> For the complete documentation index, see [llms.txt](https://epsilla-inc.gitbook.io/epsilladb/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://epsilla-inc.gitbook.io/epsilladb/epsilla-vector-database/readme-1.md). # 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](https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/)). **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.\\

Example: Build a tax assistant chatbot augmented by IRS publications

### 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.