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30 Days of LLMs: Day 18 — LLM Chat Interface & Document Ingestion

For Day 18 of the W&B 30 Days of LLMs, we guide you through building a baseline Large Language Model application with an interactive chat interface. Discover the process of document ingestion and utilizing Langchain and Chroma.
Created on December 8|Last edited on December 10
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Day 18 — LLM Chat Interface & Document Ingestion

In Day 18 of the Weights & Biases 30 Days of LLMs, we start the hands-on development of a foundational Large Language Model (LLM) application. Our Machine Learning Engineer, Darek Kleczek, at Weights & Biases, will lead you through the essential phases of integrating documents into a vector store for a dynamic chat interface.

Chapter Highlights

  • Constructing a Chat Interface: Learn the initial steps of building a baseline application with a chat interface for user interaction.
  • Document Ingestion Process: Explore the detailed process of loading, chunking, and embedding documents for efficient retrieval in LLM applications.
  • Utilizing Langchain and Chroma: See how Langchain and Chroma DB vector store are used for processing and storing embeddings.
  • Version Control with Weights & Biases: Understand the importance of version controlling your documents and prompts using Weights & Biases artifacts.
  • Practical Implementation: Follow the hands-on approach to run the ingestion script, specifying parameters like document directory, chunk size, and overlap.

Key Course Information

  • No deep machine learning knowledge is needed, just some familiarity with Python programming.
  • Strategies for continual enhancement of your LLM applications.
  • Unique perspectives on the LLM tools used by Weights & Biases.

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Preview of the Next Chapter

Don't miss our next chapter, which focuses on further enhancing and refining LLM applications.
















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