mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
feat: implement get chat completions APIs (#2200)
# What does this PR do? * Provide sqlite implementation of the APIs introduced in https://github.com/meta-llama/llama-stack/pull/2145. * Introduced a SqlStore API: llama_stack/providers/utils/sqlstore/api.py and the first Sqlite implementation * Pagination support will be added in a future PR. ## Test Plan Unit test on sql store: <img width="1005" alt="image" src="https://github.com/user-attachments/assets/9b8b7ec8-632b-4667-8127-5583426b2e29" /> Integration test: ``` INFERENCE_MODEL="llama3.2:3b-instruct-fp16" llama stack build --template ollama --image-type conda --run ``` ``` LLAMA_STACK_CONFIG=http://localhost:5001 INFERENCE_MODEL="llama3.2:3b-instruct-fp16" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-fp16" -k 'inference_store and openai' ```
This commit is contained in:
ehhuang
GitHub
parent
633bb9c5b3
commit
549812f51e
71 changed files with 1111 additions and 10 deletions
|
|
@ -43,8 +43,20 @@ def get_provider_dependencies(
|
|||
# Extract providers based on config type
|
||||
if isinstance(config, DistributionTemplate):
|
||||
providers = config.providers
|
||||
|
||||
# TODO: This is a hack to get the dependencies for internal APIs into build
|
||||
# We should have a better way to do this by formalizing the concept of "internal" APIs
|
||||
# and providers, with a way to specify dependencies for them.
|
||||
run_configs = config.run_configs
|
||||
additional_pip_packages: list[str] = []
|
||||
if run_configs:
|
||||
for run_config in run_configs.values():
|
||||
run_config_ = run_config.run_config(name="", providers={}, container_image=None)
|
||||
if run_config_.inference_store:
|
||||
additional_pip_packages.extend(run_config_.inference_store.pip_packages)
|
||||
elif isinstance(config, BuildConfig):
|
||||
providers = config.distribution_spec.providers
|
||||
additional_pip_packages = config.additional_pip_packages
|
||||
deps = []
|
||||
registry = get_provider_registry(config)
|
||||
for api_str, provider_or_providers in providers.items():
|
||||
|
|
@ -72,6 +84,9 @@ def get_provider_dependencies(
|
|||
else:
|
||||
normal_deps.append(package)
|
||||
|
||||
if additional_pip_packages:
|
||||
normal_deps.extend(additional_pip_packages)
|
||||
|
||||
return list(set(normal_deps)), list(set(special_deps))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from llama_stack.apis.vector_dbs import VectorDB, VectorDBInput
|
|||
from llama_stack.apis.vector_io import VectorIO
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqlStoreConfig
|
||||
|
||||
LLAMA_STACK_BUILD_CONFIG_VERSION = "2"
|
||||
LLAMA_STACK_RUN_CONFIG_VERSION = "2"
|
||||
|
|
@ -314,6 +315,13 @@ Configuration for the persistence store used by the distribution registry. If no
|
|||
a default SQLite store will be used.""",
|
||||
)
|
||||
|
||||
inference_store: 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.""",
|
||||
)
|
||||
|
||||
# registry of "resources" in the distribution
|
||||
models: list[ModelInput] = Field(default_factory=list)
|
||||
shields: list[ShieldInput] = Field(default_factory=list)
|
||||
|
|
@ -362,6 +370,10 @@ class BuildConfig(BaseModel):
|
|||
description="Path to directory containing external provider implementations. The providers packages will be resolved from this directory. "
|
||||
"pip_packages MUST contain the provider package name.",
|
||||
)
|
||||
additional_pip_packages: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="Additional pip packages to install in the distribution. These packages will be installed in the distribution environment.",
|
||||
)
|
||||
|
||||
@field_validator("external_providers_dir")
|
||||
@classmethod
|
||||
|
|
|
|||
|
|
@ -140,7 +140,7 @@ async def resolve_impls(
|
|||
|
||||
sorted_providers = sort_providers_by_deps(providers_with_specs, run_config)
|
||||
|
||||
return await instantiate_providers(sorted_providers, router_apis, dist_registry)
|
||||
return await instantiate_providers(sorted_providers, router_apis, dist_registry, run_config)
|
||||
|
||||
|
||||
def specs_for_autorouted_apis(apis_to_serve: list[str] | set[str]) -> dict[str, dict[str, ProviderWithSpec]]:
|
||||
|
|
@ -243,7 +243,10 @@ def sort_providers_by_deps(
|
|||
|
||||
|
||||
async def instantiate_providers(
|
||||
sorted_providers: list[tuple[str, ProviderWithSpec]], router_apis: set[Api], dist_registry: DistributionRegistry
|
||||
sorted_providers: list[tuple[str, ProviderWithSpec]],
|
||||
router_apis: set[Api],
|
||||
dist_registry: DistributionRegistry,
|
||||
run_config: StackRunConfig,
|
||||
) -> dict:
|
||||
"""Instantiates providers asynchronously while managing dependencies."""
|
||||
impls: dict[Api, Any] = {}
|
||||
|
|
@ -258,7 +261,7 @@ async def instantiate_providers(
|
|||
if isinstance(provider.spec, RoutingTableProviderSpec):
|
||||
inner_impls = inner_impls_by_provider_id[f"inner-{provider.spec.router_api.value}"]
|
||||
|
||||
impl = await instantiate_provider(provider, deps, inner_impls, dist_registry)
|
||||
impl = await instantiate_provider(provider, deps, inner_impls, dist_registry, run_config)
|
||||
|
||||
if api_str.startswith("inner-"):
|
||||
inner_impls_by_provider_id[api_str][provider.provider_id] = impl
|
||||
|
|
@ -308,6 +311,7 @@ async def instantiate_provider(
|
|||
deps: dict[Api, Any],
|
||||
inner_impls: dict[str, Any],
|
||||
dist_registry: DistributionRegistry,
|
||||
run_config: StackRunConfig,
|
||||
):
|
||||
provider_spec = provider.spec
|
||||
if not hasattr(provider_spec, "module"):
|
||||
|
|
@ -327,7 +331,7 @@ async def instantiate_provider(
|
|||
method = "get_auto_router_impl"
|
||||
|
||||
config = None
|
||||
args = [provider_spec.api, deps[provider_spec.routing_table_api], deps]
|
||||
args = [provider_spec.api, deps[provider_spec.routing_table_api], deps, run_config]
|
||||
elif isinstance(provider_spec, RoutingTableProviderSpec):
|
||||
method = "get_routing_table_impl"
|
||||
|
||||
|
|
|
|||
|
|
@ -7,8 +7,10 @@
|
|||
from typing import Any
|
||||
|
||||
from llama_stack.distribution.datatypes import RoutedProtocol
|
||||
from llama_stack.distribution.stack import StackRunConfig
|
||||
from llama_stack.distribution.store import DistributionRegistry
|
||||
from llama_stack.providers.datatypes import Api, RoutingTable
|
||||
from llama_stack.providers.utils.inference.inference_store import InferenceStore
|
||||
|
||||
from .routing_tables import (
|
||||
BenchmarksRoutingTable,
|
||||
|
|
@ -45,7 +47,9 @@ async def get_routing_table_impl(
|
|||
return impl
|
||||
|
||||
|
||||
async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: dict[str, Any]) -> Any:
|
||||
async def get_auto_router_impl(
|
||||
api: Api, routing_table: RoutingTable, deps: dict[str, Any], run_config: StackRunConfig
|
||||
) -> Any:
|
||||
from .routers import (
|
||||
DatasetIORouter,
|
||||
EvalRouter,
|
||||
|
|
@ -76,6 +80,12 @@ async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: dict
|
|||
if dep_api in deps:
|
||||
api_to_dep_impl[dep_name] = deps[dep_api]
|
||||
|
||||
# TODO: move pass configs to routers instead
|
||||
if api == Api.inference and run_config.inference_store:
|
||||
inference_store = InferenceStore(run_config.inference_store)
|
||||
await inference_store.initialize()
|
||||
api_to_dep_impl["store"] = inference_store
|
||||
|
||||
impl = api_to_routers[api.value](routing_table, **api_to_dep_impl)
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -32,8 +32,11 @@ from llama_stack.apis.inference import (
|
|||
EmbeddingsResponse,
|
||||
EmbeddingTaskType,
|
||||
Inference,
|
||||
ListOpenAIChatCompletionResponse,
|
||||
LogProbConfig,
|
||||
Message,
|
||||
OpenAICompletionWithInputMessages,
|
||||
Order,
|
||||
ResponseFormat,
|
||||
SamplingParams,
|
||||
StopReason,
|
||||
|
|
@ -73,6 +76,8 @@ from llama_stack.log import get_logger
|
|||
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.inference.stream_utils import stream_and_store_openai_completion
|
||||
from llama_stack.providers.utils.telemetry.tracing import get_current_span
|
||||
|
||||
logger = get_logger(name=__name__, category="core")
|
||||
|
|
@ -141,10 +146,12 @@ class InferenceRouter(Inference):
|
|||
self,
|
||||
routing_table: RoutingTable,
|
||||
telemetry: Telemetry | None = None,
|
||||
store: InferenceStore | None = None,
|
||||
) -> None:
|
||||
logger.debug("Initializing InferenceRouter")
|
||||
self.routing_table = routing_table
|
||||
self.telemetry = telemetry
|
||||
self.store = store
|
||||
if self.telemetry:
|
||||
self.tokenizer = Tokenizer.get_instance()
|
||||
self.formatter = ChatFormat(self.tokenizer)
|
||||
|
|
@ -607,9 +614,31 @@ class InferenceRouter(Inference):
|
|||
|
||||
provider = self.routing_table.get_provider_impl(model_obj.identifier)
|
||||
if stream:
|
||||
return await provider.openai_chat_completion(**params)
|
||||
response_stream = await provider.openai_chat_completion(**params)
|
||||
if self.store:
|
||||
return stream_and_store_openai_completion(response_stream, model, self.store, messages)
|
||||
return response_stream
|
||||
else:
|
||||
return await self._nonstream_openai_chat_completion(provider, params)
|
||||
response = await self._nonstream_openai_chat_completion(provider, params)
|
||||
if self.store:
|
||||
await self.store.store_chat_completion(response, messages)
|
||||
return response
|
||||
|
||||
async def list_chat_completions(
|
||||
self,
|
||||
after: str | None = None,
|
||||
limit: int | None = 20,
|
||||
model: str | None = None,
|
||||
order: Order | None = Order.desc,
|
||||
) -> ListOpenAIChatCompletionResponse:
|
||||
if self.store:
|
||||
return await self.store.list_chat_completions(after, limit, model, order)
|
||||
raise NotImplementedError("List chat completions is not supported: inference store is not configured.")
