forked from phoenix-oss/llama-stack-mirror
0bc5d05243
# What does this PR do? Remove the default log handlers for notebook to avoid polluting logs
442 lines
14 KiB
Python
442 lines
14 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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import inspect
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import json
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import logging
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import os
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import queue
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import threading
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from concurrent.futures import ThreadPoolExecutor
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from enum import Enum
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from pathlib import Path
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from typing import Any, Generator, get_args, get_origin, Optional, TypeVar
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import httpx
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import yaml
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from llama_stack_client import (
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APIResponse,
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AsyncAPIResponse,
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AsyncLlamaStackClient,
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AsyncStream,
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LlamaStackClient,
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NOT_GIVEN,
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)
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from pydantic import BaseModel, TypeAdapter
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from rich.console import Console
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from termcolor import cprint
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from llama_stack.distribution.build import print_pip_install_help
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from llama_stack.distribution.configure import parse_and_maybe_upgrade_config
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from llama_stack.distribution.datatypes import Api
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from llama_stack.distribution.resolver import ProviderRegistry
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from llama_stack.distribution.server.endpoints import get_all_api_endpoints
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from llama_stack.distribution.stack import (
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construct_stack,
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get_stack_run_config_from_template,
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redact_sensitive_fields,
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replace_env_vars,
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)
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from llama_stack.providers.utils.telemetry.tracing import (
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end_trace,
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setup_logger,
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start_trace,
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)
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T = TypeVar("T")
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def in_notebook():
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try:
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from IPython import get_ipython
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if "IPKernelApp" not in get_ipython().config: # pragma: no cover
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return False
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except ImportError:
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return False
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except AttributeError:
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return False
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return True
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def stream_across_asyncio_run_boundary(
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async_gen_maker,
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pool_executor: ThreadPoolExecutor,
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path: Optional[str] = None,
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) -> Generator[T, None, None]:
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result_queue = queue.Queue()
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stop_event = threading.Event()
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async def consumer():
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# make sure we make the generator in the event loop context
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gen = await async_gen_maker()
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await start_trace(path, {"__location__": "library_client"})
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try:
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async for item in await gen:
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result_queue.put(item)
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except Exception as e:
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print(f"Error in generator {e}")
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result_queue.put(e)
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except asyncio.CancelledError:
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return
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finally:
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result_queue.put(StopIteration)
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stop_event.set()
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await end_trace()
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def run_async():
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# Run our own loop to avoid double async generator cleanup which is done
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# by asyncio.run()
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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task = loop.create_task(consumer())
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loop.run_until_complete(task)
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finally:
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# Handle pending tasks like a generator's athrow()
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pending = asyncio.all_tasks(loop)
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if pending:
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loop.run_until_complete(
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asyncio.gather(*pending, return_exceptions=True)
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)
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loop.close()
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future = pool_executor.submit(run_async)
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try:
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# yield results as they come in
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while not stop_event.is_set() or not result_queue.empty():
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try:
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item = result_queue.get(timeout=0.1)
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if item is StopIteration:
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break
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if isinstance(item, Exception):
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raise item
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yield item
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except queue.Empty:
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continue
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finally:
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future.result()
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def convert_pydantic_to_json_value(value: Any) -> Any:
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if isinstance(value, Enum):
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return value.value
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elif isinstance(value, list):
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return [convert_pydantic_to_json_value(item) for item in value]
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elif isinstance(value, dict):
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return {k: convert_pydantic_to_json_value(v) for k, v in value.items()}
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elif isinstance(value, BaseModel):
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return json.loads(value.model_dump_json())
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else:
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return value
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def convert_to_pydantic(annotation: Any, value: Any) -> Any:
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if isinstance(annotation, type) and annotation in {str, int, float, bool}:
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return value
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origin = get_origin(annotation)
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if origin is list:
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item_type = get_args(annotation)[0]
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try:
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return [convert_to_pydantic(item_type, item) for item in value]
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except Exception:
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print(f"Error converting list {value}")
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return value
