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Merge branch 'main' into add-watsonx-inference-adapter

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Sajikumar JS 2025-03-20 10:18:05 +05:30
commit 28e6c8478b
308 changed files with 33749 additions and 5102 deletions
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12
llama_stack/providers/remote/agents/sample/config.py
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@ -1,12 +0,0 @@
# 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.
from pydantic import BaseModel
class SampleConfig(BaseModel):
host: str = "localhost"
port: int = 9999

17
llama_stack/providers/remote/agents/sample/sample.py
View file

@ -1,17 +0,0 @@
# 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.
from llama_stack.apis.agents import Agents
from .config import SampleConfig
class SampleAgentsImpl(Agents):
def __init__(self, config: SampleConfig):
self.config = config
async def initialize(self):
pass

16
llama_stack/providers/remote/datasetio/huggingface/config.py
View file

@ -3,9 +3,10 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from pydantic import BaseModel
from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.providers.utils.kvstore.config import (
KVStoreConfig,
SqliteKVStoreConfig,
@ -13,6 +14,13 @@ from llama_stack.providers.utils.kvstore.config import (
class HuggingfaceDatasetIOConfig(BaseModel):
kvstore: KVStoreConfig = SqliteKVStoreConfig(
db_path=(RUNTIME_BASE_DIR / "huggingface_datasetio.db").as_posix()
) # Uses SQLite config specific to HF storage
kvstore: KVStoreConfig
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
return {
"kvstore": SqliteKVStoreConfig.sample_run_config(
__distro_dir__=__distro_dir__,
db_name="huggingface_datasetio.db",
)
}

65
llama_stack/providers/remote/datasetio/huggingface/huggingface.py
View file

@ -4,13 +4,13 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict, List, Optional
from urllib.parse import parse_qs, urlparse
import datasets as hf_datasets
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
from llama_stack.apis.datasetio import DatasetIO, IterrowsResponse
from llama_stack.apis.datasets import Dataset
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
from llama_stack.providers.utils.kvstore import kvstore_impl
from .config import HuggingfaceDatasetIOConfig
@ -18,22 +18,14 @@ from .config import HuggingfaceDatasetIOConfig
DATASETS_PREFIX = "datasets:"
def load_hf_dataset(dataset_def: Dataset):
if dataset_def.metadata.get("path", None):
dataset = hf_datasets.load_dataset(**dataset_def.metadata)
else:
df = get_dataframe_from_url(dataset_def.url)
def parse_hf_params(dataset_def: Dataset):
uri = dataset_def.source.uri
parsed_uri = urlparse(uri)
params = parse_qs(parsed_uri.query)
params = {k: v[0] for k, v in params.items()}
path = parsed_uri.path.lstrip("/")
if df is None:
raise ValueError(f"Failed to load dataset from {dataset_def.url}")
dataset = hf_datasets.Dataset.from_pandas(df)
# drop columns not specified by schema
if dataset_def.dataset_schema:
dataset = dataset.select_columns(list(dataset_def.dataset_schema.keys()))
return dataset
return path, params
class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
@ -64,7 +56,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
key = f"{DATASETS_PREFIX}{dataset_def.identifier}"
await self.kvstore.set(
key=key,
value=dataset_def.json(),
value=dataset_def.model_dump_json(),
)
self.dataset_infos[dataset_def.identifier] = dataset_def
@ -73,41 +65,34 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
await self.kvstore.delete(key=key)
del self.dataset_infos[dataset_id]
async def get_rows_paginated(
async def iterrows(
self,
dataset_id: str,
rows_in_page: int,
page_token: Optional[str] = None,
filter_condition: Optional[str] = None,
) -> PaginatedRowsResult:
start_index: Optional[int] = None,
limit: Optional[int] = None,
) -> IterrowsResponse:
dataset_def = self.dataset_infos[dataset_id]
loaded_dataset = load_hf_dataset(dataset_def)
path, params = parse_hf_params(dataset_def)
loaded_dataset = hf_datasets.load_dataset(path, **params)
if page_token and not page_token.isnumeric():
raise ValueError("Invalid page_token")
start_index = start_index or 0
if page_token is None or len(page_token) == 0:
next_page_token = 0
else:
next_page_token = int(page_token)
start = next_page_token
if rows_in_page == -1:
if limit is None or limit == -1:
end = len(loaded_dataset)
else:
end = min(start + rows_in_page, len(loaded_dataset))
end = min(start_index + limit, len(loaded_dataset))
rows = [loaded_dataset[i] for i in range(start, end)]
rows = [loaded_dataset[i] for i in range(start_index, end)]
return PaginatedRowsResult(
rows=rows,
total_count=len(rows),
next_page_token=str(end),
return IterrowsResponse(
data=rows,
next_start_index=end if end < len(loaded_dataset) else None,
)
async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
dataset_def = self.dataset_infos[dataset_id]
loaded_dataset = load_hf_dataset(dataset_def)
path, params = parse_hf_params(dataset_def)
loaded_dataset = hf_datasets.load_dataset(path, **params)
# Convert rows to HF Dataset format
new_dataset = hf_datasets.Dataset.from_list(rows)

