🚀 beanllm

Production-ready LLM toolkit with Clean Architecture and unified interface for multiple providers

beanllm is a comprehensive, production-ready toolkit for building LLM applications with a unified interface across OpenAI, Anthropic, Google, DeepSeek, Perplexity, and Ollama. Built with Clean Architecture and SOLID principles for maintainability and scalability.

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📚 Documentation

• 📖 Quick Start Guide - Get started in 5 minutes
• 📘 API Reference - Complete API documentation
• 🏗️ Architecture Guide - Design principles and patterns
• ⚡ Advanced Features - Structured Outputs, Prompt Caching, Tool Calling
• 🆕 2024-2025 Updates - Latest features and integrations
• 💡 Examples - 15+ working examples
• 📦 PyPI Package - Installation and releases

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✨ Key Features

🎯 Core Features

• 🔄 Unified Interface - Single API for 7 LLM providers (OpenAI, Claude, Gemini, DeepSeek, Perplexity, Ollama)
• 🎛️ Intelligent Adaptation - Automatic parameter conversion between providers
• 📊 Model Registry - Auto-detect available models from API keys
• 🔍 CLI Tools - Inspect models and capabilities from command line
• 💰 Cost Tracking - Accurate token counting and cost estimation
• 🏗️ Clean Architecture - Layered architecture with clear separation of concerns

📄 RAG & Document Processing

• 📑 Document Loaders - PDF, DOCX, XLSX, PPTX (Docling), Jupyter Notebooks, HTML, CSV, TXT
• 🚀 beanPDFLoader - Advanced PDF processing with 3-layer architecture
  • ⚡ Fast Layer (PyMuPDF): ~2s/100 pages, image extraction
  • 🎯 Accurate Layer (pdfplumber): 95% accuracy, table extraction
  • 🤖 ML Layer (marker-pdf): 98% accuracy, structure-preserving Markdown
• ✂️ Smart Text Splitters - Semantic chunking with tiktoken
• 🗄️ Vector Search - Chroma, FAISS, Pinecone, Qdrant, Weaviate, Milvus, LanceDB, pgvector
• 🎯 RAG Pipeline - Complete question-answering system in one line
• 📊 RAG Evaluation - TruLens integration, context recall metrics

🧠 Embeddings

• 📝 Text Embeddings - OpenAI, Gemini, Voyage, Jina, Mistral, Cohere, HuggingFace, Ollama
• 🌏 Multilingual - Qwen3-Embedding-8B (top multilingual model)
• 💻 Code Embeddings - Specialized embeddings for code search
• 🖼️ Vision Embeddings - CLIP, SigLIP, MobileCLIP for image-text matching
• 🎨 Advanced Features - Matryoshka (dimension reduction), MMR search, hard negative mining

👁️ Vision AI

• ✂️ Segmentation - SAM 3 (zero-shot segmentation)
• 🎯 Object Detection - YOLOv12 (latest detection/segmentation)
• 🤖 Vision-Language - Qwen3-VL (VQA, OCR, captioning, 128K context)
• 🖼️ Image Understanding - Florence-2 (detection, captioning, VQA)
• 🔍 Vision RAG - Image-based question answering with CLIP embeddings

🎙️ Audio Processing

• 🎤 Speech-to-Text - 8 STT engines with multilingual support
  • ⚡ SenseVoice-Small: 15x faster than Whisper-Large, emotion recognition, 한국어 지원
  • 🏢 Granite Speech 8B: Open ASR Leaderboard #2 (WER 5.85%), enterprise-grade
  • 🔥 Whisper V3 Turbo, Distil-Whisper, Parakeet TDT, Canary, Moonshine
• 🔊 Text-to-Speech - Multi-provider TTS (OpenAI, Azure, Google)
• 🎧 Audio RAG - Search and QA across audio files

🤖 Advanced LLM Features

• 🛠️ Tools & Agents - Function calling with ReAct pattern
• 🧠 Memory Systems - Buffer, window, token-based, summary memory
• ⛓️ Chains - Sequential, parallel, and custom chain composition
• 📊 Output Parsers - Pydantic, JSON, datetime, enum parsing
• 💫 Streaming - Real-time response streaming
• 🎯 Structured Outputs - 100% schema accuracy (OpenAI strict mode)
• 💾 Prompt Caching - 85% latency reduction, 10x cost savings (Anthropic)
• ⚡ Parallel Tool Calling - Concurrent function execution

