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Why Vector Database is important

 Indexing Vectors into Vector Databases:

  • Developers can index vectors generated by embeddings into a vector database.
  • This enables finding similar assets by querying for neighboring vectors.

Operationalizing Embedding Models:

  • Vector databases provide a method to operationalize embedding models.
  • Application development benefits from database capabilities like resource management, security controls, scalability, fault tolerance, and efficient information retrieval through sophisticated query languages.

Empowering Developers:

  • Vector databases empower developers to create unique application experiences.
  • Users could snap photographs on their smartphones to search for similar images.

Metadata Extraction and Hybrid Search:

  • Developers can use other machine learning models to automate metadata extraction from content like images and scanned documents.
  • They can index metadata alongside vectors to enable hybrid search on both keywords and vectors.
  • Fusion of semantic understanding into relevancy ranking improves search results.

Innovations in Generative AI:

  • Generative artificial intelligence (AI) introduces new models like ChatGPT that can generate text and manage complex conversations.
  • Some models operate on multiple modalities, allowing users to describe a landscape and generate an image that fits the description.

Challenges with Generative Models:

  • Generative models are prone to hallucinations, potentially causing issues like misleading information.

Vector Databases and Generative AI:

  • Vector databases can complement generative AI models.
  • They provide an external knowledge base for generative AI chatbots, ensuring the provision of trustworthy information.
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