whoosh
Typesense
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whoosh | Typesense | |
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5 | 129 | |
527 | 17,876 | |
- | 4.4% | |
0.0 | 9.8 | |
4 months ago | 8 days ago | |
Python | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
whoosh
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Milli-py: Python bindings for Milli, an embeddable high-performance search engine
The only other embeddable search engine I'm aware off, Whoosh, is brilliant but building the index was quite slow, and search performance degraded quite a lot as number of documents increase (performance is strictly a non-goal). Meilisearch was comparatively faster, I didn't like managing a server to get "just search" in my scripts and applications. However, their underlying engine Milli solves both issues I had, and all that was needed creating bindings for it.
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Meilisearch v1.0 – the open-source Rust alternative to Algolia and Elasticsearch
Is it really "just a single statically linked binary"?
I'd love to use Meilisearch as you describe, but their so-called SDKs are just about for the search client, you still need the HTTP server listening on localhost.
I would love to see something like SQLite based off Meilisearch (i.e. a fully selfcontained library like https://github.com/mchaput/whoosh). Do you know if such a thing exists?
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Faster Full Text Search
For our full text search, we used whoosh, which works pretty well for moderately big amount of data.
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We upgraded an old, 3PB large, Elasticsearch cluster without downtime
Nearly a decade ago (oh god) I converted some overdesigned five node ES mess to https://github.com/mchaput/whoosh. It's (obviously) not the fastest or anything, but it was more than good enough for low-dozens of GBs of mostly static data.
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Starting a KF Discord Bot
Your best bet is to start using a proper search library rather than the simple loop with 'in' checks that you have now. A search lib will handle things like Unicode/ASCII similarities, removal of stop words, stemming, TF-IDF (and other) weighting, etc. and will be massively faster as well. Quite a few pages come up if you Google "python search engine", also Whoosh looks promising.
Typesense
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Website Search Hurts My Feelings
There are actually plenty of non-ES products that are way easier to integrate and tune (and get better results with less effort).
- Typesense (https://github.com/typesense/typesense)
- Algolia
- Google Programmable Search Engine (https://programmablesearchengine.google.com/about/)
- Remote Machine Learning and Searching on a Raspberry Pi 5
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Open Source alternatives to tools you Pay for
Typesense - Open Source Alternative to Algolia
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DNS record "hn.algolia.com" is gone
If you like your penny take a look at Typesense https://typesense.org/ - nothing to complain here. Especially nothing complain about pricing.
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Vector databases: analyzing the trade-offs
I work on Typesense [1] (historically considered an open source alternative to Algolia).
We then launched vector search in Jan 2023, and just last week we launched the ability to generate embeddings from within Typesense.
You'd just need to send JSON data, and Typesense can generate embeddings for your data using OpenAI, PaLM API, or built-in models like S-BERT, E-5, etc (running on a GPU if you prefer) [2]
You can then do a hybrid (keyword + semantic) search by just sending the search keywords to Typesense, and Typesense will automatically generate embeddings for you internally and return a ranked list of keyword results weaved with semantic results (using Rank Fusion).
You can also combine filtering, faceting, typo tolerance, etc - the things Typesense already had.
[1] https://github.com/typesense/typesense
[2] https://typesense.org/docs/0.25.0/api/vector-search.html
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Creating an advanced search engine with PostgreSQL
For something small with a minimal footprint, I'd recommend Typesense. https://github.com/typesense/typesense
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Obsidian Publish full text search
I haven’t used Publish, but I’d assume you could use something like https://typesense.org/ to index and search the vault.
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DynamoDB search options
A cheaper option would be to use https://typesense.org. You can use DynamoDb streams to automatically load records. It has worked well for me.
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[Guide] A Tour Through the Python Framework Galaxy: Discovering the Stars
Try tigris | typesense for faster search
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Is it worth using Postgres' builtin full-text search or should I go straight to Elastic?
I’m also checking out Typesense as a possibility for replacing Elastic: https://typesense.org/
What are some alternatives?
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
Search Engine Parser - Lightweight package to query popular search engines and scrape for result titles, links and descriptions
pysolr - Pysolr — Python Solr client
Apache Solr - Apache Lucene and Solr open-source search software
elasticsearch-dsl-py - High level Python client for Elasticsearch
meilisearch-laravel-scout - MeiliSearch integration for Laravel Scout
query-builder - sql query builder library for crystal-lang
loki - Like Prometheus, but for logs.
query.cr - Query abstraction for Crystal Language. Used by active_record.cr library.
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.