semantic-search-through-wikipedia-with-weaviate
awesome-vector-search
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| semantic-search-through-wikipedia-with-weaviate | awesome-vector-search | |
|---|---|---|
| 9 | 20 | |
| 223 | 1,275 | |
| - | 5.5% | |
| 3.2 | 6.1 | |
| 11 months ago | 18 days ago | |
| Python | ||
| MIT License | MIT License |
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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
awesome-vector-search
- Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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Reality check on good embedding model (and this idea in general)
Probably. But there are a number of free open source ones. For example, I've got a document that I'm doing embedding-keys for that has about 8000 sentences. Here's a list of some [ https://github.com/currentslab/awesome-vector-search ]
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Rye, meet GPT3 ... and vice versa :)
note: search for vector databases not written in Go but with Go clients, in case there is anything more local/lightweight: https://github.com/currentslab/awesome-vector-search
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Vector database built for scalable similarity search
https://github.com/currentslab/awesome-vector-search
I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Supporting sequence of vectors does seems like a fresh air to the vector search service. I have added marqo to the list of awesome vector search (disclosure: I am the maintainer of the list) to increase your exposure.
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What are vector search engines?
If you want a proper curated list of various libraries and standalone services of vector search engines, refer to this awesome GitHub repository by Currents API.
- List of vector search libraries
- List of curated vector search libraries
- A GitHub repository that collects awesome vector search framework/engine, library, cloud service, and research papers
- Find anything fast with Google's vector search technology
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/
pgvector - Open-source vector similarity search for Postgres
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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.
biggraph-wikidata-search-with-weaviate - Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine
Milvus - A cloud-native vector database, storage for next generation AI applications
google-research - Google Research
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.