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@llama_index

Advanced QA over a lot of Tabular Data (combine text-to-SQL with RAG) 📊🪄 Our brand-new mini course 🧑‍🏫 is a comprehensive overview of how you can build simple-to-advanced query pipelines from scratch, by composing components into complex DAGs. Presenting this in three levels: 1️⃣ Basic text-to-pandas / SQL 2️⃣ Query-time table retrieval in text-to-SQL prompt 3️⃣ Query-time row retrieval in text-to-SQL prompt Steps 2 and 3 introduce RAG concepts by vectorizing the tables and rows for few-shot example selection. Adding on these layers ensures that your pipeline can scale to more tables (table retrieval), and that your queries are less prone to failure with the right examples. Uses WikiTableQuestions as a dataset (@IcePasupat et al.) Logged with @ArizePhoenix tracing (works with any of our observability partners). Video (part 2 of our advanced RAG orchestration series): https://t.co/7MxTuPc3dY Colab: https://t.co/DcC3fNjO0V Source docs: SQL: https://t.co/blb1Xfs0wN Pandas: https://t.co/us6PEUfk49

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