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When dealing with multiple CSV files having different columns, it’s essential to have an efficient method for querying and extracting relevant information. Azure OpenAI and LangChain provide a robust combination for handling such scenarios.  In this article, we’ll explore how to load multiple CSV files, process them, and ask questions across all of them. Let’s get started by installing the required packages. Install Required Packages Here are the ones which you need to install: ! pip install openai ! pip install langchain ! pip install pandas ! pip install langchain_experimental ! pip install langchain_openai ! pip install tabulate Import Required Packages Here are the packages which we need to import to get started: from langchain_openai import AzureOpenAI from langchain_experimental.agents import create_csv_agent Read Configuration First of all, we need to set few variables with information from Azure portal and Azure OpenAI Studio : api_type = "azure" api_base = "

In this article, I’ll show you how can you get your hands dirty with Langchain agents. If you are not aware what Langchain is, I would recommend you to watch my recording here, wherein I just briefed about it. Langchain agents are the ones who uses LLM to determine what needs to be done and in which order. You can have a quick look at the available agents in the documentation, but I’ll list them here too. zero-shot-react-description react-docstore self-ask-with-search conversational-react-description Here agents work via tools and tools are nothing but those are functions which will be used by agents to interact with outside world. List of tools which are available today are: python_repl serpapi wolfram-alpha requests terminal pal-math pal-colored-objects llm-math open-meteo-api news-api tmdb-api google-search searx-search google-serper, etc In this article, I’m covering serpapi . Import Required Packages In order to get started, we need to import these below packages: from langchain