This starter project is designed to process `.mdx` files in the `pages` directory to use as custom context within OpenAI Text Completion prompts. It involves building a custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase. The project includes steps for pre-processing knowledge base, storing embeddings in Postgres, performing vector similarity search, and injecting content into OpenAI GPT-3 text completion prompt.

HASH is a self-building, open-source database which grows, structures and checks itself. With it, we're creating a platform for decision-making, which helps you integrate, understand and use data in a variety of different ways.

The BlinkID Android SDK is a comprehensive solution for implementing secure document scanning and extraction. It offers powerful capabilities for extracting data from a wide range of identification documents. The SDK provides features for integrating document scanning into Android apps, including camera requirements, SDK resource pre-bundling, customizing the UX, changing default strings and localization, troubleshooting integration difficulties, and using the SDK through various methods. It also offers options for completely custom UX with low-level API integration. The SDK size is optimized for different processor architectures, and API documentation is available for reference. For any questions or support, users can contact the Microblink team at help.microblink.com.

horde-engine is a wrapper around ComfyUI designed to run inference pipelines visually designed in the ComfyUI GUI. It enables users to design inference pipelines in ComfyUI and then call them programmatically, maintaining compatibility with the existing horde implementation. The library provides features for processing Horde payloads, initializing the library, downloading and validating models, and generating images based on input data. It also includes custom nodes for preprocessing and tasks such as face restoration and QR code generation. The project depends on various open source projects and bundles some dependencies within the library itself. Users can design ComfyUI pipelines, convert them to the backend format, and run them using the run_image_pipeline() method in hordelib.comfy.Comfy(). The project is actively developed and tested using git, tox, and a specific model directory structure.

Termax is an LLM agent in your terminal that converts natural language to commands. It is featured by: - Personalized Experience: Optimize the command generation with RAG. - Various LLMs Support: OpenAI GPT, Anthropic Claude, Google Gemini, Mistral AI, and more. - Shell Extensions: Plugin with popular shells like `zsh`, `bash` and `fish`. - Cross Platform: Able to run on Windows, macOS, and Linux.

Datalore is a terminal tool for generating structured datasets from local files like PDFs, Word docs, images, and text. It extracts content, uses semantic search to understand context, applies instructions through a generated schema, and outputs clean, structured data. Perfect for converting raw or unstructured local documents into ready-to-use datasets for training, analysis, or experimentation, all without manual formatting.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

Pax is a framework to configure and run machine learning experiments on top of Jax.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

The `vectara-agentic` Python library is designed for developing powerful AI assistants using Vectara and Agentic-RAG. It supports various agent types, includes pre-built tools for domains like finance and legal, and enables easy creation of custom AI assistants and agents. The library provides tools for summarizing text, rephrasing text, legal tasks like summarizing legal text and critiquing as a judge, financial tasks like analyzing balance sheets and income statements, and database tools for inspecting and querying databases. It also supports observability via LlamaIndex and Arize Phoenix integration.

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.

This repository, safety-tooling, is designed to be shared across various AI Safety projects. It provides an LLM API with a common interface for OpenAI, Anthropic, and Google models. The aim is to facilitate collaboration among AI Safety researchers, especially those with limited software engineering backgrounds, by offering a platform for contributing to a larger codebase. The repo can be used as a git submodule for easy collaboration and updates. It also supports pip installation for convenience. The repository includes features for installation, secrets management, linting, formatting, Redis configuration, testing, dependency management, inference, finetuning, API usage tracking, and various utilities for data processing and experimentation.

The Slack Bot is a tool designed to enhance the workflow of development teams by integrating with Jenkins, GitHub, GitLab, and Jira. It allows for custom commands, macros, crons, and project-specific commands to be implemented easily. Users can interact with the bot through Slack messages, execute commands, and monitor job progress. The bot supports features like starting and monitoring Jenkins jobs, tracking pull requests, querying Jira information, creating buttons for interactions, generating images with DALL-E, playing quiz games, checking weather, defining custom commands, and more. Configuration is managed via YAML files, allowing users to set up credentials for external services, define custom commands, schedule cron jobs, and configure VCS systems like Bitbucket for automated branch lookup in Jenkins triggers.

