deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/retrievers/pineconedenseretriever.mdx

---
title: "PineconeEmbeddingRetriever"
id: pineconedenseretriever
slug: "/pineconedenseretriever"
description: "An embedding-based Retriever compatible with the Pinecone Document Store."
---

# PineconeEmbeddingRetriever

An embedding-based Retriever compatible with the Pinecone 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 the semantic search pipeline 3. After a Text Embedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx)   in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of a [PineconeDocumentStore](../../document-stores/pinecone-document-store.mdx)                                                                                                                                                                                   |
| **Mandatory run variables**            | `query_embedding`: A vector representing the query (a list of floats)                                                                                                                                                                                                     |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                          |
| **API reference**                      | [Pinecone](/reference/integrations-pinecone)                                                                                                                                                                                                                                     |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pinecone                                                                                                                                                                                |

</div>

## Overview

The `PineconeEmbeddingRetriever` is an embedding-based Retriever compatible with the `PineconeDocumentStore`. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `PineconeDocumentStore` based on the outcome.

When using the `PineconeEmbeddingRetriever` in your NLP system, make sure it has the query and Document embeddings available. You can do so by adding a Document Embedder to your indexing Pipeline and a Text Embedder to your query Pipeline.

In addition to the `query_embedding`, the `PineconeEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `PineconeDocumentStore` is initialized: these include the `dimension` of the embeddings and the distance `metric` to use.

## Usage

### On its own

This Retriever needs the `PineconeDocumentStore` and indexed Documents to run.

```python
from haystack_integrations.components.retrievers.pinecone import (
    PineconeEmbeddingRetriever,
)
from haystack_integrations.document_stores.pinecone import PineconeDocumentStore

## Make sure you have the PINECONE_API_KEY environment variable set
document_store = PineconeDocumentStore(
    index="my_index_with_documents",
    namespace="my_namespace",
    dimension=768,
)

retriever = PineconeEmbeddingRetriever(document_store=document_store)

## using an imaginary vector to keep the example simple, example run query:
retriever.run(query_embedding=[0.1] * 768)
```

### In a pipeline

Install the dependencies you’ll need:

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

Use this Retriever in a query Pipeline like this:

```python
from haystack.document_stores.types import DuplicatePolicy
from haystack import Document
from haystack import Pipeline
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)
from haystack_integrations.components.retrievers.pinecone import (
    PineconeEmbeddingRetriever,
)
from haystack_integrations.document_stores.pinecone import PineconeDocumentStore

## Make sure you have the PINECONE_API_KEY environment variable set
document_store = PineconeDocumentStore(
    index="my_index",
    namespace="my_namespace",
    dimension=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()
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)

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

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    PineconeEmbeddingRetriever(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])
```

The example output would be:

```python
Document(id=cfe93bc1c274908801e6670440bf2bbba54fad792770d57421f85ffa2a4fcc94, content: 'There are over 7,000 languages spoken around the world today.', score: 0.87717235, embedding: vector of size 768)
```