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本页 26 段还没译成日本語,下面显示的是英文原文。Retriever integrations
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A retriever 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 other sources.
Retrievers accept a string query as input and return a list of Document objects as output.
Note that all vector stores can be cast to retrievers. Refer to the vector store integration docs for available vector stores. This page lists custom retrievers, implemented via subclassing BaseRetriever.
Integration components
- Typical retrieval flow: Document loaders → Text splitters → Embedding models → Vector stores → Retrievers (this page) → Chat models
- Agent capabilities: Tools, Middleware
- Execution and state: Sandboxes, Backends, Checkpointers, Stores
Bring-your-own documents
The below retrievers allow you to index and search a custom corpus of documents.
External index
The below retrievers will search over an external index (e.g., constructed from Internet data or similar).
| Retriever | Source | Package |
|---|---|---|
ApifySearchRetriever | Internet search via the Apify RAG Web Browser | langchain-apify |
DiffbotKnowledgeGraphRetriever | Entity search over the Diffbot Knowledge Graph | langchain-diffbot |
DiffbotWebSearchRetriever | Internet search via the Diffbot Web Search API | langchain-diffbot |
ParallelSearchRetriever | Internet search via the Parallel Search API | langchain-parallel |
PerplexitySearchRetriever | Internet search via the Perplexity Search API | langchain-perplexity |
YouRetriever | Internet search | langchain-youdotcom |