Search Results for "python-kinterbasdb" - Page 9
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Showing 2989 open source projects for "python-kinterbasdb"
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Airweave
Airweave lets agents search any app
Airweave is an open-source platform that enables agents to semantically search across various applications, databases, and APIs. By transforming disparate data sources into a unified, searchable knowledge base, Airweave facilitates intelligent information retrieval through REST APIs or the MCP protocol. It's particularly useful for building AI agents that require access to structured and unstructured data across multiple platforms. -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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Cog
Package and deploy machine learning models using Docker containers
...It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. Cog automatically generates a RESTful HTTP API for running predictions, enabling models to be accessed programmatically through a built-in prediction server. -
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OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows
The OpenAI Agents Python SDK is a powerful yet lightweight framework for developing multi-agent workflows. This framework enables developers to create and manage agents that can coordinate tasks autonomously, using a set of instructions, tools, guardrails, and handoffs. The SDK allows users to configure workflows in which agents can pass control to other agents as necessary, ensuring dynamic task management. -
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gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. ... -
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Claude Skills
Public repository for Agent Skills
Claude Skills is a public repository that showcases and serves as a collection of skills — modular, reusable packages of instructions, scripts, and resources that Claude and other compatible agents can dynamically discover and load to extend their capabilities on specialized tasks. Rather than relying on handcrafted prompts every time, Skills teach an AI agent procedural knowledge and task-specific workflows so it can apply that expertise reliably, whether the task involves document... -
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LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
LTX-2 is a powerful, open-source toolkit developed by Lightricks that provides a modular, high-performance base for building real-time graphics and visual effects applications. It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries,... -
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OpenLLM
Operating LLMs in production
...Built-in supports a wide range of open-source LLMs and model runtime, including Llama 2， StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder, and more. Serve LLMs over RESTful API or gRPC with one command, query via WebUI, CLI, our Python/Javascript client, or any HTTP client. -
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OpenPlanter
Language-model investigation agent with a terminal UI
...The system is structured to support experimentation and customization, making it suitable for both research and DIY agriculture projects. With its modular Python codebase, users can adapt the platform for different plant types, hardware setups, or automation strategies. Overall, OpenPlanter aims to simplify the creation of programmable, data-driven plant care systems. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Pyreft
ReFT: Representation Finetuning for Language Models
PyreFT is a tool by Stanford NLP for fine-tuning transformer models with an emphasis on efficient, resource-conserving training and customizability for NLP tasks. -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. -
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FlashAttention
Fast and memory-efficient exact attention
FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. The project provides implementations of FlashAttention, FlashAttention-2, and newer iterations... -
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AgentRun
The easiest, and fastest way to run AI-generated Python code safely
AgentRun is a framework for building autonomous AI agents capable of executing complex tasks with minimal human intervention. It provides a structured environment for defining agent behaviors, managing workflows, and integrating AI models to achieve specific goals. -
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LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
...The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. These operators allow tasks such as semantic filtering, ranking, clustering, and summarization to be expressed directly within data processing pipelines. ... -
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BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
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Audiomentations
A Python library for audio data augmentation
A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. -
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LuxTTS
A high-quality rapid TTS voice cloning model
...The project supports zero-shot voice cloning, meaning it can adapt to a reference speaker’s voice with minimal example data, enabling realistic and personalized synthetic speech. Intended for developers, hobbyists, and creators, the repository includes installation instructions, usage examples, and Python APIs that make it feasible to integrate the model in local workflows, web demos, or production systems. Its design emphasizes efficiency and practicality, fitting within modest GPU memory footprints. -
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MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
...Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. The project provides a straightforward CLI (mlx_audio.tts.generate) as well as a Python API for programmatic generation of audio, including parameters for voice choice, speed, language hints, output format, and sample rate. It includes examples such as audiobook generation to demonstrate long-form synthesis and joined audio segments. On top of that, MLX-Audio offers a modern web interface powered by FastAPI, with real-time waveform and 3D visualizations, file upload, and audio management. -
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Granite TSFM
Foundation Models for Time Series
...The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. Issues and examples in the tracker illustrate common tasks such as slicing inference windows or using pipeline helpers that return pandas DataFrames, grounding the library in day-to-day time-series operations. The ecosystem around TSFM also includes a community cookbook of “recipes” that showcase capabilities and patterns. ... -
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Docling
Get your documents ready for gen AI
...It supports advanced PDF understanding, including layout detection, table extraction, and reading order analysis, enabling high-fidelity document intelligence pipelines. Docling is designed to run efficiently on commodity hardware and can be used both as a Python API and a command-line tool. Its modular architecture allows developers to extend functionality and integrate specialized models for tasks such as OCR and audio transcription. Overall, Docling serves as a comprehensive preprocessing layer for AI applications that require reliable, structured access to complex document data. -
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FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
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Elasticsearch MCP Server
A Model Context Protocol (MCP) server implementation
This MCP server implementation provides interaction capabilities with Elasticsearch and OpenSearch, enabling functionalities such as document searching, index analysis, and cluster management through a set of tools. -
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Memary
The Open Source Memory Layer For Autonomous Agents
Memary is a journaling and personal memory management application that helps users record and retrieve past experiences. It focuses on simplicity, ease of use, and structured data storage for personal reflections and knowledge tracking. -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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npcpy
The AI toolkit for the AI developer
npcpy is a Python-based agent framework and command-line toolkit (the NPC Shell) for developers to build, test, and integrate AI agents into their workflows, including both command-line and GUI interfaces via NPC Studio. Welcome to npcpy, the core library of the NPC Toolkit that supercharges natural language processing pipelines and agent tooling. npcpy is a flexible framework for building state-of-the-art applications and conducting novel research with LLMs.
