project-lyra/cortex/llm/llm_router.py

# llm_router.py
import os
import httpx
import json
import logging

logger = logging.getLogger(__name__)

# ------------------------------------------------------------
# Load backend registry from root .env
# ------------------------------------------------------------

BACKENDS = {
    "PRIMARY": {
        "provider": os.getenv("LLM_PRIMARY_PROVIDER", "").lower(),
        "url": os.getenv("LLM_PRIMARY_URL", ""),
        "model": os.getenv("LLM_PRIMARY_MODEL", "")
    },
    "SECONDARY": {
        "provider": os.getenv("LLM_SECONDARY_PROVIDER", "").lower(),
        "url": os.getenv("LLM_SECONDARY_URL", ""),
        "model": os.getenv("LLM_SECONDARY_MODEL", "")
    },
    "OPENAI": {
        "provider": os.getenv("LLM_OPENAI_PROVIDER", "").lower(),
        "url": os.getenv("LLM_OPENAI_URL", ""),
        "model": os.getenv("LLM_OPENAI_MODEL", ""),
        "api_key": os.getenv("OPENAI_API_KEY", "")
    },
    "FALLBACK": {
        "provider": os.getenv("LLM_FALLBACK_PROVIDER", "").lower(),
        "url": os.getenv("LLM_FALLBACK_URL", ""),
        "model": os.getenv("LLM_FALLBACK_MODEL", "")
    },
}

DEFAULT_BACKEND = "PRIMARY"

# Reusable async HTTP client
http_client = httpx.AsyncClient(timeout=120.0)


# ------------------------------------------------------------
# Public call
# ------------------------------------------------------------
async def call_llm(
    prompt: str = None,
    messages: list = None,
    backend: str | None = None,
    temperature: float = 0.7,
    max_tokens: int = 512,
):
    """
    Call an LLM backend.

    Args:
        prompt: String prompt (for completion-style APIs like mi50)
        messages: List of message dicts (for chat-style APIs like Ollama/OpenAI)
        backend: Which backend to use (PRIMARY, SECONDARY, OPENAI, etc.)
        temperature: Sampling temperature
        max_tokens: Maximum tokens to generate
    """
    backend = (backend or DEFAULT_BACKEND).upper()

    if backend not in BACKENDS:
        raise RuntimeError(f"Unknown backend '{backend}'")

    cfg = BACKENDS[backend]
    provider = cfg["provider"]
    url = cfg["url"]
    model = cfg["model"]

    if not url or not model:
        raise RuntimeError(f"Backend '{backend}' missing url/model in env")

    # -------------------------------
    # Provider: MI50 (llama.cpp server)
    # -------------------------------
    if provider == "mi50":
        payload = {
            "prompt": prompt,
            "n_predict": max_tokens,
            "temperature": temperature,
            "stop": ["User:", "\nUser:", "Assistant:", "\n\n\n"]
        }
        try:
            r = await http_client.post(f"{url}/completion", json=payload)
            r.raise_for_status()
            data = r.json()
            return data.get("content", "")
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (mi50): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from mi50: {e}")
            raise RuntimeError(f"Invalid response format (mi50): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (mi50): {type(e).__name__}: {str(e)}")

    # -------------------------------
    # Provider: OLLAMA (your 3090)
    # -------------------------------
    if provider == "ollama":
        # Use messages array if provided, otherwise convert prompt to single user message
        if messages:
            chat_messages = messages
        else:
            chat_messages = [{"role": "user", "content": prompt}]

        payload = {
            "model": model,
            "messages": chat_messages,
            "stream": False,
            "options": {
                "temperature": temperature,
                "num_predict": max_tokens
            }
        }
        try:
            r = await http_client.post(f"{url}/api/chat", json=payload)
            r.raise_for_status()
            data = r.json()
            return data["message"]["content"]
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (ollama): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from ollama: {e}")
            raise RuntimeError(f"Invalid response format (ollama): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (ollama): {type(e).__name__}: {str(e)}")


    # -------------------------------
    # Provider: OPENAI
    # -------------------------------
    if provider == "openai":
        headers = {
            "Authorization": f"Bearer {cfg['api_key']}",
            "Content-Type": "application/json"
        }
        payload = {
            "model": model,
            "messages": [
                {"role": "user", "content": prompt}
            ],
            "temperature": temperature,
            "max_tokens": max_tokens,
        }
        try:
            r = await http_client.post(f"{url}/chat/completions", json=payload, headers=headers)
            r.raise_for_status()
            data = r.json()
            return data["choices"][0]["message"]["content"]
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (openai): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from openai: {e}")
            raise RuntimeError(f"Invalid response format (openai): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (openai): {type(e).__name__}: {str(e)}")

    # -------------------------------
    # Unknown provider
    # -------------------------------
    raise RuntimeError(f"Provider '{provider}' not implemented.")