'document retrieval' 검색 결과

Design memory systems that help AI agents retrieve relevant past information. Choose short-term and long-term memory structures, compare vector storage options, and review document splitting and retrieval strategies for different kinds of knowledge and experience.

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• Design memory for information used across conversations
• Choose storage for experiences, facts, and procedures
• Review retrieval when an agent misses saved information

Extract references between imported documents and classify how they relate, such as relying on, extending, or refuting another work. Store these relationships as typed links so explicit graph queries can trace connections through the collection.

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• Tracing which papers extend earlier research
• Checking which decisions overrule or distinguish a case
• Following the documents a brief relies on

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

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• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Retrieve Chinese medical guidelines, drug information, and regulatory documents when original sources are inaccessible. It checks document identity and version before using alternative copies, while keeping summaries and reposts separate from independent clinical evidence.

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• Finding a guideline when its original page is inaccessible
• Checking whether an alternative PDF matches the original version
• Reviewing fallback rules for Chinese medical sources

Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

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Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

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DSPy로 질문 응답과 문서 검색을 결합한 AI 처리를 만듭니다. 입출력을 정의한 구성 요소를 조합하고 학습 예시와 평가 지표로 프롬프트를 자동 조정합니다.

• 문서 검색을 결합한 질문 응답 만들기
• 학습 예시로 프롬프트 조정하기
• 텍스트에서 구조화된 데이터 추출하기

Retrieve facts from named scientific, regulatory, economic, and other databases through documented APIs. Get results with recorded filters, retrieval dates, and record counts so another person can repeat the lookup and assess its completeness.

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• Look up gene or compound records by identifier
• Collect clinical trials matching defined criteria
• Retrieve economic statistics with documented sources

Evaluate how Chatbox answers questions about large attachments using synthetic and real documents. Check whether the model retrieves file content when needed, skips retrieval for unrelated requests, and investigate failures through the actual application flow.

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• Verify retrieval for questions about large attachments
• Test file-related questions without explicit search requests
• Check that unrelated requests skip file retrieval

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

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• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Turn documents into a growing Markdown wiki and a knowledge graph, a map of entities and their relationships. Preserve source text, update linked pages as new material arrives, and answer questions with supporting evidence.

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• Build a reusable knowledge base from documents
• Trace relationships between terms and topics
• Answer questions with source evidence

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

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• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Develop applications with the Claude API using examples from Anthropic's official cookbooks. Explore document summarization, image analysis, document retrieval, external tool calls, and prompt caching through practical Python code and implementation guidance.

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• Learning basic Claude API integration
• Building an assistant that searches documents
• Extracting text and information from images

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Convert PDF, PowerPoint, Excel, and Word documents into structured Markdown for search and information extraction. Outputs include document metadata, a parsing confidence score, and the parser used, with fallback parsers available when earlier attempts fail.

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• Preparing documents for a searchable knowledge base
• Structuring contract text for information extraction
• Checking parsing confidence before manual review

Convert PDF, Word, and Excel documents into structured Markdown for search and knowledge bases. The skill tries three parsers in sequence when needed and returns metadata, a confidence score, and the parser used.

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• Prepare documents for retrieval-augmented generation
• Structure documents for an internal knowledge base
• Extract structured text from contracts

Create documents when several research or execution tools fail. Delegate PDFs and complex layouts to shell_agent, a separate agent, or save Markdown directly, while distinguishing verified facts from general guidance and checking the output.

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• Drafting reports when several data retrieval tools fail
• Delegating PDF templates and complex document layouts
• Saving Markdown drafts with clear information limitations

Produce Word, PDF, and HTML documents when retrieval or conversion tools fail. The workflow switches to available knowledge with stated limitations, prepares characters for PDF conversion, tries alternative tools, and checks the resulting files.

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• Drafting a report when search or file reading fails
• Producing Word, PDF, and HTML from one source
• Trying alternative tools after PDF conversion failures

Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.

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• Continue drafting after repeated web retrieval failures
• Preserve task requirements during network errors
• Mark information that could not be externally verified