huawei-cloud-openviking-embedding-switch

Q: How do I install huawei-cloud-openviking-embedding-switch?

Run npx skills add https://github.com/huaweicloud/huaweicloud-skills --skill huawei-cloud-openviking-embedding-switch in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/huawei-cloud-openviking-embedding-switch/ (personal) or .claude/skills/huawei-cloud-openviking-embedding-switch/ (project).

by huaweicloudAI & Agents

Switch OpenViking's embedding model to a local llama-server (or any OpenAI-compatible embedding endpoint) running inside a bwrap sandbox managed by job-env-manager. Handles the full lifecycle: detect current config, validate the target embedding endpoint, modify ov.conf, delete incompatible vectordb index when dimension changes, restart the openviking-server process in the sandbox, and verify the new collection dimension. Use this skill when the user wants to: (1) switch the OpenViking embedding model, (2) change the embedding dimension, (3) fix EmbeddingRebuildRequiredError after a dimension mismatch, (4) rebuild the vectordb index after an embedding model change, (5) use a local llama-server for OpenViking embeddings. Trigger words: "切换OpenViking embedding", "OpenViking embedding模型", "OpenViking向量化模型", "openviking embedding switch", "change openviking embedding model", "配置openviking embedding", "openviking llama embedding", "bge embedding openviking", "切换向量化模型", "OpenViking模型切换".

Installs: 16.1K
Last 24h: +104
Trending rank: #1946
Security: Pass

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/huaweicloud/huaweicloud-skills --skill huawei-cloud-openviking-embedding-switch

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads huawei-cloud-openviking-embedding-switch automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill facilitates switching the embedding model for OpenViking environments managed via job-env-manager. It uses local REST APIs and system commands for configuration updates, process management, and health verification. While automated scanners flagged certain command patterns, analysis confirms they are used safely for local data processing.

Socket PassNo alerts

Snyk PassLOW ·Risk: LOW · No issues

SKILL.md

The instructions your agent reads when the skill is triggered.

OpenViking Embedding Model Switch

概述

Switch the embedding model used by OpenViking to a local llama-server or any OpenAI-compatible endpoint, with proper vectordb index rebuild and sandbox-safe restart.

⚠️ Single-purpose skill — all operations go through the job-env-manager REST API (http://127.0.0.1:8090). Never run openviking-server directly on the host.

OpenViking is an AI context database that uses vector embeddings for semantic search. Its embedding model is configured in ov.conf under the embedding.dense section. When switching to a different embedding model (especially one with a different vector dimension), the existing vectordb index must be deleted and rebuilt — otherwise OpenViking raises EmbeddingRebuildRequiredError on startup.

Architecture

OpenViking Embedding Model Switch
├── Detect current config     (Read ov.conf embedding.dense section)
├── Validate endpoint         (Check llama-server /v1/embeddings)
├── Modify ov.conf            (Update provider, model, api_base, dimension)
├── Delete vectordb index     (If dimension changed: rm -rf vectordb/context)
├── Restart server            (Kill + exec, NOT stop/start)
└── Verify                    (Health + PID + dimension + log check)

┌─────────────────────────────────────────────────────┐
│                    Host                              │
│                                                      │
│  ┌─────────────┐    REST API   ┌──────────────────┐ │
│  │  Agent       │─────────────▶│  job-env-manager  │ │
│  │  (this skill)│              │  :8090            │ │
│  └─────────────┘              └────────┬─────────┘ │
│                                        │            │
│         ┌──────────────────────────────┼──────┐    │
│         │  bwrap sandbox (openviking)   │      │    │
│         │                               ▼      │    │
│         │  ┌────────────────────────────────┐  │    │
│         │  │  openviking-server :1933       │  │    │
│         │  │  ├── ov.conf (embedding config)│  │    │
│         │  │  ├── vectordb/context/         │  │    │
│         │  │  └── viking/ (metadata)        │  │    │
│         │  └────────────────────────────────┘  │    │
│         └──────────────────────────────────────┘    │
│                                                      │
│         ┌──────────────────────────────────────┐    │
│         │  bwrap sandbox (llama)                │    │
│         │  ┌────────────────────────────────┐  │    │
│         │  │  llama-server :18200           │  │    │
│         │  │  --embeddings --model bge-...  │  │    │
│         │  └────────────────────────────────┘  │    │
│         └──────────────────────────────────────┘    │
│                                                      │
│  Both sandboxes use --share-net, so 127.0.0.1        │
│  endpoints are mutually reachable.                   │
└─────────────────────────────────────────────────────┘

Prerequisites

Prerequisite check: job-env-manager running

curl -s http://127.0.0.1:8090/api/v1/envs/openviking | python3 -c "import sys,json; print(json.load(sys.stdin)['state'])"

job-env-manager running on http://127.0.0.1:8090
OpenViking environment deployed and running (state = running)
llama-server running at 127.0.0.1:{port} with --embeddings flag
curl and python3 available on the host
No AK/SK or Huawei Cloud credentials required

IAM Permission Policies

This skill operates on local bwrap sandboxes via the job-env-manager REST API and does not access Huawei Cloud services — no Huawei Cloud IAM policies required. Equivalent access controls are listed in references/iam-policies.md.

核心命令 (Core Workflow)

Task 1: Detect Current Configuration

SANDBOX_DIR=$(curl -s http://127.0.0.1:8090/api/v1/envs/openviking \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['cwd'])")

Read ov.conf under the sandbox directory to get the current embedding.dense section (provider, model, dimension).