13
llama_stack/providers/remote/inference/databricks/config.py
View file

@ -4,6 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from pydantic import BaseModel, Field
@ -20,3 +21,15 @@ class DatabricksImplConfig(BaseModel):
default=None,
description="The Databricks API token",
)
@classmethod
def sample_run_config(
cls,
url: str = "${env.DATABRICKS_URL}",
api_token: str = "${env.DATABRICKS_API_TOKEN}",
**kwargs: Any,
) -> Dict[str, Any]:
return {
"url": url,
"api_token": api_token,
}

4
llama_stack/providers/remote/inference/fireworks/models.py
View file

@ -24,10 +24,6 @@ MODEL_ENTRIES = [
"accounts/fireworks/models/llama-v3p1-405b-instruct",
CoreModelId.llama3_1_405b_instruct.value,
),
build_hf_repo_model_entry(
"accounts/fireworks/models/llama-v3p2-1b-instruct",
CoreModelId.llama3_2_1b_instruct.value,
),
build_hf_repo_model_entry(
"accounts/fireworks/models/llama-v3p2-3b-instruct",
CoreModelId.llama3_2_3b_instruct.value,

51
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -6,6 +6,7 @@
import logging
import warnings
from functools import lru_cache
from typing import AsyncIterator, List, Optional, Union
from openai import APIConnectionError, AsyncOpenAI, BadRequestError
@ -82,12 +83,42 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
# )
self._config = config
# make sure the client lives longer than any async calls
self._client = AsyncOpenAI(
base_url=f"{self._config.url}/v1",
api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
timeout=self._config.timeout,
)
@lru_cache # noqa: B019
def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
"""
For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However,
some models are hosted on different URLs. This function returns the appropriate client
for the given provider_model_id.
This relies on lru_cache and self._default_client to avoid creating a new client for each request
or for each model that is hosted on https://integrate.api.nvidia.com/v1.
:param provider_model_id: The provider model ID
:return: An OpenAI client
"""
@lru_cache # noqa: B019
def _get_client_for_base_url(base_url: str) -> AsyncOpenAI:
"""
Maintain a single OpenAI client per base_url.
"""
return AsyncOpenAI(
base_url=base_url,
api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
timeout=self._config.timeout,
)
special_model_urls = {
"meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct",
"meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
}
base_url = f"{self._config.url}/v1"
if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
base_url = special_model_urls[provider_model_id]
return _get_client_for_base_url(base_url)
async def completion(
self,
@ -105,9 +136,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
await check_health(self._config) # this raises errors
provider_model_id = self.get_provider_model_id(model_id)
request = convert_completion_request(
request=CompletionRequest(
model=self.get_provider_model_id(model_id),
model=provider_model_id,
content=content,
sampling_params=sampling_params,
response_format=response_format,
@ -118,7 +150,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
)
try:
response = await self._client.completions.create(**request)
response = await self._get_client(provider_model_id).completions.create(**request)
except APIConnectionError as e:
raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
@ -206,6 +238,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
await check_health(self._config) # this raises errors
provider_model_id = self.get_provider_model_id(model_id)
request = await convert_chat_completion_request(
request=ChatCompletionRequest(
model=self.get_provider_model_id(model_id),
@ -221,7 +254,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
)
try:
response = await self._client.chat.completions.create(**request)
response = await self._get_client(provider_model_id).chat.completions.create(**request)
except APIConnectionError as e:
raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e