🕸️ Graph & Multi-Agent

• 📊 Graph Workflows - LangGraph-style DAG execution
• 🤝 Multi-Agent - Sequential, parallel, hierarchical, debate patterns
• 💾 State Management - Automatic state threading and checkpoints
• 📞 Communication - Inter-agent message passing

🏭 Production Features

• 📈 Evaluation - BLEU, ROUGE, LLM-as-Judge, RAG metrics, context recall
• 👤 Human-in-the-Loop - Feedback collection and hybrid evaluation
• 🔄 Continuous Evaluation - Scheduled evaluation and tracking
• 📉 Drift Detection - Model performance monitoring
• 🎯 Fine-tuning - OpenAI fine-tuning API integration
• 🛡️ Error Handling - Retry, circuit breaker, rate limiting
• 📊 Tracing - Distributed tracing with OpenTelemetry

⚡ Performance Optimizations (v0.2.1)

Algorithm Optimizations:

• 🚀 Model Parameter Lookup: 100× speedup (O(n) → O(1)) - Pre-cached dictionary lookup
• 🔍 Hybrid Search: 10-50% faster top-k selection (O(n log n) → O(n log k)) - heapq.nlargest() optimization
• 📁 Directory Loading: 1000× faster pattern matching (O(n×m×p) → O(n×m)) - Pre-compiled regex patterns

Code Quality:

• 🧹 Duplicate Code: ~100+ lines eliminated via helper methods (CSV loader, cache consolidation)
• 🛡️ Error Handling: Standardized utilities in base provider (reduces boilerplate across all providers)
• 🏗️ Architecture: Single Responsibility, DRY principle, Template Method pattern

Impact:

• Model-heavy workflows: 10-30% faster
• Large-scale RAG: 20-50% faster
• Directory scanning: 50-90% faster

🏗️ Project Structure Improvements (v0.2.1)

Phase 1: Configuration & Cleanup:

• ✅ MANIFEST.in: Fixed package name bug (llmkit → beanllm)
• ✅ Dependencies: Moved pytest to dev, added version caps (prevents breaking changes)
• ✅ .env.example: Created template with all required API keys
• ✅ Cleanup: Removed ~396MB of unnecessary files (caches, build artifacts, bytecode)
• ✅ Simplified: Eliminated duplicate re-export layers (vector_stores/, embeddings.py)

Phase 2: Code Quality & Utilities:

• ✨ DependencyManager: Centralized dependency checking (261 duplicates → 1)
• ✨ LazyLoadMixin: Deferred initialization pattern (23 duplicates → 1)
• ✨ StructuredLogger: Consistent logging (510+ calls unified)
• ✨ Module Naming: _source_providers/ → providers/, _source_models/ → models/

Phase 3: God Class Decomposition (5,930 lines → 23 files):

• 📦 vision/models.py (1,845 lines) → 4 files (sam, florence, yolo, + 4 more models)
• 📦 vector_stores/implementations.py (1,650 lines) → 9 files (8 stores + re-exports)
• 📦 loaders/loaders.py (1,435 lines) → 8 files (7 loaders + re-exports)

Impact:

• Disk space: -396MB (-99%)
• Code duplication: -90% (794 → ~80)
• God classes: 5 → 0 (all decomposed ✅)
• Average file size: ~200 lines (was 1,500+)
• New modules: +21 focused files
• Utility modules: +3 (reusable)
• Configuration bugs: 0 (all fixed)
• Module naming: 100% consistent
• Backward compatibility: Maintained (re-exports)

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📦 Installation

Using pip

# Basic installation
pip install beanllm

# Specific providers
pip install beanllm[openai]
pip install beanllm[anthropic]
pip install beanllm[gemini]
pip install beanllm[all]

# ML-based PDF processing
pip install beanllm[ml]

# Development tools
pip install beanllm[dev,all]

Using Poetry (권장)

git clone https://github.com/yourusername/beanllm.git
cd beanllm
poetry install --extras all
poetry shell

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🚀 Quick Start

Environment Setup

Create .env file in project root:

# LLM Providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
DEEPSEEK_API_KEY=sk-...
PERPLEXITY_API_KEY=pplx-...
OLLAMA_HOST=http://localhost:11434

💬 Basic Chat

import asyncio
from beanllm import Client

async def main():
    # Unified interface - works with any provider
    client = Client(model="gpt-4o")
    response = await client.chat(
        messages=[{"role": "user", "content": "Explain quantum computing"}]
    )
    print(response.content)

