LangChain's Pinecone upsert speed increased by 5X

In the latest release v0.0.281 of the LangChain Python client, the speed of upserts to Pinecone indexes has been increased up to 5 times by utilizing asynchronous calls, significantly improving processing time for large batches of vectors. The update introduces the parameter embeddings_chunk_size, optimizing the time spent on embedding models, such as OpenAI's text-embedding-ada-002, which, combined with an appropriate batch size, drastically reduces the time needed for upserting documents. Moreover, the update includes quality-of-life improvements, such as consolidating the from_texts and add_texts methods to ensure consistent performance and separating the batching of embeddings from the index upsert process. The update also features an automatic setting for thread_pool size, with a default pool_threads value of 4 to enhance asynchronous workloads while preventing API rate limits. Users are encouraged to experiment with these new settings by installing the updated LangChain package and providing feedback on their experience.