diff --git a/libs/oracledb/langchain_oracledb/vectorstores/oraclevs.py b/libs/oracledb/langchain_oracledb/vectorstores/oraclevs.py index 4a9d748..ef13634 100644 --- a/libs/oracledb/langchain_oracledb/vectorstores/oraclevs.py +++ b/libs/oracledb/langchain_oracledb/vectorstores/oraclevs.py @@ -26,6 +26,7 @@ Iterable, List, Optional, + Sequence, Tuple, Type, TypeVar, @@ -57,6 +58,7 @@ format="%(asctime)s - %(levelname)s - %(message)s", ) +INTERNAL_ID_KEY = "__orcl_internal_doc_id" # define a type variable that can be any kind of function T = TypeVar("T", bound=Callable[..., Any]) @@ -1008,33 +1010,33 @@ async def context(connection: Any) -> None: def get_processed_ids( - texts: Iterable[str], + texts: Optional[Iterable[str]] = None, metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, -) -> List[str]: +) -> Tuple[List[str], List[str]]: + original_ids = None if ids: # if ids are provided, hash them to maintain consistency - processed_ids = [ - hashlib.sha256(_id.encode()).hexdigest()[:16].upper() for _id in ids - ] + original_ids = [_id if _id else str(uuid.uuid4()) for _id in ids] elif metadatas and all("id" in metadata for metadata in metadatas): # if no ids are provided but metadatas with ids are, generate # ids from metadatas - processed_ids = [ - hashlib.sha256(metadata["id"].encode()).hexdigest()[:16].upper() + original_ids = [ + str(metadata["id"]) if metadata["id"] else str(uuid.uuid4()) for metadata in metadatas ] else: + input_seq = texts or metadatas or [] # generate new ids if none are provided - generated_ids = [ - str(uuid.uuid4()) for _ in texts + original_ids = [ + str(uuid.uuid4()) for _ in input_seq ] # uuid4 is more standard for random UUIDs - processed_ids = [ - hashlib.sha256(_id.encode()).hexdigest()[:16].upper() - for _id in generated_ids - ] - return processed_ids + processed_ids = [ + hashlib.sha256(_id.encode()).hexdigest()[:16].upper() for _id in original_ids + ] + + return processed_ids, original_ids def _get_delete_ddl( @@ -1044,7 +1046,7 @@ def _get_delete_ddl( raise ValueError("No ids provided to delete.") # compute SHA-256 hashes of the ids and truncate them - hashed_ids = [hashlib.sha256(_id.encode()).hexdigest()[:16].upper() for _id in ids] + hashed_ids = get_processed_ids(ids=[_id for _id in ids if _id])[0] # constructing the SQL statement with individual placeholders placeholders = ", ".join([":id" + str(i + 1) for i in range(len(hashed_ids))]) @@ -1057,6 +1059,26 @@ def _get_delete_ddl( return ddl, bind_vars +def _get_by_ids_select( + table_name: str, ids: Optional[Sequence[str]] = None +) -> Tuple[str, Dict]: + if ids is None: + raise ValueError("No ids provided to select.") + + # compute SHA-256 hashes of the ids and truncate them + hashed_ids = get_processed_ids(ids=[_id for _id in ids if _id])[0] + + # constructing the SQL statement with individual placeholders + placeholders = ", ".join([":id" + str(i + 1) for i in range(len(hashed_ids))]) + + ddl = f"SELECT text, metadata FROM {table_name} WHERE id IN ({placeholders})" + + # preparing bind variables + bind_vars = {f"id{i}": hashed_id for i, hashed_id in enumerate(hashed_ids, start=1)} + + return ddl, bind_vars + + def mmr_from_docs_embeddings( docs_scores_embeddings: List[Tuple[Document, float, NDArray[np.float32]]], embedding: List[float], @@ -1099,7 +1121,7 @@ def _get_similarity_search_query( where_clause = _generate_where_clause(filter, bind_variables) query = f""" - SELECT id, + SELECT text, metadata, vector_distance(embedding, :embedding, @@ -1143,6 +1165,87 @@ def output_type_string_handler(cursor: Any, metadata: Any) -> Any: return cursor.var(oracledb.DB_TYPE_LONG_NVARCHAR, arraysize=cursor.arraysize) +def _read_similarity_output( + results: List, has_similarity_score: bool = False, has_embeddings: bool = False +) -> List: + """ + Reads the SELECT queries, expects the following order: + result[0] - text + result[1] - metadata + result[2] - distance (optional) + result[3] - embedding (optional) + """ + docs: Any = [] + for result in results: + id = None + metadata = result[1] or {} + + if INTERNAL_ID_KEY in metadata: + id = metadata[INTERNAL_ID_KEY] + del metadata[INTERNAL_ID_KEY] + + page_content_str = result[0] if result[0] is not None else "" + + if not isinstance(page_content_str, str): + raise Exception("Unexpected type:", type(page_content_str)) + + doc = Document( + page_content=page_content_str, + metadata=metadata, + **({"id": id} if id is not None else {}), + ) + + if not has_similarity_score and not has_embeddings: + docs.append(doc) + continue + + temp_res: Any = [] + temp_res.append(doc) + + if has_similarity_score: + distance = result[2] + temp_res.append(distance) + + if has_embeddings: + # assuming result[3] is already in the correct format; + # adjust if necessary + current_embedding = ( + np.array(result[3], dtype=np.float32) + if result[3] + else np.empty(0, dtype=np.float32) + ) + temp_res.append(current_embedding) + + docs.append(tuple(temp_res)) + + return