Delphi, a San Francisco-based AI startup named after the Ancient Greek oracle, was grappling with a modern issue: its “Digital Minds,” personalized chatbots that embody an end-user’s voice through their content, were overwhelmed by data.
Each Delphi combines numerous sources like books and social media to engage users in meaningful dialogue. Creators and experts were leveraging them to connect with audiences.
However, each new media upload added complexity to Delphi’s system, threatening real-time interactivity.
Fortunately, Delphi solved its scaling issues with the managed vector database Pinecone.
AI Scaling Challenges
Power limitations, token costs, and delays are reshaping enterprise AI. Discover how top teams are:
- Using energy strategically
- Enhancing inference efficiency
- Maximizing ROI with sustainable systems
Sign up here: https://bit.ly/4mwGngO
Open source only goes so far
Delphi initially used open-source vector stores, but they quickly proved inadequate. Index sizes grew, searches slowed, and scaling became complex. Latency during events threatened conversational quality.
Delphi’s team spent weeks fine-tuning systems instead of building features.
Pinecone’s managed database offered a superior solution, supporting SOC 2 compliance and efficient data management. Each Digital Mind now has a private namespace, ensuring privacy and efficient data retrieval.
Data can be removed with one API call, and retrievals are fast, meeting performance benchmarks easily.
“Pinecone frees us to focus on performance rather than infrastructure,” said Samuel Spelsberg, Delphi’s CTO.
The architecture behind the scale
The system relies on a retrieval-augmented generation (RAG) pipeline. Content is processed and embedded with models, stored in Pinecone, and retrieved quickly to interact using a large language model.
This setup lets Delphi manage real-time chats without excessive costs.
Jeffrey Zhu, Pinecone’s VP of Product, highlighted their shift from memory-driven to storage-driven architecture, improving scalability and cost efficiency.
Pinecone adapts indexing automatically, making Delphi’s varied workloads manageable across diverse creator data sizes.
Variance among creators
Delphi hosts Digital Minds with varying content sizes, from social media to vast data archives. Pinecone’s architecture supports over 100 million vectors, ensuring consistent performance even during spikes.
Delphi manages around 20 global queries per second, scaling without issues.
Toward a million digital minds
Delphi aims to host millions of Digital Minds, requiring substantial namespace support.
Spelsberg envisions expansive growth fueled by reliable performance. Pinecone’s design caters to bursty usages, crucial for Delphi’s planned scaling.
Why RAG still matters and will for the foreseeable future
Despite growing model capabilities, RAG remains vital for delivering concise and relevant information, controlling costs and improving model outcomes.
Pinecone’s work on context engineering demonstrates how retrieval enhances large model performance, emphasizing efficient information management.
From Black Mirror to enterprise-grade
Once known for cloning historical figures, Delphi now emphasizes its practical use cases in education and professional development.
Partnering with Pinecone, Delphi supports enterprise-grade reliability and security, moving away from mere novelty.
Leave a Reply