Hi there,
Large WhatsApp exports can push Pinecone past its 1000 dimension and per-upsert size limits. A reliable pattern is to preprocess the chat into smaller overlapping chunks (for example 500-700 characters with 30 percent overlap) before embedding. This keeps tokens per vector low and improves semantic recall during query time.
When you upsert, batch in groups of 100 vectors and enable async with exponential back-off. I have seen timeouts disappear when requests stay under 2 MB and you give the index time to persist. Also double-check that you set pod_type to p1.x1 so memory isn’t starved.
For retrieval, include a metadata field like chat_id or date so you can filter instead of scanning the full namespace. This reduces latency dramatically when the dataset grows.
A couple of questions:
• How many total messages end up in a single job and which embedding model are you using?
• Is real-time ingestion a requirement, or can the workflow run in scheduled batches?
This is general guidance based on my experience with similar projects.