searchcli-volcengine-ai-search

name	searchcli-volcengine-ai-search
description	CLI for integrating Volcengine AI Search, recommendation, and conversational retrieval into agent and business systems
triggers	["integrate AI search into my application","set up Volcengine search CLI","configure AI-powered search and recommendations","onboard data to Volcengine AI Search","build conversational retrieval with SearchCLI","tune search quality with Viking","create item search with SearchCLI","run search evaluation and tuning"]

SearchCLI (Volcengine AI Search)

Skill by ara.so — Devtools Skills collection.

SearchCLI is the open-source CLI for Volcengine AI Search. It provides stable, tunable search, recommendation, and conversational retrieval capabilities for agent systems and business applications. Built with TypeScript, it offers agent-friendly workflows with installable skills, dry-runs, confirmation gates, and automated quality tuning.

What It Does

• Item & Catalog Search: Build search on structured business data
• Recommendation Flows: Connect recommendations to application scenes and user behavior
• Conversational Retrieval: Create RAG-style chat experiences grounded in application search
• Agent Workflows: Onboard data, configure applications, and validate behavior with reviewable steps
• Automated Tuning: Text-similarity evaluation and query tuning with LLM-based relevance judging

Installation

Requirements

• Node.js 20 or newer
• Git
• Volcengine AK/SK with AI Search access

Install SearchCLI

git clone [e-mail]:volcengine/SearchCLI.git vs
cd vs
bash ./scripts/install.sh

Install as Agent Skills (for AI Agents)

npx skills add "[e-mail]:volcengine/SearchCLI.git" -y -g

This installs bundled skills:

• vs-shared
• vs-item-onboarding
• vs-search
• vs-search-tuning
• vs-chat
• vs-recommend

Authentication

Using Environment Variables

If VIKING_AK and VIKING_SK are already in your environment:

vs auth import-env
vs auth status --json

Interactive Login

vs auth login
vs auth status --json
vs doctor --json

LLM Configuration (for Search Tuning)

For query generation and LLM-based relevance judging:

# Interactive setup
vs llm login
vs llm status --json

# Or import from environment
# Requires: VIKING_LLM_BASE_URL, VIKING_LLM_API_KEY, VIKING_LLM_MODEL
vs llm import-env
vs llm status --json

Verify LLM connectivity:

vs search tune llm-check --live --json

Core Command Groups

Authentication & Setup

vs auth login                    # Interactive authentication
vs auth import-env              # Import AK/SK from environment
vs auth status --json           # Check auth status
vs llm login                    # Configure LLM for tuning
vs llm import-env               # Import LLM config from env
vs doctor --json                # System health check

Item Onboarding

vs item profile --file ./items.json --pretty
vs item plan --file ./items.json --goal "Build item search"
vs item apply --plan-dir ./.viking/item-plans/<plan> --dry-run
vs item apply --plan-dir ./.viking/item-plans/<plan> --confirm-review --wait-ready --run-trials

Dataset Management

vs dataset create --data @dataset-create.json
vs dataset ingest --dataset-id <id> --fields @<file>
vs dataset list --json
vs dataset describe --dataset-id <id> --json

Application Management

vs app list --json
vs app describe --app-id <id> --json
vs app create --data @app-config.json
vs app update --app-id <id> --data @update.json

Search Operations

vs search run --app-id <id> --query "search term" --json
vs search tune query-generate --dataset-id <id> --output ./queries.json
vs search tune plan --queries @queries.json --dataset-id <id>
vs search tune run --plan-dir ./plan --output ./results.json
vs search tune report --results @results.json --pretty

Recommendation

vs recommend run --app-id <id> --user-id <uid> --json

Conversational Retrieval

vs chat run --app-id <id> --query "What are the best products?" --json

Workflows

Dataset + App Provisioning (Full Onboarding)

Complete workflow for creating both dataset and search application:

# 1. Profile your data
vs item profile --file ./items.json --pretty

# 2. Generate provisioning plan
vs item plan --file ./items.json --goal "Build product search"

# 3. Review plan with dry-run
vs item apply --plan-dir ./.viking/item-plans/<plan-id> --dry-run

# 4. Execute with confirmations and runtime verification
vs item apply \
  --plan-dir ./.viking/item-plans/<plan-id> \
  --confirm-review \
  --wait-ready \
  --run-trials

Dataset-Only Provisioning

When you only need data ingestion without application setup:

# Generate dataset-only plan
vs item plan --file ./items.json --goal "Ingest product data" --skip-app

# Create dataset
vs dataset create --data @dataset-create.json

# Ingest data
vs dataset ingest --dataset-id <dataset-id> --fields @<normalized-items-artifact>

The --skip-app flag works with vs item provision and vs item apply as a guardrail.

