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sync model validation with cli catalog

This commit is contained in:
CadeYu 2026-03-25 21:23:02 +08:00
parent f362a160c3
commit 8793336dad
8 changed files with 192 additions and 125 deletions
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81
cli/utils.py
View File

@ -4,6 +4,7 @@ from typing import List, Optional, Tuple, Dict
from rich.console import Console
from cli.models import AnalystType
from tradingagents.llm_clients.model_catalog import get_model_options
console = Console()
@ -129,48 +130,11 @@ def select_research_depth() -> int:
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Define shallow thinking llm engine options with their corresponding model names
# Ordering: medium → light → heavy (balanced first for quick tasks)
# Within same tier, newer models first
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
],
"anthropic": [
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
],
"xai": [
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
],
"openrouter": [
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
],
"ollama": [
("Qwen3:latest (8B, local)", "qwen3:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
],
}
choice = questionary.select(
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
for display, value in get_model_options(provider, "quick")
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
@ -194,50 +158,11 @@ def select_shallow_thinking_agent(provider) -> str:
def select_deep_thinking_agent(provider) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# Define deep thinking llm engine options with their corresponding model names
# Ordering: heavy → medium → light (most capable first for deep tasks)
# Within same tier, newer models first
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
],
"anthropic": [
("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
],
"xai": [
("Grok 4 - Flagship model", "grok-4-0709"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
],
"openrouter": [
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
],
"ollama": [
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("Qwen3:latest (8B, local)", "qwen3:latest"),
],
}
choice = questionary.select(
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
for display, value in get_model_options(provider, "deep")
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(

52
tests/test_model_validation.py Normal file
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@ -0,0 +1,52 @@
import unittest
import warnings
from tradingagents.llm_clients.base_client import BaseLLMClient
from tradingagents.llm_clients.model_catalog import get_known_models
from tradingagents.llm_clients.validators import validate_model
class DummyLLMClient(BaseLLMClient):
def __init__(self, provider: str, model: str):
self.provider = provider
super().__init__(model)
def get_llm(self):
self.warn_if_unknown_model()
return object()
def validate_model(self) -> bool:
return validate_model(self.provider, self.model)
class ModelValidationTests(unittest.TestCase):
def test_cli_catalog_models_are_all_validator_approved(self):
for provider, models in get_known_models().items():
if provider in ("ollama", "openrouter"):
continue
for model in models:
with self.subTest(provider=provider, model=model):
self.assertTrue(validate_model(provider, model))
def test_unknown_model_emits_warning_for_strict_provider(self):
client = DummyLLMClient("openai", "not-a-real-openai-model")
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
client.get_llm()
self.assertEqual(len(caught), 1)
self.assertIn("not-a-real-openai-model", str(caught[0].message))
self.assertIn("openai", str(caught[0].message))
def test_openrouter_and_ollama_accept_custom_models_without_warning(self):
for provider in ("openrouter", "ollama"):
client = DummyLLMClient(provider, "custom-model-name")
with self.subTest(provider=provider):
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
client.get_llm()
self.assertEqual(caught, [])

1
tradingagents/llm_clients/anthropic_client.py
View File

@ -14,6 +14,7 @@ class AnthropicClient(BaseLLMClient):
def get_llm(self) -> Any:
"""Return configured ChatAnthropic instance."""
self.warn_if_unknown_model()
llm_kwargs = {"model": self.model}
for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):

22
tradingagents/llm_clients/base_client.py
View File

@ -1,5 +1,6 @@
from abc import ABC, abstractmethod
from typing import Any, Optional
import warnings
class BaseLLMClient(ABC):
@ -10,6 +11,27 @@ class BaseLLMClient(ABC):
self.base_url = base_url
self.kwargs = kwargs
def get_provider_name(self) -> str:
"""Return the provider name used in warning messages."""
provider = getattr(self, "provider", None)
if provider:
return str(provider)
return self.__class__.__name__.removesuffix("Client").lower()
def warn_if_unknown_model(self) -> None:
"""Warn when the model is outside the known list for the provider."""
if self.validate_model():
return
warnings.warn(
(
f"Model '{self.model}' is not in the known model list for "
f"provider '{self.get_provider_name()}'. Continuing anyway."
),
RuntimeWarning,
stacklevel=2,
)
@abstractmethod
def get_llm(self) -> Any:
"""Return the configured LLM instance."""

