forked from phoenix/litellm-mirror
trying to add docs
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ishaan-jaff
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f08772b0",
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"metadata": {},
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"source": [
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"# Alibaba Cloud MaxCompute\n",
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"\n",
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">[Alibaba Cloud MaxCompute](https://www.alibabacloud.com/product/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.\n",
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"\n",
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"The `MaxComputeLoader` lets you execute a MaxCompute SQL query and loads the results as one document per row."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "067b7213",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting pyodps\n",
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" Downloading pyodps-0.11.4.post0-cp39-cp39-macosx_10_9_universal2.whl (2.0 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m0m\n",
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"\u001b[?25hRequirement already satisfied: charset-normalizer>=2 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (3.1.0)\n",
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"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)\n",
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"Requirement already satisfied: idna>=2.5 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (3.4)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/newboy/anaconda3/envs/langchain/lib/python3.9/site-packages (from pyodps) (2023.5.7)\n",
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"Installing collected packages: pyodps\n",
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"Successfully installed pyodps-0.11.4.post0\n"
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]
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}
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],
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"source": [
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"!pip install pyodps"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19641457",
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"metadata": {},
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"source": [
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"## Basic Usage\n",
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"To instantiate the loader you'll need a SQL query to execute, your MaxCompute endpoint and project name, and you 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`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "71a0da4b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import MaxComputeLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d4770c4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"base_query = \"\"\"\n",
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"SELECT *\n",
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"FROM (\n",
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" SELECT 1 AS id, 'content1' AS content, 'meta_info1' AS meta_info\n",
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" UNION ALL\n",
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" SELECT 2 AS id, 'content2' AS content, 'meta_info2' AS meta_info\n",
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" UNION ALL\n",
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" SELECT 3 AS id, 'content3' AS content, 'meta_info3' AS meta_info\n",
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") mydata;\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1616c174",
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"metadata": {},
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"outputs": [],
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"source": [
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"endpoint = \"<ENDPOINT>\"\n",
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"project = \"<PROJECT>\"\n",
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"ACCESS_ID = \"<ACCESS ID>\"\n",
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"SECRET_ACCESS_KEY = \"<SECRET ACCESS KEY>\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "e5c25041",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = MaxComputeLoader.from_params(\n",
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" base_query,\n",
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" endpoint,\n",
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" project,\n",
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" access_id=ACCESS_ID,\n",
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" secret_access_key=SECRET_ACCESS_KEY,\n",
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")\n",
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"data = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "311e74ea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[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={})]\n"
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]
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}
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],
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"source": [
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"print(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "a4d8c388",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"id: 1\n",
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"content: content1\n",
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"meta_info: meta_info1\n"
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]
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}
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],
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"source": [
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"print(data[0].page_content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "f2422e6c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{}\n"
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]
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}
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],
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"source": [
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"print(data[0].metadata)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "85e07e28",
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"metadata": {},
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"source": [
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"## Specifying Which Columns are Content vs Metadata\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "a7b9d726",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = MaxComputeLoader.from_params(\n",
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" base_query,\n",
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" endpoint,\n",
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" project,\n",
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" page_content_columns=[\"content\"], # Specify Document page content\n",
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" metadata_columns=[\"id\", \"meta_info\"], # Specify Document metadata\n",
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" access_id=ACCESS_ID,\n",
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" secret_access_key=SECRET_ACCESS_KEY,\n",
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")\n",
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"data = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "532c19e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content: content1\n"
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]
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}
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],
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"source": [
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"print(data[0].page_content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "5fe4990a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'id': 1, 'meta_info': 'meta_info1'}\n"
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]
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}
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],
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"source": [
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"print(data[0].metadata)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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