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+import array
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+import json
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+import uuid
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+from contextlib import contextmanager
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+from typing import Any
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+
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+import numpy
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+import oracledb
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+from flask import current_app
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+from pydantic import BaseModel, model_validator
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+
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+from core.rag.datasource.entity.embedding import Embeddings
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+from core.rag.datasource.vdb.vector_base import BaseVector
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+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
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+from core.rag.datasource.vdb.vector_type import VectorType
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+from core.rag.models.document import Document
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+from extensions.ext_redis import redis_client
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+from models.dataset import Dataset
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+
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+oracledb.defaults.fetch_lobs = False
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+
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+
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+class OracleVectorConfig(BaseModel):
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+ host: str
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+ port: int
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+ user: str
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+ password: str
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+ database: str
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+
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+ @model_validator(mode='before')
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+ def validate_config(cls, values: dict) -> dict:
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+ if not values["host"]:
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+ raise ValueError("config ORACLE_HOST is required")
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+ if not values["port"]:
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+ raise ValueError("config ORACLE_PORT is required")
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+ if not values["user"]:
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+ raise ValueError("config ORACLE_USER is required")
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+ if not values["password"]:
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+ raise ValueError("config ORACLE_PASSWORD is required")
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+ if not values["database"]:
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+ raise ValueError("config ORACLE_DB is required")
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+ return values
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+
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+
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+SQL_CREATE_TABLE = """
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+CREATE TABLE IF NOT EXISTS {table_name} (
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+ id varchar2(100)
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+ ,text CLOB NOT NULL
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+ ,meta JSON
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+ ,embedding vector NOT NULL
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+)
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+"""
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+
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+
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+class OracleVector(BaseVector):
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+ def __init__(self, collection_name: str, config: OracleVectorConfig):
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+ super().__init__(collection_name)
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+ self.pool = self._create_connection_pool(config)
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+ self.table_name = f"embedding_{collection_name}"
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+
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+ def get_type(self) -> str:
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+ return VectorType.ORACLE
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+
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+ def numpy_converter_in(self, value):
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+ if value.dtype == numpy.float64:
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+ dtype = "d"
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+ elif value.dtype == numpy.float32:
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+ dtype = "f"
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+ else:
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+ dtype = "b"
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+ return array.array(dtype, value)
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+
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+ def input_type_handler(self, cursor, value, arraysize):
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+ if isinstance(value, numpy.ndarray):
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+ return cursor.var(
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+ oracledb.DB_TYPE_VECTOR,
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+ arraysize=arraysize,
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+ inconverter=self.numpy_converter_in,
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+ )
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+
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+ def numpy_converter_out(self, value):
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+ if value.typecode == "b":
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+ dtype = numpy.int8
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+ elif value.typecode == "f":
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+ dtype = numpy.float32
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+ else:
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+ dtype = numpy.float64
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+ return numpy.array(value, copy=False, dtype=dtype)
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+
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+ def output_type_handler(self, cursor, metadata):
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+ if metadata.type_code is oracledb.DB_TYPE_VECTOR:
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+ return cursor.var(
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+ metadata.type_code,
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+ arraysize=cursor.arraysize,
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+ outconverter=self.numpy_converter_out,
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+ )
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+ def _create_connection_pool(self, config: OracleVectorConfig):
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+ return oracledb.create_pool(user=config.user, password=config.password, dsn="{}:{}/{}".format(config.host, config.port, config.database), min=1, max=50, increment=1)
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+
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+
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+ @contextmanager
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+ def _get_cursor(self):
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+ conn = self.pool.acquire()
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+ conn.inputtypehandler = self.input_type_handler
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+ conn.outputtypehandler = self.output_type_handler
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+ cur = conn.cursor()
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+ try:
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+ yield cur
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+ finally:
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+ cur.close()
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+ conn.commit()
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+ conn.close()
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+
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+ def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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+ dimension = len(embeddings[0])
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+ self._create_collection(dimension)
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+ return self.add_texts(texts, embeddings)
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+
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+ def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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+ values = []
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+ pks = []
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+ for i, doc in enumerate(documents):
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+ doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
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+ pks.append(doc_id)
