VectorIndex supports Retrieval Augmented Generation (RAG). For e.g., you can convert text into vector embeddings and store them in a vector store. Select AI will augment the natural language prompt by retrieving content from the vector store using semantic similarity search.

1. `VectorIndex` Object Model¶

2. `VectorIndexAttributes`¶

A VectorIndexAttributes object can be created with select_ai.VectorIndexAttributes(). Also check vector index attributes

Attributes of a vector index help to manage and configure the behavior of the vector index.

Parameters:

chunk_size (int) – Text size of chunking the input data.
chunk_overlap (int) – Specifies the amount of overlapping characters between adjacent chunks of text.
location (str) – Location of the object store.
match_limit (int) – Specifies the maximum number of results to return in a vector search query
object_storage_credential_name (str) – Name of the credentials for accessing object storage.
profile_name (str) – Name of the AI profile which is used for embedding source data and user prompts.
refresh_rate (int) – Interval of updating data in the vector store. The unit is minutes.
similarity_threshold (float) – Defines the minimum level of similarity required for two items to be considered a match
vector_distance_metric (VectorDistanceMetric) – Specifies the type of distance calculation used to compare vectors in a database
vector_db_provider (VectorDBProvider) – Name of the Vector database provider. Default value is “oracle”
vector_db_endpoint (str) – Endpoint to access the Vector database
vector_db_credential_name (str) – Name of the credentials for accessing Vector database
vector_dimension (int) – Specifies the number of elements in each vector within the vector store
vector_table_name (str) – Specifies the name of the table or collection to store vector embeddings and chunked data

2.1. `OracleVectorIndexAttributes`¶

class select_ai.OracleVectorIndexAttributes(chunk_size: int | None = None, chunk_overlap: int | None = None, location: str | None = None, match_limit: int | None = None, object_storage_credential_name: str | None = None, profile_name: str | None = None, refresh_rate: int | None = None, similarity_threshold: float | None = None, vector_distance_metric: VectorDistanceMetric | None = None, vector_db_endpoint: str | None = None, vector_db_credential_name: str | None = None, vector_db_provider: VectorDBProvider | None = VectorDBProvider.ORACLE, vector_dimension: int | None = None, vector_table_name: str | None = None, pipeline_name: str | None = None)¶: Oracle specific vector index attributes

3. `VectorIndex` API¶

A VectorIndex object can be created with select_ai.VectorIndex()

class select_ai.VectorIndex(profile: BaseProfile | None = None, index_name: str | None = None, description: str | None = None, attributes: VectorIndexAttributes | None = None)¶

VectorIndex objects let you manage vector indexes

Parameters:

index_name (str) – The name of the vector index
description (str) – The description of the vector index
attributes (select_ai.VectorIndexAttributes) – The attributes of the vector index

create(replace: bool | None = False)¶

Create a vector index in the database and populates the index: with data from an object store bucket using an async scheduler job

Parameters:: replace (bool) – Replace vector index if it exists
Returns:: None

delete(include_data: bool | None = True, force: bool | None = False)¶

This procedure removes a vector store index

Parameters:

include_data (bool) – Indicates whether to delete both the customer’s vector store and vector index along with the vector index object
force (bool) – Indicates whether to ignore errors that occur if the vector index does not exist

Returns:

None

Raises:

oracledb.DatabaseError

disable()¶

This procedure disables a vector index object in the current database. When disabled, an AI profile cannot use the vector index, and the system does not load data into the vector store as new data is added to the object store and does not perform indexing, searching or querying based on the index.

Returns:: None
Raises:: oracledb.DatabaseError

enable()¶

This procedure enables or activates a previously disabled vector index object. Generally, when you create a vector index, by default it is enabled such that the AI profile can use it to perform indexing and searching.

Returns:: None
Raises:: oracledb.DatabaseError

get_attributes() → VectorIndexAttributes¶

Get attributes of this vector index

Returns:: select_ai.VectorIndexAttributes
Raises:: VectorIndexNotFoundError

get_profile() → Profile¶

Get Profile object linked to this vector index

Returns:: select_ai.Profile
Raises:: ProfileNotFoundError

classmethod list(index_name_pattern: str = '.*') → Iterator[VectorIndex]¶

List Vector Indexes

Parameters:: index_name_pattern (str) – Regular expressions can be used to specify a pattern. Function REGEXP_LIKE is used to perform the match. Default value is “.*” i.e. match all vector indexes.
Returns:: Iterator[VectorIndex]

set_attribute(attribute_name: str, attribute_value: str | int | float)¶

This procedure updates an existing vector store index with a specified value of the vector index attribute.