    # Switch providers seamlessly
    client = Client(model="claude-sonnet-4-20250514")
    response = await client.chat(
        messages=[{"role": "user", "content": "Same question, different provider"}]
    )

    # Streaming
    async for chunk in client.stream_chat(
        messages=[{"role": "user", "content": "Tell me a story"}]
    ):
        print(chunk, end="", flush=True)

asyncio.run(main())

📚 RAG in One Line

import asyncio
from beanllm import RAGChain

async def main():
    # Create RAG system from documents
    rag = RAGChain.from_documents("docs/")

    # Ask questions
    answer = await rag.query("What is this document about?")
    print(answer)

    # With sources
    result = await rag.query("Explain the main concept", include_sources=True)
    print(result.answer)
    for source in result.sources:
        print(f"📄 Source: {source.metadata.get('source', 'unknown')}")

    # Streaming query
    async for chunk in rag.stream_query("Tell me more"):
        print(chunk, end="", flush=True)

asyncio.run(main())

🛠️ Tools & Agents

import asyncio
from beanllm import Agent, Tool

async def main():
    # Define tools
    @Tool.from_function
    def calculator(expression: str) -> str:
        """Evaluate a math expression"""
        return str(eval(expression))

    @Tool.from_function
    def get_weather(city: str) -> str:
        """Get weather for a city"""
        return f"Sunny, 22°C in {city}"

    # Create agent
    agent = Agent(
        model="gpt-4o-mini",
        tools=[calculator, get_weather],
        max_iterations=10
    )

    # Run agent
    result = await agent.run("What is 25 * 17? Also what's the weather in Seoul?")
    print(result.answer)
    print(f"⏱️ Steps: {result.total_steps}")

asyncio.run(main())

🕸️ Graph Workflows

import asyncio
from beanllm import StateGraph, Client

async def main():
    client = Client(model="gpt-4o-mini")

    # Create graph
    graph = StateGraph()

    async def analyze(state):
        response = await client.chat(
            messages=[{"role": "user", "content": f"Analyze: {state['input']}"}]
        )
        state["analysis"] = response.content
        return state

    async def improve(state):
        response = await client.chat(
            messages=[{"role": "user", "content": f"Improve: {state['input']}"}]
        )
        state["improved"] = response.content
        return state

    def decide(state):
        score = 0.9 if "excellent" in state["analysis"].lower() else 0.5
        return "good" if score > 0.8 else "bad"

    # Build graph
    graph.add_node("analyze", analyze)
    graph.add_node("improve", improve)
    graph.add_conditional_edges("analyze", decide, {
        "good": "END",
        "bad": "improve"
    })
    graph.add_edge("improve", "END")
    graph.set_entry_point("analyze")

    # Run
    result = await graph.invoke({"input": "Draft proposal"})
    print(result)

asyncio.run(main())

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🎨 Advanced Features

🎯 Structured Outputs (100% Schema Accuracy)

from openai import AsyncOpenAI

client = AsyncOpenAI()

response = await client.chat.completions.create(
    model="gpt-4o-2024-08-06",
    messages=[{"role": "user", "content": "Extract: John Doe, 30, [email protected]"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "user_info",
            "strict": True,  # ✅ 100% accuracy
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"},
                    "email": {"type": "string"}
                },
                "required": ["name", "age", "email"]
            }
        }
    }
)

💾 Prompt Caching (10x Cost Savings)

from anthropic import AsyncAnthropic

client = AsyncAnthropic()

response = await client.messages.create(
    model="claude-sonnet-4-20250514",
    system=[{
        "type": "text",
        "text": "Long system prompt..." * 1000,
        "cache_control": {"type": "ephemeral"}  # 💰 10x cheaper
    }],
    messages=[{"role": "user", "content": "Question"}],
    extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"}
)

# Check cache savings
print(f"💾 Cache created: {response.usage.cache_creation_input_tokens}")
print(f"⚡ Cache read: {response.usage.cache_read_input_tokens}")

See Advanced Features Guide for more details.

