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Adopt Mintlify-quality writing patterns across 11 existing docs and add 3 new docs. Adds Steps, Tabs, AccordionGroup, and mermaid diagrams for better readability. Priority 1 (major expansion): agent-market, resource, scheduled-task, mcp-market Priority 2 (structural): memory, web-search, tts-stt, vision, chain-of-thought Priority 3 (minor): artifacts, agent New docs: chat, file-upload, skills-and-tools Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
286 lines
10 KiB
Plaintext
286 lines
10 KiB
Plaintext
---
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title: Resource Library
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description: >-
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Your personal knowledge hub — documents, AI-generated images, and drafts in
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one place. Agents answer from your data.
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tags:
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- LobeHub
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- Resource Library
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- Knowledge Base
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- Semantic Search
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- File Upload
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- Notion Import
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---
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# Resource Library
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The Resource Library is your personal knowledge hub. Documents uploaded in conversations, AI-generated images, and drafts you create — all collected here. Centralize your knowledge so Agents can answer from your data, not just from general training. Manage and access everything in one place.
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## Overview
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The Resource Library provides:
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- **Semantic Search**: Vector-based search that understands meaning, not just keywords
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- **Document Retrieval**: Agents read and reference your uploaded files
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- **Multiple Formats**: Support for documents, images, audio, video, and more
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- **Persistent Storage**: Knowledge persists across conversations
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- **Chunked Processing**: Large documents split into searchable chunks
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## How It Works
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```mermaid
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graph LR
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A[Upload Documents] --> B[Chunk & Embed]
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B --> C[Vector Store]
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D[User Query] --> E[Semantic Search]
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E --> C
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C --> F[Relevant Chunks]
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F --> G[Agent Response]
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```
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<Steps>
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### Upload Documents
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Upload files to your Resource Library through direct upload, conversation uploads, or Notion import.
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### Embedding Process
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Documents are:
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1. Split into manageable chunks
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2. Converted to vector embeddings
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3. Stored in the vector database
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4. Associated with metadata (filename, chunk position)
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### Semantic Search
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When queried, the system:
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1. Converts your query to a vector embedding
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2. Finds the most similar chunks via cosine similarity
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3. Returns files and excerpts ranked by relevance
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### Agent Response
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The Agent reads the most relevant chunks and incorporates them into its response — finding content even when the exact words don't match. For example, searching "how to login" can find content about "authentication" or "sign in".
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</Steps>
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## Where Resources Come From
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- **Conversation uploads** — Files uploaded during chats are automatically saved to the Resource Library. Upload a PDF for summarization, and it stays available for future use.
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- **AI-generated content** — Images created in the Drawing module are saved automatically. View, download, and reuse them in conversations or drafts anytime.
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- **Manual uploads** — Upload files or folders directly to build your knowledge base. Supported formats: documents, images, video, audio, and more.
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- **Drafts** — Pages created in the writing module are stored here for unified management.
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- **Notion import** — Import content from Notion into LobeHub for seamless knowledge management across platforms.
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**Access the Resource Library** — Click the **Resources** icon at the bottom of the left sidebar on the LobeHub main interface.
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## Creating a Resource Library
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Organize your resources by theme, project, or type. Separate libraries keep everything structured.
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Click **New Library** in the left panel of the Resource Library page, enter a name, add an optional description, and click **Create**.
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### Uploading Folders
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Choose to upload a folder to batch upload all files within it.
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### Connecting Notion
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Export documents from Notion, then import the Notion ZIP file via the **Connect Notion** page. Once imported, you can continue editing these documents within LobeHub.
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## Search Best Practices
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### Resolve References
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Knowledge Base uses vector-based semantic search. Always resolve pronouns and references to concrete entities.
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- ❌ "What does it do?" / "Tell me about that" / "How does this work?"
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- ✅ "What does the authentication system do?" / "How does the payment processing workflow work?"
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**Why?** Vector search works by semantic similarity. Pronouns like "it" or "that" have no semantic meaning on their own and produce poor results.
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### Query Formulation
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<AccordionGroup>
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<Accordion title="Be Specific">
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Use precise terminology and context:
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- "user authentication with OAuth 2.0" ✅
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- "login stuff" ❌
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</Accordion>
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<Accordion title="Include Context">
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Add relevant details to narrow scope:
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- "React component testing with Jest" ✅
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- "testing" ❌
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</Accordion>
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<Accordion title="Use Full Names">
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Expand abbreviations on first search:
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- "JSON Web Token authentication" ✅
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- "JWT auth" (use after establishing context) ✅
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</Accordion>
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<Accordion title="Natural Language">
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Write queries as complete questions or phrases:
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- "How to implement password reset functionality" ✅
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- "password reset how" ❌
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</Accordion>
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</AccordionGroup>
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## Workflow Strategies
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### Two-Step Search Pattern
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The recommended approach for getting the best results:
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1. **Search first** — Use semantic search to discover relevant files and review the excerpts
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2. **Read selected files** — Retrieve the full content from the most relevant results
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This gives the Agent complete context to answer your question accurately.
