Return VectorStoreRetriever initialized from this VectorStore.
as_retriever(
self,
**kwargs: Any = {},
) -> VectorStoreRetrieverExamples:
# 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"}}
)| Name | Type | Description |
|---|---|---|
**kwargs | Any | Default: {}Keyword arguments to pass to the search function. Can include:
|