hack/tools/scripts/llm-key-hunter/results/_cache/content/baiyu858_phyagentos-rekep-real-plugin.json

{"repo": "baiyu858/phyagentos-rekep-real-plugin", "files": {"runtime/vlm_client.py": {"c532a28ddd69809bbc63ac0604d72866582e047f": {"t": 1785768916.0084245, "n": 6872, "h": "21ef7dad67d01f23", "c": "import base64\nimport os\nimport time\nimport requests\n\n\nDEFAULT_VLM_MODEL = \"gpt-5.4\"\n\n\ndef _read_env(name):\n    value = os.environ.get(name)\n    return value.strip() if isinstance(value, str) else \"\"\n\n\ndef _read_int_env(name, default=0, lower=0, upper=10):\n    raw = _read_env(name)\n    if not raw:\n        return int(default)\n    try:\n        value = int(raw)\n    except Exception:\n        value = int(default)\n    return max(int(lower), min(int(upper), value))\n\n\ndef _read_float_env(name, default=1.0, lower=0.0, upper=30.0):\n    raw = _read_env(name)\n    if not raw:\n        return float(default)\n    try:\n        value = float(raw)\n    except Exception:\n        value = float(default)\n    return max(float(lower), min(float(upper), value))\n\n\ndef resolve_vlm_config(default_model=None):\n    api_key = _read_env(\"REKEP_VLM_API_KEY\")\n    api_key_env = \"\"\n\n    if not api_key:\n        configured_env = _read_env(\"REKEP_VLM_API_KEY_ENV\")\n        if configured_env:\n            api_key_env = configured_env\n            api_key = _read_env(configured_env)\n\n    if not api_key:\n        for fallback_env in (\"DMXAPI_API_KEY\", \"OPENAI_API_KEY\"):\n            fallback_value = _read_env(fallback_env)\n            if fallback_value:\n                api_key_env = fallback_env\n                api_key = fallback_value\n                break\n\n    return {\n        \"api_key\": api_key,\n        \"api_key_env\": api_key_env,\n        \"base_url\": _read_env(\"REKEP_VLM_BASE_URL\"),\n        \"model\": _read_env(\"REKEP_VLM_MODEL\") or default_model or DEFAULT_VLM_MODEL,\n    }\n\n\ndef vlm_ready(default_model=None):\n    return bool(resolve_vlm_config(default_model=default_model)[\"api_key\"])\n\n\ndef get_vlm_request_config(default_model=None):\n    config = resolve_vlm_config(default_model=default_model)\n    if not config[\"api_key\"]:\n        source = config[\"api_key_env\"] or \"REKEP_VLM_API_KEY / REKEP_VLM_API_KEY_ENV / DMXAPI_API_KEY / OPENAI_API_KEY\"\n        raise RuntimeError(f\"VLM API key is required ({source})\")\n    return config\n\n\ndef encode_image_bytes(image_bytes):\n    return base64.b64encode(image_bytes).decode(\"utf-8\")\n\n\ndef build_image_messages(question, image_bytes, system_prompt=None):\n    contents = [\n        {\n            \"type\": \"text\",\n            \"text\": question,\n        },\n        {\n            \"type\": \"image_url\",\n            \"image_url\": {\n                \"url\": f\"data:image/png;base64,{encode_image_bytes(image_bytes)}\",\n            },\n        },\n    ]\n    messages = []\n    if system_prompt:\n        messages.append({\"role\": \"system\", \"content\": system_prompt})\n    messages.append({\"role\": \"user\", \"content\": contents})\n    return messages\n\n\ndef _resolve_chat_completions_url(config):\n    base_url = config[\"base_url\"] or \"https://api.openai.com/v1\"\n    return f\"{base_url.rstrip('/')}/chat/completions\"\n\n\ndef _extract_message_text(payload):\n    choices = payload.get(\"choices\", [])\n    if not choices:\n        return \"\"\n    message = choices[0].get(\"message\", {})\n    content = message.get(\"content\", \"\")\n    if isinstance(content, str):\n        return content.strip()\n    if isinstance(content, list):\n        text_parts = []\n        for item in content:\n            if isinstance(item, dict) and item.get(\"type\") == \"text\":\n                