docs + + +# SQL queries to insert data into tables. +# INSERT_QUERY is used when we do not wish to update the row when there is duplicate id. +# MERGE_QUERY is used when we wish to update the row when there is duplicate id. +# both expect values in the order (id, embedding, metadata, text) + +INSERT_QUERY = ( + "INSERT INTO {table_name} (id, embedding, metadata, text) VALUES ({values})" +) +MERGE_QUERY = """ +MERGE INTO {table_name} t +USING ( + VALUES ({values}) +) s(id, embedding, metadata, text) +ON (t.id = s.id) +WHEN MATCHED THEN + UPDATE SET + t.embedding = s.embedding, + t.metadata = s.metadata, + t.text = s.text +WHEN NOT MATCHED THEN + INSERT (id, embedding, metadata, text) + VALUES (s.id, s.embedding, s.metadata, s.text) +""" + + class OracleVS(VectorStore): """`OracleVS` vector store. @@ -1151,6 +1254,11 @@ class OracleVS(VectorStore): - a connection string associated with a OracleDBCluster having deployed an Search index + - `mutate_on_duplicate` controls what happens when a document with an + existing ID is provided. + If False (default), the existing document is not updated. + If True, the document with the same ID will be updated. + Example: .. code-block:: python @@ -1178,6 +1286,7 @@ def __init__( distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = "What is a Oracle database", params: Optional[Dict[str, Any]] = None, + mutate_on_duplicate: Optional[bool] = False, ): """ Initialize the OracleVS store. @@ -1195,6 +1304,7 @@ def __init__( distance_strategy, query, params, + mutate_on_duplicate, ) embedding_dim = self.get_embedding_dimension() @@ -1213,6 +1323,7 @@ async def acreate( distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = "What is a Oracle database", params: Optional[Dict[str, Any]] = None, + mutate_on_duplicate: Optional[bool] = False, ) -> OracleVS: """ Initialize the OracleVS store with async connection. @@ -1229,6 +1340,7 @@ async def context(connection: Any) -> None: distance_strategy, query, params, + mutate_on_duplicate, ) embedding_dim = await self.aget_embedding_dimension() @@ -1250,6 +1362,7 @@ def _initialize( distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = "What is a Oracle database", params: Optional[Dict[str, Any]] = None, + mutate_on_duplicate: Optional[bool] = False, ) -> None: if not (hasattr(connection, "thin") and connection.thin): if oracledb.clientversion()[:2] < (23, 4): @@ -1280,6 +1393,7 @@ def _initialize( self.table_name = _quote_indentifier(table_name) self.distance_strategy = distance_strategy self.params = params + self.mutate_on_duplicate = mutate_on_duplicate @property def embeddings(self) -> Optional[Embeddings]: @@ -1361,20 +1475,43 @@ def add_texts( **kwargs: Any, ) -> list[str]: """Add more texts to the vectorstore index. + + Duplicate id handling behavior is controlled by the `mutate_on_duplicate` + parameter passed when creating an `OracleVS` instance: + If False: Existing rows with the same id are left unchanged; + duplicate rows are skipped and their ids are not returned + (i.e., they are not reported as successfully inserted). + If True: Existing rows with the same id are updated (upsert behavior); + returned ids include successful inserts and updates. + Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids for the texts that are being added to the vector store. kwargs: vectorstore specific parameters + + Returns: + List[str]: The ids successfully inserted (and, when mutate_on_duplicate=True, + also those successfully updated). """ texts = list(texts) - processed_ids = get_processed_ids(texts, metadatas, ids) + processed_ids, original_ids = get_processed_ids(texts, metadatas, ids) if not metadatas: metadatas = [{} for _ in texts] + for i, _id in enumerate(original_ids): + if INTERNAL_ID_KEY in metadatas[i]: + raise ValueError( + f'Metadata key "{INTERNAL_ID_KEY}" is ' + "reserved and cannot used in metadata." + ) + metadatas[i][INTERNAL_ID_KEY] = _id + + # with OracleEmbeddings, embeddings are generated in the database during insert; + # they are not sent back to Python to be written again. docs: Any if not isinstance(self.embeddings, OracleEmbeddings): embeddings = self._embed_documents(texts) @@ -1391,20 +1528,45 @@ def add_texts( ) ] else: - docs = list(zip(processed_ids, metadatas, texts)) + docs = [] + for _1, _2, _3 in zip(processed_ids, metadatas, texts): + docs.append( + {"id": _1, "meta": _2, "text": _3, "param": self.embeddings.params} + ) connection = _get_connection(self.client) if connection is None: raise ValueError("Failed to acquire a connection.") with connection.cursor() as cursor: + # self.mutate_on_duplicate controls how inserts handle existing IDs. + # If False: + # uses INSERT_QUERY. + # existing rows having the same ID as the inserted row are not updated. + # with batcherrors=True, duplicate rows are skipped and their IDs + # are not included in the `add_texts` return value (i.e., not + # reported as successfully