PineconeEmbeddings

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)