|
||||
|
||||
async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
|
||||
if self.store:
|
||||
return await self.store.get_chat_completion(completion_id)
|
||||
raise NotImplementedError("Get chat completion is not supported: inference store is not configured.")
|
||||
|
||||
async def _nonstream_openai_chat_completion(self, provider: Inference, params: dict) -> OpenAIChatCompletion:
|
||||
response = await provider.openai_chat_completion(**params)
|
||||
|
|
|
|||
123
llama_stack/providers/utils/inference/inference_store.py
Normal file
123
llama_stack/providers/utils/inference/inference_store.py
Normal file
|
|
@ -0,0 +1,123 @@
|
|||
# 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 (
|
||||
ListOpenAIChatCompletionResponse,
|
||||
OpenAIChatCompletion,
|
||||
OpenAICompletionWithInputMessages,
|
||||
OpenAIMessageParam,
|
||||
Order,
|
||||
)
|
||||
from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
|
||||
|
||||
from ..sqlstore.api import ColumnDefinition, ColumnType
|
||||
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl
|
||||
|
||||
|
||||
class InferenceStore:
|
||||
def __init__(self, sql_store_config: SqlStoreConfig):
|
||||
if not sql_store_config:
|
||||
sql_store_config = SqliteSqlStoreConfig(
|
||||
db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
|
||||
)
|
||||
self.sql_store_config = sql_store_config
|
||||
self.sql_store = None
|
||||
|
||||
async def initialize(self):
|
||||
"""Create the necessary tables if they don't exist."""
|
||||
self.sql_store = sqlstore_impl(self.sql_store_config)
|
||||
await self.sql_store.create_table(
|
||||
"chat_completions",
|
||||
{
|
||||
"id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
|
||||
"created": ColumnType.INTEGER,
|
||||
"model": ColumnType.STRING,
|
||||
"choices": ColumnType.JSON,
|
||||
"input_messages": ColumnType.JSON,
|
||||
},
|
||||
)
|
||||
|
||||
async def store_chat_completion(
|
||||
self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam]
|
||||
) -> None:
|
||||
if not self.sql_store:
|
||||
raise ValueError("Inference store is not initialized")
|
||||
|
||||
data = chat_completion.model_dump()
|
||||
|
||||
await self.sql_store.insert(
|
||||
"chat_completions",
|
||||
{
|
||||
"id": data["id"],
|
||||
"created": data["created"],
|
||||
"model": data["model"],
|
||||
"choices": data["choices"],
|
||||
"input_messages": [message.model_dump() for message in input_messages],
|
||||
},
|
||||
)
|
||||
|
||||
async def list_chat_completions(
|
||||
self,
|
||||
after: str | None = None,
|
||||
limit: int | None = 50,
|
||||
model: str | None = None,
|
||||
order: Order | None = Order.desc,
|
||||
) -> ListOpenAIChatCompletionResponse:
|
||||
"""
|
||||
List chat completions from the database.
|
||||
|
||||
:param after: The ID of the last chat completion to return.
|
||||
:param limit: The maximum number of chat completions to return.
|
||||
:param model: The model to filter by.
|
||||
:param order: The order to sort the chat completions by.
|
||||
"""
|
||||
if not self.sql_store:
|
||||
raise ValueError("Inference store is not initialized")
|
||||
|
||||
# TODO: support after
|
||||
if after:
|
||||
raise NotImplementedError("After is not supported for SQLite")
|
||||
if not order:
|
||||
order = Order.desc
|
||||
|
||||
rows = await self.sql_store.fetch_all(
|
||||
"chat_completions",
|
||||
where={"model": model} if model else None,
|
||||
order_by=[("created", order.value)],
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
data = [
|
||||
OpenAICompletionWithInputMessages(
|
||||
id=row["id"],
|
||||
created=row["created"],
|
||||
model=row["model"],
|
||||
choices=row["choices"],
|
||||
input_messages=row["input_messages"],
|
||||
)
|
||||
for row in rows
|
||||
]
|
||||
return ListOpenAIChatCompletionResponse(
|
||||
data=data,
|
||||
# TODO: implement has_more
|
||||
has_more=False,
|
||||
first_id=data[0].id if data else "",
|
||||
last_id=data[-1].id if data else "",
|
||||
)
|
||||
|
||||
async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
|
||||
if not self.sql_store:
|
||||
raise ValueError("Inference store is not initialized")
|
||||
|
||||
row = await self.sql_store.fetch_one("chat_completions", where={"id": completion_id})
|
||||
if not row:
|
||||
raise ValueError(f"Chat completion with id {completion_id} not found") from None
|
||||
return OpenAICompletionWithInputMessages(
|
||||
id=row["id"],
|
||||
created=row["created"],
|
||||
model=row["model"],
|
||||
choices=row["choices"],
|
||||
input_messages=row["input_messages"],
|
||||
)
|
||||
129
llama_stack/providers/utils/inference/stream_utils.py
Normal file
129
llama_stack/providers/utils/inference/stream_utils.py
Normal file
|
|
@ -0,0 +1,129 @@
|
|||
# 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 collections.abc import AsyncIterator
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChatCompletionChunk,
|
||||
OpenAIChatCompletionToolCall,
|
||||
OpenAIChatCompletionToolCallFunction,
|
||||
OpenAIChoice,
|
||||
OpenAIChoiceLogprobs,
|
||||
OpenAIMessageParam,
|
||||
)
|
||||
from llama_stack.providers.utils.inference.inference_store import InferenceStore
|
||||
|
||||
|
||||
async def stream_and_store_openai_completion(
|
||||
provider_stream: AsyncIterator[OpenAIChatCompletionChunk],
|
||||
model: str,
|
||||
store: InferenceStore,
|
||||
input_messages: list[OpenAIMessageParam],
|
||||
) -> AsyncIterator[OpenAIChatCompletionChunk]:
|
||||
"""
|
||||
Wraps a provider's stream, yields chunks, and stores the full completion at the end.
