Method call_with_system

autofigure/utils/llm_client.py:117–180

Call the LLM with a system prompt and user message. Args: system_prompt: System message user_contents: List of user content items temperature: Sampling temperature max_tokens: Maximum tokens in response Returns: R

(
        self,
        system_prompt: str,
        user_contents: List[Any],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None,
    )

            return None

    def call_with_system(
        self,
        system_prompt: str,
        user_contents: List[Any],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None,
    ) -> Optional[str]:
        """
        Call the LLM with a system prompt and user message.

        Args:
            system_prompt: System message
            user_contents: List of user content items
            temperature: Sampling temperature
            max_tokens: Maximum tokens in response

        Returns:
            Response text, or None on failure
        """
        try:
            from openai import OpenAI

            if not self.api_key:
                print("[LLMClient] ERROR: API key not provided!")
                return None

            client = OpenAI(base_url=self.base_url, api_key=self.api_key)

            # Build user message content
            user_content: List[Dict[str, Any]] = []
            for part in user_contents:
                if isinstance(part, str):
                    user_content.append({"type": "text", "text": part})
                elif isinstance(part, Image.Image):
                    buf = io.BytesIO()
                    part.save(buf, format="PNG")
                    image_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
                    user_content.append({
                        "type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{image_b64}"}
                    })

            messages = [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_content},
            ]

            kwargs = {
                "model": self.model,
                "messages": messages,
                "temperature": temperature,
            }
            if max_tokens:
                kwargs["max_tokens"] = max_tokens

            completion = client.chat.completions.create(**kwargs)

            if completion and completion.choices: