Get synchronous index instance.

Get asynchronous index instance.

Access the query embedding object if available.

Run more texts through the embeddings and add to the vectorstore.

Upsert optimization is done by chunking the embeddings and upserting them. This is done to avoid memory issues and optimize using HTTP based embeddings. For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, embedding_chunk_size>1000 and batch_size~64 for best performance. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. namespace: Optional pinecone namespace to add the texts to. batch_size: Batch size to use when adding the texts to the vectorstore. embedding_chunk_size: Chunk size to use when embedding the texts. async_req: Whether runs asynchronously. Defaults to True. id_prefix: Optional string to use as an ID prefix when upserting vectors.

Asynchronously run more texts through the embeddings and add to the vectorstore.

Upsert optimization is done by chunking the embeddings and upserting them. This is done to avoid memory issues and optimize using HTTP based embeddings. For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, embedding_chunk_size>1000 and batch_size~64 for best performance. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. namespace: Optional pinecone namespace to add the texts to. batch_size: Batch size to use when adding the texts to the vectorstore. embedding_chunk_size: Chunk size to use when embedding the texts. id_prefix: Optional string to use as an ID prefix when upserting vectors.

Return documents most similar to the given embedding vector.

Wraps similarity_search_by_vector_with_score but strips the scores.

Return documents most similar to the given embedding vector asynchronously.

Wraps asimilarity_search_by_vector_with_score but strips the scores.

Return pinecone documents most similar to query, along with scores.

Asynchronously return pinecone documents most similar to query, along with scores.

Return pinecone documents most similar to embedding, along with scores.

Return pinecone documents most similar to embedding, along with scores asynchronously.

Return pinecone documents most similar to query.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance asynchronously.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return a Pinecone Index instance.

Construct Pinecone wrapper from raw documents.

Load pinecone vectorstore from index name.

Delete by vector IDs or filter. Args: ids: List of ids to delete. delete_all: Whether delete all vectors in the index. filter: Dictionary of conditions to filter vectors to delete. namespace: Namespace to search in. Default will search in '' namespace.