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

---
title: "OracleEmbeddingRetriever"
id: oracleembeddingretriever
slug: "/oracleembeddingretriever"
description: "An embedding-based Retriever compatible with the Oracle Document Store."
---

# OracleEmbeddingRetriever

An embedding-based Retriever compatible with the Oracle Document Store.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx) in a RAG pipeline 2. The last component in a semantic search pipeline 3. After a Text Embedder and 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_embedding`: A vector representing the query (a list of floats) |
| **Output variables**                   | `documents`: A list of documents |
| **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 `OracleEmbeddingRetriever` is an embedding-based Retriever compatible with `OracleDocumentStore`. It uses Oracle AI Vector Search to compare query and document embeddings, fetching the most relevant documents based on vector similarity.

When using `OracleEmbeddingRetriever` in a pipeline, make sure embeddings are available for both documents (at index time) and queries (at query time). Use a Document Embedder in your indexing pipeline and a Text Embedder in your query pipeline.

The distance metric (COSINE, EUCLIDEAN, or DOT) is configured on the `OracleDocumentStore`. In addition to `query_embedding`, 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
```

The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:

```shell
pip install sentence-transformers-haystack
```

## Usage

### On its own

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

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

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 = OracleEmbeddingRetriever(document_store=document_store)

# using a fake vector to keep the example simple
retriever.run(query_embedding=[0.1] * 768)
```

### In a Pipeline

```python
from haystack import Document, Pipeline
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.components.embedders.sentence_transformers import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.utils import Secret

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

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_embedder = SentenceTransformersDocumentEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2",
)
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings["documents"],
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
)
query_pipeline.add_component(
    "retriever",
    OracleEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "How many languages are there?"
result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])
```