inserted). + # + # If True: + # uses MERGE_QUERY. + # existing rows having the same ID as the inserted row are updated + # with the new data ("upsert"). + # the ID is included in the `add_texts` return value, + # indicating a successful insert/update. + selected_query = ( + INSERT_QUERY if not self.mutate_on_duplicate else MERGE_QUERY + ) if not isinstance(self.embeddings, OracleEmbeddings): - cursor.setinputsizes(None, None, oracledb.DB_TYPE_JSON, None) + cursor.setinputsizes( + None, oracledb.DB_TYPE_VECTOR, oracledb.DB_TYPE_JSON, None + ) cursor.executemany( - f"INSERT INTO {self.table_name} (id, embedding, metadata, " - f"text) VALUES (:1, :2, :3, :4)", + selected_query.format( + table_name=self.table_name, values=":1, :2, :3, :4" + ), docs, + batcherrors=True, ) - connection.commit() + else: if self.embeddings.proxy: cursor.execute( @@ -1412,22 +1574,31 @@ def add_texts( proxy=self.embeddings.proxy, ) - cursor.setinputsizes(None, oracledb.DB_TYPE_JSON, None) + cursor.setinputsizes( + meta=oracledb.DB_TYPE_JSON, param=oracledb.DB_TYPE_JSON + ) + cursor.executemany( - f"INSERT INTO {self.table_name} (id, metadata, " - f"text) VALUES (:1, :2, :3)", + selected_query.format( + table_name=self.table_name, + values=( + ":id, dbms_vector_chain.utl_to_embedding(:text,json(:param)), " # noqa: E501 + ":meta, :text" + ), + ), docs, + batcherrors=True, ) - cursor.setinputsizes(oracledb.DB_TYPE_JSON) - update_sql = ( - f"UPDATE {self.table_name} " - "SET embedding = dbms_vector_chain.utl_to_embedding(text, json(:1))" - ) - cursor.execute(update_sql, [self.embeddings.params]) - connection.commit() + error_indices = [error.offset for error in cursor.getbatcherrors()] + connection.commit() - return processed_ids + # do not change the input dict list + for i in range(len(original_ids)): + del metadatas[i][INTERNAL_ID_KEY] + + inserted_ids = [i for j, i in enumerate(original_ids) if j not in error_indices] + return inserted_ids @_ahandle_exceptions async def aadd_texts( @@ -1439,20 +1610,43 @@ async def aadd_texts( **kwargs: Any, ) -> list[str]: """Add more texts to the vectorstore index, async. + + Duplicate id handling behavior is controlled by the `mutate_on_duplicate` + parameter passed when creating an `OracleVS` instance: + If False: Existing rows with the same id are left unchanged; + duplicate rows are skipped and their ids are not returned + (i.e., they are not reported as successfully inserted). + If True: Existing rows with the same id are updated (upsert behavior); + returned ids include successful inserts and updates. + Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids for the texts that are being added to the vector store. kwargs: vectorstore specific parameters + + Returns: + List[str]: The ids successfully inserted (and, when mutate_on_duplicate=True, + also those successfully updated). """ texts = list(texts) - processed_ids = get_processed_ids(texts, metadatas, ids) + processed_ids, original_ids = get_processed_ids(texts, metadatas, ids) if not metadatas: metadatas = [{} for _ in texts] + for i, _id in enumerate(original_ids): + if INTERNAL_ID_KEY in metadatas[i]: + raise ValueError( + f'Metadata key "{INTERNAL_ID_KEY}" is ' + "reserved and cannot used in metadata." + ) + metadatas[i][INTERNAL_ID_KEY] = _id + + # with OracleEmbeddings, embeddings are generated in the database during insert; + # they are not sent back to Python to be written again. docs: Any if not isinstance(self.embeddings, OracleEmbeddings): embeddings = await self._aembed_documents(texts) @@ -1469,20 +1663,32 @@ async def aadd_texts( ) ] else: - docs = list(zip(processed_ids, metadatas, texts)) + docs = [] + for _1, _2, _3 in zip(processed_ids, metadatas, texts): + docs.append( + {"id": _1, "meta": _2, "text": _3, "param": self.embeddings.params} + ) - async def context(connection: Any) -> None: + async def context(connection: Any) -> List[str]: if connection is None: raise ValueError("Failed to acquire a connection.") with connection.cursor() as cursor: + # self.mutate_on_duplicate controls how inserts handle existing IDs, + # behavior is identical to the synchronous `add_texts` method. + selected_query = ( + INSERT_QUERY if not self.mutate_on_duplicate else MERGE_QUERY + ) if not isinstance(self.embeddings, OracleEmbeddings): - cursor.setinputsizes(None, None, oracledb.DB_TYPE_JSON, None) + cursor.setinputsizes( + None, oracledb.DB_TYPE_VECTOR, oracledb.DB_TYPE_JSON, None + ) await cursor.executemany( - f"INSERT INTO {self.table_name} (id, embedding, metadata, " - f"text) VALUES (:1, :2, :3, :4)", + selected_query.format( + table_name=self.table_name, values=":1, :2, :3, :4" + ), docs, + batcherrors=True, ) - await connection.commit() else: if self.embeddings.proxy: await cursor.execute( @@ -1490,24 +1696,34 @@ async def context(connection: Any) -> None: proxy=self.embeddings.proxy, ) - cursor.setinputsizes(None, oracledb.DB_TYPE_JSON, None) + cursor.setinputsizes( + meta=oracledb.DB_TYPE_JSON, param=oracledb.DB_TYPE_JSON + ) + await cursor.executemany( - f"INSERT INTO {self.table_name} (id, metadata, " - f"text) VALUES (:1, :2, :3)", + selected_query.format( + table_name=self.table_name, + values=( + ":id, dbms_vector_chain.utl_to_embedding(:text,json(:param)), " # noqa: E501 + ":meta, :text" + ), + ), docs, + batcherrors=True, ) - cursor.setinputsizes(oracledb.DB_TYPE_JSON) - update_sql = ( - f"UPDATE {self.table_name} " - "SET embedding = dbms_vector_chain.utl_to_embedding(text, json(:1))" # noqa: E501 - ) - await cursor.execute(update_sql, [self.embeddings.params]) - await connection.commit() + error_indices = [error.offset for error in cursor.getbatcherrors()] + await connection.commit() + return error_indices - await _handle_context(self.client, context) + error_indices = await _handle_context(self.client, context) - return processed_ids + # do not change the input dict list + for i in range(len(original_ids)): + del metadatas[i][INTERNAL_ID_KEY] + + inserted_ids = [i for j, i in enumerate(original_ids) if j not in error_indices] + return inserted_ids def similarity_search( self, @@ -1640,20 +1856,9 @@ def similarity_search_by_vector_with_relevance_scores( cursor.execute(query, **params) results = cursor.fetchall() - for result in results: - metadata = result[2] or {} - page_content_str = result[1] if result[1] is not None else "" - - if not isinstance(page_content_str, str): - raise Exception("Unexpected type:", type(page_content_str)) - - doc = Document( - page_content=page_content_str, - metadata=metadata, - ) - distance = result[3] - - docs_and_scores.append((doc, distance)) + docs_and_scores = _read_similarity_output( + results, has_similarity_score=True + ) return docs_and_scores @@ -1666,8 +1871,6 @@ async def asimilarity_search_by_vector_with_relevance_scores( filter: Optional[dict] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: - docs_and_scores = [] - embedding_arr: Any = array.array("f", embedding) db_filter = kwargs.get("db_filter", None) @@ -1698,19 +1901,9 @@ async def context(connection: Any) -> List: await cursor.execute(query, **params) results = await cursor.fetchall() - for result in results: - metadata = result[2] or {} - page_content_str = result[1] if result[1] is not None else "" - if not isinstance(page_content_str, str): - raise Exception("Unexpected type:", type(page_content_str)) - - doc = Document( - page_content=page_content_str, - metadata=metadata, - ) - distance = result[3] - - docs_and_scores.append((doc, distance)) + docs_and_scores = _read_similarity_output( + results, has_similarity_score=True + ) return docs_and_scores @@ -1759,24 +1952,9 @@ def similarity_search_by_vector_returning_embeddings( cursor.execute(query, **params) results = cursor.fetchall() - for result in results: - page_content_str = result[1] if result[1] is not None else "" - if not isinstance(page_content_str, str): - raise Exception("Unexpected type:", type(page_content_str)) - metadata = result[2] or {} - - document = Document(page_content=page_content_str, metadata=metadata) - distance = result[3] - - # assuming result[4] is already in the correct format; - # adjust if necessary - current_embedding = ( - np.array(result[4], dtype=np.float32) - if result[4] - else np.empty(0, dtype=np.float32) - ) - - documents.append((document, distance, current_embedding)) + documents = _read_similarity_output( + results, has_similarity_score=True, has_embeddings=True + ) return documents @@ -1791,8 +1969,6 @@ async def asimilarity_search_by_vector_returning_embeddings( ) -> List[Tuple[Document, float, NDArray[np.float32]]]: embedding_arr: Any = array.array("f", embedding) - documents = [] - db_filter = kwargs.get("db_filter", None) if db_filter: if filter: @@ -1820,27 +1996,9 @@ async def context(connection: Any) -> List: await cursor.execute(query, **params) results = await cursor.fetchall() - - for result in results: - page_content_str = result[1] if result[1] is not None else "" - if not isinstance(page_content_str, str): - raise Exception("Unexpected type:", type(page_content_str)) - metadata = result[2] or {} - - document = Document( - page_content=page_content_str, metadata=metadata - ) - distance = result[3] - - # assuming result[4] is already in the correct format; - # adjust if necessary - current_embedding = ( - np.array(result[4], dtype=np.float32) - if result[4] - else np.empty(0, dtype=np.float32) - ) - - documents.append((document, distance, current_embedding)) + documents = _read_similarity_output( + results, has_similarity_score=True, has_embeddings=True + ) return documents @@ -2114,6 +2272,57 @@ async def amax_marginal_relevance_search( ) return documents + @_handle_exceptions + def get_by_ids(self, ids: Sequence[str], /) -> list[Document]: + """Get documents by vector IDs. + Args: + self: An instance of the class + ids: The ids of the documents to get. + **kwargs + """ + + ddl, bind_vars = _get_by_ids_select(self.table_name, ids) + + connection = _get_connection(self.client) + if connection is None: + raise ValueError("Failed to acquire a connection.") + + documents = [] + with connection.cursor() as cursor: + cursor.outputtypehandler = output_type_string_handler + cursor.execute(ddl, bind_vars) + results = cursor.fetchall() + + documents = _read_similarity_output(results) + + return documents + + @_ahandle_exceptions + async def aget_by_ids(self, ids: Sequence[str], /) -> list[Document]: + """Get documents by vector IDs. + Args: + self: An instance of the class + ids: The ids of the documents to get. + **kwargs + """ + + ddl, bind_vars = _get_by_ids_select(self.table_name, ids) + + async def context(connection: Any) -> List: + documents = [] + with connection.cursor() as cursor: + cursor.outputtypehandler = output_type_string_handler + await cursor.execute(ddl, bind_vars) + results = 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platform_machine == \"x86_64\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.13\"" files = [ {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6116fad3e049e04791c0256a9778c16237837c08b27ed8c8401e2e45de8d60cd"}, {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d461264ecb429c84c8879a7153499ddc7b19b5f8d84c204307491989a365588e"}, @@ -1768,6 +2519,20 @@ files = [ {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:86c58044c824bf3c173c49a2dbc7a6c8b53cb4e4dca50068be0bf64e9dab3f7f"}, ] +[[package]] +name = "nvidia-cuda-runtime-cu12" +version = "12.8.90" +description = "CUDA Runtime native Libraries" +optional = false +python-versions = ">=3" +groups = ["test"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\"" +files = [ + {file = 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+description = "CUFFT native runtime libraries" +optional = false +python-versions = ">=3" +groups = ["test"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\"" +files = [ + {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"}, + {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74"}, + {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-win_amd64.whl", hash = "sha256:7a64a98ef2a7c47f905aaf8931b69a3a43f27c55530c698bb2ed7c75c0b42cb7"}, +] + +[package.dependencies] +nvidia-nvjitlink-cu12 = "*" + [[package]] name = "nvidia-cufile-cu12" version = "1.11.1.6" @@ -1811,12 +2610,25 @@ description = "cuFile GPUDirect libraries" optional = false python-versions = ">=3" groups = ["test"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.13\"" files = [ {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc23469d1c7e52ce6c1d55253273d32c565dd22068647f3aa59b3c6b005bf159"}, {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:8f57a0051dcf2543f6dc2b98a98cb2719c37d3cee1baba8965d57f3bbc90d4db"}, ] +[[package]] +name = "nvidia-cufile-cu12" +version = "1.13.1.3" +description = "cuFile GPUDirect libraries" +optional = false +python-versions = ">=3" +groups = ["test"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\"" +files = [ + {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc"}, + {file = 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python-versions = ">=3" groups = ["test"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.13\"" files = [ {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0ce237ef60acde1efc457335a2ddadfd7610b892d94efee7b776c64bb1cac9e0"}, {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e9e49843a7707e42022babb9bcfa33c29857a93b88020c4e4434656a655b698c"}, @@ -1854,6 +2680,25 @@ nvidia-cublas-cu12 = "*" nvidia-cusparse-cu12 = "*" nvidia-nvjitlink-cu12 = "*" +[[package]] +name = "nvidia-cusolver-cu12" +version = "11.7.3.90" +description = "CUDA solver native runtime libraries" +optional = false +python-versions = ">=3" +groups = ["test"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\"" +files = [ + {file = 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hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b"}, + {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-win_amd64.whl", hash = "sha256:9a33604331cb2cac199f2e7f5104dfbb8a5a898c367a53dfda9ff2acb6b6b4dd"}, +] + +[package.dependencies] +nvidia-nvjitlink-cu12 = "*" + [[package]] name = "nvidia-cusparselt-cu12" version = "0.6.3" @@ -1880,13 +2742,27 @@ description = "NVIDIA cuSPARSELt" optional = false python-versions = "*" groups = ["test"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version >= \"3.13\"" files = [ {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8371549623ba601a06322af2133c4a44350575f5a3108fb75f3ef20b822ad5f1"}, {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:e5c8a26c36445dd2e6812f1177978a24e2d37cacce7e090f297a688d1ec44f46"}, {file = 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34f5988..083a006 100644 --- a/libs/oracledb/pyproject.toml +++ b/libs/oracledb/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-oracledb" -version = "1.0.2" +version = "1.0.3" description = "An integration