GITHUB: github.com/ron...
TELEGRAM: t.me/ttyoutube...
1. *Introduction and Objective:*
- Introducing the tutorial topic on using multiple functions within LangChain utility.
- Mentioning a viewer request to demonstrate chaining functions where each function's output feeds into the next.
2. *Setting Up Environment:*
- Importing necessary modules (`chat.openai` and possibly `prompts.PromptTemplate`).
3. *Defining Functions:*
- *Function 1 (`generate_article`):*
- Generates an article on a given topic.
- Uses a prompt template to structure the request.
- Demonstrates how to use the generated content.
- *Function 2 (`translate_article`):*
- Takes the content from Function 1 as input.
- Translates the content into a specified language (e.g., Spanish, Bengali, German).
- Uses another prompt template to structure the translation request.
- Outputs the translated content.
4. *Implementation:*
- Shows how the LLM (likely GPT model) controls the execution flow between these functions.
- Demonstrates executing the functions with different parameters (e.g., different topics, languages).
5. *Conclusion and Call to Action:*
- Encourages viewers to explore further content on LangChain and LLM applications.
- Asks for subscriptions, engagement, and feedback to improve content.
Overall, your script outlines a practical demonstration of using advanced capabilities of OpenAI's language models to automate sequential tasks using natural language prompts. This approach leverages the power of AI to chain multiple functions seamlessly, showcasing its potential in various applications.
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