Showing 3 open source projects for "server chat"

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    Google Workspace MCP Server

    Google Workspace MCP Server

    Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms

    Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically.
    Downloads: 4 This Week
    Last Update:
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  • 2
    ChatGLM3

    ChatGLM3

    ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat

    ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit),...
    Downloads: 1 This Week
    Last Update:
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  • 3
    Canopy

    Canopy

    Retrieval Augmented Generation (RAG) framework

    ...The system provides a complete pipeline for transforming raw text data into searchable embeddings, storing them in a vector database, and retrieving relevant context for language model responses. It is designed to handle many of the complex components required for a RAG workflow, including document chunking, embedding generation, prompt construction, and chat history management. Developers can use Canopy to quickly build chat systems that answer questions using their own data instead of relying solely on the pretrained knowledge of the language model. The framework includes a built-in server and command-line interface that allow users to experiment with RAG pipelines and compare outputs between retrieval-augmented responses and standard LLM responses.
    Downloads: 0 This Week
    Last Update:
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