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