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Merge branch 'main' into use-openai-for-ollama

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This commit is contained in:
Matthew Farrellee 2025-09-15 15:31:03 -04:00
commit 91fb6f42cb
74 changed files with 8761 additions and 971 deletions
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17
llama_stack/cli/verify_download.py
View file

@ -48,15 +48,12 @@ def setup_verify_download_parser(parser: argparse.ArgumentParser) -> None:
parser.set_defaults(func=partial(run_verify_cmd, parser=parser))
def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str:
# NOTE: MD5 is used here only for download integrity verification,
# not for security purposes
# TODO: switch to SHA256
md5_hash = hashlib.md5(usedforsecurity=False)
def calculate_sha256(filepath: Path, chunk_size: int = 8192) -> str:
sha256_hash = hashlib.sha256()
with open(filepath, "rb") as f:
for chunk in iter(lambda: f.read(chunk_size), b""):
md5_hash.update(chunk)
return md5_hash.hexdigest()
sha256_hash.update(chunk)
return sha256_hash.hexdigest()
def load_checksums(checklist_path: Path) -> dict[str, str]:
@ -64,10 +61,10 @@ def load_checksums(checklist_path: Path) -> dict[str, str]:
with open(checklist_path) as f:
for line in f:
if line.strip():
md5sum, filepath = line.strip().split(" ", 1)
sha256sum, filepath = line.strip().split(" ", 1)
# Remove leading './' if present
filepath = filepath.lstrip("./")
checksums[filepath] = md5sum
checksums[filepath] = sha256sum
return checksums
@ -88,7 +85,7 @@ def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) -
matches = False
if exists:
actual_hash = calculate_md5(full_path)
actual_hash = calculate_sha256(full_path)
matches = actual_hash == expected_hash
results.append(

13
llama_stack/core/datatypes.py
View file

@ -431,6 +431,12 @@ class ServerConfig(BaseModel):
)
class InferenceStoreConfig(BaseModel):
sql_store_config: SqlStoreConfig
max_write_queue_size: int = Field(default=10000, description="Max queued writes for inference store")
num_writers: int = Field(default=4, description="Number of concurrent background writers")
class StackRunConfig(BaseModel):
version: int = LLAMA_STACK_RUN_CONFIG_VERSION
@ -464,11 +470,12 @@ Configuration for the persistence store used by the distribution registry. If no
a default SQLite store will be used.""",
)
inference_store: SqlStoreConfig | None = Field(
inference_store: InferenceStoreConfig | SqlStoreConfig | None = Field(
default=None,
description="""
Configuration for the persistence store used by the inference API. If not specified,
a default SQLite store will be used.""",
Configuration for the persistence store used by the inference API. Can be either a
InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (deprecated).
If not specified, a default SQLite store will be used.""",
)
# registry of "resources" in the distribution

5
llama_stack/core/routers/__init__.py
View file

@ -78,7 +78,10 @@ async def get_auto_router_impl(
# TODO: move pass configs to routers instead
if api == Api.inference and run_config.inference_store:
inference_store = InferenceStore(run_config.inference_store, policy)
inference_store = InferenceStore(
config=run_config.inference_store,
policy=policy,
)
await inference_store.initialize()
api_to_dep_impl["store"] = inference_store

19
llama_stack/core/routers/inference.py
View file

@ -63,7 +63,7 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.telemetry.tracing import get_current_span
from llama_stack.providers.utils.telemetry.tracing import enqueue_event, get_current_span
logger = get_logger(name=__name__, category="core::routers")
@ -90,6 +90,11 @@ class InferenceRouter(Inference):
async def shutdown(self) -> None:
logger.debug("InferenceRouter.shutdown")
if self.store:
try:
await self.store.shutdown()
except Exception as e:
logger.warning(f"Error during InferenceStore shutdown: {e}")
async def register_model(
self,
@ -160,7 +165,7 @@ class InferenceRouter(Inference):
metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
if self.telemetry:
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
async def _count_tokens(
@ -431,7 +436,7 @@ class InferenceRouter(Inference):
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
# these metrics will show up in the client response.
response.metrics = (
@ -537,7 +542,7 @@ class InferenceRouter(Inference):
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
# these metrics will show up in the client response.
response.metrics = (
metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
@ -664,7 +669,7 @@ class InferenceRouter(Inference):
"completion_tokens",
"total_tokens",
]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
enqueue_event(metric)
# Return metrics in response
async_metrics = [
@ -710,7 +715,7 @@ class InferenceRouter(Inference):
)
for metric in completion_metrics:
if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
enqueue_event(metric)
# Return metrics in response
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics]
@ -806,7 +811,7 @@ class InferenceRouter(Inference):
model=model,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
yield chunk
finally:

1
llama_stack/distributions/ci-tests/build.yaml
View file

@ -17,6 +17,7 @@ distribution_spec:
- provider_type: remote::vertexai
- provider_type: remote::groq
- provider_type: remote::sambanova
- provider_type: remote::azure
- provider_type: inline::sentence-transformers
vector_io:
- provider_type: inline::faiss

7
llama_stack/distributions/ci-tests/run.yaml
View file

@ -81,6 +81,13 @@ providers:
config:
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY:=}
- provider_id: ${env.AZURE_API_KEY:+azure}
provider_type: remote::azure
config:
api_key: ${env.AZURE_API_KEY:=}
api_base: ${env.AZURE_API_BASE:=}
api_version: ${env.AZURE_API_VERSION:=}
api_type: ${env.AZURE_API_TYPE:=}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
vector_io:

1
llama_stack/distributions/starter-gpu/build.yaml
View file

@ -18,6 +18,7 @@ distribution_spec:
- provider_type: remote::vertexai
- provider_type: remote::groq
- provider_type: remote::sambanova
- provider_type: remote::azure
- provider_type: inline::sentence-transformers
vector_io:
- provider_type: inline::faiss

7
llama_stack/distributions/starter-gpu/run.yaml
View file

@ -81,6 +81,13 @@ providers:
config:
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY:=}
- provider_id: ${env.AZURE_API_KEY:+azure}
provider_type: remote::azure
config:
api_key: ${env.AZURE_API_KEY:=}
api_base: ${env.AZURE_API_BASE:=}
api_version: ${env.AZURE_API_VERSION:=}
api_type: ${env.AZURE_API_TYPE:=}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
vector_io:

1
llama_stack/distributions/starter/build.yaml
View file

@ -18,6 +18,7 @@ distribution_spec:
- provider_type: remote::vertexai
- provider_type: remote::groq
- provider_type: remote::sambanova
- provider_type: remote::azure
- provider_type: inline::sentence-transformers
vector_io:
- provider_type: inline::faiss

7
llama_stack/distributions/starter/run.yaml
View file

@ -81,6 +81,13 @@ providers:
config:
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY:=}
- provider_id: ${env.AZURE_API_KEY:+azure}
provider_type: remote::azure
config:
api_key: ${env.AZURE_API_KEY:=}
api_base: ${env.AZURE_API_BASE:=}
api_version: ${env.AZURE_API_VERSION:=}
api_type: ${env.AZURE_API_TYPE:=}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
vector_io:

