llmpic 0.2.2

LLM-powered chart generation Python SDK — create charts with natural language

LLMPIC

Natural Language → Production Charts. One line.

中文  |  Documentation  |  API Reference  |  User Guide

---

from llmpic import llmPIC

lp = llmPIC(api_key="sk-...", base_url="https://api.openai.com/v1")

lp.plot("Monthly sales trend, 12 months").show()   # Jupyter inline
lp.plot("CPU usage over 30 days").save()            # → ~/llmpic_charts/
lp.bar("Sales by region").data(df).style({"color_scheme":"warm"}).save("bar.png")

---

💡 Why llmpic?

Traditional Python charting means wrestling with matplotlib's verbose API — plt.subplots(), ax.set_xticklabels(), fig.tight_layout(), hundreds of functions to memorize, 15–40 lines for a single chart. Data scientists spend more time googling matplotlib syntax than analyzing data.

llmpic brings Python visualization into the LLM era. Describe what you want in plain English, Chinese, Japanese, or Korean — get a production-quality matplotlib chart instantly.

	Traditional matplotlib	llmpic
Lines of code	15–40 lines	1–3 lines
API knowledge	100+ functions	0 (natural language)
Chart types	Manual selection	11 types + auto-detect
Iteration	Rewrite entire block	result.edit("make bars red")
Jupyter	plt.show() only	result.show() inline
Multi-format	Separate savefig calls	Single save() — PNG/SVG/PDF
Error recovery	Manual debugging	Auto-fix with LLM (up to 2 rounds)
Concurrency	Manual threading	AsyncllmPIC.batch() parallel generation
Security	None	Dual-layer: 32 regex + optional LLM review

👀 See the difference for yourself

Task: A grouped bar chart comparing 4 regions × 4 quarters, with value labels, custom colors, dashed grid, title, axis labels, and a legend.

Traditional matplotlib — 30+ lines, 100+ API calls to memorize

llmpic — 1 line, plain English

import matplotlib.pyplot as plt import numpy as np regions = ["North", "South", "East", "West"] quarters = ["Q1", "Q2", "Q3", "Q4"] data = np.array([ [120, 145, 160, 180], [ 95, 110, 130, 155], [140, 165, 180, 200], [ 80, 95, 110, 125], ]) x = np.arange(len(regions)) w = 0.2 colors = ["#4C72B0", "#55A868", "#C44E52", "#8172B2"] fig, ax = plt.subplots(figsize=(10, 6), dpi=150) for i, q in enumerate(quarters): bars = ax.bar(x + i * w, data[:, i], w, label=q, color=colors[i]) for bar in bars: h = bar.get_height() ax.text(bar.get_x() + bar.get_width() / 2, h + 1, f"{h:.0f}", ha="center", va="bottom", fontsize=9) ax.set_title("Quarterly Sales by Region (2025)", fontsize=14, pad=12) ax.set_xlabel("Region") ax.set_ylabel("Sales (K USD)") ax.set_xticks(x + w * 1.5) ax.set_xticklabels(regions) ax.legend(title="Quarter", loc="upper left") ax.grid(axis="y", linestyle="--", alpha=0.4) fig.tight_layout() plt.savefig("sales.png", dpi=150) plt.close()

from llmpic import llmPIC lp = llmPIC(api_key="sk-...", base_url="https://api.openai.com/v1") # Step 1: render — LLM writes matplotlib code, # safety check + sandbox execute it for you. result = lp.bar( "Quarterly sales by region 2025: " "North=[120,145,160,180], " "South=[95,110,130,155], " "East=[140,165,180,200], " "West=[80,95,110,125]. " "Add value labels, dashed grid, legend." ).render() result.save("sales.png") # Want to tweak it later? Use natural language: v2 = result.edit("make Q4 bars red, " "title 'Annual Report'") v2.save("sales_v2.png") # Need the underlying matplotlib code? It's right there: print(result.code) That's it. A chart that conveys the same insight — generated, not hand-coded. No set_xticklabels. No bar.get_x() + bar.get_width()/2. No Stack Overflow tabs open.

💥 30+ lines of API drudgery → a few lines of plain English. llmpic still hands you the generated matplotlib code via result.code if you want to fine-tune it manually. The exact rendered output depends on the LLM's code generation — if you need pixel-perfect control, result.edit("...") lets you iterate without rewriting from scratch.