Parameters:

attribute_name (str) – Custom attribute name
attribute_value (Union[str, int, float]) – Attribute Value

set_attributes(attributes: VectorIndexAttributes = None)¶

This procedure updates an existing vector store index with a specified value of the vector index attributes. Specify multiple attributes by passing an object of type :class VectorIndexAttributes

Parameters:: attributes (select_ai.VectorIndexAttributes) – Use this to update multiple attribute values
Returns:: None
Raises:: oracledb.DatabaseError

Check the examples below to understand how to create vector indexes

3.1. Create vector index¶

In the following example, vector database provider is Oracle and objects (to create embedding for) reside in OCI’s object store

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


select_ai.connect(user=user, password=password, dsn=dsn)
# Configure an AI provider with an embedding model
# of your choice
provider = select_ai.OCIGenAIProvider(
    region="us-chicago-1",
    oci_apiformat="GENERIC",
    embedding_model="cohere.embed-english-v3.0",
)

# Create an AI profile to use the Vector index with
profile_attributes = select_ai.ProfileAttributes(
    credential_name="my_oci_ai_profile_key",
    provider=provider,
)
profile = select_ai.Profile(
    profile_name="oci_vector_ai_profile",
    attributes=profile_attributes,
    description="MY OCI AI Profile",
    replace=True,
)

# Specify objects to create an embedding for. In this example,
# the objects reside in ObjectStore and the vector database is
# Oracle
vector_index_attributes = select_ai.OracleVectorIndexAttributes(
    location="https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph",
    object_storage_credential_name="my_oci_ai_profile_key",
)

# Create a Vector index object
vector_index = select_ai.VectorIndex(
    index_name="test_vector_index",
    attributes=vector_index_attributes,
    description="Test vector index",
    profile=profile,
)
vector_index.create(replace=True)
print("Created vector index: test_vector_index")

output:

Created vector index: test_vector_index

3.2. List vector index¶

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")

select_ai.connect(user=user, password=password, dsn=dsn)
vector_index = select_ai.VectorIndex()
for index in vector_index.list(index_name_pattern="^test"):
    print("Vector index", index.index_name)
    print("Vector index profile", index.profile)

output:

Vector index TEST_VECTOR_INDEX
Vector index profile Profile(profile_name=oci_vector_ai_profile, attributes=ProfileAttributes(annotations=None, case_sensitive_values=None, comments=None, constraints=None, conversation=None, credential_name='my_oci_ai_profile_key', enable_sources=None, enable_source_offsets=None, enforce_object_list=None, max_tokens=1024, object_list=None, object_list_mode=None, provider=OCIGenAIProvider(embedding_model=None, model=None, provider_name='oci', provider_endpoint=None, region='us-chicago-1', oci_apiformat='GENERIC', oci_compartment_id=None, oci_endpoint_id=None, oci_runtimetype=None), seed=None, stop_tokens=None, streaming=None, temperature=None, vector_index_name='test_vector_index'), description=None)

3.3. Get vector index attributes¶

You can fetch the vector index attributes and associated AI profile using vector_index.get_attributes() and vector_index.get_profile() methods respectively.

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")

select_ai.connect(user=user, password=password, dsn=dsn)

vector_index = select_ai.VectorIndex(
    index_name="test_vector_index",
)

print(vector_index.get_attributes())
print(vector_index.get_profile())

output:

OracleVectorIndexAttributes(chunk_size=1024, chunk_overlap=128, location='https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph', match_limit=5, object_storage_credential_name='my_oci_ai_profile_key', profile_name='oci_vector_ai_profile', refresh_rate=1450, similarity_threshold=0.5, vector_distance_metric='COSINE', vector_db_endpoint=None, vector_db_credential_name=None, vector_db_provider=<VectorDBProvider.ORACLE: 'oracle'>, vector_dimension=None, vector_table_name=None, pipeline_name='TEST_VECTOR_INDEX$VECPIPELINE')

Profile(profile_name=oci_vector_ai_profile, attributes=ProfileAttributes(annotations=None, case_sensitive_values=None, comments=None, constraints=None, conversation=None, credential_name='my_oci_ai_profile_key', enable_custom_source_uri=None, enable_sources=None, enable_source_offsets=None, enforce_object_list=None, max_tokens=1024, object_list=None, object_list_mode=None, provider=OCIGenAIProvider(embedding_model='cohere.embed-english-v3.0', model=None, provider_name='oci', provider_endpoint=None, region='us-chicago-1', oci_apiformat='GENERIC', oci_compartment_id=None, oci_endpoint_id=None, oci_runtimetype=None), seed=None, stop_tokens=None, streaming=None, temperature=None, vector_index_name='test_vector_index'), description=MY OCI AI Profile)

