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Talk to Django

Learn how to use LLMs for Django ORM Data

0:00
8/5/2024
In this course, I show you how to implement these queries using text-to-sql and semantic search (aka natural language search) within a Django project.
This happens thanks two a few key technologies at play:
  • Neon's built-in support for pgvector -- which has optimized vector datatypes to your database to do vector-based queries. These kinds of vectors are numerical representations of your text data because machines are good at numbers
  • Embeddings via sentence-transformers and OpenAI (I show both). Embeddings turn your text into vectors such that 👆 works
  • Numpy and pgvector-python so that you can do things like Cosine Similiarty for searching your database (e.g. search for similar vectors in your database)
  • Jupyter for rapid prototyping all of this for Django ORM models (django database models)
  • OpenAI for LLM and Embeddings -- LLM is used to infer the database results to give a natural language response back (instead of just raw SQL or a Django queryset)
  • Llama Index for performing Text to SQL, RAG, and natural language text to SQL with semantic search
It's an exciting time to build projects on Django. Are you ready to roll
Talk to Django

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