PineconeEmbeddings#

class langchain_pinecone.embeddings.PineconeEmbeddings[source]#

Bases: BaseModel, Embeddings

PineconeEmbeddings embedding model.

Example

from langchain_pinecone import PineconeEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_core.documents import Document

# Initialize embeddings with a specific model
embeddings = PineconeEmbeddings(model="multilingual-e5-large")

# Embed a single query
query_embedding = embeddings.embed_query("What is machine learning?")

# Embed multiple documents
docs = ["Document 1 content", "Document 2 content"]
doc_embeddings = embeddings.embed_documents(docs)

# Use with PineconeVectorStore
from pinecone import Pinecone

pc = Pinecone(api_key="your-api-key")
index = pc.Index("your-index-name")

vectorstore = PineconeVectorStore(
    index=index,
    embedding=embeddings
)

# Add documents to vector store
vectorstore.add_documents([
    Document(page_content="Hello, world!"),
    Document(page_content="This is a test.")
])

# Search for similar documents
results = vectorstore.similarity_search("hello", k=2)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param batch_size: int | None = None#

Batch size for embedding documents.

param dimension: int | None = None#
param document_params: Dict [Optional]#

Parameters for embedding document

param model: str [Required]#

Model to use for example โ€˜multilingual-e5-largeโ€™.

param pinecone_api_key: SecretStr [Optional] (alias 'api_key')#

Pinecone API key.

If not provided, will look for the PINECONE_API_KEY environment variable.

param query_params: Dict [Optional]#

Parameters for embedding query.

param show_progress_bar: bool = False#
async aembed_documents(
texts: List[str],
) โ†’ List[List[float]][source]#

Asynchronous Embed search docs.

Parameters:

texts (List[str]) โ€“ List of text to embed.

Returns:

List of embeddings.

Return type:

List[List[float]]

async aembed_query(
text: str,
) โ†’ List[float][source]#

Asynchronously embed query text.

Parameters:

text (str)

Return type:

List[float]

embed_documents(
texts: List[str],
) โ†’ List[List[float]][source]#

Embed search docs.

Parameters:

texts (List[str])

Return type:

List[List[float]]

embed_query(
text: str,
) โ†’ List[float][source]#

Embed query text.

Parameters:

text (str)

Return type:

List[float]

property async_client: PineconeAsyncio#

Lazily initialize the async client.