Search Results for "t-sql" - Page 2
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Showing 118 open source projects for "t-sql"
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Aix-DB
Based on the LangChain/LangGraph framework
...The system is designed as a ChatBI solution that allows users to query datasets using natural language and receive structured insights, charts, and visualizations automatically. Built on frameworks such as LangChain and LangGraph, Aix-DB integrates retrieval-augmented generation and Text-to-SQL capabilities to convert user questions into executable database queries. The platform supports multiple types of data sources and provides an end-to-end pipeline that includes intent recognition, SQL generation, database execution, and visual presentation of results. Its architecture includes multiple layers such as a web interface, API gateway, AI service layer, and data storage layer that support relational databases, vector stores, graph databases, and file systems. -
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civitai
Open platform for sharing and discovering Stable Diffusion models
Civitai is an open source project that provides the codebase for a platform designed to share and manage generative AI models used for image generation. It focuses primarily on models compatible with Stable Diffusion and related technologies, allowing creators to upload, organize, and distribute custom AI models and related resources. These resources can include textual inversions, hypernetworks, aesthetic gradients, and variational autoencoders that modify or extend the capabilities of... -
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LlamaParse
Parse files for optimal RAG
LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers. -
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PostgresML
The GPU-powered AI application database
...Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database. -
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MCP Server MySQL
A Model Context Protocol server
A Model Context Protocol server that provides access to MySQL databases, enabling Large Language Models to inspect database schemas and execute SQL queries, facilitating seamless database interactions. -
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MCP Snowflake Server
A Model Context Protocol (MCP) server implementation
An MCP server implementation that facilitates database interactions with Snowflake, allowing execution of SQL queries and presentation of data insights as resources. -
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SAG
SQL-Driven RAG Engine
SAG is an open-source SQL-driven retrieval-augmented generation engine that dynamically constructs knowledge graphs during query processing. Instead of relying on a static knowledge graph prepared in advance, the system automatically builds relational structures between entities while processing user queries. Documents are first decomposed into atomic semantic events, which are then represented using multidimensional natural language vectors. -
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Deepnote
Deepnote is a drop-in replacement for Jupyter
...Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. The system supports programming languages such as Python, R, and SQL and allows users to execute and analyze data directly within interactive notebooks. Deepnote emphasizes team-based data science by enabling real-time collaboration similar to shared document editors, allowing multiple users to work simultaneously on the same notebook environment. -
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Logfire MCP
The Logfire MCP Server is here
The Logfire MCP Server is a Model Context Protocol server that allows AI applications to access OpenTelemetry traces and metrics sent to Logfire. It enables retrieval and analysis of telemetry data, enhancing debugging and observability workflows. -
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KOM
Kubernetes Operations Manager
A Kubernetes Operations Manager (kom) that serves as an SDK-level tool, encapsulating functionalities of kubectl and client-go, providing a comprehensive suite of features for managing Kubernetes resources efficiently. -
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1Panel
1Panel provides an intuitive web interface and MCP Server
1Panel is a comprehensive Linux server management dashboard and MCP server built in Go. It offers UI control over websites, containers, databases, file systems, LLMs, backups, and monitoring, streamlining typical admin workflows via web. -
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Memobase
Fast backend for long-term AI user memory via structured profiles
...The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks. -
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Pydantic Logfire
Python observability platform for tracing apps, metrics, and logs
Pydantic Logfire is an observability platform designed to help developers monitor, analyze, and understand the behavior of their applications in real time. It is built by the team behind Pydantic and follows a philosophy of combining powerful capabilities with ease of use, making it accessible to entire engineering teams. Pydantic Logfire provides deep visibility into application performance by capturing traces, metrics, and logs through an OpenTelemetry-based architecture. It is... -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. In addition to conceptual study materials, the project includes interview question banks and case study prompts that simulate real hiring scenarios. ... -
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MemMachine
Universal memory layer for AI Agents
...Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores (e.g., graph databases for episodic lines of reasoning and SQL for user facts) to support robust, context-aware intelligence in agents. It offers flexible APIs, a Python SDK, REST interfaces, and MCP (Model Context Protocol) connectivity to integrate seamlessly with agent frameworks receiving and storing memories over time, effectively boosting relevance, continuity, and tailored behavior. -
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and... -
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Sail
A drop-in Apache Spark replacement written in Rust
...It is built entirely in Rust, eliminating JVM overhead and enabling predictable performance, fast startup times, and improved memory safety compared to traditional big data frameworks. Sail is compatible with the Spark Connect protocol, which means existing Spark SQL and DataFrame workloads can run without code changes, making adoption seamless for teams already using Spark-based pipelines. The framework is designed to operate across a variety of environments, including local machines, Kubernetes clusters, and cloud deployments, allowing flexible scaling based on workload requirements. It also emphasizes cost efficiency, with benchmarks showing significant performance improvements and reduced infrastructure usage compared to traditional systems. -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
...The repository focuses on core data science fundamentals rather than acting as a software framework, which makes it especially useful as a study and revision resource. Its content is organized into subject-specific documents that cover machine learning, deep learning, statistics, probability, Python, SQL and databases, and resume-based interview questions. That structure makes it practical for users who want to study by topic, strengthen weak areas, or simulate the range of questions they may encounter in interviews. -
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Data Science Interviews
Data science interview questions and answers
...Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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Open Gauss
Project-scoped Lean workflow orchestrator from Math, Inc.
...It is based on the PostgreSQL ecosystem but significantly extends its capabilities through architectural optimizations, AI-driven features, and enterprise-level enhancements. The database organizes data using the relational model, storing structured information in tables composed of rows and columns while supporting standard SQL for querying and management. One of its defining strengths is its optimization for multi-core and distributed environments, allowing it to efficiently process high volumes of concurrent transactions with minimal latency. OpenGauss also incorporates AI-based optimization techniques, such as intelligent query planning, performance prediction, and automated tuning, which help reduce operational complexity and improve efficiency. -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. HeavyDB was originally developed as part of the OmniSci platform (formerly MapD) and is commonly used for large-scale analytics and geospatial data processing. ... -
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DBHub
Universal database MCP server connecting to MySQL, PostgreSQL
...It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH tunneling options for secure access into private networks. A demo mode ships with an in-memory SQLite “employee” dataset so users can try the tools immediately without provisioning a database. The project lives in the Bytebase org alongside database DevSecOps tooling, underscoring a production focus on safe and auditable DB interaction. -
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LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
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JimuReport
Open source drag-and-drop reporting and dashboard builder platform
...JimuReport also includes components for building interactive charts, data tables, and analytical displays that can be used in enterprise applications. It can connect to multiple data sources and retrieve data through SQL queries, APIs, or other structured formats. It can be embedded into Java applications using Spring Boot integration modules. -
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Sec-Context
AI Code Security Anti-Patterns distilled from 150+ sources
...It compiles insights from over 150 industry and academic sources into structured reference documents that outline real-world security problems such as hardcoded secrets, SQL injection, cross-site scripting, command injection, weak password storage, and other frequent issues that occur when code is auto-generated without context of best practices. Each anti-pattern is paired with a secure coding alternative and explanation, offering educational value for both humans and automated review agents designed to flag or correct unsafe patterns.