87
llama_stack/providers/remote/inference/passthrough/passthrough.py
View file

@ -4,12 +4,15 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import AsyncGenerator, List, Optional
from typing import Any, AsyncGenerator, Dict, List, Optional
from llama_stack_client import LlamaStackClient
from llama_stack_client import AsyncLlamaStackClient
from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionMessage,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
@ -24,6 +27,7 @@ from llama_stack.apis.inference import (
ToolPromptFormat,
)
from llama_stack.apis.models import Model
from llama_stack.distribution.library_client import convert_pydantic_to_json_value, convert_to_pydantic
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from .config import PassthroughImplConfig
@ -46,7 +50,7 @@ class PassthroughInferenceAdapter(Inference):
async def register_model(self, model: Model) -> Model:
return model
def _get_client(self) -> LlamaStackClient:
def _get_client(self) -> AsyncLlamaStackClient:
passthrough_url = None
passthrough_api_key = None
provider_data = None
@ -71,7 +75,7 @@ class PassthroughInferenceAdapter(Inference):
)
passthrough_api_key = provider_data.passthrough_api_key
return LlamaStackClient(
return AsyncLlamaStackClient(
base_url=passthrough_url,
api_key=passthrough_api_key,
provider_data=provider_data,
@ -91,7 +95,7 @@ class PassthroughInferenceAdapter(Inference):
client = self._get_client()
model = await self.model_store.get_model(model_id)
params = {
request_params = {
"model_id": model.provider_resource_id,
"content": content,
"sampling_params": sampling_params,
@ -100,10 +104,13 @@ class PassthroughInferenceAdapter(Inference):
"logprobs": logprobs,
}
params = {key: value for key, value in params.items() if value is not None}
request_params = {key: value for key, value in request_params.items() if value is not None}
# cast everything to json dict
json_params = self.cast_value_to_json_dict(request_params)
# only pass through the not None params
return client.inference.completion(**params)
return await client.inference.completion(**json_params)
async def chat_completion(
self,
@ -120,10 +127,14 @@ class PassthroughInferenceAdapter(Inference):
) -> AsyncGenerator:
if sampling_params is None:
sampling_params = SamplingParams()
client = self._get_client()
model = await self.model_store.get_model(model_id)
params = {
# TODO: revisit this remove tool_calls from messages logic
for message in messages:
if hasattr(message, "tool_calls"):
message.tool_calls = None
request_params = {
"model_id": model.provider_resource_id,
"messages": messages,
"sampling_params": sampling_params,
@ -135,10 +146,41 @@ class PassthroughInferenceAdapter(Inference):
"logprobs": logprobs,
}
params = {key: value for key, value in params.items() if value is not None}
# only pass through the not None params
return client.inference.chat_completion(**params)
request_params = {key: value for key, value in request_params.items() if value is not None}
# cast everything to json dict
json_params = self.cast_value_to_json_dict(request_params)
if stream:
return self._stream_chat_completion(json_params)
else:
return await self._nonstream_chat_completion(json_params)
async def _nonstream_chat_completion(self, json_params: Dict[str, Any]) -> ChatCompletionResponse:
client = self._get_client()
response = await client.inference.chat_completion(**json_params)
return ChatCompletionResponse(
completion_message=CompletionMessage(
content=response.completion_message.content.text,
stop_reason=response.completion_message.stop_reason,
tool_calls=response.completion_message.tool_calls,
),
logprobs=response.logprobs,
)
async def _stream_chat_completion(self, json_params: Dict[str, Any]) -> AsyncGenerator:
client = self._get_client()
stream_response = await client.inference.chat_completion(**json_params)
async for chunk in stream_response:
chunk = chunk.to_dict()
# temporary hack to remove the metrics from the response
chunk["metrics"] = []
chunk = convert_to_pydantic(ChatCompletionResponseStreamChunk, chunk)
yield chunk
async def embeddings(
self,
@ -151,10 +193,29 @@ class PassthroughInferenceAdapter(Inference):
client = self._get_client()
model = await self.model_store.get_model(model_id)
return client.inference.embeddings(
return await client.inference.embeddings(
model_id=model.provider_resource_id,
contents=contents,
text_truncation=text_truncation,
output_dimension=output_dimension,
task_type=task_type,
)
def cast_value_to_json_dict(self, request_params: Dict[str, Any]) -> Dict[str, Any]:
json_params = {}
for key, value in request_params.items():
json_input = convert_pydantic_to_json_value(value)
if isinstance(json_input, dict):
json_input = {k: v for k, v in json_input.items() if v is not None}
elif isinstance(json_input, list):
json_input = [x for x in json_input if x is not None]
new_input = []
for x in json_input:
if isinstance(x, dict):
x = {k: v for k, v in x.items() if v is not None}
new_input.append(x)
json_input = new_input
json_params[key] = json_input
return json_params

