Showing 6 open source projects for "ai chat"

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  • 1
    DocsGPT

    DocsGPT

    Private AI platform for agents, enterprise search and RAG pipelines

    ...Works with Qdrant, MongoDB, and Elasticsearch and more. Deploy via Docker or Kubernetes with full data sovereignty. Build embeddable chat and search widgets, automate multi-step workflows with AI agents, and integrate via Slack, Telegram, Discord, or REST API. Enterprise features include RBAC, 99.9% uptime SLA, and dedicated support. MIT licensed.
    Downloads: 3 This Week
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  • 2
    KoboldCpp

    KoboldCpp

    Run GGUF models easily with a UI or API. One File. Zero Install.

    KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features.
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    Downloads: 525 This Week
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  • 3
    Edge GPT

    Edge GPT

    Reverse engineered API of Microsoft's Bing Chat

    EdgeGPT is a reverse-engineered client project that exposed a programmatic interface to Microsoft’s Bing Chat experience (often associated with “Edge” due to access patterns and requirements at the time). It was built to let developers run prompts and retrieve responses through scripts and applications, effectively turning the chat experience into something that could be automated and integrated into tools. The repository gained popularity because it provided a practical way to experiment...
    Downloads: 0 This Week
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  • 4
    mindflow

    mindflow

    AI-powered CLI git wrapper, boilerplate code generator, chat history

    I-powered CLI git wrapper, boilerplate code generator, chat history manager, and code search engine to streamline your dev workflow. The ChatGPT-powered swiss army knife for the modern developer! We provide an AI-powered CLI git wrapper, boilerplate code generator, code search engine, a conversation history manager, and much more! Configure the model used for generating responses by running mf config and selecting either GPT 3.5 Turbo (default) or GPT 4.
    Downloads: 1 This Week
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  • Attack Surface Management | Criminal IP ASM Icon
    Attack Surface Management | Criminal IP ASM

    For security operations, threat-intelligence and risk teams wanting a tool to get access to auto-monitored assets exposed to attack surfaces

    Criminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
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  • 5
    revChatGPT

    revChatGPT

    This app allows you to chat with ChatGPT using reverse-engineered API

    This app allows you to chat with ChatGPT using a reverse-engineered API library called revChatGPT. Replies from the Chatbot are streamed back to the user in real-time, which gives the user an experience similar to how ChatGPT streams back its answers. To get started with the app, you'll need to create an account on OpenAI's ChatGPT and save your credentials. You can choose from three authentication methods: Email/Password, Session token, or Access token. Once you have your credentials, you...
    Downloads: 0 This Week
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  • 6
    Seq2seq Chatbot for Keras

    Seq2seq Chatbot for Keras

    This repository contains a new generative model of chatbot

    This repository contains a new generative model of chatbot based on seq2seq modeling. The trained model available here used a small dataset composed of ~8K pairs of context (the last two utterances of the dialogue up to the current point) and respective response. The data were collected from dialogues of English courses online. This trained model can be fine-tuned using a closed-domain dataset to real-world applications. The canonical seq2seq model became popular in neural machine...
    Downloads: 0 This Week
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