AnythingLLM – Open source full-stack AI client that supports local deployment and API integration

What is AnythingLLM

AnythingLLM is an open source, free and full-stack AI client that supports multimodal interaction. AnythingLLM supports a variety of input methods such as text, images, and audio, converting any document or content into context for use in conversations by various language models (LLM). AnythingLLM supports local operation and remote deployment, providing multi-user management, workspace isolation, rich document format support, and powerful API integration. All data is stored locally by default, ensuring privacy and security. AnythingLLM supports a variety of popular LLM and vector databases, suitable for individual users, developers and businesses.

The main functions of AnythingLLM

• Multimodal interaction: Supports various input methods such as text, images and audio, providing a richer interactive experience.
• Document processing and context management: Divide documents into independent “workspaces”, supports multiple formats (such as PDF, TXT, DOCX, etc.), maintains context isolation, and ensures clarity of conversations.
• Multi-user support and permission management: The Docker version supports multi-user instances, and administrators can control user permissions, which is suitable for team collaboration.
• AI Agent and Tool Integration: Supports running AI agents in the workspace, performing tasks such as web browsing and code running, and expanding application functions.
• Local deployment and privacy protection: By default, all data (including models, documents, and chat history) is stored locally to ensure privacy and data security.
• Strong API support: Provides a complete developer API to facilitate users to develop and integrate customly.
• Cloud deployment is ready: Supports a variety of cloud platforms (such as AWS, GCP, etc.), which facilitates users to deploy remotely according to their needs.

AnythingLLM’s project address

The technical principles of anythingLLM

• front end: Built with ViteJS and React, it provides a simple and easy-to-use user interface, and supports drag and drop uploading functions such as documents.
• rear end: Based on NodeJS and Express, it is responsible for handling user interaction, document analysis, vector database management and communication with LLM.
• Document processing: Based on the NodeJS server parsing and processing uploaded documents, converting them into vector embedding, and storing them in a vector database.
• Vector database: Use vector databases such as LanceDB to convert document content into vector embedding, which facilitates quick retrieval of relevant contexts in conversations.
• LLM Integration: Supports a variety of open source and commercial LLMs (such as OpenAI, Hugging Face, etc.), and users choose appropriate models according to their needs.
• AI Agent: Run an AI agent in the workspace. The agent can perform various tasks (such as web browsing, code execution, etc.) and expand the functions of the application.

AnythingLLM supports models and databases

• Large Language Models (LLMs): Supports a variety of open source and closed source models, such as OpenAI, Google Gemini Pro, Hugging Face, etc.
• Embed Model: Supports AnythingLLM native embedded devices, OpenAI, etc.
• Voice to text and text to pronunciation: Supports multiple voice models, including OpenAI and ElevenLabs.
• Vector database: Support LanceDB, Pinecone, Chroma, etc.

AnythingLLM usage and deployment

• Desktop version：
  • System requirements：
    • operating system: Supports Windows, MacOS and Linux.
    • Hardware requirements: It is recommended to have at least 8GB of memory, 16GB or higher is recommended.
  • Download and install: Visit AnythingLLM Official website. Select the corresponding installation package according to the operating system.
  • Installer：
    • Windows: Double-click the installer and follow the prompts to complete the installation.
    • MacOS: Double-click the DMG file and drag the application into the Applications folder.
    • Linux: Install DEB or RPM files based on the package manager.
  • Start the application: After the installation is complete, open the AnythingLLM application.
  • Initialize settings：
    • Select a model: When you first start, select a language model (LLM).
    • Configure vector database: Select the default vector database (such as LanceDB) or configure other supported databases.
  • Create a workspace: Click “New Workspace” to create a separate workspace for the project or document. Upload documents (such as PDF, TXT, DOCX, etc.), apply automatically parses and generates vector embeddings, and store them in the vector database.
  • Start a conversation：
    • Enter a question or command in the workspace, and the application generates intelligent answers based on the uploaded document content.
    • Supports multimodal interaction, uploading pictures or audio files, and the application processes them according to the content.
• Docker version：
  • System requirements:
    • operating system: Supports Linux, Windows (WSL2) and MacOS.
    • Hardware requirements: It is recommended to have at least 8GB of memory, 16GB or higher is recommended.
    • Docker environment: Docker and Docker Compose need to be installed.
  • Deployment steps：
    • Access the GitHub repository: Go to AnythingLLM GitHub repository。
    • Cloning the repository：

git clone https://github.com/Mintplex-Labs/anything-llm.git
cd anything-llm

• • Configure environment variables：

    • Run the following command in the project root directory to generate .env document:

• • Access the app: Open the browser, visit http://localhost:3000, and enter the web interface of AnythingLLM.
  • How to use：
    • Create a workspace: Similar to the desktop version, create a workspace and upload documents.
    • Multi-user management: Docker version supports multi-user login and permission management, and administrators set user permissions in the background.
    • Embedded chat widget: Docker version supports the generation of embedded chat widgets and supports embedding into websites.
  • Advanced features：
    • Custom integration: Extend application functions based on API and plug-ins.
    • Cloud platform deployment: Support deployment on cloud platforms such as AWS, GCP, Digital Ocean, etc.

AnythingLLM application scenarios

• Internal knowledge management and Q&A: The enterprise uploads internal documents (such as knowledge base, manuals, project documents, etc.) to the workspace of AnythingLLM. Employees use dialogue to quickly query and obtain relevant information to improve work efficiency.
• Academic research and literature compilation: Researchers upload a large number of academic documents, papers, etc. to the workspace to quickly extract key information, summarize views, and assist in research work.
• Personal study and material sorting: Students or individual learners import learning materials (such as e-books, notes, etc.), review and consolidate knowledge in dialogue form, and improve learning efficiency.
• Content creation: Content creators obtain creative inspiration, polish text or generate outlines to assist in the creative process.
• Multilingual document translation and understanding: Users upload multilingual documents to quickly obtain translated versions or key information of document content, breaking the language barrier.

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