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RAG (Retrieval Augmented Generation) is the process of querying a (large) set of documents with natural language, leveraging vector search and llms. While it has recently become widely accessible to develop a Proof-Of-Concept RAG using OpenAI and one of the various open-source contributions (e.g. langchain), building a performant RAG that brings value to users is challenging.
This talk will focus on learnings from building a RAG for a medical company, to allow doctors to query drug documentation with natural language, using tools like Chainlit, Qdrant and Langsmith.
Naturally, a product question emerged: how to effectively leverage LLMs that can never guarantee 100% accuracy in the health sector?
We will explain how we addressed this challenge, as well as the various technical improvements implemented to enhance both the retrieval (vector search) and generation (llm) metrics of our RAG.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
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28 июл 2024
