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Extract, store and retrieve structured data from unstructured data by LLMs

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SC92113/LLM-Function-Calling-and-Data-Extraction

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LLM-Function-Calling-and-Data-Extraction

☰ Table of Contents

🎯 Goal

Build a Dialogue Data Extraction System

  • Part 1 - Process breakdown
    • Defining required data to be extracted
    • Building database to store extracted data
    • Defining tools to populate the database
    • Building tools to retrieve information out
  • Part 2 - Building the whole extraction system

Quick access to notebook: Dialogue_Data_Extraction_System.ipynb

💡 Key concepts in the project

Function calling

  • Single function calling
  • Multiple function calling
  • Parallel function calling
  • Nested function calling
  • No call

Function calling with external tools

  • API interfacing
  • Internal Python tool

Structured extraction

  • Simple method
  • Data class method

Function calling use cases

  • Use case 1: extract structured data from unstructured data
  • Use case 2: extract the most current data from web to self-learn and update
  • Use case 3: retrieve insights from internal database
  • Use case 4: generate valid JSON file

📚 References

🛠️ This project is supported by DeepLearning.AI and Nexusflow.

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