18
llama_stack/distributions/starter/starter.py
View file

@ -59,6 +59,7 @@ ENABLED_INFERENCE_PROVIDERS = [
"cerebras",
"nvidia",
"bedrock",
"azure",
]
INFERENCE_PROVIDER_IDS = {
@ -68,6 +69,7 @@ INFERENCE_PROVIDER_IDS = {
"cerebras": "${env.CEREBRAS_API_KEY:+cerebras}",
"nvidia": "${env.NVIDIA_API_KEY:+nvidia}",
"vertexai": "${env.VERTEX_AI_PROJECT:+vertexai}",
"azure": "${env.AZURE_API_KEY:+azure}",
}
@ -277,5 +279,21 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
"http://localhost:11434",
"Ollama URL",
),
"AZURE_API_KEY": (
"",
"Azure API Key",
),
"AZURE_API_BASE": (
"",
"Azure API Base",
),
"AZURE_API_VERSION": (
"",
"Azure API Version",
),
"AZURE_API_TYPE": (
"azure",
"Azure API Type",
),
},
)

12
llama_stack/providers/inline/tool_runtime/rag/context_retriever.py
View file

@ -8,7 +8,7 @@
from jinja2 import Template
from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.inference import UserMessage
from llama_stack.apis.inference import OpenAIUserMessageParam
from llama_stack.apis.tools.rag_tool import (
DefaultRAGQueryGeneratorConfig,
LLMRAGQueryGeneratorConfig,
@ -61,16 +61,16 @@ async def llm_rag_query_generator(
messages = [interleaved_content_as_str(content)]
template = Template(config.template)
content = template.render({"messages": messages})
rendered_content: str = template.render({"messages": messages})
model = config.model
message = UserMessage(content=content)
response = await inference_api.chat_completion(
model_id=model,
message = OpenAIUserMessageParam(content=rendered_content)
response = await inference_api.openai_chat_completion(
model=model,
messages=[message],
stream=False,
)
query = response.completion_message.content
query = response.choices[0].message.content
return query

121
llama_stack/providers/inline/tool_runtime/rag/memory.py
View file

@ -45,10 +45,7 @@ from llama_stack.apis.vector_io import (
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
from llama_stack.providers.utils.memory.vector_store import (
content_from_doc,
parse_data_url,
)
from llama_stack.providers.utils.memory.vector_store import parse_data_url
from .config import RagToolRuntimeConfig
from .context_retriever import generate_rag_query
@ -60,6 +57,47 @@ def make_random_string(length: int = 8):
return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length))
async def raw_data_from_doc(doc: RAGDocument) -> tuple[bytes, str]:
"""Get raw binary data and mime type from a RAGDocument for file upload."""
if isinstance(doc.content, URL):
if doc.content.uri.startswith("data:"):
parts = parse_data_url(doc.content.uri)
mime_type = parts["mimetype"]
data = parts["data"]
if parts["is_base64"]:
file_data = base64.b64decode(data)
else:
file_data = data.encode("utf-8")
return file_data, mime_type
else:
async with httpx.AsyncClient() as client:
r = await client.get(doc.content.uri)
r.raise_for_status()
mime_type = r.headers.get("content-type", "application/octet-stream")
return r.content, mime_type
else:
if isinstance(doc.content, str):
content_str = doc.content
else:
content_str = interleaved_content_as_str(doc.content)
if content_str.startswith("data:"):
parts = parse_data_url(content_str)
mime_type = parts["mimetype"]
data = parts["data"]
if parts["is_base64"]:
file_data = base64.b64decode(data)
else:
file_data = data.encode("utf-8")
return file_data, mime_type
else:
return content_str.encode("utf-8"), "text/plain"
class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime):
def __init__(
self,
@ -95,46 +133,52 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
return
for doc in documents:
if isinstance(doc.content, URL):
if doc.content.uri.startswith("data:"):
parts = parse_data_url(doc.content.uri)
file_data = base64.b64decode(parts["data"]) if parts["is_base64"] else parts["data"].encode()
mime_type = parts["mimetype"]
else:
async with httpx.AsyncClient() as client:
response = await client.get(doc.content.uri)
file_data = response.content
mime_type = doc.mime_type or response.headers.get("content-type", "application/octet-stream")
else:
content_str = await content_from_doc(doc)
file_data = content_str.encode("utf-8")
mime_type = doc.mime_type or "text/plain"
try:
try:
file_data, mime_type = await raw_data_from_doc(doc)
except Exception as e:
log.error(f"Failed to extract content from document {doc.document_id}: {e}")
continue
file_extension = mimetypes.guess_extension(mime_type) or ".txt"
filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}")
file_extension = mimetypes.guess_extension(mime_type) or ".txt"
filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}")
file_obj = io.BytesIO(file_data)
file_obj.name = filename
file_obj = io.BytesIO(file_data)
file_obj.name = filename
upload_file = UploadFile(file=file_obj, filename=filename)
upload_file = UploadFile(file=file_obj, filename=filename)
created_file = await self.files_api.openai_upload_file(
file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS
)
try:
created_file = await self.files_api.openai_upload_file(
file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS
)
except Exception as e:
log.error(f"Failed to upload file for document {doc.document_id}: {e}")
continue
chunking_strategy = VectorStoreChunkingStrategyStatic(
static=VectorStoreChunkingStrategyStaticConfig(
max_chunk_size_tokens=chunk_size_in_tokens,
chunk_overlap_tokens=chunk_size_in_tokens // 4,
chunking_strategy = VectorStoreChunkingStrategyStatic(
static=VectorStoreChunkingStrategyStaticConfig(
max_chunk_size_tokens=chunk_size_in_tokens,
chunk_overlap_tokens=chunk_size_in_tokens // 4,
)
)
)
await self.vector_io_api.openai_attach_file_to_vector_store(
vector_store_id=vector_db_id,
file_id=created_file.id,
attributes=doc.metadata,
chunking_strategy=chunking_strategy,
)
try:
await self.vector_io_api.openai_attach_file_to_vector_store(
vector_store_id=vector_db_id,
file_id=created_file.id,
attributes=doc.metadata,
chunking_strategy=chunking_strategy,
)
except Exception as e:
log.error(
f"Failed to attach file {created_file.id} to vector store {vector_db_id} for document {doc.document_id}: {e}"
)
continue
except Exception as e:
log.error(f"Unexpected error processing document {doc.document_id}: {e}")
continue
async def query(
self,
@ -274,7 +318,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
if query_config:
query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
else:
# handle someone passing an empty dict
query_config = RAGQueryConfig()
query = kwargs["query"]
@ -285,6 +328,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
)
return ToolInvocationResult(
content=result.content,
content=result.content or [],
metadata=result.metadata,
)

2
llama_stack/providers/registry/batches.py
View file

@ -13,7 +13,7 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.batches,
provider_type="inline::reference",
pip_packages=["openai"],
pip_packages=[],
module="llama_stack.providers.inline.batches.reference",
config_class="llama_stack.providers.inline.batches.reference.config.ReferenceBatchesImplConfig",
api_dependencies=[

