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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.
LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization , chatbots , and code analysis .
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.
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 ...
Chroma or ChromaDB is an open-source vector database tailored to applications with large language models. [1]Its headquarters are in San Francisco.In April 2023, it raised 18 million US dollars as seed funding.
Download as PDF; Printable version; In other projects ... In other projects Wikidata item; Appearance. ... VectorDB was a database of sequence information for common ...
Described by its developers as an ACID-compliant transactional database with native graph storage and processing, [3] Neo4j is available in a non-open-source "community edition" licensed with a modification of the GNU General Public License, with online backup and high availability extensions licensed under a closed-source commercial license. [4]
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.