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🎯 Model Support

🤖 LLM Providers (7 providers)

• OpenAI: GPT-5, GPT-4o, GPT-4.1, GPT-4o-mini
• Anthropic: Claude Opus 4, Claude Sonnet 4.5, Claude Haiku 3.5
• Google: Gemini 2.5 Pro, Gemini 2.5 Flash
• DeepSeek: DeepSeek-V3 (671B MoE, open-source top performance)
• Perplexity: Sonar (real-time web search + LLM)
• Meta: Llama 3.3 70B (via Ollama)
• Ollama: Local LLM support

🎤 Speech-to-Text (8 engines)

• SenseVoice-Small: 15x faster than Whisper-Large, emotion recognition
• Granite Speech 8B: Open ASR Leaderboard #2 (WER 5.85%)
• Whisper V3 Turbo: Latest OpenAI model
• Distil-Whisper: 6x faster with similar accuracy
• Parakeet TDT: Real-time optimized (RTFx >2000)
• Canary: Multilingual + translation
• Moonshine: On-device optimized

👁️ Vision Models

• SAM 3: Zero-shot segmentation
• YOLOv12: Latest object detection
• Qwen3-VL: Vision-language model (VQA, OCR, captioning)
• Florence-2: Microsoft multimodal model

🧠 Embeddings

• Qwen3-Embedding-8B: Top multilingual model
• Code Embeddings: Specialized for code search
• CLIP/SigLIP: Vision-text embeddings
• OpenAI: text-embedding-3-small/large
• Voyage, Jina, Cohere, Mistral: Alternative providers

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🏗️ Architecture

beanllm follows Clean Architecture with SOLID principles.

┌─────────────────────────────────────────────────────┐
│                  Facade Layer                       │
│  사용자 친화적 API (Client, RAGChain, Agent)       │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│                 Handler Layer                       │
│  Controller 역할 (입력 검증, 에러 처리)             │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│                 Service Layer                       │
│  비즈니스 로직 (인터페이스 + 구현체)                │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│                 Domain Layer                        │
│  핵심 비즈니스 (엔티티, 인터페이스, 규칙)          │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│            Infrastructure Layer                     │
│  외부 시스템 (Provider, Vector Store 구현)          │
└─────────────────────────────────────────────────────┘

자세한 아키텍처 설명은 **ARCHITECTURE.md**를 참고하세요.

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🔧 CLI Usage

# List available models
beanllm list

# Show model details
beanllm show gpt-4o

# Check providers
beanllm providers

# Quick summary
beanllm summary

# Export model info
beanllm export > models.json

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🧪 Testing

# Run all tests
pytest

# With coverage
pytest --cov=src/beanllm --cov-report=html

# Specific module
pytest tests/test_facade/ -v

Test Coverage: 61% (624 tests, 593 passed)

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🛠️ Development

Using Makefile (권장)

# Install dev tools
make install-dev

# Quick auto-fix
make quick-fix

# Type check
make type-check

# Lint check
make lint

# Run all checks
make all

Manual

# Install in editable mode
pip install -e ".[dev,all]"

# Format code
ruff format src/beanllm

# Lint
ruff check src/beanllm

# Type check
mypy src/beanllm

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🗺️ Roadmap

✅ Completed (2024-2025)

• ✅ Clean Architecture & SOLID principles
• ✅ Unified multi-provider interface (7 providers)
• ✅ RAG pipeline & document processing
• ✅ beanPDFLoader with 3-layer architecture
• ✅ Vision AI (SAM 3, YOLOv12, Qwen3-VL)
• ✅ Audio processing (8 STT engines)
• ✅ Embeddings (Qwen3-Embedding-8B, Matryoshka, Code)
• ✅ Vector stores (Milvus, LanceDB, pgvector)
• ✅ RAG evaluation (TruLens, HyDE)
• ✅ Advanced features (Structured Outputs, Prompt Caching, Parallel Tool Calling)
• ✅ Tools, agents, graph workflows
• ✅ Multi-agent systems
• ✅ Production features (evaluation, monitoring, cost tracking)

📋 Planned

• ⬜ Benchmark system
• ⬜ Advanced agent frameworks integration

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

Inspired by:

• LangChain - LLM application framework
• LangGraph - Graph workflow patterns
• Anthropic Claude - Clear code philosophy

Special thanks to:

• OpenAI, Anthropic, Google, DeepSeek, Perplexity for APIs
• Ollama team for local LLM support
• Open-source AI community

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📧 Contact

• GitHub: https://github.com/leebeanbin/beanllm
• Issues: https://github.com/leebeanbin/beanllm/issues
• Discussions: https://github.com/leebeanbin/beanllm/discussions

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Built with ❤️ for the LLM community

Transform your LLM applications from prototype to production with beanllm.