Load pinecone vectorstore from index name.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, text_key: str = 'text', namespace: Optional[str] = None, index_name: Optional[str] = None, upsert_kwargs: Optional[dict] = None, pool_threads: int = 4, embeddings_chunk_size: int = 1000, **kwargs: Any) → Pinecone[source]¶
Construct Pinecone wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Adds the documents to a provided Pinecone index
This is intended to be a quick way to get started.
The pool_threads affects the speed of the upsert operations.
.. rubric:: Example
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
    texts,
    embeddings,
    index_name="langchain-demo"
)
classmethod get_pinecone_index(index_name: Optional[str], pool_threads: int = 4) → Index[source]¶
Return a Pinecone Index instance.
Parameters
index_name – Name of the index to use.
pool_threads – Number of threads to use for index upsert.
Returns
Pinecone Index instance.