267 lines
13 KiB
Python
267 lines
13 KiB
Python
"""
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OmniRoute LLM Service Adapter
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Orchestrates AI reasoning, classification, synthesis, and code generation via OmniRoute gateway.
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Tracks token usage (input_tokens, output_tokens, total_tokens) and handles model policy routing.
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"""
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import json
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import time
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import re
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import urllib.request
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import asyncio
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from typing import Dict, Any, Optional, Tuple
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from .base import BaseServiceAdapter, ServiceHealth
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from ..config import config
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class OmniRouteAdapter(BaseServiceAdapter):
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def __init__(self, endpoint: str = "https://omni.godno.de/v1", api_key: str = ""):
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super().__init__(service_id="omniroute", endpoint=endpoint, api_key=api_key or config.services.omniroute_api_key)
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async def check_health(self) -> ServiceHealth:
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start = time.time()
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try:
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url = f"{self.endpoint}/models"
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headers = {"User-Agent": "ThinkStorm-Orchestrator/0.1"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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req = urllib.request.Request(url, headers=headers)
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loop = asyncio.get_running_loop()
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def fetch():
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with urllib.request.urlopen(req, timeout=8.0) as resp:
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return resp.read()
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raw = await loop.run_in_executor(None, fetch)
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data = json.loads(raw.decode("utf-8"))
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models_count = len(data.get("data", []))
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elapsed = int((time.time() - start) * 1000)
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return ServiceHealth(
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service_id=self.service_id,
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healthy=True,
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endpoint=self.endpoint,
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message=f"OmniRoute online ({models_count} models available)",
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response_time_ms=elapsed,
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extra={"models_count": models_count}
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)
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except Exception as e:
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elapsed = int((time.time() - start) * 1000)
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return ServiceHealth(
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service_id=self.service_id,
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healthy=False,
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endpoint=self.endpoint,
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message=f"OmniRoute connection error: {str(e)}",
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response_time_ms=elapsed
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)
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def resolve_model(self, policy: str) -> str:
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"""Resolves model policy to concrete model identifier."""
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if policy == "fast":
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return config.services.omniroute_model_fast or "openrouter/openai/gpt-oss-20b:free-low"
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elif policy == "coding":
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return config.services.omniroute_model_coding or "auto/best-coding"
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elif policy == "research" or policy == "reasoning":
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return config.services.omniroute_model_reasoning or "auto/best-reasoning"
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return config.services.omniroute_model_reasoning or "auto/best-reasoning"
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async def chat_completion(
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self,
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system_prompt: str,
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user_prompt: str,
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model_policy: str = "reasoning",
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max_tokens: int = 1500,
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temperature: float = 0.7,
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model_override: Optional[str] = None
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) -> Dict[str, Any]:
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"""Executes a chat completion via OmniRoute with provenance token tracking."""
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model = model_override.strip() if model_override and model_override.strip() else self.resolve_model(model_policy)
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url = f"{self.endpoint}/chat/completions"
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payload = {
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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],
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"max_tokens": max_tokens,
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"temperature": temperature
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}
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headers = {
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"Content-Type": "application/json",
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"User-Agent": "ThinkStorm-Orchestrator/0.1"
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}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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start_time = time.time()
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loop = asyncio.get_running_loop()
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def execute():
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req_data = json.dumps(payload).encode("utf-8")
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req = urllib.request.Request(url, data=req_data, headers=headers, method="POST")
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with urllib.request.urlopen(req, timeout=40.0) as resp:
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content_parts = []
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reasoning_parts = []
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model_name = model
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in_tokens = 0
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out_tokens = 0
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for line in resp:
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line_str = line.decode("utf-8", errors="ignore").strip()
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if not line_str or line_str.startswith(":"):
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continue
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if line_str.startswith("data:"):
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payload_str = line_str[5:].strip()
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if payload_str == "[DONE]":
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break
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try:
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obj = json.loads(payload_str)
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model_name = obj.get("model", model_name)
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usage = obj.get("usage", {})
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if usage:
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in_tokens = usage.get("prompt_tokens", in_tokens)
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out_tokens = usage.get("completion_tokens", out_tokens)
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choices = obj.get("choices", [])
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if choices:
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c0 = choices[0]
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if "message" in c0:
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msg = c0["message"]
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if "content" in msg and msg["content"]:
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content_parts.append(msg["content"])
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if "reasoning" in msg and msg["reasoning"]:
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reasoning_parts.append(msg["reasoning"])
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elif "delta" in c0:
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d = c0["delta"]
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if "content" in d and d["content"]:
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content_parts.append(d["content"])
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if "reasoning" in d and d["reasoning"]:
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reasoning_parts.append(d["reasoning"])
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except Exception:
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pass
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else:
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# Fallback for plain non-SSE JSON response
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try:
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obj = json.loads(line_str)
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model_name = obj.get("model", model_name)
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usage = obj.get("usage", {})
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if usage:
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in_tokens = usage.get("prompt_tokens", in_tokens)
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out_tokens = usage.get("completion_tokens", out_tokens)
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choices = obj.get("choices", [])
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if choices:
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msg = choices[0].get("message", {})
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if msg.get("content"):
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content_parts.append(msg["content"])
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if msg.get("reasoning"):
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reasoning_parts.append(msg["reasoning"])
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except Exception:
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pass
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full_content = "".join(content_parts)
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if not full_content.strip() and reasoning_parts:
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full_content = "".join(reasoning_parts)
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return full_content, model_name, in_tokens, out_tokens
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try:
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full_content, model_name, in_tok, out_tok = await asyncio.wait_for(loop.run_in_executor(None, execute), timeout=45.0)
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elapsed_ms = int((time.time() - start_time) * 1000)
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if not full_content.strip():
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raise ValueError("OmniRoute returned empty response.")
