RAG from scratch: Part 11 (Query Structuring)

Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. Problem: We interact w/ databases using domain-specific languages (e.g., SQL, Cypher for Relational and Graph DBs). And, many vectorstores have metadata that can allow for structured queries to filter chunks. But RAG systems ingest questions in natural language. Idea: A great deal of work has focused on query structuring, the process of text-to-DSL where DSL is a domain specific language required to interact with a given database. This converts user questions into structured queries. Below are links that dive into text-to-SQL/Cypher, and the below video overviews query structuring for vectorstores using function calling. Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_10_and_11.ipynb References: 1/ Blog with links to various tutorials and templates: https://blog.langchain.dev/query-construction/ 2/ Deep dive on graphDBs (c/o @neo4j): https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/ 3/ Query structuring docs: https://python.langchain.com/docs/use_cases/query_analysis/techniques/structuring 4/ Self-query retriever docs: https://python.langchain.com/docs/modules/data_connection/retrievers/self_query