llama-stack/llama_stack/apis/agents/agents.py
Dinesh Yeduguru a5c57cd381
agents to use tools api (#673)
# What does this PR do?

PR #639 introduced the notion of Tools API and ability to invoke tools
through API just as any resource. This PR changes the Agents to start
using the Tools API to invoke tools. Major changes include:
1) Ability to specify tool groups with AgentConfig
2) Agent gets the corresponding tool definitions for the specified tools
and pass along to the model
3) Attachements are now named as Documents and their behavior is mostly
unchanged from user perspective
4) You can specify args that can be injected to a tool call through
Agent config. This is especially useful in case of memory tool, where
you want the tool to operate on a specific memory bank.
5) You can also register tool groups with args, which lets the agent
inject these as well into the tool call.
6) All tests have been migrated to use new tools API and fixtures
including client SDK tests
7) Telemetry just works with tools API because of our trace protocol
decorator


## Test Plan
```
pytest -s -v -k fireworks llama_stack/providers/tests/agents/test_agents.py  \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

pytest -s -v -k together  llama_stack/providers/tests/tools/test_tools.py \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/agents/test_agents.py
```
run.yaml:
https://gist.github.com/dineshyv/0365845ad325e1c2cab755788ccc5994

Notebook:
https://colab.research.google.com/drive/1ck7hXQxRl6UvT-ijNRZ-gMZxH1G3cN2d?usp=sharing
2025-01-08 19:01:00 -08:00

