🔍Perform RAG with SQL and tabular data using embedding models and large language models.
💡Convert Excel files, CSV files, and SQL databases into SQL, vector, or graph databases.
🔄Interact with SQL and vector databases using LLAM agents and large language models.
🔎Explore the power of Knowledge Graph for complex queries on SQL and vector databases.
🌐Understand the difference between RAG and Q&A projects and their implementations.
What is the difference between a RAG project and a Q&A project?
—A RAG project utilizes embedding models and large language models to perform retrieval-based and generative tasks. Q&A projects mainly focus on query-based interactions with databases.
Can Excel files, CSV files, and SQL databases be converted into different types of databases?
—Yes, Excel files, CSV files, and SQL databases can be converted into SQL, vector, or graph databases based on your project requirements.
How can LLAM agents and large language models be used to interact with SQL and vector databases?
—LLAM agents can convert user questions into queries that SQL and vector databases understand. Large language models retrieve relevant content from the databases and provide accurate answers.
What is the role of Knowledge Graph in performing complex queries?
—Knowledge Graph enables leveraging knowledge within databases and performing complex queries that traditional pipelines cannot handle. It opens up new possibilities for extracting precise information.
What are the key techniques covered in this video?
—The video covers performing RAG with SQL and tabular data, converting different file types into databases, interacting with databases using LLAM agents and large language models, and utilizing Knowledge Graph for complex queries.
00:00Introduction to Advanced R Series: Performing RAG with SQL and Tabular Data
01:28Difference between RAG projects and Q&A projects
03:05Techniques to convert Excel files, CSV files, and SQL databases into different types of databases
06:08Interacting with SQL and vector databases using LLAM agents and large language models
09:59Exploring the power of Knowledge Graph for complex queries
