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