Fabric is an open-source framework for augmenting humans using AI. It provides a structured approach to breaking down problems into individual components and applying AI to them one at a time. Fabric includes a collection of pre-defined Patterns (prompts) that can be used for a variety of tasks, such as extracting the most interesting parts of YouTube videos and podcasts, writing essays, summarizing academic papers, creating AI art prompts, and more. Users can also create their own custom Patterns. Fabric is designed to be easy to use, with a command-line interface and a variety of helper apps. It is also extensible, allowing users to integrate it with their own AI applications and infrastructure.

ChatData is a robust chat-with-documents application designed to extract information and provide answers by querying the MyScale free knowledge base or uploaded documents. It leverages the Retrieval Augmented Generation (RAG) framework, millions of Wikipedia pages, and arXiv papers. Features include self-querying retriever, VectorSQL, session management, and building a personalized knowledge base. Users can effortlessly navigate vast data, explore academic papers, and research documents. ChatData empowers researchers, students, and knowledge enthusiasts to unlock the true potential of information retrieval.

AIBotPublic is an open-source version of AIBotPro, a comprehensive AI tool that provides various features such as knowledge base construction, AI drawing, API hosting, and more. It supports custom plugins and parallel processing of multiple files. The tool is built using bootstrap4 for the frontend, .NET6.0 for the backend, and utilizes technologies like SqlServer, Redis, and Milvus for database and vector database functionalities. It integrates third-party dependencies like Baidu AI OCR, Milvus C# SDK, Google Search, and more to enhance its capabilities.

ChatWiki is an open-source knowledge base AI question-answering system. It is built on large language models (LLM) and retrieval-augmented generation (RAG) technologies, providing out-of-the-box data processing, model invocation capabilities, and helping enterprises quickly build their own knowledge base AI question-answering systems. It offers exclusive AI question-answering system, easy integration of models, data preprocessing, simple user interface design, and adaptability to different business scenarios.

This starter project is designed to process `.mdx` files in the `pages` directory to use as custom context within OpenAI Text Completion prompts. It involves building a custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase. The project includes steps for pre-processing knowledge base, storing embeddings in Postgres, performing vector similarity search, and injecting content into OpenAI GPT-3 text completion prompt.

AutoFlow is an open source graph rag based knowledge base tool built on top of TiDB Vector and LlamaIndex and DSPy. It features a Perplexity-style Conversational Search page and an Embeddable JavaScript Snippet for easy integration into websites. The tool allows for comprehensive coverage and streamlined search processes through sitemap URL scraping.

Chipper provides a web interface, CLI, and architecture for pipelines, document chunking, web scraping, and query workflows. It is built with Haystack, Ollama, Hugging Face, Docker, Tailwind, and ElasticSearch, running locally or as a Dockerized service. Originally created to assist in creative writing, it now offers features like local Ollama and Hugging Face API, ElasticSearch embeddings, document splitting, web scraping, audio transcription, user-friendly CLI, and Docker deployment. The project aims to be educational, beginner-friendly, and a playground for AI exploration and innovation.

RapidRAG is a project focused on Knowledge QA with LLM, combining Questions & Answers based on local knowledge base with a large language model. The project aims to provide a flexible and deployment-friendly solution for building a knowledge question answering system. It is modularized, allowing easy replacement of parts and simple code understanding. The tool supports various document formats and can utilize CPU for most parts, with the large language model interface requiring separate deployment.

OP Vault is a tool that leverages the OP Stack (OpenAI + Pinecone Vector Database) to allow users to upload custom knowledgebase files and ask questions about their contents. It provides a user-friendly Golang server and React frontend for querying human-readable content like books and documents, making it valuable for knowledge extraction and question-answering. Users can upload entire libraries, receive specific answers with file and section references, and explore the power of the OP Stack in a practical interface.

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.