watsonx Assistant
Build Retrieval-Augmented Generation Chatbots with watsonx Assistant and Milvus or Zilliz Cloud
Use this integration for FreeWhat is watsonx Assistant?
IBM® WatsonX™ AI and Data Platform is a solution that helps developers build custom AI applications all on one platform. WatsonX includes four components:
- watsonx.ai—a solution to train, validate, tune and deploy models.
- Watsonx.data—a data store for your data
- Watsonx.governance—a set of tools to manage the end-to-end lifecycle governance of data
- watsonx Assistant—a conversational artificial intelligence platform to help developers build AI-powered voice agents and chatbots.
Why watsonx Assistant and Zilliz Cloud (Managed Milvus)
IBM watsonx Assistant is a conversational AI platform that allows developers to create chatbots to improve customer service and provide sales and support assistance. Because these chatbots will utilize your company's private data associated with your products and customer details, you can be assured that all your data remains private. This enables you to provide more comprehensive support, boosting productivity and improving your bottom line.
watsonx Assistant works with Zilliz Cloud to become the basis of a Retrieval Augmented Generation (RAG) framework. This gives you the assurance of using your private data combined with the powerful language generation capabilities of a large language model. Here are some reasons why this solution is useful:
- Efficient Storage and Retrieval: Vector databases efficiently store and retrieve high-dimensional vectors. In the context of watsonx Assistant, where large document collections and embeddings generated by LLMs are common, a vector database can help manage these vectors effectively.
- Fast Similarity Search: Vector databases are optimized for similarity search operations, which are crucial for tasks like semantic document search and retrieval-augmented generation (RAG) pipelines. By indexing vectors and enabling fast similarity search, a vector database can significantly speed up these operations in watsonx Assistant.
- Scalability: As document collections and the number of vectors grow, scalability becomes essential. Vector databases are designed to scale horizontally, allowing watsonx Assistant to effectively handle large-scale deployments and growing data volumes.
- Integration with Watson AI Services: By utilizing Watson AI Services, you can convert and store vector embeddings generated from text, audio, or image files into Zilliz Cloud. This multi-modal collection of embeddings can represent customer conversations, screenshots of errors from billing documents reported by customers, pictures of damaged products, etc., and detailed product and sales information commonly used when working with customers. This comprehensive collection of data is what makes customer intimacy possible.
In addition, watsonx has a number of foundation models that support naturelan languages other than English such as Japanese, German, Spanish, French, Portuguese and more.
How the IBM watsonx Assistant works with Zilliz Cloud Works
Retrieval Augmented Generation (RAG)
IBM watsonx has a no-code Retrieval Augmented Generation solution that integrates with Zilliz Cloud and reduces the need to feed and retrain the LLM model. Users can simply upload the latest business documentation or policies, and the model will retrieve information and return an updated response.
- Collect all relevant data (business text, audio files, images, etc.) for your chatbot. Convert this unstructured data set with one of the watsonx Foundation Models that best matches your requirements and store these vector embeddings in Zilliz Cloud.
- When the user asks a question, also known as the retrieval portion, Watsonx Assistant leverages Zilliz Cloud's semantic search capabilities to retrieve relevant content stored as vector embeddings.
- The query results are combined with the question as a prompt and sent to a large language model, like IBM's Granite, to synthesize and generate a conversational answer grounded in that content.
Learn how to use watsonx with Milvus
Check out these tutorials to learn how to use watsonx with Milvus
How to build a watsonX Assistant with Milvus as a Vector Database