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implement embedding generation in supported inference providers (#589)

Browse source 
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

```
This commit is contained in:
Dinesh Yeduguru 2024-12-12 11:17:39 -08:00
parent 6a23f24ee0
commit d362d2d740
32 changed files with 597 additions and 143 deletions
Download patch file Download diff file Expand all files Collapse all files

6
llama_stack/providers/tests/inference/conftest.py
View file

@ -18,6 +18,12 @@ def pytest_addoption(parser):
default=None,
help="Specify the inference model to use for testing",
)
parser.addoption(
"--embedding-model",
action="store",
default=None,
help="Specify the embedding model to use for testing",
)
def pytest_configure(config):

23
llama_stack/providers/tests/inference/fixtures.py
View file

@ -9,9 +9,9 @@ import os
import pytest
import pytest_asyncio
from llama_stack.apis.models import ModelInput
from llama_stack.apis.models import ModelInput, ModelType
from llama_stack.distribution.datatypes import Api, Provider
from llama_stack.providers.inline.inference.meta_reference import (
MetaReferenceInferenceConfig,
)
@ -47,6 +47,9 @@ def inference_meta_reference(inference_model) -> ProviderFixture:
inference_model = (
[inference_model] if isinstance(inference_model, str) else inference_model
)
# If embedding dimension is set, use the 8B model for testing
if os.getenv("EMBEDDING_DIMENSION"):
inference_model = ["meta-llama/Llama-3.1-8B-Instruct"]
return ProviderFixture(
providers=[
@ -85,7 +88,7 @@ def inference_ollama(inference_model) -> ProviderFixture:
inference_model = (
[inference_model] if isinstance(inference_model, str) else inference_model
)
if "Llama3.1-8B-Instruct" in inference_model:
if inference_model and "Llama3.1-8B-Instruct" in inference_model:
pytest.skip("Ollama only supports Llama3.2-3B-Instruct for testing")
return ProviderFixture(
@ -232,11 +235,23 @@ INFERENCE_FIXTURES = [
async def inference_stack(request, inference_model):
fixture_name = request.param
inference_fixture = request.getfixturevalue(f"inference_{fixture_name}")
model_type = ModelType.llm
metadata = {}
if os.getenv("EMBEDDING_DIMENSION"):
model_type = ModelType.embedding_model
metadata["embedding_dimension"] = get_env_or_fail("EMBEDDING_DIMENSION")
test_stack = await construct_stack_for_test(
[Api.inference],
{"inference": inference_fixture.providers},
inference_fixture.provider_data,
models=[ModelInput(model_id=inference_model)],
models=[
ModelInput(
model_id=inference_model,
model_type=model_type,
metadata=metadata,
)
],
)
return test_stack.impls[Api.inference], test_stack.impls[Api.models]

62
llama_stack/providers/tests/inference/test_embeddings.py Normal file
View file

@ -0,0 +1,62 @@
# 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 pytest
from llama_stack.apis.inference import EmbeddingsResponse, ModelType
# How to run this test:
# pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py
class TestEmbeddings:
@pytest.mark.asyncio
async def test_embeddings(self, inference_model, inference_stack):
inference_impl, models_impl = inference_stack
model = await models_impl.get_model(inference_model)
if model.model_type != ModelType.embedding_model:
pytest.skip("This test is only applicable for embedding models")
response = await inference_impl.embeddings(
model_id=inference_model,
contents=["Hello, world!"],
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) > 0
assert all(isinstance(embedding, list) for embedding in response.embeddings)
assert all(
isinstance(value, float)
for embedding in response.embeddings
for value in embedding
)
@pytest.mark.asyncio
async def test_batch_embeddings(self, inference_model, inference_stack):
inference_impl, models_impl = inference_stack
model = await models_impl.get_model(inference_model)
if model.model_type != ModelType.embedding_model:
pytest.skip("This test is only applicable for embedding models")
texts = ["Hello, world!", "This is a test", "Testing embeddings"]
response = await inference_impl.embeddings(
model_id=inference_model,
contents=texts,
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) == len(texts)
assert all(isinstance(embedding, list) for embedding in response.embeddings)
assert all(
isinstance(value, float)
for embedding in response.embeddings
for value in embedding
)
embedding_dim = len(response.embeddings[0])
assert all(len(embedding) == embedding_dim for embedding in response.embeddings)

80
llama_stack/providers/tests/memory/conftest.py
View file

@ -6,9 +6,65 @@
import pytest
from ..conftest import get_provider_fixture_overrides
from ..inference.fixtures import INFERENCE_FIXTURES
from .fixtures import MEMORY_FIXTURES
DEFAULT_PROVIDER_COMBINATIONS = [
pytest.param(
{
"inference": "meta_reference",
"memory": "faiss",
},
id="meta_reference",
marks=pytest.mark.meta_reference,
),
pytest.param(
{
"inference": "ollama",
"memory": "pgvector",
},
id="ollama",
marks=pytest.mark.ollama,
),
pytest.param(
{
"inference": "together",
"memory": "chroma",
},
id="chroma",
marks=pytest.mark.chroma,
),
pytest.param(
{
"inference": "bedrock",
"memory": "qdrant",
},
id="qdrant",
marks=pytest.mark.qdrant,
),
pytest.param(
{
"inference": "fireworks",
"memory": "weaviate",
},
id="weaviate",
marks=pytest.mark.weaviate,
),
]
def pytest_addoption(parser):
parser.addoption(
"--embedding-model",
action="store",
default=None,
help="Specify the embedding model to use for testing",
)
def pytest_configure(config):
for fixture_name in MEMORY_FIXTURES:
config.addinivalue_line(
@ -18,12 +74,22 @@ def pytest_configure(config):
def pytest_generate_tests(metafunc):
if "embedding_model" in metafunc.fixturenames:
model = metafunc.config.getoption("--embedding-model")
if not model:
raise ValueError(
"No embedding model specified. Please provide a valid embedding model."
