Overview
AnythingLLM is the all-in-one AI application designed to transform any document, resource, or piece of content into context that any LLM can use as reference during chatting. This full-stack application provides a complete private AI platform where users can build custom ChatGPT-style assistants, implement document Q&A systems, and deploy intelligent agents without writing code.
Unlike simple chat interfaces, AnythingLLM delivers a comprehensive workspace environment where organizations can organize documents into separate workspaces, each with isolated contexts and specific AI configurations. This workspace model enables teams to maintain different knowledge bases for various projects, departments, or clients while using a single installation.
The platform excels at RAG (Retrieval-Augmented Generation) implementations, handling the entire pipeline from document ingestion to intelligent retrieval. AnythingLLM supports importing documents from local files, websites, GitHub repositories, YouTube transcripts, and cloud storage services. The built-in document processor extracts text from PDFs, Word documents, PowerPoint presentations, spreadsheets, and various other formats, automatically chunking and embedding content for optimal retrieval.
AnythingLLM provides ultimate flexibility in AI provider selection. Connect to OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Cohere, or use self-hosted options like Ollama, LM Studio, and LocalAI. This multi-provider architecture enables organizations to choose models based on cost, performance, privacy requirements, or specific capabilities for each workspace.
The agent system transforms AnythingLLM into an autonomous assistant capable of executing tasks beyond simple question-answering. Agents can browse websites, search the web, save files, run SQL queries, execute code, and interact with external APIs through a growing library of tools and plugins. This extensibility makes AnythingLLM suitable for complex workflows requiring multi-step reasoning and external integrations.
For enterprises, AnythingLLM offers sophisticated user management with role-based access control, workspace permissions, and activity logging. Administrators control which users access specific workspaces, monitor usage patterns, and enforce security policies across the organization. The platform supports both cloud-hosted and fully on-premise deployments based on privacy and compliance requirements.
Key Features
Multi-Workspace Organization
Create isolated workspaces for different projects or departments. Each workspace maintains separate document collections, AI configurations, and user permissions.
Universal Document Processing
Ingest PDFs, DOCX, PPTX, XLSX, TXT, websites, GitHub repos, YouTube videos. Automatic text extraction, chunking, and vectorization for optimal retrieval.
Flexible LLM Integration
Support for 15+ LLM providers including OpenAI, Claude, Gemini, Cohere, and self-hosted options like Ollama. Switch providers per workspace for cost and performance optimization.
Intelligent Agent System
Deploy autonomous agents with tools for web browsing, file management, SQL queries, code execution, and custom API integrations. Multi-step reasoning for complex tasks.
Enterprise User Management
Role-based access control with workspace-level permissions. User activity tracking, usage analytics, and administrative controls for secure multi-tenant deployments.
Custom Embeddings & Vectorization
Choice of embedding providers including OpenAI, Azure, Cohere, or local models. Bring your own vector database (Pinecone, Weaviate, Chroma, Qdrant, Lance) or use built-in storage.
Common Use Cases
- **Enterprise Knowledge Base**: Centralized platform for company documentation, policies, and procedures with AI-powered Q&A for employees
- **Customer Support Assistant**: Upload product documentation and support articles for AI agents to reference when helping customers
- **Research & Analysis Platform**: Ingest research papers, reports, and data sources for scientific literature review and analysis
- **Legal Document Review**: Process contracts, case law, and legal documents for AI-assisted research and drafting
- **Code Documentation**: Import GitHub repositories for AI-powered code search, documentation generation, and technical Q&A
- **Sales Enablement**: Maintain product information, competitive intelligence, and sales materials for AI-assisted proposal generation
Installation Guide
Install AnythingLLM on Ubuntu VPS using Docker for easiest deployment. Pull official image and configure with environment variables for database connection, storage path, and authentication settings.
For production deployments, use Docker Compose with PostgreSQL for data persistence. Configure volumes for document storage, vector database, and application data. Set up environment variables for JWT secret, server URL, and LLM provider API keys.
Initial setup requires creating admin user through command-line interface or environment variables. Configure workspaces through web UI after first launch. Set default LLM provider and embedding model in system settings.
Connect to self-hosted LLM providers like Ollama by configuring network access between containers. For Ollama on same VPS, use Docker network or host mode to enable communication. Configure API endpoints and authentication credentials for each provider.
Implement reverse proxy with Nginx for SSL termination and custom domain. Enable authentication via built-in user management or integrate with existing identity providers. Configure file upload limits and storage quotas based on expected document volume.
Configuration Tips
AnythingLLM configuration managed through environment variables and admin web interface. Set DATABASE_URL for PostgreSQL connection, STORAGE_DIR for document and vector storage paths, and JWT_SECRET for secure sessions.
Configure LLM providers in workspace settings with API keys, endpoint URLs, and model selection. Set embedding provider and dimension settings for vector search. Choose vector database backend (built-in LanceDB, Pinecone, Weaviate, Chroma, Qdrant) based on scale requirements.
Customize chat behavior with system prompts, temperature settings, and context window sizes per workspace. Configure document processing pipelines with chunk size, overlap, and metadata extraction rules. Set up agent tools and permissions for each workspace.
Best practices include regular database backups, monitoring disk usage for document storage, implementing rate limiting for API calls to external providers, segregating workspaces by security requirements, and enabling audit logging for compliance. Use environment-specific configurations for development and production environments.
Technical Requirements
System Requirements
- මතකය: 4GB
- CPU: 2 cores
- ගබඩාව: 20GB
Dependencies
- ✓ Docker
- ✓ PostgreSQL or SQLite
- ✓ LLM provider API key or Ollama
- ✓ Vector database (built-in or external)
මෙම ලිපිය ශ්රේණිගත කරන්න