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Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
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Key Concepts
LLMs
Wrappers around Large Language Models (in particular, the "generate" ability of large language models) are at the core of LangChain functionality.
The core method that these classes expose is a generate method, which takes in a list of strings and returns an LLMResult (which contains outputs for all input strings).
Read more about LLMResult. This interface operates over a list of strings because often the lists of strings can be batched to the LLM provider,
providing speed and efficiency gains.
For convenience, this class also exposes a simpler, more user friendly interface (via __call__).
The interface for this takes in a single string, and returns a single string.
Generation
The output of a single generation. Currently in LangChain this is just the generated text, although could be extended in the future to contain log probs or the like.
LLMResult
The full output of a call to the generate method of the LLM class.
Since the generate method takes as input a list of strings, this returns a list of results.
Each result consists of a list of generations (since you can request N generations per input string).
This also contains a llm_output attribute which contains provider-specific information about the call.