Imagine a world where language models not only comprehend data but also seamlessly interact with their environment, forging a new era of AI applications. This is precisely the vision realized by LangChain, a groundbreaking framework that harnesses the power of OpenAI's GPT and other language models, enabling developers to create ChatGPT-like chatbots on their own data. LangChain is a paradigm shift in AI development, providing a versatile platform that facilitates the creation of data-aware and agentic applications.

Data Awareness: LangChain empowers language models with the ability to connect to external sources of data. This means that, beyond merely processing language, these models can now access and utilize data from various repositories, databases, or real-time streams. This data-awareness supercharges the language models, enabling them to provide more contextually relevant responses and perform tasks that involve dynamic data retrieval.

Agentic Interaction: LangChain goes beyond the passive nature of traditional language models. It allows these models to actively engage with their environment. This interaction can be seen as the ability of a language model to initiate actions, make decisions, and influence the world around it. In essence, LangChain grants language models a level of agency, making them more versatile and adaptable to various applications.

Langchain modules

In the practice notebook, you will implement various Langchain modules/components that includes:

• Model I/O
  • Chat Models
  • PromptTemplate
  • Output Parsers
• Chains
• Memory
• Retrieval
• Agents

Reference:

• Introduction to Langchain documentation