|
||||
"""
|
||||
id = None
|
||||
created = None
|
||||
choices_data: dict[int, dict[str, Any]] = {}
|
||||
|
||||
try:
|
||||
async for chunk in provider_stream:
|
||||
if id is None and chunk.id:
|
||||
id = chunk.id
|
||||
if created is None and chunk.created:
|
||||
created = chunk.created
|
||||
|
||||
if chunk.choices:
|
||||
for choice_delta in chunk.choices:
|
||||
idx = choice_delta.index
|
||||
if idx not in choices_data:
|
||||
choices_data[idx] = {
|
||||
"content_parts": [],
|
||||
"tool_calls_builder": {},
|
||||
"finish_reason": None,
|
||||
"logprobs_content_parts": [],
|
||||
}
|
||||
current_choice_data = choices_data[idx]
|
||||
|
||||
if choice_delta.delta:
|
||||
delta = choice_delta.delta
|
||||
if delta.content:
|
||||
current_choice_data["content_parts"].append(delta.content)
|
||||
if delta.tool_calls:
|
||||
for tool_call_delta in delta.tool_calls:
|
||||
tc_idx = tool_call_delta.index
|
||||
if tc_idx not in current_choice_data["tool_calls_builder"]:
|
||||
# Initialize with correct structure for _ToolCallBuilderData
|
||||
current_choice_data["tool_calls_builder"][tc_idx] = {
|
||||
"id": None,
|
||||
"type": "function",
|
||||
"function_name_parts": [],
|
||||
"function_arguments_parts": [],
|
||||
}
|
||||
builder = current_choice_data["tool_calls_builder"][tc_idx]
|
||||
if tool_call_delta.id:
|
||||
builder["id"] = tool_call_delta.id
|
||||
if tool_call_delta.type:
|
||||
builder["type"] = tool_call_delta.type
|
||||
if tool_call_delta.function:
|
||||
if tool_call_delta.function.name:
|
||||
builder["function_name_parts"].append(tool_call_delta.function.name)
|
||||
if tool_call_delta.function.arguments:
|
||||
builder["function_arguments_parts"].append(tool_call_delta.function.arguments)
|
||||
if choice_delta.finish_reason:
|
||||
current_choice_data["finish_reason"] = choice_delta.finish_reason
|
||||
if choice_delta.logprobs and choice_delta.logprobs.content:
|
||||
# Ensure that we are extending with the correct type
|
||||
current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content)
|
||||
yield chunk
|
||||
finally:
|
||||
if id:
|
||||
assembled_choices: list[OpenAIChoice] = []
|
||||
for choice_idx, choice_data in choices_data.items():
|
||||
content_str = "".join(choice_data["content_parts"])
|
||||
assembled_tool_calls: list[OpenAIChatCompletionToolCall] = []
|
||||
if choice_data["tool_calls_builder"]:
|
||||
for tc_build_data in choice_data["tool_calls_builder"].values():
|
||||
if tc_build_data["id"]:
|
||||
func_name = "".join(tc_build_data["function_name_parts"])
|
||||
func_args = "".join(tc_build_data["function_arguments_parts"])
|
||||
assembled_tool_calls.append(
|
||||
OpenAIChatCompletionToolCall(
|
||||
id=tc_build_data["id"],
|
||||
type=tc_build_data["type"], # No or "function" needed, already set
|
||||
function=OpenAIChatCompletionToolCallFunction(name=func_name, arguments=func_args),
|
||||
)
|
||||
)
|
||||
message = OpenAIAssistantMessageParam(
|
||||
role="assistant",
|
||||
content=content_str if content_str else None,
|
||||
tool_calls=assembled_tool_calls if assembled_tool_calls else None,
|
||||
)
|
||||
logprobs_content = choice_data["logprobs_content_parts"]
|
||||
final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None
|
||||
|
||||
assembled_choices.append(
|
||||
OpenAIChoice(
|
||||
finish_reason=choice_data["finish_reason"],
|
||||
index=choice_idx,
|
||||
message=message,
|
||||
logprobs=final_logprobs,
|
||||
)
|
||||
)
|
||||
|
||||
final_response = OpenAIChatCompletion(
|
||||
id=id,
|
||||
choices=assembled_choices,
|
||||
created=created or int(datetime.now(timezone.utc).timestamp()),
|
||||
model=model,
|
||||
object="chat.completion",
|
||||
)
|
||||
await store.store_chat_completion(final_response, input_messages)
|
||||
90
llama_stack/providers/utils/sqlstore/api.py
Normal file
90
llama_stack/providers/utils/sqlstore/api.py
Normal file
|
|
@ -0,0 +1,90 @@
|
|||
# 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 collections.abc import Mapping
|
||||
from enum import Enum
|
||||
from typing import Any, Literal, Protocol
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class ColumnType(Enum):
|
||||
INTEGER = "INTEGER"
|
||||
STRING = "STRING"
|
||||
TEXT = "TEXT"
|
||||
FLOAT = "FLOAT"
|
||||
BOOLEAN = "BOOLEAN"
|
||||
JSON = "JSON"
|
||||
DATETIME = "DATETIME"
|
||||
|
||||
|
||||
class ColumnDefinition(BaseModel):
|
||||
type: ColumnType
|
||||
primary_key: bool = False
|
||||
nullable: bool = True
|
||||
default: Any = None
|
||||
|
||||
|
||||
class SqlStore(Protocol):
|
||||
"""
|
||||
A protocol for a SQL store.
|
||||
"""
|
||||
|
||||
async def create_table(self, table: str, schema: Mapping[str, ColumnType | ColumnDefinition]) -> None:
|
||||
"""
|
||||
Create a table.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def insert(self, table: str, data: Mapping[str, Any]) -> None:
|
||||
"""
|
||||
Insert a row into a table.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def fetch_all(
|
||||
self,
|
||||
table: str,
|
||||
where: Mapping[str, Any] | None = None,
|
||||
limit: int | None = None,
|
||||
order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Fetch all rows from a table.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def fetch_one(
|
||||
self,
|
||||
table: str,
|
||||
where: Mapping[str, Any] | None = None,
|
||||
order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""
|
||||
Fetch one row from a table.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def update(
|
||||
self,
|
||||
table: str,
|
||||
data: Mapping[str, Any],
|
||||
where: Mapping[str, Any],
|
||||
) -> None:
|
||||
"""
|
||||
Update a row in a table.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def delete(
|
||||
self,
|
||||
table: str,
|
||||
where: Mapping[str, Any],
|
||||
) -> None:
|
||||
"""
|
||||
Delete a row from a table.
|
||||
"""
|
||||
pass
|
||||
161
llama_stack/providers/utils/sqlstore/sqlite/sqlite.py
Normal file
161
llama_stack/providers/utils/sqlstore/sqlite/sqlite.py
Normal file
|
|
@ -0,0 +1,161 @@
|
|||
# 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 collections.abc import Mapping
|
||||
from typing import Any, Literal
|
||||
|
||||
from sqlalchemy import (
|
||||
JSON,
|
||||
Boolean,
|
||||
Column,
|
||||
DateTime,
|
||||
Float,
|
||||
Integer,
|
||||
MetaData,
|
||||
String,
|
||||
Table,
|
||||
Text,
|
||||
select,
|
||||
)
|
||||
from sqlalchemy.ext.asyncio import create_async_engine
|
||||
|
||||
from ..api import ColumnDefinition, ColumnType, SqlStore
|
||||
from ..sqlstore import SqliteSqlStoreConfig
|
||||
|
||||
TYPE_MAPPING: dict[ColumnType, Any] = {
|
||||
ColumnType.INTEGER: Integer,
|
||||
ColumnType.STRING: String,
|
||||
ColumnType.FLOAT: Float,
|
||||
ColumnType.BOOLEAN: Boolean,
|
||||
ColumnType.DATETIME: DateTime,
|
||||
ColumnType.TEXT: Text,
|
||||
ColumnType.JSON: JSON,
|
||||
}
|
||||
|
||||
|
||||
class SqliteSqlStoreImpl(SqlStore):
|
||||
def __init__(self, config: SqliteSqlStoreConfig):
|
||||
self.engine = create_async_engine(config.engine_str)
|
||||
self.metadata = MetaData()
|
||||
|
||||
async def create_table(
|
||||
self,
|
||||
table: str,
|
||||
schema: Mapping[str, ColumnType | ColumnDefinition],
|
||||
) -> None:
|
||||
if not schema:
|
||||
raise ValueError(f"No columns defined for table '{table}'.")
|
||||
|
||||
sqlalchemy_columns: list[Column] = []
|
||||
|
||||
for col_name, col_props in schema.items():
|
||||
col_type = None
|
||||
is_primary_key = False
|
||||
is_nullable = True # Default to nullable
|
||||
|
||||
if isinstance(col_props, ColumnType):
|
||||
col_type = col_props
|
||||
elif isinstance(col_props, ColumnDefinition):
|
||||
col_type = col_props.type
|
||||
is_primary_key = col_props.primary_key
|
||||
is_nullable = col_props.nullable
|
||||
|
||||
sqlalchemy_type = TYPE_MAPPING.get(col_type)
|
||||
if not sqlalchemy_type:
|
||||
raise ValueError(f"Unsupported column type '{col_type}' for column '{col_name}'.")