text_parts.append(str(item.get(\"text\", \"\")))\n        return \"\\n\".join(part for part in text_parts if part).strip()\n    return str(content).strip()\n\n\ndef request_chat_completion(\n    *,\n    messages,\n    default_model=None,\n    temperature=0.0,\n    max_tokens=512,\n    timeout=300,\n):\n    config = get_vlm_request_config(default_model=default_model)\n    max_retries = _read_int_env(\"REKEP_VLM_MAX_RETRIES\", default=2, lower=0, upper=10)\n    retry_backoff_s = _read_float_env(\"REKEP_VLM_RETRY_BACKOFF_S\", default=1.0, lower=0.0, upper=60.0)\n    retry_status_codes = {429, 500, 502, 503, 504}\n    payload = {\n        \"model\": config[\"model\"],\n        \"messages\": messages,\n    }\n    if temperature is not None:\n        payload[\"temperature\"] = float(temperature)\n    if max_tokens is not None:\n        payload[\"max_tokens\"] = int(max_tokens)\n\n    url = _resolve_chat_completions_url(config)\n    headers = {\n        \"Authorization\": f\"Bearer {config['api_key']}\",\n        \"Content-Type\": \"application/json\",\n    }\n\n    last_exc = None\n    for attempt in range(max_retries + 1):\n        try:\n            response = requests.post(\n                url,\n                headers=headers,\n                json=payload,\n                timeout=timeout,\n            )\n        except requests.RequestException as exc:\n            last_exc = exc\n            if attempt < max_retries:\n                sleep_s = retry_backoff_s * (2**attempt)\n                if sleep_s > 0:\n                    time.sleep(sleep_s)\n                continue\n            raise RuntimeError(\n                f\"VLM request failed after {max_retries + 1} attempts \"\n                f\"(model={config['model']}, base_url={config['base_url'] or 'https://api.openai.com/v1'}): {exc}\"\n            ) from exc\n\n        status_code = int(response.status_code)\n        if status_code in retry_status_codes and attempt < max_retries:\n            sleep_s = retry_backoff_s * (2**attempt)\n            if sleep_s > 0:\n                time.sleep(sleep_s)\n            continue\n\n        if status_code >= 400:\n            body = response.text.strip().replace(\"\\n\", \" \")\n            if len(body) > 400:\n                body = f\"{body[:400]}...(truncated)\"\n            raise requests.HTTPError(\n                f\"{status_code} error from VLM endpoint \"\n                f\"(model={config['model']}, base_url={config['base_url'] or 'https://api.openai.com/v1'}): {body}\",\n                response=response,\n            )\n\n        try:\n            return response.json(), config\n        except Exception as exc:\n            body = response.text.strip().replace(\"\\n\", \" \")\n            if len(body) > 400:\n                body = f\"{body[:400]}...(truncated)\"\n            raise RuntimeError(\n                f\"VLM response JSON parse failed \"\n                f\"(model={config['model']}, base_url={config['base_url'] or 'https://api.openai.com/v1'}): {body}\"\n            ) from exc\n\n    raise RuntimeError(\n        f\"VLM request failed after {max_retries + 1} attempts \"\n        f\"(model={config['model']}, base_url={config['base_url'] or 'https://api.openai.com/v1'})\"\n    ) from last_exc\n\n\ndef ask_image_question(\n    *,\n    image_bytes,\n    question,\n    default_model=None,\n    system_prompt=None,\n    temperature=0.0,\n    max_tokens=512,\n):\n    messages = build_image_messages(question, image_bytes, system_prompt=system_prompt)\n    response, config = request_chat_completion(\n        messages=messages,\n        default_model=default_model,\n        temperature=temperature,\n        max_tokens=max_tokens,\n    )\n    answer = _extract_message_text(response)\n    return answer, config\n"}}}}