semantic-search-through-wikipedia-with-weaviate
sample-apps
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| semantic-search-through-wikipedia-with-weaviate | sample-apps | |
|---|---|---|
| 9 | 3 | |
| 223 | 281 | |
| - | 3.9% | |
| 3.2 | 9.6 | |
| 11 months ago | 8 days ago | |
| Python | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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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
sample-apps
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[P] I'm building a Neural Search Plugin for Elastic/Opensearch
See this blog post https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-1/ and the open source sample app it describes: https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking
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Find anything fast with Google's vector search technology
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Vespa.ai supports combining dense vector search with keyword search and ranking, see https://docs.google.com/presentation/d/1vWKhSvFH-4MFcs4aNa9C...
There is also a Vespa sample application (open source, Apache 2) demonstrating multiple different retrieval and ranking strategies over at https://github.com/vespa-engine/sample-apps/blob/master/msma...
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What Are Some Open Source NLP Framework Pipelines For QA Task
Look up Vespa.ai. https://github.com/vespa-engine/sample-apps/tree/master/dense-passage-retrieval-with-ann
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/
awesome-vector-search - Collections of vector search related libraries, service and research papers
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
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
pgvector - Open-source vector similarity search for Postgres
google-research - Google Research