package connecting Oracle Database and LangChain" authors = [] readme = "README.md" @@ -12,7 +12,7 @@ license = "UPL" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = ">=0.3.15,<0.4" +langchain-core = ">=0.3.15" langchain-community = ">=0.3.0" oracledb = ">=2.2.0" pydantic = ">=2,<3" @@ -28,6 +28,10 @@ syrupy = "^4.0.2" pytest-asyncio = "^0.23.2" pytest-watcher = "^0.3.4" sentence-transformers = "^5.0.0" +langchain-tests = [ + { version = "^0.3.21", python = "<3.10" }, + { version = "^1.0.0", python = ">=3.10" } +] [tool.poetry.group.codespell] optional = true diff --git a/libs/oracledb/tests/integration_tests/embeddings/__init__.py b/libs/oracledb/tests/integration_tests/embeddings/__init__.py new file mode 100644 index 0000000..9d558f0 --- /dev/null +++ b/libs/oracledb/tests/integration_tests/embeddings/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) 2025 Oracle and/or its affiliates. +# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/ diff --git a/libs/oracledb/tests/integration_tests/embeddings/test_standard.py b/libs/oracledb/tests/integration_tests/embeddings/test_standard.py new file mode 100644 index 0000000..5e6fcb5 --- /dev/null +++ b/libs/oracledb/tests/integration_tests/embeddings/test_standard.py @@ -0,0 +1,32 @@ +from typing import Type + +import oracledb +import pytest +from langchain_tests.integration_tests import EmbeddingsIntegrationTests + +from langchain_oracledb import OracleEmbeddings + +username = "" +password = "" +dsn = "" + +try: + oracledb.connect(user=username, password=password, dsn=dsn) +except Exception as e: + pytest.skip( + allow_module_level=True, + reason=f"Database connection failed: {e}, skipping tests.", + ) + + +class TestOracleEmbeddingsModelIntegration(EmbeddingsIntegrationTests): + @property + def embeddings_class(self) -> Type[OracleEmbeddings]: + # Return the embeddings model class to test here + return OracleEmbeddings + + @property + def embedding_model_params(self) -> dict: + # Return initialization parameters for the model. + conn = oracledb.connect(user=username, password=password, dsn=dsn) + return {"conn": conn, "params": {"provider": "database", "model": "allminilm"}} diff --git a/libs/oracledb/tests/integration_tests/vectorstores/test_oraclevs.py b/libs/oracledb/tests/integration_tests/vectorstores/test_oraclevs.py index 6700295..f5003b3 100644 --- a/libs/oracledb/tests/integration_tests/vectorstores/test_oraclevs.py +++ b/libs/oracledb/tests/integration_tests/vectorstores/test_oraclevs.py @@ -13,6 +13,7 @@ import sys import threading +import numpy as np import oracledb import pytest from langchain_community.embeddings import HuggingFaceEmbeddings @@ -20,6 +21,7 @@ from langchain_oracledb.embeddings import OracleEmbeddings from langchain_oracledb.vectorstores.oraclevs import ( + INTERNAL_ID_KEY, OracleVS, _acreate_table, _aindex_exists, @@ -1164,6 +1166,7 @@ def test_add_texts_test() -> None: vs_obj = OracleVS(connection, model, "TB7", DistanceStrategy.EUCLIDEAN_DISTANCE) ids6 = ['"Good afternoon"', '"India"'] vs_obj.add_texts(texts2, ids=ids6) + assert len(vs_obj.add_texts(texts2, ids=ids6)) == 0 drop_table_purge(connection, "TB7") # 4. Add records with ids and metadatas @@ -1207,30 +1210,6 @@ def add(val: str) -> None: thread_2.join() drop_table_purge(connection, "TB10") - # 7. Add 2 same record concurrently - # Expectation:Successful, For one of the insert,get primary key violation error - def add1(val: str) -> None: - model = HuggingFaceEmbeddings( - model_name="sentence-transformers/all-mpnet-base-v2" - ) - vs_obj = OracleVS( - connection, model, "TB11", DistanceStrategy.EUCLIDEAN_DISTANCE - ) - texts = [val] - ids10 = texts - vs_obj.add_texts(texts, ids=ids10) - - try: - thread_1 = threading.Thread(target=add1, args=("Sri Ram")) - thread_2 = threading.Thread(target=add1, args=("Sri Ram")) - thread_1.start() - thread_2.start() - thread_1.join() - thread_2.join() - except Exception: - pass - drop_table_purge(connection, "TB11") - # 8. create object with table name of type # Expectation:U1 does not exist with pytest.raises(RuntimeError): @@ -1316,6 +1295,7 @@ async def test_add_texts_test_async() -> None: ) ids6 = ['"Good afternoon"', '"India"'] await vs_obj.aadd_texts(texts2, ids=ids6) + assert len(await vs_obj.aadd_texts(texts2, ids=ids6)) == 0 await adrop_table_purge(connection, "TB7") # 4. Add records with ids and metadatas @@ -1361,26 +1341,6 @@ async def add(val: str) -> None: await asyncio.gather(task_1, task_2) await adrop_table_purge(connection, "TB10") - # 7. Add 2 same record concurrently - # Expectation:Successful, For one of the insert,get primary key violation error - async def add1(val: str) -> None: - model = HuggingFaceEmbeddings( - model_name="sentence-transformers/all-mpnet-base-v2" - ) - vs_obj = await OracleVS.acreate( - connection, model, "TB11", DistanceStrategy.EUCLIDEAN_DISTANCE - ) - texts = [val] - ids10 = texts - await vs_obj.aadd_texts(texts, ids=ids10) - - with