Hi @manishm112005, and welcome to the Pinecone community forums!
Thank you for your question.
The from_texts method is a Langchain method (not a Pinecone method).
Please have a look at our Langchain guide here which demonstrates how to create a vectorstore from documents and also how to add more information using the from_texts method:
import os
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
os.environ['OPENAI_API_KEY'] = '<YOUR_OPENAI_API_KEY>'
os.environ['PINECONE_API_KEY'] = '<YOUR_PINECONE_API_KEY>'
index_name = "<YOUR_PINECONE_INDEX_NAME>"
embeddings = OpenAIEmbeddings()
# path to an example text file
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
vectorstore_from_docs = PineconeVectorStore.from_documents(
docs,
index_name=index_name,
embedding=embeddings
)
texts = ["Tonight, I call on the Senate to: Pass the Freedom to Vote Act.", "ne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.", "One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence."]
vectorstore_from_texts = PineconeVectorStore.from_texts(
texts,
index_name=index_name,
embedding=embeddings
)
Hope this helps!
Best,
Zack