Alternatives to Jina logo

Alternatives to Jina

Algolia, Solr, SpaCy, rasa NLU, and Gensim are the most popular alternatives and competitors to Jina.
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What is Jina and what are its top alternatives?

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
Jina is a tool in the Search Tools category of a tech stack.
Jina is an open source tool with GitHub stars and GitHub forks. Here’s a link to Jina's open source repository on GitHub

Top Alternatives to Jina

  • Algolia

    Algolia

    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard. ...

  • Solr

    Solr

    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites. ...

  • SpaCy

    SpaCy

    It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages. ...

  • rasa NLU

    rasa NLU

    rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries. ...

  • Gensim

    Gensim

    It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. ...

  • Amazon Comprehend

    Amazon Comprehend

    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications. ...

  • Transformers

    Transformers

    It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. ...

  • Google Cloud Natural Language API

    Google Cloud Natural Language API

    You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage. ...

Jina alternatives & related posts

Algolia logo

Algolia

1K
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Developer-friendly API and complete set of tools for building search
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PROS OF ALGOLIA
  • 124
    Ultra fast
  • 95
    Super easy to implement
  • 73
    Modern search engine
  • 71
    Excellent support
  • 70
    Easy setup, fast and relevant
  • 46
    Typos handling
  • 40
    Search analytics
  • 31
    Designed to search records, not pages
  • 30
    Multiple datacenters
  • 30
    Distributed Search Network
  • 10
    Smart Highlighting
  • 9
    Search as you type
  • 8
    Instantsearch.js
  • 8
    Multi-attributes
  • 6
    Super fast, easy to set up
  • 5
    Amazing uptime
  • 5
    Database search
  • 4
    Realtime
  • 4
    Great documentation
  • 4
    Highly customizable
  • 4
    Github-awesome-autocomple
  • 3
    Powerful Search
  • 3
    Beautiful UI
  • 3
    Places.js
  • 2
    Integrates with just about everything
  • 2
    Awesome aanltiycs and typos hnadling
  • 1
    Fast response time
  • 1
    Smooth platform
  • 1
    Github integration
  • 1
    Developer-friendly frontend libraries
CONS OF ALGOLIA
  • 10
    Expensive

related Algolia posts

Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Tim Specht
‎Co-Founder and CTO at Dubsmash · | 16 upvotes · 318.4K views

Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

#SearchAsAService

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Solr logo

Solr

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A blazing-fast, open source enterprise search platform
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PROS OF SOLR
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Apache Software Foundation
  • 5
    Restful
  • 4
    Great Search engine
  • 2
    Security built-in
CONS OF SOLR
    Be the first to leave a con

    related Solr posts

    Ganesa Vijayakumar
    Full Stack Coder | Module Lead · | 19 upvotes · 2.6M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

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    Shared insights
    on
    SolrSolrLuceneLucene
    at

    "Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.

    Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."

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    SpaCy logo

    SpaCy

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    233
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    Industrial-Strength Natural Language Processing in Python
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    PROS OF SPACY
    • 11
      Speed
    • 2
      No vendor lock-in
    CONS OF SPACY
    • 1
      Requires creating a training set and managing training

    related SpaCy posts

    rasa NLU logo

    rasa NLU

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    Conversational AI platform, for personalized conversations at scale
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    PROS OF RASA NLU
    • 8
      Open Source
    • 6
      Self Hosted
    • 5
      Docker Image
    • 3
      Comes with rasa_core
    • 1
      Enterprise Ready
    CONS OF RASA NLU
    • 3
      No interface provided
    • 1
      Wdfsdf

    related rasa NLU posts

    Gensim logo

    Gensim

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    A python library for Topic Modelling
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    PROS OF GENSIM
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      CONS OF GENSIM
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        related Gensim posts

        Biswajit Pathak
        Project Manager at Sony · | 5 upvotes · 42.8K views

        Can you please advise which one to choose FastText Or Gensim, in terms of:

        1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
        2. Performance
        3. Customization of Intermediate steps
        4. FastText and Gensim both have the same underlying libraries
        5. Use cases each one tries to solve
        6. Unsupervised Vs Supervised dimensions
        7. Ease of Use.

        Please mention any other points that I may have missed here.

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        Amazon Comprehend logo

        Amazon Comprehend

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        Discover insights and relationships in text
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        PROS OF AMAZON COMPREHEND
          Be the first to leave a pro
          CONS OF AMAZON COMPREHEND
          • 2
            Multi-lingual

          related Amazon Comprehend posts

          Transformers logo

          Transformers

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          State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0
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          PROS OF TRANSFORMERS
            Be the first to leave a pro
            CONS OF TRANSFORMERS
              Be the first to leave a con

              related Transformers posts

              Google Cloud Natural Language API logo

              Google Cloud Natural Language API

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              Derive insights from unstructured text using Google machine learning
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              PROS OF GOOGLE CLOUD NATURAL LANGUAGE API
                Be the first to leave a pro
                CONS OF GOOGLE CLOUD NATURAL LANGUAGE API
                • 2
                  Multi-lingual

                related Google Cloud Natural Language API posts