35
llama_stack/providers/registry/inference.py
View file

@ -75,7 +75,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="vllm",
pip_packages=["openai"],
pip_packages=[],
module="llama_stack.providers.remote.inference.vllm",
config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
description="Remote vLLM inference provider for connecting to vLLM servers.",
@ -151,9 +151,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="databricks",
pip_packages=[
"openai",
],
pip_packages=[],
module="llama_stack.providers.remote.inference.databricks",
config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
description="Databricks inference provider for running models on Databricks' unified analytics platform.",
@ -163,9 +161,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="nvidia",
pip_packages=[
"openai",
],
pip_packages=[],
module="llama_stack.providers.remote.inference.nvidia",
config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.",
@ -175,7 +171,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="runpod",
pip_packages=["openai"],
pip_packages=[],
module="llama_stack.providers.remote.inference.runpod",
config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig",
description="RunPod inference provider for running models on RunPod's cloud GPU platform.",
@ -207,7 +203,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="gemini",
pip_packages=["litellm", "openai"],
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.gemini",
config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator",
@ -218,7 +214,7 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="vertexai",
pip_packages=["litellm", "google-cloud-aiplatform", "openai"],
pip_packages=["litellm", "google-cloud-aiplatform"],
module="llama_stack.providers.remote.inference.vertexai",
config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig",
provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator",
@ -248,7 +244,7 @@ Available Models:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="groq",
pip_packages=["litellm", "openai"],
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.groq",
config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator",
@ -270,7 +266,7 @@ Available Models:
api=Api.inference,
adapter=AdapterSpec(
adapter_type="sambanova",
pip_packages=["litellm", "openai"],
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.sambanova",
config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator",
@ -299,4 +295,19 @@ Available Models:
description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="azure",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.azure",
config_class="llama_stack.providers.remote.inference.azure.AzureConfig",
provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator",
description="""
Azure OpenAI inference provider for accessing GPT models and other Azure services.
Provider documentation
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
""",
),
),
]

2
llama_stack/providers/registry/scoring.py
View file

@ -38,7 +38,7 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.scoring,
provider_type="inline::braintrust",
pip_packages=["autoevals", "openai"],
pip_packages=["autoevals"],
module="llama_stack.providers.inline.scoring.braintrust",
config_class="llama_stack.providers.inline.scoring.braintrust.BraintrustScoringConfig",
api_dependencies=[

15
llama_stack/providers/remote/inference/azure/__init__.py Normal file
View file

@ -0,0 +1,15 @@
# 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 .config import AzureConfig
async def get_adapter_impl(config: AzureConfig, _deps):
from .azure import AzureInferenceAdapter
impl = AzureInferenceAdapter(config)
await impl.initialize()
return impl

64
llama_stack/providers/remote/inference/azure/azure.py Normal file
View file

@ -0,0 +1,64 @@
# 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 urllib.parse import urljoin
from llama_stack.apis.inference import ChatCompletionRequest
from llama_stack.providers.utils.inference.litellm_openai_mixin import (
LiteLLMOpenAIMixin,
)
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from .config import AzureConfig
from .models import MODEL_ENTRIES
class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
def __init__(self, config: AzureConfig) -> None:
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
litellm_provider_name="azure",
api_key_from_config=config.api_key.get_secret_value(),
provider_data_api_key_field="azure_api_key",
openai_compat_api_base=str(config.api_base),
)
self.config = config
# Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
get_api_key = LiteLLMOpenAIMixin.get_api_key
def get_base_url(self) -> str:
"""
Get the Azure API base URL.
Returns the Azure API base URL from the configuration.
"""
return urljoin(str(self.config.api_base), "/openai/v1")
async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
# Get base parameters from parent
params = await super()._get_params(request)
# Add Azure specific parameters
provider_data = self.get_request_provider_data()
if provider_data:
if getattr(provider_data, "azure_api_key", None):
params["api_key"] = provider_data.azure_api_key
if getattr(provider_data, "azure_api_base", None):
params["api_base"] = provider_data.azure_api_base
if getattr(provider_data, "azure_api_version", None):
params["api_version"] = provider_data.azure_api_version
if getattr(provider_data, "azure_api_type", None):
params["api_type"] = provider_data.azure_api_type
else:
params["api_key"] = self.config.api_key.get_secret_value()
params["api_base"] = str(self.config.api_base)
params["api_version"] = self.config.api_version
params["api_type"] = self.config.api_type
return params

63
llama_stack/providers/remote/inference/azure/config.py Normal file
View file

@ -0,0 +1,63 @@
# 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
from pydantic import BaseModel, Field, HttpUrl, SecretStr
from llama_stack.schema_utils import json_schema_type
class AzureProviderDataValidator(BaseModel):
azure_api_key: SecretStr = Field(
description="Azure API key for Azure",
)
azure_api_base: HttpUrl = Field(
description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)",
)
azure_api_version: str | None = Field(
default=None,
description="Azure API version for Azure (e.g., 2024-06-01)",
)
azure_api_type: str | None = Field(
default="azure",
description="Azure API type for Azure (e.g., azure)",
)
@json_schema_type
class AzureConfig(BaseModel):
api_key: SecretStr = Field(
description="Azure API key for Azure",
)
api_base: HttpUrl = Field(
description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)",
)
api_version: str | None = Field(
default_factory=lambda: os.getenv("AZURE_API_VERSION"),
description="Azure API version for Azure (e.g., 2024-12-01-preview)",
)
api_type: str | None = Field(
default_factory=lambda: os.getenv("AZURE_API_TYPE", "azure"),
description="Azure API type for Azure (e.g., azure)",
)
@classmethod
def sample_run_config(
cls,
api_key: str = "${env.AZURE_API_KEY:=}",
api_base: str = "${env.AZURE_API_BASE:=}",
api_version: str = "${env.AZURE_API_VERSION:=}",
api_type: str = "${env.AZURE_API_TYPE:=}",
**kwargs,
) -> dict[str, Any]:
return {
"api_key": api_key,
"api_base": api_base,
"api_version": api_version,
"api_type": api_type,
}

28
llama_stack/providers/remote/inference/azure/models.py Normal file
View file

@ -0,0 +1,28 @@
# 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.providers.utils.inference.model_registry import (
ProviderModelEntry,
)
# https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions
LLM_MODEL_IDS = [
"gpt-5",
"gpt-5-mini",
"gpt-5-nano",
"gpt-5-chat",
"o1",
"o1-mini",
"o3-mini",
"o4-mini",
"gpt-4.1",
"gpt-4.1-mini",
"gpt-4.1-nano",
]
SAFETY_MODELS_ENTRIES = list[ProviderModelEntry]()
MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + SAFETY_MODELS_ENTRIES

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

@ -53,6 +53,43 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
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,
@ -166,8 +203,13 @@ class BedrockInferenceAdapter(
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": bedrock_model,
"modelId": inference_profile_id,
"body": json.dumps(
{
"prompt": prompt,
@ -185,6 +227,11 @@ class BedrockInferenceAdapter(
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
# Convert foundation model ID to inference profile ID
region_name = self.client.meta.region_name
inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name)
embeddings = []
for content in contents:
assert not content_has_media(content), "Bedrock does not support media for embeddings"
@ -193,7 +240,7 @@ class BedrockInferenceAdapter(
body = json.dumps(input_body)
response = self.client.invoke_model(
body=body,
modelId=model.provider_resource_id,
modelId=inference_profile_id,
accept="application/json",
contentType="application/json",
)

