The Future of Search with Nils Reimers and Erika Cardenas - Weaviate Podcast #97!
Jun 11, 2024
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Join the 97th Weaviate Podcast with Nils Reimers and Erika Cardenas as they delve into AI-powered search technology. Topics include Compass embeddings, multi-aspect chunking, ColBERT, RAG Evaluation, and more. Discover the challenges and solutions in embeddings, LLMs, smart chunking strategies, evaluating chatbots, and optimizing vector databases with quantization methods.
Cohere's Compass embeddings address challenges faced by traditional embeddings when dealing with longer text for refined search results.
Innovative approaches like Cohere's Compass embeddings create a knowledge graph for more precise and granular search results.
Traditional embedding models tend to overlook specific details and nuances in text, but Cohere's Compass embeddings aim to tackle this issue for better differentiation of information.
Deep dives
Advancements in AI Power Search, Cohere's Compass Embeddings
Recent advancements in AI power search are discussed, focusing on Cohere's latest Compass embeddings. These new embeddings are being tested with beta customers to address challenges faced by traditional embeddings when dealing with longer text. The goal is to provide more refined search results in scenarios involving multi-aspect data and longer documents.
Challenges in Embeddings and Search Quality Enhancements
The discussion delves into the challenges posed by traditional embeddings in search, especially when handling longer documents and diverse contextual information. It emphasizes the need for innovative approaches like Cohere's Compass embeddings, which aim to create a knowledge graph of dense embeddings to enable more precise and granular search results.
Importance of Addressing Information Loss in Embeddings
The importance of addressing information loss in embeddings, particularly when dealing with diverse and longer documents, is highlighted. Traditional embedding models tend to overlook specific details and nuances in text, leading to compromised search quality. Cohere's approach with Compass embeddings aims to tackle this issue by creating a multi-aspect knowledge graph for better differentiation of information.
Quantization for Enhanced Search Performance
The conversation shifts towards the significance of quantization in improving search performance, especially in scenarios with a massive scale of data. Introducing int8 embeddings directly in the model training phase and utilizing product quantization binary embeddings are explored as methods to reduce memory footprint in vector databases and enhance search efficiency.
Evaluation Methods and Challenges in Search Technologies
The discussion extends to evaluating complex systems like search technologies and the challenges involved in assessing their effectiveness. The importance of aligning evaluation criteria with business needs, such as factual correctness over fluency, and the role of human judgment in refining search quality are emphasized. Incorporating diverse perspectives and expert insights in evaluation processes is crucial for shaping robust and reliable search solutions.
Hey everyone! I am SUPER excited to publish our 97th Weaviate Podcast on the state of AI-powered Search technology featuring Nils Reimers and Erika Cardenas! Erika and I have been super excited about Cohere's latest works to advance RAG and Search and it was amazing getting to pick Nils' brain about all these topics!
We began with the development of Compass! Nils explains the current problem with embeddings as a soup!! For example, imagine embedding this video description, the first part is about the launch of a podcast, whereas this part is about an embedding algorithm -- how do we form representations of multi-aspect chunks of text?
We dove into all the details of this from the distinction of multi-aspect embeddings with LLM or "smart" chunkers, ColBERT, "Embed Small, Retrieve Big", and many other topics as well from Cross Encoder Re-rankers to Data Cleaning with Generative Feedback Loops, RAG Evaluation, Vector Quantization, and more!
I really hope you enjoy the podcast! It was such an educational experience for Erika and I and we really hope you enjoy it as well!
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