pytest.raises(RuntimeError): - task_1 = asyncio.create_task(add1("Sri Ram")) - task_2 = asyncio.create_task(add1("Sri Ram")) - await asyncio.gather(task_1, task_2) - - await adrop_table_purge(connection, "TB11") - # 8. create object with table name of type # Expectation:U1 does not exist with pytest.raises(RuntimeError): @@ -1694,7 +1654,9 @@ def test_perform_search_test() -> None: vs.similarity_search(query, 2, filter=db_filter) # Similarity search with relevance score - vs.similarity_search_with_score(query, 2) + res = vs.similarity_search_with_score(query, 2) + assert all(isinstance(_r[1], float) for _r in res) + assert res[0][1] <= res[1][1] # Similarity search with relevance score with filter vs.similarity_search_with_score(query, 2, filter=db_filter) @@ -1786,7 +1748,9 @@ async def test_perform_search_test_async() -> None: await vs.asimilarity_search(query, 2, filter=db_filter) # Similarity search with relevance score - await vs.asimilarity_search_with_score(query, 2) + res = await vs.asimilarity_search_with_score(query, 2) + assert all(isinstance(_r[1], float) for _r in res) + assert res[0][1] <= res[1][1] # Similarity search with relevance score with filter await vs.asimilarity_search_with_score(query, 2, filter=db_filter) @@ -2517,6 +2481,14 @@ def test_oracle_embeddings() -> None: res = vs_obj.similarity_search("database", 1) assert "Database" in res[0].page_content + assert "100" == res[0].id + + embedding = model.embed_query("Database Document") + res = vs_obj.similarity_search_by_vector_returning_embeddings(embedding, 1) # type: ignore + + # distance + assert all(np.isclose([res[0][1]], [0])) # type: ignore + assert all(np.isclose(res[0][2], embedding)) # type: ignore drop_table_purge(connection, "TB1") @@ -2555,6 +2527,14 @@ async def test_oracle_embeddings_async(caplog: pytest.LogCaptureFixture) -> None res = await vs_obj.asimilarity_search("database", 1) assert "Database" in res[0].page_content + assert "100" == res[0].id + + embedding = model.embed_query("Database Document") + res = await vs_obj.asimilarity_search_by_vector_returning_embeddings(embedding, 1) # type: ignore + + # distance + assert all(np.isclose([res[0][1]], [0])) # type: ignore + assert all(np.isclose(res[0][2], embedding)) # type: ignore await adrop_table_purge(connection, "TB1") @@ -2986,3 +2966,75 @@ def model1(_) -> list[float]: # type: ignore[no-untyped-def] result = await vs.asimilarity_search("Hello", k=3, filter=_f) await adrop_table_purge(connection, "TB10") + + +################################## +####### test_reserved ###### +################################## + + +def test_reserved() -> None: + try: + connection = oracledb.connect(user=username, password=password, dsn=dsn) + except Exception: + sys.exit(1) + + drop_table_purge(connection, "TB1") + + embedder_params = {"provider": "database", "model": "allminilm"} + proxy = "" + + # instance + model = OracleEmbeddings(conn=connection, params=embedder_params, proxy=proxy) + + vs_obj = OracleVS(connection, model, "TB1", DistanceStrategy.EUCLIDEAN_DISTANCE) + + texts = ["Database Document", "Code Document"] + metadata = [ + {"id": "100", "link": "Document Example Test 1", INTERNAL_ID_KEY: "my_temp_id"}, + {"id": "101", "link": "Document Example Test 2"}, + ] + + with pytest.raises(ValueError, match="reserved"): + vs_obj.add_texts(texts, metadata, ids=["1", "2"]) + + drop_table_purge(connection, "TB1") + + connection.close() + + +@pytest.mark.asyncio +async def test_reserved_async() -> None: + try: + connection = await oracledb.connect_async( + user=username, password=password, dsn=dsn + ) + + connection_sync = oracledb.connect(user=username, password=password, dsn=dsn) + except Exception: + sys.exit(1) + + await adrop_table_purge(connection, "TB1") + + embedder_params = {"provider": "database", "model": "allminilm"} + proxy = "" + + # instance + model = OracleEmbeddings(conn=connection_sync, params=embedder_params, proxy=proxy) + + vs_obj = await OracleVS.acreate( + connection, model, "TB1", DistanceStrategy.EUCLIDEAN_DISTANCE + ) + + texts = ["Database Document", "Code Document"] + metadata = [ + {"id": "100", "link": "Document Example Test 1", INTERNAL_ID_KEY: "my_temp_id"}, + {"id": "101", "link": "Document Example Test 2"}, + ] + + with pytest.raises(ValueError, match="reserved"): + await vs_obj.aadd_texts(texts, metadata, ids=["1", "2"]) + + await adrop_table_purge(connection, "TB1") + + connection.close() diff --git a/libs/oracledb/tests/integration_tests/vectorstores/test_standard.py b/libs/oracledb/tests/integration_tests/vectorstores/test_standard.py new file mode 100644 index 0000000..9f00cd3 --- /dev/null +++ b/libs/oracledb/tests/integration_tests/vectorstores/test_standard.py @@ -0,0 +1,120 @@ +from collections.abc import Generator +from typing import AsyncGenerator + +import oracledb +import pytest +import pytest_asyncio +from langchain_core.vectorstores import VectorStore +from