Video Dataset Provisioning

Always specify --type video explicitly for video datasets:

# Dataset + App
vs item profile --file ./videos.jsonl --type video --pretty
vs item plan --file ./videos.jsonl --type video --goal "Build video search"
vs item apply --plan-dir ./.viking/item-plans/<plan> --confirm-review --wait-ready

# Dataset-only
vs item plan --file ./videos.jsonl --type video --goal "Video data" --skip-app
vs dataset create --data @dataset-create.json
vs dataset ingest --dataset-id <id> --fields @<artifact>

Important: For video datasets, prefer dataset-create.json over --schema @schema.json to include DataFieldConfig and avoid MissingParameter.DefaultFieldStrategy errors.

Search Quality Tuning

Automated evaluation and tuning workflow:

# 1. Generate evaluation queries
vs search tune query-generate \
  --dataset-id <dataset-id> \
  --output ./eval-queries.json

# 2. Create tuning plan
vs search tune plan \
  --queries @eval-queries.json \
  --dataset-id <dataset-id>

# 3. Run evaluation
vs search tune run \
  --plan-dir ./.viking/search-tune-plans/<plan-id> \
  --output ./results.json

# 4. Generate report
vs search tune report \
  --results @results.json \
  --pretty

Code Examples

TypeScript: Item Data Structure

// items.json - Product catalog
[
  {
    "id": "prod-001",
    "name": "Wireless Headphones",
    "description": "High-quality Bluetooth headphones with noise cancellation",
    "category": "Electronics",
    "price": 149.99,
    "tags": ["audio", "bluetooth", "noise-canceling"],
    "in_stock": true
  },
  {
    "id": "prod-002",
    "name": "Smart Watch",
    "description": "Fitness tracking smartwatch with heart rate monitor",
    "category": "Wearables",
    "price": 299.99,
    "tags": ["fitness", "health", "smartwatch"],
    "in_stock": true
  }
]

TypeScript: Dataset Create Payload

// dataset-create.json
{
  "Name": "product-catalog-v1",
  "Type": "item",
  "Schema": {
    "Fields": [
      {
        "FieldName": "id",
        "FieldType": "String",
        "IsPrimaryKey": true
      },
      {
        "FieldName": "name",
        "FieldType": "String"
      },
      {
        "FieldName": "description",
        "FieldType": "Text"
      },
      {
        "FieldName": "price",
        "FieldType": "Float"
      },
      {
        "FieldName": "tags",
        "FieldType": "List<String>"
      }
    ]
  },
  "DataFieldConfig": {
    "DefaultFields": {
      "Title": "name",
      "Body": "description"
    }
  }
}

TypeScript: Search Application Config

// app-config.json
{
  "Name": "product-search-app",
  "DatasetId": "<dataset-id>",
  "SearchConfig": {
    "RankingStrategy": "TextSimilarity",
    "TextSimilarityConfig": {
      "Fields": ["name", "description"],
      "Weights": {
        "name": 2.0,
        "description": 1.0
      }
    }
  }
}

Bash: Complete Onboarding Script

#!/bin/bash
set -e

# Environment check
vs doctor --json

# 1. Profile and plan
vs item profile --file ./products.json --pretty
vs item plan --file ./products.json --goal "E-commerce product search"

# Get the latest plan directory
PLAN_DIR=$(ls -dt ./.viking/item-plans/* | head -1)

# 2. Review with dry-run
vs item apply --plan-dir "$PLAN_DIR" --dry-run

# 3. Execute provisioning
vs item apply \
  --plan-dir "$PLAN_DIR" \
  --confirm-review \
  --wait-ready \
  --run-trials

echo "Onboarding complete. Check ./.viking/item-plans/ for artifacts."

TypeScript: Programmatic Search

import { execSync } from 'child_process';

function runSearch(appId: string, query: string): any {
  const result = execSync(
    `vs search run --app-id ${appId} --query "${query}" --json`,
    { encoding: 'utf-8' }
  );
  return JSON.parse(result);
}

function runRecommendation(appId: string, userId: string): any {
  const result = execSync(
    `vs recommend run --app-id ${appId} --user-id ${userId} --json`,
    { encoding: 'utf-8' }
  );
  return JSON.parse(result);
}

// Usage
const searchResults = runSearch('app-123', 'wireless headphones');
console.log('Search results:', searchResults);

const recommendations = runRecommendation('app-123', 'user-456');
console.log('Recommendations:', recommendations);

TypeScript: Evaluation Query Generation

// eval-queries.json structure
[
  {
    "query": "wireless bluetooth headphones",
    "expectedRelevance": {
      "prod-001": "high",
      "prod-003": "medium"
    }
  },
  {
    "query": "fitness tracking watch",
    "expectedRelevance": {
      "prod-002": "high"
    }
  }
]

