oracle 23ai vector search
This commit is contained in:
Oracle Public Cloud User
parent
5eca495d3e
commit
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@ -1845,6 +1845,16 @@ PINECONE_DIMENSION = int(os.getenv("PINECONE_DIMENSION", 1536)) # or 3072, 1024
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PINECONE_METRIC = os.getenv("PINECONE_METRIC", "cosine")
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PINECONE_CLOUD = os.getenv("PINECONE_CLOUD", "aws") # or "gcp" or "azure"
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# ORACLE23AI (Oracle23ai vector search)
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ORACLE_DB_USER = os.environ.get("ORACLE_DB_USER", "DEMOUSER")
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ORACLE_DB_PASSWORD = os.environ.get("ORACLE_DB_PASSWORD", "Welcome123456")
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ORACLE_DB_DSN = os.environ.get("ORACLE_DB_DSN", "medium")
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ORACLE_WALLET_DIR = os.environ.get("ORACLE_WALLET_DIR", "/home/opc/adb_wallet")
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ORACLE_WALLET_PASSWORD = os.environ.get("ORACLE_WALLET_PASSWORD", "Welcome1")
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ORACLE_VECTOR_LENGTH = os.environ.get("ORACLE_VECTOR_LENGTH", 768)
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####################################
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# Information Retrieval (RAG)
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####################################
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@ -0,0 +1,699 @@
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from typing import Optional, List, Dict, Any
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from decimal import Decimal
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import os
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import oracledb
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from open_webui.retrieval.vector.main import (
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VectorDBBase,
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VectorItem,
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SearchResult,
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GetResult,
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)
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from open_webui.config import (
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ORACLE_DB_USER,
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ORACLE_DB_PASSWORD,
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ORACLE_DB_DSN,
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ORACLE_WALLET_DIR,
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ORACLE_WALLET_PASSWORD,
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ORACLE_VECTOR_LENGTH,
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)
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class Oracle23aiClient(VectorDBBase):
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"""
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Oracle Vector Database Client for vector similarity search using Oracle Database 23ai.
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This client provides an interface to store, retrieve, and search vector embeddings
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in an Oracle database. It uses connection pooling for efficient database access
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and supports vector similarity search operations.
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Attributes:
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pool: Connection pool for Oracle database connections
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"""
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def __init__(self) -> None:
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"""
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Initialize the Oracle23aiClient with a connection pool.
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Creates a connection pool with min=2 and max=10 connections, initializes
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the database schema if needed, and sets up necessary tables and indexes.
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Raises:
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ValueError: If required configuration parameters are missing
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Exception: If database initialization fails
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"""
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try:
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if not ORACLE_DB_DSN:
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raise ValueError("ORACLE_DB_DSN is required for Oracle Vector Search")
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self.pool = oracledb.create_pool(
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user=ORACLE_DB_USER,
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password=ORACLE_DB_PASSWORD,
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dsn=ORACLE_DB_DSN,
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min=2,
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max=10,
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increment=1,
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config_dir=ORACLE_WALLET_DIR,
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wallet_location=ORACLE_WALLET_DIR,
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wallet_password=ORACLE_WALLET_PASSWORD
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)
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print(f" >>> Creating Connection Pool [{ORACLE_DB_USER}:**@{ORACLE_DB_DSN}]")
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with self.get_connection() as connection:
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print("Connection version:", connection.version)
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self._initialize_database(connection)
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print("Oracle Vector Search initialization complete.")
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except Exception as e:
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print(f"Error during Oracle Vector Search initialization: {e}")
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raise
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def get_connection(self):
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"""
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Acquire a connection from the connection pool.
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Returns:
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connection: A database connection with output type handler configured
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"""
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connection = self.pool.acquire()
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connection.outputtypehandler = self._output_type_handler
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return connection
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def _output_type_handler(self, cursor, metadata):
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"""
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Handle Oracle vector type conversion.