|
||||
|
||||
sqlalchemy_columns.append(
|
||||
Column(col_name, sqlalchemy_type, primary_key=is_primary_key, nullable=is_nullable)
|
||||
)
|
||||
|
||||
# Check if table already exists in metadata, otherwise define it
|
||||
if table not in self.metadata.tables:
|
||||
sqlalchemy_table = Table(table, self.metadata, *sqlalchemy_columns)
|
||||
else:
|
||||
sqlalchemy_table = self.metadata.tables[table]
|
||||
|
||||
# Create the table in the database if it doesn't exist
|
||||
# checkfirst=True ensures it doesn't try to recreate if it's already there
|
||||
async with self.engine.begin() as conn:
|
||||
await conn.run_sync(self.metadata.create_all, tables=[sqlalchemy_table], checkfirst=True)
|
||||
|
||||
async def insert(self, table: str, data: Mapping[str, Any]) -> None:
|
||||
async with self.engine.begin() as conn:
|
||||
await conn.execute(self.metadata.tables[table].insert(), data)
|
||||
await conn.commit()
|
||||
|
||||
async def fetch_all(
|
||||
self,
|
||||
table: str,
|
||||
where: Mapping[str, Any] | None = None,
|
||||
limit: int | None = None,
|
||||
order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
async with self.engine.begin() as conn:
|
||||
query = select(self.metadata.tables[table])
|
||||
if where:
|
||||
for key, value in where.items():
|
||||
query = query.where(self.metadata.tables[table].c[key] == value)
|
||||
if limit:
|
||||
query = query.limit(limit)
|
||||
if order_by:
|
||||
if not isinstance(order_by, list):
|
||||
raise ValueError(
|
||||
f"order_by must be a list of tuples (column, order={['asc', 'desc']}), got {order_by}"
|
||||
)
|
||||
for order in order_by:
|
||||
if not isinstance(order, tuple):
|
||||
raise ValueError(
|
||||
f"order_by must be a list of tuples (column, order={['asc', 'desc']}), got {order_by}"
|
||||
)
|
||||
name, order_type = order
|
||||
if order_type == "asc":
|
||||
query = query.order_by(self.metadata.tables[table].c[name].asc())
|
||||
elif order_type == "desc":
|
||||
query = query.order_by(self.metadata.tables[table].c[name].desc())
|
||||
else:
|
||||
raise ValueError(f"Invalid order '{order_type}' for column '{name}'")
|
||||
result = await conn.execute(query)
|
||||
if result.rowcount == 0:
|
||||
return []
|
||||
return [dict(row._mapping) for row in result]
|
||||
|
||||
async def fetch_one(
|
||||
self,
|
||||
table: str,
|
||||
where: Mapping[str, Any] | None = None,
|
||||
order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
rows = await self.fetch_all(table, where, limit=1, order_by=order_by)
|
||||
if not rows:
|
||||
return None
|
||||
return rows[0]
|
||||
|
||||
async def update(
|
||||
self,
|
||||
table: str,
|
||||
data: Mapping[str, Any],
|
||||
where: Mapping[str, Any],
|
||||
) -> None:
|
||||
if not where:
|
||||
raise ValueError("where is required for update")
|
||||
|
||||
async with self.engine.begin() as conn:
|
||||
stmt = self.metadata.tables[table].update()
|
||||
for key, value in where.items():
|
||||
stmt = stmt.where(self.metadata.tables[table].c[key] == value)
|
||||
await conn.execute(stmt, data)
|
||||
await conn.commit()
|
||||
|
||||
async def delete(self, table: str, where: Mapping[str, Any]) -> None:
|
||||
if not where:
|
||||
raise ValueError("where is required for delete")
|
||||
|
||||
async with self.engine.begin() as conn:
|
||||
stmt = self.metadata.tables[table].delete()
|
||||
for key, value in where.items():
|
||||
stmt = stmt.where(self.metadata.tables[table].c[key] == value)
|
||||
await conn.execute(stmt)
|
||||
await conn.commit()
|
||||
72
llama_stack/providers/utils/sqlstore/sqlstore.py
Normal file
72
llama_stack/providers/utils/sqlstore/sqlstore.py
Normal file
|
|
@ -0,0 +1,72 @@
|
|||
# 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 enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Annotated, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
|
||||
|
||||
from .api import SqlStore
|
||||
|
||||
|
||||
class SqlStoreType(Enum):
|
||||
sqlite = "sqlite"
|
||||
postgres = "postgres"
|
||||
|
||||
|
||||
class SqliteSqlStoreConfig(BaseModel):
|
||||
type: Literal["sqlite"] = SqlStoreType.sqlite.value
|
||||
db_path: str = Field(
|
||||
default=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
|
||||
description="Database path, e.g. ~/.llama/distributions/ollama/sqlstore.db",
|
||||
)
|
||||
|
||||
@property
|
||||
def engine_str(self) -> str:
|
||||
return "sqlite+aiosqlite:///" + Path(self.db_path).expanduser().as_posix()
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(cls, __distro_dir__: str, db_name: str = "sqlstore.db"):
|
||||
return cls(
|
||||
type="sqlite",
|
||||
db_path="${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + db_name,
|
||||
)
|
||||
|
||||
# TODO: move this when we have a better way to specify dependencies with internal APIs
|
||||
@property
|
||||
def pip_packages(self) -> list[str]:
|
||||
return ["sqlalchemy[asyncio]"]
|
||||
|
||||
|
||||
class PostgresSqlStoreConfig(BaseModel):
|
||||
type: Literal["postgres"] = SqlStoreType.postgres.value
|
||||
|
||||
@property
|
||||
def pip_packages(self) -> list[str]:
|
||||
raise NotImplementedError("Postgres is not implemented yet")
|
||||
|
||||
|
||||
SqlStoreConfig = Annotated[
|
||||
SqliteSqlStoreConfig | PostgresSqlStoreConfig,
|
||||
Field(discriminator="type", default=SqlStoreType.sqlite.value),
|
||||
]
|
||||
|
||||
|
||||
def sqlstore_impl(config: SqlStoreConfig) -> SqlStore:
|
||||
if config.type == SqlStoreType.sqlite.value:
|
||||
from .sqlite.sqlite import SqliteSqlStoreImpl
|
||||
|
||||
impl = SqliteSqlStoreImpl(config)
|
||||
elif config.type == SqlStoreType.postgres.value:
|
||||
raise NotImplementedError("Postgres is not implemented yet")
|
||||
else:
|
||||
raise ValueError(f"Unknown sqlstore type {config.type}")
|
||||
|
||||
return impl
|
||||
|
|
@ -29,3 +29,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -96,6 +96,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta.llama3-1-8b-instruct-v1:0
|
||||
|
|
|
|||
|
|
@ -29,3 +29,5 @@ distribution_spec:
|
|||
- remote::tavily-search
|
||||
- inline::rag-runtime
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -99,6 +99,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: llama3.1-8b
|
||||
|
|
|
|||
|
|
@ -30,3 +30,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -99,6 +99,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
|
||||
|
|
|
|||
|
|
@ -30,3 +30,6 @@ distribution_spec:
|
|||
- remote::tavily-search
|
||||
- inline::rag-runtime
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -99,6 +99,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -95,6 +95,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -31,6 +31,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -67,6 +68,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -105,6 +107,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"sqlite-vec",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -145,6 +148,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -184,6 +188,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -221,6 +226,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -259,6 +265,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -297,6 +304,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -335,6 +343,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"sqlite-vec",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -379,6 +388,7 @@
|
|||
"scipy",
|
||||
"sentence-transformers",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"torch",
|
||||
"torchao==0.8.0",
|
||||
"torchvision",
|
||||
|
|
@ -414,6 +424,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"uvicorn"
|
||||
|
|
@ -452,6 +463,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"torch",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -490,6 +502,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"sqlite-vec",
|
||||
"together",
|
||||
"tqdm",
|
||||
|
|
@ -528,6 +541,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -566,6 +580,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -599,6 +614,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"uvicorn",
|
||||
|
|
@ -637,6 +653,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"sqlite-vec",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -678,6 +695,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -716,6 +734,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"together",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -755,6 +774,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"sqlite-vec",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
|
|
@ -794,6 +814,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
@ -833,6 +854,7 @@
|
|||
"scikit-learn",
|
||||
"scipy",
|
||||
"sentencepiece",
|
||||
"sqlalchemy[asyncio]",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
|
|
|
|||
|
|
@ -31,3 +31,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -111,6 +111,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