3
llama_stack/providers/remote/inference/runpod/__init__.py
View file

@ -5,10 +5,11 @@
# the root directory of this source tree.
from .config import RunpodImplConfig
from .runpod import RunpodInferenceAdapter
async def get_adapter_impl(config: RunpodImplConfig, _deps):
from .runpod import RunpodInferenceAdapter
assert isinstance(config, RunpodImplConfig), f"Unexpected config type: {type(config)}"
impl = RunpodInferenceAdapter(config)
await impl.initialize()

9
llama_stack/providers/remote/inference/runpod/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
@ -21,3 +21,10 @@ class RunpodImplConfig(BaseModel):
default=None,
description="The API token",
)
@classmethod
def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
return {
"url": "${env.RUNPOD_URL:}",
"api_token": "${env.RUNPOD_API_TOKEN:}",
}

1
llama_stack/providers/remote/inference/runpod/runpod.py
View file

@ -8,7 +8,6 @@ from typing import AsyncGenerator
from openai import OpenAI
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.models.llama.datatypes import Message
# from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper

10
llama_stack/providers/remote/inference/sambanova/sambanova.py
View file

@ -42,9 +42,7 @@ from llama_stack.models.llama.datatypes import (
TopKSamplingStrategy,
TopPSamplingStrategy,
)
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
process_chat_completion_stream_response,
)
@ -293,14 +291,12 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
if not tool_calls:
return []
for call in tool_calls:
call_function_arguments = json.loads(call.function.arguments)
compitable_tool_calls = [
ToolCall(
call_id=call.id,
tool_name=call.function.name,
arguments=call_function_arguments,
arguments=json.loads(call.function.arguments),
arguments_json=call.function.arguments,
)
for call in tool_calls
]

17
llama_stack/providers/remote/inference/sample/__init__.py
View file

@ -1,17 +0,0 @@
# 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.
from typing import Any
from .config import SampleConfig
async def get_adapter_impl(config: SampleConfig, _deps) -> Any:
from .sample import SampleInferenceImpl
impl = SampleInferenceImpl(config)
await impl.initialize()
return impl

12
llama_stack/providers/remote/inference/sample/config.py
View file

@ -1,12 +0,0 @@
# 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.
from pydantic import BaseModel
class SampleConfig(BaseModel):
host: str = "localhost"
port: int = 9999

23
llama_stack/providers/remote/inference/sample/sample.py
View file

@ -1,23 +0,0 @@
# 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.
from llama_stack.apis.inference import Inference
from llama_stack.apis.models import Model
from .config import SampleConfig
class SampleInferenceImpl(Inference):
def __init__(self, config: SampleConfig):
self.config = config
async def register_model(self, model: Model) -> None:
# these are the model names the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def initialize(self):
pass