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Args:
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cursor: Oracle database cursor
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metadata: Metadata for the column
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Returns:
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A variable with appropriate conversion for vector types
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"""
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if metadata.type_code is oracledb.DB_TYPE_VECTOR:
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return cursor.var(metadata.type_code, arraysize=cursor.arraysize,
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outconverter=list)
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def _initialize_database(self, connection) -> None:
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"""
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Initialize database schema, tables and indexes.
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Creates the document_chunk table and necessary indexes if they don't exist.
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Args:
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connection: Oracle database connection
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Raises:
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Exception: If schema initialization fails
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"""
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with connection.cursor() as cursor:
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print(f" >>> Creating Table document_chunk")
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cursor.execute(f"""
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BEGIN
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EXECUTE IMMEDIATE '
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CREATE TABLE IF NOT EXISTS document_chunk (
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id VARCHAR2(255) PRIMARY KEY,
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collection_name VARCHAR2(255) NOT NULL,
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text CLOB,
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vmetadata JSON,
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vector vector(*, float32)
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)
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';
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EXCEPTION
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WHEN OTHERS THEN
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IF SQLCODE != -955 THEN
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RAISE;
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END IF;
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END;
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""")
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print(f" >>> Creating Table document_chunk_collection_name_idx")
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cursor.execute("""
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BEGIN
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EXECUTE IMMEDIATE '
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CREATE INDEX IF NOT exists document_chunk_collection_name_idx
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ON document_chunk (collection_name)
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';
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EXCEPTION
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WHEN OTHERS THEN
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IF SQLCODE != -955 THEN
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RAISE;
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END IF;
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END;
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""")
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print(f" >>> Creating VECTOR INDEX document_chunk_vector_ivf_idx")
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cursor.execute("""
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BEGIN
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EXECUTE IMMEDIATE '
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create vector index IF NOT EXISTS document_chunk_vector_ivf_idx on document_chunk(vector)
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organization neighbor partitions
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distance cosine
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with target accuracy 95
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PARAMETERS (type IVF, NEIGHBOR PARTITIONS 100)
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';
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EXCEPTION
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WHEN OTHERS THEN
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IF SQLCODE != -955 THEN
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RAISE;
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END IF;
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END;
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""")
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connection.commit()
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def check_vector_length(self) -> None:
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"""
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Check vector length compatibility (placeholder).
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This method would check if the configured vector length matches the database schema.
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Currently implemented as a placeholder.
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"""
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pass
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def _vector_to_blob(self, vector: List[float]) -> bytes:
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"""
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Convert a vector to Oracle BLOB format.
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Args:
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vector (List[float]): The vector to convert
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Returns:
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bytes: The vector in Oracle BLOB format
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"""
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import array
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return array.array("f", vector)
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def adjust_vector_length(self, vector: List[float]) -> List[float]:
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"""
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Adjust vector to the expected length if needed.
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Args:
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vector (List[float]): The vector to adjust
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Returns:
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List[float]: The adjusted vector
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"""
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return vector
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def _decimal_handler(self, obj):
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"""
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Handle Decimal objects for JSON serialization.
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Args:
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obj: Object to serialize
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Returns:
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float: Converted decimal value
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Raises:
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TypeError: If object is not JSON serializable
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"""
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if isinstance(obj, Decimal):
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return float(obj)
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raise TypeError(f"{obj} is not JSON serializable")
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def _metadata_to_json(self, metadata: Dict) -> str:
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"""
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Convert metadata dictionary to JSON string.
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Args:
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metadata (Dict): Metadata dictionary
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Returns:
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str: JSON representation of metadata
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"""
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import json
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return json.dumps(metadata, default=self._decimal_handler) if metadata else "{}"
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def _json_to_metadata(self, json_str: str) -> Dict:
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"""
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Convert JSON string to metadata dictionary.
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Args:
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json_str (str): JSON string
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Returns:
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Dict: Metadata dictionary
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"""
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import json
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return json.loads(json_str) if json_str else {}
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def insert(self, collection_name: str, items: List[VectorItem]) -> None:
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"""
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Insert vector items into the database.
