Weaviate Agent 技能

开发并查询使用 Weaviate 的 AI 应用。

官方包提供接入、搜索与 Agent 开发指引，并附带 Weaviate 流程所需脚本和资源。

适用场景

安装技能包，配置 Weaviate 地址与凭据，并提供所需外部模型服务密钥；通过 quickstart 验证访问。

engram_memory
Add persistent, long-term memory to LLM agents and chatbots using Engram, Weaviate's managed memory server. Use when an app needs to remember things across sessions — user preferences, profiles, past interactions, or lessons an agent learns over time (continual learning). Covers storing memories (string, conversation, pre-extracted input), semantic/keyword/hybrid search, scoping by user and topic, async run tracking, and chatbot/agent integration patterns.
weaviate/agent-skills
weaviate-cookbooks
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
weaviate/agent-skills
weaviate
Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from PDF/CSV/JSON/JSONL files, create example data and collection creation.
weaviate/agent-skills