2Fopen-llms/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praneethravuri%2Fopen-llms/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praneethravuri%2Fopen-llms/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/praneethravuri","download_url":"https://codeload.github.com/praneethravuri/open-llms/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praneethravuri%2Fopen-llms/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29117065,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-05T05:31:32.482Z","status":"ssl_error","status_checked_at":"2026-02-05T05:31:29.075Z","response_time":65,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","axios","chatbot","chatgpt","fastapi","huggingface","llm","llm-aggregator","llms","machine-learning","nextjs","nlp","python","react","tailwindcss","transformers","typescript"],"created_at":"2024-10-13T22:11:06.708Z","updated_at":"2026-02-05T08:32:37.013Z","avatar_url":"https://github.com/praneethravuri.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# OpenLLMs 💬🤖\nA chat application that allows users to interact with pre-trained open-source LLM models for question answering. The application features a chat interface where users can input questions, and the application responds with answers generated by the selected models. The aim of this project is to demonstrate how to integrate pre-trained transformer models with a modern web frontend using Next.js and work with multiple LLMs simultaneously.\n\n![Alt Text](demo.gif)\n\n## Table of Contents\n\n- [Tech Stack](#tech-stack)\n- [Installation](#installation)\n- [Running the Application](#running-the-application)\n- [Usage](#usage)\n\n## Tech Stack\n\n- **Frontend**: Next.js 14, React, TypeScript, Tailwind CSS\n- **Backend**: FastAPI, Python, SearxNG\n- **Machine Learning**: Hugging Face Transformers for LLMs\n- **Other Libraries**: Axios (for HTTP requests), CORS Middleware\n\n## Installation\n\nFollow these steps to set up and run the application on your local machine.\n\n### Prerequisites\n\n- Node.js 14+\n- Python 3.7+\n- Git\n- Docker\n\n### Steps\n\n1. **Clone the Repository**\n\n    ```bash\n    git clone https://github.com/praneethravuri/open-llms.git\n    ```\n\n2. **Backend Setup**\n\n    Navigate to the backend directory, create a virtual environment, and install the required dependencies.\n\n    ```bash\n    cd backend\n    python -m venv venv\n    source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n    pip install -r requirements.txt\n    ```\n\n    If the `requirements.txt` file does not exist, create it with the following content:\n\n    ```txt\n    fastapi\n    uvicorn\n    transformers\n    torch\n    tensorflow\n    sentence_transformers\n    nltk\n    tf-keras\n    language_tool_python\n    textblob\n    pymongo\n    ```\n\n    Additionally, install PyTorch with CUDA support:\n    ```bash\n    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n    ```\n\n3. **Frontend Setup**\n\n    Navigate to the frontend directory and install the required dependencies.\n\n    ```bash\n    cd frontend\n    npm install\n    ```\n\n## Running the Application 🚀\n\n### Backend\n\n1. **Start the FastAPI Server**\n\n    ```bash\n    uvicorn app.main:app --reload --host 0.0.0.0 --port 8000\n    ```\n\n2. **Start SearXNG***\n\n    Navigate to the searxng-docker directory and start SearxNG using Docker.\n\n    ```bash\n    cd searxng-docker\n    docker-compose up\n    ```\n\n\n### Frontend\n\n1. **Start the Next.js Development Server**\n\n    ```bash\n    npm run dev\n    ```\n\n### Access the Application\n\nOpen your browser and navigate to `http://localhost:3000` to see the chat interface.\n\n## Usage\n\n1. **Interact with the Chat Interface**\n\n    - Open the chat interface in your browser.\n    - Type a question in the input box at the bottom.\n    - Press the send button or hit enter to send your question.\n    - The application will respond with an answer generated by the selected model.\n\n2. **Interact with Different LLMs**\n\n    - Select a particular pre-trained LLM.\n    - Type a question in the input box at the bottom.\n    - Press the send button or hit enter to send your question.\n    - The application will respond with an answer generated by the selected model.\n\nBy following these steps, you will be able to interact with various pre-trained language models through a modern and intuitive web interface. 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