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Args:
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collection_name (str): Name of the collection
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items (List[VectorItem]): List of vector items to insert
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Raises:
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Exception: If insertion fails
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Example:
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>>> client = Oracle23aiClient()
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>>> items = [
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... {"id": "1", "text": "Sample text", "vector": [0.1, 0.2, ...], "metadata": {"source": "doc1"}},
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... {"id": "2", "text": "Another text", "vector": [0.3, 0.4, ...], "metadata": {"source": "doc2"}}
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... ]
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>>> client.insert("my_collection", items)
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"""
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print(f"Oracle23aiClient:Inserting {len(items)} items into collection '{collection_name}'.")
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with self.get_connection() as connection:
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try:
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with connection.cursor() as cursor:
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for item in items:
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vector_blob = self._vector_to_blob(item["vector"])
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metadata_json = self._metadata_to_json(item["metadata"])
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cursor.execute("""
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INSERT INTO document_chunk
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(id, collection_name, text, vmetadata, vector)
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VALUES (:id, :collection_name, :text, :metadata, :vector)
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""", {
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'id': item["id"],
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'collection_name': collection_name,
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'text': item["text"],
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'metadata': metadata_json,
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'vector': vector_blob
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})
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connection.commit()
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print(f"Oracle23aiClient:Inserted {len(items)} items into collection '{collection_name}'.")
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except Exception as e:
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connection.rollback()
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print(f"Error during insert: {e}")
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raise
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def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
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"""
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Update or insert vector items into the database.
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If an item with the same ID exists, it will be updated;
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otherwise, it will be inserted.
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Args:
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collection_name (str): Name of the collection
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items (List[VectorItem]): List of vector items to upsert
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Raises:
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Exception: If upsert operation fails
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Example:
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>>> client = Oracle23aiClient()
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>>> items = [
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... {"id": "1", "text": "Updated text", "vector": [0.1, 0.2, ...], "metadata": {"source": "doc1"}},
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... {"id": "3", "text": "New item", "vector": [0.5, 0.6, ...], "metadata": {"source": "doc3"}}
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... ]
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>>> client.upsert("my_collection", items)
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"""
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with self.get_connection() as connection:
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try:
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with connection.cursor() as cursor:
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for item in items:
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vector_blob = self._vector_to_blob(item["vector"])
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metadata_json = self._metadata_to_json(item["metadata"])
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cursor.execute("""
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MERGE INTO document_chunk d
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USING (SELECT :id as id FROM dual) s
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ON (d.id = s.id)
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WHEN MATCHED THEN
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UPDATE SET
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collection_name = :collection_name,
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text = :text,
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vmetadata = :metadata,
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vector = :vector
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WHEN NOT MATCHED THEN
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INSERT (id, collection_name, text, vmetadata, vector)
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VALUES (:id, :collection_name, :text, :metadata, :vector)
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""", {
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'id': item["id"],
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'collection_name': collection_name,
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'text': item["text"],
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'metadata': metadata_json,
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'vector': vector_blob,
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'id': item["id"],
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'collection_name': collection_name,
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'text': item["text"],
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'metadata': metadata_json,
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'vector': vector_blob
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})
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connection.commit()
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print(f"Upserted {len(items)} items into collection '{collection_name}'.")
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except Exception as e:
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connection.rollback()
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print(f"Error during upsert: {e}")
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raise
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def search(
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self,
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collection_name: str,
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vectors: List[List[float]],
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limit: Optional[int] = None
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) -> Optional[SearchResult]:
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"""
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Search for similar vectors in the database.
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Performs vector similarity search using cosine distance.
