Weaviate
semantic-search-through-wikipedia-with-weaviate
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| Weaviate | semantic-search-through-wikipedia-with-weaviate | |
|---|---|---|
| 76 | 9 | |
| 9,436 | 223 | |
| 4.8% | - | |
| 10.0 | 3.2 | |
| 6 days ago | 11 months ago | |
| Go | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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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.
Weaviate
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
- FLaNK Stack 29 Jan 2024
- Qdrant, the Vector Search Database, raised $28M in a Series A round
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How to use Weaviate to store and query vector embeddings
In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.
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Choosing vector database: a side-by-side comparison
This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424
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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?
Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:
1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.
2. Parameter tuning. lowering `efConstruction` can speed up imports.
3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.
In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.
- Weaviate vector database
- Weaviate 1.21: Support for ImageBind and GPT4all and more
- Weaviate Vector Database
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
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
faiss - A library for efficient similarity search and clustering of dense vectors.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
pgvector - Open-source vector similarity search for Postgres
biggraph-wikidata-search-with-weaviate - Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine
awesome-vector-search - Collections of vector search related libraries, service and research papers
jina - ☁️ Build multimodal AI applications with cloud-native stack
google-research - Google Research
vald - Vald. A Highly Scalable Distributed Vector Search Engine
Elasticsearch - Free and Open, Distributed, RESTful Search Engine