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restructure cli (#656)

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Adil Hafeez 2025-12-25 14:55:29 -08:00 • committed by GitHub
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425
arch/tools/cli/config_generator.py
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@ -1,425 +0,0 @@
import json
import os
from cli.utils import convert_legacy_listeners
from jinja2 import Environment, FileSystemLoader
import yaml
from jsonschema import validate
from urllib.parse import urlparse
from copy import deepcopy
SUPPORTED_PROVIDERS_WITH_BASE_URL = [
"azure_openai",
"ollama",
"qwen",
"amazon_bedrock",
"arch",
]
SUPPORTED_PROVIDERS_WITHOUT_BASE_URL = [
"deepseek",
"groq",
"mistral",
"openai",
"gemini",
"anthropic",
"together_ai",
"xai",
"moonshotai",
"zhipu",
]
SUPPORTED_PROVIDERS = (
SUPPORTED_PROVIDERS_WITHOUT_BASE_URL + SUPPORTED_PROVIDERS_WITH_BASE_URL
)
def get_endpoint_and_port(endpoint, protocol):
endpoint_tokens = endpoint.split(":")
if len(endpoint_tokens) > 1:
endpoint = endpoint_tokens[0]
port = int(endpoint_tokens[1])
return endpoint, port
else:
if protocol == "http":
port = 80
else:
port = 443
return endpoint, port
def validate_and_render_schema():
ENVOY_CONFIG_TEMPLATE_FILE = os.getenv(
"ENVOY_CONFIG_TEMPLATE_FILE", "envoy.template.yaml"
)
ARCH_CONFIG_FILE = os.getenv("ARCH_CONFIG_FILE", "/app/arch_config.yaml")
ARCH_CONFIG_FILE_RENDERED = os.getenv(
"ARCH_CONFIG_FILE_RENDERED", "/app/arch_config_rendered.yaml"
)
ENVOY_CONFIG_FILE_RENDERED = os.getenv(
"ENVOY_CONFIG_FILE_RENDERED", "/etc/envoy/envoy.yaml"
)
ARCH_CONFIG_SCHEMA_FILE = os.getenv(
"ARCH_CONFIG_SCHEMA_FILE", "arch_config_schema.yaml"
)
env = Environment(loader=FileSystemLoader(os.getenv("TEMPLATE_ROOT", "./")))
template = env.get_template(ENVOY_CONFIG_TEMPLATE_FILE)
try:
validate_prompt_config(ARCH_CONFIG_FILE, ARCH_CONFIG_SCHEMA_FILE)
except Exception as e:
print(str(e))
exit(1) # validate_prompt_config failed. Exit
with open(ARCH_CONFIG_FILE, "r") as file:
arch_config = file.read()
with open(ARCH_CONFIG_SCHEMA_FILE, "r") as file:
arch_config_schema = file.read()
config_yaml = yaml.safe_load(arch_config)
_ = yaml.safe_load(arch_config_schema)
inferred_clusters = {}
# Convert legacy llm_providers to model_providers
if "llm_providers" in config_yaml:
if "model_providers" in config_yaml:
raise Exception(
"Please provide either llm_providers or model_providers, not both. llm_providers is deprecated, please use model_providers instead"
)
config_yaml["model_providers"] = config_yaml["llm_providers"]
del config_yaml["llm_providers"]
listeners, llm_gateway, prompt_gateway = convert_legacy_listeners(
config_yaml.get("listeners"), config_yaml.get("model_providers")
)
config_yaml["listeners"] = listeners
endpoints = config_yaml.get("endpoints", {})
# Process agents section and convert to endpoints
agents = config_yaml.get("agents", [])
filters = config_yaml.get("filters", [])
agents_combined = agents + filters
agent_id_keys = set()
for agent in agents_combined:
agent_id = agent.get("id")
if agent_id in agent_id_keys:
raise Exception(
f"Duplicate agent id {agent_id}, please provide unique id for each agent"
)
agent_id_keys.add(agent_id)
agent_endpoint = agent.get("url")
if agent_id and agent_endpoint:
urlparse_result = urlparse(agent_endpoint)
if urlparse_result.scheme and urlparse_result.hostname:
protocol = urlparse_result.scheme
port = urlparse_result.port
if port is None:
if protocol == "http":
port = 80
else:
port = 443
endpoints[agent_id] = {
"endpoint": urlparse_result.hostname,
"port": port,
"protocol": protocol,
}
# override the inferred clusters with the ones defined in the config
for name, endpoint_details in endpoints.items():
inferred_clusters[name] = endpoint_details
# Only call get_endpoint_and_port for manually defined endpoints, not agent-derived ones
if "port" not in endpoint_details:
endpoint = inferred_clusters[name]["endpoint"]
protocol = inferred_clusters[name].get("protocol", "http")
(
inferred_clusters[name]["endpoint"],
inferred_clusters[name]["port"],
) = get_endpoint_and_port(endpoint, protocol)
print("defined clusters from arch_config.yaml: ", json.dumps(inferred_clusters))
if "prompt_targets" in config_yaml:
for prompt_target in config_yaml["prompt_targets"]:
