How To Use output parser with Langchain Agents|Tutorial:39

#ai #llm #langchain #openai #learntocode2023
The text is a comprehensive tutorial by Ronnie on the "Total Technology Zone" channel, focusing on Tutorial 39 about using output parsers with Lang chain agents. Here's a summary of the key points covered in the tutorial:

1. **Introduction to Output Parsing:** The tutorial builds on the previous session, moving from creating a parsing experience with a prompt template to utilizing a structured output parser with Lang chain agents. Ronnie aims to demonstrate how responses from Lang chain agents can be converted into a desired format using output parsers.

2. **Tutorial Structure:** Ronnie outlines the steps for the tutorial, including the use of an output parser with a response schema, writing an efficient prompt, and validating the generated response. The tutorial is designed to be straightforward and impactful in demonstrating the utility of output parsers.

3. **Coding Demonstration:**
- **Initialization and Imports:** Ronnie starts by importing necessary modules from Lang chain, including tools for using Google Search API, and setting up the environment.
- **Response Schema Creation:** A schema is defined for the desired output, specifying fields like brand name, product name, description, price, and rating. This schema guides the parsing of the agent's response into a structured format.
- **Prompt Template and Execution:** Ronnie demonstrates how to create a prompt template and execute it using the Lang chain agent, emphasizing the simplicity of the template for the purpose of the tutorial.
- **Output Parsing:** The tutorial showcases how to set up an output parser using the response schema and generate format instructions for parsing the agent's response.
- **Example Queries:** Ronnie runs example queries to demonstrate the process of querying for the best Android phone in England and India, showing how the output parser converts the responses into the structured format defined by the response schema.

4. **Conclusion and Encouragement:**
- Ronnie concludes the tutorial by emphasizing the significance of output parsing in filtering and structuring responses from Lang chain agents according to specific needs.
- He encourages viewers to subscribe to the channel, share videos, and provide feedback to help reach a larger audience and improve future content.

The tutorial effectively demonstrates the application of output parsers with Lang chain agents, providing viewers with practical skills for structuring and customizing the responses from AI agents according to predefined schemas.