# Retrievers

***

Retriever nodes return 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.

### Retriever Nodes:

* [Cohere Rerank Retriever](/integrations/langchain/retrievers/cohere-rerank-retriever.md)
* [Embeddings Filter Retriever](/integrations/langchain/retrievers/embeddings-filter-retriever.md)
* [HyDE Retriever](/integrations/langchain/retrievers/hyde-retriever.md)
* [LLM Filter Retriever](/integrations/langchain/retrievers/llm-filter-retriever.md)
* [Multi Query Retriever](/integrations/langchain/retrievers/multi-query-retriever.md)
* [Prompt Retriever](/integrations/langchain/retrievers/prompt-retriever.md)
* [Reciprocal Rank Fusion Retriever](/integrations/langchain/retrievers/reciprocal-rank-fusion-retriever.md)
* [Similarity Score Threshold Retriever](/integrations/langchain/retrievers/similarity-score-threshold-retriever.md)
* [Vector Store Retriever](/integrations/langchain/retrievers/vector-store-retriever.md)
* [Voyage AI Rerank Retriever](/integrations/langchain/retrievers/page.md)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.flowiseai.com/integrations/langchain/retrievers.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.