fix: handle oversized text in embedding requests with recursive chunking
When message text exceeds the embedding model's context length, recursively
split it until all chunks can be embedded successfully.
Changes:
- `tpuf_client.py`: Add `_split_text_in_half()` helper for recursive splitting
- `tpuf_client.py`: Add `_generate_embeddings_with_chunking()` that retries
with splits on context length errors
- `tpuf_client.py`: Store `message_id` and `chunk_index` columns in Turbopuffer
- `tpuf_client.py`: Deduplicate query results by `message_id`
- `tpuf_client.py`: Use `LettaInvalidArgumentError` instead of `ValueError`
- `tpuf_client.py`: Move LLMClient import to top of file
- `openai_client.py`: Remove fixed truncation (chunking handles this now)
- Add tests for `_split_text_in_half` and chunked query deduplication
🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
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