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from __future__ import annotations
import logging
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING
from app.config import settings
from app.generator.prompts import (
ENHANCE_SYSTEM_PROMPT,
PROOFREAD_SYSTEM_PROMPT,
RICS_PROMPT,
VALIDATE_SYSTEM_PROMPT,
build_enhance_prompt,
build_lcel_invoke_vars,
build_proofread_prompt,
build_validate_prompt,
max_context_tokens_for_survey_level,
max_output_tokens_for_survey_level,
top_p_for_ai_involvement,
)
from app.llm.llm_throttle import throttled_llm_call
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from app.models.schemas import WritingStyleProfile
class AsyncLLMAdapter(ABC):
@abstractmethod
async def generate_section(
self,
skeleton: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
document_context: list[str] | None = None,
style_anchor: str | None = None,
hierarchy_section_snippets: list[str] | None = None,
paragraph_snippets: list[str] | None = None,
identity_facts: str | None = None,
survey_level: int | None = None,
reference_only_context: bool = False,
ai_percent: int | None = None,
interference_level: str | None = None,
) -> str: ...
@abstractmethod
async def proofread(
self,
text: str,
bullets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.15,
creativity_hint: str = "",
) -> str: ...
@abstractmethod
async def enhance(
self,
text: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
) -> str: ...
@abstractmethod
async def validate_section_compliance(
self,
*,
survey_level: int | None,
section_code: str,
bullets: list[str],
evidence_snippets: list[str],
text: str,
) -> str: ...
@abstractmethod
async def constrained_weave(
self,
*,
section_code: str,
section_title: str | None,
bullets: list[str],
standard_passages: list[str],
survey_level: int | None = None,
tenant_id: str | None = None,
) -> str: ...
class AsyncOpenAIAdapter(AsyncLLMAdapter):
"""Async generate mode via LangChain LCEL; proofread/enhance/validate via OpenAI ChatCompletions."""
def __init__(self) -> None:
from langchain_openai import ChatOpenAI
from openai import AsyncOpenAI
self._client = AsyncOpenAI(api_key=settings.openai_api_key)
self._model = settings.chat_model
self._lc_llm = ChatOpenAI(
model=self._model,
temperature=0.2,
max_tokens=settings.max_output_tokens,
api_key=settings.openai_api_key,
max_retries=3,
)
async def _call_async(
self,
*,
system: str,
user: str,
phase: str,
section_id: str | None,
max_tokens: int | None = None,
temperature: float = 0.2,
survey_level: int | None = None,
interference_level: str | None = None,
tenant_id: str | None = None,
) -> str:
from app.llm.llm_throttle import make_cache_hit_slot
from app.llm.prompt_cache import (
build_chat_messages,
log_openai_cache_usage,
openai_extra_kwargs,
prompt_caching_active,
)
cache_slot = make_cache_hit_slot()
async def _do_call() -> str:
messages = build_chat_messages(system=system, user=user)
extra = openai_extra_kwargs(
phase=phase,
model=self._model,
survey_level=survey_level,
interference_level=interference_level,
tenant_id=tenant_id,
)
response = await self._client.chat.completions.create(
model=self._model,
messages=messages,
max_tokens=max_tokens or settings.max_output_tokens,
temperature=temperature,
**extra,
)
if prompt_caching_active():
cache_slot[0] = log_openai_cache_usage(
response, phase=phase, section_id=section_id
)
return (response.choices[0].message.content or "").strip()
return await throttled_llm_call(
phase=phase,
section_id=section_id,
cache_hit_out=cache_slot,
call=_do_call,
)
async def generate_section(
self,
skeleton: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
document_context: list[str] | None = None,
style_anchor: str | None = None,
hierarchy_section_snippets: list[str] | None = None,
paragraph_snippets: list[str] | None = None,
identity_facts: str | None = None,
survey_level: int | None = None,
reference_only_context: bool = False,
ai_percent: int | None = None,
interference_level: str | None = None,
tenant_id: str | None = None,
) -> str:
from langchain_core.output_parsers import StrOutputParser
fine = paragraph_snippets if paragraph_snippets is not None else snippets
out_tokens = max_output_tokens_for_survey_level(
survey_level, interference_level=interference_level
)
ctx_tokens = max_context_tokens_for_survey_level(
survey_level, interference_level=interference_level
)
vars_ = build_lcel_invoke_vars(
skeleton=skeleton,
bullets=bullets,
snippets=None,
