> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OracleSummary integration

> Integrate with the OracleSummary tool using LangChain JavaScript.

<Tip>
  **Compatibility**: Only available on Node.js.
</Tip>

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](https://www.oracle.com/database/free/#resources). For background on user administration, refer to the official [Oracle guide](https://docs.oracle.com/en/database/oracle/oracle-database/26/dbseg/configuring-privilege-and-role-authorization.html#GUID-16473474-7F47-4E40-A592-01836E7D911C).

## Overview

### Integration details

| Class           | Package                                                                                  | Local | [PY support](https://python.langchain.com/docs/integrations/tools/oracleai) |
| :-------------- | :--------------------------------------------------------------------------------------- | :---: | :-------------------------------------------------------------------------: |
| `OracleSummary` | [`@oracle/langchain-oracledb`](https://www.npmjs.com/package/@oracle/langchain-oracledb) |   ✅   |                                      ✅                                      |

## Setup

To access OracleSummary, install the `@oracle/langchain-oracledb` helpers (with `@langchain/core`) and make sure the [Oracle Database driver](https://node-oracledb.readthedocs.io/en/latest/) prerequisites are satisfied for your environment.

### Credentials

Set credentials (or use a secrets manager) for the Oracle user that owns your summarization configuration:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export ORACLE_USER=testuser
export ORACLE_PASSWORD=testuser
export ORACLE_DSN="localhost:1521/free"
```

If you plan to call third-party providers such as OCI Generative AI or Hugging Face, create credentials inside Oracle Database first (for example `OCI_CRED`, `HF_CRED`) using the PL/SQL helpers documented in the [Oracle AI Vector Search guide](https://www.oracle.com/pls/topic/lookup?ctx=dblatest\&id=GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57).

### Installation

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @oracle/langchain-oracledb @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @oracle/langchain-oracledb @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @oracle/langchain-oracledb @langchain/core
  ```
</CodeGroup>

## Instantiate the tool

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import oracledb from "oracledb";
import { OracleSummary } from "@oracle/langchain-oracledb";

const connection = await oracledb.getConnection({
  user: process.env.ORACLE_USER,
  password: process.env.ORACLE_PASSWORD,
  connectionString: process.env.ORACLE_DSN,
});

const summary = new OracleSummary(
  connection,
  {
    provider: "database",
    glevel: "S",
    numParagraphs: 1,
    language: "english",
  },
  process.env.HTTP_PROXY, // optional
);
```

The third constructor argument is an optional proxy string. Supply it only when outbound requests must traverse an HTTP proxy (for example, when invoking Hugging Face endpoints).

## Summarize text with in-database models

Run ONNX summarization models directly in Oracle Database to keep data on the same host as your transactional workloads.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const result = await summary.getSummary(
  "Oracle Database combines relational and vector workloads so you can build secure RAG pipelines.",
);

console.log(result); // => concise summary text
```

## Use managed providers

Switch the `provider` parameter to route summarization requests through [OCI Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) or Hugging Face. Provide the credential name you registered in Oracle Database and, if required, a proxy string.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const ociSummary = new OracleSummary(
  connection,
  {
    provider: "ocigenai",
    credential_name: "OCI_CRED",
    url: "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/summarizeText",
    model: "cohere.command",
  },
  process.env.HTTP_PROXY,
);

const hfSummary = new OracleSummary(connection, {
  provider: "huggingface",
  credential_name: "HF_CRED",
  url: "https://api-inference.huggingface.co/models/",
  model: "facebook/bart-large-cnn",
  wait_for_model: "true",
});
```

## Chain with LangChain tools

You can wrap `OracleSummary` in a custom LangChain tool or runnable to integrate it with agent tool-calling.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const summarizeTool = tool(
  async ({ text }: { text: string }) => summary.getSummary(text),
  {
    name: "oracle_summary",
    description: "Summarize Oracle-sourced documents.",
    schema: z.object({
      text: z.string().describe("Full text to summarize"),
    }),
  },
);

const toolSummary = await summarizeTool.invoke({ text });
```

## Next steps

* Load content with [`OracleDocLoader`](/oss/javascript/integrations/document_loaders/file_loaders/oracleai)
* Chunk and normalize with [`OracleTextSplitter`](/oss/javascript/integrations/document_loaders/file_loaders/oracleai#chunk-documents)
* Generate embeddings using [`OracleEmbeddings`](/oss/javascript/integrations/embeddings/oracleai) and store them in [`OracleVS`](/oss/javascript/integrations/vectorstores/oracleai)

***

## API reference

For detailed documentation of all `OracleSummary` parameters and return types, see the [Oracle LangChain Oracle DB repository](https://github.com/oracle/langchain-oracle/tree/main/libs/js/langchain-oracledb).

***

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