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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 document summaries using OracleSummary from the Oracle AI Vector Search LangChain integration.
Why summarization here? Summaries are a practical way to compress long documents into retrieval-friendly content (previews, metadata, or condensed context) while keeping governance and operational guarantees close to the data.
If you are just starting with Oracle Database, consider exploring the free Oracle 26 AI. For an end-to-end setup walkthrough, see the Oracle AI Vector Search demo notebook. For background on user administration, refer to the official Oracle guide.

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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 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!")

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Generate summary

The Oracle AI Vector Search LangChain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, OCIGENAI, HuggingFace, among others, allowing users to select the provider that best meets their needs. To utilize these capabilities, users must configure the summary parameters as specified. For detailed information on these parameters, please consult the Oracle AI Vector Search Guide book. Note: The users may need to set proxy if they want to use some 3rd party summary generation providers other than Oracle’s in-house and default provider: ‘database’. If you don’t have proxy, please remove the proxy parameter when you instantiate the OracleSummary. 
# proxy to be used when we instantiate summary and embedder object
proxy = "<proxy>"
The following sample code will show how to generate summary: 
from langchain_oracledb.utilities.oracleai import OracleSummary
from langchain_core.documents import Document

"""
# using 'ocigenai' provider
summary_params = {
    "provider": "ocigenai",
    "credential_name": "OCI_CRED",
    "url": "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/summarizeText",
    "model": "cohere.command",
}

# using 'huggingface' provider
summary_params = {
    "provider": "huggingface",
    "credential_name": "HF_CRED",
    "url": "https://api-inference.huggingface.co/models/",
    "model": "facebook/bart-large-cnn",
    "wait_for_model": "true"
}
"""

# using 'database' provider
summary_params = {
    "provider": "database",
    "glevel": "S",
    "numParagraphs": 1,
    "language": "english",
}

# get the summary instance
# Remove proxy if not required
summ = OracleSummary(conn=conn, params=summary_params, proxy=proxy)
summary = summ.get_summary(
    "In the heart of the forest, "
    + "a lone fox ventured out at dusk, seeking a lost treasure. "
    + "With each step, memories flooded back, guiding its path. "
    + "As the moon rose high, illuminating the night, the fox unearthed "
    + "not gold, but a forgotten friendship, worth more than any riches."
)

print(f"Summary generated by OracleSummary: {summary}")

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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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