Fidelity Investments and real-time vector search for Amazon MemoryDB (DAT337)

Here is a detailed summary of the video transcription in Markdown format, broken into sections:

Overview of Amazon MemoryDB

  • MemoryDB is AWS's fastest database, fully-managed and durable through a multi-AZ transaction log.
  • It provides 49 9's of durability and prioritizes open-source compatibility with Redis OSS and Valkry.
  • MemoryDB enables vector search capabilities, allowing customers to store millions of vectors and provide single-digit millisecond vector search queries, updates, and CRUD operations.
  • Vector search for MemoryDB enables use cases such as fast semantic search, durable semantic caching, retrieval-augmented generation, and anomaly detection.

Fidelity's Use Cases and Decision Matrix

Financial Markets Use Case

  • Fidelity has two types of market participants: analysts (focused on historical data) and traders (interested in current earnings data).
  • Unstructured data (research notes, SEC filings) is chunked, converted to embeddings, and stored in a vector database with metadata.
  • The system ensures information barriers between analysts and traders through role-based access control.

Self-Help Incident Management Use Case

  • Fidelity leverages a pre-built knowledge base to enable individuals to resolve technical issues.
  • Incident descriptions and resolutions are chunked, embedded, and stored in a vector database with metadata.
  • Users can search the knowledge base by providing context (categories, applications, assignment groups), and the system returns the top matching results.

Capability Matrix and Decision Factors

  • Fidelity built a capability matrix to evaluate vector database requirements, including data storage, consumption, processing, security, and non-functional requirements.
  • Key decision factors included multi-cloud portability, managed service capabilities, ease of operations and support, and performance benchmarking.

MemoryDB as the Chosen Vector Store

  • Fidelity found that MemoryDB met their requirements, providing single-digit millisecond latency with high recall, non-lossy compression, and out-of-the-box monitoring and scaling capabilities.
  • MemoryDB's token-based authentication, ability to store data within Fidelity's VPC, and ease of backup and restore were also important factors in the decision.
  • Fidelity was able to implement their use cases effectively by leveraging MemoryDB's capabilities, such as defining key spaces for information barriers and using metadata filters for efficient searching.

Conclusion

  • The presenters provided links to a GitHub repository with MemoryDB APIs, a blog on semantic caching, and the integration with the Langchain framework for building generative AI applications.
  • Attendees were encouraged to provide feedback through a survey.

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