Exploring Retrieval-Augmented Generation: Going Beyond Vector Search for AI

Exploring the frontiers of retrieval augmented generation, where human feedback, chunking, and relevancy signals are crucial to unlocking the full potential of AI applications.

1. Anton is co-founder of Chroma, a company building beyond vector search for Retrieval Augmented Generation (RAG).
2. The basic RAG loop involves embedding a corpus of documents in a vector store, finding nearest neighbor vectors for query embeddings, and returning associated documents.
3. This open-loop RAG application is just the beginning, with more powerful applications possible through additional capabilities.
4. Human feedback is essential to adapt data, embeddings models, and memory for specific tasks and users.
5. Memory used in RAG applications needs to support self-updates from agents, enabling constantly dynamic updating datasets.
6. Agents must be able to store interactions with the world and update data based on those interactions.
7. The simple RAG loop is the foundation for many current applications but more complex systems will require advanced retrieval capabilities.
8. Returning all relevant information without irrelevant distractions is a key challenge in retrieval, as distractors can significantly degrade AI application performance.
9. Determining which embedding model works best for your dataset is another open question that requires effective evaluation methods.
10. Open-source tooling and human feedback on relevance are ways to build better benchmarks for RAG applications.
11. Embedding models with the same training objectives tend to learn similar representations, suggesting it may be possible to project one model's embedding space into another using a simple linear tr
12. Chunking determines what results are available to the model; different types of chunking produce different relevancy in return results.
13. Determining relevance for retrieved results is essential; this can be achieved through human feedback, auxiliary reranking models, and augmented retrieval methods.
14. Chroma supports keyword-based search and metadata-based filtering to improve retrieval relevance.
15. Conditional relevancy signals per user, task, model, and instance can be generated algorithmically using language models that understand query semantics and dataset content.
16. Chroma is building a horizontally scalable cluster version of their product for enterprise use.
17. Chroma Cloud technical preview will be available in December 2021, with hybrid deployments coming in January 2022.
18. Multimodal data support (e.g., images and voice) is being added to Chroma to enable multimedia retrieval augmented systems.
19. Making relevancy, aesthetic quality, and other factors work together seamlessly in multimodal retrieval applications will be important for their success.
20. Chroma's focus is on building a one-stop solution for the data layer, allowing application developers to concentrate on application logic.
21. The company is working on model selection and providing an easy-to-use AI solution that "just works" for developers.
22. GPT Vision API and Gemini's image understanding capabilities will soon be available, further expanding the potential use cases for Chroma.
23. Retrieval augmented generation is a powerful tool for AI applications but requires addressing several challenges to realize its full potential.
24. Open-source lightweight language models can contribute significantly to RAG and retrieval systems development.

Source: AI Engineer via YouTube

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