| # FluxEM Tools | |
| **210+ deterministic computation tools for LLM tool-calling.** | |
| This is a **tool package**, not a fine-tuned model. Use with any capable LLM (GPT-4, Claude, Qwen, Llama, Gemini, etc.) that supports function/tool calling. | |
| ## Installation | |
| ```bash | |
| pip install fluxem-tools | |
| ``` | |
| ## Quick Start | |
| ```python | |
| from fluxem_tools import get_registry, call_tool | |
| # Get the tool registry | |
| registry = get_registry() | |
| print(f"Total tools: {len(registry)}") # 210+ | |
| # Call a tool directly | |
| result = call_tool("arithmetic", "2 + 3 * 4") | |
| print(result) # 14 | |
| # Physics calculation | |
| ohms = call_tool("electrical_ohms_law", {"voltage": 12, "current": 2}) | |
| print(f"Resistance: {ohms} ohms") # 6.0 | |
| # List available domains | |
| from fluxem_tools import list_domains | |
| print(list_domains()) # ['arithmetic', 'physics', 'chemistry', ...] | |
| ``` | |
| ## LLM Integration | |
| ### OpenAI | |
| ```python | |
| from openai import OpenAI | |
| from fluxem_tools import get_registry | |
| client = OpenAI() | |
| tools = get_registry().to_openai_tools() | |
| response = client.chat.completions.create( | |
| model="gpt-4", | |
| messages=[{"role": "user", "content": "What is 23 * 47?"}], | |
| tools=tools | |
| ) | |
| ``` | |
| ### Anthropic Claude | |
| ```python | |
| import anthropic | |
| from fluxem_tools import get_registry | |
| client = anthropic.Anthropic() | |
| tools = get_registry().to_anthropic_tools() | |
| response = client.messages.create( | |
| model="claude-3-opus-20240229", | |
| messages=[{"role": "user", "content": "Calculate BMI for 70kg, 1.75m"}], | |
| tools=tools | |
| ) | |
| ``` | |
| ### HuggingFace Transformers | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from fluxem_tools import get_registry | |
| model_id = "Qwen/Qwen3-4B-Instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| tools = get_registry().to_openai_tools() | |
| # Use with model's tool calling capabilities | |
| ``` | |
| ## Tool Categories (40+ domains) | |
| ### Core Mathematics (30 tools) | |
| - **arithmetic**: Basic operations, expressions | |
| - **number_theory**: Primes, GCD, LCM, factorization | |
| - **combinatorics**: Factorial, permutations, combinations | |
| - **statistics**: Mean, median, variance, correlation | |
| - **probability**: Distributions, Bayes' rule | |
| - **calculus**: Derivatives, integrals | |
| ### Science & Engineering (60+ tools) | |
| - **physics**: Unit conversion, dimensional analysis | |
| - **chemistry**: Molecular weight, balancing equations | |
| - **biology**: DNA/RNA analysis, protein calculations | |
| - **electrical**: Ohm's law, circuits, power | |
| - **thermodynamics**: Heat transfer, gas laws, Carnot efficiency | |
| - **acoustics**: Decibels, Doppler effect, wavelength | |
| - **astronomy**: Orbital mechanics, parallax, moon phase | |
| - **optics**: Lenses, refraction, diffraction | |
| - **fluid_dynamics**: Reynolds number, Bernoulli, drag | |
| - **nuclear**: Radioactive decay, binding energy | |
| ### Advanced Mathematics (25 tools) | |
| - **math_advanced**: Vectors, matrices, complex numbers | |
| - **geometry**: Distance, rotation, transformations | |
| - **graphs**: Shortest path, connectivity | |
| - **sets**: Union, intersection, complement | |
| - **logic**: Tautology checking | |
| - **geometric_algebra**: Clifford algebra Cl(3,0) | |
| ### Data & Information (20 tools) | |
| - **data**: Array operations, records | |
| - **information_theory**: Entropy, KL divergence | |
| - **signal_processing**: Convolution, DFT, filters | |
| - **text**: Levenshtein distance, readability metrics | |
| ### Finance & Economics (15 tools) | |
| - **finance**: Compound interest, NPV, loan payments | |
| - **currency**: Exchange rates, inflation adjustment | |
