Source code for langchain_community.embeddings.oracleai
# Authors:
# Harichandan Roy (hroy)
# David Jiang (ddjiang)
#
# -----------------------------------------------------------------------------
# oracleai.py
# -----------------------------------------------------------------------------
from __future__ import annotations
import json
import logging
import traceback
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra
if TYPE_CHECKING:
from oracledb import Connection
logger = logging.getLogger(__name__)
"""OracleEmbeddings class"""
[docs]class OracleEmbeddings(BaseModel, Embeddings):
"""获取嵌入向量"""
"""Oracle数据库连接"""
conn: Any
"""嵌入参数"""
params: Dict[str, Any]
"""代理"""
proxy: Optional[str] = None
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
class Config:
"""此pydantic对象的配置。"""
extra = Extra.forbid
""" 1 - 用户需要具有创建存储过程、创建挖掘模型、创建任何目录权限。
2 - 将创建存储过程、创建挖掘模型、创建任何目录的权限授予<用户>;"""
[docs] @staticmethod
def load_onnx_model(
conn: Connection, dir: str, onnx_file: str, model_name: str
) -> None:
"""将一个ONNX模型加载到Oracle数据库中。
参数:
conn: Oracle连接,
dir: Oracle目录,
onnx_file: ONNX文件名,
model_name: 模型的名称。
"""
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:
logger.info(f"An exception occurred :: {ex}")
traceback.print_exc()
cursor.close()
raise
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""使用OracleEmbeddings计算文档嵌入。
参数:
texts:要嵌入的文本列表。
返回:
每个输入文本的嵌入列表。
"""
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
)
for text in texts:
cursor.execute(
"select t.* "
+ "from dbms_vector_chain.utl_to_embeddings(:content, "
+ "json(:params)) t",
content=text,
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:
logger.info(f"An exception occurred :: {ex}")
traceback.print_exc()
cursor.close()
raise
[docs] def embed_query(self, text: str) -> List[float]:
"""使用OracleEmbeddings计算查询嵌入。
参数:
text:要嵌入的文本。
返回:
文本的嵌入。
"""
return self.embed_documents([text])[0]
# uncomment the following code block to run the test
"""
# A sample unit test.
''' get the Oracle connection '''
conn = oracledb.connect(
user="",
password="",
dsn="")
print("Oracle connection is established...")
''' params '''
embedder_params = {"provider":"database", "model":"demo_model"}
proxy = ""
''' instance '''
embedder = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy)
embed = embedder.embed_query("Hello World!")
print(f"Embedding generated by OracleEmbeddings: {embed}")
conn.close()
print("Connection is closed.")
"""