“document retrieval”的搜索结果

Google Docs skill for creating, reading, and updating documents via the gws CLI. Covers document creation, content retrieval, batch updates, and sharing.

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Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".

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使用Qdrant将文本等内容表示为向量，实现相似内容搜索。支持按类别或日期筛选、为AI回答检索资料、实时推荐，以及内存和分布式部署的配置调整。

• 为AI回答检索参考资料
• 按类别和日期筛选相似内容
• 实现实时推荐

Design and evaluate how an AI agent remembers user preferences and history across conversations. Compare storage formats and memory frameworks, plan updates and compression, and distinguish personal memory from shared document retrieval while addressing sensitive information.

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• Design user memory that persists across conversations
• Compare storage formats, Mem0, and Memobase
• Evaluate recall and cross-conversation reasoning

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

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• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

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• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Develop cryptocurrency trading bots with Hummingbot documentation and code examples. Explore exchange connections, price and order book data, and strategies such as market making, which places buy and sell orders, or arbitrage across markets.

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• Developing a cryptocurrency trading bot
• Exploring market making and arbitrage strategies
• Checking exchange connection settings

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

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• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Retrieve public eQTL Catalogue association data, linking genes and genetic variants, along with study metadata. Filter requests to specific identifiers and receive concise Markdown summaries or raw JSON when machine-readable output is requested.

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• Look up association data for a specific gene
• Filter association results by a genetic variant
• Check study metadata in the public catalogue

Develop and deploy Cloudflare Workers, manage related storage and services, and troubleshoot configuration with Wrangler. The skill checks official documentation and configuration definitions before preparing commands, with guidance for local development, environments, secrets, and logs.

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• Develop and deploy a Cloudflare Worker
• Configure KV, R2, and D1 resources
• Separate staging and production settings

Write and review Cloudflare Workers code using current official guidance. Check configuration, asynchronous tasks, large response handling, secrets, and service connections, with findings supported by documentation, code locations, or validation results.

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• Writing a new Cloudflare Worker
• Reviewing Workers code before production use
• Checking wrangler.jsonc and service bindings

Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.

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• Choose services for a Cloudflare application
• Select storage for databases and files
• Build AI agents and semantic search

Build AI agents on Cloudflare Workers with the Agents SDK. Implement persistent state, real-time chat, scheduled tasks, multi-step background workflows, and MCP servers, using current Cloudflare documentation to guide implementation.

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• Build agents that retain and synchronize state
• Create real-time chat with resumable streaming
• Implement scheduled tasks and multi-step workflows

Audit page loading speed with Chrome DevTools MCP, a tool connection for browser inspection. Measure performance indicators, investigate network delays and layout shifts, check accessibility gaps, and produce prioritized fixes with estimated impact and relevant code findings.

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• Investigate slow page loads
• Find causes of layout shifts
• Identify improvements related to Lighthouse scores

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Find learning materials across papers, official documentation, and code repositories, then prioritize them using source evidence, current goals, and reading history. It also supports detailed reading explanations and integrating findings into a specified note.

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• Finding papers and technical learning materials
• Choosing what to read next
• Understanding a paper's methods and limitations

Design and improve prompts and structured responses for AI applications. The skill also guides evaluation, retrieval augmented generation, which uses retrieved material to inform answers, and agent architecture, with references and scripts for production workflows.

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• Improving prompts and response formats
• Evaluating answers based on retrieved documents
• Designing AI agent systems

Teach the user a topic as an adaptive tutor — retrieval practice, spaced repetition with decay, and persistent memory in ~/.drill-me/. Use when the user wants to learn or be drilled on something, says "drill me on X", "teach me X", or wants to study a topic, a codebase, or a document.

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Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)

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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.

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Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.

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INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

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