community[patch]: add possibility to search by vector in OpenSearchVe…

…ctorSearch (langchain-ai#17878)

- **Description:** implements the missing `similarity_search_by_vector`
function for `OpenSearchVectorSearch`
- **Issue:** N/A
- **Dependencies:** N/A

libs/community/langchain_community/vectorstores/opensearch_vector_search.py

@@ -516,6 +516,15 @@ def similarity_search(
         docs_with_scores = self.similarity_search_with_score(query, k, **kwargs)
         return [doc[0] for doc in docs_with_scores]
 
+    def similarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to the embedding vector."""
+        docs_with_scores = self.similarity_search_with_score_by_vector(
+            embedding, k, **kwargs
+        )
+        return [doc[0] for doc in docs_with_scores]
+
     def similarity_search_with_score(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Tuple[Document, float]]:
@@ -534,46 +543,69 @@ def similarity_search_with_score(
         Optional Args:
             same as `similarity_search`
         """
+        embedding = self.embedding_function.embed_query(query)
+        return self.similarity_search_with_score_by_vector(embedding, k, **kwargs)
+
+    def similarity_search_with_score_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Return docs and it's scores most similar to the embedding vector.
+
+        By default, supports Approximate Search.
+        Also supports Script Scoring and Painless Scripting.
+
+        Args:
+            embedding: Embedding vector to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents along with its scores most similar to the query.
 
+        Optional Args:
+            same as `similarity_search`
+        """
         text_field = kwargs.get("text_field", "text")
         metadata_field = kwargs.get("metadata_field", "metadata")
 
-        hits = self._raw_similarity_search_with_score(query=query, k=k, **kwargs)
+        hits = self._raw_similarity_search_with_score_by_vector(
+            embedding=embedding, k=k, **kwargs
+        )
 
         documents_with_scores = [
             (
                 Document(
                     page_content=hit["_source"][text_field],
-                    metadata=hit["_source"]
-                    if metadata_field == "*" or metadata_field not in hit["_source"]
-                    else hit["_source"][metadata_field],
+                    metadata=(
+                        hit["_source"]
+                        if metadata_field == "*" or metadata_field not in hit["_source"]
+                        else hit["_source"][metadata_field]
+                    ),
                 ),
                 hit["_score"],
             )
             for hit in hits
         ]
         return documents_with_scores
 
-    def _raw_similarity_search_with_score(
-        self, query: str, k: int = 4, **kwargs: Any
+    def _raw_similarity_search_with_score_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
     ) -> List[dict]:
         """Return raw opensearch documents (dict) including vectors,
-        scores most similar to query.
+        scores most similar to the embedding vector.
 
         By default, supports Approximate Search.
         Also supports Script Scoring and Painless Scripting.
 
         Args:
-            query: Text to look up documents similar to.
+            embedding: Embedding vector to look up documents similar to.
             k: Number of Documents to return. Defaults to 4.
 
         Returns:
-            List of dict with its scores most similar to the query.
+            List of dict with its scores most similar to the embedding.
 
         Optional Args:
             same as `similarity_search`
         """
-        embedding = self.embedding_function.embed_query(query)
         search_type = kwargs.get("search_type", "approximate_search")
         vector_field = kwargs.get("vector_field", "vector_field")
         index_name = kwargs.get("index_name", self.index_name)
@@ -702,7 +734,9 @@ def max_marginal_relevance_search(
         embedding = self.embedding_function.embed_query(query)
 
         # Do ANN/KNN search to get top fetch_k results where fetch_k >= k
-        results = self._raw_similarity_search_with_score(query, fetch_k, **kwargs)
+        results = self._raw_similarity_search_with_score_by_vector(
+            embedding, fetch_k, **kwargs
+        )
 
         embeddings = [result["_source"][vector_field] for result in results]