Graphene
Typesense
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Graphene | Typesense | |
---|---|---|
19 | 129 | |
7,971 | 17,876 | |
0.5% | 4.4% | |
5.0 | 9.8 | |
about 1 month ago | 3 days ago | |
Python | C++ | |
MIT License | 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.
Graphene
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Who moved my error codes? Adding error types to your GoLang GraphQL Server
And gqlgen is not alone in this. We found several more GraphQL frameworks that don’t take it upon themselves to address this problem. Widely used GraphQL server implementations, such as graphql-go/graphql and Python’s graphene, have the exact same gap of exposing messages of unexpected errors by default.
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Using GraphQL with Strawberry, FastAPI, and Next.js
There are multiple Python-based GraphQL libraries and they all vary slightly from each other. For the longest time, Graphene was a natural choice as it was the oldest and was used in production at different companies, but now other newer libraries have also started gaining some traction.
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Wasmer and Trademarks
> But you need to know that Wasmer and its sibling projects will stay free forever. We are open source lovers, all of us, and we have a strong background on working on open source projects before joining Wasmer too.
That's great. I'm sure the closed source paid 10x faster editions of the graphene Python GraphQL server written by Syrus "CEO of Graphene" and CEO of wasmer will be open sourced in this spirit.
https://github.com/graphql-python/graphene/issues/268#issuec...
http://graphql-quiver.com/
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graphene django with cloudinary model field
CloudinaryField is a custom model field not a django built in one and graphene-python doesn't know what do with it. See the list of types it does https://github.com/graphql-python/graphene/tree/master/graphene/types
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[Python] What minimal application server do you run your Python x GraphQL services with? Django, Flask....
Thanks for the reply, I was looking at Strawberry a bit but not enough documentation for a graphql noob like me... Hate to turn this into tech support but could you potentially answer a question for me? I'm having a really hard time figuring out how to do this. Given this example: https://github.com/graphql-python/graphene/blob/master/examples/simple_example.py and a slight modification:
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Strawberry Django Plus: Enchanted Strawberry GraphQL integration with Django
Graphene was one of the first (if not the first) lib built on top of graphql-core to provide an easy to use api to build graphql applications. It's been around since 2015 and has a lot of integrations built for it (e.g. Django, SQL Alchemy, etc).
- Graphene – Python GraphQL Library
- Graphene 3.0 is released
- Graphene 3.0 Is Released
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?
strawberry - A GraphQL library for Python that leverages type annotations 🍓
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
ariadne - Python library for implementing GraphQL servers using schema-first approach.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
tartiflette-aiohttp - tartiflette-aiohttp is a wrapper of aiohttp which includes the Tartiflette GraphQL Engine, do not hesitate to take a look of the Tartiflette project.
Apache Solr - Apache Lucene and Solr open-source search software
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
meilisearch-laravel-scout - MeiliSearch integration for Laravel Scout
CherryPy - CherryPy is a pythonic, object-oriented HTTP framework. https://cherrypy.dev
loki - Like Prometheus, but for logs.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.