|
||||
|
|
|
|||
|
|
@ -106,6 +106,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
|
||||
|
|
|
|||
|
|
@ -26,3 +26,5 @@ distribution_spec:
|
|||
- remote::tavily-search
|
||||
- inline::rag-runtime
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -99,6 +99,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: groq/llama3-8b-8192
|
||||
|
|
|
|||
|
|
@ -29,3 +29,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -107,6 +107,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -102,6 +102,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -30,3 +30,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -107,6 +107,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -102,6 +102,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -30,3 +30,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -111,6 +111,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: Llama-3.3-70B-Instruct
|
||||
|
|
|
|||
|
|
@ -29,3 +29,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -117,6 +117,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -107,6 +107,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -24,3 +24,6 @@ distribution_spec:
|
|||
tool_runtime:
|
||||
- inline::rag-runtime
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -92,6 +92,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -80,6 +80,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta/llama3-8b-instruct
|
||||
|
|
|
|||
|
|
@ -32,3 +32,6 @@ distribution_spec:
|
|||
- remote::model-context-protocol
|
||||
- remote::wolfram-alpha
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -112,6 +112,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -110,6 +110,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -33,3 +33,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -125,6 +125,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: openai/gpt-4o
|
||||
|
|
|
|||
|
|
@ -31,3 +31,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -111,6 +111,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-8B-Instruct
|
||||
|
|
|
|||
|
|
@ -106,6 +106,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-8B-Instruct
|
||||
|
|
|
|||
|
|
@ -31,3 +31,6 @@ distribution_spec:
|
|||
- remote::model-context-protocol
|
||||
- remote::wolfram-alpha
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -115,6 +115,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -108,6 +108,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -22,3 +22,5 @@ distribution_spec:
|
|||
- remote::model-context-protocol
|
||||
- remote::wolfram-alpha
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -82,6 +82,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: sambanova/Meta-Llama-3.1-8B-Instruct
|
||||
|
|
|
|||
|
|
@ -35,3 +35,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -133,6 +133,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: openai/gpt-4o
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ from llama_stack.distribution.distribution import get_provider_registry
|
|||
from llama_stack.distribution.utils.dynamic import instantiate_class_type
|
||||
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
|
||||
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
|
||||
|
||||
def get_model_registry(
|
||||
|
|
@ -117,6 +118,10 @@ class RunConfigSettings(BaseModel):
|
|||
__distro_dir__=f"~/.llama/distributions/{name}",
|
||||
db_name="registry.db",
|
||||
),
|
||||
inference_store=SqliteSqlStoreConfig.sample_run_config(
|
||||
__distro_dir__=f"~/.llama/distributions/{name}",
|
||||
db_name="inference_store.db",
|
||||
),
|
||||
models=self.default_models or [],
|
||||
shields=self.default_shields or [],
|
||||
tool_groups=self.default_tool_groups or [],
|
||||
|
|
@ -146,14 +151,20 @@ class DistributionTemplate(BaseModel):
|
|||
available_models_by_provider: dict[str, list[ProviderModelEntry]] | None = None
|
||||
|
||||
def build_config(self) -> BuildConfig:
|
||||
additional_pip_packages: list[str] = []
|
||||
for run_config in self.run_configs.values():
|
||||
run_config_ = run_config.run_config(self.name, self.providers, self.container_image)
|
||||
if run_config_.inference_store:
|
||||
additional_pip_packages.extend(run_config_.inference_store.pip_packages)
|
||||
|
||||
return BuildConfig(
|
||||
name=self.name,
|
||||
distribution_spec=DistributionSpec(
|
||||
description=self.description,
|
||||
container_image=self.container_image,
|
||||
providers=self.providers,
|
||||
),
|
||||
image_type="conda", # default to conda, can be overridden
|
||||
additional_pip_packages=additional_pip_packages,
|
||||
)
|
||||
|
||||
def generate_markdown_docs(self) -> str:
|
||||
|
|
|
|||
|
|
@ -30,3 +30,6 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -102,6 +102,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -101,6 +101,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -31,3 +31,6 @@ distribution_spec:
|
|||
- remote::model-context-protocol
|
||||
- remote::wolfram-alpha
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -111,6 +111,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
|
||||
|
|
|
|||
|
|
@ -106,6 +106,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
|
||||
|
|
|
|||
|
|
@ -35,3 +35,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -135,6 +135,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: openai/gpt-4o
|
||||
|
|
|
|||
|
|
@ -30,3 +30,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -106,6 +106,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
|
|
|
|||
|
|
@ -28,3 +28,5 @@ distribution_spec:
|
|||
- inline::rag-runtime
|
||||
- remote::model-context-protocol
|
||||
image_type: conda
|
||||
additional_pip_packages:
|
||||
- sqlalchemy[asyncio]
|
||||
|
|
|
|||
|
|
@ -103,6 +103,9 @@ providers:
|
|||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: meta-llama/llama-3-3-70b-instruct
|
||||
|
|
|
|||
|
|
@ -70,6 +70,8 @@ unit = [
|
|||
"chardet",
|
||||
"qdrant-client",
|
||||
"opentelemetry-exporter-otlp-proto-http",
|
||||
"sqlalchemy",
|
||||
"sqlalchemy[asyncio]>=2.0.41",
|
||||
]
|
||||
# These are the core dependencies required for running integration tests. They are shared across all
|
||||
# providers. If a provider requires additional dependencies, please add them to your environment
|
||||
|
|
|
|||
|
|
@ -222,3 +222,105 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex
|
|||
streamed_content.append(chunk.choices[0].delta.content.lower().strip())
|
||||
assert len(streamed_content) > 0
|
||||
assert expected.lower() in "".join(streamed_content)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"stream",
|
||||
[
|
||||
True,
|
||||
False,
|
||||
],
|
||||
)
|
||||
def test_inference_store(openai_client, client_with_models, text_model_id, stream):
|
||||
skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id)
|
||||
client = openai_client
|
||||
# make a chat completion
|
||||
message = "Hello, world!"
|
||||
response = client.chat.completions.create(
|
||||
model=text_model_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": message,
|
||||
}
|
||||
],
|
||||
stream=stream,
|
||||
)
|
||||
if stream:
|
||||
# accumulate the streamed content
|
||||
content = ""
|
||||
response_id = None
|
||||
for chunk in response:
|
||||
if response_id is None:
|
||||
response_id = chunk.id
|
||||
content += chunk.choices[0].delta.content
|
||||
else:
|
||||
response_id = response.id
|
||||
content = response.choices[0].message.content
|
||||
|
||||
responses = client.chat.completions.list()
|
||||
assert response_id in [r.id for r in responses.data]
|
||||
|
||||
retrieved_response = client.chat.completions.retrieve(response_id)
|
||||
assert retrieved_response.id == response_id
|
||||
assert retrieved_response.input_messages[0]["content"] == message
|
||||
assert retrieved_response.choices[0].message.content == content
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"stream",
|
||||
[
|
||||
True,
|
||||
False,
|
||||
],
|
||||
)
|
||||
def test_inference_store_tool_calls(openai_client, client_with_models, text_model_id, stream):
|
||||
skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id)
|
||||
client = openai_client
|
||||
# make a chat completion
|
||||
message = "What's the weather in Tokyo? Use the get_weather function to get the weather."
|
||||
response = client.chat.completions.create(
|
||||
model=text_model_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": message,
|
||||
}
|
||||
],
|
||||
stream=stream,
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the weather in a given city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {"type": "string", "description": "The city to get the weather for"},
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
)
|
||||
if stream:
|
||||
# accumulate the streamed content
|
||||
content = ""
|
||||
response_id = None
|
||||
for chunk in response:
|
||||
if response_id is None:
|
||||
response_id = chunk.id
