SequentialChain with LangChain - Python Example (explained)
Sequential chains consist of steps where each step takes one input and produces one output. It is one of the most popular #LLM variant in #LangChain.
In simple words, LangChain is a framework that simplifies the process of creating generative AI application interfaces basically in within the NLP context.
In Sequential chains, the output from one step becomes the input for the next. You can simply setup individual chains, define input and output for them and combine them all into logical structure with LangChain.
This tutorial covers both some theory and hands-on practice, so it can be useful either for beginner and advanced user in Large Language Models developing. The whole code is written in purely Python.
As the main LLM framework we used here in the video, is OpenAI's gpt-3.5-turbo (the current version of #chatgpt ). With this video we will develop a logical sequential chain which takes an input the employee's performance review, and delivers the output as personalized plan forr improvement based on summarized employee's weaknesses. That is almost real-life example you go deeper with this tutorial
The content of the video:
0:00 - Intro
0:23 - Idea of the example
2:41 - Hands-on with Python
12:08 - Explaining results
After this tutorial you will be more familiar with prompt engineering using LLM templates (instructions what to do for individual chains), defining templates for LLM and designing custom chains for you business use case.
Useful links:
Official documentation about LangChain Sequential Chains: https://python.langchain.com/docs/mod...
Full Python code and input data at Github repo: https://github.com/vb100/langchain_se...
Enjoy and have fun!
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