devdocs-to-llm

The devdocs-to-llm repository is a work-in-progress tool that aims to convert documentation from DevDocs format to Long Language Model (LLM) format. This tool is designed to streamline the process of converting documentation for use with LLMs, making it easier for developers to leverage large language models for various tasks. By automating the conversion process, developers can quickly adapt DevDocs content for training and fine-tuning LLMs, enabling them to create more accurate and contextually relevant language models.

This repository contains a Rest-Backend for a Conversational Agent that allows embedding documents, semantic search, QA based on documents, and document processing with Large Language Models. It uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries, includes a vector database, and provides a REST API built with FastAPI. The project also features semantic search, secret management for API keys, installation instructions, and development guidelines for both backend and frontend components.

Tara is a powerful node for ComfyUI that integrates Large Language Models (LLMs) to enhance and automate workflow processes. With Tara, you can create complex, intelligent workflows that refine and generate content, manage API keys, and seamlessly integrate various LLMs into your projects. It comprises nodes for handling OpenAI-compatible APIs, saving and loading API keys, composing multiple texts, and using predefined templates for OpenAI and Groq. Tara supports OpenAI and Grok models with plans to expand support to together.ai and Replicate. Users can install Tara via Git URL or ComfyUI Manager and utilize it for tasks like input guidance, saving and loading API keys, and generating text suitable for chaining in workflows.

This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

The Neo4j GenAI Workshop repository contains notebooks for a workshop focusing on building a Neo4j Graph, text embedding, and providing demos for content generation. The workshop includes data staging, loading, and exploration using Cypher queries. It also covers improvements in LLM response quality, GPT-4 usage, and vector search speed. The repository has undergone multiple updates to enhance course quality, simplify content, and provide better explainers and examples.

pgai simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL. It brings embedding and generation AI models closer to the database, allowing users to create embeddings, retrieve LLM chat completions, reason over data for classification, summarization, and data enrichment directly from within PostgreSQL in a SQL query. The tool requires an OpenAI API key and a PostgreSQL client to enable AI functionality in the database. Users can install pgai from source, run it in a pre-built Docker container, or enable it in a Timescale Cloud service. The tool provides functions to handle API keys using psql or Python, and offers various AI functionalities like tokenizing, detokenizing, embedding, chat completion, and content moderation.

Kuebiko is a Twitch Chat Bot that reads twitch chat and generates text-to-speech responses using Google Cloud API and OpenAI's GPT-3 text completion model. It allows users to set up their own VTuber AI similar to 'Neuro-Sama'. The project is built with Python and requires setting up various API keys and configurations to enable the bot functionality. Users can customize the voice of their VTuber and route audio using VBAudio Cable. Kuebiko provides a unique way to interact with viewers through chat responses and captions in OBS.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

Scale's LLM Engine is an open-source Python library, CLI, and Helm chart that provides everything you need to serve and fine-tune foundation models, whether you use Scale's hosted infrastructure or do it in your own cloud infrastructure using Kubernetes.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

LitLytics is an affordable analytics platform leveraging LLMs for automated data analysis. It simplifies analytics for teams without data scientists, generates custom pipelines, and allows customization. Cost-efficient with low data processing costs. Scalable and flexible, works with CSV, PDF, and plain text data formats.

LangSim is a tool developed to address the challenge of using simulation tools in computational chemistry and materials science, which typically require cryptic input files or programming experience. The tool provides a Large Language Model (LLM) extension with agents to couple the LLM to scientific simulation codes and calculate physical properties from a natural language interface. It aims to simplify the process of interacting with simulation tools by enabling users to query the large language model directly from a Python environment or a web-based interface.

Agentok Studio is a visual tool built for AutoGen, a cutting-edge agent framework from Microsoft and various contributors. It offers intuitive visual tools to simplify the construction and management of complex agent-based workflows. Users can create workflows visually as graphs, chat with agents, and share flow templates. The tool is designed to streamline the development process for creators and developers working on next-generation Multi-Agent Applications.

The devdocs-to-llm repository is a work-in-progress tool that aims to convert documentation from DevDocs format to Long Language Model (LLM) format. This tool is designed to streamline the process of converting documentation for use with LLMs, making it easier for developers to leverage large language models for various tasks. By automating the conversion process, developers can quickly adapt DevDocs content for training and fine-tuning LLMs, enabling them to create more accurate and contextually relevant language models.

Dstack is an open-source orchestration engine for running AI workloads in any cloud. It supports a wide range of cloud providers (such as AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, CUDO, RunPod, etc.) as well as on-premises infrastructure. With Dstack, you can easily set up and manage dev environments, tasks, services, and pools for your AI workloads.

The one-click-llms repository provides templates for quickly setting up an API for language models. It includes advanced inferencing scripts for function calling and offers various models for text generation and fine-tuning tasks. Users can choose between Runpod and Vast.AI for different GPU configurations, with recommendations for optimal performance. The repository also supports Trelis Research and offers templates for different model sizes and types, including multi-modal APIs and chat models.

StarCoder2-Instruct is an open-source pipeline that introduces StarCoder2-15B-Instruct-v0.1, a self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. It generates instruction-response pairs to fine-tune StarCoder-15B without human annotations or data from proprietary LLMs. The tool is primarily finetuned for Python code generation tasks that can be verified through execution, with potential biases and limitations. Users can provide response prefixes or one-shot examples to guide the model's output. The model may have limitations with other programming languages and out-of-domain coding tasks.

The enhance_llm repository contains three main parts: 1. Vector model domain fine-tuning based on llama_index and qwen fine-tuning BGE vector model. 2. Large model domain fine-tuning based on PEFT fine-tuning qwen1.5-7b-chat, with sft and dpo. 3. High-order retrieval enhanced generation (RAG) system based on the above domain work, implementing a two-stage RAG system. It includes query rewriting, recall reordering, retrieval reordering, multi-turn dialogue, and more. The repository also provides hardware and environment configurations along with star history and licensing information.

The 'fms-fsdp' repository is a companion to the Foundation Model Stack, providing a (pre)training example to efficiently train FMS models, specifically Llama2, using native PyTorch features like FSDP for training and SDPA implementation of Flash attention v2. It focuses on leveraging FSDP for training efficiently, not as an end-to-end framework. The repo benchmarks training throughput on different GPUs, shares strategies, and provides installation and training instructions. It trained a model on IBM curated data achieving high efficiency and performance metrics.

CogVLM2 is a new generation of open source models that offer significant improvements in benchmarks such as TextVQA and DocVQA. It supports 8K content length, image resolution up to 1344 * 1344, and both Chinese and English languages. The project provides basic calling methods, fine-tuning examples, and OpenAI API format calling examples to help developers quickly get started with the model.

liboai is a simple C++17 library for the OpenAI API, providing developers with access to OpenAI endpoints through a collection of methods and classes. It serves as a spiritual port of OpenAI's Python library, 'openai', with similar structure and features. The library supports various functionalities such as ChatGPT, Audio, Azure, Functions, Image DALL·E, Models, Completions, Edit, Embeddings, Files, Fine-tunes, Moderation, and Asynchronous Support. Users can easily integrate the library into their C++ projects to interact with OpenAI services.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.