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Taylor
a065b4b9c6
Adds a Python `nvidia` embedding provider for Nemotron 3.5 Embed 1B and 8B. It works with NVIDIA's hosted API and self-hosted NIM `/v1` endpoints, and uses the existing `openai` client. The OpenAI provider can't send NVIDIA's passage/query input types or pick Nemotron's dimensions. - Documents are embedded as `passage`, searches as `query`. - Default dimensions are 2048 (1B) and 4096 (8B), and `dim` is checked against each model's supported range. Self-hosted model IDs need an explicit `dim`. - Response count, order, dimensions and values are checked. Null and empty text get a null vector without a request. - Each request is retried on its own with LanceDB's retry policy. Invalid input or responses fail immediately. - `NVIDIA_API_KEY` is only sent to the hosted API. Custom endpoints need an explicit `api_key` (a `$var:` reference), and endpoints restored from table metadata must be allowed with `allow_endpoint`. - Request dimensions are sent through `extra_body`, preserving the NVIDIA wire contract while supporting the declared minimum `openai==1.6.1`. Python and text only. Docs are in `integrations/embedding/nvidia`. Originally authored by Taylor (@tsmithv11). ### Testing - `test_nvidia_embeddings.py`: 45 passed, 2 deselected with both the locked `openai==2.44.0` and minimum `openai==1.6.1`. These use the real OpenAI client with mock HTTP transport and real local tables. - Embedding, registry, field-metadata and trust suites: 163 passed, 4 skipped, 10 deselected. - Historical live experiments: hosted 8B passed a five-request smoke test; hosted 1B returned HTTP 500. No live request was made in this stage, so current hosted 1B availability was not verified. Enterprise coverage uses the repository mock HTTP server, not a live deployment.