Super Data Science: ML & AI Podcast with Jon Krohn 710: LangChain: Create LLM Applications Easily in Python
Sep 1, 2023
Kris Ograbek discusses LangChain, niching down, and continuous improvement in AI. They touch on data preprocessing, word embeddings, and chat GPT for enhancing daily interactions. The conversation also explores the transition to hosting the Super Data Science podcast and the importance of vector embeddings for large language models.
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Use LangChain Loaders For Any Data
- Use LangChain loaders to import diverse data sources with two lines of code.
- Convert inputs into LangChain Documents so all downstream steps stay consistent and simple.
Chunk Documents With Overlap
- Split large documents into overlapping chunks using a text splitter to avoid cutting sentences.
- Include overlap so adjacent chunks preserve context across boundaries.
Embeddings Turn Meaning Into Positions
- Embeddings map chunk meaning into a high-dimensional space so semantic similarity equals proximity.
- Queries embed into the same space to retrieve the most relevant chunks for an LLM context window.