Building Multi Stage Reasoning System With LangChain
In this technical guide, we delve into the construction of multi-stage reasoning systems using LangChain, focusing on creating two advanced AI systems.
This article is part 3 of 5, of our series on working LLMs.
Download the source code from the comment section.
Our first project, JekyllHyde
, is a pioneering AI tool designed for self-commenting and moderating. It employs one Large Language Model (LLM) to generate reactive comments and another to evaluate and flag any negative responses. This involves intricate steps in building prompts, establishing multiple LLM Chains, and integrating inputs from both preceding LLMs and external sources.
The second endeavor, DaScie
(pronounced "dae-see"), is conceptualized as an LLM-based agent specialized in data science. It will operate on data stored in a vector database through ChromaDB, utilizing LangChain agents, the ChromaDB library, the Pandas Dataframe Agent, and a Python Read-Eval-Print Loop (REPL) tool. This system aims to demonstrate a comprehensive approach to managing and analyzing data science tasks.
By the end of this notebook, you will have developed the skills to build custom prompt templates, create basic LLM chains linking prompts and LLMs, and construct sequential LLMChains
for sophisticated multi-stage reasoning analysis. Additionally, you'll learn to employ langchain agents in developing semi-automated systems that leverage LLM-centric agents for internet searches and dataset analysis, thereby mastering the fundamentals of advanced AI system development.
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