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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
Text Splitter
This class is responsible for splitting long pieces of text into smaller components. It contains different ways for splitting text (on characters, using Spacy, etc) as well as different ways for measuring length (token based, character based, etc).
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). These are particularly useful when implementing semantic search functionality. They expose separate methods for embedding queries versus embedding documents.
Vectorstores
These are datastores that store embeddings of documents in vector form. They expose a method for passing in a string and finding similar documents.
Python REPL
Sometimes, for complex calculations, rather than have an LLM generate the answer directly, it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. In order to easily do that, we provide a simple Python REPL to execute commands in. This interface will only return things that are printed - therefor, if you want to use it to calculate an answer, make sure to have it print out the answer.
Bash
It can often be useful to have an LLM generate bash commands, and then run them. A common use case this is for letting it interact with your local file system. We provide an easy component to execute bash commands.
Requests Wrapper
The web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
Google Search
This uses the official Google Search API to look up information on the web.
SerpAPI
This uses SerpAPI, a third party search API engine, to interact with Google Search.