A simple Perplexity AI clone.
The repository provides a minimal PPLX implementation in TypeScript, allowing front-end developers to get started with it. It relies on the OpenAI API, but implements RAG by scraping and parsing HTML from Google web pages, rather than using a search engine API.
A near perfect replica of Perplexity AI's "Search" function in Python.
This repository is the Python version of clarity-ai, which directly returns an HTML file constructed in a specific format on the front end. If you are a Python developer and do not care about the front-end implementation, this tiny pplx search repository simplifies the backend representation of pplx and is very suitable for learning.
float32.app is a large language model-driven search assistant that can help you quickly find the answers you want. Based on RAG technology, float32 can access internet information to provide more accurate responses.［https://float32.app/］
float32.app支持自托管,你可以在本地搭建一个float32.app服务。服务相关代码可以参考backend/exec/svr/...
为了能运行,你需要:
- OpenAI API 服务
- 必须。包括 API Key 和 EndPoint（如适用）
- Serper.dev 服务
- 必须。用于获取搜索引擎结果。包括一个 API Key。
- MySQL 数据库
- 可选。用于历史服务,如没有 MySQL 数据库,则历史服务/分享服务不可用。
前端使用 pnpm + React + Vite + MobX + TDesign 的结构。请使用以下命令以启动开发服务器:
cd frontend
bash tdesign.sh # 获取 TDesign 资源
pnpm i
pnpm devBuilding a quick conversation-based search demo with Lepton AI.(search.lepton.run)
- Built-in support for LLM
- Built-in support for search engine
- Customizable pretty UI interface
- Shareable, cached search results The repository is built with TypeScript and Python for the front-end and back-end, it depends on the Lepton third-party package for deployment, and it does not support continuous question-answering.
Build a Perplexity-Inspired Answer Engine Using Next.js, Groq, Mixtral, Langchain, OpenAI, Brave & Serper[https://www.youtube.com/watch?v=kFC-OWw7G8k&feature=youtu.be]
This repository contains the code and instructions needed to build a sophisticated answer engine that leverages the capabilities of Groq, Mistral AI's Mixtral, Langchain.JS, Brave Search, Serper API, and OpenAI. Designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries, this project is an ideal starting point for developers interested in natural language processing and search technologies.
- Next.js: A React framework for building server-side rendered and static web applications.
- Tailwind CSS: A utility-first CSS framework for rapidly building custom user interfaces.
- Vercel AI SDK: The Vercel AI SDK is a library for building AI-powered streaming text and chat UIs.
- Groq & Mixtral: Technologies for processing and understanding user queries.
- Langchain.JS: A JavaScript library focused on text operations, such as text splitting and embeddings.
- Brave Search: A privacy-focused search engine used for sourcing relevant content and images.
- Serper API: Used for fetching relevant video and image results based on the user's query.
- OpenAI Embeddings: Used for creating vector representations of text chunks.
- Cheerio: Utilized for HTML parsing, allowing the extraction of content from web pages.
- Ollama (Optional): Used for streaming inference and embeddings.
RAG Search API(thinkany.ai)
rag-search is a backend API of thinkany. If you are a front-end programmer focusing on building web UI, you can directly use the open-source pplx-rag implementation provided by thinkany by integrating this API.