Alibaba Cloud MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing. MaxCompute supports various data importing solutions and distributed computing models, enabling users to effectively query massive datasets, reduce production costs, and ensure data security.
The MaxComputeLoader lets you execute a MaxCompute SQL query and loads the results as one document per row. 
%pip install --upgrade --quiet  pyodps

Collecting pyodps
  Downloading pyodps-0.11.4.post0-cp39-cp39-macosx_10_9_universal2.whl (2.0 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.0/2.0 MB 1.7 MB/s eta 0:00:0000:0100:010m
Requirement already satisfied: charset-normalizer>=2 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (3.1.0)
Requirement already satisfied: urllib3<2.0,>=1.26.0 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (1.26.15)
Requirement already satisfied: idna>=2.5 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (2023.5.7)
Installing collected packages: pyodps
Successfully installed pyodps-0.11.4.post0

​
Basic Usage

To instantiate the loader you’ll need a SQL query to execute, your MaxCompute endpoint and project name, and your access ID and secret access key. The access ID and secret access key can either be passed in direct via the access_id and secret_access_key parameters or they can be set as environment variables MAX_COMPUTE_ACCESS_ID and MAX_COMPUTE_SECRET_ACCESS_KEY. 
from langchain_community.document_loaders import MaxComputeLoader

base_query = """
SELECT *
FROM (
    SELECT 1 AS id, 'content1' AS content, 'meta_info1' AS meta_info
    UNION ALL
    SELECT 2 AS id, 'content2' AS content, 'meta_info2' AS meta_info
    UNION ALL
    SELECT 3 AS id, 'content3' AS content, 'meta_info3' AS meta_info
) mydata;
"""

endpoint = "<ENDPOINT>"
project = "<PROJECT>"
ACCESS_ID = "<ACCESS ID>"
SECRET_ACCESS_KEY = "<SECRET ACCESS KEY>"

loader = MaxComputeLoader.from_params(
    base_query,
    endpoint,
    project,
    access_id=ACCESS_ID,
    secret_access_key=SECRET_ACCESS_KEY,
)
data = loader.load()

print(data)

[Document(page_content='id: 1\ncontent: content1\nmeta_info: meta_info1', metadata={}), Document(page_content='id: 2\ncontent: content2\nmeta_info: meta_info2', metadata={}), Document(page_content='id: 3\ncontent: content3\nmeta_info: meta_info3', metadata={})]

print(data[0].page_content)

id: 1
content: content1
meta_info: meta_info1

print(data[0].metadata)

{}

​
Specifying Which Columns are Content vs Metadata

You can configure which subset of columns should be loaded as the contents of the Document and which as the metadata using the page_content_columns and metadata_columns parameters. 
loader = MaxComputeLoader.from_params(
    base_query,
    endpoint,
    project,
    page_content_columns=["content"],  # Specify Document page content
    metadata_columns=["id", "meta_info"],  # Specify Document metadata
    access_id=ACCESS_ID,
    secret_access_key=SECRET_ACCESS_KEY,
)
data = loader.load()

print(data[0].page_content)

content: content1

print(data[0].metadata)

{'id': 1, 'meta_info': 'meta_info1'}