langchain_tests.integration_tests.vectorstores import VectorStoreIntegrationTests + +from langchain_oracledb import OracleVS +from langchain_oracledb.embeddings import OracleEmbeddings +from langchain_oracledb.vectorstores.oraclevs import adrop_table_purge, drop_table_purge + +username = "" +password = "" +dsn = "" + +try: + oracledb.connect(user=username, password=password, dsn=dsn) +except Exception as e: + pytest.skip( + allow_module_level=True, + reason=f"Database connection failed: {e}, skipping tests.", + ) + + +class TestOracleVSStandardSync(VectorStoreIntegrationTests): + @property + def has_async(self) -> bool: + """Configurable property to enable or disable sync tests.""" + return False + + @pytest.fixture() + def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore[override] + """Get an empty vectorstore for unit tests.""" + conn = oracledb.connect(user=username, password=password, dsn=dsn) + drop_table_purge(conn, "standard_tests") + store = OracleVS( + conn, + embedding_function=self.get_embeddings(), + table_name="standard_tests", + mutate_on_duplicate=True, + ) + yield store + + +class TestOracleVSOracleEmbeddingsStandardSync(VectorStoreIntegrationTests): + @property + def has_async(self) -> bool: + """Configurable property to enable or disable sync tests.""" + return False + + @pytest.fixture() + def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore[override] + """Get an empty vectorstore for unit tests.""" + conn = oracledb.connect(user=username, password=password, dsn=dsn) + drop_table_purge(conn, "standard_tests") + embedder_params = {"provider": "database", "model": "allminilm"} + proxy = "" + + # instance + model = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy) + + store = OracleVS( + conn, + embedding_function=model, + table_name="standard_tests", + mutate_on_duplicate=True, + ) + yield store + + +class TestOracleVSStandardAsync(VectorStoreIntegrationTests): + @property + def has_sync(self) -> bool: + """Configurable property to enable or disable sync tests.""" + return False + + @pytest_asyncio.fixture + async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: + """Get an empty vectorstore for unit tests (async version).""" + + conn = await oracledb.connect_async(user=username, password=password, dsn=dsn) + await adrop_table_purge(conn, "standard_tests") + + store = await OracleVS.acreate( + conn, + embedding_function=self.get_embeddings(), + table_name="standard_tests", + mutate_on_duplicate=True, + ) + yield store + + +class TestOracleVSOracleEmbeddingsStandardAsync(VectorStoreIntegrationTests): + @property + def has_sync(self) -> bool: + """Configurable property to enable or disable sync tests.""" + return False + + @pytest_asyncio.fixture + async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: + """Get an empty vectorstore for unit tests (async version).""" + + conn = await oracledb.connect_async(user=username, password=password, dsn=dsn) + await adrop_table_purge(conn, "standard_tests") + embedder_params = {"provider": "database", "model": "allminilm"} + proxy = "" + + # OracleEmbeddings does not support asyncconnection + conn_syn = oracledb.connect(user=username, password=password, dsn=dsn) + model = OracleEmbeddings(conn=conn_syn, params=embedder_params, proxy=proxy) + + store = await OracleVS.acreate( + conn, + embedding_function=model, + table_name="standard_tests", + mutate_on_duplicate=True, + ) + yield store diff --git a/libs/oracledb/tests/unit_tests/embeddings/__init__.py b/libs/oracledb/tests/unit_tests/embeddings/__init__.py new file mode 100644 index 0000000..9d558f0 --- /dev/null +++ b/libs/oracledb/tests/unit_tests/embeddings/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) 2025 Oracle and/or its affiliates. +# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/ diff --git a/libs/oracledb/tests/unit_tests/embeddings/test_standard.py b/libs/oracledb/tests/unit_tests/embeddings/test_standard.py new file mode 100644 index 0000000..39e4b8a --- /dev/null +++ b/libs/oracledb/tests/unit_tests/embeddings/test_standard.py @@ -0,0 +1,32 @@ +from typing import Type + +import oracledb +import pytest +from langchain_tests.unit_tests import EmbeddingsUnitTests + +from langchain_oracledb import OracleEmbeddings + +username = "" +password = "" +dsn = "" + +try: + oracledb.connect(user=username, password=password, dsn=dsn) +except Exception as e: + pytest.skip( + allow_module_level=True, + reason=f"Database connection failed: {e}, skipping tests.", + ) + + +class TestOracleEmbeddingsModelIntegration(EmbeddingsUnitTests): + @property + def embeddings_class(self) -> Type[OracleEmbeddings]: + # Return the embeddings model class to test here + return OracleEmbeddings + + @property + def embedding_model_params(self) -> dict: + # Return initialization parameters for the model. + conn = oracledb.connect(user=username, password=password, dsn=dsn) + return {"conn": conn, "params": {"provider": "database", "model": "allminilm"}}