197
llama_stack/providers/remote/inference/vllm/vllm.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.
import json
from collections.abc import AsyncGenerator, AsyncIterator
from collections.abc import AsyncGenerator
from typing import Any
import httpx
@ -38,13 +38,6 @@ from llama_stack.apis.inference import (
LogProbConfig,
Message,
ModelStore,
OpenAIChatCompletion,
OpenAICompletion,
OpenAIEmbeddingData,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
OpenAIMessageParam,
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
@ -71,11 +64,11 @@ from llama_stack.providers.utils.inference.openai_compat import (
convert_message_to_openai_dict,
convert_tool_call,
get_sampling_options,
prepare_openai_completion_params,
process_chat_completion_stream_response,
process_completion_response,
process_completion_stream_response,
)
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
completion_request_to_prompt,
content_has_media,
@ -288,7 +281,7 @@ async def _process_vllm_chat_completion_stream_response(
yield c
class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
# automatically set by the resolver when instantiating the provider
__provider_id__: str
model_store: ModelStore | None = None
@ -296,7 +289,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
def __init__(self, config: VLLMInferenceAdapterConfig) -> None:
self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
self.config = config
self.client = None
async def initialize(self) -> None:
if not self.config.url:
@ -308,8 +300,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
return self.config.refresh_models
async def list_models(self) -> list[Model] | None:
self._lazy_initialize_client()
assert self.client is not None # mypy
models = []
async for m in self.client.models.list():
model_type = ModelType.llm # unclear how to determine embedding vs. llm models
@ -340,8 +330,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
HealthResponse: A dictionary containing the health status.
"""
try:
client = self._create_client() if self.client is None else self.client
_ = [m async for m in client.models.list()] # Ensure the client is initialized
_ = [m async for m in self.client.models.list()] # Ensure the client is initialized
return HealthResponse(status=HealthStatus.OK)
except Exception as e:
return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
@ -351,19 +340,14 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
raise ValueError("Model store not set")
return await self.model_store.get_model(model_id)
def _lazy_initialize_client(self):
if self.client is not None:
return
def get_api_key(self):
return self.config.api_token
log.info(f"Initializing vLLM client with base_url={self.config.url}")
self.client = self._create_client()
def get_base_url(self):
return self.config.url
def _create_client(self):
return AsyncOpenAI(
base_url=self.config.url,
api_key=self.config.api_token,
http_client=httpx.AsyncClient(verify=self.config.tls_verify),
)
def get_extra_client_params(self):
return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)}
async def completion(
self,
@ -374,7 +358,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]:
self._lazy_initialize_client()
if sampling_params is None:
sampling_params = SamplingParams()
model = await self._get_model(model_id)
@ -406,7 +389,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: LogProbConfig | None = None,
tool_config: ToolConfig | None = None,
) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
self._lazy_initialize_client()
if sampling_params is None:
sampling_params = SamplingParams()
model = await self._get_model(model_id)
@ -479,16 +461,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
yield chunk
async def register_model(self, model: Model) -> Model:
# register_model is called during Llama Stack initialization, hence we cannot init self.client if not initialized yet.
# self.client should only be created after the initialization is complete to avoid asyncio cross-context errors.
# Changing this may lead to unpredictable behavior.
client = self._create_client() if self.client is None else self.client
try:
model = await self.register_helper.register_model(model)
except ValueError:
pass # Ignore statically unknown model, will check live listing
try:
res = await client.models.list()
res = await self.client.models.list()
except APIConnectionError as e:
raise ValueError(
f"Failed to connect to vLLM at {self.config.url}. Please check if vLLM is running and accessible at that URL."
@ -543,8 +521,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
self._lazy_initialize_client()
assert self.client is not None
model = await self._get_model(model_id)
kwargs = {}
@ -560,154 +536,3 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
embeddings = [data.embedding for data in response.data]
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,
input: str | list[str],
encoding_format: str | None = "float",
dimensions: int | None = None,
user: str | None = None,
) -> OpenAIEmbeddingsResponse:
self._lazy_initialize_client()
assert self.client is not None
model_obj = await self._get_model(model)
assert model_obj.model_type == ModelType.embedding
# Convert input to list if it's a string
input_list = [input] if isinstance(input, str) else input
# Call vLLM embeddings endpoint with encoding_format
response = await self.client.embeddings.create(
model=model_obj.provider_resource_id,
input=input_list,
dimensions=dimensions,
encoding_format=encoding_format,
)
# Convert response to OpenAI format
data = [
OpenAIEmbeddingData(
embedding=embedding_data.embedding,
index=i,
)
for i, embedding_data in enumerate(response.data)
]
# Not returning actual token usage since vLLM doesn't provide it
usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
return OpenAIEmbeddingsResponse(
data=data,
model=model_obj.provider_resource_id,
usage=usage,
)
async def openai_completion(
self,
model: str,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
suffix: str | None = None,
) -> OpenAICompletion:
self._lazy_initialize_client()
model_obj = await self._get_model(model)
extra_body: dict[str, Any] = {}
if prompt_logprobs is not None and prompt_logprobs >= 0:
extra_body["prompt_logprobs"] = prompt_logprobs
if guided_choice:
extra_body["guided_choice"] = guided_choice
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
prompt=prompt,
best_of=best_of,
echo=echo,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
logprobs=logprobs,
max_tokens=max_tokens,
n=n,
presence_penalty=presence_penalty,
seed=seed,
stop=stop,
stream=stream,
stream_options=stream_options,
temperature=temperature,
top_p=top_p,
user=user,
extra_body=extra_body,
)
return await self.client.completions.create(**params) # type: ignore
async def openai_chat_completion(
self,
model: str,
messages: list[OpenAIMessageParam],
frequency_penalty: float | None = None,
function_call: str | dict[str, Any] | None = None,
functions: list[dict[str, Any]] | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_completion_tokens: int | None = None,
max_tokens: int | None = None,
n: int | None = None,
parallel_tool_calls: bool | None = None,
presence_penalty: float | None = None,
response_format: OpenAIResponseFormatParam | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
tool_choice: str | dict[str, Any] | None = None,
tools: list[dict[str, Any]] | None = None,
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
self._lazy_initialize_client()
model_obj = await self._get_model(model)
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id,
messages=messages,
frequency_penalty=frequency_penalty,
function_call=function_call,
functions=functions,
logit_bias=logit_bias,
logprobs=logprobs,
max_completion_tokens=max_completion_tokens,
max_tokens=max_tokens,
n=n,
parallel_tool_calls=parallel_tool_calls,
presence_penalty=presence_penalty,
response_format=response_format,
seed=seed,
stop=stop,
stream=stream,
stream_options=stream_options,
temperature=temperature,
tool_choice=tool_choice,
tools=tools,
top_logprobs=top_logprobs,
top_p=top_p,
user=user,
)
return await self.client.chat.completions.create(**params) # type: ignore

