semantic-search-through-wikipedia-with-weaviate
Elasticsearch
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| semantic-search-through-wikipedia-with-weaviate | Elasticsearch | |
|---|---|---|
| 9 | 91 | |
| 223 | 67,632 | |
| - | 1.2% | |
| 3.2 | 10.0 | |
| 11 months ago | 2 days ago | |
| Python | Java | |
| MIT License | GNU General Public License v3.0 or later |
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semantic-search-through-wikipedia-with-weaviate
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Named entity recognition extraction from website
Although the Wikipedia demo dataset does not have NER enabled, you can play around with the interface. You can create a custom setup for NER using this configurator. Good luck!
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Find anything fast with Google's vector search technology
* Wikipedia demo dataset: https://github.com/semi-technologies/semantic-search-through...
- Semantic search through Wikipedia with the Weaviate vector search engine
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[D] Are you seeing any compelling use cases of semantic search being leveraged at scale?
Semantic search through Wikipedia with the Weaviate vector search engine
- [P] Semantic search through a vectorized Wikipedia
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Semantic search through complete EN-language Wikipedia with the Weaviate vector search engine
The source code to run the dataset yourself is completely open on Github
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Semantic search using GraphQL through the complete EN-Wikipedia
Github
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[P] Semantic search through Wikipedia with Weaviate and Sentence-BERT transformers
Github: https://github.com/semi-technologies/semantic-search-through-Wikipedia-with-Weaviate
Elasticsearch
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Elasticsearch Version 9
You could check out their GitHub and see what is going on https://github.com/elastic/elasticsearch/issues
- One .gitignore to rule them all
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
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Do we think about vector dbs wrong?
I believe the 1024 limit has been upped in recent versions of Elasticsearch
https://github.com/elastic/elasticsearch/issues/92458
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Elasticsearch VS openobserve - a user suggested alternative
2 projects | 30 Aug 2023
- A dedicated Elasticsearch query language (ES|QL)
- Fleet datastreams: custom index templates
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Integrating Elasticsearch with Node.js Applications
Elasticsearch is written in Java and its source code is available on Github.
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Murmur3 hash plugin for nested objects?
I don't think the murmur3 hash implementation has changed since it was added as the default in version 2.0 (see the [changes](https://github.com/elastic/elasticsearch/commits/main/server/src/main/java/org/elasticsearch/cluster/routing/Murmur3HashFunction.java)). The plugin itself has seen [more changes](https://github.com/elastic/elasticsearch/commits/main/plugins/mapper-murmur3) but that's IMO because of internals and not visible changes in the calculations.
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Mongo or Mysql for 10tb of JSON documents, I'm questioning my previous choice.
Mysql is not as open source as postgres (long story). And you can see how open elasticsearch is by just having access to the bugs database https://github.com/elastic/elasticsearch/issue
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
OpenSearch - 🔎 Open source distributed and RESTful search engine.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
bleve - A modern text/numeric/geo-spatial/vector indexing library for go
biggraph-wikidata-search-with-weaviate - Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine
pgvector - Open-source vector similarity search for Postgres
awesome-vector-search - Collections of vector search related libraries, service and research papers
Whoosh
google-research - Google Research
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow