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Hardik Shah 2025-06-13 16:03:50 -07:00 • committed by GitHub
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37 changed files with 2105 additions and 63 deletions
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35
llama_stack/providers/remote/inference/ollama/ollama.py
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@ -33,7 +33,6 @@ from llama_stack.apis.inference import (
JsonSchemaResponseFormat,
LogProbConfig,
Message,
OpenAIEmbeddingsResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
@ -46,6 +45,8 @@ from llama_stack.apis.inference.inference import (
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAICompletion,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
OpenAIMessageParam,
OpenAIResponseFormatParam,
)
@ -62,8 +63,10 @@ from llama_stack.providers.utils.inference.model_registry import (
from llama_stack.providers.utils.inference.openai_compat import (
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
b64_encode_openai_embeddings_response,
get_sampling_options,
prepare_openai_completion_params,
prepare_openai_embeddings_params,
process_chat_completion_response,
process_chat_completion_stream_response,
process_completion_response,
@ -386,7 +389,35 @@ class OllamaInferenceAdapter(
dimensions: int | None = None,
user: str | None = None,
) -> OpenAIEmbeddingsResponse:
raise NotImplementedError()
model_obj = await self._get_model(model)
if model_obj.model_type != ModelType.embedding:
raise ValueError(f"Model {model} is not an embedding model")
if model_obj.provider_resource_id is None:
raise ValueError(f"Model {model} has no provider_resource_id set")
# Note, at the moment Ollama does not support encoding_format, dimensions, and user parameters
params = prepare_openai_embeddings_params(
model=model_obj.provider_resource_id,
input=input,
encoding_format=encoding_format,
dimensions=dimensions,
user=user,
)
response = await self.openai_client.embeddings.create(**params)
data = b64_encode_openai_embeddings_response(response.data, encoding_format)
usage = OpenAIEmbeddingUsage(
prompt_tokens=response.usage.prompt_tokens,
total_tokens=response.usage.total_tokens,
)
# TODO: Investigate why model_obj.identifier is used instead of response.model
return OpenAIEmbeddingsResponse(
data=data,
model=model_obj.identifier,
usage=usage,
)
async def openai_completion(
self,