LangChain Tutorials and Experiments

Repository for experimenting with LangChain. I use Python 3.9

Models

LangChain differentiates between 3 types of models: LLMs, Chat models and Text embedding models.

Prompts

LLM inputs are managed using prompts. PromptTemplates help construct prompts from multiple components and can be zero-shot or few-shot.

Chains

Chains combine LLMs with other components for application creation. For example

• Combining LLMs with prompt templates
• Combining multiple LLMs sequentially by taking the first LLM’s output as the input for the second LLM
• Combining LLMs with external data, e.g., for question answering
• Combining LLMs with long-term memory, e.g., for chat history. BY keeping all/ k conversations or by summarizing.

Agents

LLMs gebreken can (partly) be omzeild by using supplementary tools such as search, calculators and lookup. Agents decide when to use which tool.

Tutorial Links

• Towards Data Science