themex 0.1.0a1.post3

A theme extraction tool using LLMs

themex

⚠️ Caution: This package is under active development and is currently not stable. Interfaces, file structure, and behaviour may change without notice.

themex is a flexible, modular framework designed to support large language model (LLM) tasks across social care, health, and research contexts — including thematic extraction, sentiment analysis, and more.

It supports both local HuggingFace models and remote APIs (such as Azure OpenAI), with configurable prompts, structured outputs, and logging.

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📦 Installation

pip install themex

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📁 Project Structure

llm-theme-miner/
├── poetry.lock
├── pyproject.toml
├── README.md
└── themex/
    ├── llm_runner                    # Core logic for calling LLMs
    │   ├── direct_runner.py
    │   ├── hf_runner.py
    │   ├── langchain_runner.py
    │   ├── schema.py
    │   └── utils.py
    ├── logger.py                     # Logging utilities
    ├── paths.py                      # Default paths and file naming logic
    ├── prompts/                      # Prompt template files
    └── utils.py                      # General utility functions

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🚀 Quick Start

This framework supports flexible execution of large language models (LLMs) via local or remote backends. You can choose to run models on your own machine ("execution_mode": "local") or through hosted APIs like Azure OpenAI and OpenRouter ("execution_mode": "remote").

🔐 API Key Configuration

By default, API keys are loaded from a .env file:

# For Azure OpenAI
AZURE_API_KEY=your_azure_key
AZURE_ENDPOINT=https://your-resource-name.openai.azure.com/
AZURE_DEPLOYMENT_NAME=your_deployment_name

# For OpenRouter
OPENROUTER_API_KEY=your_openrouter_key

If not found, you can pass them as parameters:

# For Azure
api_key="your_azure_key", azure_endpoint="https://...", deployment_name="your_deployment_name",

# For OpenRouter
api_key="your_openrouter_key"

Example 1 - Using a local HuggingFace model

from themex.llm_runner.direct_runner import run_llm
from pathlib import Path
from multiprocessing import Process

p = Process(target=run_llm, kwargs={
    "execution_mode": "local",
    "provider": "huggingface",
    "model_id": "meta-llama/Meta-Llama-3-8B-Instruct",
    "inputs": ["This is an example comment."],
    "sys_tmpl": Path("./prompts/system_prompt.txt"),
    "user_tmpl": Path("./prompts/theming_sentiment.txt"),
    "gen_args": {
        "temperature": 0.7,
        "max_new_tokens": 300
    },
    "output_filename": "output.csv",
    "csv_logger_filepath": "log.csv",
    "extra_inputs": {
        "question": "What are the strengths and weaknesses in this case?",
        "domain": "Strength"
    }
})
p.start()
p.join()

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Example 2 - Using Azure OpenAI remotely

from themex.llm_runner.direct_runner import run_llm
from pathlib import Path
from multiprocessing import Process

p = Process(target=run_llm, kwargs={
    "execution_mode": "remote",
    "provider": "azure",
    "model_id": "gpt-4.1",
    "api_version": "2025-01-01-preview",
    "inputs": ["This is an example comment."],
    "sys_tmpl": Path("./prompts/system_prompt.txt"),
    "user_tmpl": Path("./prompts/theming_sentiment.txt"),
    "gen_args": {
        "temperature": 0.4,
    },
    "output_filename": "azure_output.csv",
    "csv_logger_filepath": "azure_log.csv",
    "extra_inputs": {
        "question": "What are the strengths and weaknesses in this case?",
        "domain": "Strength"
    }
})
p.start()
p.join()

💡 Note on Multi-Process Execution

The examples use Python's multiprocessing.Process to run each task in a separate subprocess.

This is not mandatory, but can be helpful, particularly when using local models (e.g. with execution_mode="local").

Running in a subprocess ensures that memory (especially GPU memory) is fully released after the task completes, helping prevent memory leaks or out-of-memory errors during batch processing.

Feel free to adapt the structure for your own scheduling or orchestration needs.

Example 3 - Using LangChain with OpenRouter as LLM Backend

from themex.llm_runner.langchain_runner import run_chain_openrouter_async 

results, failed = await run_chain_openrouter_async(
    model_name="meta-llama/llama-3.3-70b-instruct:free",
    "inputs": ["This is an example comment."],
    sys_tmpl=Path("./prompts/system_prompt.txt"),
    user_tmpl=Path("./prompts/theming_sentiment.txt"),
    output_filename="output.csv",
    csv_logger_filepath="log.csv",
    gen_args={"temperature": 0.0}
)

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📄 Output Format (Example)

The example output assumes that you are using the prompts included in this repository.

👉 View prompt template on GitHub

In this setup, the prompt is written in a step-by-step manner and bundles multiple sub-tasks into a single instruction block. However, instead of executing everything sequentially, you can distribute these sub-tasks by launching them as separate multiprocessing.Process workers. Each worker handles one step of the prompt, and you can then aggregate their outputs at the end to form the final result. What we found is the longer the prompt, the worse the performance.

🧠 Field Definitions

• evidence: A verbatim quote from the original input text that supports or illustrates the identified topic. It serves as direct justification for the theme.
• root_cause: If the impact is "negative", this field provides a short explanatory phrase reflecting the likely underlying structural, procedural, or systemic cause of the issue. It is not a restatement of the evidence, but an inferred explanation.

The framework saves structured outputs to CSV. Fields depend on prompt structure, but may include:

comment_id	model_id	domain	topic	evidence	impact	root_cause	sentiment
1	gpt-4.1	Strength	Family Contact Support	...	positive		positive

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🧾 CSV Logger Output (Optional)

If csv_logger_filepath is specified, the framework will save an additional per-call log file capturing key runtime statistics, LLM behaviour, and inputs/outputs.

✅ When is it created?

• Only when csv_logger_filepath is explicitly set in run_llm parameters
• If omitted, no logger file is generated

📋 Example fields in the logger:

comment_id	context_len	current_mem_MB	do_sample	extra_fields	generated_token_len	increment_MB	input_len	input_token_len	max_new_tokens	model_id	output	peak_mem_MB	raw_output	system_prompt	temperature	tokens_per_sec	torch_dtype	total_time_sec	user_prompt
id		1.57		{"domain": "Strength"}	55	1.57	1	991		gpt-4.1	…	1.63	…	…	0.2	40.86	None	1.35	…

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⚙️ Key Parameters

Parameter	Description
execution_mode	"local" or "remote"
provider	"huggingface" / "azure"
model_id	Model name or deployment ID
api_version	Azure API version if applicable
inputs	List of input strings
sys_tmpl	Path to system prompt
user_tmpl	Path to user prompt
gen_args	Dict of generation parameters (e.g. temperature, max_tokens)
output_filename	Where to save the result
csv_logger_filepath	Filepath for detailed logs
extra_inputs	Additional template fields (e.g. domain, question)

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🧪 Development Status

This project is still in development. Breaking changes are likely.
Use with caution in production environments.

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📬 Contact

To report bugs, request features, or contribute ideas, please open an issue on GitHub or contact the maintainer.

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