max_context_tokens=ctx_tokens,
style_profile=style_profile,
creativity_hint=creativity_hint,
document_snippets=document_context,
section_snippets=None,
hierarchy_section_snippets=hierarchy_section_snippets,
paragraph_snippets=fine,
style_anchor=style_anchor,
identity_facts=identity_facts,
survey_level=survey_level,
reference_only_context=reference_only_context,
ai_percent=ai_percent,
interference_level=interference_level,
)
top_p = top_p_for_ai_involvement(ai_percent)
chain = (
RICS_PROMPT
| self._lc_llm.bind(
temperature=temperature, max_tokens=out_tokens, top_p=top_p
)
| StrOutputParser()
)
from app.llm.prompt_cache import (
build_chat_messages,
log_openai_cache_usage,
openai_extra_kwargs,
prompt_caching_active,
)
phase = "generate_section"
if prompt_caching_active():
system = str(vars_.get("system_content") or "")
user = str(vars_.get("user_content") or "")
extra = openai_extra_kwargs(
phase=phase,
model=self._model,
survey_level=survey_level,
interference_level=interference_level,
tenant_id=tenant_id,
)
from app.llm.llm_throttle import make_cache_hit_slot
cache_slot = make_cache_hit_slot()
async def _cached_generate() -> str:
response = await self._client.chat.completions.create(
model=self._model,
messages=build_chat_messages(system=system, user=user),
max_tokens=out_tokens,
temperature=temperature,
top_p=top_p,
**extra,
)
cache_slot[0] = log_openai_cache_usage(
response, phase=phase, section_id=None
)
return (response.choices[0].message.content or "").strip()
return await throttled_llm_call(
phase=phase,
section_id=None,
cache_hit_out=cache_slot,
call=_cached_generate,
)
from app.llm.lcel_invoke import ainvoke_lcel_chain
return await ainvoke_lcel_chain(
chain,
vars_,
phase=phase,
section_id=None,
)
async def proofread(
self,
text: str,
bullets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.15,
creativity_hint: str = "",
) -> str:
from app.generator.prompts import build_proofread_prompt
from app.chunking.splitter import count_tokens
user_prompt = build_proofread_prompt(
text=text,
bullets=bullets,
style_profile=style_profile,
creativity_hint=creativity_hint,
)
out_cap = max(700, min(2400, count_tokens(text or "") + 200))
return await self._call_async(
system=PROOFREAD_SYSTEM_PROMPT,
user=user_prompt,
phase="proofread",
section_id=None,
max_tokens=out_cap,
temperature=temperature,
)
async def enhance(
self,
text: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
) -> str:
from app.chunking.splitter import count_tokens
user_prompt = build_enhance_prompt(
text=text,
bullets=bullets,
snippets=snippets,
max_context_tokens=settings.max_context_tokens,
style_profile=style_profile,
creativity_hint=creativity_hint,
)
out_cap = max(900, min(2400, count_tokens(text or "") + 800))
return await self._call_async(
system=ENHANCE_SYSTEM_PROMPT,
user=user_prompt,
phase="enhance",
section_id=None,
max_tokens=out_cap,
temperature=temperature,
)
async def validate_section_compliance(
self,
*,
survey_level: int | None,
section_code: str,
bullets: list[str],
evidence_snippets: list[str],
text: str,
) -> str:
user_prompt = build_validate_prompt(
survey_level=survey_level,
section_code=section_code,
bullets=bullets,
evidence_snippets=evidence_snippets,
text=text,
)
result = await self._call_async(
system=VALIDATE_SYSTEM_PROMPT,
user=user_prompt,
phase="validate_section",
section_id=section_code,
max_tokens=220,
temperature=0.0,
)
return (result or "").strip()
async def constrained_weave(
self,
*,
section_code: str,
section_title: str | None,
bullets: list[str],
standard_passages: list[str],
survey_level: int | None = None,
tenant_id: str | None = None,
) -> str:
from app.generator.prompts import _ASSEMBLY_SYSTEM_CORE
cleaned_passages = [
str(p).strip() for p in (standard_passages or []) if str(p).strip()
]
cleaned_bullets = [str(b).strip() for b in (bullets or []) if str(b).strip()]
if not cleaned_passages or not cleaned_bullets:
return ""
title_part = f" — {section_title}" if section_title else ""
user = (
f"SECTION: {section_code}{title_part}\n\n"
"STANDARD SOURCE PASSAGES (preserve wording; weave NOTES facts into the slots):\n"
+ "\n".join(f"- {p}" for p in cleaned_passages)
+ "\n\nINSPECTOR'S RAW NOTES (substitute these specifics into the standards):\n"
+ "\n".join(f"- {b}" for b in cleaned_bullets)
+ "\n\nProduce the structurally-routed output now. Standard wording stays, "
"note facts replace generic slots, no new sentences, no new claims."