2
llama_stack/providers/remote/inference/together/config.py
View file

@ -26,5 +26,5 @@ class TogetherImplConfig(BaseModel):
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
return {
"url": "https://api.together.xyz/v1",
"api_key": "${env.TOGETHER_API_KEY}",
"api_key": "${env.TOGETHER_API_KEY:}",
}

71
llama_stack/providers/remote/inference/together/together.py
View file

@ -6,7 +6,7 @@
from typing import AsyncGenerator, List, Optional, Union
from together import Together
from together import AsyncTogether
from llama_stack.apis.common.content_types import (
InterleavedContent,
@ -59,12 +59,15 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
def __init__(self, config: TogetherImplConfig) -> None:
ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
self.config = config
self._client = None
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
pass
if self._client:
await self._client.close()
self._client = None
async def completion(
self,
@ -91,35 +94,32 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
else:
return await self._nonstream_completion(request)
def _get_client(self) -> Together:
together_api_key = None
config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
if config_api_key:
together_api_key = config_api_key
else:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
return Together(api_key=together_api_key)
def _get_client(self) -> AsyncTogether:
if not self._client:
together_api_key = None
config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
if config_api_key:
together_api_key = config_api_key
else:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
self._client = AsyncTogether(api_key=together_api_key)
return self._client
async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
params = await self._get_params(request)
r = self._get_client().completions.create(**params)
client = self._get_client()
r = await client.completions.create(**params)
return process_completion_response(r)
async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
params = await self._get_params(request)
# if we shift to TogetherAsyncClient, we won't need this wrapper
async def _to_async_generator():
s = self._get_client().completions.create(**params)
for chunk in s:
yield chunk
stream = _to_async_generator()
client = await self._get_client()
stream = await client.completions.create(**params)
async for chunk in process_completion_stream_response(stream):
yield chunk
@ -184,25 +184,21 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
params = await self._get_params(request)
client = self._get_client()
if "messages" in params:
r = self._get_client().chat.completions.create(**params)
r = await client.chat.completions.create(**params)
else:
r = self._get_client().completions.create(**params)
r = await client.completions.create(**params)
return process_chat_completion_response(r, request)
async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
params = await self._get_params(request)
client = self._get_client()
if "messages" in params:
stream = await client.chat.completions.create(**params)
else:
stream = await client.completions.create(**params)
# if we shift to TogetherAsyncClient, we won't need this wrapper
async def _to_async_generator():
if "messages" in params:
s = self._get_client().chat.completions.create(**params)
else:
s = self._get_client().completions.create(**params)
for chunk in s:
yield chunk
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(stream, request):
yield chunk
@ -240,7 +236,8 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
assert all(not content_has_media(content) for content in contents), (
"Together does not support media for embeddings"
)
r = self._get_client().embeddings.create(
client = self._get_client()
r = await client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
)

5
llama_stack/providers/remote/inference/vllm/config.py
View file

@ -25,6 +25,10 @@ class VLLMInferenceAdapterConfig(BaseModel):
default="fake",
description="The API token",
)
tls_verify: bool = Field(
default=True,
description="Whether to verify TLS certificates",
)
@classmethod
def sample_run_config(
@ -36,4 +40,5 @@ class VLLMInferenceAdapterConfig(BaseModel):
"url": url,
"max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
"api_token": "${env.VLLM_API_TOKEN:fake}",
"tls_verify": "${env.VLLM_TLS_VERIFY:true}",
}

