mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-28 10:54:19 +00:00
554ada57b0
# What does this PR do? This PR adds OpenAI compatibility for Ollama embeddings. Closes https://github.com/meta-llama/llama-stack/issues/2428 Summary of changes: - `llama_stack/providers/remote/inference/ollama/ollama.py` - Implements the OpenAI embeddings endpoint for Ollama, replacing the NotImplementedError with a full function that validates the model, prepares parameters, calls the client, encodes embedding data (optionally in base64), and returns a correctly structured response. - Updates import statements to include the new embedding response utilities. - `llama_stack/providers/utils/inference/litellm_openai_mixin.py` - Refactors the embedding data encoding logic to use a new shared utility (`b64_encode_openai_embeddings_response`) instead of inline base64 encoding and packing logic. - Cleans up imports accordingly. - `llama_stack/providers/utils/inference/openai_compat.py` - Adds `b64_encode_openai_embeddings_response` to handle encoding OpenAI embedding outputs (including base64 support) in a reusable way. - Adds `prepare_openai_embeddings_params` utility for standardizing embedding parameter preparation. - Updates imports to include the new embedding data class. - `tests/integration/inference/test_openai_embeddings.py` - Removes `"remote::ollama"` from the list of providers that skip OpenAI embeddings tests, since support is now implemented. ## Note There was one minor issue, which required me to override the `OpenAIEmbeddingsResponse.model` name with `self._get_model(model).identifier` name, which is very unsatisfying. ## Test Plan Unit Tests and integration tests --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
278 lines
10 KiB
Python
278 lines
10 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 base64
|
|
import struct
|
|
|
|
import pytest
|
|
from openai import OpenAI
|
|
|
|
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
|
|
|
|
|
|
def decode_base64_to_floats(base64_string: str) -> list[float]:
|
|
"""Helper function to decode base64 string to list of float32 values."""
|
|
embedding_bytes = base64.b64decode(base64_string)
|
|
float_count = len(embedding_bytes) // 4 # 4 bytes per float32
|
|
embedding_floats = struct.unpack(f"{float_count}f", embedding_bytes)
|
|
return list(embedding_floats)
|
|
|
|
|
|
def provider_from_model(client_with_models, model_id):
|
|
models = {m.identifier: m for m in client_with_models.models.list()}
|
|
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
|
|
provider_id = models[model_id].provider_id
|
|
providers = {p.provider_id: p for p in client_with_models.providers.list()}
|
|
return providers[provider_id]
|
|
|
|
|
|
def skip_if_model_doesnt_support_variable_dimensions(model_id):
|
|
if "text-embedding-3" not in model_id:
|
|
pytest.skip("{model_id} does not support variable output embedding dimensions")
|
|
|
|
|
|
@pytest.fixture(params=["openai_client", "llama_stack_client"])
|
|
def compat_client(request, client_with_models):
|
|
if request.param == "openai_client" and isinstance(client_with_models, LlamaStackAsLibraryClient):
|
|
pytest.skip("OpenAI client tests not supported with library client")
|
|
return request.getfixturevalue(request.param)
|
|
|
|
|
|
def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
|
|
provider = provider_from_model(client, model_id)
|
|
if provider.provider_type in (
|
|
"inline::meta-reference",
|
|
"remote::bedrock",
|
|
"remote::cerebras",
|
|
"remote::databricks",
|
|
"remote::runpod",
|
|
"remote::sambanova",
|
|
"remote::tgi",
|
|
):
|
|
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI embeddings.")
|
|
|
|
|
|
@pytest.fixture
|
|
def openai_client(client_with_models):
|
|
base_url = f"{client_with_models.base_url}/v1/openai/v1"
|
|
return OpenAI(base_url=base_url, api_key="fake")
|
|
|
|
|
|
def test_openai_embeddings_single_string(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with a single string input."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_text = "Hello, world!"
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text,
|
|
encoding_format="float",
|
|
)
|
|
|
|
assert response.object == "list"
|
|
assert response.model == embedding_model_id
|
|
assert len(response.data) == 1
|
|
assert response.data[0].object == "embedding"
|
|
assert response.data[0].index == 0
|
|
assert isinstance(response.data[0].embedding, list)
|
|
assert len(response.data[0].embedding) > 0
|
|
assert all(isinstance(x, float) for x in response.data[0].embedding)
|
|
|
|
|
|
def test_openai_embeddings_multiple_strings(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with multiple string inputs."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_texts = ["Hello, world!", "How are you today?", "This is a test."]
