How to build AI applications with Python and LangChain

You can build AI applications using only a large language model (LLM) like the one that powers ChatGPT, but you’ll eventually hit the limitations that all LLMs have, including:

• Their lack of “memory”. Unless you resort to prompting tricks, an LLM will not “remember” any prior prompts.
• The difficulty with getting them to incorporate new information. LLMs know nothing outside their training data, and it takes a lot of work to augment their information from sources such as the web, PDFs, spreadsheets, databases, and other places.
• They’re designed to respond with sentences. This is great if you need conversational output, but what if you need what an application would consider to be structured output, such as JSON?
• They all have different APIs. If you want to switch away from OpenAI’s LLM to a local one running on your server for privacy/security reasons, you’ll have to make some pretty big changes to your application.

LangChain can help solve these problems. It’s a framework that makes it easier to incorporate an LLM into a data pipeline or an application. It simplifies the process of augmenting an LLM with additional information from various sources, including APIs, and can convert an LLM’s responses into structured data. It also can abstract away the details of the LLM in your application, simplifying the process of switching LLMs.

Join us at Kforce on Wednesday, March 20th, where we’ll build an AI application using Python and LangChain. You’ll see how LangChain can help you build more sophisticated AI applications that can use additional data sources, “remember” what you told it previously, and provide structured output that your application can use. We think this session will take your AI app development to a whole new level!

There will be pizza and beverages, thanks to our food sponsor, Civo, and their upcoming Civo Navigate Local Tampa conference (happening April 16th)!