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### Iterative Refinement
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For complex queries, search multiple times with different phrasings:
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1. Start with a broad initial search ("payment processing system")
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2. Refine based on results ("payment processing error handling and retries")
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3. Read the most relevant files from combined results
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### Batch Reading
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When a topic spans multiple files, read several related files at once to give the Agent comprehensive context.
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## Managing the Resource Library
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**File chunking** — Long documents are split into smaller text segments and vectorized. This helps the Agent understand and retrieve relevant content more accurately.
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After chunking:
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- When you ask a question, the Agent quickly locates the relevant parts instead of processing the entire file
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- Vectorization lets the Agent grasp semantic meaning, not just match keywords
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- The Agent processes only relevant chunks — faster and more accurate responses
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### Batch Operations
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Select multiple files and open the menu in the top-right corner to perform batch operations: move files to a library, chunk, or delete in bulk.
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### Supported File Formats and Limits
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**Supported formats:**
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- Documents: PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx), Markdown, plain text (.txt)
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- Code: Most programming languages
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- Data: CSV, JSON, XML, HTML
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- Images: JPEG, PNG, GIF, WebP, SVG
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- Audio: MP3, WAV, M4A, FLAC
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- Video: MP4, MOV, WebM
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File size limit is typically 50 MB per file. For very large documents, consider splitting them before uploading for better search performance.
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### File Organization
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- Use clear, descriptive filenames
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- Organize by topic or category
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- Keep files updated and remove outdated ones
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- Use version numbers for evolving docs
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- Break large documents into focused files
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## Advanced Features
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### Relevance Scoring
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Search results include relevance scores (0–1). Scores above 0.85 indicate a strong match, 0.7–0.85 indicate moderate relevance worth reviewing, and below 0.7 may not be useful.
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### Excerpt Preview
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Search results include brief excerpts from matched chunks. Review these before reading full files — often the excerpt contains enough information.
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### Citation & Attribution
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When Agents use your knowledge base, best practices include:
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- Reference specific files by name
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- Include section/page numbers when available
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- Quote directly for critical information
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- Indicate when synthesizing across multiple sources
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### System Prompt Integration
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Guide knowledge base usage in your Agent's system prompt:
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```
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You are a customer support agent with access to our product documentation.
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When answering product questions:
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1. Always search the knowledge base first
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2. Cite specific documents in your responses
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3. If unsure, read the full document for context
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4. If you can't find info, let the user know
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```
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## Cross-Feature Collaboration
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- **Chat + Resource Library** — Reference resources during conversations so the Agent answers from your knowledge
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- **Pages + Resource Library** — Use materials from the Resource Library while writing to enhance content creation
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- **Drawing + Resource Library** — Save generated images to the Resource Library for centralized management of visual assets
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## Use Cases
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- **Technical documentation** — Upload API docs, guides, or specs and ask questions in natural language
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- **Company knowledge** — Centralize onboarding materials, policies, and wikis for Agent-assisted retrieval
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- **Customer support** — Upload FAQs and product documentation to power accurate support responses
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- **Research** — Collect papers and articles and let the Agent synthesize findings across sources
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- **Code documentation** — Store architecture docs and code guides to help developers understand codebases
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## Troubleshooting
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<AccordionGroup>
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<Accordion title="No Search Results">
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**Solutions**:
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- Verify documents are uploaded and processed (chunked)
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- Rephrase query with different terminology
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- Use broader, more general search terms
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- Check if topic is covered in uploaded docs
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- Resolve pronouns to concrete entity names
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</Accordion>
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<Accordion title="Poor Relevance">
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**Solutions**:
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- Reformulate query with more context
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- Try multiple related queries
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- Verify document quality and format
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- Check if chunking is appropriate
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- Use full entity names instead of pronouns
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</Accordion>
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<Accordion title="Slow Performance">
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**Solutions**:
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- Break large documents into focused files
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- Remove outdated or irrelevant files
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- Use specific queries to reduce search scope
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- Read only the most relevant files instead of all results
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</Accordion>
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</AccordionGroup>
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<Cards>
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<Card href={'/docs/usage/getting-started/agent'} title={'Agent'} />
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<Card href={'/docs/usage/getting-started/page'} title={'Pages'} />
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<Card href={'/docs/usage/getting-started/image-generation'} title={'Image Generation'} />
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</Cards>
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