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Oracle AI Database supports AI workloads where you query data by meaning (semantics), not just keywords. It combines semantic search over unstructured content with relational filtering over business data in a single system—so you can build retrieval workflows (like RAG) without introducing a separate vector database and fragmenting data across multiple platforms. This guide demonstrates how to generate embeddings for your content using OracleEmbeddings.
Why generate embeddings in (or via) Oracle? You can keep data governance and operational guarantees (security, transactions, availability) close to your AI workflow—while choosing the embedding provider model that fits your environment.

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Prerequisites

Install langchain-oracledb. The python-oracledb driver will be installed automatically as a dependency. 
pip install -qU langchain-oracledb

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Connect to Oracle Database

The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following guide that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode. 
import sys

import oracledb

# Please update with your username, password, hostname, port and service_name
username = "<username>"
password = "<password>"
dsn = "<hostname>:<port>/<service_name>"

connection = oracledb.connect(user=username, password=password, dsn=dsn)
print("Connection successful!")
For embedding generation, several provider options are available to users, including embedding generation within the database and third-party services such as OcigenAI, Hugging Face, and OpenAI. Users opting for third-party providers must establish credentials that include the requisite authentication information. Alternatively, if users select ‘database’ as their provider, they are required to load an ONNX model into the Oracle Database to facilitate embeddings.

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Load ONNX model

Oracle accommodates a variety of embedding providers, enabling users to choose between proprietary database solutions and third-party services such as OCIGENAI and HuggingFace. This selection dictates the methodology for generating and managing embeddings. Important : Should users opt for the database option, they must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required. A significant advantage of utilizing an ONNX model directly within Oracle is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls. Below is the example code to upload an ONNX model into Oracle Database: 
from langchain_oracledb.embeddings.oracleai import OracleEmbeddings

# Update the directory and file names for your ONNX model
# make sure that you have onnx file in the system
onnx_dir = "DEMO_DIR"
onnx_file = "tinybert.onnx"
model_name = "demo_model"

try:
    OracleEmbeddings.load_onnx_model(conn, onnx_dir, onnx_file, model_name)
    print("ONNX model loaded.")
except Exception as e:
    print("ONNX model loading failed!")
    sys.exit(1)

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Create credential

When selecting third-party providers for generating embeddings, users are required to establish credentials to securely access the provider’s endpoints. Important: No credentials are necessary when opting for the ‘database’ provider to generate embeddings. However, should users decide to utilize a third-party provider, they must create credentials specific to the chosen provider. Below is an illustrative example: 
try:
    cursor = conn.cursor()
    cursor.execute(
        """
       declare
           jo json_object_t;
       begin
           -- HuggingFace
           dbms_vector_chain.drop_credential(credential_name  => 'HF_CRED');
           jo := json_object_t();
           jo.put('access_token', '<access_token>');
           dbms_vector_chain.create_credential(
               credential_name   =>  'HF_CRED',
               params            => json(jo.to_string));

           -- OCIGENAI
           dbms_vector_chain.drop_credential(credential_name  => 'OCI_CRED');
           jo := json_object_t();
           jo.put('user_ocid','<user_ocid>');
           jo.put('tenancy_ocid','<tenancy_ocid>');
           jo.put('compartment_ocid','<compartment_ocid>');
           jo.put('private_key','<private_key>');
           jo.put('fingerprint','<fingerprint>');
           dbms_vector_chain.create_credential(
               credential_name   => 'OCI_CRED',
               params            => json(jo.to_string));
       end;
       """
    )
    cursor.close()
    print("Credentials created.")
except Exception as ex:
    cursor.close()
    raise

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Generate embeddings

Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the Oracle AI Vector Search Guide. Note: Users may need to configure a proxy to utilize third-party embedding generation providers, excluding the ‘database’ provider that utilizes an ONNX model. 
# proxy to be used when we instantiate summary and embedder object
proxy = "<proxy>"
The following sample code will show how to generate embeddings: 
from langchain_oracledb.embeddings.oracleai import OracleEmbeddings
from langchain_core.documents import Document

"""
# using ocigenai
embedder_params = {
    "provider": "ocigenai",
    "credential_name": "OCI_CRED",
    "url": "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/embedText",
    "model": "cohere.embed-english-light-v3.0",
}

# using huggingface
embedder_params = {
    "provider": "huggingface",
    "credential_name": "HF_CRED",
    "url": "https://api-inference.huggingface.co/pipeline/feature-extraction/",
    "model": "sentence-transformers/all-MiniLM-L6-v2",
    "wait_for_model": "true"
}
"""

# using ONNX model loaded to Oracle Database
embedder_params = {"provider": "database", "model": "demo_model"}

# If a proxy is not required for your environment, you can omit the 'proxy' parameter below
embedder = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy)
embed = embedder.embed_query("Hello World!")

""" verify """
print(f"Embedding generated by OracleEmbeddings: {embed}")

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End to end demo

Please refer to our complete demo guide Oracle AI Vector Search End-to-End Demo Guide to build an end to end RAG pipeline with the help of Oracle AI Vector Search.
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