mirror of
https://github.com/meta-llama/llama-stack.git
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471b1b248b
Migrates package structure to src/ layout following Python packaging best practices. All code moved from `llama_stack/` to `src/llama_stack/`. Public API unchanged - imports remain `import llama_stack.*`. Updated build configs, pre-commit hooks, scripts, and GitHub workflows accordingly. All hooks pass, package builds cleanly. **Developer note**: Reinstall after pulling: `pip install -e .`
82 lines
2.8 KiB
Python
82 lines
2.8 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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from openai import NOT_GIVEN
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from llama_stack.apis.inference import (
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OpenAIEmbeddingData,
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OpenAIEmbeddingsRequestWithExtraBody,
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OpenAIEmbeddingsResponse,
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OpenAIEmbeddingUsage,
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)
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from .config import GeminiConfig
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class GeminiInferenceAdapter(OpenAIMixin):
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config: GeminiConfig
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provider_data_api_key_field: str = "gemini_api_key"
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embedding_model_metadata: dict[str, dict[str, int]] = {
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"models/text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
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"models/gemini-embedding-001": {"embedding_dimension": 3072, "context_length": 2048},
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}
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def get_base_url(self):
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return "https://generativelanguage.googleapis.com/v1beta/openai/"
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async def openai_embeddings(
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self,
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params: OpenAIEmbeddingsRequestWithExtraBody,
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) -> OpenAIEmbeddingsResponse:
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"""
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Override embeddings method to handle Gemini's missing usage statistics.
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Gemini's embedding API doesn't return usage information, so we provide default values.
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"""
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# Prepare request parameters
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request_params = {
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"model": await self._get_provider_model_id(params.model),
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"input": params.input,
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"encoding_format": params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
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"dimensions": params.dimensions if params.dimensions is not None else NOT_GIVEN,
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"user": params.user if params.user is not None else NOT_GIVEN,
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}
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# Add extra_body if present
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extra_body = params.model_extra
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if extra_body:
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request_params["extra_body"] = extra_body
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# Call OpenAI embeddings API with properly typed parameters
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response = await self.client.embeddings.create(**request_params)
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data = []
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for i, embedding_data in enumerate(response.data):
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data.append(
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OpenAIEmbeddingData(
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embedding=embedding_data.embedding,
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index=i,
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)
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)
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# Gemini doesn't return usage statistics - use default values
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if hasattr(response, "usage") and response.usage:
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usage = OpenAIEmbeddingUsage(
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prompt_tokens=response.usage.prompt_tokens,
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total_tokens=response.usage.total_tokens,
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)
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else:
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usage = OpenAIEmbeddingUsage(
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prompt_tokens=0,
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total_tokens=0,
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)
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return OpenAIEmbeddingsResponse(
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data=data,
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model=params.model,
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usage=usage,
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)
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