Given a set of PDFs and the query, the most relevant pdf can be found with the help of TF-IDF. The code has not used any library to implement TF-IDF

NLP Course By Deep learning.io powered by @coursera. Taught by: Younes Bensouda Mourri, Instructor of AI at Stanford University and Łukasz Kaiser, Staff Research Scientist at Google Brain.

Distributed document search using TF-IDF algorithm.

Rust-based text search engine from scratch supporting multiple document similarity metrics (TF-IDF, BM25, BM25VA)

COVID-19 comorbidities analysis platform based on Natural Language Processing(NLP)

The extended version of simhash supports fingerprint extraction of documents and images.

COVID-19 Open Research Dataset (CORD-19) Analysis

Search through all your personal data efficiently like web search.

Apache Solr Document Search and Indexing Analysis with OCR

Document Search Engine project with TF-IDF abd Google universal sentence encoder model

Information retrieval of text document using TF-IDF weighting & Cosine Similarity Algorithm.

This open source chatbot project lets you create a chatbot that uses your own data to answer questions, thanks to the power of the OpenAI GPT-3.5 model.

A highly efficient, isomorphic, full-featured, multilingual text search engine library, providing full-text search, fuzzy matching, phonetic scoring, document indexing and more, with micro JSON state hydration/dehydration in-browser and server-side.

This code example shows how to make a chatbot for semantic search over documents using Streamlit, LangChain, and various vector databases. The chatbot lets users ask questions and get answers from a document collection. The code is in Python and can be customized for different scenarios and data.

dead simple document index and search, nothing fancy

Website in PHP to index all pdf content and easy way to find any text

Open Source Search Engine with built-in web/document crawler and an indexing method.

Mini desktop search engine with Binary Search Tree

Retrieval-Augmented Generation, or RAG, is an innovative approach that enhances the capabilities of pre-trained large language models (LLMs) by integrating them with external data sources. This technique leverages the generative power of LLMs (Large Language Model), and combines it with the precision of specialized data search mechanisms.