Best AI tools for< 问答 >

开搜AI问答搜索 is a user-friendly AI question and answer search engine that helps users filter useful information from billions of documents. It provides direct, accurate answers, automatically summarizes key points, generates outlines, mind maps, and allows for downloading. The website is free of ads and offers a seamless search experience.

AI-DOG is an intelligent partner and creation platform that explores infinite creativity. It offers a range of AI-powered tools to assist users in content creation, website optimization, and marketing. With AI-DOG, users can generate high-quality articles, train AI models, create compelling文案, optimize websites, and produce engaging videos and literary content. The platform seamlessly integrates with website backend systems, enabling automated and intelligent content publishing.

包阅AI is an intelligent AI reading assistant that covers various scenarios such as paper reading, legal analysis, scientific research, marketing, education, brand analysis, and business understanding. It supports multiple document formats like PDF, Word, PPT, EPUB, Mobi, TXT, and Markdown. The tool offers features like document interpretation, web page summarization, contract review, resume analysis, and financial document analysis. With the ability to analyze over 50,000 documents and assist more than 100,000 knowledge workers efficiently, it aims to enhance work and study productivity through AI-powered assistance.

Llama中文社区是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。 **已经基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级【Done】**。**正在对Llama3模型进行中文能力的持续迭代升级【Doing】** 我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。

IntelliQ is an open-source project aimed at providing a multi-turn question-answering system based on a large language model (LLM). The system combines advanced intent recognition and slot filling technology to enhance the depth of understanding and accuracy of responses in conversation systems. It offers a flexible and efficient solution for developers to build and optimize various conversational applications. The system features multi-turn dialogue management, intent recognition, slot filling, interface slot technology for real-time data retrieval and processing, adaptive learning for improving response accuracy and speed, and easy integration with detailed API documentation supporting multiple programming languages and platforms.

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

One API 是一个开源项目，它通过标准的 OpenAI API 格式访问所有的大模型，开箱即用。它支持多种大模型，包括 OpenAI ChatGPT 系列模型、Anthropic Claude 系列模型、Google PaLM2/Gemini 系列模型、Mistral 系列模型、百度文心一言系列模型、阿里通义千问系列模型、讯飞星火认知大模型、智谱 ChatGLM 系列模型、360 智脑、腾讯混元大模型、Moonshot AI、百川大模型、MINIMAX、Groq、Ollama、零一万物、阶跃星辰。One API 还支持配置镜像以及众多第三方代理服务，支持通过负载均衡的方式访问多个渠道，支持 stream 模式，支持多机部署，支持令牌管理，支持兑换码管理，支持渠道管理，支持用户分组以及渠道分组，支持渠道设置模型列表，支持查看额度明细，支持用户邀请奖励，支持以美元为单位显示额度，支持发布公告，设置充值链接，设置新用户初始额度，支持模型映射，支持失败自动重试，支持绘图接口，支持 Cloudflare AI Gateway，支持丰富的自定义设置，支持通过系统访问令牌调用管理 API，进而**在无需二开的情况下扩展和自定义** One API 的功能，支持 Cloudflare Turnstile 用户校验，支持用户管理，支持多种用户登录注册方式，支持主题切换，配合 Message Pusher 可将报警信息推送到多种 App 上。

This is a production-grade knowledge Q&A chatbot implementation based on AWS services and the LangChain framework, with optimizations at various stages. It supports flexible configuration and plugging of vector models and large language models. The front and back ends are separated, making it easy to integrate with IM tools (such as Feishu).

WeiXin-Dyh-AI is a backend management system that supports integrating WeChat subscription accounts with AI services. It currently supports integration with Ali AI, Moonshot, and Tencent Hyunyuan. Users can configure different AI models to simulate and interact with AI in multiple modes: text-based knowledge Q&A, text-to-image drawing, image description, text-to-voice conversion, enabling human-AI conversations on WeChat. The system allows hierarchical AI prompt settings at system, subscription account, and WeChat user levels. Users can configure AI model types, providers, and specific instances. The system also supports rules for allocating models and keys at different levels. It addresses limitations of WeChat's messaging system and offers features like text-based commands and voice support for interactions with AI.

