| NLP | LLM | LangChain ReAct | MultStepReason 2 |

Natural Language Processing (NLP) and Large Language Models (LLM) with LangChain / ReAct and Building Multi-stage Reasoning Systems

| Overview

In this notebook we're going to create AI systems:

• DataScienceAI will take the form of an LLM-based agent that will be tasked with performing data science tasks on data that will be stored in a vector database using ChromaDB. We will use LangChain agents as well as the ChromaDB library, as well as the Pandas Dataframe Agent and python REPL (Read-Eval-Print Loop) tool.

Learning Objectives

By the end of this notebook, you will be able to:

1. Build prompt template and create new prompts with different inputs
2. Create basic LLM chains to connect prompts and LLMs.
3. Construct sequential chains of multiple LLMChains to perform multi-stage reasoning analysis.
4. Use langchain agents to build semi-automated systems with an LM-centric agent to perform internet searches and dataset analysis.

ReAct