bleve
MeiliSearch
bleve | MeiliSearch | |
---|---|---|
13 | 129 | |
9,674 | 43,397 | |
0.7% | 1.5% | |
8.0 | 9.8 | |
about 13 hours ago | 9 days ago | |
Go | Rust | |
Apache License 2.0 | MIT License |
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.
bleve
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Hermes v1.7
I don't have the answer to that, but the project has been alive for many years. Seems maybe you should find the answer since you are developing a competing solution? Also it might be a good reference project for solving similar problems to yours. They do have bench tests you could play with https://github.com/blevesearch/bleve/blob/master/query_bench_test.go
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Seeking a free full text search solution for large data with progress display
I know of https://github.com/blevesearch/bleve and I think there was another project for full text search that I can't find now.
- Any Full Text Search library for json data?
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An alternative to Elasticsearch that runs on a few MBs of RAM
I would be interested in such a testbed. I would also like to know how Bleve Search (https://github.com/blevesearch/bleve) turns out.
I have for many years now a small search engine project in my free-time pipeline, but I'm before crawling even and I intend to sit for searching part after some of that.
- What is the coolest Go open source projects you have seen?
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BetterCache 2.0 (has full text search/remove, etc.)
Haha. Seriously I canβt tell the difference between these libraries https://github.com/blevesearch/bleve
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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.
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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.
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Mattermost β open-source platform for secure collaboration
Search in SQL databases is a tough beast to get it right. And given that we support MySQL and Postgres both, it gets even harder to support quirks of both of them.
In enterprise editions, the only addition is Elasticsearch. But in our open-source version, we do have support for https://github.com/blevesearch/bleve. Although, it's in beta, we have a lot of customers using it.
I am wondering if you have tried using it and didn't like it?
- A Database for 2022
MeiliSearch
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Publish/Subscribe with Sidekiq
We needed to introduce a new service for search. As we settled on using meilisearch, we needed a way to sync updates on our models with the records in meilisearch. We could've continued to use callbacks but we needed something better.
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The Mechanics of Silicon Valley Pump and Dump Schemes
Meilisearch
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What is Hybrid Search?
In this case, a good strategy is to use vector search only when the keyword/prefix search returns none or just a small number of results. A good candidate for this is MeiliSearch. It uses custom ranking rules to provide results as fast as the user can type.
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Create a ChatBot with VertexAI and LibreChat
With the VertexAI endpoint set up and tested, our next step is to work with LibreChat. LibreChat is an open-source ChatGPT clone that can integrate with various AI models, including the PaLM 2 models via the VertexAI API. It's built using React, MongoDB, and Meilisearch technologies.
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Pg_bm25: Elastic-Quality Full Text Search Inside Postgres
Meilisearch seems like it is the best open source option.
https://www.meilisearch.com/
- Looking for an easy installable search engine for a shared hosting account? Any ideas?
- Meilisearch: Build an intuitive search experience in a snap
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Vector storage is coming to Meilisearch to empower search through AI
Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice (Hugging Face, OpenAI). As we published the first v1.3 release candidate, you can try out vector search today.
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[N] Open-source search engine Meilisearch launches vector search
I work at Meilisearch, an open-source search engine built in Rust. π¦
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Creating search engine for your local network - Is it even possible?
https://www.meilisearch.com/ https://github.com/meilisearch
What are some alternatives?
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch β‘ π β¨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
elastic - Deprecated: Use the official Elasticsearch client for Go at https://github.com/elastic/go-elasticsearch
zincsearch - ZincSearch . A lightweight alternative to elasticsearch that requires minimal resources, written in Go.
goriak - goriak - Go language driver for Riak KV
elasticsql - convert sql to elasticsearch DSL in golang(go)
Searx - Privacy-respecting metasearch engine
goes
sonic - π¦ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
elastigo - A Go (golang) based Elasticsearch client library.
rust-postgres - Native PostgreSQL driver for the Rust programming language