---

✨ Features

• 🗣️ Natural Language Input — English, Chinese, Japanese, Korean
• 📊 11 Chart Types — Line, Scatter, Bar, Pie, Histogram, Heatmap, Boxplot, Area, Radar, Subplots, Auto-detect
• 🔗 Fluent Builder API — chain .data() → .style() → .format() → .save() / .render()
• 📓 Jupyter Inline — result.show() renders directly below notebook cells
• ⚡ Async Batch — AsyncllmPIC.batch() generates multiple charts concurrently (total time ≈ slowest one)
• 🔧 Auto-Fix — Failed code executions are auto-corrected by the LLM (up to 2 rounds)
• ✏️ Iterative Editing — result.edit("make bars red, increase title size") refines charts with natural language
• 📦 Multi-Format — PNG (raster), SVG (vector), PDF (print) from a single save()
• 🌍 Multi-Language Labels — Auto-detects query language and matches chart titles/labels
• 🛡️ Dual Safety — 32 precompiled regex patterns (~0ms) + optional LLM semantic review
• 💻 Cross-Platform — Windows / Linux / macOS, automatic CJK font configuration
• 🔄 Exponential Retry — LLM calls retry with backoff (1s, 2s, 4s) on transient failures
• 📊 Structured Output — Uses JSON mode for reliable code extraction from LLM responses

---

📦 Installation

pip install llmpic          # All-in-one: matplotlib, numpy, openai, pandas, seaborn, scikit-learn, scipy

Requirements:

• Python ≥ 3.10
• An OpenAI-compatible API endpoint (OpenAI, Azure, DeepSeek, GLM, Ollama, vLLM, etc.)
• For CJK charts: appropriate fonts (auto-detected on first run)

Verify Installation

from llmpic import llmPIC, AsyncllmPIC, ChartResult, PlotBuilder, AsyncPlotBuilder
import llmpic
print(llmpic.__version__)  # → "0.2.2"

---

🚀 Quick Start

1. Initialize the SDK

from llmpic import llmPIC

# OpenAI
lp = llmPIC(
    api_key="sk-your-openai-key",
    base_url="https://api.openai.com/v1",
    model="gpt-4o",
)

# DeepSeek
lp = llmPIC(
    api_key="sk-your-deepseek-key",
    base_url="https://api.deepseek.com/v1",
    model="deepseek-chat",
)

# Any OpenAI-compatible endpoint (Azure, GLM, Ollama, vLLM, etc.)
lp = llmPIC(
    api_key="your-key",
    base_url="https://your-endpoint/v1",
    model="your-model",
)

2. Your First Chart

# Basic — describe it, get a chart
lp.plot("12-month sales trend").save("sales.png")

# Jupyter inline display
lp.plot("CPU usage over 30 days").render().show()

# Default save path (falls through to home directory)
lp.plot("Temperature trend").save()  # → ~/llmpic_charts/chart_20250101_120000.png

3. With Real Data

import pandas as pd

df = pd.read_csv("sales.csv")
lp.bar("Sales by region").data(df).style({
    "color_scheme": "warm",
    "figsize": [12, 7],
    "title_fontsize": 16,
}).save("regional_sales.png")

4. Common Patterns (30-second cookbook)

# Line chart with SVG output
lp.plot("CPU trend").format('svg').save("cpu.svg")

# Scatter with DataFrame
lp.scatter("Age vs income correlation").data(df).save("scatter.png")

# Pie with inline data
lp.pie("Market share: A=40%, B=25%, C=20%, D=15%").save("pie.png")

# Heatmap correlation matrix
lp.heatmap("Feature correlation").data(corr_df).save("heatmap.png")

# Multi-chart dashboard
lp.subplots("2x2: trend line, region bar, customer scatter, growth histogram").save("dash.png")

# Let the LLM pick the best chart type
lp.custom("Analyze user retention trends").data(df).save("auto.png")

# Access generated code
result = lp.plot("Test").render()
print(result.code)           # The matplotlib code the LLM wrote
print(result.token_usage)    # {'input': 320, 'output': 180}

# Edit an existing chart
result = lp.plot("Sales: Jan=100, Feb=120").render()
result.edit("Change to bar chart, use red color").edit(
    "Title 'Q1 Revenue', add grid").save("final.png")