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Args:
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collection_name (str): Name of the collection to search
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vectors (List[List[float]]): Query vectors to find similar items for
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limit (Optional[int]): Maximum number of results to return per query
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Returns:
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Optional[SearchResult]: Search results containing ids, distances, documents, and metadata
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Example:
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>>> client = Oracle23aiClient()
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>>> query_vector = [0.1, 0.2, 0.3, ...] # Must match VECTOR_LENGTH
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>>> results = client.search("my_collection", [query_vector], limit=5)
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>>> if results:
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... print(f"Found {len(results.ids[0])} matches")
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... for i, (id, dist) in enumerate(zip(results.ids[0], results.distances[0])):
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... print(f"Match {i+1}: id={id}, distance={dist}")
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"""
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print(f"Oracle23aiClient:Searching items from collection '{collection_name}'.")
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try:
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if not vectors:
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return None
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limit = limit or 10
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num_queries = len(vectors)
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ids = [[] for _ in range(num_queries)]
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distances = [[] for _ in range(num_queries)]
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documents = [[] for _ in range(num_queries)]
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metadatas = [[] for _ in range(num_queries)]
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with self.get_connection() as connection:
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with connection.cursor() as cursor:
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for qid, vector in enumerate(vectors):
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vector_blob = self._vector_to_blob(vector)
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cursor.execute("""
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SELECT dc.id, dc.text,
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JSON_SERIALIZE(dc.vmetadata) as vmetadata,
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VECTOR_DISTANCE(dc.vector, :query_vector, COSINE) as distance
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FROM document_chunk dc
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WHERE dc.collection_name = :collection_name
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ORDER BY VECTOR_DISTANCE(dc.vector, :query_vector, COSINE)
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FETCH APPROX FIRST :limit ROWS ONLY
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""", {
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'query_vector': vector_blob,
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'collection_name': collection_name,
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'limit': limit
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})
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results = cursor.fetchall()
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for row in results:
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ids[qid].append(row[0])
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documents[qid].append(row[1].read() if isinstance(row[1], oracledb.LOB) else str(row[1]))
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metadatas[qid].append(row[2].read() if isinstance(row[2], oracledb.LOB) else row[2])
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distances[qid].append(float(row[3]))
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return SearchResult(
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ids=ids,
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distances=distances,
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documents=documents,
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metadatas=metadatas
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)
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except Exception as e:
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print(f"Error during search: {e}")
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import traceback
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print(traceback.format_exc())
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return None
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def query(
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self,
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collection_name: str,
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filter: Dict[str, Any],
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limit: Optional[int] = None
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) -> Optional[GetResult]:
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"""
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Query items based on metadata filters.
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Retrieves items that match specified metadata criteria.
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Args:
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collection_name (str): Name of the collection to query
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filter (Dict[str, Any]): Metadata filters to apply
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limit (Optional[int]): Maximum number of results to return
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Returns:
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Optional[GetResult]: Query results containing ids, documents, and metadata
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Example:
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>>> client = Oracle23aiClient()
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>>> filter = {"source": "doc1", "category": "finance"}
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>>> results = client.query("my_collection", filter, limit=20)
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>>> if results:
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... print(f"Found {len(results.ids[0])} matching documents")
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"""
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print(f"Oracle23aiClient:Querying items from collection '{collection_name}'.")
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try:
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limit = limit or 100
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query = """
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SELECT id, text, vmetadata
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FROM document_chunk
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WHERE collection_name = :collection_name
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"""
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params = {'collection_name': collection_name}
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for i, (key, value) in enumerate(filter.items()):
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param_name = f"value_{i}"
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query += f" AND JSON_VALUE(vmetadata, '$.{key}' RETURNING VARCHAR2(4096)) = :{param_name}"
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params[param_name] = str(value)
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query += " FETCH FIRST :limit ROWS ONLY"
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params['limit'] = limit
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with self.get_connection() as connection:
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with connection.cursor() as cursor:
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cursor.execute(query, params)
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results = cursor.fetchall()
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if not results:
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return None
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ids = [[row[0] for row in results]]
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documents = [[row[1].read() if isinstance(row[1], oracledb.LOB) else str(row[1]) for row in results]]
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metadatas = [[row[2].read() if isinstance(row[2], oracledb.LOB) else row[2] for row in results]]
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return GetResult(
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ids=ids,
|
||||
documents=documents,
|
||||
metadatas=metadatas
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error during query: {e}")
|
||||
import traceback
|
||||
print(traceback.format_exc())
|
||||
return None
|
||||
|
||||
def get(
|
||||
self,
|
||||
collection_name: str,
|
||||
limit: Optional[int] = None
|
||||
) -> Optional[GetResult]:
|
||||
"""
|
||||
Get all items in a collection.