3.4. Update vector index attributes¶

To update attributes, use either vector_index.set_attribute() or vector_index.set_attributes()

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")
select_ai.connect(user=user, password=password, dsn=dsn)
vector_index = select_ai.VectorIndex(
    index_name="test_vector_index",
)

# Use vector_index.set_attributes to update a multiple attributes
updated_attributes = select_ai.OracleVectorIndexAttributes(
    refresh_rate=1450,
)
vector_index.set_attributes(attributes=updated_attributes)

# Use vector_index.set_attribute to update a single attribute
vector_index.set_attribute(
    attribute_name="similarity_threshold", attribute_value=0.5
)
print(vector_index.get_attributes())

output:

OracleVectorIndexAttributes(chunk_size=1024, chunk_overlap=128, location='https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph', match_limit=5, object_storage_credential_name='my_oci_ai_profile_key', profile_name='oci_vector_ai_profile', refresh_rate=1450, similarity_threshold=0.5, vector_distance_metric='COSINE', vector_db_endpoint=None, vector_db_credential_name=None, vector_db_provider=<VectorDBProvider.ORACLE: 'oracle'>, vector_dimension=None, vector_table_name=None, pipeline_name='TEST_VECTOR_INDEX$VECPIPELINE')

3.5. RAG using vector index¶

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")

select_ai.connect(user=user, password=password, dsn=dsn)
profile = select_ai.Profile(profile_name="oci_vector_ai_profile")
r = profile.narrate("list the conda environments in my object store")
print(r)

output:

The conda environments in your object store are:
 1. fccenv
 2. myrenv
 3. fully-loaded-mlenv
 4. graphenv

 These environments are listed in the provided data as separate JSON documents, each containing information about a specific conda environment.

 Sources:
   - fccenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/fccenv-manifest.json)
   - myrenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/myrenv-manifest.json)
   - fully-loaded-mlenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/fully-loaded-mlenv-manifest.json)
   - graphenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/graphenv-manifest.json)

3.6. Delete vector index¶

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")

select_ai.connect(user=user, password=password, dsn=dsn)
vector_index = select_ai.VectorIndex(index_name="test_vector_index")
vector_index.delete(force=True)
print("Deleted vector index: test_vector_index")

output:

Deleted vector index: test_vector_index

4. `AsyncVectorIndex` API¶

A AsyncVectorIndex object can be created with select_ai.AsyncVectorIndex()

class select_ai.AsyncVectorIndex(profile: BaseProfile | None = None, index_name: str | None = None, description: str | None = None, attributes: VectorIndexAttributes | None = None)¶

AsyncVectorIndex objects let you manage vector indexes using async APIs. Use this for non-blocking concurrent requests

Parameters:

index_name (str) – The name of the vector index
description (str) – The description of the vector index
attributes (VectorIndexAttributes) – The attributes of the vector index

async create(replace: bool | None = False) → None¶

Create a vector index in the database and populates it with data from an object store bucket using an async scheduler job

Parameters:: replace (bool) – True to replace existing vector index

async delete(include_data: bool | None = True, force: bool | None = False) → None¶

This procedure removes a vector store index.

Parameters:

include_data (bool) – Indicates whether to delete both the customer’s vector store and vector index along with the vector index object.
force (bool) – Indicates whether to ignore errors that occur if the vector index does not exist.

Returns:

None

Raises:

oracledb.DatabaseError

async disable() → None¶

This procedure disables a vector index object in the current database. When disabled, an AI profile cannot use the vector index, and the system does not load data into the vector store as new data is added to the object store and does not perform indexing, searching or querying based on the index.

Returns:: None
Raises:: oracledb.DatabaseError

async enable() → None¶

This procedure enables or activates a previously disabled vector index object. Generally, when you create a vector index, by default it is enabled such that the AI profile can use it to perform indexing and searching.

Returns:: None
Raises:: oracledb.DatabaseError

async get_attributes() → VectorIndexAttributes¶

Get attributes of a vector index

Returns:: select_ai.VectorIndexAttributes
Raises:: VectorIndexNotFoundError

async get_profile() → AsyncProfile¶

Get AsyncProfile object linked to this vector index

Returns:: select_ai.AsyncProfile
Raises:: ProfileNotFoundError

classmethod list(index_name_pattern: str = '.*') → AsyncGenerator[VectorIndex, None]¶

List Vector Indexes.