15
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -7,6 +7,7 @@ import json
import logging
from typing import AsyncGenerator, List, Optional, Union
import httpx
from openai import AsyncOpenAI
from openai.types.chat.chat_completion_chunk import (
ChatCompletionChunk as OpenAIChatCompletionChunk,
@ -89,15 +90,12 @@ def _convert_to_vllm_tool_calls_in_response(
if not tool_calls:
return []
call_function_arguments = None
for call in tool_calls:
call_function_arguments = json.loads(call.function.arguments)
return [
ToolCall(
call_id=call.id,
tool_name=call.function.name,
arguments=call_function_arguments,
arguments=json.loads(call.function.arguments),
arguments_json=call.function.arguments,
)
for call in tool_calls
]
@ -182,6 +180,7 @@ async def _process_vllm_chat_completion_stream_response(
call_id=tool_call_buf.call_id,
tool_name=tool_call_buf.tool_name,
arguments=args,
arguments_json=args_str,
),
parse_status=ToolCallParseStatus.succeeded,
),
@ -229,7 +228,11 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def initialize(self) -> None:
log.info(f"Initializing VLLM client with base_url={self.config.url}")
self.client = AsyncOpenAI(base_url=self.config.url, api_key=self.config.api_token)
self.client = AsyncOpenAI(
base_url=self.config.url,
api_key=self.config.api_token,
http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
)
async def shutdown(self) -> None:
pass

9
llama_stack/providers/remote/agents/sample/__init__.py → llama_stack/providers/remote/safety/nvidia/__init__.py
View file

@ -4,14 +4,15 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any
from .config import SampleConfig
from .config import NVIDIASafetyConfig
async def get_adapter_impl(config: SampleConfig, _deps) -> Any:
from .sample import SampleAgentsImpl
async def get_adapter_impl(config: NVIDIASafetyConfig, _deps) -> Any:
from .nvidia import NVIDIASafetyAdapter
impl = SampleAgentsImpl(config)
impl = NVIDIASafetyAdapter(config)
await impl.initialize()
return impl

37
llama_stack/providers/remote/safety/nvidia/config.py Normal file
View file

@ -0,0 +1,37 @@
# 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 os
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from llama_stack.schema_utils import json_schema_type
@json_schema_type
class NVIDIASafetyConfig(BaseModel):
"""
Configuration for the NVIDIA Guardrail microservice endpoint.
Attributes:
guardrails_service_url (str): A base url for accessing the NVIDIA guardrail endpoint, e.g. http://0.0.0.0:7331
config_id (str): The ID of the guardrails configuration to use from the configuration store
(https://developer.nvidia.com/docs/nemo-microservices/guardrails/source/guides/configuration-store-guide.html)
"""
guardrails_service_url: str = Field(
default_factory=lambda: os.getenv("GUARDRAILS_SERVICE_URL", "http://0.0.0.0:7331"),
description="The url for accessing the guardrails service",
)
config_id: Optional[str] = Field(default="self-check", description="Config ID to use from the config store")
@classmethod
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
return {
"guardrails_service_url": "${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}",
"config_id": "self-check",
}

