llama-stack-mirror/llama_stack/providers/inline/agents/meta_reference/agents.py
Ihar Hrachyshka 9e6561a1ec
chore: enable pyupgrade fixes (#1806)
# What does this PR do?

The goal of this PR is code base modernization.

Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
2025-05-01 14:23:50 -07:00

273 lines
8.8 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.
import json
import logging
import shutil
import uuid
from collections.abc import AsyncGenerator
from llama_stack.apis.agents import (
Agent,
AgentConfig,
AgentCreateResponse,
Agents,
AgentSessionCreateResponse,
AgentStepResponse,
AgentToolGroup,
AgentTurnCreateRequest,
AgentTurnResumeRequest,
Document,
ListAgentSessionsResponse,
ListAgentsResponse,
OpenAIResponseInputMessage,
OpenAIResponseInputTool,
OpenAIResponseObject,
Session,
Turn,
)
from llama_stack.apis.inference import (
Inference,
ToolConfig,
ToolResponse,
ToolResponseMessage,
UserMessage,
)
from llama_stack.apis.safety import Safety
from llama_stack.apis.tools import ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO
from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_impl
from .agent_instance import ChatAgent
from .config import MetaReferenceAgentsImplConfig
from .openai_responses import OpenAIResponsesImpl
logger = logging.getLogger()
logger.setLevel(logging.INFO)
class MetaReferenceAgentsImpl(Agents):
def __init__(
self,
config: MetaReferenceAgentsImplConfig,
inference_api: Inference,
vector_io_api: VectorIO,
safety_api: Safety,
tool_runtime_api: ToolRuntime,
tool_groups_api: ToolGroups,
):
self.config = config
self.inference_api = inference_api
self.vector_io_api = vector_io_api
self.safety_api = safety_api
self.tool_runtime_api = tool_runtime_api
self.tool_groups_api = tool_groups_api
self.in_memory_store = InmemoryKVStoreImpl()
self.openai_responses_impl = None
async def initialize(self) -> None:
self.persistence_store = await kvstore_impl(self.config.persistence_store)
self.openai_responses_impl = OpenAIResponsesImpl(
self.persistence_store,
inference_api=self.inference_api,
tool_groups_api=self.tool_groups_api,
tool_runtime_api=self.tool_runtime_api,
)
# check if "bwrap" is available
if not shutil.which("bwrap"):
logger.warning("Warning: `bwrap` is not available. Code interpreter tool will not work correctly.")
async def create_agent(
self,
agent_config: AgentConfig,
) -> AgentCreateResponse:
agent_id = str(uuid.uuid4())
await self.persistence_store.set(
key=f"agent:{agent_id}",
value=agent_config.model_dump_json(),
)
return AgentCreateResponse(
agent_id=agent_id,
)
async def _get_agent_impl(self, agent_id: str) -> ChatAgent:
agent_config = await self.persistence_store.get(
key=f"agent:{agent_id}",
)
if not agent_config:
raise ValueError(f"Could not find agent config for {agent_id}")
try:
agent_config = json.loads(agent_config)
except json.JSONDecodeError as e:
raise ValueError(f"Could not JSON decode agent config for {agent_id}") from e
try:
agent_config = AgentConfig(**agent_config)
except Exception as e:
raise ValueError(f"Could not validate(?) agent config for {agent_id}") from e
return ChatAgent(
agent_id=agent_id,
agent_config=agent_config,
inference_api=self.inference_api,
safety_api=self.safety_api,
vector_io_api=self.vector_io_api,
tool_runtime_api=self.tool_runtime_api,
tool_groups_api=self.tool_groups_api,
persistence_store=(
self.persistence_store if agent_config.enable_session_persistence else self.in_memory_store
),
)
async def create_agent_session(
self,
agent_id: str,
session_name: str,
) -> AgentSessionCreateResponse:
agent = await self._get_agent_impl(agent_id)
session_id = await agent.create_session(session_name)
return AgentSessionCreateResponse(
session_id=session_id,
)