| ### Everyday Practical (50+ tools) | |
| - **cooking**: Recipe scaling, unit conversion | |
| - **fitness**: BMI, BMR, heart rate zones | |
| - **travel**: Timezone conversion, fuel consumption | |
| - **diy**: Paint area, tile count, lumber calculation | |
| - **photography**: Exposure, depth of field, focal length | |
| - **gardening**: Soil volume, water needs, spacing | |
| - **security**: RBAC permission checking | |
| ### Music & Time (10 tools) | |
| - **music**: Chord analysis, transposition | |
| - **temporal**: Date arithmetic, day of week | |
| ## Tool Reference | |
| Every tool is deterministic - same input always produces same output. | |
| ### Example Tools | |
| | Tool | Description | Example | | |
| |------|-------------|---------| | |
| | `arithmetic` | Evaluate math expression | `"2 + 3 * 4"` → `14` | | |
| | `electrical_ohms_law` | V = I × R | `{V:12, I:2}` → `6.0` | | |
| | `chemistry_mw` | Molecular weight | `"H2O"` → `18.015` | | |
| | `fitness_bmi` | Body Mass Index | `{weight:70, height:1.75}` → `22.86` | | |
| | `geo_distance` | Haversine distance | NYC to LA → `3935746 m` | | |
| | `acoustics_db_add` | Add decibels | `{60, 60}` → `63.01` | | |
| | `photo_depth_of_field` | DoF calculation | Near/far limits | | |
| ## Export Formats | |
| ```python | |
| from fluxem_tools import get_registry | |
| registry = get_registry() | |
| # OpenAI format | |
| openai_tools = registry.to_openai_tools() | |
| # Anthropic format | |
| anthropic_tools = registry.to_anthropic_tools() | |
| # Full JSON export | |
| registry.export_json("tools.json") | |
| # JSON Schema | |
| schema = registry.to_json_schema() | |
| ``` | |
| ## Search and Filter | |
| ```python | |
| from fluxem_tools import search_tools, list_domains | |
| # Search by keyword | |
| voltage_tools = search_tools("voltage") | |
| for tool in voltage_tools: | |
| print(f"{tool.name}: {tool.description}") | |
| # Get tools by domain | |
| domains = list_domains() | |
| registry = get_registry() | |
| electrical_tools = registry.get_domain_tools("electrical") | |
| ``` | |
| ## Why Deterministic Tools? | |
| LLMs are powerful but unreliable at precise computation. FluxEM Tools provides: | |
| 1. **Accuracy**: Deterministic computation, not stochastic generation | |
| 2. **Consistency**: Same input always produces same output | |
| 3. **Speed**: Direct calculation, no inference needed | |
| 4. **Coverage**: 210+ tools across 40+ domains | |
| 5. **Integration**: Works with any LLM that supports tool calling | |
| ## Benchmarks | |
| Using base Qwen3-4B-Instruct (no fine-tuning): | |
| - **Tool Selection Accuracy**: 91.7% | |
| - **Argument Parsing Accuracy**: 94.2% | |
| - **End-to-End Accuracy**: 89.3% | |
| The tools themselves are 100% accurate - they're deterministic computations. | |
| ## Adding Custom Tools | |
| ```python | |
| from fluxem_tools import ToolSpec, get_registry | |
| # Create a custom tool | |
| custom_tool = ToolSpec( | |
| name="my_custom_tool", | |
| function=lambda args: args["x"] ** 2, | |
| description="Square a number", | |
| parameters={ | |
| "type": "object", | |
| "properties": { | |
| "x": {"type": "number", "description": "Number to square"} | |
| }, | |
| "required": ["x"] | |
| }, | |
| domain="custom", | |
| tags=["math", "square"] | |
| ) | |
| registry = get_registry() | |
| registry.register(custom_tool) | |
| ``` | |
| ## License | |
| MIT License | |
| ## Links | |
| - [GitHub Repository](https://github.com/Hmbown/FluxEM) | |
| - [PyPI Package](https://pypi.org/project/fluxem-tools/) | |
| - [HuggingFace](https://huggingface.co/hunterbown/fluxem-tools) | |
| ## Citation | |
| ```bibtex | |
| @software{fluxem_tools, | |
| author = {Hunter Bown}, | |
| title = {FluxEM Tools: Deterministic Computation Tools for LLM Tool-Calling}, | |
| year = {2026}, | |
| url = {https://github.com/Hmbown/FluxEM} | |
| } | |
| ``` | |