Careful consideration is needed to optimize and differentiate the usage of RAGs in natural language processing.
Understanding embeddings and exploiting vector databases are key elements in the successful implementation of RAG models.
Deep dives
The Rise of RAGs: Optimizing and Differentiating
RAGs (Retrieval-Augmented Generative) models are gaining popularity in the LLM (Large Language Model) space. They allow for improved performance and easier deployment of ML models. One key optimization method discussed is the chunking and pre-processing of data for better results. The type and size of data chunks can vary depending on the desired application, such as conversational chatbots or blog writing assistance. Additionally, the need for evaluation tools and guardrails is emphasized to ensure reliable and trustworthy outputs. Multi-modal RAGs, involving images and text, hold great potential but also come with challenges, including potential compounded hallucinations. Overall, RAGs offer a powerful approach to natural language processing, but careful consideration is needed to optimize and differentiate their usage.
Understanding Embeddings and the Role of Vector Databases
Embeddings models are essential for converting data into numerical representations, enabling effective analysis and processing. In the RAG stack, these embeddings are stored in vector databases, which provide semantic stores for efficient mathematical operations. The selection of embeddings models may require training on specific data for better accuracy. Furthermore, using the output from the second to last layer of embeddings models allows for capturing the semantic meaning of the input. Similarly, the role of Large Language Models (LLMs), such as GPT and others, in the RAG stack is highlighted. However, differentiating between embedding models and LLMs is crucial, as they serve distinct purposes. Overall, understanding embeddings and exploiting vector databases are key elements in the successful implementation of RAG models.
Considerations and Trade-offs in RAG Optimization
RAG optimization involves various considerations and trade-offs. The pre-processing of data, including chunking and splitting, affects the performance and relevance of RAG models. The size and context overlap of data chunks depend on the specific application and user interaction. Storage and computational requirements should be balanced with the desired quality and latency. Additionally, the evaluation of RAG outputs, especially in multi-modal applications, helps address potential hallucinations. While multi-modal RAGs offer exciting possibilities, there are challenges in dealing with compounded hallucinations. The need for prompt engineering and convenient access to prompt customization for improved user experience is also highlighted. Overall, optimizing RAGs involves trade-offs between compute, storage, latency, and quality, requiring careful consideration.
The Versatility and Limitations of RAGs
RAGs provide versatility across various applications, making them suitable for different use cases. While they excel in applications like chatbots, document search, and Q&A systems, there are limitations in their usage. RAGs may not be well-suited for structured data, such as user tracking or specific characteristics mapping. For certain use cases like fashion AI, where similarity search suffices, employing RAGs may not be necessary. Moreover, the challenges of video search and video-to-video matching highlight the computational complexity and potential issues of compounded hallucinations. The ongoing development of multi-modal RAGs opens up new possibilities, but further research and advancements are required. As the RAG landscape continues to evolve, determining the most appropriate use cases will be essential for successful implementation.
Yujian is working as a Developer Advocate at Zilliz, where they develop and write tutorials for proof of concepts for large language model applications. They also give talks on vector databases, LLM Apps, semantic search, and tangential spaces.
MLOps podcast #206 with Yujian Tang, Developer Advocate at Zilliz, RAG Has Been Oversimplified, brought to us by our Premium Brand Partner, Zilliz
// Abstract
In the world of development, Retrieval Augmented Generation (RAG) has often been oversimplified. Despite the industry's push, the practical application of RAG reveals complexities beyond its apparent simplicity. This talk delves into the nuanced challenges and considerations developers encounter when working with RAG, providing a candid exploration of the intricacies often overlooked in the broader narrative.
// Bio
Yujian Tang is a Developer Advocate at Zilliz. He has a background as a software engineer working on AutoML at Amazon. Yujian studied Computer Science, Statistics, and Neuroscience with research papers published to conferences including IEEE Big Data. He enjoys drinking bubble tea, spending time with family, and being near water.
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// Related Links
Website: zilliz.com
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Timestamps:
[00:00] Yujian's preferred coffee
[00:17] Takeaways
[02:42] Please like, share, and subscribe to our MLOps channels!
[02:55] The hero of the LLM space
[05:42] Embeddings into Vector databases
[09:15] What is large and what is small LLM consensus
[10:10] QA Bot behind the scenes
[13:59] Fun fact getting more context
[17:05] RAGs eliminate the ability of LLMs to hallucinate
[18:50] Critical part of the rag stack
[19:57] Building citations
[20:48] Difference between context and relevance
[26:11] Missing prompt tooling
[27:46] Similarity search
[29:54] RAG Optimization
[33:03] Interacting with LLMs and tradeoffs
[35:22] RAGs not suited for
[39:33] Fashion App
[42:43] Multimodel Rags vs LLM RAGs
[44:18] Multimodel use cases
[46:50] Video citations
[47:31] Wrap up
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