Memory

Alibaba Cloud AI Search OpenSearch

Push documents and run HA or SQL searches in OpenSearch Vector Search Edition with the Python SDK.

What it does

Connect to OpenSearch Vector Search Edition through the alibabacloud-ha3engine Python SDK, push add/delete document updates, and execute HA or SQL-style searches. Configure credentials and resource identifiers with environment variables, verify access with a minimal read-only query, then save response summaries and reproducibility evidence under the designated output directory.

When to use it

  • Loading documents into an OpenSearch data source
  • Running vector and keyword retrieval with HA queries
  • Querying structured data with SQL-style syntax
  • Validating SDK connectivity and permissions

The skill document

Category: provider

OpenSearch Vector Search Edition

Use the ha3engine SDK to push documents and execute HA/SQL searches. This skill focuses on API/SDK usage only (no console steps).

Prerequisites

  • Install SDK (recommended in a venv to avoid PEP 668 limits):
python3 -m venv .venv
. .venv/bin/activate
python -m pip install alibabacloud-ha3engine
  • Provide connection config via environment variables:
    • OPENSEARCH_ENDPOINT (API domain)
    • OPENSEARCH_INSTANCE_ID
    • OPENSEARCH_USERNAME
    • OPENSEARCH_PASSWORD
    • OPENSEARCH_DATASOURCE (data source name)
    • OPENSEARCH_PK_FIELD (primary key field name)
import os
from alibabacloud_ha3engine import models, client
from Tea.exceptions import TeaException, RetryError

cfg = models.Config(
    endpoint=os.getenv("OPENSEARCH_ENDPOINT"),
    instance_id=os.getenv("OPENSEARCH_INSTANCE_ID"),
    protocol="http",
    access_user_name=os.getenv("OPENSEARCH_USERNAME"),
    access_pass_word=os.getenv("OPENSEARCH_PASSWORD"),
)
ha3 = client.Client(cfg)

def push_docs():
    data_source = os.getenv("OPENSEARCH_DATASOURCE")
    pk_field = os.getenv("OPENSEARCH_PK_FIELD", "id")

    documents = [
        {"fields": {"id": 1, "title": "hello", "content": "world"}, "cmd": "add"},
        {"fields": {"id": 2, "title": "faq", "content": "vector search"}, "cmd": "add"},
    ]
    req = models.PushDocumentsRequestModel({}, documents)
    return ha3.push_documents(data_source, pk_field, req)


def search_ha():
    # HA query example. Replace cluster/table names as needed.
    query_str = (
        "config=hit:5,format:json,qrs_chain:search"
        "&&query=title:hello"
        "&&cluster=general"
    )
    ha_query = models.SearchQuery(query=query_str)
    req = models.SearchRequestModel({}, ha_query)
    return ha3.search(req)

try:
    print(push_docs().body)
    print(search_ha())
except (TeaException, RetryError) as e:
    print(e)

Script quickstart

python skills/ai/search/alicloud-ai-search-opensearch/scripts/quickstart.py

Environment variables:

  • OPENSEARCH_ENDPOINT
  • OPENSEARCH_INSTANCE_ID
  • OPENSEARCH_USERNAME
  • OPENSEARCH_PASSWORD
  • OPENSEARCH_DATASOURCE
  • OPENSEARCH_PK_FIELD (optional, default id)
  • OPENSEARCH_CLUSTER (optional, default general)

Optional args: --cluster, --hit, --query.

from alibabacloud_ha3engine import models

sql = "select * from &&kvpair=trace:INFO;formatType:json"
sql_query = models.SearchQuery(sql=sql)
req = models.SearchRequestModel({}, sql_query)
resp = ha3.search(req)
print(resp)

Notes for Claude Code/Codex

  • Use push_documents for add/delete updates.
  • Large query strings (>30KB) should use the RESTful search API.
  • HA queries are fast and flexible for vector + keyword retrieval; SQL is helpful for structured data.

Error handling

  • Auth errors: verify username/password and instance access.
  • 4xx on push: check schema fields and pk_field alignment.
  • 5xx: retry with backoff.

Validation

mkdir -p output/alicloud-ai-search-opensearch
for f in skills/ai/search/alicloud-ai-search-opensearch/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-ai-search-opensearch/validate.txt

Pass criteria: command exits 0 and output/alicloud-ai-search-opensearch/validate.txt is generated.

Output And Evidence

  • Save artifacts, command outputs, and API response summaries under output/alicloud-ai-search-opensearch/.
  • Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Workflow

  1. Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.
  2. Run one minimal read-only query first to verify connectivity and permissions.
  3. Execute the target operation with explicit parameters and bounded scope.
  4. Verify results and save output/evidence files.

References

  • SDK package: alibabacloud-ha3engine

  • Demos: data push and HA/SQL search demos in OpenSearch docs

  • Source list: references/sources.md

Questions people ask

What setup is required before running the examples?
Install `alibabacloud-ha3engine`, preferably in a virtual environment, and provide the endpoint, instance ID, username, password, data source, and primary-key field through environment variables.
Can it perform both document updates and searches?
Yes. It uses `push_documents` for add/delete updates and `search` for HA queries or SQL-style searches.
How should failures and large queries be handled?
Check credentials and instance access for authentication errors, verify schema and primary-key alignment for push 4xx responses, and retry 5xx responses with backoff. Query strings larger than 30 KB should use the RESTful search API.

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