diff --git a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb index 4eb477da0e..e6f7c0da45 100644 --- a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb +++ b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2023-09-09T08:02:16.802456Z", @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2023-09-09T08:02:28.174088Z", @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2023-09-09T08:02:25.452472Z", @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-09-09T08:04:16.696625Z", @@ -541,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -574,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -606,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -869,6 +869,113 @@ " print(\"-\" * 80)\n", " print(doc.page_content)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter Performance Optimization with Custom Columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To allow flexible metadata values, all metadata is stored as JSON in the metadata column by default. If some of the used metadata keys and value types are known, they can be stored in additional columns instead by creating the target table with the key names as column names and passing them to the HanaDB constructor via the specific_metadata_columns list. Metadata keys that match those values are copied into the special column during insert. Filters use the special columns instead of the metadata JSON column for keys in the specific_metadata_columns list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new table \"PERFORMANT_CUSTOMTEXT_FILTER\" with three \"standard\" columns and one additional column\n", + "my_own_table_name = \"PERFORMANT_CUSTOMTEXT_FILTER\"\n", + "cur = connection.cursor()\n", + "cur.execute(\n", + " (\n", + " f\"CREATE TABLE {my_own_table_name} (\"\n", + " \"CUSTOMTEXT NVARCHAR(500), \"\n", + " \"MY_TEXT NVARCHAR(2048), \"\n", + " \"MY_METADATA NVARCHAR(1024), \"\n", + " \"MY_VECTOR REAL_VECTOR )\"\n", + " )\n", + ")\n", + "\n", + "# Create a HanaDB instance with the own table\n", + "db = HanaDB(\n", + " connection=connection,\n", + " embedding=embeddings,\n", + " table_name=my_own_table_name,\n", + " content_column=\"MY_TEXT\",\n", + " metadata_column=\"MY_METADATA\",\n", + " vector_column=\"MY_VECTOR\",\n", + " specific_metadata_columns=[\"CUSTOMTEXT\"],\n", + ")\n", + "\n", + "# Add a simple document with some metadata\n", + "docs = [\n", + " Document(\n", + " page_content=\"Some other text\",\n", + " metadata={\n", + " \"start\": 400,\n", + " \"end\": 450,\n", + " \"doc_name\": \"other.txt\",\n", + " \"CUSTOMTEXT\": \"Filters on this value are very performant\",\n", + " },\n", + " )\n", + "]\n", + "db.add_documents(docs)\n", + "\n", + "# Check if data has been inserted into our own table\n", + "cur.execute(f\"SELECT * FROM {my_own_table_name} LIMIT 1\")\n", + "rows = cur.fetchall()\n", + "print(\n", + " rows[0][0]\n", + ") # Value of column \"CUSTOMTEXT\". Should be \"Filters on this value are very performant\"\n", + "print(rows[0][1]) # The text\n", + "print(\n", + " rows[0][2]\n", + ") # The metadata without the \"CUSTOMTEXT\" data, as this is extracted into a sperate column\n", + "print(rows[0][3]) # The vector\n", + "\n", + "cur.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The special columns are completely transparent to the rest of the langchain interface. Everything works as it did before, just more performant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "docs = [\n", + " Document(\n", + " page_content=\"Some more text\",\n", + " metadata={\n", + " \"start\": 800,\n", + " \"end\": 950,\n", + " \"doc_name\": \"more.txt\",\n", + " \"CUSTOMTEXT\": \"Another customtext value\",\n", + " },\n", + " )\n", + "]\n", + "db.add_documents(docs)\n", + "\n", + "advanced_filter = {\"CUSTOMTEXT\": {\"$like\": \"%value%\"}}\n", + "query = \"What's up?