146
llama_stack/providers/utils/inference/inference_store.py
View file

@ -3,6 +3,11 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
from typing import Any
from sqlalchemy.exc import IntegrityError
from llama_stack.apis.inference import (
ListOpenAIChatCompletionResponse,
OpenAIChatCompletion,
@ -10,24 +15,43 @@ from llama_stack.apis.inference import (
OpenAIMessageParam,
Order,
)
from llama_stack.core.datatypes import AccessRule
from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.core.datatypes import AccessRule, InferenceStoreConfig
from llama_stack.log import get_logger
from ..sqlstore.api import ColumnDefinition, ColumnType
from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl
from ..sqlstore.sqlstore import SqlStoreConfig, SqlStoreType, sqlstore_impl
logger = get_logger(name=__name__, category="inference_store")
class InferenceStore:
def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]):
if not sql_store_config:
sql_store_config = SqliteSqlStoreConfig(
db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
def __init__(
self,
config: InferenceStoreConfig | SqlStoreConfig,
policy: list[AccessRule],
):
# Handle backward compatibility
if not isinstance(config, InferenceStoreConfig):
# Legacy: SqlStoreConfig passed directly as config
config = InferenceStoreConfig(
sql_store_config=config,
)
self.sql_store_config = sql_store_config
self.config = config
self.sql_store_config = config.sql_store_config
self.sql_store = None
self.policy = policy
# Disable write queue for SQLite to avoid concurrency issues
self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite
# Async write queue and worker control
self._queue: asyncio.Queue[tuple[OpenAIChatCompletion, list[OpenAIMessageParam]]] | None = None
self._worker_tasks: list[asyncio.Task[Any]] = []
self._max_write_queue_size: int = config.max_write_queue_size
self._num_writers: int = max(1, config.num_writers)
async def initialize(self):
"""Create the necessary tables if they don't exist."""
self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config))
@ -42,23 +66,109 @@ class InferenceStore:
},
)
if self.enable_write_queue:
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
for _ in range(self._num_writers):
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
else:
logger.info("Write queue disabled for SQLite to avoid concurrency issues")
async def shutdown(self) -> None:
if not self._worker_tasks:
return
if self._queue is not None:
await self._queue.join()
for t in self._worker_tasks:
if not t.done():
t.cancel()
for t in self._worker_tasks:
try:
await t
except asyncio.CancelledError:
pass
self._worker_tasks.clear()
async def flush(self) -> None:
"""Wait for all queued writes to complete. Useful for testing."""
if self.enable_write_queue and self._queue is not None:
await self._queue.join()
async def store_chat_completion(
self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam]
) -> None:
if not self.sql_store:
if self.enable_write_queue:
if self._queue is None:
raise ValueError("Inference store is not initialized")
try:
self._queue.put_nowait((chat_completion, input_messages))
except asyncio.QueueFull:
logger.warning(
f"Write queue full; adding chat completion id={getattr(chat_completion, 'id', '<unknown>')}"
)
await self._queue.put((chat_completion, input_messages))
else:
await self._write_chat_completion(chat_completion, input_messages)
async def _worker_loop(self) -> None:
assert self._queue is not None
while True:
try:
item = await self._queue.get()
except asyncio.CancelledError:
break
chat_completion, input_messages = item
try:
await self._write_chat_completion(chat_completion, input_messages)
except Exception as e: # noqa: BLE001
logger.error(f"Error writing chat completion: {e}")
finally:
self._queue.task_done()
async def _write_chat_completion(
self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam]
) -> None:
if self.sql_store is None:
raise ValueError("Inference store is not initialized")
data = chat_completion.model_dump()
record_data = {
"id": data["id"],
"created": data["created"],
"model": data["model"],
"choices": data["choices"],
"input_messages": [message.model_dump() for message in input_messages],
}
await self.sql_store.insert(
table="chat_completions",
data={
"id": data["id"],
"created": data["created"],
"model": data["model"],
"choices": data["choices"],
"input_messages": [message.model_dump() for message in input_messages],
},
try:
await self.sql_store.insert(
table="chat_completions",
data=record_data,
)
except IntegrityError as e:
# Duplicate chat completion IDs can be generated during tests especially if they are replaying
# recorded responses across different tests. No need to warn or error under those circumstances.
# In the wild, this is not likely to happen at all (no evidence) so we aren't really hiding any problem.
# Check if it's a unique constraint violation
error_message = str(e.orig) if e.orig else str(e)
if self._is_unique_constraint_error(error_message):
# Update the existing record instead
await self.sql_store.update(table="chat_completions", data=record_data, where={"id": data["id"]})
else:
# Re-raise if it's not a unique constraint error
raise
def _is_unique_constraint_error(self, error_message: str) -> bool:
"""Check if the error is specifically a unique constraint violation."""
error_lower = error_message.lower()
return any(
indicator in error_lower
for indicator in [
"unique constraint failed", # SQLite
"duplicate key", # PostgreSQL
"unique violation", # PostgreSQL alternative
"duplicate entry", # MySQL
]
)
async def list_chat_completions(

28
llama_stack/providers/utils/inference/openai_mixin.py
View file

@ -67,6 +67,17 @@ class OpenAIMixin(ABC):
"""
pass
def get_extra_client_params(self) -> dict[str, Any]:
"""
Get any extra parameters to pass to the AsyncOpenAI client.
Child classes can override this method to provide additional parameters
such as timeout settings, proxies, etc.
:return: A dictionary of extra parameters
"""
return {}
@property
def client(self) -> AsyncOpenAI:
"""
@ -78,6 +89,7 @@ class OpenAIMixin(ABC):
return AsyncOpenAI(
api_key=self.get_api_key(),
base_url=self.get_base_url(),
**self.get_extra_client_params(),
)
async def _get_provider_model_id(self, model: str) -> str:
@ -124,10 +136,15 @@ class OpenAIMixin(ABC):
"""
Direct OpenAI completion API call.
"""
if guided_choice is not None:
logger.warning("guided_choice is not supported by the OpenAI API. Ignoring.")
if prompt_logprobs is not None:
logger.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.")
# Handle parameters that are not supported by OpenAI API, but may be by the provider
# prompt_logprobs is supported by vLLM
# guided_choice is supported by vLLM
# TODO: test coverage
extra_body: dict[str, Any] = {}
if prompt_logprobs is not None and prompt_logprobs >= 0:
extra_body["prompt_logprobs"] = prompt_logprobs
if guided_choice:
extra_body["guided_choice"] = guided_choice
# TODO: fix openai_completion to return type compatible with OpenAI's API response
return await self.client.completions.create( # type: ignore[no-any-return]
@ -150,7 +167,8 @@ class OpenAIMixin(ABC):
top_p=top_p,
user=user,
suffix=suffix,
)
),
extra_body=extra_body,
)
async def openai_chat_completion(

