Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
# Key Concepts
## LLMs
Wrappers around Large Language Models (in particular, the "generate" ability of large language models) are at the core of LangChain functionality.
2023-03-02 05:18:09 +00:00
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 ](#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.
Docs refactor (#480)
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>
2023-01-02 16:24:09 +00:00
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.
