time-travel-math-parser 2025.12.20185553
The package lets users input a textual description of a claim about paradox‑free time travel backed by mathematics. It then uses an LLM together with llmatch‑messages to parse the text, extract the co
License: MIT
Requires Python: >=3.9

# time-travel-math-parser

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**Time‑Travel Math Parser** transforms free‑form textual claims about paradox‑free time‑travel into a clean, structured JSON format that downstream tools can consume reliably.

---

## Highlights

- **LLM‑powered extraction** using the `llmatch-messages` library to enforce a strict output format.
- **Works out‑of‑the‑box with the free tier of LLM7** thanks to a sensible default implementation (`ChatLLM7` from `langchain_llm7`).
- **Easy to swap LLMs** – you can pass any `langchain` compatible `BaseChatModel` (OpenAI, Anthropic, Google, etc.) to replace the default.

---

## Installation

```bash
pip install time-travel-math-parser
```

---

## Quick Start

```python
from time_travel_math_parser import time_travel_math_parser

user_input = """
The paper proves that any universe with a closed timelike curve will eventually resolve paradoxes by introducing a feedback loop that nullifies causal inconsistencies. 
Key assumptions: (1) spacetime is differentiable, (2) causality violation is limited to the region within the wormhole throat, and (3) quantum fluctuations are suppressed.
"""

# Using the default ChatLLM7
output = time_travel_math_parser(user_input)

print(output)
```

`output` will be a list of extracted JSON objects, for example:

```json
[
  {
    "claim": "any universe with a closed timelike curve will eventually resolve paradoxes ...",
    "theorem_summary": "...feedback loop that nullifies causal inconsistencies",
    "assumptions": [
      "spacetime is differentiable",
      "causality violation is limited to the region within the wormhole throat",
      "quantum fluctuations are suppressed"
    ],
    "paradox_handling": "...",
    "confidence_score": 0.92
  }
]
```

---

## Using Your Own LLM

`time_travel_math_parser` accepts an optional `llm` argument that should be an instance of `langchain` `BaseChatModel`.  
Below are examples for the three most common LLM back‑ends.

### OpenAI

```python
from langchain_openai import ChatOpenAI
from time_travel_math_parser import time_travel_math_parser

llm = ChatOpenAI()          # configure API key through environment variable or kwargs
result = time_travel_math_parser(user_input, llm=llm)
```

### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from time_travel_math_parser import time_travel_math_parser

llm = ChatAnthropic()
result = time_travel_math_parser(user_input, llm=llm)
```

### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from time_travel_math_parser import time_travel_math_parser

llm = ChatGoogleGenerativeAI()
result = time_travel_math_parser(user_input, llm=llm)
```

---

## Parameters

| Parameter | Type | Description |
|-----------|------|-------------|
| `user_input` | `str` | The free‑form text describing the time‑travel claim to parse. |
| `llm` | `Optional[BaseChatModel]` | A LangChain chat model instance. If omitted, `ChatLLM7` (free tier) is used. |
| `api_key` | `Optional[str]` | LLM7 API key. If not supplied, the function looks for `LLM7_API_KEY` in the environment; if still missing it falls back to the default public key. |

---

## Rate Limits

LLM7’s free tier provides ample throughput for most development and testing workloads.  
If you require higher limits, supply a personal API key:

```bash
export LLM7_API_KEY=YOUR_KEY
```

or:

```python
time_travel_math_parser(user_input, api_key="YOUR_KEY")
```

Free keys are available from <https://token.llm7.io/>.

---

## Contributing & Issues

If you encounter bugs or have feature requests, please open an issue on GitHub:
<https://github.com/chigwell/time-travel-math-parser/issues>

Feel free to fork and submit pull requests!

---

## License

MIT © Eugene Evstafev – <hi@eugene.plus>

---

## Author

Eugene Evstafev (`chigwell`)  
<https://github.com/chigwell>  
LinkedIn: <https://linkedin.com/in/chigwell>