Configuration

Environment Variables

# Required for authentication
export VIKING_AK="your-access-key"
export VIKING_SK="your-secret-key"

# Optional: LLM for search tuning
export VIKING_LLM_BASE_URL="https://api.openai.com/v1"
export VIKING_LLM_API_KEY="sk-..."
export VIKING_LLM_MODEL="gpt-4"

# Then import
vs auth import-env
vs llm import-env

Credential Storage

SearchCLI stores credentials securely:

• API Keys: Local secure credential store (not plain text config)
• LLM Base URL & Model: Stored as non-secret config
• Plan Artifacts: ./.viking/ directory (safe to version control after removing sensitive data)

Output Formats

Most commands support --json for machine-readable output:

vs auth status --json
vs dataset list --json
vs search run --app-id <id> --query "test" --json

Use --pretty for human-readable formatting:

vs item profile --file ./items.json --pretty
vs search tune report --results @results.json --pretty

Common Patterns

Incremental Data Updates

# Update existing dataset
vs dataset ingest --dataset-id <id> --fields @new-items.json --mode append

# Replace all data
vs dataset ingest --dataset-id <id> --fields @all-items.json --mode replace

Multi-Stage Verification

# Always use dry-run first
vs item apply --plan-dir <plan> --dry-run

# Then execute with confirmations
vs item apply --plan-dir <plan> --confirm-review

# Add runtime trials for end-to-end verification
vs item apply --plan-dir <plan> --confirm-review --run-trials

Search Result Validation

# Run search and save results
vs search run \
  --app-id <app-id> \
  --query "test query" \
  --json > search-results.json

# Use in scripts for validation
jq '.Results | length' search-results.json

Skill-Based Agent Integration

// Agent can invoke skills directly
await agent.useSkill('vs-item-onboarding', {
  file: './products.json',
  goal: 'Build product search',
  skipApp: false
});

await agent.useSkill('vs-search', {
  appId: 'app-123',
  query: 'wireless headphones'
});

Troubleshooting

Authentication Failures

# Check current auth status
vs auth status --json

# Re-authenticate
vs auth login

# Verify credentials are set
echo $VIKING_AK
echo $VIKING_SK

# Full diagnostic
vs doctor --json

Dataset Creation Errors

Error: MissingParameter.DefaultFieldStrategy (common with video datasets)

# ❌ Don't use schema-only for video datasets
vs dataset create --name video-data --type video --schema @schema.json

# ✅ Use full create payload with DataFieldConfig
vs dataset create --data @dataset-create.json

Solution: Always use dataset-create.json from the plan artifact, which includes both Schema and DataFieldConfig.

Item Ingestion Issues

# Verify dataset exists and is ready
vs dataset describe --dataset-id <id> --json

# Check item data format
vs item profile --file ./items.json --pretty

# Use normalized artifact from plan
vs dataset ingest --dataset-id <id> --fields @<normalized-artifact>

LLM Connectivity Issues

# Check LLM configuration
vs llm status --json

# Test LLM connection
vs search tune llm-check --live --json

# Reconfigure if needed
vs llm login

Plan Execution Failures

# Always start with dry-run
vs item apply --plan-dir <plan> --dry-run

# Check plan artifacts
ls -la ./.viking/item-plans/<plan>/

# Review plan.json for details
cat ./.viking/item-plans/<plan>/plan.json | jq .

Search Tuning Not Working

# Verify LLM is configured
vs llm status --json
vs search tune llm-check --live --json

# Regenerate queries if format is wrong
vs search tune query-generate --dataset-id <id> --output ./new-queries.json

# Check plan directory structure
ls -la ./.viking/search-tune-plans/<plan>/

Permission Errors

# Verify AK/SK has correct permissions
vs doctor --json

# Check specific resource access
vs dataset list --json
vs app list --json

Agent Skill Installation

# List installed skills
vs skill list

# Reinstall all skills
vs skill install all

# Validate skill definitions
vs skill validate

Best Practices

1. Always use dry-run first: --dry-run shows what will happen without executing
2. Use confirmation gates: --confirm-review adds human review step for critical operations
3. Prefer dataset-create.json: Especially for video datasets, use full payloads over schema-only
4. Store credentials securely: Never commit AK/SK to version control; use environment variables
5. Version control plans: The .viking/ directory contains reproducible plans (clean sensitive data first)
6. Use JSON output for automation: --json flag provides machine-readable output
7. Validate with runtime trials: --run-trials ensures end-to-end functionality
8. Type video datasets explicitly: Always pass --type video for video data

Related Resources

• Agent Quick Start Guide
• Contributing Guidelines
• Security Policy
• Code of Conduct