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d362d2d740
This PR adds the ability to generate embeddings in all supported inference providers. ``` pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py -k "bedrock" --inference-model="amazon.titan-embed-text-v2:0" --env EMBEDDING_DIMENSION=1024 pytest -v -s -k "vllm" --inferrence-model="intfloat/e5-mistral-7b-instruct" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=4096 --env VLLM_URL="http://localhost:9798/v1" pytest -v -s --inference-model="nomic-ai/nomic-embed-text-v1.5" llama_stack/providers/tests/inference/test_embeddings.py -k "fireworks" --env FIREWORKS_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=128 pytest -v -s --inference-model="togethercomputer/m2-bert-80M-2k-retrieval" llama_stack/providers/tests/inference/test_embeddings.py -k "together" --env TOGETHER_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=768 pytest -v -s -k "ollama" --inference-model="all-minilm:v8" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384 torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="sentence-transformers/all-MiniLM-L6-v2" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384 ```
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import pytest
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from llama_stack.apis.inference import EmbeddingsResponse, ModelType
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# How to run this test:
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# pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py
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class TestEmbeddings:
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@pytest.mark.asyncio
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async def test_embeddings(self, inference_model, inference_stack):
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inference_impl, models_impl = inference_stack
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model = await models_impl.get_model(inference_model)
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if model.model_type != ModelType.embedding_model:
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pytest.skip("This test is only applicable for embedding models")
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response = await inference_impl.embeddings(
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model_id=inference_model,
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contents=["Hello, world!"],
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)
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assert isinstance(response, EmbeddingsResponse)
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assert len(response.embeddings) > 0
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assert all(isinstance(embedding, list) for embedding in response.embeddings)
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assert all(
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isinstance(value, float)
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for embedding in response.embeddings
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for value in embedding
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)
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@pytest.mark.asyncio
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async def test_batch_embeddings(self, inference_model, inference_stack):
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inference_impl, models_impl = inference_stack
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model = await models_impl.get_model(inference_model)
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if model.model_type != ModelType.embedding_model:
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pytest.skip("This test is only applicable for embedding models")
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texts = ["Hello, world!", "This is a test", "Testing embeddings"]
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response = await inference_impl.embeddings(
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model_id=inference_model,
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contents=texts,
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)
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assert isinstance(response, EmbeddingsResponse)
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assert len(response.embeddings) == len(texts)
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assert all(isinstance(embedding, list) for embedding in response.embeddings)
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assert all(
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isinstance(value, float)
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for embedding in response.embeddings
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for value in embedding
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)
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embedding_dim = len(response.embeddings[0])
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assert all(len(embedding) == embedding_dim for embedding in response.embeddings)
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