mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-05-01 14:33:10 +03:00
7c37249f80
This release bundles substantial work since v0.2.3: - Structured-output Research Manager, Trader, and Portfolio Manager (canonical with_structured_output pattern, single LLM call per agent, rendered markdown preserves the existing report shape). - LangGraph checkpoint resume for crash recovery (--checkpoint flag). - Persistent decision log replacing the per-agent BM25 memory, with deferred reflection driven by yfinance returns + alpha vs SPY. - DeepSeek, Qwen, GLM, and Azure OpenAI provider support; dynamic OpenRouter model selection. - Docker support; cache and logs moved to ~/.tradingagents/ to fix Docker permission issues. - Windows UTF-8 encoding fix on every file I/O site. - 5-tier rating consistency (Buy / Overweight / Hold / Underweight / Sell) across Research Manager, Portfolio Manager, signal processor, memory log. Plus the small quality items in this commit: 1. Suppress noisy Pydantic serializer warnings from OpenAI Responses-API parse path by defaulting structured-output to method="function_calling" (root-cause fix, not a warnings filter — same typed result, no warnings). 2. Ship scripts/smoke_structured_output.py so contributors can verify their provider's structured-output path with one command. 3. Add opt-in memory_log_max_entries config — when set, oldest resolved memory log entries are pruned once the cap is exceeded; pending entries (unresolved) are never pruned. 4. backend_url default changed from the OpenAI URL to None so the per-provider client falls back to its native endpoint instead of leaking OpenAI's URL into Gemini / other clients. CHANGELOG.md added with the full v0.2.4 entry. 92 tests pass without API keys.
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
import os
|
|
from typing import Any, Optional
|
|
|
|
from langchain_openai import ChatOpenAI
|
|
|
|
from .base_client import BaseLLMClient, normalize_content
|
|
from .validators import validate_model
|
|
|
|
|
|
class NormalizedChatOpenAI(ChatOpenAI):
|
|
"""ChatOpenAI with normalized content output.
|
|
|
|
The Responses API returns content as a list of typed blocks
|
|
(reasoning, text, etc.). This normalizes to string for consistent
|
|
downstream handling.
|
|
"""
|
|
|
|
def invoke(self, input, config=None, **kwargs):
|
|
return normalize_content(super().invoke(input, config, **kwargs))
|
|
|
|
def with_structured_output(self, schema, *, method=None, **kwargs):
|
|
"""Wrap with structured output, defaulting to function_calling for OpenAI.
|
|
|
|
langchain-openai's Responses-API-parse path (the default for json_schema
|
|
when use_responses_api=True) calls response.model_dump(...) on the OpenAI
|
|
SDK's union-typed parsed response, which makes Pydantic emit ~20
|
|
PydanticSerializationUnexpectedValue warnings per call. The function-calling
|
|
path returns a plain tool-call shape that does not trigger that
|
|
serialization, so it is the cleaner choice for our combination of
|
|
use_responses_api=True + with_structured_output. Both paths use OpenAI's
|
|
strict mode and produce the same typed Pydantic instance.
|
|
"""
|
|
if method is None:
|
|
method = "function_calling"
|
|
return super().with_structured_output(schema, method=method, **kwargs)
|
|
|
|
# Kwargs forwarded from user config to ChatOpenAI
|
|
_PASSTHROUGH_KWARGS = (
|
|
"timeout", "max_retries", "reasoning_effort",
|
|
"api_key", "callbacks", "http_client", "http_async_client",
|
|
)
|
|
|
|
# Provider base URLs and API key env vars
|
|
_PROVIDER_CONFIG = {
|
|
"xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
|
|
"deepseek": ("https://api.deepseek.com", "DEEPSEEK_API_KEY"),
|
|
"qwen": ("https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "DASHSCOPE_API_KEY"),
|
|
"glm": ("https://api.z.ai/api/paas/v4/", "ZHIPU_API_KEY"),
|
|
"openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
|
|
"ollama": ("http://localhost:11434/v1", None),
|
|
}
|
|
|
|
|
|
class OpenAIClient(BaseLLMClient):
|
|
"""Client for OpenAI, Ollama, OpenRouter, and xAI providers.
|
|
|
|
For native OpenAI models, uses the Responses API (/v1/responses) which
|
|
supports reasoning_effort with function tools across all model families
|
|
(GPT-4.1, GPT-5). Third-party compatible providers (xAI, OpenRouter,
|
|
Ollama) use standard Chat Completions.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
base_url: Optional[str] = None,
|
|
provider: str = "openai",
|
|
**kwargs,
|
|
):
|
|
super().__init__(model, base_url, **kwargs)
|
|
self.provider = provider.lower()
|
|
|
|
def get_llm(self) -> Any:
|
|
"""Return configured ChatOpenAI instance."""
|
|
self.warn_if_unknown_model()
|
|
llm_kwargs = {"model": self.model}
|
|
|
|
# Provider-specific base URL and auth
|
|
if self.provider in _PROVIDER_CONFIG:
|
|
base_url, api_key_env = _PROVIDER_CONFIG[self.provider]
|
|
llm_kwargs["base_url"] = base_url
|
|
if api_key_env:
|
|
api_key = os.environ.get(api_key_env)
|
|
if api_key:
|
|
llm_kwargs["api_key"] = api_key
|
|
else:
|
|
llm_kwargs["api_key"] = "ollama"
|
|
elif self.base_url:
|
|
llm_kwargs["base_url"] = self.base_url
|
|
|
|
# Forward user-provided kwargs
|
|
for key in _PASSTHROUGH_KWARGS:
|
|
if key in self.kwargs:
|
|
llm_kwargs[key] = self.kwargs[key]
|
|
|
|
# Native OpenAI: use Responses API for consistent behavior across
|
|
# all model families. Third-party providers use Chat Completions.
|
|
if self.provider == "openai":
|
|
llm_kwargs["use_responses_api"] = True
|
|
|
|
return NormalizedChatOpenAI(**llm_kwargs)
|
|
|
|
def validate_model(self) -> bool:
|
|
"""Validate model for the provider."""
|
|
return validate_model(self.provider, self.model)
|