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Intro In the previous article I gave a very brief overview of LangChain, describing its main concepts with some examples with unstructured data in pdf format. Following the same approach, in this article we will give a brief introduction to Agents and proceed by trying to answer an ambitious question: leveraging these new AI tools, can we carry out data analysis on our DB without any knowledge of SQL nor of the data model, simply starting from a text prompt in natural language?...

Intro For those unfamiliar with it, LangChain is a framework for developing applications that make use of LLMs. As the name suggests, LangChain is based on the concept of LLM Chain, which combines 3 elements: Prompt Templates: they refer to a reproducible way to generate a prompt. Contains a text string (“the model”), which can accept a series of parameters from the end user and generates the definitive prompt which is passed as input to the model The language model (LLM): LangChain integrates with the most important providers (OpenAI, Cohere, Hugging Face, etc) Output Parsers: allow to extract structure data form from the answers returned by the linguistic model The framework has 2 very interesting features:...