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_texts,
|
|
)
|
|
|
|
assert response.object == "list"
|
|
assert response.model == embedding_model_id
|
|
assert len(response.data) == len(input_texts)
|
|
|
|
for i, embedding_data in enumerate(response.data):
|
|
assert embedding_data.object == "embedding"
|
|
assert embedding_data.index == i
|
|
assert isinstance(embedding_data.embedding, list)
|
|
assert len(embedding_data.embedding) > 0
|
|
assert all(isinstance(x, float) for x in embedding_data.embedding)
|
|
|
|
|
|
def test_openai_embeddings_with_encoding_format_float(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with float encoding format."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_text = "Test encoding format"
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text,
|
|
encoding_format="float",
|
|
)
|
|
|
|
assert response.object == "list"
|
|
assert len(response.data) == 1
|
|
assert isinstance(response.data[0].embedding, list)
|
|
assert all(isinstance(x, float) for x in response.data[0].embedding)
|
|
|
|
|
|
def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with custom dimensions parameter."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
|
|
|
|
input_text = "Test dimensions parameter"
|
|
dimensions = 16
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text,
|
|
dimensions=dimensions,
|
|
)
|
|
|
|
assert response.object == "list"
|
|
assert len(response.data) == 1
|
|
# Note: Not all models support custom dimensions, so we don't assert the exact dimension
|
|
assert isinstance(response.data[0].embedding, list)
|
|
assert len(response.data[0].embedding) > 0
|
|
|
|
|
|
def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with user parameter."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_text = "Test user parameter"
|
|
user_id = "test-user-123"
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text,
|
|
user=user_id,
|
|
)
|
|
|
|
assert response.object == "list"
|
|
assert len(response.data) == 1
|
|
assert isinstance(response.data[0].embedding, list)
|
|
assert len(response.data[0].embedding) > 0
|
|
|
|
|
|
def test_openai_embeddings_empty_list_error(compat_client, client_with_models, embedding_model_id):
|
|
"""Test that empty list input raises an appropriate error."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
with pytest.raises(Exception): # noqa: B017
|
|
compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=[],
|
|
)
|
|
|
|
|
|
def test_openai_embeddings_invalid_model_error(compat_client, client_with_models, embedding_model_id):
|
|
"""Test that invalid model ID raises an appropriate error."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
with pytest.raises(Exception): # noqa: B017
|
|
compat_client.embeddings.create(
|
|
model="invalid-model-id",
|
|
input="Test text",
|
|
)
|
|
|
|
|
|
def test_openai_embeddings_different_inputs_different_outputs(compat_client, client_with_models, embedding_model_id):
|
|
"""Test that different inputs produce different embeddings."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_text1 = "This is the first text"
|
|
input_text2 = "This is completely different content"
|
|
|
|
response1 = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text1,
|
|
)
|
|
|
|
response2 = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text2,
|
|
)
|
|
|
|
embedding1 = response1.data[0].embedding
|
|
embedding2 = response2.data[0].embedding
|
|
|
|
assert len(embedding1) == len(embedding2)
|
|
# Embeddings should be different for different inputs
|
|
assert embedding1 != embedding2
|
|
|
|
|
|
def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with base64 encoding format."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
|
|
|
|
input_text = "Test base64 encoding format"
|
|
dimensions = 12
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_text,
|
|
encoding_format="base64",
|
|
dimensions=dimensions,
|
|
)
|
|
|
|
# Validate response structure
|
|
assert response.object == "list"
|
|
assert len(response.data) == 1
|
|
|
|
# With base64 encoding, embedding should be a string, not a list
|
|
embedding_data = response.data[0]
|
|
assert embedding_data.object == "embedding"
|
|
assert embedding_data.index == 0
|
|
assert isinstance(embedding_data.embedding, str)
|
|
|
|
# Verify it's valid base64 and decode to floats
|
|
embedding_floats = decode_base64_to_floats(embedding_data.embedding)
|
|
|
|
# Verify we got valid floats
|
|
assert len(embedding_floats) == dimensions, f"Got embedding length {len(embedding_floats)}, expected {dimensions}"
|
|
assert all(isinstance(x, float) for x in embedding_floats)
|
|
|
|
|
|
def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id):
|
|
"""Test OpenAI embeddings endpoint with base64 encoding for batch processing."""
|
|
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
|
|
|
input_texts = ["First text for base64", "Second text for base64", "Third text for base64"]
|
|
|
|
response = compat_client.embeddings.create(
|
|
model=embedding_model_id,
|
|
input=input_texts,
|
|
encoding_format="base64",
|
|
)
|
|
|
|
# Validate response structure
|
|
assert response.object == "list"
|
|
assert response.model == embedding_model_id
|
|
assert len(response.data) == len(input_texts)
|
|
|
|
# Validate each embedding in the batch
|
|
embedding_dimensions = []
|
|
for i, embedding_data in enumerate(response.data):
|
|
assert embedding_data.object == "embedding"
|
|
assert embedding_data.index == i
|
|
|
|
# With base64 encoding, embedding should be a string, not a list
|
|
assert isinstance(embedding_data.embedding, str)
|
|
embedding_floats = decode_base64_to_floats(embedding_data.embedding)
|
|
assert len(embedding_floats) > 0
|
|
assert all(isinstance(x, float) for x in embedding_floats)
|
|
embedding_dimensions.append(len(embedding_floats))
|
|
|
|
# All embeddings should have the same dimensionality
|
|
assert all(dim == embedding_dimensions[0] for dim in embedding_dimensions)
|