csv-ai-analyzer：用 AI 分析 CSV 与 Excel 文件的开源应用

🚀 Try it live →

App preview:

A modern, elegant application to analyze your CSV and Excel (.xlsx) files with Artificial Intelligence using multiple AI providers (OpenAI, Anthropic, Google, Mistral, and more). Privacy-first: the app does not store your data. Use a self-hosted/custom endpoint to keep processing entirely local; otherwise API calls go to the selected provider. Self-hostable with Docker.

✨ Features

📁 File Upload & Parsing

• Drag & Drop or file selection — supports CSV and Excel (.xlsx) files
• Automatic detection of delimiters (comma, semicolon, tab) for CSV files
• Configurable settings: delimiter, header row, encoding
• Type inference for columns (text, number, date)

📋 Data Visualization

• Interactive table with sorting and pagination
• Data preview with automatic formatting
• Smooth navigation for large datasets

🤖 AI-Powered Analysis

• Multiple AI providers: OpenAI, Anthropic Claude, Google Gemini, Mistral, and more
• Custom endpoint support: Connect to Ollama, LM Studio, vLLM, or any OpenAI-compatible API
• Intelligent analysis of your data with AI-generated insights
• Markdown rendering: AI responses rendered with full Markdown support (headings, lists, tables, syntax-highlighted code blocks)
• Streaming with Markdown: Real-time streaming responses are rendered progressively as formatted Markdown
• Smart chart suggestions tailored to your dataset
• Chart types: Bar, Line, Pie, Scatter, Area

🔒 Privacy

• Local or direct-to-provider: the app does not store your data. Processing can happen in your browser when using a self-hosted/custom endpoint; otherwise API calls go directly to the chosen AI provider.
• API keys stored locally in your browser
• No tracking or third-party cookies

🐳 Self-Hostable

• Docker ready: Deploy in seconds with a single command
• Custom AI endpoints: Use your own LLM server (Ollama, LM Studio, vLLM, etc.)
• Full control: Host on your own infrastructure

🚀 Installation

Option 1: Local Development

# Clone the repo
git clone https://github.com/maxgfr/csv-ai-analyzer.git
cd csv-ai-analyzer

# Install dependencies
pnpm install

# Run in development
pnpm dev

The application will be accessible at http://localhost:3000

Auto-updating the models catalog

This project ships a static public/models.json that contains the models catalog fetched from https://models.dev/api.json.

• You can regenerate the file locally with:

pnpm run fetch-models

• A GitHub Action is configured to run this script once per day and automatically commit public/models.json if it changes.

  • Workflow: .github/workflows/update-models.yml
  • Runs daily (UTC 06:00) and is also triggerable manually with workflow_dispatch.

If you'd like the file to be refreshed more/less often you can update the cron schedule in the workflow file.

Option 2: Docker (Self-Hosting)

# Build the Docker image
docker build -t csv-ai-analyzer .

# Run the container
docker run -p 3000:3000 csv-ai-analyzer

Or use Docker Compose:

# docker-compose.yml
version: '3.8'
services:
  csv-ai-analyzer:
    build: .
    ports:
      - "3000:3000"
    restart: unless-stopped

docker compose up -d

The application will be accessible at http://localhost:3000

Or use the live version directly!

🎮 Usage

1. Upload a File

Drag and drop your CSV or Excel (.xlsx) file, or click to select a file.

2. Configure Parsing (Optional)

If automatic detection doesn't work perfectly, adjust the settings:

• Custom delimiter
• Header row choice
• File encoding

3. Configure your AI Provider

Click the ⚙️ icon to configure your AI provider:

• Choose a provider: OpenAI, Anthropic, Google, Mistral, or others
• Enter your API key: Each provider has its own API key format
• Custom Endpoint: Enable "Use Custom Endpoint" to connect to local/self-hosted OpenAI-compatible APIs (Ollama, LM Studio, vLLM, etc.)

Using Custom Endpoints (Ollama, LM Studio, vLLM, etc.)

1. Toggle "Use Custom Endpoint" in settings
2. Enter your API Base URL (e.g., http://localhost:11434/v1 for Ollama)
3. Enter your model name (e.g., llama3.2, mistral, codellama)
4. API key is optional for most local servers

Example configurations:

4. Analysis & Charts

Click "Run Complete Analysis" and the AI will analyze your data, detect anomalies, and suggest relevant visualizations.

📦 csv-charts-ai — Standalone Library

The core of this project is published as a standalone npm package csv-charts-ai — a full-featured library for AI-powered CSV analysis, chart generation, and interactive visualization. It works with any LLM provider (OpenAI, Anthropic, Google, Mistral, Ollama, or any OpenAI-compatible endpoint).

Use it in React apps, Node.js scripts, APIs, or CLI tools.

Installation

pnpm add csv-charts-ai

Core utilities are bundled. AI providers are pluggable — install only the ones you need (@ai-sdk/openai, @ai-sdk/anthropic, etc.). Optional peer dependencies (for React chart components only): react, recharts.

