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new feature: reasoning_content for compatible models in ainvoke_response()

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Cristiano Hoshikawa
2026-08-21 07:40:59 -03:00
parent 8b908df58f
commit e063e4b500
13 changed files with 1726 additions and 7 deletions
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4
agent_framework_oci/libs/agent_framework/src/agent_framework/llm/__init__.py
View File

@@ -0,0 +1,4 @@
from .base import LLMProvider
from .types import LLMResponse
__all__ = ["LLMProvider", "LLMResponse"]

21
agent_framework_oci/libs/agent_framework/src/agent_framework/llm/base.py
View File

@@ -1,6 +1,25 @@
from abc import ABC, abstractmethod
from typing import Any
from .types import LLMResponse
class LLMProvider(ABC):
@abstractmethod
async def ainvoke(self, messages: list[dict[str, str]], **kwargs: Any) -> str: ...
async def ainvoke(self, messages: list[dict[str, str]], **kwargs: Any) -> str:
"""Legacy API. Must keep returning only the textual answer."""
...
async def ainvoke_response(
self,
messages: list[dict[str, str]],
**kwargs: Any,
) -> LLMResponse:
"""Rich opt-in API with a backward-compatible fallback.
Custom providers that only implement ``ainvoke`` continue to work. They
simply expose ``content`` and leave optional provider metadata/reasoning
empty until they choose to override this method.
"""
content = await self.ainvoke(messages, **kwargs)
return LLMResponse(content=str(content or ""))

121
agent_framework_oci/libs/agent_framework/src/agent_framework/llm/providers.py
View File

