llama-stack-mirror/llama_stack/distribution/stack.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

224 lines
7.2 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 logging
import os
import re
from pathlib import Path
from typing import Any, Dict, Optional
import pkg_resources
import yaml
from termcolor import colored
from llama_stack.apis.agents import Agents
from llama_stack.apis.batch_inference import BatchInference
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Datasets
from llama_stack.apis.eval import Eval
from llama_stack.apis.eval_tasks import EvalTasks
from llama_stack.apis.inference import Inference
from llama_stack.apis.inspect import Inspect
from llama_stack.apis.memory import Memory
from llama_stack.apis.memory_banks import MemoryBanks
from llama_stack.apis.models import Models
from llama_stack.apis.post_training import PostTraining
from llama_stack.apis.safety import Safety
from llama_stack.apis.scoring import Scoring
from llama_stack.apis.scoring_functions import ScoringFunctions
from llama_stack.apis.shields import Shields
from llama_stack.apis.synthetic_data_generation import SyntheticDataGeneration
from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import ToolGroups, ToolRuntime
from llama_stack.distribution.datatypes import StackRunConfig
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.resolver import ProviderRegistry, resolve_impls
from llama_stack.distribution.store.registry import create_dist_registry
from llama_stack.providers.datatypes import Api
log = logging.getLogger(__name__)
LLAMA_STACK_API_VERSION = "alpha"
class LlamaStack(
MemoryBanks,
Inference,
BatchInference,
Agents,
Safety,
SyntheticDataGeneration,
Datasets,
Telemetry,
PostTraining,
Memory,
Eval,
EvalTasks,
Scoring,
ScoringFunctions,
DatasetIO,
Models,
Shields,
Inspect,
ToolGroups,
ToolRuntime,
):
pass
RESOURCES = [
("models", Api.models, "register_model", "list_models"),
("shields", Api.shields, "register_shield", "list_shields"),
("memory_banks", Api.memory_banks, "register_memory_bank", "list_memory_banks"),
("datasets", Api.datasets, "register_dataset", "list_datasets"),
(
"scoring_fns",
Api.scoring_functions,
"register_scoring_function",
"list_scoring_functions",
),
("eval_tasks", Api.eval_tasks, "register_eval_task", "list_eval_tasks"),
("tool_groups", Api.tool_groups, "register_tool_group", "list_tool_groups"),
]
async def register_resources(run_config: StackRunConfig, impls: Dict[Api, Any]):
for rsrc, api, register_method, list_method in RESOURCES:
objects = getattr(run_config, rsrc)
if api not in impls:
continue
method = getattr(impls[api], register_method)
for obj in objects:
await method(**obj.model_dump())
method = getattr(impls[api], list_method)
for obj in await method():
log.info(
f"{rsrc.capitalize()}: {colored(obj.identifier, 'white', attrs=['bold'])} served by {colored(obj.provider_id, 'white', attrs=['bold'])}",
)
log.info("")
class EnvVarError(Exception):
def __init__(self, var_name: str, path: str = ""):
self.var_name = var_name
self.path = path
super().__init__(
f"Environment variable '{var_name}' not set or empty{f' at {path}' if path else ''}"
)
def redact_sensitive_fields(data: Dict[str, Any]) -> Dict[str, Any]:
"""Redact sensitive information from config before printing."""
sensitive_patterns = ["api_key", "api_token", "password", "secret"]
def _redact_dict(d: Dict[str, Any]) -> Dict[str, Any]:
result = {}
for k, v in d.items():
if isinstance(v, dict):
result[k] = _redact_dict(v)
elif isinstance(v, list):
result[k] = [_redact_dict(i) if isinstance(i, dict) else i for i in v]
elif any(pattern in k.lower() for pattern in sensitive_patterns):
result[k] = "********"
else:
result[k] = v
return result
return _redact_dict(data)
def replace_env_vars(config: Any, path: str = "") -> Any:
if isinstance(config, dict):
result = {}
for k, v in config.items():
try:
result[k] = replace_env_vars(v, f"{path}.{k}" if path else k)
except EnvVarError as e:
raise EnvVarError(e.var_name, e.path) from None
return result
elif isinstance(config, list):
result = []
for i, v in enumerate(config):
try:
result.append(replace_env_vars(v, f"{path}[{i}]"))
except EnvVarError as e:
raise EnvVarError(e.var_name, e.path) from None
return result
elif isinstance(config, str):
pattern = r"\${env\.([A-Z0-9_]+)(?::([^}]*))?}"
def get_env_var(match):
env_var = match.group(1)
default_val = match.group(2)
value = os.environ.get(env_var)
if not value:
if default_val is None:
raise EnvVarError(env_var, path)
else:
value = default_val
# expand "~" from the values
return os.path.expanduser(value)
try:
return re.sub(pattern, get_env_var, config)
except EnvVarError as e:
raise EnvVarError(e.var_name, e.path) from None
return config
def validate_env_pair(env_pair: str) -> tuple[str, str]:
"""Validate and split an environment variable key-value pair."""
try:
key, value = env_pair.split("=", 1)
key = key.strip()
if not key:
raise ValueError(f"Empty key in environment variable pair: {env_pair}")
if not all(c.isalnum() or c == "_" for c in key):
raise ValueError(
f"Key must contain only alphanumeric characters and underscores: {key}"
)
return key, value
except ValueError as e:
raise ValueError(
f"Invalid environment variable format '{env_pair}': {str(e)}. Expected format: KEY=value"
) from e
# Produces a stack of providers for the given run config. Not all APIs may be
# asked for in the run config.
async def construct_stack(
run_config: StackRunConfig, provider_registry: Optional[ProviderRegistry] = None
) -> Dict[Api, Any]:
dist_registry, _ = await create_dist_registry(
run_config.metadata_store, run_config.image_name
)
impls = await resolve_impls(
run_config, provider_registry or get_provider_registry(), dist_registry
)
await register_resources(run_config, impls)
return impls
def get_stack_run_config_from_template(template: str) -> StackRunConfig:
template_path = pkg_resources.resource_filename(
"llama_stack", f"templates/{template}/run.yaml"
)
if not Path(template_path).exists():
raise ValueError(f"Template '{template}' not found at {template_path}")
with open(template_path) as f:
run_config = yaml.safe_load(f)
return StackRunConfig(**replace_env_vars(run_config))