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https://github.com/hwchase17/langchain
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28 lines
1.4 KiB
Markdown
28 lines
1.4 KiB
Markdown
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# 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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## CombineDocuments Chains
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These are a subset of chains designed to work with documents. There are two pieces to consider:
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1. The underlying chain method (eg, how the documents are combined)
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2. Use cases for these types of chains.
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For the first, please see [this documentation](combine_docs.md) for more detailed information on the types of chains LangChain supports.
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For the second, please see the Use Cases section for more information on [question answering](/use_cases/question_answering.md),
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[question answering with sources](/use_cases/qa_with_sources.md), and [summarization](/use_cases/summarization.md).
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