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huawei-cloud-openviking-embedding-switch

huaweicloud/huaweicloud-skills

Switch OpenViking's embedding model to a local llama-server or OpenAI-compatible endpoint with automatic vectordb index rebuild.

What is huawei-cloud-openviking-embedding-switch?

Manages the complete lifecycle of changing OpenViking's embedding model, including configuration updates, vectordb index deletion when dimensions change, and sandbox-safe server restart via job-env-manager. Use this when switching embedding models, fixing dimension mismatches, or rebuilding the vectordb index after model changes.

  • Detects current embedding configuration from ov.conf
  • Validates target embedding endpoint connectivity and dimension
  • Modifies ov.conf with new provider, model, api_base, and dimension settings
  • Deletes incompatible vectordb index when embedding dimension changes
  • Restarts openviking-server inside bwrap sandbox without overwriting config
  • Verifies health, PID change, collection dimension, and logs with automatic rollback on failure

How to install huawei-cloud-openviking-embedding-switch

npx skills add https://github.com/huaweicloud/huaweicloud-skills --skill huawei-cloud-openviking-embedding-switch
Prerequisites
  • job-env-manager running on http://127.0.0.1:8090
  • OpenViking environment deployed and in running state
  • llama-server or compatible embedding endpoint running at 127.0.0.1 with --embeddings flag
  • curl and python3 available on the host
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How to use huawei-cloud-openviking-embedding-switch

  1. 1.Verify job-env-manager is running: curl http://127.0.0.1:8090/api/v1/envs/openviking
  2. 2.Identify the target embedding endpoint (llama-server port and model name)
  3. 3.Run the skill with target provider, model, api_base URL, and port parameters
  4. 4.The skill detects current config, validates the endpoint, and auto-corrects dimension if needed
  5. 5.Skill modifies ov.conf, deletes vectordb index if dimension changed, and restarts the server
  6. 6.Monitor health checks and log verification; automatic rollback occurs on failure

Use cases

Good for
  • Switch from one embedding model to another (e.g., bge-small-zh-v1.5 to bge-large-zh-v1.5)
  • Fix EmbeddingRebuildRequiredError caused by dimension mismatch between config and vectordb
  • Migrate OpenViking to use a local llama-server for embeddings instead of cloud-based provider
  • Rebuild vectordb index after changing embedding model dimensions
  • Configure OpenViking to use a custom OpenAI-compatible embedding endpoint
Who it's for
  • OpenViking administrators managing embedding model configurations
  • DevOps engineers deploying local embedding services with OpenViking
  • Teams using job-env-manager for sandbox-based service orchestration
  • Users troubleshooting vectordb dimension mismatch errors

huawei-cloud-openviking-embedding-switch FAQ

Why can't I use stop/start to restart openviking-server?

The stop/start API re-runs start.sh which overwrites ov.conf with TokenHub credentials, losing your embedding configuration changes. This skill uses kill + exec instead to preserve the modified config.

What happens if the embedding endpoint dimension doesn't match ov.conf?

The skill auto-corrects the dimension in ov.conf to match the actual endpoint output, then deletes the old vectordb index to prevent EmbeddingRebuildRequiredError on startup.

How long does the embedding model switch take?

Typically 5-15 seconds: endpoint validation (~1s), config modification (~1s), vectordb deletion if needed (~2-5s), server restart (~3-5s), and health verification (~3-5s).

What if the health check fails after restart?

The skill automatically restores ov.conf from the backup (ov.conf.bak) and exits with an error, leaving the system in its previous working state.

Can I use this skill with cloud-based embedding providers?

Yes, the skill works with any OpenAI-compatible embedding endpoint. Set api_base to the cloud provider's URL and api_key to your credentials; local llama-server is just one example.

Full instructions (SKILL.md)

Source of truth, from huaweicloud/huaweicloud-skills.


name: huawei-cloud-openviking-embedding-switch description: | 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". tags:

  • openviking
  • embedding
  • llama
  • vectordb
  • job-env-manager

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:

FieldDescription
providerEmbedding provider name
modelModel name (e.g., bge-small-zh-v1.5)
api_keyAPI key for the endpoint (empty for local)
api_baseEndpoint URL (e.g., http://127.0.0.1:18200/v1)
dimensionVector 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:

  1. 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.
  2. Clean up stale lock files: .openviking.pid and vectordb LOCK files.
  3. 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

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

Parameter Confirmation

ParameterRequiredDescriptionExample
MODEL_NAMEYesEmbedding model namebge-small-zh-v1.5
LLAMA_PORTYesllama-server port18200
TARGET_DIMENSIONYesVector dimension (auto-corrected if wrong)512
# Usage
bash scripts/switch-embedding-model.sh <model_name> <llama_port> <dimension>

Common Embedding Model Dimensions

ModelDimensionTypical Use
bge-small-zh-v1.5512Lightweight Chinese embedding
bge-large-zh-v1.51024High-quality Chinese embedding
bge-small-en-v1.5384Lightweight English embedding
bge-base-en-v1.5768General-purpose English embedding
Qwen3-Embedding-0.6B1024Qwen3 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

DocumentDescription
config-reference.mdov.conf embedding section field reference
guardrails.mdSafety rules: sandbox execution, restart sequence, rollback
iam-policies.mdEquivalent access controls (no Huawei Cloud IAM needed)
verification-method.mdStep-by-step verification for each workflow
related-commands.mdCommon job-env-manager and curl commands
acceptance-criteria.mdAcceptance criteria for a successful switch
troubleshooting.mdTroubleshooting for common failure scenarios
dataflow-diagram.mdMermaid data flow diagram
demo/example-input.jsonExample input for the switch workflow