)
from app.llm.llm_throttle import make_cache_hit_slot
from app.llm.prompt_cache import (
build_chat_messages,
log_openai_cache_usage,
openai_extra_kwargs,
prompt_caching_active,
)
cache_slot = make_cache_hit_slot()
phase = "constrained_weave"
async def _do_call() -> str:
msgs = build_chat_messages(system=_ASSEMBLY_SYSTEM_CORE, user=user)
extra = openai_extra_kwargs(
phase=phase,
model=self._model,
survey_level=survey_level,
tenant_id=tenant_id,
)
response = await self._client.chat.completions.create(
model=self._model,
messages=msgs,
max_tokens=600,
temperature=0.0,
top_p=0.1,
**extra,
)
if prompt_caching_active():
cache_slot[0] = log_openai_cache_usage(
response, phase=phase, section_id=section_code
)
return (response.choices[0].message.content or "").strip()
return await throttled_llm_call(
phase=phase,
section_id=section_code,
cache_hit_out=cache_slot,
call=_do_call,
)
class MockAsyncLLMAdapter(AsyncLLMAdapter):
"""Deterministic mock adapter for async paths without OpenAI."""
def __init__(self) -> None:
pass
async def generate_section(
self,
skeleton: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
document_context: list[str] | None = None,
style_anchor: str | None = None,
hierarchy_section_snippets: list[str] | None = None,
paragraph_snippets: list[str] | None = None,
identity_facts: str | None = None,
survey_level: int | None = None,
reference_only_context: bool = False,
ai_percent: int | None = None,
interference_level: str | None = None,
) -> str:
style_note = f" (style: {style_profile.tone})" if style_profile else ""
summary = "; ".join(bullets[:3]) if bullets else "No facts provided"
return f"Based on the available information{style_note}: {summary}."
async def proofread(
self,
text: str,
bullets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.15,
creativity_hint: str = "",
) -> str:
return (
f"{text}\n---NOTES---\n"
"No OpenAI key configured — proofreading not available in mock mode."
)
async def enhance(
self,
text: str,
bullets: list[str],
snippets: list[str],
style_profile: "WritingStyleProfile | None" = None,
temperature: float = 0.2,
creativity_hint: str = "",
) -> str:
extra = (
f" Additional context from {len(snippets)} retrieved source(s) noted."
if snippets
else ""
)
return (
f"{text}{extra} "
"[No OpenAI key configured — full technical enhancement requires OPENAI_API_KEY.]"
)
async def validate_section_compliance(
self,
*,
survey_level: int | None,
section_code: str,
bullets: list[str],
evidence_snippets: list[str],
text: str,
) -> str:
return "PASS"
async def constrained_weave(
self,
*,
section_code: str,
section_title: str | None,
bullets: list[str],
standard_passages: list[str],
survey_level: int | None = None,
tenant_id: str | None = None,
) -> str:
return ""
_async_llm_adapter_instance: AsyncLLMAdapter | None = None
def get_async_llm_adapter() -> AsyncLLMAdapter:
"""Return a singleton async adapter (real OpenAI when key configured)."""
global _async_llm_adapter_instance
if _async_llm_adapter_instance is not None:
return _async_llm_adapter_instance
if settings.openai_api_key:
_async_llm_adapter_instance = AsyncOpenAIAdapter()
else:
_async_llm_adapter_instance = MockAsyncLLMAdapter()
return _async_llm_adapter_instance
|