deepset-ai--haystack/docs-website/docs/pipeline-components/retrievers/oraclekeywordretriever.mdx

---
title: "OracleKeywordRetriever"
id: oraclekeywordretriever
slug: "/oraclekeywordretriever"
description: "A keyword-based Retriever that fetches documents matching a query from the Oracle Document Store using Oracle's DBMS_SEARCH full-text index."
---

# OracleKeywordRetriever

A keyword-based Retriever that fetches documents matching a query from the Oracle Document Store.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. Before a [`PromptBuilder`](../builders/promptbuilder.mdx) in a RAG pipeline 2. The last component in a keyword search pipeline 3. Before a [`TransformersExtractiveReader`](../readers/transformersextractivereader.mdx) in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of an [OracleDocumentStore](../../document-stores/oracledocumentstore.mdx) |
| **Mandatory run variables**            | `query`: A string |
| **Output variables**                   | `documents`: A list of documents matching the query |
| **API reference**                      | [Oracle](/reference/integrations-oracle) |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/oracle |
| **Package name**                       | `oracle-haystack` |

</div>

## Overview

The `OracleKeywordRetriever` is a keyword-based Retriever compatible with `OracleDocumentStore`. It uses Oracle's DBMS_SEARCH full-text index — automatically created when the document store is initialized — to search documents by keyword relevance.

This retriever works without embeddings, making it suitable for keyword-only pipelines or as the keyword branch of a hybrid search pipeline.

In addition to `query`, the retriever accepts `top_k` (maximum documents to return) and `filters` to narrow the search space.

## Installation

To run Oracle Database 23ai locally with Docker:

```shell
docker run -d --name oracle23ai \
  -p 1521:1521 \
  -e ORACLE_PASSWORD=oracle \
  container-registry.oracle.com/database/free:latest
```

Install the Oracle integration for Haystack:

```shell
pip install oracle-haystack
```

## Usage

### On its own

This Retriever needs an `OracleDocumentStore` and indexed documents to run.

```python
from haystack.utils import Secret
from haystack_integrations.document_stores.oracle import (
    OracleDocumentStore,
    OracleConnectionConfig,
)
from haystack_integrations.components.retrievers.oracle import OracleKeywordRetriever

document_store = OracleDocumentStore(
    connection_config=OracleConnectionConfig(
        user=Secret.from_env_var("ORACLE_USER"),
        password=Secret.from_env_var("ORACLE_PASSWORD"),
        dsn=Secret.from_env_var("ORACLE_DSN"),
    ),
    embedding_dim=768,
)

retriever = OracleKeywordRetriever(document_store=document_store)
retriever.run(query="my keyword query")
```

### In a RAG pipeline

```python
from haystack import Document, Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.document_stores.types import DuplicatePolicy
from haystack.utils import Secret

from haystack_integrations.document_stores.oracle import (
    OracleDocumentStore,
    OracleConnectionConfig,
)
from haystack_integrations.components.retrievers.oracle import OracleKeywordRetriever

prompt_template = [
    ChatMessage.from_user(
        """
    Given these documents, answer the question.\nDocuments:
    {% for doc in documents %}
        {{ doc.content }}
    {% endfor %}

    \nQuestion: {{question}}
    \nAnswer:
        """,
    ),
]

document_store = OracleDocumentStore(
    connection_config=OracleConnectionConfig(
        user=Secret.from_env_var("ORACLE_USER"),
        password=Secret.from_env_var("ORACLE_PASSWORD"),
        dsn=Secret.from_env_var("ORACLE_DSN"),
    ),
    embedding_dim=768,
)

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors.",
    ),
    Document(
        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_store.write_documents(documents=documents, policy=DuplicatePolicy.SKIP)

retriever = OracleKeywordRetriever(document_store=document_store)

rag_pipeline = Pipeline()
rag_pipeline.add_component(name="retriever", instance=retriever)
rag_pipeline.add_component(
    instance=ChatPromptBuilder(template=prompt_template, required_variables="*"),
    name="prompt_builder",
)
rag_pipeline.add_component(instance=OpenAIChatGenerator(), name="llm")

rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder.prompt", "llm.messages")

question = "How many languages are there?"
result = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)
print(result["llm"]["replies"][0].text)
```