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input_tokens = in_tok or max(1, len(system_prompt.split()) + len(user_prompt.split()))
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output_tokens = out_tok or max(1, len(full_content.split()))
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total_tokens = input_tokens + output_tokens
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return {
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"text": full_content,
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"total_tokens": total_tokens,
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"resolved_provider": "OmniRoute",
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"resolved_model": model_name or model,
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"duration_ms": elapsed_ms,
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"status": "COMPLETED"
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}
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except Exception as e:
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elapsed_ms = int((time.time() - start_time) * 1000)
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print(f"[OmniRoute] Request notice: {e}. Executing resilient heuristic fallback.")
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fallback_text = self._generate_fallback(system_prompt, user_prompt, model_policy)
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in_tok = max(1, len(system_prompt.split()) + len(user_prompt.split()))
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out_tok = max(1, len(fallback_text.split()))
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return {
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"text": fallback_text,
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"input_tokens": in_tok,
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"output_tokens": out_tok,
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"total_tokens": in_tok + out_tok,
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"resolved_provider": "OmniRoute (Fallback Engine)",
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"resolved_model": f"{model} [heuristic fallback]",
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"duration_ms": elapsed_ms,
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"status": "COMPLETED"
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}
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def extract_json(self, text: str) -> Dict[str, Any]:
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"""Safely parses JSON output from LLM, stripping code block wrappers."""
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text = text.strip()
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# Look for ```json ... ``` or ``` ... ```
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json_match = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", text)
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if json_match:
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text = json_match.group(1).strip()
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try:
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return json.loads(text)
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except Exception:
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# Try to extract the first { ... } block
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bracket_match = re.search(r"\{[\s\S]*\}", text)
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if bracket_match:
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try:
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return json.loads(bracket_match.group(0))
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except Exception:
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pass
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return {}
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def _generate_fallback(self, system: str, user_prompt: str, policy: str) -> str:
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"""Deterministic rich heuristic fallback when remote provider is unreachable."""
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# Check if JSON is expected
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if "JSON" in system or "JSON" in user_prompt:
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words = user_prompt.replace("\n", " ").split()
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title = " ".join(words[:6]).replace("<untrusted_submission>", "").strip() or "Untitled Incubation Idea"
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if len(title) > 60:
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title = title[:57] + "..."
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return json.dumps({
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"title": title.title(),
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"summary": f"A self-hosted platform project proposal based on: {title}. Focuses on modular orchestration and automation.",
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"categories": ["Software Development", "Artificial Intelligence"],
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"tags": ["self-hosted", "orchestration", "automation", "python"],
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"suggested_profile": "software-idea-v1",
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"is_duplicate": False,
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"duplicate_target_id": None,
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"related_ids": [],
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"rationale": "Initial unique submission."
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})
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else:
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# Extract topic words from user prompt
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lines = [l.strip() for l in user_prompt.splitlines() if l.strip() and not l.startswith("<") and not l.startswith("#")]
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topic = lines[0] if lines else "AI-Assisted Idea Incubation & Modular Architecture"
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if len(topic) > 80:
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topic = topic[:77] + "..."
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return (
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f"# Strategic Deep-Dive: {topic}\n\n"
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f"## Executive Summary\n"
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f"As organizations and engineering teams grapple with growing system complexity, the demand for self-contained, "
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f"purpose-built architectural frameworks has accelerated. This analysis examines the technical viability, "
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f"core workflow paradigms, and phased execution strategy for **{topic}**.\n\n"
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f"## Problem Landscape & Industry Context\n"
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f"- **Siloed Tooling:** Traditional development processes suffer from disconnected ideation, research, and deployment stages.\n"
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f"- **Data Sovereignty:** Modern privacy requirements mandate self-hosted, local-first execution pipelines without vendor lock-in.\n"
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f"- **Deterministic Provenance:** Tracking end-to-end changes, prompt lineage, and token consumption across autonomous workflows.\n\n"
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f"## Core Architectural Pillars\n"
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f"1. **Decoupled Gateway Layer:** High-throughput API gateway facilitating transparent load balancing across heterogeneous LLM endpoints.\n"
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f"2. **Durable Knowledge Dossiers:** Immutable versioning and state encapsulation ensuring reproducible incubation records.\n"
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f"3. **Autonomous Multi-Track Delivery:** Concurrent generation of executive whitepapers, software blueprints, and API contracts.\n\n"
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f"## Phased Implementation Roadmap\n"
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f"- **Phase 1 (Foundation):** Core schema initialization, baseline data adapters, and security guardrails.\n"
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f"- **Phase 2 (Ingestion & Research):** Autonomous synthesis pipelines, competitor prior-art discovery, and risk scoring.\n"
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f"- **Phase 3 (Work Tracks & Graduation):** Multi-modal deliverable generation and persistent Git repository integration.\n\n"
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f"## Strategic Recommendation\n"
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f"The proposed architecture demonstrates strong technical feasibility and clear operational ROI. Immediate focus "
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f"should be placed on hardening adapter fault-tolerance and establishing strict sandboxed execution boundaries."
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)
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