vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
)
vector_search_as_retriever = DatabricksVectorSearch(
vs_index,
).as_retriever()
immediate = PromptTemplate(
)
chain = (
vector_search_as_retriever,
| immediate
| StrOutputParser()
)mlflow.langchain.autolog()
mlflow.fashions.set_retriever_schema(
)
mlflow.fashions.set_model(mannequin=chain)
uc_registered_model_info = mlflow.register_model(model_uri=model_uri,
title=UC_MODEL_NAME)
Analysis evaluate software & create an agent serving endpoint
deployment_info = brokers.deploy(model_name=UC_MODEL_NAME,
model_version=uc_model.model)
brokers.set_permissions(model_name=UC_MODEL_NAME,
permission_level=brokers.PermissionLevel.CAN_QUERY)
As a leading provider of advanced materials science, Corning’s expertise in glass and ceramic technologies enables numerous industrial and scientific applications, emphasizing the importance of accurate comprehension and execution of our knowledge. Utilizing the Databricks Mosaic AI Agent Framework, we developed a cutting-edge AI analysis assistant capable of indexing vast quantities of documents, including hundreds of thousands of papers and US patent office records. Having an LLM-powered assistant capable of accurately responding to questions was crucial for our organization – this enabled our researchers to efficiently complete tasks they were previously handling. We leveraged the Databricks Mosaic AI Agent Framework to build a pioneering Hello Hey Generative AI solution, seamlessly integrating it with U.S. data assets. patent workplace information. Through the strategic deployment of the Databricks Knowledge Intelligence Platform, we witnessed a substantial enhancement in data retrieval speed, response quality, and overall accuracy.
As a renowned expert in software engineering, Denis Kamotsky shares his insights on the future of technology.
eval_results = mlflow.consider(
)
As the world’s leading manufacturer, Lippert harnesses the power of data and artificial intelligence to craft meticulously designed products, tailored solutions, and unparalleled customer experiences. The Mosaic AI Agent Framework has revolutionized our operations by enabling us to evaluate the efficacy of our GenAI functions, confidently showcasing the precision of our outputs while retaining complete control over our data sources. Thanks to the Databricks Knowledge Intelligence Platform, I am confidently deploying to manufacturing.
— Kenan Colson, VP Knowledge & AI, Lippert
production-ready, scalable API
deployment_info = brokers.deploy(model_name=UC_MODEL_NAME,
model_version=MODEL_VERSION_NUMBER)The Mosaic AI Agent Framework enables rapid experimentation with augmented large language models, ensuring that personal information remains securely contained and under our comprehensive management. The smooth integration with MLflow and Mannequin Serving enables our machine learning engineering team to effortlessly scale from proof-of-concept to production-level deployment, minimizing technical complexities along the way.
Ben Halsall, Analytics Director at Burberry
Ford Direct is at the vanguard of the digital revolution reshaping the automotive industry. As the central information hub for Ford and Lincoln dealerships, we designed a comprehensive chatbot to empower sales teams by providing real-time insights into performance metrics, including seller efficiency, inventory levels, behavioral trends, and customer interaction statistics. The Databricks Mosaic AI Agent Framework enabled us to integrate our proprietary knowledge and documentation seamlessly into a generative AI solution powered by RAG. The integration of Mosaic AI with Databricks Delta Tables and Unity Catalog enabled seamless updates to our vector indexes in real-time, ensuring that our supply chain information remained current without requiring manual intervention to our deployed models.
Tom Thomas, Vice President of Analytics, Ford Direct.
