Function call_llm

src/codegraphcontext/viz/server.py:331–392

(system_prompt: str, user_prompt: str)

        raise HTTPException(status_code=500, detail=str(e))

def call_llm(system_prompt: str, user_prompt: str) -> str:
    gemini_key = os.getenv("GEMINI_API_KEY")
    openai_key = os.getenv("OPENAI_API_KEY")
    atlascloud_key = os.getenv("ATLASCLOUD_API_KEY") or os.getenv("ATLAS_CLOUD_API_KEY")
    
    if not gemini_key and not openai_key and not atlascloud_key:
        raise ValueError(
            "GEMINI_API_KEY, OPENAI_API_KEY, or ATLASCLOUD_API_KEY environment variable is required."
        )
    
    if gemini_key:
        url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key={gemini_key}"
        payload = {
            "contents": [
                {
                    "role": "user",
                    "parts": [{"text": f"{system_prompt}\n\nUser Prompt:\n{user_prompt}"}]
                }
            ]
        }
        res = requests.post(url, json=payload, timeout=30)
        if res.status_code != 200:
            raise RuntimeError(f"Gemini API returned error {res.status_code}: {res.text}")
        data = res.json()
        try:
            return data["candidates"][0]["content"]["parts"][0]["text"]
        except (KeyError, IndexError) as e:
            raise RuntimeError(f"Failed to parse Gemini API response: {data}") from e
    else:
        if openai_key:
            provider_name = "OpenAI"
            api_key = openai_key
            url = "https://api.openai.com/v1/chat/completions"
            model = "gpt-4o-mini"
        else:
            provider_name = "Atlas Cloud"
            api_key = atlascloud_key
            base_url = os.getenv("ATLASCLOUD_API_BASE") or os.getenv("ATLAS_CLOUD_API_BASE")
            base_url = (base_url or "https://api.atlascloud.ai/v1").rstrip("/")
            url = f"{base_url}/chat/completions"
            model = os.getenv("ATLASCLOUD_MODEL") or os.getenv("ATLAS_CLOUD_MODEL")
            model = model or "deepseek-ai/deepseek-v4-pro"

        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {api_key}"
        }
        payload = {
            "model": model,
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
        }
        res = requests.post(url, json=payload, headers=headers, timeout=30)
        if res.status_code != 200:
            raise RuntimeError(f"{provider_name} API returned error {res.status_code}: {res.text}")
        data = res.json()