– Utilize Firestore vector search for product recommendations and chatbots by leveraging exact K-nearest neighbor (KNN) search
– Seamlessly integrate Firestore vector search with popular orchestration frameworks like LangChain and LlamaIndex
– Simplify the process of computing vector embeddings and performing vector searches with the new Firestore extension, making it easier for developers to create web and mobile applications that incorporate vector search functionality.
Google Cloud Next ’24 announced Firestore vector search in preview, allowing developers to perform vector search on transactional Firestore data without the need to copy data to another solution. Integration with popular orchestration frameworks like LangChain and LlamaIndex is available, along with a Firestore extension to compute vector embeddings and create web services for easier vector searches from web or mobile applications.
To use K-nearest neighbor (KNN) vector search in Firestore, developers first generate vector embeddings that represent different types of data in a continuous vector space. Embeddings capture similarities between entities and can be calculated using services like the Vertex AI text-embeddings API. Once generated, developers can store embeddings in Firestore using supported SDKs. For example, an embedding for data in the “description” field in the “beans” collection can be added as a vector value to the “embedding_field” using the NodeJS SDK.
Overall, Firestore’s new vector search capabilities streamline the process of implementing AI-powered solutions like product recommendations and chatbots by providing efficient and simple KNN search functionality on transactional data. Through integrations with popular frameworks and the availability of a Firestore extension, developers can easily incorporate vector search into their applications for enhanced user experiences.