---

📊 Chart Types

Method	Chart Type	Best For	LLM Hint
.plot()	Line	Trends, time series, continuous data	ax.plot()
.scatter()	Scatter	Correlation, cluster visualization	ax.scatter()
.bar()	Bar / Barh	Categorical comparison, rankings	ax.bar() / ax.barh()
.pie()	Pie	Proportions, market share	ax.pie(autopct='%1.1f%%')
.hist()	Histogram	Distributions, frequency analysis	ax.hist()
.heatmap()	Heatmap	Correlation matrices, 2D density	ax.imshow() / sns.heatmap()
.boxplot()	Boxplot	Statistical distribution comparison	ax.boxplot() / sns.boxplot()
.area()	Area	Stacked trends, composition over time	ax.fill_between() / ax.stackplot()
.radar()	Radar	Multi-dim comparison, capability assessment	Polar axes
.subplots()	Dashboard	Multi-chart composite views	plt.subplots(nrows, ncols)
.custom()	Auto	LLM picks the best type automatically	Context-aware selection

---

🔗 Core Workflow

  sdk.plot("query")            ← describe the chart in natural language
    → PlotBuilder
      .data(df)                ← attach real data (optional)
      .style({...})            ← customize appearance (optional)
      .format('png')           ← choose output format (optional)
      .render()                ← trigger: LLM → safety → sandbox → ChartResult
      .save("path.png")        ← trigger + save to file

The Builder Pattern

# All chart methods return a PlotBuilder. Builders are lazy — nothing runs
# until .render(), .save(), or accessing .image_bytes / .code.

builder = lp.plot("Monthly sales")  # Returns PlotBuilder — NOTHING generated yet
builder = builder.data(df)           # Attach data
builder = builder.style({"figsize": [12, 6]})  # Set style
builder = builder.format('svg')      # Set format

result = builder.render()  # ← NOW everything runs: LLM → safety → sandbox
result.save("chart.svg")

ChartResult — Your Swiss Army Knife

result = lp.plot("CPU trend").render()

# Basic info
result.success           # bool
result.code              # The matplotlib code (str)
result.token_usage       # {'input': 320, 'output': 180}
result.size_kb           # 45.2

# Save to file
result.save()            # → ~/llmpic_charts/chart_{timestamp}.png
result.save("out.png")   # PNG
result.save("out.svg")   # SVG
result.save("out.pdf")   # PDF

# Display in Jupyter
result.show()

# Base64 for web embedding
result.base64()          # data:image/png;base64,...
result.base64_svg()      # data:image/svg+xml;base64,...

# Access alternative formats (lazy — re-renders on first access)
result.svg_bytes         # SVG as bytes
result.pdf_bytes         # PDF as bytes
result.svg               # SVG as string

# Edit with natural language
v2 = result.edit("Change to bar chart, use warm colors")
v3 = v2.edit("Increase title size to 18")

---

⚡ Async & Batch

Use AsyncllmPIC for concurrent chart generation — total time ≈ slowest single chart.

from llmpic import AsyncllmPIC
import asyncio

async def main():
    lp = AsyncllmPIC(
        api_key="sk-...",
        base_url="https://api.openai.com/v1",
        model="gpt-4o",
    )

    # Batch: 5 charts generated concurrently
    results = await lp.batch([
        ("plot",     "12-month sales trend"),
        ("bar",      "Regional sales comparison"),
        ("pie",      "Market share distribution"),
        ("scatter",  "Customer age vs spend"),
        ("heatmap",  "Feature correlation matrix"),
    ])

    for i, r in enumerate(results):
        if r.success:
            r.save(f"batch_{i}.png")
            print(f"[{i}] OK — {r.size_kb:.1f}KB, "
                  f"tokens: in={r.token_usage['input']} out={r.token_usage['output']}")
        else:
            print(f"[{i}] FAIL: {r.error_message}")

asyncio.run(main())

Manual Concurrent Rendering

async def main():
    lp = AsyncllmPIC(...)

    # Fine-grained control with builders
    builders = [
        lp.plot("CPU trend").format('png'),
        lp.bar("Sales").data(df).style({"color_scheme": "warm"}).format('svg'),
        lp.pie("Market share").format('pdf'),
    ]
    results = await asyncio.gather(*[b.render() for b in builders])
    # All three run concurrently

asyncio.run(main())

---

✏️ Iterative Editing

Don't rewrite prompts for small tweaks — use edit() to refine charts incrementally.

# Start
result = lp.plot("Monthly sales: Jan=100, Feb=120, Mar=90, Apr=150").render()

# Iterate — each .edit() returns a NEW ChartResult (never mutates originals)
result = result.edit("Change to bar chart")
result = result.edit("Make bars red, use warm color scheme")
result = result.edit("Add grid lines, increase title font size to 18")
result = result.edit("Title 'Q1 2025 Sales Report', add y-axis label 'Revenue (K USD)'")

result.save("final.png")
result.show()

How it works: Each edit() sends the current code + your edit request to the LLM, which returns modified code. The new code passes through the same safety check → sandbox execution pipeline. Original ChartResult is never mutated.

