Summary Vector Search with OpenAI Embeddings using Lucene arxiv.org
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One Line
The paper demonstrates the use of OpenAI embeddings and Lucene for vector search on the MS MARCO passage ranking test collection, questioning the necessity of a separate vector store.
Slides
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Key Points
- Vector search using OpenAI embeddings and Lucene is demonstrated.
- The authors challenge the belief that a dedicated vector store is necessary for leveraging deep neural networks in search.
- Lucene is used to index the embedding vectors and evaluate the performance on the MS MARCO development set queries.
- Alternative means to achieve the capabilities of vector stores are discussed.
- Lucene is compared to Faiss, noting differences in query throughput and scalability.
- Academic papers and conference proceedings related to information retrieval and dense passage retrieval are mentioned, including "A Proposed Conceptual Framework for a Representational Approach to Information Retrieval" by Jimmy Lin in 2021.
Summaries
30 word summary
This paper shows how OpenAI embeddings and Lucene can be used for vector search on the MS MARCO passage ranking test collection, challenging the need for a dedicated vector store.
44 word summary
This paper demonstrates vector search using OpenAI embeddings and Lucene on the MS MARCO passage ranking test collection. It challenges the belief that a dedicated vector store is necessary for leveraging deep neural networks in search. The authors encode the entire corpus using OpenAI
299 word summary
This paper presents a demonstration of vector search using OpenAI embeddings and Lucene on the MS MARCO passage ranking test collection. The authors challenge the belief that a dedicated vector store is necessary for leveraging deep neural networks in search. They show that Lucene
The article discusses vector search with OpenAI embeddings using Lucene. The authors demonstrate the effectiveness of OpenAI embeddings by encoding the entire corpus and indexing the embedding vectors using Lucene. They evaluate the performance on MS MARCO development set queries and queries from
Modern enterprise architectures are complex, and adding a vector store component increases this complexity. While vector stores offer new capabilities, it is important to consider if these capabilities can be achieved through alternative means. Many organizations have already invested in search within the Lucene ecosystem
The summary presents the key points from the excerpted text in a more concise form:
The implementation of state-of-the-art vector search using generative AI can be easily achieved by combining existing components. The logical scoring model maps to the OpenAI embedding API
This summary provides an overview of the main points discussed in the excerpted text.
The text discusses the results of vector search experiments using OpenAI embeddings and Lucene. The results include comparisons with other models and mention variations in results due to indexing.
The document discusses the vector search capabilities of Lucene and its potential for improvement in performance. It compares Lucene to Faiss, noting that Lucene has slower query throughput but better scalability. The paper acknowledges alternative options, including fully managed services like Ves
This summary provides a list of academic papers and conference proceedings related to information retrieval and dense passage retrieval.
The first paper mentioned is "A Proposed Conceptual Framework for a Representational Approach to Information Retrieval" by Jimmy Lin in 2021.