str.encode

response = requests.post(self.API_URL, headers=self.headers, json=payload)
        try: 
            return response.json()['error']
        except:
            return response.json()
    def encode(self, query, show_progress_bar=False):
        data = self.query({
            "inputs": query,
        })
        if type(data) is str:
            raise(CloudLoadingException('Cloud model not ready'))
        else:
            return np.array(data)
    def similarity_func(self, query_embedding, pooled_embeddings):
        return (pooled_embeddings @ query_embedding.T).squeeze(axis=1)

class MagiIndexer:
    def __init__(
        self, 
        datasets: List[GitHubCorpusRawTextDataset], 
        model, 
        embedding_file: str = None
    ) -> Tuple[List[GithubQueryResult], int]:
        '''
        Arguments:

response = await self._request("GET", endpoint, params=params)
        if response and isinstance(response, dict) and "content" in response:
            try:
                # 解码Base64编码的文件内容
                content = base64.b64decode(response["content"].encode()).decode()
                response["decoded_content"] = content
            except Exception as e:
                print(f"解码文件内容时出错: {str(e)}")

        return response

    async def get_directory_content(self, owner: str, repo: str, path: str, ref: Optional[str] = None) -> List[Dict]:
        """获取目录内容

        Args:
            owner: 仓库所有者
            repo: 仓库名
            path: 目录路径
            ref: 分支名、标签名或提交SHA

        Returns:
            目录内容列表
        """
        # 注意：此方法与get_file_content使用相同的端点，但对于目录会返回列表