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elif origin is dict:
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key_type, val_type = get_args(annotation)
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try:
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return {k: convert_to_pydantic(val_type, v) for k, v in value.items()}
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except Exception:
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print(f"Error converting dict {value}")
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return value
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try:
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# Handle Pydantic models and discriminated unions
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return TypeAdapter(annotation).validate_python(value)
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except Exception as e:
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cprint(
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f"Warning: direct client failed to convert parameter {value} into {annotation}: {e}",
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"yellow",
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)
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return value
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class LlamaStackAsLibraryClient(LlamaStackClient):
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def __init__(
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self,
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config_path_or_template_name: str,
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skip_logger_removal: bool = False,
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custom_provider_registry: Optional[ProviderRegistry] = None,
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):
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super().__init__()
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self.async_client = AsyncLlamaStackAsLibraryClient(
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config_path_or_template_name, custom_provider_registry
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)
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self.pool_executor = ThreadPoolExecutor(max_workers=4)
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self.skip_logger_removal = skip_logger_removal
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def initialize(self):
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if in_notebook():
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import nest_asyncio
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nest_asyncio.apply()
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if not self.skip_logger_removal:
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self._remove_root_logger_handlers()
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return asyncio.run(self.async_client.initialize())
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def _remove_root_logger_handlers(self):
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"""
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Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
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"""
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root_logger = logging.getLogger()
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for handler in root_logger.handlers[:]:
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root_logger.removeHandler(handler)
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print(f"Removed handler {handler.__class__.__name__} from root logger")
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def _get_path(
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self,
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cast_to: Any,
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options: Any,
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*,
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stream=False,
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stream_cls=None,
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):
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return options.url
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def request(self, *args, **kwargs):
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path = self._get_path(*args, **kwargs)
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if kwargs.get("stream"):
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return stream_across_asyncio_run_boundary(
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lambda: self.async_client.request(*args, **kwargs),
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self.pool_executor,
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path=path,
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)
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else:
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async def _traced_request():
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await start_trace(path, {"__location__": "library_client"})
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try:
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return await self.async_client.request(*args, **kwargs)
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finally:
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await end_trace()
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return asyncio.run(_traced_request())
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class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
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def __init__(
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self,
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config_path_or_template_name: str,
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custom_provider_registry: Optional[ProviderRegistry] = None,
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):
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super().__init__()
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# when using the library client, we should not log to console since many
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# of our logs are intended for server-side usage
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current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
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os.environ["TELEMETRY_SINKS"] = ",".join(
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sink for sink in current_sinks if sink != "console"
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)
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if config_path_or_template_name.endswith(".yaml"):
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config_path = Path(config_path_or_template_name)
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if not config_path.exists():
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raise ValueError(f"Config file {config_path} does not exist")
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config_dict = replace_env_vars(yaml.safe_load(config_path.read_text()))
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config = parse_and_maybe_upgrade_config(config_dict)
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else:
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# template
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config = get_stack_run_config_from_template(config_path_or_template_name)
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self.config_path_or_template_name = config_path_or_template_name
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self.config = config
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self.custom_provider_registry = custom_provider_registry
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async def initialize(self):
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try:
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self.impls = await construct_stack(
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self.config, self.custom_provider_registry
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)
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except ModuleNotFoundError as _e:
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cprint(
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"Using llama-stack as a library requires installing dependencies depending on the template (providers) you choose.\n",
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"yellow",
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)
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if self.config_path_or_template_name.endswith(".yaml"):
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print_pip_install_help(self.config.providers)
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else:
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prefix = "!" if in_notebook() else ""
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cprint(
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f"Please run:\n\n{prefix}llama stack build --template {self.config_path_or_template_name} --image-type venv\n\n",
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"yellow",
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)
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return False
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if Api.telemetry in self.impls:
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setup_logger(self.impls[Api.telemetry])
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console = Console()
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console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:")
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# Redact sensitive information before printing
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safe_config = redact_sensitive_fields(self.config.model_dump())
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console.print(yaml.dump(safe_config, indent=2))
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endpoints = get_all_api_endpoints()
