llama-stack-mirror/llama_stack/distribution/stack.py
Ashwin Bharambe 754feba61f
feat: add a configurable category-based logger (#1352)
A self-respecting server needs good observability which starts with
configurable logging. Llama Stack had little until now. This PR adds a
`logcat` facility towards that. Callsites look like:

```python
logcat.debug("inference", f"params to ollama: {params}")
```

- the first parameter is a category. there is a static list of
categories in `llama_stack/logcat.py`
- each category can be associated with a log-level which can be
configured via the `LLAMA_STACK_LOGGING` env var.
- a value `LLAMA_STACK_LOGGING=inference=debug;server=info"` does the
obvious thing. there is a special key called `all` which is an alias for
all categories

## Test Plan

Ran with `LLAMA_STACK_LOGGING="all=debug" llama stack run fireworks` and
saw the following:


![image](https://github.com/user-attachments/assets/d24b95ab-3941-426c-9ea0-a4c62542e6f0)

Hit it with a client-sdk test case and saw this:


![image](https://github.com/user-attachments/assets/3fee8c6c-986e-4125-a09c-f5dc019682e2)
2025-03-02 18:51:14 -08:00

230 lines
8.1 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 importlib.resources
import os
import re
from typing import Any, Dict, Optional
import yaml
from termcolor import colored
from llama_stack import logcat
from llama_stack.apis.agents import Agents
from llama_stack.apis.batch_inference import BatchInference
from llama_stack.apis.benchmarks import Benchmarks
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.files import Files
from llama_stack.apis.inference import Inference
from llama_stack.apis.inspect import Inspect
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 RAGToolRuntime, ToolGroups, ToolRuntime
from llama_stack.apis.vector_dbs import VectorDBs
from llama_stack.apis.vector_io import VectorIO
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
class LlamaStack(
VectorDBs,
Inference,
BatchInference,
Agents,
Safety,
SyntheticDataGeneration,
Datasets,
Telemetry,
PostTraining,
VectorIO,
Eval,
Benchmarks,
Scoring,
ScoringFunctions,
DatasetIO,
Models,
Shields,
Inspect,
ToolGroups,
ToolRuntime,
RAGToolRuntime,
Files,
):
pass
RESOURCES = [
("models", Api.models, "register_model", "list_models"),
("shields", Api.shields, "register_shield", "list_shields"),
("vector_dbs", Api.vector_dbs, "register_vector_db", "list_vector_dbs"),
("datasets", Api.datasets, "register_dataset", "list_datasets"),
(
"scoring_fns",
Api.scoring_functions,
"register_scoring_function",
"list_scoring_functions",
),
("benchmarks", Api.benchmarks, "register_benchmark", "list_benchmarks"),
("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)
response = await method()
objects_to_process = response.data if hasattr(response, "data") else response
for obj in objects_to_process:
logcat.debug(
"core",
f"{rsrc.capitalize()}: {colored(obj.identifier, 'white', attrs=['bold'])} served by {colored(obj.provider_id, 'white', attrs=['bold'])}",
)
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):
# Updated pattern to support both default values (:) and conditional values (+)
pattern = r"\${env\.([A-Z0-9_]+)(?:([:\+])([^}]*))?}"
def get_env_var(match):
env_var = match.group(1)
operator = match.group(2) # ':' for default, '+' for conditional
value_expr = match.group(3)
env_value = os.environ.get(env_var)
if operator == ":": # Default value syntax: ${env.FOO:default}
if not env_value:
if value_expr is None:
raise EnvVarError(env_var, path)
else:
value = value_expr
else:
value = env_value
elif operator == "+": # Conditional value syntax: ${env.FOO+value_if_set}
if env_value:
value = value_expr
else:
# If env var is not set, return empty string for the conditional case
value = ""
else: # No operator case: ${env.FOO}
if not env_value:
raise EnvVarError(env_var, path)
value = env_value
# 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 = importlib.resources.files("llama_stack") / f"templates/{template}/run.yaml"
with importlib.resources.as_file(template_path) as path:
if not path.exists():
raise ValueError(f"Template '{template}' not found at {template_path}")
run_config = yaml.safe_load(path.open())
return StackRunConfig(**replace_env_vars(run_config))