> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chunkr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Making an excel agent

## Overview

An Excel agent combines Chunkr's parsing power with an interactive viewer and LLM capabilities. Users can view native Excel cells, search for tables, and let the LLM read/write to specific cell ranges while keeping everything synchronized.

## Architecture

```mermaid theme={"system"}
graph LR
    H[Excel File] --> I[Chunkr]
    I --> A
    A --> J[Embeddings]
    J --> B
    
    subgraph "Tools"
        A[Chunkr Parsed Output]
        B[Vector DB]
        D[Sync Code]
    end
    
    subgraph "Agent (LLM)"
        E[Excel Agent]
    end
    
    subgraph "User Interface"
        F[Excel Viewer]
        G[User]
    end
    
    A --> E
    B --> E
    D --> E
    
    E --> F
    F --> G
    G --> F
    
    E -.->|Requests changes| D
    D -.->|Modifies parsed data| A
    D -.->|Syncs data changes| F
    D -.->|Updates embeddings| B
    
    style H fill:#e1f5fe
    style I fill:#f3e5f5
    style E fill:#fce4ec,stroke:#333,stroke-width:3px
    style F fill:#e8f5e8
    style A fill:#fff3e0
    style J fill:#e8f5e8
    style B fill:#f0f4c3
```

## How It Works

### 1. **Chunkr Output as Foundation**

* Chunkr parses the Excel file **once** into structured data
* The viewer renders native cells directly from this parsed output
* All components work with the same underlying data structure

### 2. **LLM Integration**

* **Read Operations**: LLM analyzes specific cell ranges for patterns and insights
* **Write Operations**: LLM requests data changes through the sync code
* **No Re-parsing**: Changes are made to existing parsed data, Chunkr doesn't re-parse

### 3. **Sync Code Coordination**

* Sync code is the central coordinator for all data modifications
* LLM sends change requests to sync code rather than modifying data directly
* Sync code updates parsed data, viewer, and vector DB embeddings simultaneously
* Ensures all components stay synchronized with a single source of truth

### 4. **Search & Discovery**

* Chunkr parsed output gets converted into embeddings for semantic understanding
* Vector DB stores these embeddings for efficient similarity search
* Enables semantic search for more intelligent data discovery
* LLM can quickly locate specific data ranges through natural language queries

## Example Workflow

1. **Upload Excel file** → Chunkr parses structure
2. **Create embeddings** → Convert parsed data to vectors for search
3. **View native cells** → Viewer displays from parsed output
4. **Search for tables** → Find specific tables through natural language in vector DB
5. **LLM operations** → Read/write to cell ranges
6. **Auto-update** → Viewer reflects changes immediately

The agent acts as the central coordinator, ensuring seamless interaction between the user interface and the underlying tools while maintaining data consistency through the Chunkr output.