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| import {CategoryTable, IndexTable} from '@theme/FeatureTables' | |
| # Retrievers | |
| A [retriever](/docs/concepts/#retrievers) is an interface that returns documents given an unstructured query. | |
| It is more general than a vector store. | |
| A retriever does not need to be able to store documents, only to return (or retrieve) them. | |
| Retrievers can be created from vector stores, but are also broad enough to include [Wikipedia search](/docs/integrations/retrievers/wikipedia/) and [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/). | |
| Retrievers accept a string query as input and return a list of [Documents](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) as output. | |
| For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers). | |
| Note that all [vector stores](/docs/concepts/#vector-stores) can be [cast to retrievers](/docs/how_to/vectorstore_retriever/). | |
| Refer to the vector store [integration docs](/docs/integrations/vectorstores/) for available vector stores. | |
| This page lists custom retrievers, implemented via subclassing [BaseRetriever](/docs/how_to/custom_retriever/). | |
| ## Bring-your-own documents | |
| The below retrievers allow you to index and search a custom corpus of documents. | |
| <CategoryTable category="document_retrievers" /> | |
| ## External index | |
| The below retrievers will search over an external index (e.g., constructed from Internet data or similar). | |
| <CategoryTable category="external_retrievers" /> | |
| ## All retrievers | |
| <IndexTable /> | |