PineconeEmbeddingRetriever

Retrieves documents from the PineconeDocumentStore, based on their dense embeddings.

Basic Information

• Type: haystack_integrations.pinecone.src.haystack_integrations.components.retrievers.pinecone.embedding_retriever.PineconeEmbeddingRetriever

Inputs

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	Maximum number of Documents to return.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		List of Document similar to query_embedding.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

Retrieves documents from the PineconeDocumentStore, based on their dense embeddings.

Usage example:

import os
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

os.environ["PINECONE_API_KEY"] = "YOUR_PINECONE_API_KEY"
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..."),
             Document(content="In certain places, 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?"

res = query_pipeline.run({"text_embedder": {"text": query}})
assert res['retriever']['documents'][0].content == "There are over 7,000 languages spoken around the world today."

Usage Example

components:
  PineconeEmbeddingRetriever:
    type: pinecone.src.haystack_integrations.components.retrievers.pinecone.embedding_retriever.PineconeEmbeddingRetriever
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	PineconeDocumentStore		The Pinecone Document Store.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents.
top_k	int	10	Maximum number of Documents to return.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
query_embedding	List[float]		Embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	Maximum number of Documents to return.