How Delphi achieved sub 100ms retrieval with Pinecone
2 days ago
- #AI
- #VectorDatabase
- #Scaling
- Delphi, an AI startup, creates personalized chatbots called 'Digital Minds' that mimic users based on their data.
- The company faced scaling issues with its AI systems due to increasing data complexity.
- Pinecone's managed vector database helped Delphi solve its scaling problems with features like SOC 2 compliance and namespace isolation.
- Delphi uses a retrieval-augmented generation (RAG) pipeline to maintain real-time conversations efficiently.
- Pinecone's architecture dynamically manages data storage and compute, reducing costs and improving scalability.
- Delphi aims to scale to millions of Digital Minds, requiring support for millions of namespaces in a single index.
- RAG remains crucial for efficiency and accuracy in AI applications, despite advancements in large language models.
- Delphi is shifting its focus from novelty AI clones to enterprise-grade tools for knowledge sharing and training.
- Future plans include an 'interview mode' for Digital Minds to interactively gather information from creators.
- Both Delphi and Pinecone are focused on scaling their technologies to support more sophisticated and widespread applications.