name = prompt_target.get("endpoint", {}).get("name", None)
if not name:
continue
if name not in inferred_clusters:
raise Exception(
f"Unknown endpoint {name}, please add it in endpoints section in your arch_config.yaml file"
)
arch_tracing = config_yaml.get("tracing", {})
llms_with_endpoint = []
llms_with_endpoint_cluster_names = set()
updated_model_providers = []
model_provider_name_set = set()
llms_with_usage = []
model_name_keys = set()
model_usage_name_keys = set()
print("listeners: ", listeners)
for listener in listeners:
if (
listener.get("model_providers") is None
or listener.get("model_providers") == []
):
continue
print("Processing listener with model_providers: ", listener)
name = listener.get("name", None)
for model_provider in listener.get("model_providers", []):
if model_provider.get("usage", None):
llms_with_usage.append(model_provider["name"])
if model_provider.get("name") in model_provider_name_set:
raise Exception(
f"Duplicate model_provider name {model_provider.get('name')}, please provide unique name for each model_provider"
)
model_name = model_provider.get("model")
print("Processing model_provider: ", model_provider)
if model_name in model_name_keys:
raise Exception(
f"Duplicate model name {model_name}, please provide unique model name for each model_provider"
)
model_name_keys.add(model_name)
if model_provider.get("name") is None:
model_provider["name"] = model_name
model_provider_name_set.add(model_provider.get("name"))
model_name_tokens = model_name.split("/")
if len(model_name_tokens) < 2:
raise Exception(
f"Invalid model name {model_name}. Please provide model name in the format <provider>/<model_id>."
)
provider = model_name_tokens[0]
# Validate azure_openai and ollama provider requires base_url
if (provider in SUPPORTED_PROVIDERS_WITH_BASE_URL) and model_provider.get(
"base_url"
) is None:
raise Exception(
f"Provider '{provider}' requires 'base_url' to be set for model {model_name}"
)
model_id = "/".join(model_name_tokens[1:])
if provider not in SUPPORTED_PROVIDERS:
if (
model_provider.get("base_url", None) is None
or model_provider.get("provider_interface", None) is None
):
raise Exception(
f"Must provide base_url and provider_interface for unsupported provider {provider} for model {model_name}. Supported providers are: {', '.join(SUPPORTED_PROVIDERS)}"
)
provider = model_provider.get("provider_interface", None)
elif model_provider.get("provider_interface", None) is not None:
raise Exception(
f"Please provide provider interface as part of model name {model_name} using the format <provider>/<model_id>. For example, use 'openai/gpt-3.5-turbo' instead of 'gpt-3.5-turbo' "
)
if model_id in model_name_keys:
raise Exception(
f"Duplicate model_id {model_id}, please provide unique model_id for each model_provider"
)
model_name_keys.add(model_id)
for routing_preference in model_provider.get("routing_preferences", []):
if routing_preference.get("name") in model_usage_name_keys:
raise Exception(
f"Duplicate routing preference name \"{routing_preference.get('name')}\", please provide unique name for each routing preference"
)
model_usage_name_keys.add(routing_preference.get("name"))
model_provider["model"] = model_id
model_provider["provider_interface"] = provider
model_provider_name_set.add(model_provider.get("name"))
if model_provider.get("provider") and model_provider.get(
"provider_interface"
):
raise Exception(
"Please provide either provider or provider_interface, not both"
)
if model_provider.get("provider"):
provider = model_provider["provider"]
model_provider["provider_interface"] = provider
del model_provider["provider"]
updated_model_providers.append(model_provider)
if model_provider.get("base_url", None):
base_url = model_provider["base_url"]
urlparse_result = urlparse(base_url)
base_url_path_prefix = urlparse_result.path
if base_url_path_prefix and base_url_path_prefix != "/":
# we will now support base_url_path_prefix. This means that the user can provide base_url like http://example.com/path and we will extract /path as base_url_path_prefix
model_provider["base_url_path_prefix"] = base_url_path_prefix
if urlparse_result.scheme == "" or urlparse_result.scheme not in [
"http",
"https",
]:
raise Exception(
"Please provide a valid URL with scheme (http/https) in base_url"
)
protocol = urlparse_result.scheme