)
params = [pytest.param(model, id="")]
metafunc.parametrize("embedding_model", params, indirect=True)
if "memory_stack" in metafunc.fixturenames:
metafunc.parametrize(
"memory_stack",
[
pytest.param(fixture_name, marks=getattr(pytest.mark, fixture_name))
for fixture_name in MEMORY_FIXTURES
],
indirect=True,
available_fixtures = {
"inference": INFERENCE_FIXTURES,
"memory": MEMORY_FIXTURES,
}
combinations = (
get_provider_fixture_overrides(metafunc.config, available_fixtures)
or DEFAULT_PROVIDER_COMBINATIONS
)
metafunc.parametrize("memory_stack", combinations, indirect=True)

30
llama_stack/providers/tests/memory/fixtures.py
View file

@ -10,6 +10,8 @@ import tempfile
import pytest
import pytest_asyncio
from llama_stack.apis.inference import ModelInput, ModelType
from llama_stack.distribution.datatypes import Api, Provider
from llama_stack.providers.inline.memory.chroma import ChromaInlineImplConfig
from llama_stack.providers.inline.memory.faiss import FaissImplConfig
@ -105,14 +107,30 @@ MEMORY_FIXTURES = ["faiss", "pgvector", "weaviate", "remote", "chroma"]
@pytest_asyncio.fixture(scope="session")
async def memory_stack(request):
fixture_name = request.param
fixture = request.getfixturevalue(f"memory_{fixture_name}")
async def memory_stack(embedding_model, request):
fixture_dict = request.param
providers = {}
provider_data = {}
for key in ["inference", "memory"]:
fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
providers[key] = fixture.providers
if fixture.provider_data:
provider_data.update(fixture.provider_data)
test_stack = await construct_stack_for_test(
[Api.memory],
{"memory": fixture.providers},
fixture.provider_data,
[Api.memory, Api.inference],
providers,
provider_data,
models=[
ModelInput(
model_id=embedding_model,
model_type=ModelType.embedding_model,
metadata={
"embedding_dimension": get_env_or_fail("EMBEDDING_DIMENSION"),
},
)
],
)
return test_stack.impls[Api.memory], test_stack.impls[Api.memory_banks]

26
llama_stack/providers/tests/memory/test_memory.py
View file

@ -45,12 +45,14 @@ def sample_documents():
]
async def register_memory_bank(banks_impl: MemoryBanks) -> MemoryBank:
async def register_memory_bank(
banks_impl: MemoryBanks, embedding_model: str
) -> MemoryBank:
bank_id = f"test_bank_{uuid.uuid4().hex}"
return await banks_impl.register_memory_bank(
memory_bank_id=bank_id,
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
embedding_model=embedding_model,
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
@ -59,11 +61,11 @@ async def register_memory_bank(banks_impl: MemoryBanks) -> MemoryBank:
class TestMemory:
@pytest.mark.asyncio
async def test_banks_list(self, memory_stack):
async def test_banks_list(self, memory_stack, embedding_model):
_, banks_impl = memory_stack
# Register a test bank
registered_bank = await register_memory_bank(banks_impl)
registered_bank = await register_memory_bank(banks_impl, embedding_model)
try:
# Verify our bank shows up in list
@ -84,7 +86,7 @@ class TestMemory:
)
@pytest.mark.asyncio
async def test_banks_register(self, memory_stack):
async def test_banks_register(self, memory_stack, embedding_model):
_, banks_impl = memory_stack
bank_id = f"test_bank_{uuid.uuid4().hex}"
@ -94,7 +96,7 @@ class TestMemory:
await banks_impl.register_memory_bank(
memory_bank_id=bank_id,
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
embedding_model=embedding_model,
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
@ -109,7 +111,7 @@ class TestMemory:
await banks_impl.register_memory_bank(
memory_bank_id=bank_id,
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
embedding_model=embedding_model,
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
@ -126,13 +128,15 @@ class TestMemory:
await banks_impl.unregister_memory_bank(bank_id)
@pytest.mark.asyncio
async def test_query_documents(self, memory_stack, sample_documents):
async def test_query_documents(
self, memory_stack, embedding_model, sample_documents
):
memory_impl, banks_impl = memory_stack
with pytest.raises(ValueError):
await memory_impl.insert_documents("test_bank", sample_documents)
registered_bank = await register_memory_bank(banks_impl)
registered_bank = await register_memory_bank(banks_impl, embedding_model)
await memory_impl.insert_documents(
registered_bank.memory_bank_id, sample_documents
)
@ -165,13 +169,13 @@ class TestMemory:
# Test case 5: Query with threshold on similarity score
query5 = "quantum computing" # Not directly related to any document
params5 = {"score_threshold": 0.2}
params5 = {"score_threshold": 0.01}
response5 = await memory_impl.query_documents(
registered_bank.memory_bank_id, query5, params5
)
assert_valid_response(response5)
print("The scores are:", response5.scores)
assert all(score >= 0.2 for score in response5.scores)
assert all(score >= 0.01 for score in response5.scores)
def assert_valid_response(response: QueryDocumentsResponse):