154
llama_stack/providers/remote/safety/nvidia/nvidia.py Normal file
View file

@ -0,0 +1,154 @@
# 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 logging
from typing import Any, List, Optional
import requests
from llama_stack.apis.inference import Message
from llama_stack.apis.safety import RunShieldResponse, Safety, SafetyViolation, ViolationLevel
from llama_stack.apis.shields import Shield
from llama_stack.distribution.library_client import convert_pydantic_to_json_value
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from .config import NVIDIASafetyConfig
logger = logging.getLogger(__name__)
class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
def __init__(self, config: NVIDIASafetyConfig) -> None:
"""
Initialize the NVIDIASafetyAdapter with a given safety configuration.
Args:
config (NVIDIASafetyConfig): The configuration containing the guardrails service URL and config ID.
"""
print(f"Initializing NVIDIASafetyAdapter({config.guardrails_service_url})...")
self.config = config
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
pass
async def register_shield(self, shield: Shield) -> None:
if not shield.provider_resource_id:
raise ValueError("Shield model not provided.")
async def run_shield(
self, shield_id: str, messages: List[Message], params: Optional[dict[str, Any]] = None
) -> RunShieldResponse:
"""
Run a safety shield check against the provided messages.
Args:
shield_id (str): The unique identifier for the shield to be used.
messages (List[Message]): A list of Message objects representing the conversation history.
params (Optional[dict[str, Any]]): Additional parameters for the shield check.
Returns:
RunShieldResponse: The response containing safety violation details if any.
Raises:
ValueError: If the shield with the provided shield_id is not found.
"""
shield = await self.shield_store.get_shield(shield_id)
if not shield:
raise ValueError(f"Shield {shield_id} not found")
self.shield = NeMoGuardrails(self.config, shield.shield_id)
return await self.shield.run(messages)
class NeMoGuardrails:
"""
A class that encapsulates NVIDIA's guardrails safety logic.
Sends messages to the guardrails service and interprets the response to determine
if a safety violation has occurred.
"""
def __init__(
self,
config: NVIDIASafetyConfig,
model: str,
threshold: float = 0.9,
temperature: float = 1.0,
):
"""
Initialize a NeMoGuardrails instance with the provided parameters.
Args:
config (NVIDIASafetyConfig): The safety configuration containing the config ID and guardrails URL.
model (str): The identifier or name of the model to be used for safety checks.
threshold (float, optional): The threshold for flagging violations. Defaults to 0.9.
temperature (float, optional): The temperature setting for the underlying model. Must be greater than 0. Defaults to 1.0.
Raises:
ValueError: If temperature is less than or equal to 0.
AssertionError: If config_id is not provided in the configuration.
"""
self.config_id = config.config_id
self.model = model
assert self.config_id is not None, "Must provide config id"
if temperature <= 0:
raise ValueError("Temperature must be greater than 0")
self.temperature = temperature
self.threshold = threshold
self.guardrails_service_url = config.guardrails_service_url
async def run(self, messages: List[Message]) -> RunShieldResponse:
"""
Queries the /v1/guardrails/checks endpoint of the NeMo guardrails deployed API.
Args:
messages (List[Message]): A list of Message objects to be checked for safety violations.
Returns:
RunShieldResponse: If the response indicates a violation ("blocked" status), returns a
RunShieldResponse with a SafetyViolation; otherwise, returns a RunShieldResponse with violation set to None.
Raises:
requests.HTTPError: If the POST request fails.
"""
headers = {
"Accept": "application/json",
}
request_data = {
"model": self.model,
"messages": convert_pydantic_to_json_value(messages),
"temperature": self.temperature,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"max_tokens": 160,
"stream": False,
"guardrails": {
"config_id": self.config_id,
},
}
response = requests.post(
url=f"{self.guardrails_service_url}/v1/guardrail/checks", headers=headers, json=request_data
)
response.raise_for_status()
if "Content-Type" in response.headers and response.headers["Content-Type"].startswith("application/json"):
response_json = response.json()
if response_json["status"] == "blocked":
user_message = "Sorry I cannot do this."
metadata = response_json["rails_status"]
return RunShieldResponse(
violation=SafetyViolation(
user_message=user_message,
violation_level=ViolationLevel.ERROR,
metadata=metadata,
)
)
return RunShieldResponse(violation=None)

17
llama_stack/providers/remote/safety/sample/__init__.py
View file

@ -1,17 +0,0 @@
# 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.
from typing import Any
from .config import SampleConfig
async def get_adapter_impl(config: SampleConfig, _deps) -> Any:
from .sample import SampleSafetyImpl
impl = SampleSafetyImpl(config)
await impl.initialize()
return impl

12
llama_stack/providers/remote/safety/sample/config.py
View file

@ -1,12 +0,0 @@
# 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.
from pydantic import BaseModel
class SampleConfig(BaseModel):
host: str = "localhost"
port: int = 9999

23
llama_stack/providers/remote/safety/sample/sample.py
View file

@ -1,23 +0,0 @@
# 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.
from llama_stack.apis.safety import Safety
from llama_stack.apis.shields import Shield
from .config import SampleConfig
class SampleSafetyImpl(Safety):
def __init__(self, config: SampleConfig):
self.config = config
async def register_shield(self, shield: Shield) -> None:
# these are the safety shields the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def initialize(self):
pass

