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requêtes de données

  • Effortlessly analyze data in Google Sheets using natural language.
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    What is Octo?
    Octo revolutionizes how users analyze data in Google Sheets. With its advanced natural language processing capabilities, Octo allows users to type queries just like they would ask a colleague for information. This reduces the need for complex formulas and enhances accessibility for users of all skill levels. The extension is designed to support a variety of data analysis tasks, making it the perfect companion for professionals, students, and anyone using Google Sheets for data management.
  • Text2SQL.AI allows users to convert plain text into SQL queries effortlessly using AI technology.
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    What is Text2SQL.AI?
    Text2SQL.AI is a robust AI-powered platform designed to convert natural language into optimized SQL queries. It provides an intuitive user interface where users can input their requirements in plain English. The AI engine then translates these inputs into precise SQL commands, enabling efficient and accurate database management. This tool is ideal for individuals and businesses looking to streamline their database querying processes without needing in-depth SQL knowledge.
  • An open-source LLM-based agent framework using ReAct pattern for dynamic reasoning with tool execution and memory support.
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    What is llm-ReAct?
    llm-ReAct implements the ReAct (Reasoning and Acting) architecture for large language models, enabling seamless integration of chain-of-thought reasoning with external tool execution and memory storage. Developers can configure a toolkit of custom tools—such as web search, database queries, file operations, and calculators—and instruct the agent to plan multi-step tasks, invoking tools as needed to retrieve or process information. The built-in memory module preserves conversational state and past actions, supporting more context-aware agent behaviors. With modular Python code and support for OpenAI APIs, llm-ReAct simplifies experimentation and deployment of intelligent agents that can adaptively solve problems, automate workflows, and provide context-rich responses.
  • Open-source framework for orchestrating LLM-powered agents with memory, tool integrations, and pipelines for automating complex workflows across domains.
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    What is OmniSteward?
    OmniSteward is a modular AI agent orchestration platform built on Python that connects to OpenAI, local LLMs, and supports custom models. It provides memory modules to store context, toolkits for API calls, web search, code execution, and database queries. Users define agent templates with prompts, workflows, and triggers. The framework orchestrates multiple agents in parallel, manages conversation history, and automates tasks via pipelines. It also includes logging, monitoring dashboards, plugin architecture, and integration with third-party services. OmniSteward simplifies creating domain-specific assistants for research, operations, marketing, and more, offering flexibility, scalability, and open-source transparency for enterprises and developers.
  • Simplify your data querying with Qquest's AI-powered tool.
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    What is Qquest?
    Qquest is a Chrome extension that simplifies data querying through advanced generative AI technology. By converting intricate and complex queries into easy, conversational exchanges, it allows users to retrieve and manage their data in a much more intuitive manner. Ideal for business professionals and data enthusiasts, Qquest empowers users to enhance their data interactions without the need for extensive technical knowledge.
  • Hands-on Python-based workshop for building AI Agents with OpenAI API and custom tools integrations.
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    What is AI Agent Workshop?
    AI Agent Workshop is a comprehensive repository offering practical examples and templates for developing AI Agents with Python. The workshop includes Jupyter notebooks demonstrating agent frameworks, tool integrations (e.g., web search, file operations, database queries), memory mechanisms, and multi-step reasoning. Users learn to configure custom agent planners, define tool schemas, and implement loop-based conversational workflows. Each module presents exercises on handling failures, optimizing prompts, and evaluating agent outputs. The codebase supports OpenAI’s function calling and LangChain connectors, allowing seamless extension for domain-specific tasks. Ideal for developers seeking to prototype autonomous assistants, task automation bots, or question-answering agents, it provides a step-by-step path from basic agents to advanced workflows.
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