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Welcome back to the Total Technology Zone! In this tutorial, hosted by Ronnie, we explore how to tag documents using LangChain and the GPT-4 Omni model. Tagging, or labeling, documents is crucial for various applications, including sentiment analysis, language detection, style classification, topic identification, and more. This tutorial will walk you through the process of implementing document tagging for a specific use case.
*Tutorial Highlights:*
1. *Introduction:*
Overview of the tutorial’s objective: document tagging using LangChain and GPT-4.
Explanation of document tagging: labeling documents with classes such as sentiment, language, style, cover topics, or political tendency.
Example: Labeling a text like "The food was not good" with sentiment (unhappy), language (English), style (informal), and topic (food).
2. *Use Case:*
Applying document tagging to student reviews of a restaurant.
Demonstrating how to label each review with sentiment, emotion, and specific items mentioned.
3. *Project Setup:*
Install necessary libraries: LangChain, OpenAI, and Pydantic.
Import required modules for data tagging.
4. *Creating the Data Model:*
Define the base model for the document tags using Pydantic.
Example: Creating a data model for restaurant reviews with fields for sentiment, emotion, and specific items.
5. *Creating the Prompt Template:*
Define a prompt template to extract desired information from text passages.
Set up the prompt to extract properties such as sentiment, emotion, and items.
6. *Configuring LangChain:*
Set up the LangChain environment and create a tagging chain.
Use the GPT-4 model for generating structured output.
7. *Loading and Processing the Data:*
Load the text data using LangChain's text loader.
Split the text into individual reviews for tagging.
8. *Tagging the Documents:*
Iterate through the reviews and tag each one using the tagging chain.
Display the tagged output with sentiment, emotion, and items for each review.
9. *Practical Applications:*
Discuss potential use cases of document tagging in various industries.
Benefits for sentiment analysis, customer feedback processing, and more.
10. *Conclusion and Next Steps:*
Recap of the tutorial’s key points.
Encourage viewers to experiment with document tagging for their own use cases.
Request for feedback and suggestions for future tutorials.
By the end of this tutorial, you'll have a solid understanding of how to tag documents using LangChain and the GPT-4 Omni model. This skill is essential for developing applications that require text analysis, sentiment detection, and other NLP tasks.
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*Additional Resources:*
Tutorial 95: Generating Synthetic Data Using LangChain and GPT-4 Omni Model
Tutorial 94: Chat and Plot with Your Excel File Using LangChain and GPT-4 Omni Model
Tutorial 93: Extract Information from PDF Tables
*About the Host:*
Ronnie is dedicated to providing practical, use-case-driven tutorials to help you enhance your tech skills and tackle real-world challenges. Stay tuned for more insightful tutorials and advanced tech solutions!
Happy learning and see you in the next video!
Негізгі бет document classification , labeling , tagging using LangChain|Tutorial:96
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