|
||||
content += chunk.choices[0].delta.content
|
||||
else:
|
||||
response_id = response.id
|
||||
content = response.choices[0].message.content
|
||||
|
||||
responses = client.chat.completions.list()
|
||||
assert response_id in [r.id for r in responses.data]
|
||||
|
||||
retrieved_response = client.chat.completions.retrieve(response_id)
|
||||
assert retrieved_response.id == response_id
|
||||
assert retrieved_response.input_messages[0]["content"] == message
|
||||
assert retrieved_response.choices[0].message.tool_calls[0].function.name == "get_weather"
|
||||
assert retrieved_response.choices[0].message.tool_calls[0].function.arguments == '{"city":"Tokyo"}'
|
||||
|
|
|
|||
62
tests/unit/utils/test_sqlstore.py
Normal file
62
tests/unit/utils/test_sqlstore.py
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
# 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 tempfile import TemporaryDirectory
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.providers.utils.sqlstore.api import ColumnType
|
||||
from llama_stack.providers.utils.sqlstore.sqlite.sqlite import SqliteSqlStoreImpl
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sqlite_sqlstore():
|
||||
with TemporaryDirectory() as tmp_dir:
|
||||
db_name = "test.db"
|
||||
sqlstore = SqliteSqlStoreImpl(
|
||||
SqliteSqlStoreConfig(
|
||||
db_path=tmp_dir + "/" + db_name,
|
||||
)
|
||||
)
|
||||
await sqlstore.create_table(
|
||||
table="test",
|
||||
schema={
|
||||
"id": ColumnType.INTEGER,
|
||||
"name": ColumnType.STRING,
|
||||
},
|
||||
)
|
||||
await sqlstore.insert("test", {"id": 1, "name": "test"})
|
||||
await sqlstore.insert("test", {"id": 12, "name": "test12"})
|
||||
rows = await sqlstore.fetch_all("test")
|
||||
assert rows == [{"id": 1, "name": "test"}, {"id": 12, "name": "test12"}]
|
||||
|
||||
row = await sqlstore.fetch_one("test", {"id": 1})
|
||||
assert row == {"id": 1, "name": "test"}
|
||||
|
||||
row = await sqlstore.fetch_one("test", {"name": "test12"})
|
||||
assert row == {"id": 12, "name": "test12"}
|
||||
|
||||
# order by
|
||||
rows = await sqlstore.fetch_all("test", order_by=[("id", "asc")])
|
||||
assert rows == [{"id": 1, "name": "test"}, {"id": 12, "name": "test12"}]
|
||||
|
||||
rows = await sqlstore.fetch_all("test", order_by=[("id", "desc")])
|
||||
assert rows == [{"id": 12, "name": "test12"}, {"id": 1, "name": "test"}]
|
||||
|
||||
# limit
|
||||
rows = await sqlstore.fetch_all("test", limit=1)
|
||||
assert rows == [{"id": 1, "name": "test"}]
|
||||
|
||||
# update
|
||||
await sqlstore.update("test", {"name": "test123"}, {"id": 1})
|
||||
row = await sqlstore.fetch_one("test", {"id": 1})
|
||||
assert row == {"id": 1, "name": "test123"}
|
||||
|
||||
# delete
|
||||
await sqlstore.delete("test", {"id": 1})
|
||||
rows = await sqlstore.fetch_all("test")
|
||||
assert rows == [{"id": 12, "name": "test12"}]
|
||||
107
uv.lock
generated
107
uv.lock
generated
|
|
@ -1,5 +1,4 @@
|
|||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
|
|
@ -875,6 +874,58 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/89/30/2bd0eb03a7dee7727cd2ec643d1e992979e62d5e7443507381cce0455132/googleapis_common_protos-1.67.0-py2.py3-none-any.whl", hash = "sha256:579de760800d13616f51cf8be00c876f00a9f146d3e6510e19d1f4111758b741", size = 164985 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "greenlet"
|
||||
version = "3.2.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/34/c1/a82edae11d46c0d83481aacaa1e578fea21d94a1ef400afd734d47ad95ad/greenlet-3.2.2.tar.gz", hash = "sha256:ad053d34421a2debba45aa3cc39acf454acbcd025b3fc1a9f8a0dee237abd485", size = 185797 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/05/66/910217271189cc3f32f670040235f4bf026ded8ca07270667d69c06e7324/greenlet-3.2.2-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:c49e9f7c6f625507ed83a7485366b46cbe325717c60837f7244fc99ba16ba9d6", size = 267395 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a8/36/8d812402ca21017c82880f399309afadb78a0aa300a9b45d741e4df5d954/greenlet-3.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3cc1a3ed00ecfea8932477f729a9f616ad7347a5e55d50929efa50a86cb7be7", size = 625742 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7b/77/66d7b59dfb7cc1102b2f880bc61cb165ee8998c9ec13c96606ba37e54c77/greenlet-3.2.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7c9896249fbef2c615853b890ee854f22c671560226c9221cfd27c995db97e5c", size = 637014 },
|
||||
{ url = "https://files.pythonhosted.org/packages/36/a7/ff0d408f8086a0d9a5aac47fa1b33a040a9fca89bd5a3f7b54d1cd6e2793/greenlet-3.2.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7409796591d879425997a518138889d8d17e63ada7c99edc0d7a1c22007d4907", size = 632874 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a1/75/1dc2603bf8184da9ebe69200849c53c3c1dca5b3a3d44d9f5ca06a930550/greenlet-3.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7791dcb496ec53d60c7f1c78eaa156c21f402dda38542a00afc3e20cae0f480f", size = 631652 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7b/74/ddc8c3bd4c2c20548e5bf2b1d2e312a717d44e2eca3eadcfc207b5f5ad80/greenlet-3.2.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d8009ae46259e31bc73dc183e402f548e980c96f33a6ef58cc2e7865db012e13", size = 580619 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/f2/40f26d7b3077b1c7ae7318a4de1f8ffc1d8ccbad8f1d8979bf5080250fd6/greenlet-3.2.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:fd9fb7c941280e2c837b603850efc93c999ae58aae2b40765ed682a6907ebbc5", size = 1109809 },
|
||||
{ url = "https://files.pythonhosted.org/packages/c5/21/9329e8c276746b0d2318b696606753f5e7b72d478adcf4ad9a975521ea5f/greenlet-3.2.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:00cd814b8959b95a546e47e8d589610534cfb71f19802ea8a2ad99d95d702057", size = 1133455 },
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/1e/0dca9619dbd736d6981f12f946a497ec21a0ea27262f563bca5729662d4d/greenlet-3.2.2-cp310-cp310-win_amd64.whl", hash = "sha256:d0cb7d47199001de7658c213419358aa8937df767936506db0db7ce1a71f4a2f", size = 294991 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a3/9f/a47e19261747b562ce88219e5ed8c859d42c6e01e73da6fbfa3f08a7be13/greenlet-3.2.2-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:dcb9cebbf3f62cb1e5afacae90761ccce0effb3adaa32339a0670fe7805d8068", size = 268635 },
|
||||
{ url = "https://files.pythonhosted.org/packages/11/80/a0042b91b66975f82a914d515e81c1944a3023f2ce1ed7a9b22e10b46919/greenlet-3.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bf3fc9145141250907730886b031681dfcc0de1c158f3cc51c092223c0f381ce", size = 628786 },
|
||||
{ url = "https://files.pythonhosted.org/packages/38/a2/8336bf1e691013f72a6ebab55da04db81a11f68e82bb691f434909fa1327/greenlet-3.2.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:efcdfb9df109e8a3b475c016f60438fcd4be68cd13a365d42b35914cdab4bb2b", size = 640866 },
|
||||
{ url = "https://files.pythonhosted.org/packages/f8/7e/f2a3a13e424670a5d08826dab7468fa5e403e0fbe0b5f951ff1bc4425b45/greenlet-3.2.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4bd139e4943547ce3a56ef4b8b1b9479f9e40bb47e72cc906f0f66b9d0d5cab3", size = 636752 },
|
||||
{ url = "https://files.pythonhosted.org/packages/fd/5d/ce4a03a36d956dcc29b761283f084eb4a3863401c7cb505f113f73af8774/greenlet-3.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71566302219b17ca354eb274dfd29b8da3c268e41b646f330e324e3967546a74", size = 636028 },
|
||||
{ url = "https://files.pythonhosted.org/packages/4b/29/b130946b57e3ceb039238413790dd3793c5e7b8e14a54968de1fe449a7cf/greenlet-3.2.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3091bc45e6b0c73f225374fefa1536cd91b1e987377b12ef5b19129b07d93ebe", size = 583869 },
|
||||
{ url = "https://files.pythonhosted.org/packages/ac/30/9f538dfe7f87b90ecc75e589d20cbd71635531a617a336c386d775725a8b/greenlet-3.2.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:44671c29da26539a5f142257eaba5110f71887c24d40df3ac87f1117df589e0e", size = 1112886 },
|
||||
{ url = "https://files.pythonhosted.org/packages/be/92/4b7deeb1a1e9c32c1b59fdca1cac3175731c23311ddca2ea28a8b6ada91c/greenlet-3.2.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c23ea227847c9dbe0b3910f5c0dd95658b607137614eb821e6cbaecd60d81cc6", size = 1138355 },
|
||||
{ url = "https://files.pythonhosted.org/packages/c5/eb/7551c751a2ea6498907b2fcbe31d7a54b602ba5e8eb9550a9695ca25d25c/greenlet-3.2.2-cp311-cp311-win_amd64.whl", hash = "sha256:0a16fb934fcabfdfacf21d79e6fed81809d8cd97bc1be9d9c89f0e4567143d7b", size = 295437 },
|
||||
{ url = "https://files.pythonhosted.org/packages/2c/a1/88fdc6ce0df6ad361a30ed78d24c86ea32acb2b563f33e39e927b1da9ea0/greenlet-3.2.2-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:df4d1509efd4977e6a844ac96d8be0b9e5aa5d5c77aa27ca9f4d3f92d3fcf330", size = 270413 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a6/2e/6c1caffd65490c68cd9bcec8cb7feb8ac7b27d38ba1fea121fdc1f2331dc/greenlet-3.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da956d534a6d1b9841f95ad0f18ace637668f680b1339ca4dcfb2c1837880a0b", size = 637242 },