Wechat AI Assistant is a project that enables multi-modal interaction with ChatGPT AI assistant within WeChat. It allows users to engage in conversations, role-playing, respond to voice messages, analyze images and videos, summarize articles and web links, and search the internet. The project utilizes the WeChatFerry library to control the Windows PC desktop WeChat client and leverages the OpenAI Assistant API for intelligent multi-modal message processing. Users can interact with ChatGPT AI in WeChat through text or voice, access various tools like bing_search, browse_link, image_to_text, text_to_image, text_to_speech, video_analysis, and more. The AI autonomously determines which code interpreter and external tools to use to complete tasks. Future developments include file uploads for AI to reference content, integration with other APIs, and login support for enterprise WeChat and WeChat official accounts.

ChatPDF is a knowledge question and answer retrieval tool based on local LLM. It supports various open-source LLM models like ChatGLM3-6b, Chinese-LLaMA-Alpaca-2, Baichuan, YI, and multiple file formats including PDF, docx, markdown, txt. The tool optimizes RAG accuracy, Chinese chunk segmentation, embedding using text2vec's sentence embedding, retrieval matching with rank_BM25, and introduces reranker module for reranking candidate sets. It also enhances candidate chunk extension context, supports custom RAG models, and provides a Gradio-based RAG conversation page for seamless dialogue.

FastGPT is a knowledge base Q&A system based on the LLM large language model, providing out-of-the-box data processing, model calling and other capabilities. At the same time, you can use Flow to visually arrange workflows to achieve complex Q&A scenarios!

DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.

Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.

Ezdata is a data processing and task scheduling system developed based on Python backend and Vue3 frontend. It supports managing multiple data sources, abstracting various data sources into a unified data model, integrating chatgpt for data question and answer functionality, enabling low-code data integration and visualization processing, scheduling single and dag tasks, and integrating a low-code data visualization dashboard system.

ChatBook provides a one-stop AI service, including basic AI dialogue, AI role agents, AI customer service, AI knowledge base, AI mind map generation, and AI PPTX generation. Users can define AI workflows freely to handle more complex business scenarios. The backend uses serverless functions with data stored in the ./data directory. The tool allows administrators to manage knowledge bases, configure keys, and review user registrations. Normal users can directly use AI models and knowledge bases after registration. The technology stack includes LLM models like Langchain, Pinecone, OpenAi, Gemini, Baidu Wenxin, Node Express for backend, and React, NextJS, MUI for frontend.

Nexior allows users to deploy their own AI application site in minutes, offering services like GPT, Midjourney, ChatDoc, QrArt, etc. Users can use the platform without any development experience, AI account purchases, API support concerns, or payment system configurations. It supports various features such as GPT 3.5/4.0, Midjourney modes, unlimited document uploads, artistic QR code generation, payment and referral systems, and user system support. Nexior is open source, free under the MIT license, and easy to configure and deploy.

LearnPrompt is a permanent, free, open-source AIGC course platform that currently supports various tools like ChatGPT, Agent, Midjourney, Runway, Stable Diffusion, AI digital humans, AI voice & music, and large model fine-tuning. The platform offers features such as multilingual support, comment sections, daily selections, and submissions. Users can explore different modules, including sound cloning, RAG, GPT-SoVits, and OpenAI Sora world model. The platform aims to continuously update and provide tutorials, examples, and knowledge systems related to AI technologies.

ChatWiki is an open-source knowledge base AI question-answering system. It is built on large language models (LLM) and retrieval-augmented generation (RAG) technologies, providing out-of-the-box data processing, model invocation capabilities, and helping enterprises quickly build their own knowledge base AI question-answering systems. It offers exclusive AI question-answering system, easy integration of models, data preprocessing, simple user interface design, and adaptability to different business scenarios.

KB Builder is an open-source knowledge base generation system based on the LLM large language model. It utilizes the RAG (Retrieval-Augmented Generation) data generation enhancement method to provide users with the ability to enhance knowledge generation and quickly build knowledge bases based on RAG. It aims to be the central hub for knowledge construction in enterprises, offering platform-based intelligent dialogue services and document knowledge base management functionality. Users can upload docx, pdf, txt, and md format documents and generate high-quality knowledge base question-answer pairs by invoking large models through the 'Parse Document' feature.

This repository provides a collection of interview questions and answers for algorithm engineers. The questions are organized by topic, and each question includes a detailed explanation of the answer. This repository is a valuable resource for anyone preparing for an algorithm engineering interview.

MedicalGPT is a training medical GPT model with ChatGPT training pipeline, implement of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(Direct Preference Optimization).

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

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