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Configure, test, benchmark, and manage embedding providers for semantic search and vector operations
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | embedding-ops |
| description | Configure, test, benchmark, and manage embedding providers for semantic search and vector operations |
| Command | Purpose |
|---|---|
do-memory-cli embedding config | Show current embedding configuration |
do-memory-cli embedding test | Test embedding provider connectivity |
do-memory-cli embedding list-providers | List available providers |
do-memory-cli embedding benchmark | Benchmark provider latency/throughput |
do-memory-cli embedding rebuild | Rebuild embedding index from episodes |
do-memory-cli embedding enable | Enable embeddings (session only) |
do-memory-cli embedding disable | Disable embeddings (session only) |
| Tool | Parameters | Purpose |
|---|---|---|
configure_embeddings | provider, model, dimension, api_key_env | Configure embedding provider |
query_semantic_memory | query, limit, threshold | Semantic similarity search |
test_embeddings | text | Test embedding generation |
generate_embedding | text, provider | Generate embedding vector |
search_by_embedding | embedding, limit | Search by raw vector |
embedding_provider_status | - | Check provider health |
Set in config file or environment:
[embeddings]
enabled = true
provider = "openai" # openai | cohere | ollama | local
model = "text-embedding-3-small"
dimension = 1536
api_key_env = "OPENAI_API_KEY"
export OPENAI_API_KEY="sk-..."
do-memory-cli embedding config
do-memory-cli embedding test
ollama pull nomic-embed-text
# Config: provider = "ollama", model = "nomic-embed-text", dimension = 768
benchmark to identify latency; consider local providerenable/disable commands affect current session only (no persistence)--semantic-search flag on episode list is declared but not yet wiredsave() is a no-op when hnsw feature is enabledCheckpoint episodes and create handoff packs for multi-agent session continuity
Create and traverse typed relationships between episodes for dependency tracking and knowledge graphs
Manage episode tags for organization, filtering, and retrieval across the memory system
Generate task playbooks and explain patterns using learned strategies from episodic memory
Record and analyze recommendation sessions and feedback to drive the self-learning loop
Use and troubleshoot the Memory MCP server for episodic memory retrieval and pattern analysis. Use when working with MCP server tools, validating the MCP implementation, or debugging MCP server issues.