llama-stack-mirror/llama_stack/providers/remote/inference/bedrock/bedrock.py
Ashwin Bharambe ecc8a554d2
Some checks failed
Integration Auth Tests / test-matrix (oauth2_token) (push) Failing after 0s
Python Package Build Test / build (3.12) (push) Failing after 1s
Unit Tests / unit-tests (3.13) (push) Failing after 4s
SqlStore Integration Tests / test-postgres (3.12) (push) Failing after 0s
SqlStore Integration Tests / test-postgres (3.13) (push) Failing after 0s
Test External Providers Installed via Module / test-external-providers-from-module (venv) (push) Has been skipped
Python Package Build Test / build (3.13) (push) Failing after 1s
Integration Tests (Replay) / Integration Tests (, , , client=, ) (push) Failing after 3s
Vector IO Integration Tests / test-matrix (push) Failing after 5s
Test External API and Providers / test-external (venv) (push) Failing after 5s
Unit Tests / unit-tests (3.12) (push) Failing after 4s
API Conformance Tests / check-schema-compatibility (push) Successful in 10s
UI Tests / ui-tests (22) (push) Successful in 40s
Pre-commit / pre-commit (push) Successful in 1m23s
feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794)
Applies the same pattern from
https://github.com/llamastack/llama-stack/pull/3777 to embeddings and
vector_stores.create() endpoints.

This should _not_ be a breaking change since (a) our tests were already
using the `extra_body` parameter when passing in to the backend (b) but
the backend probably wasn't extracting the parameters correctly. This PR
will fix that.

Updated APIs: `openai_embeddings(), openai_create_vector_store(),
openai_create_vector_store_file_batch()`
2025-10-12 19:01:52 -07:00

142 lines
4.4 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 json
from collections.abc import AsyncIterator
from botocore.client import BaseClient
from llama_stack.apis.inference import (
ChatCompletionRequest,
Inference,
OpenAIChatCompletionRequestWithExtraBody,
OpenAICompletionRequestWithExtraBody,
OpenAIEmbeddingsRequestWithExtraBody,
OpenAIEmbeddingsResponse,
)
from llama_stack.apis.inference.inference import (
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAICompletion,
)
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
from llama_stack.providers.utils.bedrock.client import create_bedrock_client
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
from .models import MODEL_ENTRIES
REGION_PREFIX_MAP = {
"us": "us.",
"eu": "eu.",
"ap": "ap.",
}
def _get_region_prefix(region: str | None) -> str:
# AWS requires region prefixes for inference profiles
if region is None:
return "us." # default to US when we don't know
# Handle case insensitive region matching
region_lower = region.lower()
for prefix in REGION_PREFIX_MAP:
if region_lower.startswith(f"{prefix}-"):
return REGION_PREFIX_MAP[prefix]
# Fallback to US for anything we don't recognize
return "us."
def _to_inference_profile_id(model_id: str, region: str = None) -> str:
# Return ARNs unchanged
if model_id.startswith("arn:"):
return model_id
# Return inference profile IDs that already have regional prefixes
if any(model_id.startswith(p) for p in REGION_PREFIX_MAP.values()):
return model_id
# Default to US East when no region is provided
if region is None:
region = "us-east-1"
return _get_region_prefix(region) + model_id
class BedrockInferenceAdapter(
ModelRegistryHelper,
Inference,
):
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
self._config = config
self._client = None
@property
def client(self) -> BaseClient:
if self._client is None:
self._client = create_bedrock_client(self._config)
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
if self._client is not None:
self._client.close()
async def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> dict:
bedrock_model = request.model
sampling_params = request.sampling_params
options = get_sampling_strategy_options(sampling_params)
if sampling_params.max_tokens:
options["max_gen_len"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
options["repetition_penalty"] = sampling_params.repetition_penalty
prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))
# Convert foundation model ID to inference profile ID
region_name = self.client.meta.region_name
inference_profile_id = _to_inference_profile_id(bedrock_model, region_name)
return {
"modelId": inference_profile_id,
"body": json.dumps(
{
"prompt": prompt,
**options,
}
),
}
async def openai_embeddings(
self,
params: OpenAIEmbeddingsRequestWithExtraBody,
) -> OpenAIEmbeddingsResponse:
raise NotImplementedError()
async def openai_completion(
self,
params: OpenAICompletionRequestWithExtraBody,
) -> OpenAICompletion:
raise NotImplementedError("OpenAI completion not supported by the Bedrock provider")
async def openai_chat_completion(
self,
params: OpenAIChatCompletionRequestWithExtraBody,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
raise NotImplementedError("OpenAI chat completion not supported by the Bedrock provider")