GENERATIVE ai
Many companies have a large amount of proprietary data stored in their databases. If there is a virtual agent that understands human language and can consult these databases, great opportunities open up for these companies. Think about customer service chatbots, they are a common example. These agents can handle customer requests, request information from the database, and provide the customer with what they need.
The benefit of such agents is not limited to interactions with external customers. Many business owners or people in companies, even in technology, may not know SQL or similar languages, but they still need to request information from the database. That's where frameworks like LangChain come into play. Such frameworks make it easy to create these useful agents/applications. Agents that can talk to humans while also talking to databases, APIs, and more.
LangChain is an open source framework for building interactive applications using large language models (LLM). It is a tool that helps LLMs connect with other sources of information and allows them to speak to the world around them. An important concept in such frameworks is Chain. Let's take a look at this concept.
What are chains?
Chains are advanced tools in this framework that combine LLM with other tools to perform more complicated tasks. Specifically, chains are interfaces that use a sequence of LLMs along with other tools, such as SQL databases, API calls, bash operators, or mathematical calculators, to complete complex work. An example could be that our application receives information from a user and passes it to our LLM model; then the LLM calls an API. The API responds to the LLM and the LLM takes the response to perform another task, and so on. As you can see, it is a chain of inputs and outputs where, in many parts of this sequence, we have LLM models handling the situation.
Now it's time to get our hands dirty and start coding a simple LLM-backed application. For this application, we are going to do…