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+ values.append(
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+ (
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+ doc_id,
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+ doc.page_content,
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+ json.dumps(doc.metadata),
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+ #array.array("f", embeddings[i]),
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+ numpy.array(embeddings[i]),
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+ )
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+ )
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+ #print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
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+ with self._get_cursor() as cur:
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+ cur.executemany(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values)
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+ return pks
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+
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+ def text_exists(self, id: str) -> bool:
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+ with self._get_cursor() as cur:
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+ cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
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+ return cur.fetchone() is not None
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+
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+ def get_by_ids(self, ids: list[str]) -> list[Document]:
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+ with self._get_cursor() as cur:
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+ cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
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+ docs = []
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+ for record in cur:
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+ docs.append(Document(page_content=record[1], metadata=record[0]))
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+ return docs
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+ #def get_ids_by_metadata_field(self, key: str, value: str):
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+ # with self._get_cursor() as cur:
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+ # cur.execute(f"SELECT id FROM {self.table_name} d WHERE d.meta.{key}='{value}'" )
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+ # idss = []
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+ # for record in cur:
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+ # idss.append(record[0])
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+ # return idss
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+
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+ #def delete_by_document_id(self, document_id: str):
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+ # ids = self.get_ids_by_metadata_field('doc_id', document_id)
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+ # if len(ids)>0:
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+ # with self._get_cursor() as cur:
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+ # cur.execute(f"delete FROM {self.table_name} d WHERE d.meta.doc_id in '%s'" % ("','".join(ids),))
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+
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+
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+ def delete_by_ids(self, ids: list[str]) -> None:
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+ with self._get_cursor() as cur:
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+ cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
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+
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+ def delete_by_metadata_field(self, key: str, value: str) -> None:
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+ with self._get_cursor() as cur:
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+ cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
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+
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+ def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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+ """
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+ Search the nearest neighbors to a vector.
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+
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+ :param query_vector: The input vector to search for similar items.
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+ :param top_k: The number of nearest neighbors to return, default is 5.
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+ :return: List of Documents that are nearest to the query vector.
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+ """
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+ top_k = kwargs.get("top_k", 5)
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+ with self._get_cursor() as cur:
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+ cur.execute(
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+ f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name} ORDER BY distance fetch first {top_k} rows only" ,[numpy.array(query_vector)]
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+ )
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+ docs = []
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+ score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
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+ for record in cur:
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+ metadata, text, distance = record
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+ score = 1 - distance
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+ metadata["score"] = score
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+ if score > score_threshold:
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+ docs.append(Document(page_content=text, metadata=metadata))
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+ return docs
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+
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+ def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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+ # do not support bm25 search
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+ return []
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+
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+ def delete(self) -> None:
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+ with self._get_cursor() as cur:
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+ cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
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+
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+ def _create_collection(self, dimension: int):
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+ cache_key = f"vector_indexing_{self._collection_name}"
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+ lock_name = f"{cache_key}_lock"
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+ with redis_client.lock(lock_name, timeout=20):
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+ collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
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+ if redis_client.get(collection_exist_cache_key):
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+ return
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+
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+ with self._get_cursor() as cur:
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+ cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
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+ redis_client.set(collection_exist_cache_key, 1, ex=3600)
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+
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+
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+class OracleVectorFactory(AbstractVectorFactory):
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+ def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> OracleVector:
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+ if dataset.index_struct_dict:
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+ class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
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+ collection_name = class_prefix
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+ else:
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+ dataset_id = dataset.id
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+ collection_name = Dataset.gen_collection_name_by_id(dataset_id)
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+ dataset.index_struct = json.dumps(
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+ self.gen_index_struct_dict(VectorType.ORACLE, collection_name))
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+
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+ config = current_app.config
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+ return OracleVector(
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+ collection_name=collection_name,
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+ config=OracleVectorConfig(
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+ host=config.get("ORACLE_HOST"),
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+ port=config.get("ORACLE_PORT"),
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+ user=config.get("ORACLE_USER"),
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+ password=config.get("ORACLE_PASSWORD"),
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+ database=config.get("ORACLE_DATABASE"),
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+ ),
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+ )
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tmuife