Parameters:: index_name_pattern (str) – Regular expressions can be used to specify a pattern. Function REGEXP_LIKE is used to perform the match. Default value is “.*” i.e. match all vector indexes.
Returns:: AsyncGenerator[VectorIndex]

async set_attribute(attribute_name: str, attribute_value: str | int | float) → None¶

This procedure updates an existing vector store index with a specified value of the vector index attribute.

Parameters:

attribute_name (str) – Custom attribute name
attribute_value (Union[str, int, float]) – Attribute Value

async set_attributes(attributes: VectorIndexAttributes) → None¶

This procedure updates an existing vector store index with a specified value of the vector index attribute. multiple attributes by passing an object of type :class VectorIndexAttributes

Parameters:: attributes (select_ai.VectorIndexAttributes) – Use this to update multiple attribute values
Returns:: None
Raises:: oracledb.DatabaseError

4.1. Async create vector index¶

import asyncio
import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


async def main():
    await select_ai.async_connect(user=user, password=password, dsn=dsn)

    provider = select_ai.OCIGenAIProvider(
        region="us-chicago-1",
        oci_apiformat="GENERIC",
        embedding_model="cohere.embed-english-v3.0",
    )
    profile_attributes = select_ai.ProfileAttributes(
        credential_name="my_oci_ai_profile_key",
        provider=provider,
    )
    async_profile = await select_ai.AsyncProfile(
        profile_name="async_oci_vector_ai_profile",
        attributes=profile_attributes,
        description="MY OCI AI Profile",
        replace=True,
    )

    vector_index_attributes = select_ai.OracleVectorIndexAttributes(
        location="https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph",
        object_storage_credential_name="my_oci_ai_profile_key",
    )

    async_vector_index = select_ai.AsyncVectorIndex(
        index_name="test_vector_index",
        attributes=vector_index_attributes,
        description="Vector index for conda environments",
        profile=async_profile,
    )
    await async_vector_index.create(replace=True)
    print("Created vector index: test_vector_index")


asyncio.run(main())

output:

created vector index: test_vector_index

4.2. Async list vector index¶

import asyncio
import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


async def main():
    await select_ai.async_connect(user=user, password=password, dsn=dsn)
    vector_index = select_ai.AsyncVectorIndex()
    async for index in vector_index.list(index_name_pattern="^test"):
        print("Vector index", index.index_name)
        print("Vector index profile", index.profile)


asyncio.run(main())

output:

Vector index TEST_VECTOR_INDEX
Vector index profile AsyncProfile(profile_name=oci_vector_ai_profile, attributes=ProfileAttributes(annotations=None, case_sensitive_values=None, comments=None, constraints=None, conversation=None, credential_name='my_oci_ai_profile_key', enable_sources=None, enable_source_offsets=None, enforce_object_list=None, max_tokens=1024, object_list=None, object_list_mode=None, provider=OCIGenAIProvider(embedding_model=None, model=None, provider_name='oci', provider_endpoint=None, region='us-chicago-1', oci_apiformat='GENERIC', oci_compartment_id=None, oci_endpoint_id=None, oci_runtimetype=None), seed=None, stop_tokens=None, streaming=None, temperature=None, vector_index_name='test_vector_index'), description=None)

4.3. Async get vector index attributes¶

You can fetch the vector index attributes and associated AI profile using async_vector_index.get_attributes() and async_vector_index.get_profile() methods respectively.

import asyncio
import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


async def main():
    await select_ai.async_connect(user=user, password=password, dsn=dsn)

    async_vector_index = select_ai.AsyncVectorIndex(
        index_name="test_vector_index",
    )
    attributes = await async_vector_index.get_attributes()
    print(attributes)
    async_profile = await async_vector_index.get_profile()
    print(async_profile)


asyncio.run(main())

output:

OracleVectorIndexAttributes(chunk_size=1024, chunk_overlap=128, location='https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph', match_limit=5, object_storage_credential_name='my_oci_ai_profile_key', profile_name='oci_vector_ai_profile', refresh_rate=1450, similarity_threshold=0.5, vector_distance_metric='COSINE', vector_db_endpoint=None, vector_db_credential_name=None, vector_db_provider=<VectorDBProvider.ORACLE: 'oracle'>, vector_dimension=None, vector_table_name=None, pipeline_name='TEST_VECTOR_INDEX$VECPIPELINE')

