diff --git a/docs/snippets/modules/chains/popular/vector_db_qa.mdx b/docs/snippets/modules/chains/popular/vector_db_qa.mdx
index 719c30eeb0..c353d16f5c 100644
--- a/docs/snippets/modules/chains/popular/vector_db_qa.mdx
+++ b/docs/snippets/modules/chains/popular/vector_db_qa.mdx
@@ -117,3 +117,38 @@ qa.run(query)
 ```
 
 </CodeOutputBlock>
+
+## Vectorstore Retriever Options
+You can adjust how documents are retrieved from your vectorstore depending on the specific task.
+
+There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the `search_type` parameter:
+
+```python
+docsearch.as_retriever(search_type="mmr")
+```
+
+You can also modify the search by passing specific search arguments through the retriever to the search function, using the `search_kwargs` keyword argument.
+
+- `k` defines how many documents are returned; defaults to 4.
+- `score_threshold` allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.
+- `fetch_k` determines the amount of documents to pass to the MMR algorithm; defaults to 20. 
+- `lambda_mult` controls the diversity of results returned by the MMR algorithm, with 1 being minimum diversity and 0 being maximum. Defaults to 0.5.
+- `filter` allows you to define a filter on what documents should be retrieved, based on the documents' metadata. This has no effect if the Vectorstore doesn't store any metadata.
+
+Some examples for how these parameters can be used:
+```python
+# Retrieve more documents with higher diversity- useful if your dataset has many similar documents
+docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25})
+
+# Fetch more documents for the MMR algorithm to consider, but only return the top 5
+docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50})
+
+# Only retrieve documents that have a relevance score above a certain threshold
+docsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8})
+
+# Only get the single most similar document from the dataset
+docsearch.as_retriever(search_kwargs={'k': 1})
+
+# Use a filter to only retrieve documents from a specific paper 
+docsearch.as_retriever(search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}})
+```
\ No newline at end of file
diff --git a/docs/snippets/modules/chains/popular/vector_db_qa_with_sources.mdx b/docs/snippets/modules/chains/popular/vector_db_qa_with_sources.mdx
index 3135593d09..564406bb61 100644
--- a/docs/snippets/modules/chains/popular/vector_db_qa_with_sources.mdx
+++ b/docs/snippets/modules/chains/popular/vector_db_qa_with_sources.mdx
@@ -3,7 +3,7 @@ Additionally, we can return the source documents used to answer the question by
 
 
 ```python
-qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True)
+qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 30}), return_source_documents=True)
 ```
 
 
diff --git a/libs/langchain/langchain/vectorstores/base.py b/libs/langchain/langchain/vectorstores/base.py
index 45ea079a21..8f27ce20b5 100644
--- a/libs/langchain/langchain/vectorstores/base.py
+++ b/libs/langchain/langchain/vectorstores/base.py
@@ -461,8 +461,62 @@ class VectorStore(ABC):
         return tags
 
     def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
+        """Return VectorStoreRetriever initialized from this VectorStore.
+
+        Args:
+            search_type (Optional[str]): Defines the type of search that
+                the Retriever should perform.
+                Can be "similarity" (default), "mmr", or
+                "similarity_score_threshold".
+            search_kwargs (Optional[Dict]): Keyword arguments to pass to the
+                search function. Can include things like:
+                    k: Amount of documents to return (Default: 4)
+                    score_threshold: Minimum relevance threshold
+                        for similarity_score_threshold
+                    fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
+                    lambda_mult: Diversity of results returned by MMR;
+                        1 for minimum diversity and 0 for maximum. (Default: 0.5)
+                    filter: Filter by document metadata
+
+        Returns:
+            VectorStoreRetriever: Retriever class for VectorStore.
+
+        Examples:
+
+        .. code-block:: python
+
+            # Retrieve more documents with higher diversity
+            # Useful if your dataset has many similar documents
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 6, 'lambda_mult': 0.25}
+            )
+
+            # Fetch more documents for the MMR algorithm to consider
+            # But only return the top 5
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 5, 'fetch_k': 50}
+            )
+
+            # Only retrieve documents that have a relevance score
+            # Above a certain threshold
+            docsearch.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={'score_threshold': 0.8}
+            )
+
+            # Only get the single most similar document from the dataset
+            docsearch.as_retriever(search_kwargs={'k': 1})
+
+            # Use a filter to only retrieve documents from a specific paper
+            docsearch.as_retriever(
+                search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
+            )
+        """
         tags = kwargs.pop("tags", None) or []
         tags.extend(self._get_retriever_tags())
+
         return VectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)