port = urlparse_result.port
if port is None:
if protocol == "http":
port = 80
else:
port = 443
endpoint = urlparse_result.hostname
model_provider["endpoint"] = endpoint
model_provider["port"] = port
model_provider["protocol"] = protocol
cluster_name = (
provider + "_" + endpoint
) # make name unique by appending endpoint
model_provider["cluster_name"] = cluster_name
# Only add if cluster_name is not already present to avoid duplicates
if cluster_name not in llms_with_endpoint_cluster_names:
llms_with_endpoint.append(model_provider)
llms_with_endpoint_cluster_names.add(cluster_name)
if len(model_usage_name_keys) > 0:
routing_model_provider = config_yaml.get("routing", {}).get(
"model_provider", None
)
if (
routing_model_provider
and routing_model_provider not in model_provider_name_set
):
raise Exception(
f"Routing model_provider {routing_model_provider} is not defined in model_providers"
)
if (
routing_model_provider is None
and "arch-router" not in model_provider_name_set
):
updated_model_providers.append(
{
"name": "arch-router",
"provider_interface": "arch",
"model": config_yaml.get("routing", {}).get("model", "Arch-Router"),
}
)
# Always add arch-function model provider if not already defined
if "arch-function" not in model_provider_name_set:
updated_model_providers.append(
{
"name": "arch-function",
"provider_interface": "arch",
"model": "Arch-Function",
}
)
if "plano-orchestrator" not in model_provider_name_set:
updated_model_providers.append(
{
"name": "plano-orchestrator",
"provider_interface": "arch",
"model": "Plano-Orchestrator",
}
)
config_yaml["model_providers"] = deepcopy(updated_model_providers)
listeners_with_provider = 0
for listener in listeners:
print("Processing listener: ", listener)
model_providers = listener.get("model_providers", None)
if model_providers is not None:
listeners_with_provider += 1
if listeners_with_provider > 1:
raise Exception(
"Please provide model_providers either under listeners or at root level, not both. Currently we don't support multiple listeners with model_providers"
)
# Validate model aliases if present
if "model_aliases" in config_yaml:
model_aliases = config_yaml["model_aliases"]
for alias_name, alias_config in model_aliases.items():
target = alias_config.get("target")
if target not in model_name_keys:
raise Exception(
f"Model alias 2 - '{alias_name}' targets '{target}' which is not defined as a model. Available models: {', '.join(sorted(model_name_keys))}"
)
arch_config_string = yaml.dump(config_yaml)
arch_llm_config_string = yaml.dump(config_yaml)
use_agent_orchestrator = config_yaml.get("overrides", {}).get(
"use_agent_orchestrator", False
)
agent_orchestrator = None
if use_agent_orchestrator:
print("Using agent orchestrator")
if len(endpoints) == 0:
raise Exception(
"Please provide agent orchestrator in the endpoints section in your arch_config.yaml file"
)
elif len(endpoints) > 1:
raise Exception(
"Please provide single agent orchestrator in the endpoints section in your arch_config.yaml file"
)
else:
agent_orchestrator = list(endpoints.keys())[0]
print("agent_orchestrator: ", agent_orchestrator)
data = {
"prompt_gateway_listener": prompt_gateway,
"llm_gateway_listener": llm_gateway,
"arch_config": arch_config_string,
"arch_llm_config": arch_llm_config_string,
"arch_clusters": inferred_clusters,
"arch_model_providers": updated_model_providers,
"arch_tracing": arch_tracing,
"local_llms": llms_with_endpoint,
"agent_orchestrator": agent_orchestrator,
"listeners": listeners,
}
rendered = template.render(data)
print(ENVOY_CONFIG_FILE_RENDERED)
print(rendered)
with open(ENVOY_CONFIG_FILE_RENDERED, "w") as file:
file.write(rendered)
with open(ARCH_CONFIG_FILE_RENDERED, "w") as file:
file.write(arch_config_string)
def validate_prompt_config(arch_config_file, arch_config_schema_file):
with open(arch_config_file, "r") as file:
arch_config = file.read()
with open(arch_config_schema_file, "r") as file:
arch_config_schema = file.read()
config_yaml = yaml.safe_load(arch_config)
config_schema_yaml = yaml.safe_load(arch_config_schema)
try:
validate(config_yaml, config_schema_yaml)
except Exception as e:
print(
f"Error validating arch_config file: {arch_config_file}, schema file: {arch_config_schema_file}, error: {e}"
)
raise e
if __name__ == "__main__":
validate_and_render_schema()