1
tradingagents/llm_clients/google_client.py
View File

@ -36,6 +36,7 @@ class GoogleClient(BaseLLMClient):
def get_llm(self) -> Any:
"""Return configured ChatGoogleGenerativeAI instance."""
self.warn_if_unknown_model()
llm_kwargs = {"model": self.model}
for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):

106
tradingagents/llm_clients/model_catalog.py Normal file
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@ -0,0 +1,106 @@
"""Shared model catalog for CLI selections and validation."""
from __future__ import annotations
from typing import Dict, List, Tuple
ModelOption = Tuple[str, str]
ProviderModeOptions = Dict[str, List[ModelOption]]
MODEL_OPTIONS: ProviderModeOptions = {
"openai": {
"quick": [
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
],
"deep": [
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
],
},
"anthropic": {
"quick": [
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"deep": [
("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
},
"google": {
"quick": [
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
],
"deep": [
("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
],
},
"xai": {
"quick": [
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
],
"deep": [
("Grok 4 - Flagship model", "grok-4-0709"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
],
},
"openrouter": {
"quick": [
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
],
"deep": [
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
],
},
"ollama": {
"quick": [
("Qwen3:latest (8B, local)", "qwen3:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
],
"deep": [
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("Qwen3:latest (8B, local)", "qwen3:latest"),
],
},
}
def get_model_options(provider: str, mode: str) -> List[ModelOption]:
"""Return shared model options for a provider and selection mode."""
return MODEL_OPTIONS[provider.lower()][mode]
def get_known_models() -> Dict[str, List[str]]:
"""Build known model names from the shared CLI catalog."""
known_models: Dict[str, List[str]] = {}
for provider, mode_options in MODEL_OPTIONS.items():
model_names = {
value
for options in mode_options.values()
for _, value in options
}
known_models[provider] = sorted(model_names)
return known_models

1
tradingagents/llm_clients/openai_client.py
View File

@ -41,6 +41,7 @@ class OpenAIClient(BaseLLMClient):
def get_llm(self) -> Any:
"""Return configured ChatOpenAI instance."""
self.warn_if_unknown_model()
llm_kwargs = {"model": self.model}
if self.provider == "xai":

53
tradingagents/llm_clients/validators.py
View File

@ -1,53 +1,12 @@
"""Model name validators for each provider.
"""Model name validators for each provider."""
from .model_catalog import get_known_models
Only validates model names - does NOT enforce limits.
Let LLM providers use their own defaults for unspecified params.
"""
VALID_MODELS = {
"openai": [
# GPT-5 series
"gpt-5.4-pro",
"gpt-5.4",
"gpt-5.2",
"gpt-5.1",
"gpt-5",
"gpt-5-mini",
"gpt-5-nano",
# GPT-4.1 series
"gpt-4.1",
"gpt-4.1-mini",
"gpt-4.1-nano",
],
"anthropic": [
# Claude 4.6 series (latest)
"claude-opus-4-6",
"claude-sonnet-4-6",
# Claude 4.5 series
"claude-opus-4-5",
"claude-sonnet-4-5",
"claude-haiku-4-5",
],
"google": [
# Gemini 3.1 series (preview)
"gemini-3.1-pro-preview",
"gemini-3.1-flash-lite-preview",
# Gemini 3 series (preview)
"gemini-3-flash-preview",
# Gemini 2.5 series
"gemini-2.5-pro",
"gemini-2.5-flash",
"gemini-2.5-flash-lite",
],
"xai": [
# Grok 4.1 series
"grok-4-1-fast-reasoning",
"grok-4-1-fast-non-reasoning",
# Grok 4 series
"grok-4-0709",
"grok-4-fast-reasoning",
"grok-4-fast-non-reasoning",
],
provider: models
for provider, models in get_known_models().items()
if provider not in ("ollama", "openrouter")
}