8
llama_stack/providers/remote/tool_runtime/bing_search/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from pydantic import BaseModel
@ -14,3 +14,9 @@ class BingSearchToolConfig(BaseModel):
api_key: Optional[str] = None
top_k: int = 3
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
return {
"api_key": "${env.BING_API_KEY:}",
}

6
llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py
View file

@ -4,8 +4,12 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from pydantic import BaseModel
class ModelContextProtocolConfig(BaseModel):
pass
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
return {}

8
llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from pydantic import BaseModel
@ -13,3 +13,9 @@ class WolframAlphaToolConfig(BaseModel):
"""Configuration for WolframAlpha Tool Runtime"""
api_key: Optional[str] = None
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
return {
"api_key": "${env.WOLFRAM_ALPHA_API_KEY:}",
}

9
llama_stack/providers/remote/vector_io/qdrant/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from pydantic import BaseModel
@ -23,4 +23,9 @@ class QdrantVectorIOConfig(BaseModel):
prefix: Optional[str] = None
timeout: Optional[int] = None
host: Optional[str] = None
path: Optional[str] = None
@classmethod
def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
return {
"api_key": "${env.QDRANT_API_KEY}",
}

20
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -6,7 +6,7 @@
import logging
import uuid
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Union
from numpy.typing import NDArray
from qdrant_client import AsyncQdrantClient, models
@ -16,12 +16,13 @@ from llama_stack.apis.inference import InterleavedContent
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
from llama_stack.providers.utils.memory.vector_store import (
EmbeddingIndex,
VectorDBWithIndex,
)
from .config import QdrantVectorIOConfig
from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
log = logging.getLogger(__name__)
CHUNK_ID_KEY = "_chunk_id"
@ -99,17 +100,19 @@ class QdrantIndex(EmbeddingIndex):
class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
def __init__(self, config: QdrantVectorIOConfig, inference_api: Api.inference) -> None:
def __init__(
self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
) -> None:
self.config = config
self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
self.client: AsyncQdrantClient = None
self.cache = {}
self.inference_api = inference_api
async def initialize(self) -> None:
pass
self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
async def shutdown(self) -> None:
self.client.close()
await self.client.close()
async def register_vector_db(
self,
@ -123,6 +126,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
self.cache[vector_db.identifier] = index
async def unregister_vector_db(self, vector_db_id: str) -> None:
if vector_db_id in self.cache:
await self.cache[vector_db_id].index.delete()
del self.cache[vector_db_id]
async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
if vector_db_id in self.cache:
return self.cache[vector_db_id]

17
llama_stack/providers/remote/vector_io/sample/__init__.py
View file

@ -1,17 +0,0 @@
# 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.
from typing import Any
from .config import SampleVectorIOConfig
async def get_adapter_impl(config: SampleVectorIOConfig, _deps) -> Any:
from .sample import SampleVectorIOImpl
impl = SampleVectorIOImpl(config)
await impl.initialize()
return impl

12
llama_stack/providers/remote/vector_io/sample/config.py
View file

@ -1,12 +0,0 @@
# 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.
from pydantic import BaseModel
class SampleVectorIOConfig(BaseModel):
host: str = "localhost"
port: int = 9999

26
llama_stack/providers/remote/vector_io/sample/sample.py
View file

@ -1,26 +0,0 @@
# 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.
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import VectorIO
from .config import SampleVectorIOConfig
class SampleVectorIOImpl(VectorIO):
def __init__(self, config: SampleVectorIOConfig):
self.config = config
async def register_vector_db(self, vector_db: VectorDB) -> None:
# these are the vector dbs the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def initialize(self):
pass
async def shutdown(self):
pass

6
llama_stack/providers/remote/vector_io/weaviate/config.py
View file

@ -4,6 +4,8 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from pydantic import BaseModel
@ -13,4 +15,6 @@ class WeaviateRequestProviderData(BaseModel):
class WeaviateVectorIOConfig(BaseModel):
pass
@classmethod
def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
return {}