\"\n", + "docs = db.similarity_search(query, k=2, filter=advanced_filter)\n", + "for doc in docs:\n", + " print(\"-\" * 80)\n", + " print(doc.page_content)" + ] } ], "metadata": { @@ -887,7 +994,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/libs/community/langchain_community/vectorstores/hanavector.py b/libs/community/langchain_community/vectorstores/hanavector.py index ca595dec93..724c3d93b2 100644 --- a/libs/community/langchain_community/vectorstores/hanavector.py +++ b/libs/community/langchain_community/vectorstores/hanavector.py @@ -1,4 +1,5 @@ """SAP HANA Cloud Vector Engine""" + from __future__ import annotations import importlib.util @@ -85,6 +86,8 @@ class HanaDB(VectorStore): metadata_column: str = default_metadata_column, vector_column: str = default_vector_column, vector_column_length: int = default_vector_column_length, + *, + specific_metadata_columns: Optional[List[str]] = None, ): # Check if the hdbcli package is installed if importlib.util.find_spec("hdbcli") is None: @@ -110,6 +113,9 @@ class HanaDB(VectorStore): self.metadata_column = HanaDB._sanitize_name(metadata_column) self.vector_column = HanaDB._sanitize_name(vector_column) self.vector_column_length = HanaDB._sanitize_int(vector_column_length) + self.specific_metadata_columns = HanaDB._sanitize_specific_metadata_columns( + specific_metadata_columns or [] + ) # Check if the table exists, and eventually create it if not self._table_exists(self.table_name): @@ -139,6 +145,8 @@ class HanaDB(VectorStore): ["REAL_VECTOR"], self.vector_column_length, ) + for column_name in self.specific_metadata_columns: + self._check_column(self.table_name, column_name) def _table_exists(self, table_name) -> bool: # type: ignore[no-untyped-def] sql_str = ( @@ -156,7 +164,9 @@ class HanaDB(VectorStore): cur.close() return False - def _check_column(self, table_name, column_name, column_type, column_length=None): # type: ignore[no-untyped-def] + def _check_column( # type: ignore[no-untyped-def] + self, table_name, column_name, column_type=None, column_length=None + ): sql_str = ( "SELECT DATA_TYPE_NAME, LENGTH FROM SYS.TABLE_COLUMNS WHERE " "SCHEMA_NAME = CURRENT_SCHEMA " @@ -170,10 +180,11 @@ class HanaDB(VectorStore): if len(rows) == 0: raise AttributeError(f"Column {column_name} does not exist") # Check data type - if rows[0][0] not in column_type: - raise AttributeError( - f"Column {column_name} has the wrong type: {rows[0][0]}" - ) + if column_type: + if rows[0][0] not in column_type: + raise AttributeError( + f"Column {column_name} has the wrong type: {rows[0][0]}" + ) # Check length, if parameter was provided if column_length is not None: if rows[0][1] != column_length: @@ -189,17 +200,20 @@ class HanaDB(VectorStore): def embeddings(self) -> Embeddings: return self.embedding + @staticmethod def _sanitize_name(input_str: str) -> str: # type: ignore[misc] # Remove characters that are not alphanumeric or underscores return re.sub(r"[^a-zA-Z0-9_]", "", input_str) + @staticmethod def _sanitize_int(input_int: any) -> int: # type: ignore[valid-type] value = int(str(input_int)) if value < -1: raise ValueError(f"Value ({value}) must not be smaller than -1") return int(str(input_int)) - def _sanitize_list_float(embedding: List[float]) -> List[float]: # type: ignore[misc] + @staticmethod + def _sanitize_list_float(embedding: List[float]) -> List[float]: for value in embedding: if not isinstance(value, float): raise ValueError(f"Value ({value}) does not have type float") @@ -208,13 +222,36 @@ class HanaDB(VectorStore): # Compile pattern only once, for better performance _compiled_pattern = re.compile("^[_a-zA-Z][_a-zA-Z0-9]*$") - def _sanitize_metadata_keys(metadata: dict) -> dict: # type: ignore[misc] + @staticmethod + def _sanitize_metadata_keys(metadata: dict) -> dict: for key in metadata.keys(): if not HanaDB._compiled_pattern.match(key): raise ValueError(f"Invalid metadata key {key}") return metadata + @staticmethod + def _sanitize_specific_metadata_columns( + specific_metadata_columns: List[str], + ) -> List[str]: + metadata_columns = [] + for c in specific_metadata_columns: + sanitized_name = HanaDB._sanitize_name(c) + metadata_columns.append(sanitized_name) + return metadata_columns + + def _split_off_special_metadata(self, metadata: dict) -> Tuple[dict, list]: + # Use provided values by default or fallback + special_metadata = [] + + if not metadata: + return {}, [] + + for column_name in self.specific_metadata_columns: + special_metadata.append(metadata.get(column_name, None)) + + return metadata, special_metadata + def add_texts( # type: ignore[override] self, texts: Iterable[str], @@ -238,30 +275,45 @@ class HanaDB(VectorStore): if embeddings is None: embeddings = self.embedding.embed_documents(list(texts)) + # Create sql parameters array + sql_params = [] + for i, text in enumerate(texts): + metadata = metadatas[i] if metadatas else {} + metadata, extracted_special_metadata = self._split_off_special_metadata( + metadata + ) + embedding = ( + embeddings[i] + if embeddings + else self.embedding.embed_documents([text])[0] + ) + sql_params.append( + ( + text, + json.dumps(HanaDB._sanitize_metadata_keys(metadata)), + f"[{','.join(map(str, embedding))}]", + *extracted_special_metadata, + ) + ) + + # Insert data into the table cur = self.connection.cursor() try: - # Insert data into the table - for i, text in enumerate(texts): - # Use provided values by default or fallback - metadata = metadatas[i] if metadatas else {} - embedding = ( - embeddings[i] - if embeddings - else self.embedding.embed_documents([text])[0] - ) - sql_str = ( - f'INSERT INTO "{self.table_name}" ("{self.content_column}", ' - f'"{self.metadata_column}", "{self.vector_column}") ' - f"VALUES (?, ?, TO_REAL_VECTOR (?));" - ) - cur.execute( - sql_str, - ( - text, - json.dumps(HanaDB._sanitize_metadata_keys(metadata)), - f"[{','.join(map(str, embedding))}]", - ), + specific_metadata_columns_string = '", "'.join( + self.specific_metadata_columns + ) + if specific_metadata_columns_string: + specific_metadata_columns_string = ( + ', "' + specific_metadata_columns_string + '"' ) + sql_str = ( + f'INSERT INTO "{self.table_name}" ("{self.content_column}", ' + f'"{self.metadata_column}", ' + f'"{self.vector_column}"{specific_metadata_columns_string}) ' + f"VALUES (?, ?, TO_REAL_VECTOR (?)" + f"{', ?' * len(self.specific_metadata_columns)});" + ) + cur.executemany(sql_str, sql_params) finally: cur.close() return [] @@ -279,6 +331,8 @@ class HanaDB(VectorStore): metadata_column: str = default_metadata_column, vector_column: str = default_vector_column, vector_column_length: int = default_vector_column_length, + *, + specific_metadata_columns: Optional[List[str]] = None, ): """Create a HanaDB instance from raw documents. This is a user-friendly interface that: @@ -297,6 +351,7 @@ class HanaDB(VectorStore): metadata_column=metadata_column, vector_column=vector_column, vector_column_length=vector_column_length, # -1 means dynamic length + specific_metadata_columns=specific_metadata_columns, ) instance.add_texts(texts, metadatas) return instance @@ -514,10 +569,12 @@ class HanaDB(VectorStore): f"Unsupported filter data-type: {type(filter_value)}" ) - where_str += ( - f" JSON_VALUE({self.metadata_column}, '$.{key}')" - f" {operator} {sql_param}" + selector = ( + f' "{key}"' + if key in self.specific_metadata_columns + else f"JSON_VALUE({self.metadata_column}, '$.{key}')" ) + where_str += f"{selector} " f"{operator} {sql_param}" return where_str, query_tuple diff --git a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py index 6a1992cc74..fd50baf529 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py +++ b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py @@ -65,6 +65,7 @@ test_setup = ConfigData() def generateSchemaName(cursor): # type: ignore[no-untyped-def] + # return "Langchain" cursor.execute( "SELECT REPLACE(CURRENT_UTCDATE, '-', '') || '_' || BINTOHEX(SYSUUID) FROM " "DUMMY;" @@ -85,6 +86,7 @@ def setup_module(module): # type: ignore[no-untyped-def] password=os.environ.get("HANA_DB_PASSWORD"), autocommit=True, sslValidateCertificate=False, + # encrypt=True ) try: cur = test_setup.conn.cursor() @@ -100,6 +102,7 @@ def setup_module(module): # type: ignore[no-untyped-def] def teardown_module(module): # type: ignore[no-untyped-def] + # return try: cur = test_setup.conn.cursor() sql_str = f"DROP SCHEMA {test_setup.schema_name} CASCADE" @@ -112,7 +115,7 @@ def teardown_module(module): # type: ignore[no-untyped-def] @pytest.fixture def texts() -> List[str]: - return ["foo", "bar", "baz"] + return ["foo", "bar", "baz", "bak", "cat"] @pytest.fixture @@ -121,6 +124,8 @@ def metadatas() -> List[str]: {"start": 0, "end": 100, "quality": "good", "ready": True}, # type: ignore[list-item] {"start": 100, "end": 200, "quality": "bad", "ready": False}, # type: ignore[list-item] {"start": 