Displaying 1 to 9 from 9 results

Haystack - Build a natural language interface for your data

  •    Python

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.

Jina - Cloud-native neural search framework for any kind of data

  •    Python

Jina is a neural search framework that empowers anyone to build SOTA & scalable deep learning search applications in minutes. It helps to build solutions for indexing, querying, understanding multi-/cross-modal data such as video, image, text, audio, source code, PDF.

Qdrant - Neural Search Engine, Vector Similarity Search Engine with extended filtering support

  •    Rust

Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

Ephyra - Question Answering System

  •    Java

Ephyra is a modular and extensible framework for open domain question answering (QA). The system retrieves accurate answers to natural language questions from the Web and other sources. The goal is to give researchers the opportunity to develop new QA techniques without worrying about the end-to-end system.

examples - Jina examples and demos to help you get started

  •    Python

These examples showcase Jina in action and provide sample code for you to work from. Copyright (c) 2021 Jina AI Limited. All rights reserved.

MySemanticSearch Sample


MySemanticSearch is a sample content management application that demonstrates semantic search capabilities introduced in SQL Server 2012. MySemanticSearch allows you to visualize tag clouds for content stored in FileTables and find similar content using semantic search.

Semantic-Search-for-Sustainable-Development - Semantic Search for Sustainable Development is experimental code for searching documents for text that "semantically" corresponds to any of the UN's Sustainable development goals/targets

  •    Python

Semantic Search for Sustainable Development is experimental code for searching documents for text that "semantically" corresponds to any of the UN's Sustainable development goals/targets. For example, it can be used to mine the national development plan documents of a country and identify pieces of text that correspond to any of the SDGs in order to verify alignment of the plan with the SDGs.

code_search - Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"

  •    Jupyter

You can use these container to reproduce the environment the authors used for this tutorial. Incase it is helpful, I have provided a requirements.txt file, however, we highly recommend using the docker containers provided below as the dependencies can be complicated to build yourself. hamelsmu/ml-gpu: Use this container for any gpu bound parts of the tutorial. We recommend running the entire tutorial on an aws p3.8xlarge and using this image.

jinabox.js - A lightweight, customizable omnibox in Javascript, for use with a Jina backend.

  •    Javascript

jinabox.js is a lightweight, customizable set of components for querying and displaying results from Jina flows. You can use it for searching text, images, videos, audio or any kind of data with a Jina backend.

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