355 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.
from datetime import datetime
from enum import Enum
from typing import (
Any,
AsyncIterator,
Dict,
List,
Literal,
Optional,
Protocol,
runtime_checkable,
Union,
)
from llama_models.schema_utils import json_schema_type, register_schema, webmethod
from pydantic import BaseModel, ConfigDict, Field
from typing_extensions import Annotated
from llama_stack.apis.common.content_types import InterleavedContent, URL
from llama_stack.apis.inference import (
CompletionMessage,
SamplingParams,
ToolCall,
ToolCallDelta,
ToolChoice,
ToolPromptFormat,
ToolResponse,
ToolResponseMessage,
UserMessage,
)
from llama_stack.apis.memory import MemoryBank
from llama_stack.apis.safety import SafetyViolation
from llama_stack.apis.tools import ToolDef
from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
class Attachment(BaseModel):
content: InterleavedContent | URL
mime_type: str
class Document(BaseModel):
content: InterleavedContent | URL
mime_type: str
class StepCommon(BaseModel):
turn_id: str
step_id: str
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
class StepType(Enum):
inference = "inference"
tool_execution = "tool_execution"
shield_call = "shield_call"
memory_retrieval = "memory_retrieval"
@json_schema_type
class InferenceStep(StepCommon):
model_config = ConfigDict(protected_namespaces=())
step_type: Literal[StepType.inference.value] = StepType.inference.value
model_response: CompletionMessage
@json_schema_type
class ToolExecutionStep(StepCommon):
step_type: Literal[StepType.tool_execution.value] = StepType.tool_execution.value
tool_calls: List[ToolCall]
tool_responses: List[ToolResponse]
@json_schema_type
class ShieldCallStep(StepCommon):
step_type: Literal[StepType.shield_call.value] = StepType.shield_call.value
violation: Optional[SafetyViolation]
@json_schema_type
class MemoryRetrievalStep(StepCommon):
step_type: Literal[StepType.memory_retrieval.value] = (
StepType.memory_retrieval.value
)
memory_bank_ids: List[str]
inserted_context: InterleavedContent
Step = Annotated[
Union[
InferenceStep,
ToolExecutionStep,
ShieldCallStep,
MemoryRetrievalStep,
],
Field(discriminator="step_type"),
]
@json_schema_type
class Turn(BaseModel):
"""A single turn in an interaction with an Agentic System."""
turn_id: str
session_id: str
input_messages: List[
Union[
UserMessage,
ToolResponseMessage,
]
]
steps: List[Step]
output_message: CompletionMessage
output_attachments: List[Attachment] = Field(default_factory=list)
started_at: datetime
completed_at: Optional[datetime] = None
@json_schema_type
class Session(BaseModel):
"""A single session of an interaction with an Agentic System."""
session_id: str
session_name: str
turns: List[Turn]
started_at: datetime
memory_bank: Optional[MemoryBank] = None
class AgentToolGroupWithArgs(BaseModel):
name: str
args: Dict[str, Any]
AgentToolGroup = register_schema(
Union[
str,
AgentToolGroupWithArgs,
],
name="AgentTool",
)
class AgentConfigCommon(BaseModel):
sampling_params: Optional[SamplingParams] = SamplingParams()
input_shields: Optional[List[str]] = Field(default_factory=list)
output_shields: Optional[List[str]] = Field(default_factory=list)
toolgroups: Optional[List[AgentToolGroup]] = Field(default_factory=list)
client_tools: Optional[List[ToolDef]] = Field(default_factory=list)
tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
tool_prompt_format: Optional[ToolPromptFormat] = Field(
default=ToolPromptFormat.json
)
max_infer_iters: int = 10
@json_schema_type
class AgentConfig(AgentConfigCommon):
model: str
instructions: str
enable_session_persistence: bool
class AgentConfigOverridablePerTurn(AgentConfigCommon):
instructions: Optional[str] = None
class AgentTurnResponseEventType(Enum):
step_start = "step_start"
step_complete = "step_complete"
step_progress = "step_progress"
turn_start = "turn_start"
turn_complete = "turn_complete"
@json_schema_type
class AgentTurnResponseStepStartPayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.step_start.value] = (
AgentTurnResponseEventType.step_start.value
)
step_type: StepType
step_id: str
metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)
@json_schema_type
class AgentTurnResponseStepCompletePayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.step_complete.value] = (
AgentTurnResponseEventType.step_complete.value
)
step_type: StepType
step_id: str
step_details: Step
@json_schema_type
class AgentTurnResponseStepProgressPayload(BaseModel):
model_config = ConfigDict(protected_namespaces=())
event_type: Literal[AgentTurnResponseEventType.step_progress.value] = (
AgentTurnResponseEventType.step_progress.value
)
step_type: StepType
step_id: str
text_delta: Optional[str] = None
tool_call_delta: Optional[ToolCallDelta] = None
@json_schema_type
class AgentTurnResponseTurnStartPayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.turn_start.value] = (
AgentTurnResponseEventType.turn_start.value
)
turn_id: str
@json_schema_type
class AgentTurnResponseTurnCompletePayload(BaseModel):
event_type: Literal[AgentTurnResponseEventType.turn_complete.value] = (
AgentTurnResponseEventType.turn_complete.value
)
turn: Turn
@json_schema_type
class AgentTurnResponseEvent(BaseModel):
"""Streamed agent execution response."""
payload: Annotated[
Union[
AgentTurnResponseStepStartPayload,
AgentTurnResponseStepProgressPayload,
AgentTurnResponseStepCompletePayload,
AgentTurnResponseTurnStartPayload,
AgentTurnResponseTurnCompletePayload,
],
Field(discriminator="event_type"),
]
@json_schema_type
class AgentCreateResponse(BaseModel):
agent_id: str
@json_schema_type
class AgentSessionCreateResponse(BaseModel):
session_id: str
@json_schema_type
class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
agent_id: str
session_id: str
# TODO: figure out how we can simplify this and make why
# ToolResponseMessage needs to be here (it is function call
# execution from outside the system)
messages: List[
Union[
UserMessage,
ToolResponseMessage,
]
]
documents: Optional[List[Document]] = None
toolgroups: Optional[List[AgentToolGroup]] = None
stream: Optional[bool] = False
@json_schema_type
class AgentTurnResponseStreamChunk(BaseModel):
"""streamed agent turn completion response."""
event: AgentTurnResponseEvent
@json_schema_type
class AgentStepResponse(BaseModel):
step: Step
@runtime_checkable
@trace_protocol
class Agents(Protocol):
@webmethod(route="/agents/create")
async def create_agent(
self,
agent_config: AgentConfig,
) -> AgentCreateResponse: ...
@webmethod(route="/agents/turn/create")
async def create_agent_turn(
self,
agent_id: str,
session_id: str,
messages: List[
Union[
UserMessage,
ToolResponseMessage,
]
],
stream: Optional[bool] = False,
documents: Optional[List[Document]] = None,
toolgroups: Optional[List[AgentToolGroup]] = None,
) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]: ...
@webmethod(route="/agents/turn/get")
async def get_agents_turn(
self, agent_id: str, session_id: str, turn_id: str
) -> Turn: ...
@webmethod(route="/agents/step/get")
async def get_agents_step(
self, agent_id: str, session_id: str, turn_id: str, step_id: str
) -> AgentStepResponse: ...
@webmethod(route="/agents/session/create")
async def create_agent_session(
self,
agent_id: str,
session_name: str,
) -> AgentSessionCreateResponse: ...
@webmethod(route="/agents/session/get")
async def get_agents_session(
self,
agent_id: str,
session_id: str,
turn_ids: Optional[List[str]] = None,
) -> Session: ...
@webmethod(route="/agents/session/delete")
async def delete_agents_session(self, agent_id: str, session_id: str) -> None: ...
@webmethod(route="/agents/delete")
async def delete_agents(
self,
agent_id: str,
) -> None: ...