Generative AI / LLM - Document Retrieval and Question Answering
With Large Language Models (LLMs), we can integrate domain-specific data to answer questions. This is especially useful for data unavailable to the model during its initial training, like a company's internal documentation or knowledge base.
This article helps you understand how to implement this architecture using LLMs and a Vector Database. We can significantly decrease the hallucinations that are commonly associated with LLMs.
Check out my Generative AI Series:
Generative AI — Getting Started with PaLM 2
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Generative AI — The Evolution of Machine Learning Engineering
/ how-to-approach-ml-problems-in-times-of-ge...
Generative AI — Best Practices for LLM Prompt Engineering
/ generative-ai-best-practices-for-llm-promp...
Generative AI — Document Retrieval and Question Answering with LLMs
/ generative-ai-document-retrieval-and-quest...
Generative AI — Mastering the Language Model Parameters for Better Outputs
/ generative-ai-mastering-the-language-model...
Generative AI - Understand and Mitigate Hallucinations in LLMs
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📓 Notebook: https://colab.research.google.com/dri...
📖 Article: / generative-ai-document-retrieval-and-quest...
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▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
00:00 Introduction
00:27 Architecture
01:16 Languages
01:45 Indexing vs. Tuning
03:33 Langchain
04:19 Data
04:40 Get data
05:10 LangChain chunking
06:02 Embedding and Vectordatabase
11:06 LLM answers questions
13:00 Bye
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