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>
* remove apps/core and apps/fern
* fix precommit
* add submodule updates in workflows
* submodule
* remove core tests
* update core revision
* Add submodules: true to all GitHub workflows
- Ensure all workflows can access git submodules
- Add submodules support to deployment, test, and CI workflows
- Fix YAML syntax issues in workflow files
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* remove core-lint
* upgrade core with latest main of oss
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Co-authored-by: Claude <noreply@anthropic.com>
