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204 | class OracleEmbeddings(BaseEmbedding):
"""Get Embeddings."""
_conn: Any = PrivateAttr()
_params: Dict[str, Any] = PrivateAttr()
_proxy: Optional[str] = PrivateAttr()
def __init__(
self,
conn: Connection,
params: Dict[str, Any],
proxy: Optional[str] = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self._conn = conn
self._proxy = proxy
self._params = params
@classmethod
def class_name(self) -> str:
return "OracleEmbeddings"
@staticmethod
def load_onnx_model(conn: Connection, dir: str, onnx_file: str, model_name: str):
"""Load an ONNX model to Oracle Database.
Args:
conn: Oracle Connection,
dir: Oracle Directory,
onnx_file: ONNX file name,
model_name: Name of the model.
Note: user needs to have create procedure,
create mining model, create any directory privilege.
"""
try:
if conn is None or dir is None or onnx_file is None or model_name is None:
raise Exception("Invalid input")
cursor = conn.cursor()
cursor.execute(
"""
begin
dbms_data_mining.drop_model(model_name => :model, force => true);
SYS.DBMS_VECTOR.load_onnx_model(:path, :filename, :model, json('{"function" : "embedding", "embeddingOutput" : "embedding" , "input": {"input": ["DATA"]}}'));
end;""",
path=dir,
filename=onnx_file,
model=model_name,
)
cursor.close()
except Exception as ex:
print(f"An exception occurred :: {ex}")
cursor.close()
raise
def _get_embedding(self, text: str) -> List[float]:
try:
import oracledb
except ImportError as e:
raise ImportError(
"Unable to import oracledb, please install with "
"`pip install -U oracledb`."
) from e
if text is None:
return None
embedding = None
try:
oracledb.defaults.fetch_lobs = False
cursor = self._conn.cursor()
if self._proxy:
cursor.execute(
"begin utl_http.set_proxy(:proxy); end;", proxy=self._proxy
)
cursor.execute(
"select t.* from dbms_vector_chain.utl_to_embeddings(:content, json(:params)) t",
content=text,
params=json.dumps(self._params),
)
row = cursor.fetchone()
if row is None:
embedding = []
else:
rdata = json.loads(row[0])
# dereference string as array
embedding = json.loads(rdata["embed_vector"])
cursor.close()
return embedding
except Exception as ex:
print(f"An exception occurred :: {ex}")
cursor.close()
raise
def _get_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using an OracleEmbeddings.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each input text.
"""
try:
import oracledb
except ImportError as e:
raise ImportError(
"Unable to import oracledb, please install with "
"`pip install -U oracledb`."
) from e
if texts is None:
return None
embeddings: List[List[float]] = []
try:
# returns strings or bytes instead of a locator
oracledb.defaults.fetch_lobs = False
cursor = self._conn.cursor()
if self._proxy:
cursor.execute(
"begin utl_http.set_proxy(:proxy); end;", proxy=self._proxy
)
chunks = []
for i, text in enumerate(texts, start=1):
chunk = {"chunk_id": i, "chunk_data": text}
chunks.append(json.dumps(chunk))
vector_array_type = self._conn.gettype("SYS.VECTOR_ARRAY_T")
inputs = vector_array_type.newobject(chunks)
cursor.execute(
"select t.* "
+ "from dbms_vector_chain.utl_to_embeddings(:content, "
+ "json(:params)) t",
content=inputs,
params=json.dumps(self._params),
)
for row in cursor:
if row is None:
embeddings.append([])
else:
rdata = json.loads(row[0])
# dereference string as array
vec = json.loads(rdata["embed_vector"])
embeddings.append(vec)
cursor.close()
return embeddings
except Exception as ex:
print(f"An exception occurred :: {ex}")
cursor.close()
raise
def _get_query_embedding(self, query: str) -> List[float]:
return self._get_embedding(query)
async def _aget_query_embedding(self, query: str) -> List[float]:
return self._get_query_embedding(query)
def _get_text_embedding(self, text: str) -> List[float]:
return self._get_embedding(text)
async def _aget_text_embedding(self, text: str) -> List[float]:
return self._get_text_embedding(text)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
return self._get_embeddings(texts)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
return self._get_text_embeddings(texts)
|
