[OpenSearch] Pass ids using from_texts and indexname in add_texts and…

… search (#10969) ### Description This PR makes the following changes to OpenSearch: 1. Pass optional ids with `from_texts` 2. Pass an optional index name with `add_texts` and `search` instead of using the same index name that was used during `from_texts` ### Issue #10967 ### Maintainers @rlancemartin, @eyurtsev, @navneet1v Signed-off-by: Naveen Tatikonda <[email protected]>
langchain-ai · Sep 23, 2023 · b0f21e2 · b0f21e2
1 parent f945426
commit b0f21e2
Showing 1 changed file with 6 additions and 2 deletions.
diff --git a/libs/langchain/langchain/vectorstores/opensearch_vector_search.py b/libs/langchain/langchain/vectorstores/opensearch_vector_search.py
@@ -374,6 +374,7 @@ def add_texts(
         """
         embeddings = self.embedding_function.embed_documents(list(texts))
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
+        index_name = _get_kwargs_value(kwargs, "index_name", self.index_name)
         text_field = _get_kwargs_value(kwargs, "text_field", "text")
         dim = len(embeddings[0])
         engine = _get_kwargs_value(kwargs, "engine", "nmslib")
@@ -392,7 +393,7 @@ def add_texts(
 
         return _bulk_ingest_embeddings(
             self.client,
-            self.index_name,
+            index_name,
             embeddings,
             texts,
             metadatas=metadatas,
@@ -526,6 +527,7 @@ def _raw_similarity_search_with_score(
         embedding = self.embedding_function.embed_query(query)
         search_type = _get_kwargs_value(kwargs, "search_type", "approximate_search")
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
+        index_name = _get_kwargs_value(kwargs, "index_name", self.index_name)
 
         if (
             self.is_aoss
@@ -601,7 +603,7 @@ def _raw_similarity_search_with_score(
         else:
             raise ValueError("Invalid `search_type` provided as an argument")
 
-        response = self.client.search(index=self.index_name, body=search_query)
+        response = self.client.search(index=index_name, body=search_query)
 
         return [hit for hit in response["hits"]["hits"]]
 
@@ -663,6 +665,7 @@ def from_texts(
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         bulk_size: int = 500,
+        ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
         """Construct OpenSearchVectorSearch wrapper from raw documents.
@@ -772,6 +775,7 @@ def from_texts(
             embeddings,
             texts,
             metadatas=metadatas,
+            ids=ids,
             vector_field=vector_field,
             text_field=text_field,
             mapping=mapping,