
Most common position in a pipeline	1. After a Text Embedder and before a `PromptBuilder` in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an `ExtractiveReader` in an extractive QA pipeline
Mandatory init variables	"document_store": An instance of a PineconeDocumentStore
Mandatory run variables	“query_embedding”: A vector representing the query (a list of floats)
Output variables	“documents”: A list of documents
API reference	Pinecone
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pinecone

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.

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:

pip install pinecone-haystack
pip install sentence-transformers

Use this Retriever in a query Pipeline like this:

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:

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