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Support hybrid LLM: local Qwen triage + OpenAI escalation

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Triage analysis runs on Qwen 8B (athena.lan) for free first-pass.
Escalation, chat, image roasts, and commands use GPT-4o via OpenAI.

Each tier gets its own base URL, API key, and concurrency settings.
Local models get /no_think and serialized requests automatically.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
AJ Isaacs
2026-02-23 12:20:07 -05:00
parent b5e401f036
commit 8a06ddbd6e
3 changed files with 49 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
.env.example
View File

@@ -1,7 +1,12 @@
DISCORD_BOT_TOKEN=your_token_here
LLM_BASE_URL=
LLM_MODEL=gpt-4o-mini
# Triage model (local llama.cpp / Ollama — leave BASE_URL empty for OpenAI)
LLM_BASE_URL=http://athena.lan:11434
LLM_MODEL=Qwen3-8B-Q6_K
LLM_API_KEY=not-needed
# Escalation model (OpenAI — leave BASE_URL empty for OpenAI)
LLM_ESCALATION_BASE_URL=
LLM_ESCALATION_MODEL=gpt-4o
LLM_API_KEY=your_openai_api_key_here
LLM_ESCALATION_API_KEY=your_openai_api_key_here
# Database
MSSQL_SA_PASSWORD=YourStrong!Passw0rd
DB_CONNECTION_STRING=DRIVER={ODBC Driver 18 for SQL Server};SERVER=localhost,1433;DATABASE=BreehaviorMonitor;UID=sa;PWD=YourStrong!Passw0rd;TrustServerCertificate=yes

22
bot.py
View File

@@ -68,15 +68,25 @@ class BCSBot(commands.Bot):
# Database (initialized async in setup_hook)
self.db = Database()
# LLM clients (OpenAI — set LLM_BASE_URL to override for local models)
# Triage LLM (local Qwen on athena for cheap first-pass analysis)
llm_base_url = os.getenv("LLM_BASE_URL", "")
llm_model = os.getenv("LLM_MODEL", "gpt-4o-mini")
llm_api_key = os.getenv("LLM_API_KEY", "")
self.llm = LLMClient(llm_base_url, llm_model, llm_api_key, db=self.db)
llm_api_key = os.getenv("LLM_API_KEY", "not-needed")
is_local = bool(llm_base_url)
self.llm = LLMClient(
llm_base_url, llm_model, llm_api_key, db=self.db,
no_think=is_local, concurrency=1 if is_local else 4,
)
# Heavy/escalation model for re-analysis, chat, and manual commands
llm_heavy_model = os.getenv("LLM_ESCALATION_MODEL", "gpt-4o")
self.llm_heavy = LLMClient(llm_base_url, llm_heavy_model, llm_api_key, db=self.db)
# Heavy/escalation LLM (OpenAI for re-analysis, chat, image roasts, commands)
esc_base_url = os.getenv("LLM_ESCALATION_BASE_URL", "")
esc_model = os.getenv("LLM_ESCALATION_MODEL", "gpt-4o")
esc_api_key = os.getenv("LLM_ESCALATION_API_KEY", llm_api_key)
esc_is_local = bool(esc_base_url)
self.llm_heavy = LLMClient(
esc_base_url, esc_model, esc_api_key, db=self.db,
no_think=esc_is_local, concurrency=1 if esc_is_local else 4,
)
# Active mode (server-wide)
modes_config = config.get("modes", {})

34
utils/llm_client.py
View File

@@ -128,15 +128,18 @@ ANALYSIS_TOOL = {
class LLMClient:
def __init__(self, base_url: str, model: str, api_key: str = "not-needed", db=None):
def __init__(self, base_url: str, model: str, api_key: str = "not-needed",
db=None, no_think: bool = False, concurrency: int = 4):
self.model = model
self.host = base_url.rstrip("/")
self._db = db
client_kwargs = {"api_key": api_key, "timeout": 120.0}
self._no_think = no_think
timeout = 600.0 if self.host else 120.0 # local models need longer for VRAM load
client_kwargs = {"api_key": api_key, "timeout": timeout}
if self.host:
client_kwargs["base_url"] = f"{self.host}/v1"
self._client = AsyncOpenAI(**client_kwargs)
self._semaphore = asyncio.Semaphore(4)
self._semaphore = asyncio.Semaphore(concurrency)
def _log_llm(self, request_type: str, duration_ms: int, success: bool,
request: str, response: str | None = None, error: str | None = None,
@@ -156,6 +159,9 @@ class LLMClient:
output_tokens=output_tokens,
))
def _append_no_think(self, text: str) -> str:
return text + "\n/no_think" if self._no_think else text
async def close(self):
await self._client.close()
@@ -168,7 +174,8 @@ class LLMClient:
user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
if channel_context:
user_content += f"=== CHANNEL INFO ===\n{channel_context}\n\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}"
user_content = self._append_no_think(user_content)
req_json = json.dumps([
{"role": "system", "content": SYSTEM_PROMPT[:500]},
@@ -299,9 +306,14 @@ class LLMClient:
first content token arrives (useful for triggering the typing indicator
only after the model starts generating).
"""
# Append /no_think to the last user message for local Qwen models
patched = list(messages)
if self._no_think and patched and patched[-1].get("role") == "user":
patched[-1] = {**patched[-1], "content": self._append_no_think(patched[-1]["content"])}
req_json = json.dumps([
{"role": "system", "content": system_prompt[:500]},
*[{"role": m["role"], "content": str(m.get("content", ""))[:200]} for m in messages],
*[{"role": m["role"], "content": str(m.get("content", ""))[:200]} for m in patched],
], default=str)
t0 = time.monotonic()
@@ -311,7 +323,7 @@ class LLMClient:
model=self.model,
messages=[
{"role": "system", "content": system_prompt},
*messages,
*patched,
],
temperature=0.8,
max_tokens=2048,
@@ -355,8 +367,11 @@ class LLMClient:
user_content: list[dict] = [
{"type": "image_url", "image_url": {"url": data_url}},
]
if user_text:
user_content.append({"type": "text", "text": user_text})
text_part = user_text or ""
if self._no_think:
text_part = (text_part + "\n/no_think").strip()
if text_part:
user_content.append({"type": "text", "text": text_part})
req_json = json.dumps([
{"role": "system", "content": system_prompt[:500]},
@@ -415,7 +430,8 @@ class LLMClient:
user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
if channel_context:
user_content += f"=== CHANNEL INFO ===\n{channel_context}\n\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}"
user_content = self._append_no_think(user_content)
req_json = json.dumps([
{"role": "system", "content": SYSTEM_PROMPT[:500]},