now
phalanx
Our great sponsors
now | phalanx | |
---|---|---|
8 | 13 | |
588 | 341 | |
- | - | |
9.7 | 0.0 | |
12 months ago | about 1 year ago | |
Python | Go | |
Apache License 2.0 | Apache License 2.0 |
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.
now
-
A Guide to Using OpenTelemetry in Jina for Monitoring and Tracing Applications
💡In this post we’re just building out a backend, and not touching on a frontend. To build your own low-code backend+frontend neural search solution, check out Jina NOW.
- I want to dive into how to make search engines
-
Best stack/service for user generated content site.
Check out Jina NOW for an end-to-end text-to-image search
-
Sophisticated neural search system using < 5 lines of code.
try it out for yourself now: https://examples.jina.ai/now/. Or if you'd prefer to go through the repo and see the magic behind the scenes: https://github.com/jina-ai/now
-
No code solution for neural search. Build a search application using < 5 lines of code
Here's the link to try it out for yourself: https://now.jina.ai
-
Deep-learning powered image search in just one line code
Github Repo
-
Built a nocode solution for text to image search engine. Checkout live demo
And here's the open-source code for developer friends here in the community - https://github.com/jina-ai/now
- Show HN: Jina NOW – lowcode solution for multimodal neural search
phalanx
-
An alternative to Elasticsearch that runs on a few MBs of RAM
Somewhat related, this guy: https://github.com/mosuka/ seems to be very passionate about search service.
He built two distributed search services:
- https://github.com/mosuka/phalanx, written in Go.
- https://github.com/mosuka/bayard, written in Rust.
-
What is the coolest Go open source projects you have seen?
Don’t forget about Phalanx if you like Bleve/Bluge.
- Cloud-native distributed search engine written in Go
-
I want to dive into how to make search engines
I've never worked on a project that encompasses as many computer science algorithms as a search engine. There are a lot of topics you can lookup in "Information Storage and Retrieval":
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes, roaring bitmaps)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, sentiment analysis etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc...)
- Compression
- Applied linear algebra
- Text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
- https://github.com/thomasjungblut/go-sstables
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. The problem is that search with good rankings often requires custom storage so calculations can be sharded among multiple nodes and you can do layered ranking without passing huge blobs of results between systems.
-
Why Writing Your Own Search Engine Is Hard (2004)
For those curious, I'm on my 3rd search engine as I keep discovering new methods of compactly and efficiently processing and querying results.
There isn't a one-size-fits all approach, but I've never worked on a project that encompasses as many computer science algorithms as a search engine.
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc...)
- text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. That might work for something small like a curated collection of a few hundred sites.
-
Show HN: I built a self hosted recommendation feed to escape Google's algorithm
Is there a tool that automatically forwards every URL + HTML of the page you visit to a webhook so you could write an endpoint that would index everything?
If not, I would love to see this add a "forward to webhook" option. I would be happy to write up a real backend that parsed the content and indexed it.
Actually, there are lots of OS projects for this: https://github.com/quickwit-oss/tantivy, https://github.com/valeriansaliou/sonic, https://github.com/mosuka/phalanx, https://github.com/meilisearch/MeiliSearch, etc...
- Phalanx is a cloud-native distributed search engine with REST API written in Go
- Phalanx v0.3.0, a distributed search engine written in Go, has been released
- Phalanx 0.2.0, a distributed search engine written in Go, has been released
- Phalanx - A cloud-native full-text search and indexing server written in Go built on top of Bluge
What are some alternatives?
search-engines - Reviewing alternative search engines
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
markov - Materials for book: "Markov Chains for programmers"
ipfs-search - Search engine for the Interplanetary Filesystem.
grub-2.0 - Grub is an AI powered Web crawler.
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
docarray - Represent, send, store and search multimodal data
go-sstables - Go library for protobuf compatible sstables, a skiplist, a recordio format and other database building blocks like a write-ahead log. Ships now with an embedded key-value store.