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endpoint_impls = {}
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for api, api_endpoints in endpoints.items():
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if api not in self.impls:
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continue
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for endpoint in api_endpoints:
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impl = self.impls[api]
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func = getattr(impl, endpoint.name)
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endpoint_impls[endpoint.route] = func
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self.endpoint_impls = endpoint_impls
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return True
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async def request(
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self,
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cast_to: Any,
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options: Any,
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*,
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stream=False,
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stream_cls=None,
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):
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if not self.endpoint_impls:
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raise ValueError("Client not initialized")
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if stream:
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return self._call_streaming(
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cast_to=cast_to,
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options=options,
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stream_cls=stream_cls,
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)
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else:
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return await self._call_non_streaming(
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cast_to=cast_to,
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options=options,
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)
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async def _call_non_streaming(
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self,
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*,
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cast_to: Any,
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options: Any,
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):
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path = options.url
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body = options.params or {}
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body |= options.json_data or {}
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func = self.endpoint_impls.get(path)
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if not func:
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raise ValueError(f"No endpoint found for {path}")
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body = self._convert_body(path, body)
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result = await func(**body)
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json_content = json.dumps(convert_pydantic_to_json_value(result))
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mock_response = httpx.Response(
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status_code=httpx.codes.OK,
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content=json_content.encode("utf-8"),
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headers={
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"Content-Type": "application/json",
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},
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request=httpx.Request(
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method=options.method,
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url=options.url,
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params=options.params,
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headers=options.headers,
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json=options.json_data,
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),
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)
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response = APIResponse(
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raw=mock_response,
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client=self,
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cast_to=cast_to,
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options=options,
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stream=False,
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stream_cls=None,
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)
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return response.parse()
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async def _call_streaming(
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self,
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*,
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cast_to: Any,
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options: Any,
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stream_cls: Any,
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):
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path = options.url
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body = options.params or {}
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body |= options.json_data or {}
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func = self.endpoint_impls.get(path)
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if not func:
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raise ValueError(f"No endpoint found for {path}")
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body = self._convert_body(path, body)
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async def gen():
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async for chunk in await func(**body):
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data = json.dumps(convert_pydantic_to_json_value(chunk))
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sse_event = f"data: {data}\n\n"
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yield sse_event.encode("utf-8")
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mock_response = httpx.Response(
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status_code=httpx.codes.OK,
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content=gen(),
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headers={
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"Content-Type": "application/json",
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},
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request=httpx.Request(
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method=options.method,
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url=options.url,
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params=options.params,
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headers=options.headers,
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json=options.json_data,
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),
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)
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# we use asynchronous impl always internally and channel all requests to AsyncLlamaStackClient
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# however, the top-level caller may be a SyncAPIClient -- so its stream_cls might be a Stream (SyncStream)
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# so we need to convert it to AsyncStream
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args = get_args(stream_cls)
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stream_cls = AsyncStream[args[0]]
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response = AsyncAPIResponse(
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raw=mock_response,
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client=self,
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cast_to=cast_to,
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options=options,
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stream=True,
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stream_cls=stream_cls,
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)
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return await response.parse()
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def _convert_body(self, path: str, body: Optional[dict] = None) -> dict:
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if not body:
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return {}
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func = self.endpoint_impls[path]
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sig = inspect.signature(func)
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# Strip NOT_GIVENs to use the defaults in signature
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body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
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# Convert parameters to Pydantic models where needed
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converted_body = {}
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for param_name, param in sig.parameters.items():
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if param_name in body:
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value = body.get(param_name)
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converted_body[param_name] = convert_to_pydantic(
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param.annotation, value
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)
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return converted_body
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