Quick Start — Full AI Analysis

import { registerProvider, fromSDK, parseCSV, analyzeData } from "csv-charts-ai";
import { createOpenAI } from "@ai-sdk/openai";

// 1. Register your AI provider(s) — once, at app startup
registerProvider("openai", fromSDK(createOpenAI));

// 2. Parse any CSV string (auto-detects delimiter, infers types)
const data = parseCSV(csvString);

// 3. Run full analysis: summary + anomalies + charts in parallel
const result = await analyzeData({
  model: { apiKey: "sk-...", model: "gpt-4o" },
  data,
});

console.log(result.summary.keyInsights);
console.log(`Found ${result.anomalies.length} anomalies`);
console.log(`Generated ${result.charts.length} chart suggestions`);

React Components

Chart components live in a separate entry point (csv-charts-ai/charts) so the core stays React-free:

import { ChartDisplay, defaultDarkTheme } from "csv-charts-ai/charts";

// Display AI-generated charts with interactive toolbar (zoom, sort, trendline, export)
<ChartDisplay data={data} charts={charts} theme={defaultDarkTheme} />

// Unstyled mode — strip all Tailwind classes and style it yourself
<ChartDisplay data={data} charts={charts} unstyled className="my-charts" />

Multi-Provider Support

Register only the providers you use, then pass config objects or LanguageModel instances:

import { registerProvider, fromSDK } from "csv-charts-ai";
import { createOpenAI } from "@ai-sdk/openai";
import { createAnthropic } from "@ai-sdk/anthropic";

registerProvider("openai", fromSDK(createOpenAI));
registerProvider("anthropic", fromSDK(createAnthropic));

// OpenAI (default)
{ apiKey: "sk-...", model: "gpt-4o" }

// Anthropic
{ apiKey: "sk-ant-...", model: "claude-sonnet-4-20250514", provider: "anthropic" }

// Ollama / vLLM / LM Studio (local, uses openai provider)
{ apiKey: "", model: "llama3", baseURL: "http://localhost:11434/v1" }

// Any Vercel AI SDK LanguageModel instance (no registration needed)
const anthropic = createAnthropic({ apiKey: "sk-ant-..." });
suggestCharts({ model: anthropic("claude-sonnet-4-20250514"), data });

AI Functions Reference

React Components Reference (csv-charts-ai/charts)

Utilities Reference

Chart types: bar, line, area, scatter, pie. Aggregations: sum, avg, count, min, max, none. Multi-series supported via groupBy.

See the full documentation with all options and examples in packages/csv-charts-ai/README.md.

🛠️ Tech Stack

📁 Project Structure

csv-ai-analyzer/
├── src/                          # Next.js application
│   ├── app/_components/          # React components
│   ├── lib/                      # Services, parsers, stores
│   └── styles/                   # Global CSS
├── packages/
│   └── csv-charts-ai/            # csv-charts-ai npm package
│       ├── src/                   # Package source (ChartDisplay, SingleChart, etc.)
│       ├── dist/                  # Built output (ESM + .d.ts)
│       └── package.json
├── pnpm-workspace.yaml           # Monorepo workspace config
└── package.json                  # Root app

📝 License

MIT - Use as you wish!

Data exploration and reliability

Imports now show the first 20 rows before confirmation. Choose a CSV delimiter, encoding and header mode, or select an Excel sheet. The original file stays in memory so Reimport with different settings can re-read it; reimport resets transforms, comparison, charts and AI results. CSV imports preserve extra cells using generated headers and report irregular row widths and renamed headers.

The table previews the transformed data. Search visible columns searches all its rows before pagination and only changes the preview; exports and AI analyses continue to use the complete transformed dataset. Local data quality counts missing values, inferred-type mismatches and repeated rows without contacting AI. Types are inferred from up to 100 rows; quality checks scan the full dataset.

Manual column selection creates charts without an API key. Numeric operations accept complete finite numbers, grouped thousands (1,234.50, 1 234.50) and decimal commas (12,50). A comma followed by exactly three digits is interpreted as a thousands separator. Ambiguous local dates use day/month/year; Excel dates are converted to ISO strings. Normalize ambiguous input before analysis.

Stop controls cancel active AI requests and retry delays. Changing the dataset invalidates dependent results, and late responses cannot populate the new analysis. Completed parts of a cancelled analysis remain available. Failed parts can be retried individually. Custom OpenAI-compatible endpoints use Chat Completions; configuring a local endpoint still requires that server's CORS policy to allow this application.

Catalog providers using @ai-sdk/openai-compatible, including Z.ai/GLM and Zhipu, use their catalog API URL and Chat Completions. Structured analyses send the expected schema as instructions with JSON-object mode, then validate the returned JSON locally. A missing catalog API URL produces a configuration error. Z.ai's public API was observed rejecting browser preflight requests from GitHub Pages (2026-09-08). Its catalog entry therefore warns that a server relay is required. The relay must be hosted separately and controlled by the user; this static application does not include a hosted relay. Network errors cannot determine whether an API key is valid.

Import, quality checks and comparisons run in Web Workers. Table operations and transforms use workers from 10,000 rows. The tested large-file scenario contains 100,000 rows; very wide files still require proportionally more browser memory.

Verification

pnpm install --frozen-lockfile
pnpm test:all
pnpm typecheck
pnpm lint
pnpm exec playwright install chromium
pnpm test:e2e
pnpm build
pnpm benchmark

Development, production build and application tests build the workspace library automatically. Browser tests cover desktop and mobile layouts and use simulated AI responses, without credentials or billable requests. See the verification report for measured results and compatibility notes.