.NET Conf: Focus on AI 2024 20. aug 2024 How to build Retrieval Augmented Generation solution on data already stored in SQL Server or Azure SQL? In this session you'll learn about existing and future options that you can start to use right tomorrow, leveraging Azure SQL ...
Run azd up - This will provision Azure resources and deploy this sample to those resources, including building the search index based on the files found in the ./data folder. Important: Beware that the resources created by this command will incur immediate costs, primarily from the AI Searc...
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search...
Click on a specific Database in the Database List on the homepage to view detailed content. Below, we will introduce each Database individually 💡 Note: When browsing online, you can only view static pages and cannot make modifications. You need to copy to your local in order to make ...
While having a data warehouse isn’t a requirement, it is a signal that your organization is far enough along on the data-maturity journey to be able to benefit from RAG. Not having a data warehouse like BigQuery, Snowflake, or similar might mean you’re not yet at that point. This ...
(#self.api_url,#headers={'Authorization': 'Bearer your_token_here'},#json={#"model": self.model_name,#"input": text,#},#)##ret = response.json() # Adjust this based on the response format of your API#pprint.pprint(ret)##return ret['data'][0]['embedding']if__name__=='__...
Oracle Database 23ai. Once the data is on Oracle Database, we have a python script that will help us to process the data and convert the oracle data into embeddings (vector column) and then a RAG function. Let's analyze a more detailed diagram for all the steps required for this use...
This query will run on a database whose schema is represented in this string: ...省略 ### Response: Based on your instructions, here is the SQL query I have generated to answer the question `{question}`: ```sql """.format(question=question) response_model = smr_client.invoke_endpoint...
Test the website before and After adding the data. Fork the sample Repository on GitHub In this step, you create a copy from the source code on your GitHub account to be able to edit it and use it later. 1. Visit the samplegithub.com/john0isaac/rag-sema...
This query will run on a database whose schema is represented in this string: ...省略 ### Response: Based on your instructions, here is the SQL query I have generated to answer the question `{question}`: ```sql """.format(question=question) response_model = smr_client.invoke_endpoint...