200, "end": 300, "quality": "ugly", "ready": True}, # type: ignore[list-item] + {"start": 200, "quality": "ugly", "ready": True, "Owner": "Steve"}, # type: ignore[list-item] + {"start": 300, "quality": "ugly", "Owner": "Steve"}, # type: ignore[list-item] ] @@ -640,14 +645,14 @@ def test_hanavector_delete_with_filter(texts: List[str], metadatas: List[dict]) table_name=table_name, ) - search_result = vectorDB.similarity_search(texts[0], 3) - assert len(search_result) == 3 + search_result = vectorDB.similarity_search(texts[0], 10) + assert len(search_result) == 5 # Delete one of the three entries assert vectorDB.delete(filter={"start": 100, "end": 200}) - search_result = vectorDB.similarity_search(texts[0], 3) - assert len(search_result) == 2 + search_result = vectorDB.similarity_search(texts[0], 10) + assert len(search_result) == 4 @pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") @@ -667,14 +672,14 @@ async def test_hanavector_delete_with_filter_async( table_name=table_name, ) - search_result = vectorDB.similarity_search(texts[0], 3) - assert len(search_result) == 3 + search_result = vectorDB.similarity_search(texts[0], 10) + assert len(search_result) == 5 # Delete one of the three entries assert await vectorDB.adelete(filter={"start": 100, "end": 200}) - search_result = vectorDB.similarity_search(texts[0], 3) - assert len(search_result) == 2 + search_result = vectorDB.similarity_search(texts[0], 10) + assert len(search_result) == 4 @pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") @@ -861,7 +866,7 @@ def test_hanavector_filter_prepared_statement_params( sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() - assert len(rows) == 2 + assert len(rows) == 3 # query_value = False query_value = "false" # type: ignore[assignment] @@ -1094,3 +1099,336 @@ def test_pgvector_with_with_metadata_filters_5( ids = [doc.metadata["id"] for doc in docs] assert len(ids) == len(expected_ids), test_filter assert set(ids).issubset(expected_ids), test_filter + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_metadata_fill( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_COLUMNS" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"Owner" NVARCHAR(100), ' + f'"quality" NVARCHAR(100));' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + vectorDB = HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["Owner", "quality"], + ) + + c = 0 + try: + sql_str = f'SELECT COUNT(*) FROM {table_name} WHERE "quality"=' f"'ugly'" + cur = test_setup.conn.cursor() + cur.execute(sql_str) + if cur.has_result_set(): + rows = cur.fetchall() + c = rows[0][0] + finally: + cur.close() + assert c == 3 + + docs = vectorDB.similarity_search("hello", k=5, filter={"quality": "good"}) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + docs = vectorDB.similarity_search("hello", k=5, filter={"start": 100}) + assert len(docs) == 1 + assert docs[0].page_content == "bar" + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 100, "quality": "good"} + ) + assert len(docs) == 0 + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 0, "quality": "good"} + ) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_metadata_via_array( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_COLUMNS_VIA_ARRAY" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"Owner" NVARCHAR(100), ' + f'"quality" NVARCHAR(100));' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + vectorDB = HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["quality"], + ) + + c = 0 + try: + sql_str = f'SELECT COUNT(*) FROM {table_name} WHERE "quality"=' f"'ugly'" + cur = test_setup.conn.cursor() + cur.execute(sql_str) + if cur.has_result_set(): + rows = cur.fetchall() + c = rows[0][0] + finally: + cur.close() + assert c == 3 + + try: + sql_str = f'SELECT COUNT(*) FROM {table_name} WHERE "Owner"=' f"'Steve'" + cur = test_setup.conn.cursor() + cur.execute(sql_str) + if cur.has_result_set(): + rows = cur.fetchall() + c = rows[0][0] + finally: + cur.close() + assert c == 0 + + docs = vectorDB.similarity_search("hello", k=5, filter={"quality": "good"}) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + docs = vectorDB.similarity_search("hello", k=5, filter={"start": 100}) + assert len(docs) == 1 + assert docs[0].page_content == "bar" + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 100, "quality": "good"} + ) + assert len(docs) == 0 + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 0, "quality": "good"} + ) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_metadata_multiple_columns( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_MULTIPLE_COLUMNS" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"quality" NVARCHAR(100), ' + f'"start" INTEGER);' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + vectorDB = HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["quality", "start"], + ) + + docs = vectorDB.similarity_search("hello", k=5, filter={"quality": "good"}) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + docs = vectorDB.similarity_search("hello", k=5, filter={"start": 100}) + assert len(docs) == 1 + assert docs[0].page_content == "bar" + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 100, "quality": "good"} + ) + assert len(docs) == 0 + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 0, "quality": "good"} + ) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_metadata_empty_columns( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_MULTIPLE_COLUMNS_EMPTY" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"quality" NVARCHAR(100), ' + f'"ready" BOOLEAN, ' + f'"start" INTEGER);' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + vectorDB = HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["quality", "ready", "start"], + ) + + docs = vectorDB.similarity_search("hello", k=5, filter={"quality": "good"}) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + docs = vectorDB.similarity_search("hello", k=5, filter={"start": 100}) + assert len(docs) == 1 + assert docs[0].page_content == "bar" + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 100, "quality": "good"} + ) + assert len(docs) == 0 + + docs = vectorDB.similarity_search( + "hello", k=5, filter={"start": 0, "quality": "good"} + ) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + + docs = vectorDB.similarity_search("hello", k=5, filter={"ready": True}) + assert len(docs) == 3 + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_metadata_wrong_type_or_non_existing( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_COLUMNS_WRONG_TYPE" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"quality" INTEGER); ' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + # Check if table is created + exception_occured = False + try: + HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["quality"], + ) + exception_occured = False + except dbapi.Error: # Nothing we should do here, hdbcli will throw an error + exception_occured = True + assert exception_occured # Check if table is created + + exception_occured = False + try: + HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["NonExistingColumn"], + ) + exception_occured = False + except AttributeError: # Nothing we should do here, hdbcli will throw an error + exception_occured = True + assert exception_occured + + +@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed") +def test_preexisting_specific_columns_for_returned_metadata_completeness( + texts: List[str], metadatas: List[dict] +) -> None: + table_name = "PREEXISTING_FILTER_COLUMNS_METADATA_COMPLETENESS" + # drop_table(test_setup.conn, table_name) + + sql_str = ( + f'CREATE TABLE "{table_name}" (' + f'"VEC_TEXT" NCLOB, ' + f'"VEC_META" NCLOB, ' + f'"VEC_VECTOR" REAL_VECTOR, ' + f'"quality" NVARCHAR(100), ' + f'"NonExisting" NVARCHAR(100), ' + f'"ready" BOOLEAN, ' + f'"start" INTEGER);' + ) + try: + cur = test_setup.conn.cursor() + cur.execute(sql_str) + finally: + cur.close() + + vectorDB = HanaDB.from_texts( + connection=test_setup.conn, + texts=texts, + metadatas=metadatas, + embedding=embedding, + table_name=table_name, + specific_metadata_columns=["quality", "ready", "start", "NonExisting"], + ) + + docs = vectorDB.similarity_search("hello", k=5, filter={"quality": "good"}) + assert len(docs) == 1 + assert docs[0].page_content == "foo" + assert docs[0].metadata["end"] == 100 + assert docs[0].metadata["start"] == 0 + assert docs[0].metadata["quality"] == "good" + assert docs[0].metadata["ready"] + assert "NonExisting" not in docs[0].metadata.keys()