llama-stack/llama_stack/providers/utils/inference/embedding_mixin.py
Ashwin Bharambe 6f9d622340
fix(api): update embeddings signature so inputs and outputs list align (#1161)
See Issue #922 

The change is slightly backwards incompatible but no callsite (in our
client codebases or stack-apps) every passes a depth-2
`List[List[InterleavedContentItem]]` (which is now disallowed.)

## 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
```

Also ran `tests/client-sdk/inference/test_embeddings.py`
2025-02-20 21:43:13 -08:00

47 lines
1.4 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
from typing import List
from llama_stack.apis.inference import (
EmbeddingsResponse,
InterleavedContentItem,
ModelStore,
)
EMBEDDING_MODELS = {}
log = logging.getLogger(__name__)
class SentenceTransformerEmbeddingMixin:
model_store: ModelStore
async def embeddings(
self,
model_id: str,
contents: List[str] | List[InterleavedContentItem],
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
embedding_model = self._load_sentence_transformer_model(model.provider_resource_id)
embeddings = embedding_model.encode(contents)
return EmbeddingsResponse(embeddings=embeddings)
def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":
global EMBEDDING_MODELS
loaded_model = EMBEDDING_MODELS.get(model)
if loaded_model is not None:
return loaded_model
log.info(f"Loading sentence transformer for {model}...")
from sentence_transformers import SentenceTransformer
loaded_model = SentenceTransformer(model)
EMBEDDING_MODELS[model] = loaded_model
return loaded_model