@@ -5,6 +5,7 @@ import os
from typing import Any
from .base import LLMProvider
from .types import LLMResponse
from .profile_resolver import LLMProfileResolver
from agent_framework.observability.token_cost import TokenUsageCollector
from agent_framework.billing.usage_repository import UsageRepository, UsageRecord
@@ -12,6 +13,62 @@ from agent_framework.billing.usage_repository import UsageRepository, UsageRecor
logger = logging.getLogger("agent_framework.llm")
def _coerce_reasoning_text(value: Any) -> str | None:
"""Normalize provider-specific reasoning payloads without inventing content."""
if value is None:
return None
if isinstance(value, str):
value = value.strip()
return value or None
if isinstance(value, (list, tuple)):
chunks: list[str] = []
for item in value:
if isinstance(item, str):
text = item
else:
text = getattr(item, "text", None) or getattr(item, "content", None)
if text is None and isinstance(item, dict):
text = item.get("text") or item.get("content")
if text:
chunks.append(str(text))
joined = "".join(chunks).strip()
return joined or None
if isinstance(value, dict):
for key in ("content", "text", "reasoning_content", "reasoning"):
text = _coerce_reasoning_text(value.get(key))
if text:
return text
return None
text = str(value).strip()
return text or None
def _extract_reasoning_content(obj: Any) -> str | None:
"""Best-effort extraction across OpenAI-compatible and OCI response shapes."""
if obj is None:
return None
for attr in ("reasoning_content", "reasoning"):
text = _coerce_reasoning_text(getattr(obj, attr, None))
if text:
return text
if isinstance(obj, dict):
for key in ("reasoning_content", "reasoning"):
text = _coerce_reasoning_text(obj.get(key))
if text:
return text
extra = getattr(obj, "model_extra", None)
if isinstance(extra, dict):
for key in ("reasoning_content", "reasoning"):
text = _coerce_reasoning_text(extra.get(key))
if text:
return text
return None
def _clean_config_value(value: Any) -> str | None:
"""Normalize values loaded from .env/YAML/PowerShell.
@@ -100,6 +157,9 @@ class MockLLMProvider(LLMProvider):
self.model = "mock-llm"
async def ainvoke(self, messages, **kwargs):
return (await self.ainvoke_response(messages, **kwargs)).content
async def ainvoke_response(self, messages, **kwargs):
profile_name = kwargs.get("profile_name", "default")
component_name = kwargs.get("component_name") or kwargs.get("component") or profile_name or "default"
generation_name = kwargs.get("generation_name") or f"llm.{component_name}"
@@ -125,7 +185,15 @@ class MockLLMProvider(LLMProvider):
generation.set_metadata(**usage)
if self.usage_repository:
await self.usage_repository.record(UsageRecord.from_usage("mock", model, generation_name, usage, llm_metadata))
return answer
return LLMResponse(
content=answer,
reasoning_content=None,
provider="mock",
model=model,
profile_name=profile_name,
usage=dict(usage),
metadata=dict(llm_metadata),
)
class OCICompatibleOpenAIProvider(LLMProvider):
@@ -208,6 +276,9 @@ class OCICompatibleOpenAIProvider(LLMProvider):
return self._clients[key]
async def ainvoke(self, messages, **kwargs):
return (await self.ainvoke_response(messages, **kwargs)).content
async def ainvoke_response(self, messages, **kwargs):
profile_name = kwargs.pop("profile_name", None)
component_name = kwargs.pop("component_name", None) or kwargs.pop("component", None) or profile_name or "default"
generation_name = kwargs.pop("generation_name", None) or f"llm.{component_name}"
@@ -227,7 +298,7 @@ class OCICompatibleOpenAIProvider(LLMProvider):
if provider == "mock":
mock = MockLLMProvider(self.settings, telemetry=self.telemetry, usage_repository=self.usage_repository)
return await mock.ainvoke(
return await mock.ainvoke_response(
messages,
model=model,
profile_name=resolved_profile_name,
@@ -241,7 +312,7 @@ class OCICompatibleOpenAIProvider(LLMProvider):
if provider == "oci_sdk":
sdk = OCISDKProvider(self.settings, telemetry=self.telemetry, usage_repository=self.usage_repository)
return await sdk.ainvoke(
return await sdk.ainvoke_response(
messages,
model=model,
temperature=temperature,
@@ -337,7 +408,9 @@ class OCICompatibleOpenAIProvider(LLMProvider):
model_parameters=model_parameters,
) as generation:
resp = await client.chat.completions.create(**request_kwargs)
answer = resp.choices[0].message.content or ""
message = resp.choices[0].message
answer = message.content or ""
reasoning_content = _extract_reasoning_content(message)
usage_metadata = self.token_collector.enrich(model, getattr(resp, "usage", None))
usage_metadata.update({
@@ -358,7 +431,15 @@ class OCICompatibleOpenAIProvider(LLMProvider):
UsageRecord.from_usage(provider, model, generation_name, usage_metadata, llm_metadata)
)
return answer
return LLMResponse(
content=answer,
reasoning_content=reasoning_content,
provider=provider,
model=model,
profile_name=resolved_profile_name,
usage=dict(usage_metadata),
metadata=dict(llm_metadata),
)
except Exception as exc:
logger.exception(
"Erro ao chamar LLM provider=%s component=%s profile=%s model=%s: %s",
@@ -576,7 +657,26 @@ class OCISDKProvider(LLMProvider):
return str(chat_response)
@staticmethod
def _extract_reasoning_content(response) -> str | None:
data = getattr(response, "data", response)
chat_response = getattr(data, "chat_response", None) or data
text = _extract_reasoning_content(chat_response)
if text:
return text
choices = getattr(chat_response, "choices", None) or []
if choices:
first = choices[0]
message = getattr(first, "message", None)
return _extract_reasoning_content(message) or _extract_reasoning_content(first)
return None
async def ainvoke(self, messages, **kwargs):
return (await self.ainvoke_response(messages, **kwargs)).content
async def ainvoke_response(self, messages, **kwargs):
import asyncio
model = _clean_config_value(kwargs.get("model") or self.model)
@@ -667,6 +767,7 @@ class OCISDKProvider(LLMProvider):
) as generation:
response = await asyncio.to_thread(client.chat, details)
answer = self._extract_answer(response)
reasoning_content = self._extract_reasoning_content(response)
usage_metadata = {
"prompt_tokens": max(1, len(str(messages)) // 4),
@@ -693,7 +794,15 @@ class OCISDKProvider(LLMProvider):
)
)
return answer
return LLMResponse(
content=answer,
reasoning_content=reasoning_content,
provider="oci_sdk",
model=model,
profile_name=profile_name,
usage=dict(usage_metadata),
metadata=dict(llm_metadata),
)
def create_llm(settings, telemetry=None, usage_repository: UsageRepository | None = None) -> LLMProvider:

22
agent_framework_oci/libs/agent_framework/src/agent_framework/llm/types.py Normal file
View File

@@ -0,0 +1,22 @@
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
@dataclass(slots=True)
class LLMResponse:
"""Canonical rich response returned by LLM providers.
``content`` preserves the legacy textual answer. ``reasoning_content`` is
optional because not every model/provider/API exposes reasoning text.
Consumers must never depend on it being present.
"""
content: str
reasoning_content: str | None = None
provider: str | None = None
model: str | None = None
profile_name: str | None = None
usage: dict[str, Any] = field(default_factory=dict)
metadata: dict[str, Any] = field(default_factory=dict)