semantic-router

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

1Backend is a flexible and scalable platform designed for running AI models on private servers and handling high-concurrency workloads. It provides a ChatGPT-like interface for users and a network-accessible API for machines, serving as a general-purpose backend framework. The platform offers on-premise ChatGPT alternatives, a microservices-first web framework, out-of-the-box services like file uploads and user management, infrastructure simplification acting as a container orchestrator, reverse proxy, multi-database support with its own ORM, and AI integration with platforms like LlamaCpp and StableDiffusion.

Bolna is an open-source platform for building voice-driven conversational applications using large language models (LLMs). It provides a comprehensive set of tools and integrations to handle various aspects of voice-based interactions, including telephony, transcription, LLM-based conversation handling, and text-to-speech synthesis. Bolna simplifies the process of creating voice agents that can perform tasks such as initiating phone calls, transcribing conversations, generating LLM-powered responses, and synthesizing speech. It supports multiple providers for each component, allowing users to customize their setup based on their specific needs. Bolna is designed to be easy to use, with a straightforward local setup process and well-documented APIs. It is also extensible, enabling users to integrate with other telephony providers or add custom functionality.

OASIS is a scalable, open-source social media simulator that integrates large language models with rule-based agents to realistically mimic the behavior of up to one million users on platforms like Twitter and Reddit. It facilitates the study of complex social phenomena such as information spread, group polarization, and herd behavior, offering a versatile tool for exploring diverse social dynamics and user interactions in digital environments. With features like scalability, dynamic environments, diverse action spaces, and integrated recommendation systems, OASIS provides a comprehensive platform for simulating social media interactions at a large scale.

OpenOrch is a daemon that transforms servers into a powerful development environment, running AI models, containers, and microservices. It serves as a blend of Kubernetes and a language-agnostic backend framework for building applications on fixed-resource setups. Users can deploy AI models and build microservices, managing applications while retaining control over infrastructure and data.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

The Cheshire Cat is a framework for building custom AIs on top of any language model. It provides an API-first approach, making it easy to add a conversational layer to your application. The Cat remembers conversations and documents, and uses them in conversation. It is extensible via plugins, and supports event callbacks, function calling, and conversational forms. The Cat is easy to use, with an admin panel that allows you to chat with the AI, visualize memory and plugins, and adjust settings. It is also production-ready, 100% dockerized, and supports any language model.

Marqo is more than a vector database, it's an end-to-end vector search engine for both text and images. Vector generation, storage and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

This repository is a sample chatbot using the Anthropic company's LLM Claude, one of the foundational models provided by Amazon Bedrock for generative AI. It allows users to have basic conversations with the chatbot, personalize it with their own instructions and external knowledge, and analyze usage for each user/bot on the administrator dashboard. The chatbot supports various languages, including English, Japanese, Korean, Chinese, French, German, and Spanish. Deployment is straightforward and can be done via the command line or by using AWS CDK. The architecture is built on AWS managed services, eliminating the need for infrastructure management and ensuring scalability, reliability, and security.

Garak is a free tool that checks if a Large Language Model (LLM) can be made to fail in a way that is undesirable. It probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. Garak's a free tool. We love developing it and are always interested in adding functionality to support applications.

AgentLang is an open-source programming language and framework designed for solving complex tasks with the help of AI agents. It allows users to build business applications rapidly from high-level specifications, making it more efficient than traditional programming languages. The language is data-oriented and declarative, with a syntax that is intuitive and closer to natural languages. AgentLang introduces innovative concepts such as first-class AI agents, graph-based hierarchical data model, zero-trust programming, declarative dataflow, resolvers, interceptors, and entity-graph-database mapping.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

WebLLM is a modular and customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU. WebLLM is fully compatible with OpenAI API. That is, you can use the same OpenAI API on any open source models locally, with functionalities including json-mode, function-calling, streaming, etc. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.

allms is a versatile and powerful library designed to streamline the process of querying Large Language Models (LLMs). Developed by Allegro engineers, it simplifies working with LLM applications by providing a user-friendly interface, asynchronous querying, automatic retrying mechanism, error handling, and output parsing. It supports various LLM families hosted on different platforms like OpenAI, Google, Azure, and GCP. The library offers features for configuring endpoint credentials, batch querying with symbolic variables, and forcing structured output format. It also provides documentation, quickstart guides, and instructions for local development, testing, updating documentation, and making new releases.

Ash AI is a tool that provides a Model Context Protocol (MCP) server for exposing tool definitions to an MCP client. It allows for the installation of dev and production MCP servers, and supports features like OAuth2 flow with AshAuthentication, tool data access, tool execution callbacks, prompt-backed actions, and vectorization strategies. Users can also generate a chat feature for their Ash & Phoenix application using `ash_oban` and `ash_postgres`, and specify LLM API keys for OpenAI. The tool is designed to help developers experiment with tools and actions, monitor tool execution, and expose actions as tool calls.

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.

The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.

Luna AI is a virtual streamer driven by a 'brain' composed of ChatterBot, GPT, Claude, langchain, chatglm, text-generation-webui, 讯飞星火, 智谱AI. It can interact with viewers in real-time during live streams on platforms like Bilibili, Douyin, Kuaishou, Douyu, or chat with you locally. Luna AI uses natural language processing and text-to-speech technologies like Edge-TTS, VITS-Fast, elevenlabs, bark-gui, VALL-E-X to generate responses to viewer questions and can change voice using so-vits-svc, DDSP-SVC. It can also collaborate with Stable Diffusion for drawing displays and loop custom texts. This project is completely free, and any identical copycat selling programs are pirated, please stop them promptly.

KULLM (구름) is a Korean Large Language Model developed by Korea University NLP & AI Lab and HIAI Research Institute. It is based on the upstage/SOLAR-10.7B-v1.0 model and has been fine-tuned for instruction. The model has been trained on 8×A100 GPUs and is capable of generating responses in Korean language. KULLM exhibits hallucination and repetition phenomena due to its decoding strategy. Users should be cautious as the model may produce inaccurate or harmful results. Performance may vary in benchmarks without a fixed system prompt.

Cria is a Python library designed for running Large Language Models with minimal configuration. It provides an easy and concise way to interact with LLMs, offering advanced features such as custom models, streams, message history management, and running multiple models in parallel. Cria simplifies the process of using LLMs by providing a straightforward API that requires only a few lines of code to get started. It also handles model installation automatically, making it efficient and user-friendly for various natural language processing tasks.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

Groma is a grounded multimodal assistant that excels in region understanding and visual grounding. It can process user-defined region inputs and generate contextually grounded long-form responses. The tool presents a unique paradigm for multimodal large language models, focusing on visual tokenization for localization. Groma achieves state-of-the-art performance in referring expression comprehension benchmarks. The tool provides pretrained model weights and instructions for data preparation, training, inference, and evaluation. Users can customize training by starting from intermediate checkpoints. Groma is designed to handle tasks related to detection pretraining, alignment pretraining, instruction finetuning, instruction following, and more.

Local RAG is an offline, open-source tool that allows users to ingest files for retrieval augmented generation (RAG) using large language models (LLMs) without relying on third parties or exposing sensitive data. It supports offline embeddings and LLMs, multiple sources including local files, GitHub repos, and websites, streaming responses, conversational memory, and chat export. Users can set up and deploy the app, learn how to use Local RAG, explore the RAG pipeline, check planned features, known bugs and issues, access additional resources, and contribute to the project.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.