14
llama_stack/providers/utils/sqlstore/authorized_sqlstore.py
View file

@ -172,6 +172,20 @@ class AuthorizedSqlStore:
return results.data[0] if results.data else None
async def update(self, table: str, data: Mapping[str, Any], where: Mapping[str, Any]) -> None:
"""Update rows with automatic access control attribute capture."""
enhanced_data = dict(data)
current_user = get_authenticated_user()
if current_user:
enhanced_data["owner_principal"] = current_user.principal
enhanced_data["access_attributes"] = current_user.attributes
else:
enhanced_data["owner_principal"] = None
enhanced_data["access_attributes"] = None
await self.sql_store.update(table, enhanced_data, where)
async def delete(self, table: str, where: Mapping[str, Any]) -> None:
"""Delete rows with automatic access control filtering."""
await self.sql_store.delete(table, where)

34
llama_stack/providers/utils/telemetry/tracing.py
View file

@ -18,6 +18,7 @@ from functools import wraps
from typing import Any
from llama_stack.apis.telemetry import (
Event,
LogSeverity,
Span,
SpanEndPayload,
@ -98,7 +99,7 @@ class BackgroundLogger:
def __init__(self, api: Telemetry, capacity: int = 100000):
self.api = api
self.log_queue: queue.Queue[Any] = queue.Queue(maxsize=capacity)
self.worker_thread = threading.Thread(target=self._process_logs, daemon=True)
self.worker_thread = threading.Thread(target=self._worker, daemon=True)
self.worker_thread.start()
self._last_queue_full_log_time: float = 0.0
self._dropped_since_last_notice: int = 0
@ -118,12 +119,16 @@ class BackgroundLogger:
self._last_queue_full_log_time = current_time
self._dropped_since_last_notice = 0
def _process_logs(self):
def _worker(self):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(self._process_logs())
async def _process_logs(self):
while True:
try:
event = self.log_queue.get()
# figure out how to use a thread's native loop
asyncio.run(self.api.log_event(event))
await self.api.log_event(event)
except Exception:
import traceback
@ -136,6 +141,19 @@ class BackgroundLogger:
self.log_queue.join()
def enqueue_event(event: Event) -> None:
"""Enqueue a telemetry event to the background logger if available.
This provides a non-blocking path for routers and other hot paths to
submit telemetry without awaiting the Telemetry API, reducing contention
with the main event loop.
"""
global BACKGROUND_LOGGER
if BACKGROUND_LOGGER is None:
raise RuntimeError("Telemetry API not initialized")
BACKGROUND_LOGGER.log_event(event)
class TraceContext:
spans: list[Span] = []
@ -256,11 +274,7 @@ class TelemetryHandler(logging.Handler):
if record.module in ("asyncio", "selector_events"):
return
global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER
if BACKGROUND_LOGGER is None:
raise RuntimeError("Telemetry API not initialized")
global CURRENT_TRACE_CONTEXT
context = CURRENT_TRACE_CONTEXT.get()
if context is None:
return
@ -269,7 +283,7 @@ class TelemetryHandler(logging.Handler):
if span is None:
return
BACKGROUND_LOGGER.log_event(
enqueue_event(
UnstructuredLogEvent(
trace_id=span.trace_id,
span_id=span.span_id,

6
llama_stack/providers/utils/vector_io/vector_utils.py
View file

@ -12,14 +12,12 @@ import uuid
def generate_chunk_id(document_id: str, chunk_text: str, chunk_window: str | None = None) -> str:
"""
Generate a unique chunk ID using a hash of the document ID and chunk text.
Note: MD5 is used only to calculate an identifier, not for security purposes.
Adding usedforsecurity=False for compatibility with FIPS environments.
Then use the first 32 characters of the hash to create a UUID.
"""
hash_input = f"{document_id}:{chunk_text}".encode()
if chunk_window:
hash_input += f":{chunk_window}".encode()
return str(uuid.UUID(hashlib.md5(hash_input, usedforsecurity=False).hexdigest()))
return str(uuid.UUID(hashlib.sha256(hash_input).hexdigest()[:32]))
def proper_case(s: str) -> str:

75
llama_stack/testing/inference_recorder.py
View file

@ -15,6 +15,8 @@ from enum import StrEnum
from pathlib import Path
from typing import Any, Literal, cast
from openai import NOT_GIVEN
from llama_stack.log import get_logger
logger = get_logger(__name__, category="testing")
@ -105,8 +107,12 @@ def _deserialize_response(data: dict[str, Any]) -> Any:
return cls.model_validate(data["__data__"])
except (ImportError, AttributeError, TypeError, ValueError) as e:
logger.warning(f"Failed to deserialize object of type {data['__type__']}: {e}")
return data["__data__"]
logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_validate: {e}")
try:
return cls.model_construct(**data["__data__"])
except Exception as e:
logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_construct: {e}")
return data["__data__"]
return data
@ -194,20 +200,15 @@ def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str:
Supported endpoints:
- '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ]
- '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ]
- '/v1/models' (OpenAI): response body is: [ { id: ... }, ... ]
Returns a list of unique identifiers or None if structure doesn't match.
"""
body = response["body"]
if endpoint == "/api/tags":
items = body.get("models")
idents = [m.model for m in items]
else:
items = body.get("data")
idents = [m.id for m in items]
items = response["body"]
idents = [m.model if endpoint == "/api/tags" else m.id for m in items]
return sorted(set(idents))
identifiers = _extract_model_identifiers()
return hashlib.sha1(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8]
return hashlib.sha256(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8]
def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None:
@ -215,28 +216,22 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]])
seen: dict[str, dict[str, Any]] = {}
for rec in records:
body = rec["response"]["body"]
if endpoint == "/api/tags":
items = body.models
elif endpoint == "/v1/models":
items = body.data
else:
items = []
for m in items:
if endpoint == "/v1/models":
if endpoint == "/v1/models":
for m in body:
key = m.id
else:
seen[key] = m
elif endpoint == "/api/tags":
for m in body.models:
key = m.model
seen[key] = m
seen[key] = m
ordered = [seen[k] for k in sorted(seen.keys())]
canonical = records[0]
canonical_req = canonical.get("request", {})
if isinstance(canonical_req, dict):
canonical_req["endpoint"] = endpoint
if endpoint == "/v1/models":
body = {"data": ordered, "object": "list"}
else:
body = ordered
if endpoint == "/api/tags":
from ollama import ListResponse
body = ListResponse(models=ordered)
@ -247,12 +242,17 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
global _current_mode, _current_storage
if _current_mode == InferenceMode.LIVE or _current_storage is None:
# Normal operation
return await original_method(self, *args, **kwargs)
if endpoint == "/v1/models":
return original_method(self, *args, **kwargs)
else:
return await original_method(self, *args, **kwargs)
# Get base URL based on client type
if client_type == "openai":
base_url = str(self._client.base_url)
# the OpenAI client methods may pass NOT_GIVEN for unset parameters; filter these out
kwargs = {k: v for k, v in kwargs.items() if v is not NOT_GIVEN}
elif client_type == "ollama":
# Get base URL from the client (Ollama client uses host attribute)
base_url = getattr(self, "host", "http://localhost:11434")
@ -296,7 +296,14 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
)
elif _current_mode == InferenceMode.RECORD:
response = await original_method(self, *args, **kwargs)
if endpoint == "/v1/models":
response = original_method(self, *args, **kwargs)
else:
response = await original_method(self, *args, **kwargs)
# we want to store the result of the iterator, not the iterator itself
if endpoint == "/v1/models":
response = [m async for m in response]
request_data = {
"method": method,
@ -376,10 +383,14 @@ def patch_inference_clients():
_original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs
)
async def patched_models_list(self, *args, **kwargs):
return await _patched_inference_method(
_original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs
)
def patched_models_list(self, *args, **kwargs):
async def _iter():
for item in await _patched_inference_method(
_original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs
):
yield item
return _iter()
# Apply OpenAI patches
AsyncChatCompletions.create = patched_chat_completions_create

