fluid-db

FluidDB is a research repository focusing on the concept of a fluid database that dynamically updates its schema based on ingested data. It enables the creation of personalized AI agents with features like adaptive schema, flexible querying, and versatile data input. The tool allows for storing unstructured data in a structured form and supports natural language queries. It aims to revolutionize database management by providing a dynamic and intuitive approach to data storage and retrieval.

Trex is a tool that transforms unstructured data into structured data by specifying a regex or context-free grammar. It intelligently restructures data to conform to the defined schema. It offers a Python client for installation and requires an API key obtained by signing up at automorphic.ai. The tool supports generating structured JSON objects based on user-defined schemas and prompts. Trex aims to provide significant speed improvements, structured custom CFG and regex generation, and generation from JSON schema. Future plans include auto-prompt generation for unstructured ETL and more intelligent models.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

CredSweeper is a tool designed to detect credentials like tokens, passwords, and API keys in directories or files. It helps users identify potential exposure of sensitive information by scanning lines, filtering, and utilizing an AI model. The tool reports lines containing possible credentials, their location, and the expected type of credential.

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

This repository contains data and code for the experiments in the SummHay paper. It includes publicly released Haystacks in conversational and news domains, along with scripts for running the pipeline, visualizing results, and benchmarking automatic evaluation. The data structure includes topics, subtopics, insights, queries, retrievers, summaries, evaluation summaries, and documents. The pipeline involves scripts for retriever scores, summaries, and evaluation scores using GPT-4o. Visualization scripts are provided for compiling and visualizing results. The repository also includes annotated samples for benchmarking and citation information for the SummHay paper.

Agently is a development framework that helps developers build AI agent native application really fast. You can use and build AI agent in your code in an extremely simple way. You can create an AI agent instance then interact with it like calling a function in very few codes like this below. Click the run button below and witness the magic. It's just that simple: python # Import and Init Settings import Agently agent = Agently.create_agent() agent\ .set_settings("current_model", "OpenAI")\ .set_settings("model.OpenAI.auth", {"api_key": ""}) # Interact with the agent instance like calling a function result = agent\ .input("Give me 3 words")\ .output([("String", "one word")])\ .start() print(result) ['apple', 'banana', 'carrot'] And you may notice that when we print the value of `result`, the value is a `list` just like the format of parameter we put into the `.output()`. In Agently framework we've done a lot of work like this to make it easier for application developers to integrate Agent instances into their business code. This will allow application developers to focus on how to build their business logic instead of figure out how to cater to language models or how to keep models satisfied.

Empower Functions is a family of large language models (LLMs) that provide GPT-4 level capabilities for real-world 'tool using' use cases. These models offer compatibility support to be used as drop-in replacements, enabling interactions with external APIs by recognizing when a function needs to be called and generating JSON containing necessary arguments based on user inputs. This capability is crucial for building conversational agents and applications that convert natural language into API calls, facilitating tasks such as weather inquiries, data extraction, and interactions with knowledge bases. The models can handle multi-turn conversations, choose between tools or standard dialogue, ask for clarification on missing parameters, integrate responses with tool outputs in a streaming fashion, and efficiently execute multiple functions either in parallel or sequentially with dependencies.

Openmacro is a multimodal personal agent that allows users to run code locally. It acts as a personal agent capable of completing and automating tasks autonomously via self-prompting. The tool provides a CLI natural-language interface for completing and automating tasks, analyzing and plotting data, browsing the web, and manipulating files. Currently, it supports API keys for models powered by SambaNova, with plans to add support for other hosts like OpenAI and Anthropic in future versions.

Motorhead is a memory and information retrieval server for LLMs. It provides three simple APIs to assist with memory handling in chat applications using LLMs. The first API, GET /sessions/:id/memory, returns messages up to a maximum window size. The second API, POST /sessions/:id/memory, allows you to send an array of messages to Motorhead for storage. The third API, DELETE /sessions/:id/memory, deletes the session's message list. Motorhead also features incremental summarization, where it processes half of the maximum window size of messages and summarizes them when the maximum is reached. Additionally, it supports searching by text query using vector search. Motorhead is configurable through environment variables, including the maximum window size, whether to enable long-term memory, the model used for incremental summarization, the server port, your OpenAI API key, and the Redis URL.

Bosquet is a tool designed for LLMOps in large language model-based applications. It simplifies building AI applications by managing LLM and tool services, integrating with Selmer templating library for prompt templating, enabling prompt chaining and composition with Pathom graph processing, defining agents and tools for external API interactions, handling LLM memory, and providing features like call response caching. The tool aims to streamline the development process for AI applications that require complex prompt templates, memory management, and interaction with external systems.

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

AwaDB is an AI native database designed for embedding vectors. It simplifies database usage by eliminating the need for schema definition and manual indexing. The system ensures real-time search capabilities with millisecond-level latency. Built on 5 years of production experience with Vearch, AwaDB incorporates best practices from the community to offer stability and efficiency. Users can easily add and search for embedded sentences using the provided client libraries or RESTful API.

A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.

This repository contains a library for constraining LLM generation to structured output, enforcing a JSON schema for precise data types and property names. It includes an acceptor/state machine framework, JSON acceptor, and JSON schema acceptor for guiding decoding in LLMs. The library provides reference implementations using Apple's MLX library and examples for function calling tasks. The tool aims to improve LLM output quality by ensuring adherence to a schema, reducing unnecessary output, and enhancing performance through pre-emptive decoding. Evaluations show performance benchmarks and comparisons with and without schema constraints.

AutoGPT is a revolutionary tool that empowers everyone to harness the power of AI. With AutoGPT, you can effortlessly build, test, and delegate tasks to AI agents, unlocking a world of possibilities. Our mission is to provide the tools you need to focus on what truly matters: innovation and creativity.

The Agent OS is an experimental framework and runtime to build sophisticated, long running, and self-coding AI agents. We believe that the most important super-power of AI agents is to write and execute their own code to interact with the world. But for that to work, they need to run in a suitable environment—a place designed to be inhabited by agents. The Agent OS is designed from the ground up to function as a long-term computing substrate for these kinds of self-evolving agents.

ChatDev IDE is a tool for building your AI agent, Whether it's NPCs in games or powerful agent tools, you can design what you want for this platform. It accelerates prompt engineering through **JavaScript Support** that allows implementing complex prompting techniques.

This library provides functionality required to build ReAct Agent using Large Language Models (LLMs).

NPi is an open-source platform providing Tool-use APIs to empower AI agents with the ability to take action in the virtual world. It is currently under active development, and the APIs are subject to change in future releases. NPi offers a command line tool for installation and setup, along with a GitHub app for easy access to repositories. The platform also includes a Python SDK and examples like Calendar Negotiator and Twitter Crawler. Join the NPi community on Discord to contribute to the development and explore the roadmap for future enhancements.

The 'ai-agents' repository is a collection of books and resources focused on developing AI agents, including topics such as GPT models, building AI agents from scratch, machine learning theory and practice, and basic methods and tools for data analysis. The repository provides detailed explanations and guidance for individuals interested in learning about and working with AI agents.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

The 'ai-app' repository is a comprehensive collection of tools and resources related to artificial intelligence, focusing on topics such as server environment setup, PyCharm and Anaconda installation, large model deployment and training, Transformer principles, RAG technology, vector databases, AI image, voice, and music generation, and AI Agent frameworks. It also includes practical guides and tutorials on implementing various AI applications. The repository serves as a valuable resource for individuals interested in exploring different aspects of AI technology.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.