Overview
Epsilla offers an open-source vector database. Our focus is on ensuring scalability, high performance, and cost-effectiveness of vector search. Epsilla bridges the gap between information retrieval and memory retention in Large Language Models. We see ourselves as the Hippocampus for AI.
Common use cases
Here are some common use cases of Epsilla vector database
1. Augmenting LLMs with Proprietary Data
Problem: LLMs don’t have latest knowledge about the world (e.g., GPT-4 has a knowledge cutoff of April 2023), and don’t have knowledge about any private data (e.g., your company's knowledge base)
Our solution: Augment LLMs by adding semantically similar information retrieved from vector database into the prompt (also known as Retrieval Augmented Generation).
Benefits: Enable the LLMs to work for your own data and knowledge. Compare to using fine-tuning, RAG has a much faster time-to-value, is much cheaper for both engineering cost and hardware cost, and support real time knowledge updates.
2. Build Better Recommendation Systems
Problem: It’s really hard to improve recommendation result relevance, and even harder to build a scalable realtime recommendation system.
Our Solution: Use embedding as the bridge between incomparable data types to leverage the hidden relevance of user behavioral data during recommendation.
Benefits: Vector DB that leverages the hidden relevance improves recommendation recall. Epsilla’s low query latency is vital to building a realtime recommendation system.
3. Find Hidden Insights From Unstructured Data
Problem: It’s really hard to analyze and query unstructured data (images, audios, videos) based on their content semantics.
Our Solution: Connect and index the unstructured data based on the semantic relevance of their content, and enable multimodal search and analytics.
Benefits: Multimodal search becomes as easy as text search. No need to manually label unstructured data and convert to structured data anymore.
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