Task 2: Validate Target Embedding Endpoint

curl -s http://127.0.0.1:${LLAMA_PORT}/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model":"${MODEL_NAME}","input":"test"}' \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(len(d['data'][0]['embedding']))"

If unreachable, STOP. The script auto-corrects the dimension if the specified value doesn't match the actual endpoint output.

Task 3: Modify ov.conf

Backs up ov.conf to ov.conf.bak before modifying. Updates the embedding.dense section:

Field	Description
`provider`	Embedding provider name
`model`	Model name (e.g., `bge-small-zh-v1.5`)
`api_key`	API key for the endpoint (empty for local)
`api_base`	Endpoint URL (e.g., `http://127.0.0.1:18200/v1`)
`dimension`	Vector dimension (auto-corrected from endpoint)

Task 4: Delete Incompatible vectordb Index

⚠️ Critical: If dimensions differ, rm -rf vectordb/context is required. Otherwise EmbeddingRebuildRequiredError on startup.

If dimension is unchanged, skip this step.

Task 5: Restart openviking-server Inside the Sandbox

⚠️ Pitfall: POST /envs/openviking/stop + start re-runs start.sh, which overwrites ov.conf with TokenHub credentials. Do not use stop/start.

Instead:

Kill old process from host: kill $PID, then poll for port 1933 release (up to 10s). If SIGTERM doesn't release the port, escalate to kill -9.
Clean up stale lock files: .openviking.pid and vectordb LOCK files.
Start new server via exec API with --max-time 15:

curl -s --max-time 15 -X POST http://127.0.0.1:8090/api/v1/envs/openviking/exec \
  -H 'Content-Type: application/json' \
  -d '{"cmd":["bash","-c","nohup /root/runtime/openviking/venv/bin/openviking-server --config /workspace/process_dir/ov.conf > /workspace/process_dir/openviking-server.log 2>&1 & sleep 2 && echo started"]}'

Task 6: Verify

Health check with retry loop (up to 30s): polls GET /health every second until healthy=true or timeout
PID change check: verifies the new server PID differs from the old one (detects port conflict false positives)
Collection dimension check: reads collection_meta.json and confirms Dimension matches target
Log error check: precise grep for Traceback|ERROR.*Application startup failed|EmbeddingRebuildRequiredError|DataDirectoryLocked (avoids false positives from "Retrying" info messages)
Rollback on failure: if health check fails or PID unchanged, restores ov.conf.bak and exits with error

Parameter Confirmation

Parameter	Required	Description	Example
`MODEL_NAME`	Yes	Embedding model name	`bge-small-zh-v1.5`
`LLAMA_PORT`	Yes	llama-server port	`18200`
`TARGET_DIMENSION`	Yes	Vector dimension (auto-corrected if wrong)	`512`

# Usage
bash scripts/switch-embedding-model.sh <model_name> <llama_port> <dimension>

Common Embedding Model Dimensions

Model	Dimension	Typical Use
`bge-small-zh-v1.5`	512	Lightweight Chinese embedding
`bge-large-zh-v1.5`	1024	High-quality Chinese embedding
`bge-small-en-v1.5`	384	Lightweight English embedding
`bge-base-en-v1.5`	768	General-purpose English embedding
`Qwen3-Embedding-0.6B`	1024	Qwen3 embedding (TokenHub default)

Verification

See references/verification-method.md for step-by-step checks and end-to-end acceptance criteria.

Quick verification:

# 1. Server healthy
curl -s http://127.0.0.1:1933/health \
  | python3 -c "import sys,json; assert json.load(sys.stdin)['healthy']; print('OK')"

# 2. Collection dimension matches target
python3 -c "import json; d=json.load(open('${SANDBOX_DIR}/data/vectordb/context/collection_meta.json')); assert d['Dimension']==${TARGET_DIMENSION}; print('OK')"

# 3. No errors in log (precise pattern)
grep -ci "Traceback\|Application startup failed\|EmbeddingRebuildRequiredError\|DataDirectoryLocked" \
  "${SANDBOX_DIR}/process_dir/openviking-server.log"
# Expected: 0

Guardrails

See references/guardrails.md for the full rules. Key principles:

Always run through job-env-manager — never execute openviking-server directly on the host
Never use stop/start restart — start.sh overwrites ov.conf with TokenHub credentials
Validate before modify — the target endpoint must respond before any config change
Rollback on failure — ov.conf.bak is restored if verification fails

References

Document	Description
config-reference.md	ov.conf embedding section field reference
guardrails.md	Safety rules: sandbox execution, restart sequence, rollback
iam-policies.md	Equivalent access controls (no Huawei Cloud IAM needed)
verification-method.md	Step-by-step verification for each workflow
related-commands.md	Common job-env-manager and curl commands
acceptance-criteria.md	Acceptance criteria for a successful switch
troubleshooting.md	Troubleshooting for common failure scenarios
dataflow-diagram.md	Mermaid data flow diagram
demo/example-input.json	Example input for the switch workflow

FAQ

What does the huawei-cloud-openviking-embedding-switch skill do?

How do I install huawei-cloud-openviking-embedding-switch?

Run npx skills add https://github.com/huaweicloud/huaweicloud-skills --skill huawei-cloud-openviking-embedding-switch in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/huawei-cloud-openviking-embedding-switch/ (personal) or .claude/skills/huawei-cloud-openviking-embedding-switch/ (project).

Which AI agents can use huawei-cloud-openviking-embedding-switch?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is huawei-cloud-openviking-embedding-switch safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains huawei-cloud-openviking-embedding-switch?

It is published by huaweicloud in huaweicloud/huaweicloud-skills. It has 16,064 recorded installs.