|
||||
|
||||
Retrieves items from a specified collection up to the limit.
|
||||
|
||||
Args:
|
||||
collection_name (str): Name of the collection to retrieve
|
||||
limit (Optional[int]): Maximum number of items to retrieve
|
||||
|
||||
Returns:
|
||||
Optional[GetResult]: Result containing ids, documents, and metadata
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> results = client.get("my_collection", limit=50)
|
||||
>>> if results:
|
||||
... print(f"Retrieved {len(results.ids[0])} documents from collection")
|
||||
"""
|
||||
try:
|
||||
limit = limit or 100
|
||||
|
||||
with self.get_connection() as connection:
|
||||
with connection.cursor() as cursor:
|
||||
cursor.execute("""
|
||||
SELECT /*+ MONITOR */ id, text, vmetadata
|
||||
FROM document_chunk
|
||||
WHERE collection_name = :collection_name
|
||||
FETCH FIRST :limit ROWS ONLY
|
||||
""", {
|
||||
'collection_name': collection_name,
|
||||
'limit': limit
|
||||
})
|
||||
|
||||
results = cursor.fetchall()
|
||||
|
||||
if not results:
|
||||
return None
|
||||
|
||||
ids = [[row[0] for row in results]]
|
||||
documents = [[row[1].read() if isinstance(row[1], oracledb.LOB) else str(row[1]) for row in results]]
|
||||
metadatas = [[row[2].read() if isinstance(row[2], oracledb.LOB) else row[2] for row in results]]
|
||||
|
||||
return GetResult(
|
||||
ids=ids,
|
||||
documents=documents,
|
||||
metadatas=metadatas
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error during get: {e}")
|
||||
import traceback
|
||||
print(traceback.format_exc())
|
||||
return None
|
||||
|
||||
def delete(
|
||||
self,
|
||||
collection_name: str,
|
||||
ids: Optional[List[str]] = None,
|
||||
filter: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""
|
||||
Delete items from the database.
|
||||
|
||||
Deletes items from a collection based on IDs or metadata filters.
|
||||
|
||||
Args:
|
||||
collection_name (str): Name of the collection to delete from
|
||||
ids (Optional[List[str]]): Specific item IDs to delete
|
||||
filter (Optional[Dict[str, Any]]): Metadata filters for deletion
|
||||
|
||||
Raises:
|
||||
Exception: If deletion fails
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> # Delete specific items by ID
|
||||
>>> client.delete("my_collection", ids=["1", "3", "5"])
|
||||
>>> # Or delete by metadata filter
|
||||
>>> client.delete("my_collection", filter={"source": "deprecated_source"})
|
||||
"""
|
||||
try:
|
||||
query = "DELETE FROM document_chunk WHERE collection_name = :collection_name"
|
||||
params = {'collection_name': collection_name}
|
||||
|
||||
if ids:
|
||||
id_list = ",".join([f"'{id}'" for id in ids])
|
||||
query += f" AND id IN ({id_list})"
|
||||
|
||||
if filter:
|
||||
for i, (key, value) in enumerate(filter.items()):
|
||||
param_name = f"value_{i}"
|
||||
query += f" AND JSON_VALUE(vmetadata, '$.{key}' RETURNING VARCHAR2(4096)) = :{param_name}"
|
||||
params[param_name] = str(value)
|
||||
|
||||
with self.get_connection() as connection:
|
||||
with connection.cursor() as cursor:
|
||||
cursor.execute(query, params)
|
||||
deleted = cursor.rowcount
|
||||
connection.commit()
|
||||
|
||||
print(f"Deleted {deleted} items from collection '{collection_name}'.")
