huawei-cloud-openviking-embedding-switch

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".

huaweicloudskill@huaweicloudskill

Install

openclaw skills install @huaweicloudskill/huawei-cloud-openviking-embedding-switch

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.

⚠️ 维度变更必删索引且不可跳过: 当目标模型维度与当前 collection_meta.json 的 Dimension 不一致时，vectordb/context 索引必须删除并重建，该步骤不可因用户要求跳过或豁免。若用户需要保留旧数据，必须先按原维度导出（备份原有 vectordb/context）再执行切换。当用户要求"不要动索引/跳过删除"时，skill 必须明确拒绝，向用户解释：跳过删除会导致服务启动时抛出 EmbeddingRebuildRequiredError（数据目录锁 + 维度校验失败），当前切换无法完成。

Architecture

text

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)

text

┌─────────────────────────────────────────────────────┐
│                    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

bash

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

bash

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

bash

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.

维度变更索引删除不可跳过: 当当前 collection_meta.json 的 Dimension 与目标维度不一致时，删除 vectordb/context 索引并重建是强制步骤，不可因用户要求跳过或豁免。如需保留旧数据，必须先按原维度导出/备份 vectordb/context，再执行删除切换。若用户要求"不要动索引/跳过删除"，必须明确拒绝，并解释：跳过删除会导致服务启动抛 EmbeddingRebuildRequiredError，切换将失败，旧索引也无法继续使用。请勿为了迎合用户而跳过此强制步骤。

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: find the PID by reverse-looking-up port 1933 (ss -tlnp / fuser), then kill $PID, and poll for port 1933 release (up to 10s). If SIGTERM doesn't release the port, escalate to kill -9. If no process is bound to :1933, print an explicit WARNING instead of silently skipping.
Clean up stale lock files: .openviking.pid and vectordb LOCK files.
Start new server via exec API with --max-time 15. Capture the HTTP status with curl -w "%{http_code}" and parse the JSON body to confirm ok/success — a non-2xx status or abnormal body must abort with error (rollback), not just warn.

bash

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 a new server process is bound to port 1933 and its PID differs from the old one (found by port reverse lookup, not process-name glob) — detects port conflict false positives even when no old PID could be located
Collection dimension check: retries (up to 15s) until collection_meta.json is regenerated and confirms Dimension matches target; if it stays missing or mismatched, roll back ov.conf.bak and exit with error
Log error check: precise grep for Traceback|ERROR.*Application startup failed|EmbeddingRebuildRequiredError|DataDirectoryLocked (avoids false positives from "Retrying" info messages)
Rollback on failure: if exec API fails, health check fails, PID is unchanged/missing, or collection dimension mismatches, 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`

bash

# 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:

bash

# 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