Jeff Huber, Co-Founder and CEO @ Chroma, discusses vector databases and their importance to AI. Topics include the basics of vector databases, integration into models, trade-offs between fine-tuning and retrieval, and creating an AI stack. They also touch on Chroma's OSS project.
Chroma and vector databases have diverse use cases in chat-your-data, agent-based systems, and hybrid models, providing developers with versatile tools for AI applications.
Vector databases, like Chroma, augment foundational models by enabling fuzzy searching, bridging the gap between general knowledge and contextual information, and enhancing the performance of AI systems.
Deep dives
Use Cases for Chroma and Vector Databases
The use cases for Chroma and vector databases are diverse, but they can be categorized into three main areas: chat-your-data, agent-based systems, and hybrid models. Chat-your-data applications involve a back-and-forth conversation between humans and AI, with the AI providing responses based on the data or documents it has been trained on. This use case is popular and widely used, accounting for a significant portion of applications built with vector databases. Agent-based systems, on the other hand, focus on AI operating autonomously and performing tasks on behalf of the user. This area of application is still in its early stages, with experimentation and development ongoing. The hybrid approach combines elements of both chat-your-data and agent-based systems, enabling bidirectional communication between humans and AI, where the AI can ask questions and seek input from users. This embedded intelligence in products and services is a promising frontier, though still emerging. Overall, the use cases for Chroma and vector databases vary across these three categories, offering developers versatile tools for creating AI-powered applications.
The Importance of Vector Databases in AI
Vector databases play a crucial role in augmenting foundational models, such as large language models, with retrieval capabilities. By integrating a vector database like Chroma, developers can bridge the gap between the language model's general knowledge and specific or contextual information needed for accurate responses. The vector database enables fuzzy searching, allowing retrieval of relevant information that supports the language model's output. For example, if a user asks a question about company time-off policy, the vector database uses fuzzy search to find related documents or paragraphs that contain information relevant to the query, even if they don't match the exact wording. This retrieval-augmented generation (RAG) workflow is becoming widely popular and enables training models to provide accurate and contextually grounded responses to users. Vector databases are a valuable tool in shaping the behavior of AI systems and enhancing their overall performance.
Fine Tuning vs. No Fine Tuning in AI Applications
In AI applications, the decision to use fine tuning, where the model is trained with specific data, or relying solely on a vector database depends on the context and requirements of the task at hand. Fine tuning is beneficial when the goal is to teach the model how to think in a new and specialized domain, enabling it to develop task-specific expertise. However, fine tuning is not always necessary and can be challenging. Conversely, a vector database provides a valuable solution for grounding a model in factual information without the need for fine tuning. It allows fuzzy searching based on the user's query, retrieving relevant information from the database to support the model's response generation. While fine tuning and vector databases can be complementary, each with its own strengths, typically, fine tuning is more relevant when specific expertise is required, while vector databases excel in providing contextual information for AI systems.
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Topic 1 - Welcome to the show. Tell us a little bit about your background, and what brought you to create Chroma.
Topic 2 - Our audience, like many out there, is learning AI as fast as they can. So, let’s start with a few basic concepts. What is a vector database, and why is it important to AI?
Topic 3 - Does this only help foundational models? What goes into the integration into a model, and can this be used with any model?
Topic 4 - Is RAG (Retrieval-Augmented Generation) possible without a vector database?
Topic 5 - What are the trade-offs between fine-tuning a model (adding the data in) vs. RAG (keeping the data external)?
Topic 6 - What else is needed to create an “AI Stack”?
Topic 7 - Let’s talk about Chroma. First off, there is the OSS project which has been a huge success. Over 3 million downloads and 9.5k Github stars and inclusion into some 10k plus projects. Tell everyone a little bit about Chroma and what makes it different. You recently also announced a major milestone, over one million instances running