467
llama_stack/ui/package-lock.json generated
View file

@ -11,7 +11,7 @@
"@radix-ui/react-collapsible": "^1.1.12",
"@radix-ui/react-dialog": "^1.1.13",
"@radix-ui/react-dropdown-menu": "^2.1.16",
"@radix-ui/react-select": "^2.2.5",
"@radix-ui/react-select": "^2.2.6",
"@radix-ui/react-separator": "^1.1.7",
"@radix-ui/react-slot": "^1.2.3",
"@radix-ui/react-tooltip": "^1.2.8",
@ -20,7 +20,7 @@
"framer-motion": "^12.23.12",
"llama-stack-client": "^0.2.21",
"lucide-react": "^0.542.0",
"next": "15.3.3",
"next": "15.5.3",
"next-auth": "^4.24.11",
"next-themes": "^0.4.6",
"react": "^19.0.0",
@ -664,9 +664,9 @@
}
},
"node_modules/@emnapi/runtime": {
"version": "1.4.3",
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.4.3.tgz",
"integrity": "sha512-pBPWdu6MLKROBX05wSNKcNb++m5Er+KQ9QkB+WVM+pW2Kx9hoSrVTnu3BdkI5eBLZoKu/J6mW/B6i6bJB2ytXQ==",
"version": "1.5.0",
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.5.0.tgz",
"integrity": "sha512-97/BJ3iXHww3djw6hYIfErCZFee7qCtrneuLa20UXFCOTCfBM2cvQHjWJ2EG0s0MtdNwInarqCTz35i4wWXHsQ==",
"license": "MIT",
"optional": true,
"dependencies": {
@ -927,9 +927,9 @@
}
},
"node_modules/@img/sharp-darwin-arm64": {
"version": "0.34.1",
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-arm64/-/sharp-darwin-arm64-0.34.1.tgz",
"integrity": "sha512-pn44xgBtgpEbZsu+lWf2KNb6OAf70X68k+yk69Ic2Xz11zHR/w24/U49XT7AeRwJ0Px+mhALhU5LPci1Aymk7A==",
"version": "0.34.3",
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-arm64/-/sharp-darwin-arm64-0.34.3.tgz",
"integrity": "sha512-ryFMfvxxpQRsgZJqBd4wsttYQbCxsJksrv9Lw/v798JcQ8+w84mBWuXwl+TT0WJ/WrYOLaYpwQXi3sA9nTIaIg==",
"cpu": [
"arm64"
],
@ -945,13 +945,13 @@
"url": "https://opencollective.com/libvips"
},
"optionalDependencies": {
"@img/sharp-libvips-darwin-arm64": "1.1.0"
"@img/sharp-libvips-darwin-arm64": "1.2.0"
}
},
"node_modules/@img/sharp-darwin-x64": {
"version": "0.34.1",
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-x64/-/sharp-darwin-x64-0.34.1.tgz",
"integrity": "sha512-VfuYgG2r8BpYiOUN+BfYeFo69nP/MIwAtSJ7/Zpxc5QF3KS22z8Pvg3FkrSFJBPNQ7mmcUcYQFBmEQp7eu1F8Q==",
"version": "0.34.3",
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-x64/-/sharp-darwin-x64-0.34.3.tgz",
"integrity": "sha512-yHpJYynROAj12TA6qil58hmPmAwxKKC7reUqtGLzsOHfP7/rniNGTL8tjWX6L3CTV4+5P4ypcS7Pp+7OB+8ihA==",
"cpu": [
"x64"
],
@ -967,13 +967,13 @@
"url": "https://opencollective.com/libvips"
},
"optionalDependencies": {
"@img/sharp-libvips-darwin-x64": "1.1.0"
"@img/sharp-libvips-darwin-x64": "1.2.0"
}
},
"node_modules/@img/sharp-libvips-darwin-arm64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-arm64/-/sharp-libvips-darwin-arm64-1.1.0.tgz",
"integrity": "sha512-HZ/JUmPwrJSoM4DIQPv/BfNh9yrOA8tlBbqbLz4JZ5uew2+o22Ik+tHQJcih7QJuSa0zo5coHTfD5J8inqj9DA==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-arm64/-/sharp-libvips-darwin-arm64-1.2.0.tgz",
"integrity": "sha512-sBZmpwmxqwlqG9ueWFXtockhsxefaV6O84BMOrhtg/YqbTaRdqDE7hxraVE3y6gVM4eExmfzW4a8el9ArLeEiQ==",
"cpu": [
"arm64"
],
@ -987,9 +987,9 @@
}
},
"node_modules/@img/sharp-libvips-darwin-x64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-x64/-/sharp-libvips-darwin-x64-1.1.0.tgz",
"integrity": "sha512-Xzc2ToEmHN+hfvsl9wja0RlnXEgpKNmftriQp6XzY/RaSfwD9th+MSh0WQKzUreLKKINb3afirxW7A0fz2YWuQ==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-x64/-/sharp-libvips-darwin-x64-1.2.0.tgz",
"integrity": "sha512-M64XVuL94OgiNHa5/m2YvEQI5q2cl9d/wk0qFTDVXcYzi43lxuiFTftMR1tOnFQovVXNZJ5TURSDK2pNe9Yzqg==",
"cpu": [
"x64"
],
@ -1003,9 +1003,9 @@
}
},
"node_modules/@img/sharp-libvips-linux-arm": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm/-/sharp-libvips-linux-arm-1.1.0.tgz",
"integrity": "sha512-s8BAd0lwUIvYCJyRdFqvsj+BJIpDBSxs6ivrOPm/R7piTs5UIwY5OjXrP2bqXC9/moGsyRa37eYWYCOGVXxVrA==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm/-/sharp-libvips-linux-arm-1.2.0.tgz",
"integrity": "sha512-mWd2uWvDtL/nvIzThLq3fr2nnGfyr/XMXlq8ZJ9WMR6PXijHlC3ksp0IpuhK6bougvQrchUAfzRLnbsen0Cqvw==",
"cpu": [
"arm"
],
@ -1019,9 +1019,9 @@
}
},
"node_modules/@img/sharp-libvips-linux-arm64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm64/-/sharp-libvips-linux-arm64-1.1.0.tgz",
"integrity": "sha512-IVfGJa7gjChDET1dK9SekxFFdflarnUB8PwW8aGwEoF3oAsSDuNUTYS+SKDOyOJxQyDC1aPFMuRYLoDInyV9Ew==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm64/-/sharp-libvips-linux-arm64-1.2.0.tgz",
"integrity": "sha512-RXwd0CgG+uPRX5YYrkzKyalt2OJYRiJQ8ED/fi1tq9WQW2jsQIn0tqrlR5l5dr/rjqq6AHAxURhj2DVjyQWSOA==",
"cpu": [
"arm64"
],
@ -1035,9 +1035,9 @@
}
},
"node_modules/@img/sharp-libvips-linux-ppc64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-ppc64/-/sharp-libvips-linux-ppc64-1.1.0.tgz",
