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Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service. Milvus is an open-source project under LF AI & Data Foundation [2] distributed under the Apache License 2.0.
When Pinecone announced a vector database at the beginning of last year, it was building something that was specifically designed for machine learning and aimed at data scientists. It turns out ...
LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.
A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
The initial phase uses dense embeddings to retrieve documents. This retrieval can be based on a variety of database formats depending on the use case, such as a vector database, summary index, tree index, or keyword table index. [38] In response to a query, a document retriever selects the most relevant documents.
In other projects Wikidata item; Appearance. move to sidebar hide ... VectorDB was a database of sequence information for common vectors used in molecular biology [1]
Distributional–relational models were first formalized, [3] [4] as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the ...
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.