danswer-ai/danswer
Danswer
Danswer is an LLM application framework and RAG engine that provides a self-hosted interface for connecting large language models to private data. It serves as an enterprise AI chat interface and agent orchestrator, enabling the creation of specialized assistants with custom instructions and knowledge bases.
The platform differentiates itself through an observability dashboard for tracking query history and token consumption, as well as a white-labeled interface for customized branding. It includes a multi-step research workflow for producing long-form reports and a sandboxed environment for executing code to analyze data and generate graphs.
Broad capabilities include identity management via single sign-on, role-based access control for securing sensitive data, and hybrid search indexing for retrieving information from internal documents. The system also supports external tool integration, web search, and the generation of images and digital artifacts.
Features
- Hybrid Search Retrievers - Combines vector and keyword search with reranking to retrieve relevant private document context for LLM prompts.
- RAG Implementations - Implements a retrieval-augmented generation architecture that connects private internal documents to LLMs.
- AI Agent Orchestration - Coordinates specialized agents that integrate custom instructions, toolsets, and sandboxed code execution.
- Self-Hosted Chat Interfaces - Provides a deployable, self-hosted web frontend for private interaction with various large language model backends.
- Chat Interfaces - Ships a white-labeled web interface for interacting with LLMs and private datasets through a chat window.
- Contextual Information Retrieval - Provides a system for fetching relevant document chunks to ground AI responses using hybrid search.
- Custom AI Assistant Development - Enables the creation of specialized AI assistants by combining unique instructions with custom knowledge bases and tools.
- Data Connectors - Implements connectors for retrieving and processing data from external services to feed into AI agents.
- LLM Application Frameworks - Serves as a platform for building AI interfaces that integrate LLMs with private data and external tools.
- Private Document Retrieval - Indexes local files using semantic search to provide grounded context for AI responses.
- RAG Context Retrieval - Indexes internal documents to retrieve relevant segments and provide factual context for LLM responses.
- Agent Building Platforms - Provides a platform for building specialized AI assistants with custom instructions and knowledge bases.
- Data Source Connectivity Tools - Includes utilities for establishing connections to external databases and APIs to index private information.
- Identity Authentication - Supports single sign-on (SSO) and session-based authentication for enterprise identity management.
- Role-Based Access Control - Implements role-based access controls to secure sensitive agents and restrict actions based on user levels.
- Role-Based Access Controls - Enforces security policies by mapping user identities to specific datasets and agent permissions.
- AI Agent Dispatchers - Provides an agent orchestration layer that routes requests to specialized assistants based on their defined roles and action sets.
- Deep Research Execution - Executes complex analytical tasks and multi-step synthesis to generate detailed research reports.
- AI-Powered Web Research - Executes iterative web searches and multi-step synthesis to generate detailed research reports with citations.
- AI Security and Governance - Provides a framework for managing user identities and implementing access controls to govern AI usage and data security.
- LLM Observability - Includes a monitoring dashboard to track query history and token consumption across different teams and models.
- Real-Time Web Search Integrations - Allows AI agents to perform live internet searches to ground reasoning with current real-time data.
- Research Step Planners - Orchestrates multi-step research planning and interpretation of web results to generate comprehensive reports.
- Code Execution Sandboxes - Provides isolated environments to securely execute code for data analysis and graph generation.
- AI Usage - Tracks token consumption, query history, and cost per provider and model via interactive charts.
- Branding Customization - Offers a white-labeled interface for customizing brand names, icons, and banners.
- Multi-User AI Chat Interfaces - Ships a multi-user chat portal with white-labeling, authentication, and collaborative conversation sharing.
- Third-Party API Integrations - Integrates third-party applications and tools using standardized authentication and API protocols.
- RAG Frameworks - Enterprise search and Q&A platform connecting to internal documentation.