"integrity": "sha512-tiXxFZFbhnkWE2LA8oQj7KYR+bWBkiV2nilRldT7bqoEZ4HiDOcePr9wVDAZPi/Id5fT1oY9iGnDq20cwUz8lQ==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-ppc64/-/sharp-libvips-linux-ppc64-1.2.0.tgz",
"integrity": "sha512-Xod/7KaDDHkYu2phxxfeEPXfVXFKx70EAFZ0qyUdOjCcxbjqyJOEUpDe6RIyaunGxT34Anf9ue/wuWOqBW2WcQ==",
"cpu": [
"ppc64"
],
@ -1051,9 +1051,9 @@
}
},
"node_modules/@img/sharp-libvips-linux-s390x": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-s390x/-/sharp-libvips-linux-s390x-1.1.0.tgz",
"integrity": "sha512-xukSwvhguw7COyzvmjydRb3x/09+21HykyapcZchiCUkTThEQEOMtBj9UhkaBRLuBrgLFzQ2wbxdeCCJW/jgJA==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-s390x/-/sharp-libvips-linux-s390x-1.2.0.tgz",
"integrity": "sha512-eMKfzDxLGT8mnmPJTNMcjfO33fLiTDsrMlUVcp6b96ETbnJmd4uvZxVJSKPQfS+odwfVaGifhsB07J1LynFehw==",
"cpu": [
"s390x"
],
@ -1067,9 +1067,9 @@
}
},
"node_modules/@img/sharp-libvips-linux-x64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-x64/-/sharp-libvips-linux-x64-1.1.0.tgz",
"integrity": "sha512-yRj2+reB8iMg9W5sULM3S74jVS7zqSzHG3Ol/twnAAkAhnGQnpjj6e4ayUz7V+FpKypwgs82xbRdYtchTTUB+Q==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-x64/-/sharp-libvips-linux-x64-1.2.0.tgz",
"integrity": "sha512-ZW3FPWIc7K1sH9E3nxIGB3y3dZkpJlMnkk7z5tu1nSkBoCgw2nSRTFHI5pB/3CQaJM0pdzMF3paf9ckKMSE9Tg==",
"cpu": [
"x64"
],
@ -1083,9 +1083,9 @@
}
},
"node_modules/@img/sharp-libvips-linuxmusl-arm64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-arm64/-/sharp-libvips-linuxmusl-arm64-1.1.0.tgz",
"integrity": "sha512-jYZdG+whg0MDK+q2COKbYidaqW/WTz0cc1E+tMAusiDygrM4ypmSCjOJPmFTvHHJ8j/6cAGyeDWZOsK06tP33w==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-arm64/-/sharp-libvips-linuxmusl-arm64-1.2.0.tgz",
"integrity": "sha512-UG+LqQJbf5VJ8NWJ5Z3tdIe/HXjuIdo4JeVNADXBFuG7z9zjoegpzzGIyV5zQKi4zaJjnAd2+g2nna8TZvuW9Q==",
"cpu": [
"arm64"
],
@ -1099,9 +1099,9 @@
}
},
"node_modules/@img/sharp-libvips-linuxmusl-x64": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-x64/-/sharp-libvips-linuxmusl-x64-1.1.0.tgz",
"integrity": "sha512-wK7SBdwrAiycjXdkPnGCPLjYb9lD4l6Ze2gSdAGVZrEL05AOUJESWU2lhlC+Ffn5/G+VKuSm6zzbQSzFX/P65A==",
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-x64/-/sharp-libvips-linuxmusl-x64-1.2.0.tgz",
"integrity": "sha512-SRYOLR7CXPgNze8akZwjoGBoN1ThNZoqpOgfnOxmWsklTGVfJiGJoC/Lod7aNMGA1jSsKWM1+HRX43OP6p9+6Q==",
"cpu": [
"x64"
],
@ -1115,9 +1115,9 @@
}
},
"node_modules/@img/sharp-linux-arm": {
"version": "0.34.1",
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm/-/sharp-linux-arm-0.34.1.tgz",
"integrity": "sha512-anKiszvACti2sGy9CirTlNyk7BjjZPiML1jt2ZkTdcvpLU1YH6CXwRAZCA2UmRXnhiIftXQ7+Oh62Ji25W72jA==",
"version": "0.34.3",
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm/-/sharp-linux-arm-0.34.3.tgz",
"integrity": "sha512-oBK9l+h6KBN0i3dC8rYntLiVfW8D8wH+NPNT3O/WBHeW0OQWCjfWksLUaPidsrDKpJgXp3G3/hkmhptAW0I3+A==",
"cpu": [
"arm"
],
@ -1133,13 +1133,13 @@
"url": "https://opencollective.com/libvips"
},
"optionalDependencies": {
"@img/sharp-libvips-linux-arm": "1.1.0"
"@img/sharp-libvips-linux-arm": "1.2.0"
}
},
"node_modules/@img/sharp-linux-arm64": {
"version": "0.34.1",
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm64/-/sharp-linux-arm64-0.34.1.tgz",
"integrity": "sha512-kX2c+vbvaXC6vly1RDf/IWNXxrlxLNpBVWkdpRq5Ka7OOKj6nr66etKy2IENf6FtOgklkg9ZdGpEu9kwdlcwOQ==",
"version": "0.34.3",
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm64/-/sharp-linux-arm64-0.34.3.tgz",
"integrity": "sha512-QdrKe3EvQrqwkDrtuTIjI0bu6YEJHTgEeqdzI3uWJOH6G1O8Nl1iEeVYRGdj1h5I21CqxSvQp1Yv7xeU3ZewbA==",
"cpu": [
"arm64"
],
@ -1155,13 +1155,35 @@
"url": "https://opencollective.com/libvips"
},
"optionalDependencies": {
"@img/sharp-libvips-linux-arm64": "1.1.0"
"@img/sharp-libvips-linux-arm64": "1.2.0"
}
},
"node_modules/@img/sharp-linux-ppc64": {
"version": "0.34.3",
"resolved": "https://registry.npmjs.org/@img/sharp-linux-ppc64/-/sharp-linux-ppc64-0.34.3.tgz",
"integrity": "sha512-GLtbLQMCNC5nxuImPR2+RgrviwKwVql28FWZIW1zWruy6zLgA5/x2ZXk3mxj58X/tszVF69KK0Is83V8YgWhLA==",
"cpu": [
"ppc64"
],
"license": "Apache-2.0",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
},
"funding": {
"url": "https://opencollective.com/libvips"
},
"optionalDependencies": {
"@img/sharp-libvips-linux-ppc64": "1.2.0"
}
},
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@ -13226,16 +13261,16 @@
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@ -13244,26 +13279,28 @@
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@ -13389,9 +13426,9 @@
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},

4
llama_stack/ui/package.json
View file

@ -16,7 +16,7 @@
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@ -25,7 +25,7 @@
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"react": "^19.0.0",