AgentSkillsCN

workers-ai-specialist

在本项目中，为 Workers AI 与 Vectorize 提供模型选择、嵌入向量、RAG 模式、AI 网关、缓存机制以及性能优化方面的指导。

SKILL.md
--- frontmatter
name: workers-ai-specialist
description: Guide model selection, embeddings, RAG patterns, AI Gateway, caching, and performance for Workers AI and Vectorize in this project.

Workers AI Specialist

Use for AI/model questions (LLaMA, BGE embeddings), RAG design, AI Gateway, and cache/latency optimization.

Project defaults

  • •Models: @cf/meta/llama-3.1-8b-instruct (QA), @cf/baai/bge-base-en-v1.5 (embed). Remote-only.
  • •Vector store: Cloudflare Vectorize (binding VECTOR_INDEX).
  • •Embedding cache: KV EMBEDDINGS_CACHE (issue #12 to implement).
  • •AI Gateway: configure via ai_gateway in wrangler.jsonc (issue #16). Start disabled; enable with real gateway ID.

Workflow

  1. •Clarify task: generation, embedding, rerank, or retrieval.
  2. •Pick model: small = speed (bge-small), base = balance (bge-base), large = quality (bge-large). For text generation, default LLaMA 3.1 8B, temperature 0–0.2 for determinism.
  3. •Retrieval:
    • •Enforce topK validation; cap length using MAX_QUERY_LENGTH.
    • •Chunking defaults: size 500, overlap 100 (env vars).
    • •Prefer cached embeddings before AI calls; 7d TTL, SHA-256 keys.
  4. •Generation:
    • •Provide system prompt with context; keep max_tokens modest (<=1024) for latency.
    • •Stream if latency sensitive; if not streaming, log latency and token counts.
  5. •AI Gateway:
    • •Enable caching (1h TTL) when ID present; note remote requirement.
    • •Respect rate limits and retry guidance; Gateway handles cache + observability.
  6. •Testing: mock env.AI.run in Vitest; seed predictable responses.

Snippets

  • •Embedding call (batched): env.AI.run('@cf/baai/bge-base-en-v1.5', { text: batch })
  • •Generation call: env.AI.run('@cf/meta/llama-3.1-8b-instruct', { messages, temperature: 0.0, max_tokens: 1024 })

Pitfalls

  • •Local dev: use wrangler dev --remote for AI/Vectorize.
  • •Keep prompts short; avoid sending redundant context; trim topK results.
  • •Log cache hit/miss; don’t fail the request on cache errors.