ByteBot: The all-in-one AI app you were looking for.
Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no fustrating set up required.
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👉 ByteBot for desktop (Mac, Windows, & Linux)! Download Now
A full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.
Watch the demo!
ByteBot is a full-stack application where you can use commercial off-the-shelf LLMs or popular open source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it.
ByteBot divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean.
Some cool features of ByteBot
- Multi-user instance support and permissioning
- Agents inside your workspace (browse the web, run code, etc)
- Custom Embeddable Chat widget for your website
- Multiple document type support (PDF, TXT, DOCX, etc)
- Manage documents in your vector database from a simple UI
- Two chat modes
conversationandquery. Conversation retains previous questions and amendments. Query is simple QA against your documents - In-chat citations
- 100% Cloud deployment ready.
- "Bring your own LLM" model.
- Extremely efficient cost-saving measures for managing very large documents. You'll never pay to embed a massive document or transcript more than once. 90% more cost effective than other document chatbot solutions.
- Full Developer API for custom integrations!
Language Learning Models:
- Any open-source llama.cpp compatible model
- OpenAI
- OpenAI (Generic)
- Azure OpenAI
- Anthropic
- Google Gemini Pro
- Hugging Face (chat models)
- Ollama (chat models)
- LM Studio (all models)
- LocalAi (all models)
- Together AI (chat models)
- Perplexity (chat models)
- OpenRouter (chat models)
- Mistral
- Groq
- Cohere
- KoboldCPP
- LiteLLM
- Text Generation Web UI
Embedder models:
- ByteBot Native Embedder (default)
- OpenAI
- Azure OpenAI
- LocalAi (all)
- Ollama (all)
- LM Studio (all)
- Cohere
Audio Transcription models:
- ByteBot Built-in (default)
- OpenAI
TTS (text-to-speech) support:
- Native Browser Built-in (default)
- OpenAI TTS
- ElevenLabs
STT (speech-to-text) support:
- Native Browser Built-in (default)
Vector Databases:
This monorepo consists of three main sections:
frontend: A viteJS + React frontend that you can run to easily create and manage all your content the LLM can use.server: A NodeJS express server to handle all the interactions and do all the vectorDB management and LLM interactions.collector: NodeJS express server that process and parses documents from the UI.docker: Docker instructions and build process + information for building from source.embed: Code specifically for generation of the embed widget.
Mintplex Labs & the community maintain a number of deployment methods, scripts, and templates that you can use to run ByteBot locally. Refer to the table below to read how to deploy on your preferred environment or to automatically deploy.
| Docker | AWS | GCP | Digital Ocean | Render.com |
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| Railway | RepoCloud |
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or set up a production ByteBot instance without Docker →
yarn setupTo fill in the required.envfiles you'll need in each of the application sections (from root of repo).- Go fill those out before proceeding. Ensure
server/.env.developmentis filled or else things won't work right.
- Go fill those out before proceeding. Ensure
yarn dev:serverTo boot the server locally (from root of repo).yarn dev:frontendTo boot the frontend locally (from root of repo).yarn dev:collectorTo then run the document collector (from root of repo).
- create issue
- create PR with branch name format of
<issue number>-<short name> - yee haw let's merge
ByteBot by Mintplex Labs Inc contains a telemetry feature that collects anonymous usage information.
More about Telemetry & Privacy for ByteBot
We use this information to help us understand how ByteBot is used, to help us prioritize work on new features and bug fixes, and to help us improve ByteBot's performance and stability.
Set DISABLE_TELEMETRY in your server or docker .env settings to "true" to opt out of telemetry. You can also do this in-app by going to the sidebar > Privacy and disabling telemetry.
We will only track usage details that help us make product and roadmap decisions, specifically:
- Typ of your installation (Docker or Desktop)
- When a document is added or removed. No information about the document. Just that the event occurred. This gives us an idea of use.
- Type of vector database in use. Let's us know which vector database provider is the most used to prioritize changes when updates arrive for that provider.
- Type of LLM in use. Let's us know the most popular choice and prioritize changes when updates arrive for that provider.
- Chat is sent. This is the most regular "event" and gives us an idea of the daily-activity of this project across all installations. Again, only the event is sent - we have no information on the nature or content of the chat itself.
You can verify these claims by finding all locations Telemetry.sendTelemetry is called. Additionally these events are written to the output log so you can also see the specific data which was sent - if enabled. No IP or other identifying information is collected. The Telemetry provider is PostHog - an open-source telemetry collection service.
- VectorAdmin: An all-in-one GUI & tool-suite for managing vector databases.
- OpenAI Assistant Swarm: Turn your entire library of OpenAI assistants into one single army commanded from a single agent.
Copyright © 2024 Mintplex Labs.
This project is MIT licensed.