AsyncProfile(profile_name=oci_vector_ai_profile, attributes=ProfileAttributes(annotations=None, case_sensitive_values=None, comments=None, constraints=None, conversation=None, credential_name='my_oci_ai_profile_key', enable_custom_source_uri=None, enable_sources=None, enable_source_offsets=None, enforce_object_list=None, max_tokens=1024, object_list=None, object_list_mode=None, provider=OCIGenAIProvider(embedding_model='cohere.embed-english-v3.0', model=None, provider_name='oci', provider_endpoint=None, region='us-chicago-1', oci_apiformat='GENERIC', oci_compartment_id=None, oci_endpoint_id=None, oci_runtimetype=None), seed=None, stop_tokens=None, streaming=None, temperature=None, vector_index_name='test_vector_index'), description=MY OCI AI Profile)

4.4. Async update vector index attributes¶

To update attributes, use either async_vector_index.set_attribute() or async_vector_index.set_attributes()

import asyncio
import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


async def main():
    await select_ai.async_connect(user=user, password=password, dsn=dsn)
    async_vector_index = select_ai.AsyncVectorIndex(
        index_name="test_vector_index",
    )

    # Use vector_index.set_attributes to update a multiple attributes
    updated_attributes = select_ai.OracleVectorIndexAttributes(
        refresh_rate=1450,
    )
    await async_vector_index.set_attributes(attributes=updated_attributes)

    # Use vector_index.set_attribute to update a single attribute
    await async_vector_index.set_attribute(
        attribute_name="similarity_threshold", attribute_value=0.5
    )
    attributes = await async_vector_index.get_attributes()
    print(attributes)


asyncio.run(main())

output:

OracleVectorIndexAttributes(chunk_size=1024, chunk_overlap=128, location='https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph', match_limit=5, object_storage_credential_name='my_oci_ai_profile_key', profile_name='oci_vector_ai_profile', refresh_rate=1450, similarity_threshold=0.5, vector_distance_metric='COSINE', vector_db_endpoint=None, vector_db_credential_name=None, vector_db_provider=<VectorDBProvider.ORACLE: 'oracle'>, vector_dimension=None, vector_table_name=None, pipeline_name='TEST_VECTOR_INDEX$VECPIPELINE')

4.5. Async RAG using vector index¶

import os

import select_ai

user = os.getenv("SELECT_AI_USER")
password = os.getenv("SELECT_AI_PASSWORD")
dsn = os.getenv("SELECT_AI_DB_CONNECT_STRING")


async def main():
    await select_ai.async_connect(user=user, password=password, dsn=dsn)
    async_profile = await select_ai.AsyncProfile(
        profile_name="async_oci_vector_ai_profile"
    )
    r = await async_profile.narrate(
        "list the conda environments in my object store"
    )
    print(r)


asyncio.run(main())

output:

The conda environments in your object store are:
1. fccenv
2. myrenv
3. fully-loaded-mlenv
4. graphenv

These environments are listed in the provided data as separate JSON documents, each containing information about a specific conda environment.

Sources:
  - fccenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/fccenv-manifest.json)
  - myrenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/myrenv-manifest.json)
  - fully-loaded-mlenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/fully-loaded-mlenv-manifest.json)
  - graphenv-manifest.json (https://objectstorage.us-ashburn-1.oraclecloud.com/n/dwcsdev/b/conda-environment/o/tenant1-pdb3/graph/graphenv-manifest.json)

1. `VectorIndex` Object Model¶

2. `VectorIndexAttributes`¶

2.1. `OracleVectorIndexAttributes`¶

3. `VectorIndex` API¶

3.1. Create vector index¶

3.2. List vector index¶

3.3. Get vector index attributes¶

3.4. Update vector index attributes¶

3.5. RAG using vector index¶

3.6. Delete vector index¶

4. `AsyncVectorIndex` API¶

4.1. Async create vector index¶

4.2. Async list vector index¶

4.3. Async get vector index attributes¶

4.4. Async update vector index attributes¶

4.5. Async RAG using vector index¶

Table of Contents

Previous topic

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This Page

1. VectorIndex Object Model¶

2. VectorIndexAttributes¶

2.1. OracleVectorIndexAttributes¶

3. VectorIndex API¶

3.1. Create vector index¶

3.2. List vector index¶

3.3. Get vector index attributes¶

3.4. Update vector index attributes¶

3.5. RAG using vector index¶

3.6. Delete vector index¶

4. AsyncVectorIndex API¶

4.1. Async create vector index¶

4.2. Async list vector index¶

4.3. Async get vector index attributes¶

4.4. Async update vector index attributes¶

4.5. Async RAG using vector index¶

1. `VectorIndex` Object Model¶

2. `VectorIndexAttributes`¶

2.1. `OracleVectorIndexAttributes`¶

3. `VectorIndex` API¶

4. `AsyncVectorIndex` API¶