mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-28 02:53:30 +00:00
e830814399
* Add distribution CLI scaffolding * More progress towards `llama distribution install` * getting closer to a distro definition, distro install + configure works * Distribution server now functioning * read existing configuration, save enums properly * Remove inference uvicorn server entrypoint and llama inference CLI command * updated dependency and client model name * Improved exception handling * local imports for faster cli * undo a typo, add a passthrough distribution * implement full-passthrough in the server * add safety adapters, configuration handling, server + clients * cleanup, moving stuff to common, nuke utils * Add a Path() wrapper at the earliest place * fixes * Bring agentic system api to toolchain Add adapter dependencies and resolve adapters using a topological sort * refactor to reduce size of `agentic_system` * move straggler files and fix some important existing bugs * ApiSurface -> Api * refactor a method out * Adapter -> Provider * Make each inference provider into its own subdirectory * installation fixes * Rename Distribution -> DistributionSpec, simplify RemoteProviders * dict key instead of attr * update inference config to take model and not model_dir * Fix passthrough streaming, send headers properly not part of body :facepalm * update safety to use model sku ids and not model dirs * Update cli_reference.md * minor fixes * add DistributionConfig, fix a bug in model download * Make install + start scripts do proper configuration automatically * Update CLI_reference * Nuke fp8_requirements, fold fbgemm into common requirements * Update README, add newline between API surface configurations * Refactor download functionality out of the Command so can be reused * Add `llama model download` alias for `llama download` * Show message about checksum file so users can check themselves * Simpler intro statements * get ollama working * Reduce a bunch of dependencies from toolchain Some improvements to the distribution install script * Avoid using `conda run` since it buffers everything * update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes * add validation for configuration input * resort imports * make optional subclasses default to yes for configuration * Remove additional_pip_packages; move deps to providers * for inline make 8b model the default * Add scripts to MANIFEST * allow installing from test.pypi.org * Fix #2 to help with testing packages * Must install llama-models at that same version first * fix PIP_ARGS --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Hardik Shah <hjshah@meta.com>
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
from typing import Any, AsyncGenerator, List
|
|
|
|
from llama_models.llama3_1.api.datatypes import StopReason, ToolResponseMessage
|
|
|
|
from llama_toolchain.agentic_system.api import (
|
|
AgenticSystem,
|
|
AgenticSystemTurnCreateRequest,
|
|
AgenticSystemTurnResponseEventType as EventType,
|
|
)
|
|
|
|
from llama_toolchain.inference.api import Message
|
|
|
|
|
|
async def execute_with_custom_tools(
|
|
system: AgenticSystem,
|
|
system_id: str,
|
|
session_id: str,
|
|
messages: List[Message],
|
|
custom_tools: List[Any],
|
|
max_iters: int = 5,
|
|
stream: bool = True,
|
|
) -> AsyncGenerator:
|
|
# first create a session, or do you keep a persistent session?
|
|
tools_dict = {t.get_name(): t for t in custom_tools}
|
|
|
|
current_messages = messages.copy()
|
|
n_iter = 0
|
|
while n_iter < max_iters:
|
|
n_iter += 1
|
|
|
|
request = AgenticSystemTurnCreateRequest(
|
|
system_id=system_id,
|
|
session_id=session_id,
|
|
messages=current_messages,
|
|
stream=stream,
|
|
)
|
|
|
|
turn = None
|
|
async for chunk in system.create_agentic_system_turn(request):
|
|
if chunk.event.payload.event_type != EventType.turn_complete.value:
|
|
yield chunk
|
|
else:
|
|
turn = chunk.event.payload.turn
|
|
|
|
message = turn.output_message
|
|
if len(message.tool_calls) == 0:
|
|
yield chunk
|
|
return
|
|
|
|
if message.stop_reason == StopReason.out_of_tokens:
|
|
yield chunk
|
|
return
|
|
|
|
tool_call = message.tool_calls[0]
|
|
if tool_call.tool_name not in tools_dict:
|
|
m = ToolResponseMessage(
|
|
call_id=tool_call.call_id,
|
|
tool_name=tool_call.tool_name,
|
|
content=f"Unknown tool `{tool_call.tool_name}` was called. Try again with something else",
|
|
)
|
|
next_message = m
|
|
else:
|
|
tool = tools_dict[tool_call.tool_name]
|
|
result_messages = await execute_custom_tool(tool, message)
|
|
next_message = result_messages[0]
|
|
|
|
yield next_message
|
|
current_messages = [next_message]
|
|
|
|
|
|
async def execute_custom_tool(tool: Any, message: Message) -> List[Message]:
|
|
result_messages = await tool.run([message])
|
|
assert (
|
|
len(result_messages) == 1
|
|
), f"Expected single message, got {len(result_messages)}"
|
|
|
|
return result_messages
|