---

📦 Output Formats

Format	Extension	Type	Best For
PNG	.png	Raster	General use, embedding, quick preview
SVG	.svg	Vector	Web embedding, printing, scaling
PDF	.pdf	Vector/Print	Reports, publications, sharing

# Method 1: Chain .format() before render
lp.plot("Trend").format('svg').save("chart.svg")
lp.plot("Trend").format('pdf').save("chart.pdf")

# Method 2: Extension auto-detection on save
result = lp.plot("Trend").render()     # Default: PNG
result.save("output.svg")              # → SVG (detected from extension)
result.save("output.pdf")              # → PDF

# Method 3: Access alternative format properties (lazy re-render)
result = lp.plot("Trend").render()     # PNG in image_bytes
svg_data = result.svg_bytes             # Re-renders as SVG (lazy, cached)
pdf_data = result.pdf_bytes             # Re-renders as PDF (lazy, cached)

---

🎨 Style Customization

Preset Color Schemes (6 built-in)

lp.plot("Trend").style({"color_scheme": "blues"}).save("b.png")
lp.plot("Trend").style({"color_scheme": "warm"}).save("w.png")
lp.plot("Trend").style({"color_scheme": "cool"}).save("c.png")
lp.plot("Trend").style({"color_scheme": "pastel"}).save("p.png")
lp.plot("Trend").style({"color_scheme": "dark"}).save("d.png")
lp.plot("Trend").style({"color_scheme": "grayscale"}).save("g.png")

All Style Keys

Key	Type	Default	Description
figsize	[int, int]	[10, 6]	Figure size in inches (width, height)
dpi	int	150	Output resolution (dots per inch)
color_scheme	str	"blues"	One of: blues warm cool pastel dark grayscale
title_fontsize	int	14	Chart title font size
label_fontsize	int	12	Axis label font size
tick_fontsize	int	10	Tick mark label font size
grid	bool	True	Show background grid
grid_alpha	float	0.3	Grid line transparency (0–1)
tight_layout	bool	True	Auto-adjust layout to avoid clipping
facecolor	str	"white"	Figure background color (any CSS color)

Style Examples

# Journal-quality figure
lp.plot("Experiment results").style({
    "figsize": [8, 5],
    "dpi": 300,
    "color_scheme": "dark",
    "title_fontsize": 16,
    "label_fontsize": 14,
}).save("journal.png")

# Presentation-ready
lp.bar("Q4 revenue").data(df).style({
    "figsize": [14, 8],
    "color_scheme": "warm",
    "title_fontsize": 22,
    "label_fontsize": 16,
    "tick_fontsize": 14,
    "grid": False,
    "facecolor": "#FAFAFA",
    "dpi": 200,
}).save("presentation.png")

# Style can also be passed as JSON string
lp.plot("Trend").style('{"color_scheme":"cool","figsize":[12,8]}').save("trend.png")

---

🛡️ Security

llmpic executes LLM-generated code in a sandboxed environment with dual-layer protection:

Layer 1: Regex Pattern Matching (~0ms overhead)

32 precompiled regex patterns block known dangerous patterns:

• System commands: os.system(), os.popen(), subprocess
• File I/O: open() (generated code shouldn't access files)
• Dynamic execution: exec(), eval(), compile(), __import__()
• Network access: socket, urllib, requests, httpx
• Dangerous modules: shutil, ctypes, pickle
• Reflection escapes: __subclasses__, __bases__, __mro__

Layer 2: Sandbox Execution

• Restricted namespace: Only safe builtins + matplotlib, numpy, pandas, seaborn
• Figure.savefig intercepted: Code cannot write files directly
• plt.show() / plt.savefig() / plt.close() blocked: All intercepted as no-ops
• Timeout fuse: ThreadPoolExecutor + timeout kills runaway code
• Serialization lock: Module-level mutex prevents matplotlib state races

Safety Level Configuration

# Fast mode (recommended for production) — regex only
lp = llmPIC(..., safety_level="fast")

# Full mode — regex + LLM semantic review (adds ~1-2s per chart)
lp = llmPIC(..., safety_level="full")

Recommendation: The sandbox already blocks all real execution paths. fast mode is sufficient for production use.

---

🌐 Provider Compatibility

llmpic works with any OpenAI-compatible API endpoint that supports chat/completions with JSON structured output:

Provider	base_url	model (example)
OpenAI	https://api.openai.com/v1	gpt-5.5, gpt-5.5-mini
DeepSeek	https://api.deepseek.com/v1	deepseek-v4-pro
Azure OpenAI	https://{resource}.openai.azure.com/openai/deployments/{deployment}	Your deployment name
GLM (Zhipu)	https://open.bigmodel.cn/api/paas/v5	glm-5
Ollama (local)	http://localhost:11434/v1	llama3, qwen3.5
vLLM (local)	http://localhost:8000/v1	Your served model
Groq	https://api.groq.com/openai/v1	llama-3.3-70b

Note: For best chart quality, use a model with strong code generation capabilities (GPT-5.5, DeepSeek-V4, GLM-5, etc.). Local models may produce less reliable code — consider increasing max_retries and max_fix_attempts.

---

🔧 Environment Variables

Store credentials in environment variables or .env files for cleaner code:

# .env file or shell
export LLMPIC_API_KEY="sk-your-key"
export LLMPIC_BASE_URL="https://api.openai.com/v1"
export LLMPIC_MODEL="gpt-4o"

import os
from llmpic import llmPIC

lp = llmPIC(
    api_key=os.getenv("LLMPIC_API_KEY"),
    base_url=os.getenv("LLMPIC_BASE_URL"),
    model=os.getenv("LLMPIC_MODEL", "gpt-4o"),
)

---

🏗 Architecture

┌─────────────────────────────────────────────────────────────┐
│                      llmPIC / AsyncllmPIC                    │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐ │
│  │  .plot() │   │  .bar()  │   │  .pie()  │   │  .custom()│ │
│  │.scatter()│   │ .hist()  │   │.heatmap()│   │   ... 11  │ │
│  └────┬─────┘   └────┬─────┘   └────┬─────┘   └────┬─────┘ │
│       └──────────────┴──────────────┴──────────────┘        │
│                          │                                   │
│                    PlotBuilder / AsyncPlotBuilder             │
│                    .data() .style() .format()                 │
│                          │                                   │
│                    .render() / .save()                        │
│                          │                                   │
│              ┌───────────┴───────────┐                      │
│              │  1. LLM Code Gen      │  OpenAI API           │
│              │     (with retry ×3)   │                      │
│              └───────────┬───────────┘                      │
│                          │                                   │
│              ┌───────────┴───────────┐                      │
│              │  2. Safety Check      │  CodeSafetyChecker    │
│              │     32 regex + LLM    │                      │
│              └───────────┬───────────┘                      │
│                          │                                   │
│              ┌───────────┴───────────┐                      │
│              │  3. Sandbox Execute   │  SandboxExecutor      │
│              │     (with auto-fix)   │  ThreadPool + timeout │
│              └───────────┬───────────┘                      │
│                          │                                   │
│                    ChartResult                               │
│         .save() .show() .edit() .base64() .svg .pdf          │
└─────────────────────────────────────────────────────────────┘

Pipeline per chart:

1. LLM Code Generation — Natural language → matplotlib Python code (JSON structured output, 3 retries with backoff)
2. Safety Check — 32 regex patterns + optional LLM semantic review
3. Sandbox Execution — ThreadPoolExecutor with timeout, Figure monkey-patching, restricted namespace
4. Auto-Fix — On execution failure, send code+error back to LLM for correction (up to 2 rounds)
5. Result — ChartResult with bytes, code, token usage, lazy format conversion

---

📖 Documentation

Document	Language	Description
API Reference	EN	Complete class, method, and parameter reference
API 参考	中文	所有类、方法、参数的详细说明
User Guide	EN	Advanced usage, best practices, troubleshooting
使用指南	中文	进阶用法、最佳实践、故障排查
Jupyter Demos	EN/中文	Ready-to-run demo notebooks
中文首页	中文	完整中文 README

---

📮 Official Contact

• Xiaohongshu (RedNote): ADW_AI
• GitHub Issues: github.com/ADW-19/llmpic/issues

---

🙏 Acknowledgements

llmpic is built on the shoulders of these excellent open-source libraries:

• SciPy — scientific computing
• scikit-learn — machine learning utilities
• pandas — data analysis & DataFrame
• NumPy — numerical computing
• Matplotlib — chart rendering engine
• seaborn — statistical data visualization

Heartfelt thanks to all maintainers and contributors of these projects.

---

📄 License

MIT © 2026 ADW-19