async def create_agent_turn(
self,
agent_id: str,
session_id: str,
messages: list[UserMessage | ToolResponseMessage],
toolgroups: list[AgentToolGroup] | None = None,
documents: list[Document] | None = None,
stream: bool | None = False,
tool_config: ToolConfig | None = None,
) -> AsyncGenerator:
request = AgentTurnCreateRequest(
agent_id=agent_id,
session_id=session_id,
messages=messages,
stream=True,
toolgroups=toolgroups,
documents=documents,
tool_config=tool_config,
)
if stream:
return self._create_agent_turn_streaming(request)
else:
raise NotImplementedError("Non-streaming agent turns not yet implemented")
async def _create_agent_turn_streaming(
self,
request: AgentTurnCreateRequest,
) -> AsyncGenerator:
agent = await self._get_agent_impl(request.agent_id)
async for event in agent.create_and_execute_turn(request):
yield event
async def resume_agent_turn(
self,
agent_id: str,
session_id: str,
turn_id: str,
tool_responses: list[ToolResponse],
stream: bool | None = False,
) -> AsyncGenerator:
request = AgentTurnResumeRequest(
agent_id=agent_id,
session_id=session_id,
turn_id=turn_id,
tool_responses=tool_responses,
stream=stream,
)
if stream:
return self._continue_agent_turn_streaming(request)
else:
raise NotImplementedError("Non-streaming agent turns not yet implemented")
async def _continue_agent_turn_streaming(
self,
request: AgentTurnResumeRequest,
) -> AsyncGenerator:
agent = await self._get_agent_impl(request.agent_id)
async for event in agent.resume_turn(request):
yield event
async def get_agents_turn(self, agent_id: str, session_id: str, turn_id: str) -> Turn:
agent = await self._get_agent_impl(agent_id)
turn = await agent.storage.get_session_turn(session_id, turn_id)
return turn
async def get_agents_step(self, agent_id: str, session_id: str, turn_id: str, step_id: str) -> AgentStepResponse:
turn = await self.get_agents_turn(agent_id, session_id, turn_id)
for step in turn.steps:
if step.step_id == step_id:
return AgentStepResponse(step=step)
raise ValueError(f"Provided step_id {step_id} could not be found")
async def get_agents_session(
self,
agent_id: str,
session_id: str,
turn_ids: list[str] | None = None,
) -> Session:
agent = await self._get_agent_impl(agent_id)
session_info = await agent.storage.get_session_info(session_id)
if session_info is None:
raise ValueError(f"Session {session_id} not found")
turns = await agent.storage.get_session_turns(session_id)
if turn_ids:
turns = [turn for turn in turns if turn.turn_id in turn_ids]
return Session(
session_name=session_info.session_name,
session_id=session_id,
turns=turns,
started_at=session_info.started_at,
)
async def delete_agents_session(self, agent_id: str, session_id: str) -> None:
await self.persistence_store.delete(f"session:{agent_id}:{session_id}")
async def delete_agent(self, agent_id: str) -> None:
await self.persistence_store.delete(f"agent:{agent_id}")
async def shutdown(self) -> None:
pass
async def list_agents(self) -> ListAgentsResponse:
pass
async def get_agent(self, agent_id: str) -> Agent:
pass
async def list_agent_sessions(
self,
agent_id: str,
) -> ListAgentSessionsResponse:
pass
# OpenAI responses
async def get_openai_response(
self,
id: str,
) -> OpenAIResponseObject:
return await self.openai_responses_impl.get_openai_response(id)
async def create_openai_response(
self,
input: str | list[OpenAIResponseInputMessage],
model: str,
previous_response_id: str | None = None,
store: bool | None = True,
stream: bool | None = False,
temperature: float | None = None,
tools: list[OpenAIResponseInputTool] | None = None,
) -> OpenAIResponseObject:
return await self.openai_responses_impl.create_openai_response(
input, model, previous_response_id, store, stream, temperature, tools
)