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Core Concepts
This section goes over the core concepts of LangChain. Understanding these will go a long way in helping you understand the codebase and how to construct chains.
PromptTemplates
PromptTemplates generically have a format method that takes in variables and returns a formatted string.
The most simple implementation of this is to have a template string with some variables in it, and then format it with the incoming variables.
More complex iterations dynamically construct the template string from few shot examples, etc.
For a more detailed explanation of how LangChain approaches prompts and prompt templates, see here.
LLMs
Wrappers around Large Language Models (in particular, the generate ability of large language models) are some of the core functionality of LangChain.
These wrappers are classes that are callable: they take in an input string, and return the generated output string.
Embeddings
These classes are very similar to the LLM classes in that they are wrappers around models, but rather than return a string they return an embedding (list of floats). This are particularly useful when implementing semantic search functionality. They expose separate methods for embedding queries versus embedding documents.
Vectorstores
These are datastores that store documents. They expose a method for passing in a string and finding similar documents.
Chains
These are pipelines that combine multiple of the above ideas. They vary greatly in complexity and are combination of generic, highly configurable pipelines and more narrow (but usually more complex) pipelines.