Chat Format
技能 已验证 活跃Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
To standardize prompt formatting across different LLM providers and enable efficient context retrieval for RAG applications.
功能
- Format prompts for multiple LLM providers
- Support for Anthropic, OpenAI, Gemini, and Ollama formats
- Integrate HNSW for context retrieval
- Create, add to, and route queries through HNSW indexes
- Check provider status
使用场景
- Preparing prompts for different LLM APIs.
- Building RAG pipelines with vector-based context retrieval.
- Ensuring consistent prompt structure across diverse LLM backends.
非目标
- General-purpose LLM inference outside of prompt formatting.
- Managing LLM provider API keys or authentication.
- Complex RAG pipeline orchestration beyond context retrieval.
工作流
- Format chat messages for a target LLM provider.
- Create an HNSW index for context retrieval.
- Add documents to the HNSW index.
- Route a query to find relevant context using the HNSW index.
- Check the status of supported LLM providers.
实践
- Prompt engineering
- LLM integration
- Vector databases
- Information retrieval
安装
请先添加 Marketplace
/plugin marketplace add ruvnet/ruflo/plugin install ruflo-ruvllm@ruflo质量评分
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