|
||||
{ url = "https://files.pythonhosted.org/packages/98/28/088af2cedf8823b6b7ab029a5626302af4ca1037cf8b998bed3a8d3cb9e2/greenlet-3.2.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9c7b15fb9b88d9ee07e076f5a683027bc3befd5bb5d25954bb633c385d8b737e", size = 651444 },
|
||||
{ url = "https://files.pythonhosted.org/packages/4a/9f/0116ab876bb0bc7a81eadc21c3f02cd6100dcd25a1cf2a085a130a63a26a/greenlet-3.2.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:752f0e79785e11180ebd2e726c8a88109ded3e2301d40abced2543aa5d164275", size = 646067 },
|
||||
{ url = "https://files.pythonhosted.org/packages/35/17/bb8f9c9580e28a94a9575da847c257953d5eb6e39ca888239183320c1c28/greenlet-3.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ae572c996ae4b5e122331e12bbb971ea49c08cc7c232d1bd43150800a2d6c65", size = 648153 },
|
||||
{ url = "https://files.pythonhosted.org/packages/2c/ee/7f31b6f7021b8df6f7203b53b9cc741b939a2591dcc6d899d8042fcf66f2/greenlet-3.2.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02f5972ff02c9cf615357c17ab713737cccfd0eaf69b951084a9fd43f39833d3", size = 603865 },
|
||||
{ url = "https://files.pythonhosted.org/packages/b5/2d/759fa59323b521c6f223276a4fc3d3719475dc9ae4c44c2fe7fc750f8de0/greenlet-3.2.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:4fefc7aa68b34b9224490dfda2e70ccf2131368493add64b4ef2d372955c207e", size = 1119575 },
|
||||
{ url = "https://files.pythonhosted.org/packages/30/05/356813470060bce0e81c3df63ab8cd1967c1ff6f5189760c1a4734d405ba/greenlet-3.2.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a31ead8411a027c2c4759113cf2bd473690517494f3d6e4bf67064589afcd3c5", size = 1147460 },
|
||||
{ url = "https://files.pythonhosted.org/packages/07/f4/b2a26a309a04fb844c7406a4501331b9400e1dd7dd64d3450472fd47d2e1/greenlet-3.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:b24c7844c0a0afc3ccbeb0b807adeefb7eff2b5599229ecedddcfeb0ef333bec", size = 296239 },
|
||||
{ url = "https://files.pythonhosted.org/packages/89/30/97b49779fff8601af20972a62cc4af0c497c1504dfbb3e93be218e093f21/greenlet-3.2.2-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:3ab7194ee290302ca15449f601036007873028712e92ca15fc76597a0aeb4c59", size = 269150 },
|
||||
{ url = "https://files.pythonhosted.org/packages/21/30/877245def4220f684bc2e01df1c2e782c164e84b32e07373992f14a2d107/greenlet-3.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2dc5c43bb65ec3669452af0ab10729e8fdc17f87a1f2ad7ec65d4aaaefabf6bf", size = 637381 },
|
||||
{ url = "https://files.pythonhosted.org/packages/8e/16/adf937908e1f913856b5371c1d8bdaef5f58f251d714085abeea73ecc471/greenlet-3.2.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:decb0658ec19e5c1f519faa9a160c0fc85a41a7e6654b3ce1b44b939f8bf1325", size = 651427 },
|
||||
{ url = "https://files.pythonhosted.org/packages/ad/49/6d79f58fa695b618654adac64e56aff2eeb13344dc28259af8f505662bb1/greenlet-3.2.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6fadd183186db360b61cb34e81117a096bff91c072929cd1b529eb20dd46e6c5", size = 645795 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5a/e6/28ed5cb929c6b2f001e96b1d0698c622976cd8f1e41fe7ebc047fa7c6dd4/greenlet-3.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1919cbdc1c53ef739c94cf2985056bcc0838c1f217b57647cbf4578576c63825", size = 648398 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9d/70/b200194e25ae86bc57077f695b6cc47ee3118becf54130c5514456cf8dac/greenlet-3.2.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3885f85b61798f4192d544aac7b25a04ece5fe2704670b4ab73c2d2c14ab740d", size = 606795 },
|
||||
{ url = "https://files.pythonhosted.org/packages/f8/c8/ba1def67513a941154ed8f9477ae6e5a03f645be6b507d3930f72ed508d3/greenlet-3.2.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:85f3e248507125bf4af607a26fd6cb8578776197bd4b66e35229cdf5acf1dfbf", size = 1117976 },
|
||||
{ url = "https://files.pythonhosted.org/packages/c3/30/d0e88c1cfcc1b3331d63c2b54a0a3a4a950ef202fb8b92e772ca714a9221/greenlet-3.2.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:1e76106b6fc55fa3d6fe1c527f95ee65e324a13b62e243f77b48317346559708", size = 1145509 },
|
||||
{ url = "https://files.pythonhosted.org/packages/90/2e/59d6491834b6e289051b252cf4776d16da51c7c6ca6a87ff97e3a50aa0cd/greenlet-3.2.2-cp313-cp313-win_amd64.whl", hash = "sha256:fe46d4f8e94e637634d54477b0cfabcf93c53f29eedcbdeecaf2af32029b4421", size = 296023 },
|
||||
{ url = "https://files.pythonhosted.org/packages/65/66/8a73aace5a5335a1cba56d0da71b7bd93e450f17d372c5b7c5fa547557e9/greenlet-3.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba30e88607fb6990544d84caf3c706c4b48f629e18853fc6a646f82db9629418", size = 629911 },
|
||||
{ url = "https://files.pythonhosted.org/packages/48/08/c8b8ebac4e0c95dcc68ec99198842e7db53eda4ab3fb0a4e785690883991/greenlet-3.2.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:055916fafad3e3388d27dd68517478933a97edc2fc54ae79d3bec827de2c64c4", size = 635251 },
|
||||
{ url = "https://files.pythonhosted.org/packages/37/26/7db30868f73e86b9125264d2959acabea132b444b88185ba5c462cb8e571/greenlet-3.2.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2593283bf81ca37d27d110956b79e8723f9aa50c4bcdc29d3c0543d4743d2763", size = 632620 },
|
||||
{ url = "https://files.pythonhosted.org/packages/10/ec/718a3bd56249e729016b0b69bee4adea0dfccf6ca43d147ef3b21edbca16/greenlet-3.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89c69e9a10670eb7a66b8cef6354c24671ba241f46152dd3eed447f79c29fb5b", size = 628851 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/9d/d1c79286a76bc62ccdc1387291464af16a4204ea717f24e77b0acd623b99/greenlet-3.2.2-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02a98600899ca1ca5d3a2590974c9e3ec259503b2d6ba6527605fcd74e08e207", size = 593718 },
|
||||
{ url = "https://files.pythonhosted.org/packages/cd/41/96ba2bf948f67b245784cd294b84e3d17933597dffd3acdb367a210d1949/greenlet-3.2.2-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:b50a8c5c162469c3209e5ec92ee4f95c8231b11db6a04db09bbe338176723bb8", size = 1105752 },
|
||||
{ url = "https://files.pythonhosted.org/packages/68/3b/3b97f9d33c1f2eb081759da62bd6162159db260f602f048bc2f36b4c453e/greenlet-3.2.2-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:45f9f4853fb4cc46783085261c9ec4706628f3b57de3e68bae03e8f8b3c0de51", size = 1125170 },
|
||||
{ url = "https://files.pythonhosted.org/packages/31/df/b7d17d66c8d0f578d2885a3d8f565e9e4725eacc9d3fdc946d0031c055c4/greenlet-3.2.2-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:9ea5231428af34226c05f927e16fc7f6fa5e39e3ad3cd24ffa48ba53a47f4240", size = 269899 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.71.0"
|
||||
|
|
@ -1495,6 +1546,7 @@ unit = [
|
|||
{ name = "opentelemetry-exporter-otlp-proto-http" },
|
||||
{ name = "pypdf" },
|
||||
{ name = "qdrant-client" },
|
||||
{ name = "sqlalchemy", extra = ["asyncio"] },
|
||||
{ name = "sqlite-vec" },
|
||||
]
|
||||
|
||||
|
|
@ -1564,6 +1616,8 @@ requires-dist = [
|
|||
{ name = "sphinxcontrib-openapi", marker = "extra == 'docs'" },
|
||||
{ name = "sphinxcontrib-redoc", marker = "extra == 'docs'" },
|
||||
{ name = "sphinxcontrib-video", marker = "extra == 'docs'" },
|
||||
{ name = "sqlalchemy", marker = "extra == 'unit'" },
|
||||
{ name = "sqlalchemy", extras = ["asyncio"], marker = "extra == 'unit'", specifier = ">=2.0.41" },
|
||||
{ name = "sqlite-vec", marker = "extra == 'unit'" },
|
||||
{ name = "streamlit", marker = "extra == 'ui'" },
|
||||
{ name = "streamlit-option-menu", marker = "extra == 'ui'" },
|
||||
|
|
@ -1577,7 +1631,6 @@ requires-dist = [
|
|||
{ name = "types-setuptools", marker = "extra == 'dev'" },
|
||||
{ name = "uvicorn", marker = "extra == 'dev'" },
|
||||
]
|
||||
provides-extras = ["dev", "unit", "test", "docs", "codegen", "ui"]
|
||||
|
||||
[[package]]
|
||||
name = "llama-stack-client"
|
||||
|
|
@ -3748,6 +3801,56 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/5d/8b/a0271fe65357860ccc52168181891e9fc9d354bfdc9be273e6a77b84f905/sphinxcontrib_video-0.4.1-py3-none-any.whl", hash = "sha256:d63ec68983dac36960557973281a616b5d9e68838369763313fc80533b1ad774", size = 10066 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sqlalchemy"
|
||||
version = "2.0.41"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "greenlet", marker = "(python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/63/66/45b165c595ec89aa7dcc2c1cd222ab269bc753f1fc7a1e68f8481bd957bf/sqlalchemy-2.0.41.tar.gz", hash = "sha256:edba70118c4be3c2b1f90754d308d0b79c6fe2c0fdc52d8ddf603916f83f4db9", size = 9689424 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/12/d7c445b1940276a828efce7331cb0cb09d6e5f049651db22f4ebb0922b77/sqlalchemy-2.0.41-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b1f09b6821406ea1f94053f346f28f8215e293344209129a9c0fcc3578598d7b", size = 2117967 },
|
||||
