Task-Oriented Agent
This tutorial demonstrates how to create a language model application that summarizes text and translates the summary to Spanish using LangChain. The application uses a combination of custom functions, structured tools, and an agent to process input text efficiently.
In today's data-rich world, the ability to quickly summarize information and translate it into different languages is invaluable. This tutorial aims to show how to leverage language models and the LangChain framework to create a tool that can: 1. Summarize lengthy text 2. Translate the summary to Spanish 3. Do both tasks in a single, streamlined process This type of tool can be useful for various applications, including content curation, multilingual communication, and rapid information processing.
What you'll learn
- 1Custom Functions: For summarization and translation
- 2Structured Tools: Wrappers for the custom functions
- 3Prompt Template: Instructions for the agent
- 4Agent: Orchestrates the use of tools based on the prompt
- 5Agent Executor: Runs the agent with specified parameters
About this tutorial
This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.
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