Merge branch 'main' into feat/litellm_sambanova_usage

llama_stack/providers/remote/datasetio/huggingface/huggingface.py

@@ -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)

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -12,6 +12,7 @@ from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.inference import (
     ChatCompletionResponse,
     ChatCompletionResponseStreamChunk,
+    CompletionMessage,
     EmbeddingsResponse,
     EmbeddingTaskType,
     Inference,
@@ -160,12 +161,14 @@ class PassthroughInferenceAdapter(Inference):
         client = self._get_client()
         response = await client.inference.chat_completion(**json_params)
 
-        response = response.to_dict()
-
-        # temporary hack to remove the metrics from the response
-        response["metrics"] = []
-
-        return convert_to_pydantic(ChatCompletionResponse, response)
+        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()

llama_stack/providers/remote/inference/vllm/config.py

@@ -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}",
         }

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -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,
@@ -229,7 +230,11 @@ 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

llama_stack/providers/remote/safety/nvidia/__init__.py

@@ -0,0 +1,18 @@
+# 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 NVIDIASafetyConfig
+
+
+async def get_adapter_impl(config: NVIDIASafetyConfig, _deps) -> Any:
+    from .nvidia import NVIDIASafetyAdapter
+
+    impl = NVIDIASafetyAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/safety/nvidia/config.py

@@ -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",
+        }

llama_stack/providers/remote/safety/nvidia/nvidia.py

@@ -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)