Vector database

A vector database management system (VDBMS) or simply vector database is a database used for storing high-dimensional vectors such as word embeddings or image embeddings.[1] Vector databases are typically used with large language models.

A vector database can be used to implement Retrieval-Augmented Generation (RAG), in which relevant information is automatically added into the context window of a large language model.[2]

Example implementations

References

  1. Evan Chaki (2023-07-31). "What is a vector database?". Microsoft. A vector database is a type of database that stores data as high-dimensional vectors, which are mathematical representations of features or attributes.
  2. Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich (2020). "Retrieval-augmented generation for knowledge-intensive NLP tasks". Advances in Neural Information Processing Systems 33: 9459–9474.
  3. Sawers, Paul (2023-04-19). "Qdrant, an open source vector database startup, wants to help AI developers leverage unstructured data". TechCrunch. Retrieved 2023-09-29.
  4. "llama_index/llama_index/vector_stores/simple.py at main · run-llama/llama_index". GitHub. Retrieved 2023-10-24.
  5. "llama_index/llama_index/vector_stores at main · run-llama/llama_index". GitHub. Retrieved 2023-10-24.
  6. "Simple Vector Store - LlamaIndex 🦙 0.6.5". gpt-index.readthedocs.io. Retrieved 2023-10-24.
  7. "Using Vector Stores - LlamaIndex 🦙 0.6.5". gpt-index.readthedocs.io. Retrieved 2023-10-24.
  8. "Thistle: A Vector Database in Rust".
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