๐ Joint Text to SQL and Semantic Search combines the power of SQL for structured data and semantic search for unstructured data.
๐ป Text-to-SQL allows users to ask questions in natural language and generate SQL statements for querying structured data.
๐ง Semantic search enables retrieval and generation of relevant information from unstructured data using vector databases.
๐ A retrieval step is performed to find similar documents based on an embedded question.
๐ The retrieved documents are fed into a synthesis model to generate a response using a language model.
๐ก Asking questions over unstructured data benefits tasks like describing scenes in history or retrieving specific dialogues.
๐ The LamaIndex allows for joint text to SQL and semantic search, combining structured and unstructured data.
๐ก Users can refine their queries and add metadata filters to effectively filter for relevant content.
๐ผ The LamaIndex features a robust query interface that can join SQL and vector databases and query them independently if needed.
๐ The video is about using a SQL statement against a database and transforming the query to get more detailed information.
๐ก The process involves using a vector database and an auto retriever module to get the desired results.
๐ The video provides an example of how to set up and use the SQL Auto Vector query engine.
โญ In this video, we learn about the LlamaIndex, which combines text to SQL and semantic search.
๐พ The LlamaIndex utilizes SQL Alchemy, a Python library, to connect with a structured database and create tables with population statistics.
๐ The LlamaIndex also builds a vector index over Wikipedia articles, associating metadata tags with each city to facilitate semantic search.
๐ The Auto Retriever can infer additional metadata filters to filter out information from the vector store.
๐ก The SQL Auto Vector query engine combines the SQL database and vector database to provide comprehensive results.
๐ The example demonstrates how the system retrieves information about the arts and culture of the city with the highest population.
๐ LlamaIndex can infer the data model for rough relationships between data and SQL or Vector databases.
๐ Semantic search can be used to find information from documents without needing the SQL database.
๐ก LlamaIndex can determine whether to use the SQL or Vector database based on the question.
๐ Some questions only require the SQL database and don't need the Vector database.