From 2542a09abc63fe0dfd86c3626c9c78fb37dfc46f Mon Sep 17 00:00:00 2001
From: Massimiliano Pronesti <massimiliano.pronesti@gmail.com>
Date: Thu, 18 Apr 2024 22:06:47 +0200
Subject: [PATCH] community[patch]: AzureSearch incorrectly converted to
 retriever (#20601)

Closes #20600.

Please see the issue for more details.
---
 .../vectorstores/azuresearch.py               | 34 +++++++++++++++++--
 1 file changed, 32 insertions(+), 2 deletions(-)

diff --git a/libs/community/langchain_community/vectorstores/azuresearch.py b/libs/community/langchain_community/vectorstores/azuresearch.py
index e2b23e8a19..b07628d5a7 100644
--- a/libs/community/langchain_community/vectorstores/azuresearch.py
+++ b/libs/community/langchain_community/vectorstores/azuresearch.py
@@ -654,6 +654,31 @@ class AzureSearch(VectorStore):
         azure_search.add_texts(texts, metadatas, **kwargs)
         return azure_search
 
+    def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever:  # type: ignore
+        """Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
+
+        Args:
+            search_type (Optional[str]): Defines the type of search that
+                the Retriever should perform.
+                Can be "similarity" (default), "hybrid", or
+                    "semantic_hybrid".
+            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:
+            AzureSearchVectorStoreRetriever: Retriever class for VectorStore.
+        """
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self._get_retriever_tags())
+        return AzureSearchVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
+
 
 class AzureSearchVectorStoreRetriever(BaseRetriever):
     """Retriever that uses `Azure Cognitive Search`."""
@@ -676,8 +701,13 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
         """Validate search type."""
         if "search_type" in values:
             search_type = values["search_type"]
-            if search_type not in ("similarity", "hybrid", "semantic_hybrid"):
-                raise ValueError(f"search_type of {search_type} not allowed.")
+            if search_type not in (
+                allowed_search_types := ("similarity", "hybrid", "semantic_hybrid")
+            ):
+                raise ValueError(
+                    f"search_type of {search_type} not allowed. Valid values are: "
+                    f"{allowed_search_types}"
+                )
         return values
 
     def _get_relevant_documents(