|
||||
except Exception as e:
|
||||
print(f"Error during delete: {e}")
|
||||
raise
|
||||
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Reset the database by deleting all items.
|
||||
|
||||
Deletes all items from the document_chunk table.
|
||||
|
||||
Raises:
|
||||
Exception: If reset fails
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> client.reset() # Warning: Removes all data!
|
||||
"""
|
||||
try:
|
||||
with self.get_connection() as connection:
|
||||
with connection.cursor() as cursor:
|
||||
cursor.execute("DELETE FROM document_chunk")
|
||||
deleted = cursor.rowcount
|
||||
connection.commit()
|
||||
print(f"Reset complete. Deleted {deleted} items from 'document_chunk' table.")
|
||||
except Exception as e:
|
||||
print(f"Error during reset: {e}")
|
||||
raise
|
||||
|
||||
def close(self) -> None:
|
||||
"""
|
||||
Close the database connection pool.
|
||||
|
||||
Properly closes the connection pool and releases all resources.
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> # After finishing all operations
|
||||
>>> client.close()
|
||||
"""
|
||||
try:
|
||||
if hasattr(self, 'pool') and self.pool:
|
||||
self.pool.close()
|
||||
print("Oracle Vector Search connection pool closed.")
|
||||
except Exception as e:
|
||||
print(f"Error closing connection pool: {e}")
|
||||
|
||||
def has_collection(self, collection_name: str) -> bool:
|
||||
"""
|
||||
Check if a collection exists.
|
||||
|
||||
Args:
|
||||
collection_name (str): Name of the collection to check
|
||||
|
||||
Returns:
|
||||
bool: True if the collection exists, False otherwise
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> if client.has_collection("my_collection"):
|
||||
... print("Collection exists!")
|
||||
... else:
|
||||
... print("Collection does not exist.")
|
||||
"""
|
||||
try:
|
||||
with self.get_connection() as connection:
|
||||
with connection.cursor() as cursor:
|
||||
cursor.execute("""
|
||||
SELECT COUNT(*)
|
||||
FROM document_chunk
|
||||
WHERE collection_name = :collection_name
|
||||
FETCH FIRST 1 ROWS ONLY
|
||||
""", {'collection_name': collection_name})
|
||||
|
||||
count = cursor.fetchone()[0]
|
||||
return count > 0
|
||||
except Exception as e:
|
||||
print(f"Error checking collection existence: {e}")
|
||||
return False
|
||||
|
||||
def delete_collection(self, collection_name: str) -> None:
|
||||
"""
|
||||
Delete an entire collection.
|
||||
|
||||
Removes all items belonging to the specified collection.
|
||||
|
||||
Args:
|
||||
collection_name (str): Name of the collection to delete
|
||||
|
||||
Example:
|
||||
>>> client = Oracle23aiClient()
|
||||
>>> client.delete_collection("obsolete_collection")
|
||||
"""
|
||||
self.delete(collection_name)
|
||||
print(f"Collection '{collection_name}' deleted.")
|
||||
|
|
@ -48,6 +48,10 @@ class Vector:
|
|||
from open_webui.retrieval.vector.dbs.chroma import ChromaClient
|
||||
|
||||
return ChromaClient()
|
||||
case VectorType.ORACLE23AI:
|
||||
from open_webui.retrieval.vector.dbs.oracle23ai import Oracle23aiClient
|
||||
|
||||
return Oracle23aiClient()
|
||||
case _:
|
||||
raise ValueError(f"Unsupported vector type: {vector_type}")
|
||||
|
||||
|
|
|
|||
|
|
@ -9,3 +9,4 @@ class VectorType(StrEnum):
|
|||
ELASTICSEARCH = "elasticsearch"
|
||||
OPENSEARCH = "opensearch"
|
||||
PGVECTOR = "pgvector"
|
||||
ORACLE23AI = "oracle23ai"
|
||||
|
|
|
|||
Loading…
Reference in New Issue