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🔍 An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT)
🔍 Google from the terminal
🦆 DuckDuckGo from the terminal
A list of software that allows searching the web with the assistance of AI: https://hf.co/spaces/felladrin/awesome-ai-web-search
A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).
ACHE is a web crawler for domain-specific search.
Minimalist web-searching platform with an AI assistant that runs directly from your browser. Uses WebLLM, Wllama and SearXNG. Demo: https://felladrin-minisearch.hf.space
Browser extension. Definitly more than a GET/POST sender. Handily use search engines via a Flexible Tool! UI has Vimium-like feature 🌐🔍 (Pure-client. No 3rd-party server needed) 大术专搜 既专又广 手敲几下 纵横去往
A Powerful Versatile Omni Search inside Emacs
PocketGroq is a powerful Python library that simplifies integration with the Groq API, offering advanced features for natural language processing, web scraping, and autonomous agent capabilities. Key Features Seamless integration with Groq API for text generation and completion Chain of Thought (CoT) reasoning for complex problem-solving and more.
Turn topics into essays in seconds!
SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.
Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines
Powerful Web and Omni Search inside Emacs
🔴🟠🔵🟢🟡🟣 Findto is an open source assistant for decentralized search. Explore the best of Web, AI, and more.
An in-memory NoSQL database implemented in Python.
Crawlzone is a fast asynchronous internet crawling framework for PHP.
SirChatalot is a Telegram bot leveraging ChatGPT, Claude or YandexGPT. It uses Whisper for speech-to-text and DALL-E, Stability AI or YandexART for image creation. It can use vision capabilities, tools and semantic search in vector DB.
RepBERT is a competitive first-stage retrieval technique. It represents documents and queries with fixed-length contextualized embeddings. The inner products of them are regarded as relevance scores. Its efficiency is comparable to bag-of-words methods.
CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.