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Author	SHA1	Message	Date
Sébastien Han	803bf0e029	fix: solve ruff B008 warnings (#1444 ) # What does this PR do? The commit addresses the Ruff warning B008 by refactoring the code to avoid calling SamplingParams() directly in function argument defaults. Instead, it either uses Field(default_factory=SamplingParams) for Pydantic models or sets the default to None and instantiates SamplingParams inside the function body when the argument is None. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-03-06 16:48:35 -08:00
Ashwin Bharambe	81ce39a607	feat(api): Add options for supporting various embedding models (#1192 ) We need to support: - asymmetric embedding models (#934) - truncation policies (#933) - varying dimensional output (#932) ## Test Plan ```bash $ cd llama_stack/providers/tests/inference $ pytest -s -v -k fireworks test_embeddings.py \ --inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784 $ pytest -s -v -k together test_embeddings.py \ --inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784 $ pytest -s -v -k ollama test_embeddings.py \ --inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784 ```	2025-02-20 22:27:12 -08:00
Botao Chen	2b995c22eb	feat: inference passthrough provider (#1166 ) ## What does this PR do? In this PR, we implement a passthrough inference provider that works for any endpoints that respect llama stack inference API definition. ## Test Plan config some endpoint that respect llama stack inference API definition and got the inference results successfully <img width="1268" alt="Screenshot 2025-02-19 at 8 52 51 PM" src="https://github.com/user-attachments/assets/447816e4-ea7a-4365-b90c-386dc7dcf4a1" />	2025-02-19 21:47:00 -08:00

Author

SHA1

Message

Date

Sébastien Han

803bf0e029

fix: solve ruff B008 warnings (#1444 )

# What does this PR do?

The commit addresses the Ruff warning B008 by refactoring the code to
avoid calling SamplingParams() directly in function argument defaults.
Instead, it either uses Field(default_factory=SamplingParams) for
Pydantic models or sets the default to None and instantiates
SamplingParams inside the function body when the argument is None.

Signed-off-by: Sébastien Han <seb@redhat.com>

2025-03-06 16:48:35 -08:00

Ashwin Bharambe

81ce39a607

feat(api): Add options for supporting various embedding models (#1192 )

We need to support:
- asymmetric embedding models (#934)
- truncation policies (#933)
- varying dimensional output (#932) 

## Test Plan

```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
   --inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$  pytest -s -v -k together test_embeddings.py \
   --inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
   --inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```

2025-02-20 22:27:12 -08:00

Botao Chen

2b995c22eb

feat: inference passthrough provider (#1166 )

##  What does this PR do?
In this PR, we implement a passthrough inference provider that works for
any endpoints that respect llama stack inference API definition.

## Test Plan
config some endpoint that respect llama stack inference API definition
and got the inference results successfully

<img width="1268" alt="Screenshot 2025-02-19 at 8 52 51 PM"
src="https://github.com/user-attachments/assets/447816e4-ea7a-4365-b90c-386dc7dcf4a1"
/>

2025-02-19 21:47:00 -08:00

3 commits