{ url = "https://files.pythonhosted.org/packages/6f/b8/cb90f23157e28946b27eb01ef401af80a1fab7553762e87df51507eaed61/sqlalchemy-2.0.41-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1936af879e3db023601196a1684d28e12f19ccf93af01bf3280a3262c4b6b4e5", size = 2107583 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9e/c2/eef84283a1c8164a207d898e063edf193d36a24fb6a5bb3ce0634b92a1e8/sqlalchemy-2.0.41-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2ac41acfc8d965fb0c464eb8f44995770239668956dc4cdf502d1b1ffe0d747", size = 3186025 },
|
||||
{ url = "https://files.pythonhosted.org/packages/bd/72/49d52bd3c5e63a1d458fd6d289a1523a8015adedbddf2c07408ff556e772/sqlalchemy-2.0.41-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81c24e0c0fde47a9723c81d5806569cddef103aebbf79dbc9fcbb617153dea30", size = 3186259 },
|
||||
{ url = "https://files.pythonhosted.org/packages/4f/9e/e3ffc37d29a3679a50b6bbbba94b115f90e565a2b4545abb17924b94c52d/sqlalchemy-2.0.41-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:23a8825495d8b195c4aa9ff1c430c28f2c821e8c5e2d98089228af887e5d7e29", size = 3126803 },
|
||||
{ url = "https://files.pythonhosted.org/packages/8a/76/56b21e363f6039978ae0b72690237b38383e4657281285a09456f313dd77/sqlalchemy-2.0.41-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:60c578c45c949f909a4026b7807044e7e564adf793537fc762b2489d522f3d11", size = 3148566 },
|
||||
{ url = "https://files.pythonhosted.org/packages/3b/92/11b8e1b69bf191bc69e300a99badbbb5f2f1102f2b08b39d9eee2e21f565/sqlalchemy-2.0.41-cp310-cp310-win32.whl", hash = "sha256:118c16cd3f1b00c76d69343e38602006c9cfb9998fa4f798606d28d63f23beda", size = 2086696 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/88/2d706c9cc4502654860f4576cd54f7db70487b66c3b619ba98e0be1a4642/sqlalchemy-2.0.41-cp310-cp310-win_amd64.whl", hash = "sha256:7492967c3386df69f80cf67efd665c0f667cee67032090fe01d7d74b0e19bb08", size = 2110200 },
|
||||
{ url = "https://files.pythonhosted.org/packages/37/4e/b00e3ffae32b74b5180e15d2ab4040531ee1bef4c19755fe7926622dc958/sqlalchemy-2.0.41-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6375cd674fe82d7aa9816d1cb96ec592bac1726c11e0cafbf40eeee9a4516b5f", size = 2121232 },
|
||||
{ url = "https://files.pythonhosted.org/packages/ef/30/6547ebb10875302074a37e1970a5dce7985240665778cfdee2323709f749/sqlalchemy-2.0.41-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9f8c9fdd15a55d9465e590a402f42082705d66b05afc3ffd2d2eb3c6ba919560", size = 2110897 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9e/21/59df2b41b0f6c62da55cd64798232d7349a9378befa7f1bb18cf1dfd510a/sqlalchemy-2.0.41-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32f9dc8c44acdee06c8fc6440db9eae8b4af8b01e4b1aee7bdd7241c22edff4f", size = 3273313 },
|
||||
{ url = "https://files.pythonhosted.org/packages/62/e4/b9a7a0e5c6f79d49bcd6efb6e90d7536dc604dab64582a9dec220dab54b6/sqlalchemy-2.0.41-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c11ceb9a1f482c752a71f203a81858625d8df5746d787a4786bca4ffdf71c6", size = 3273807 },
|
||||
{ url = "https://files.pythonhosted.org/packages/39/d8/79f2427251b44ddee18676c04eab038d043cff0e764d2d8bb08261d6135d/sqlalchemy-2.0.41-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:911cc493ebd60de5f285bcae0491a60b4f2a9f0f5c270edd1c4dbaef7a38fc04", size = 3209632 },
|
||||
{ url = "https://files.pythonhosted.org/packages/d4/16/730a82dda30765f63e0454918c982fb7193f6b398b31d63c7c3bd3652ae5/sqlalchemy-2.0.41-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:03968a349db483936c249f4d9cd14ff2c296adfa1290b660ba6516f973139582", size = 3233642 },
|
||||
{ url = "https://files.pythonhosted.org/packages/04/61/c0d4607f7799efa8b8ea3c49b4621e861c8f5c41fd4b5b636c534fcb7d73/sqlalchemy-2.0.41-cp311-cp311-win32.whl", hash = "sha256:293cd444d82b18da48c9f71cd7005844dbbd06ca19be1ccf6779154439eec0b8", size = 2086475 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9d/8e/8344f8ae1cb6a479d0741c02cd4f666925b2bf02e2468ddaf5ce44111f30/sqlalchemy-2.0.41-cp311-cp311-win_amd64.whl", hash = "sha256:3d3549fc3e40667ec7199033a4e40a2f669898a00a7b18a931d3efb4c7900504", size = 2110903 },
|
||||
{ url = "https://files.pythonhosted.org/packages/3e/2a/f1f4e068b371154740dd10fb81afb5240d5af4aa0087b88d8b308b5429c2/sqlalchemy-2.0.41-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:81f413674d85cfd0dfcd6512e10e0f33c19c21860342a4890c3a2b59479929f9", size = 2119645 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/e8/c664a7e73d36fbfc4730f8cf2bf930444ea87270f2825efbe17bf808b998/sqlalchemy-2.0.41-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:598d9ebc1e796431bbd068e41e4de4dc34312b7aa3292571bb3674a0cb415dd1", size = 2107399 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/78/8a9cf6c5e7135540cb682128d091d6afa1b9e48bd049b0d691bf54114f70/sqlalchemy-2.0.41-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a104c5694dfd2d864a6f91b0956eb5d5883234119cb40010115fd45a16da5e70", size = 3293269 },
|
||||
{ url = "https://files.pythonhosted.org/packages/3c/35/f74add3978c20de6323fb11cb5162702670cc7a9420033befb43d8d5b7a4/sqlalchemy-2.0.41-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6145afea51ff0af7f2564a05fa95eb46f542919e6523729663a5d285ecb3cf5e", size = 3303364 },
|
||||
{ url = "https://files.pythonhosted.org/packages/6a/d4/c990f37f52c3f7748ebe98883e2a0f7d038108c2c5a82468d1ff3eec50b7/sqlalchemy-2.0.41-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:b46fa6eae1cd1c20e6e6f44e19984d438b6b2d8616d21d783d150df714f44078", size = 3229072 },
|
||||
{ url = "https://files.pythonhosted.org/packages/15/69/cab11fecc7eb64bc561011be2bd03d065b762d87add52a4ca0aca2e12904/sqlalchemy-2.0.41-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41836fe661cc98abfae476e14ba1906220f92c4e528771a8a3ae6a151242d2ae", size = 3268074 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/ca/0c19ec16858585d37767b167fc9602593f98998a68a798450558239fb04a/sqlalchemy-2.0.41-cp312-cp312-win32.whl", hash = "sha256:a8808d5cf866c781150d36a3c8eb3adccfa41a8105d031bf27e92c251e3969d6", size = 2084514 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/23/4c2833d78ff3010a4e17f984c734f52b531a8c9060a50429c9d4b0211be6/sqlalchemy-2.0.41-cp312-cp312-win_amd64.whl", hash = "sha256:5b14e97886199c1f52c14629c11d90c11fbb09e9334fa7bb5f6d068d9ced0ce0", size = 2111557 },
|
||||
{ url = "https://files.pythonhosted.org/packages/d3/ad/2e1c6d4f235a97eeef52d0200d8ddda16f6c4dd70ae5ad88c46963440480/sqlalchemy-2.0.41-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4eeb195cdedaf17aab6b247894ff2734dcead6c08f748e617bfe05bd5a218443", size = 2115491 },
|
||||
{ url = "https://files.pythonhosted.org/packages/cf/8d/be490e5db8400dacc89056f78a52d44b04fbf75e8439569d5b879623a53b/sqlalchemy-2.0.41-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d4ae769b9c1c7757e4ccce94b0641bc203bbdf43ba7a2413ab2523d8d047d8dc", size = 2102827 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a0/72/c97ad430f0b0e78efaf2791342e13ffeafcbb3c06242f01a3bb8fe44f65d/sqlalchemy-2.0.41-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a62448526dd9ed3e3beedc93df9bb6b55a436ed1474db31a2af13b313a70a7e1", size = 3225224 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5e/51/5ba9ea3246ea068630acf35a6ba0d181e99f1af1afd17e159eac7e8bc2b8/sqlalchemy-2.0.41-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dc56c9788617b8964ad02e8fcfeed4001c1f8ba91a9e1f31483c0dffb207002a", size = 3230045 },
|
||||
{ url = "https://files.pythonhosted.org/packages/78/2f/8c14443b2acea700c62f9b4a8bad9e49fc1b65cfb260edead71fd38e9f19/sqlalchemy-2.0.41-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c153265408d18de4cc5ded1941dcd8315894572cddd3c58df5d5b5705b3fa28d", size = 3159357 },
|
||||
{ url = "https://files.pythonhosted.org/packages/fc/b2/43eacbf6ccc5276d76cea18cb7c3d73e294d6fb21f9ff8b4eef9b42bbfd5/sqlalchemy-2.0.41-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4f67766965996e63bb46cfbf2ce5355fc32d9dd3b8ad7e536a920ff9ee422e23", size = 3197511 },
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/2e/677c17c5d6a004c3c45334ab1dbe7b7deb834430b282b8a0f75ae220c8eb/sqlalchemy-2.0.41-cp313-cp313-win32.whl", hash = "sha256:bfc9064f6658a3d1cadeaa0ba07570b83ce6801a1314985bf98ec9b95d74e15f", size = 2082420 },
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/61/e8c1b9b6307c57157d328dd8b8348ddc4c47ffdf1279365a13b2b98b8049/sqlalchemy-2.0.41-cp313-cp313-win_amd64.whl", hash = "sha256:82ca366a844eb551daff9d2e6e7a9e5e76d2612c8564f58db6c19a726869c1df", size = 2108329 },
|
||||
{ url = "https://files.pythonhosted.org/packages/1c/fc/9ba22f01b5cdacc8f5ed0d22304718d2c758fce3fd49a5372b886a86f37c/sqlalchemy-2.0.41-py3-none-any.whl", hash = "sha256:57df5dc6fdb5ed1a88a1ed2195fd31927e705cad62dedd86b46972752a80f576", size = 1911224 },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
asyncio = [
|
||||
{ name = "greenlet" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sqlite-vec"
|
||||
version = "0.1.6"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue