Harrison/opensearch logic (#3631)

Co-authored-by: engineer-matsuo <95115586+engineer-matsuo@users.noreply.github.com>

langchain/vectorstores/opensearch_vector_search.py

@@ -35,6 +35,15 @@ def _import_bulk() -> Any:
     return bulk
 
 
+def _import_not_found_error() -> Any:
+    """Import not found error if available, otherwise raise error."""
+    try:
+        from opensearchpy.exceptions import NotFoundError
+    except ImportError:
+        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
+    return NotFoundError
+
+
 def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
     """Get OpenSearch client from the opensearch_url, otherwise raise error."""
     try:
@@ -67,11 +76,20 @@ def _bulk_ingest_embeddings(
     metadatas: Optional[List[dict]] = None,
     vector_field: str = "vector_field",
     text_field: str = "text",
+    mapping: Dict = {},
 ) -> List[str]:
     """Bulk Ingest Embeddings into given index."""
     bulk = _import_bulk()
+    not_found_error = _import_not_found_error()
     requests = []
     ids = []
+    mapping = mapping
+
+    try:
+        client.indices.get(index=index_name)
+    except not_found_error:
+        client.indices.create(index=index_name, body=mapping)
+
     for i, text in enumerate(texts):
         metadata = metadatas[i] if metadatas else {}
         _id = str(uuid.uuid4())
@@ -311,8 +329,19 @@ class OpenSearchVectorSearch(VectorStore):
         """
         embeddings = self.embedding_function.embed_documents(list(texts))
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
-        vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
         text_field = _get_kwargs_value(kwargs, "text_field", "text")
+        dim = len(embeddings[0])
+        engine = _get_kwargs_value(kwargs, "engine", "nmslib")
+        space_type = _get_kwargs_value(kwargs, "space_type", "l2")
+        ef_search = _get_kwargs_value(kwargs, "ef_search", 512)
+        ef_construction = _get_kwargs_value(kwargs, "ef_construction", 512)
+        m = _get_kwargs_value(kwargs, "m", 16)
+        vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
+
+        mapping = _default_text_mapping(
+            dim, engine, space_type, ef_search, ef_construction, m, vector_field
+        )
+
         return _bulk_ingest_embeddings(
             self.client,
             self.index_name,
@@ -321,6 +350,7 @@ class OpenSearchVectorSearch(VectorStore):
             metadatas,
             vector_field,
             text_field,
+            mapping,
         )
 
     def similarity_search(
@@ -532,8 +562,14 @@ class OpenSearchVectorSearch(VectorStore):
 
         [kwargs.pop(key, None) for key in keys_list]
         client = _get_opensearch_client(opensearch_url, **kwargs)
-        client.indices.create(index=index_name, body=mapping)
         _bulk_ingest_embeddings(
-            client, index_name, embeddings, texts, metadatas, vector_field, text_field
+            client,
+            index_name,
+            embeddings,
+            texts,
+            metadatas,
+            vector_field,
+            text_field,
+            mapping,
         )
         return cls(opensearch_url, index_name, embedding, **kwargs)

tests/integration_tests/vectorstores/test_opensearch.py

@@ -174,3 +174,26 @@ def test_appx_search_with_lucene_filter() -> None:
     )
     output = docsearch.similarity_search("foo", k=3, lucene_filter=lucene_filter_val)
     assert output == [Document(page_content="bar")]
+
+
+def test_opensearch_with_custom_field_name_appx_true() -> None:
+    """Test Approximate Search with custom field name appx true."""
+    text_input = ["test", "add", "text", "method"]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        text_input,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        is_appx_search=True,
+    )
+    output = docsearch.similarity_search("add", k=1)
+    assert output == [Document(page_content="add")]
+
+
+def test_opensearch_with_custom_field_name_appx_false() -> None:
+    """Test Approximate Search with custom field name appx true."""
+    text_input = ["test", "add", "text", "method"]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        text_input, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+    )
+    output = docsearch.similarity_search("add", k=1)
+    assert output == [Document(page_content="add")]