laurium 0.4.0

A package for extracting structured data from free text

Laurium

A Python package for extracting structured data from text and generating synthetic data using language models.

Organisations collect vast amounts of free text data containing untapped information that could provide decision makers with valuable insights. Laurium addresses this by providing tools for converting unstructured text into structured data using Large Language Models. Through prompt engineering, the package can be adapted to different use cases and data extraction requirements, unlocking the value hidden in text data.

For example, customer feedback stating "The login system crashed and I lost all my work!" contains information about the sentiment of the review, how urgently it needs to be addressed, what department is responsible for addressing the complaints and if action is required. Laurium provides the tools to extract and structure this information enabling quantitative analysis and data-driven decision making:

                                            text sentiment  urgency department action_required
The login system crashed and I lost all my work!  negative        5         IT             yes

This can be scaled to datasets which would be impossible to manually review and label.

This package started from work done by the BOLD Families project on estimating the number of children who have a parent in prison.

Install Laurium

You can install Laurium either from PyPI or from GitHub directly. If installing from PyPI, you will need to install a spaCy dependency alongside the package.

From GitHub

# using uv
uv add git+https://github.com/moj-analytical-services/laurium.git

# using pip
pip install git+https://github.com/moj-analytical-services/laurium.git

From PyPI

# using uv
uv add laurium https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl

# using pip
pip install laurium https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl

LLM Provider Setup

Laurium works with both local and cloud-based language models:

Local Models with Ollama

For running models locally without API costs:

1. Install Ollama from ollama.ai
2. Spin up a local ollama server by running in your terminal: ollama serve
3. Pull a model by running in your terminal: ollama pull qwen2.5:7b

Benefits:

• No API costs or rate limits

• Data stays local for privacy

• Works offline

Requirements:

• Sufficient disk space for model storage

• GPU recommended for faster processing

AWS Bedrock Models

For cloud-based models like Claude:

1. AWS Account with Bedrock service enabled

2. Configure AWS credentials via AWS CLI, environment variables, or IAM roles

3. Bedrock permissions for your AWS user/role

Basic Usage

Text Classification Pipeline

Laurium specializes in structured data extraction from text. Here's how to build a classification pipeline:

Using Ollama (Local)

from laurium.decoder_models import llm, prompts, pydantic_models, extract
from langchain_core.output_parsers import PydanticOutputParser
import pandas as pd
from typing import Literal

# 1. Create LLM instance
sentiment_llm = llm.create_llm(
    llm_platform="ollama", model_name="qwen2.5:7b", temperature=0.0
)

# 2. Define output schema
schema = {"ai_label": Literal[0, 1]}  # 1 for positive, 0 for negative
descriptions = {
    "ai_label": "Sentiment classification (1=positive, 0=negative)"
}

# 3. Build prompt with automatic schema integration
system_message = prompts.create_system_message(
    base_message="You are a sentiment analysis assistant. Use 1 for positive"
    "sentiment, 0 for negative sentiment.",
    keywords=["positive", "negative"],
)

extraction_prompt = prompts.create_prompt(
    system_message=system_message,
    examples=None,
    example_human_template=None,
    example_assistant_template=None,
    final_query="Analyze this text: {text}",
    schema=schema,  # Tell the LLM the output format we expect
    descriptions=descriptions,  # Provides field context to LLM
)

# 4. Create Pydantic model using same schema
OutputModel = pydantic_models.make_dynamic_example_model(
    schema=schema, descriptions=descriptions, model_name="SentimentOutput"
)

# 5. Create extractor and process data
parser = PydanticOutputParser(pydantic_object=OutputModel)
extractor = extract.BatchExtractor(
    llm=sentiment_llm, prompt=extraction_prompt, parser=parser
)

# Process your data
data = pd.DataFrame(
    {
        "text": [
            "I absolutely love this product!",
            "This is terrible, worst purchase ever.",
            "Great value for money, highly recommend!",
        ]
    }
)

results = extractor.process_chunk(data, text_column="text")
print(results.to_string(index=False))

Using AWS Bedrock

# Same code as above, but create LLM with Bedrock:
sentiment_llm = llm.create_llm(
    llm_platform="bedrock",
    model_name="anthropic.claude-3-haiku-20240307-v1:0",
    temperature=0.0,
    aws_region_name="eu-west-1",
)
# ... rest of the code remains the same

This will output something like:

                                    text  ai_label
         I absolutely love this product!         1
  This is terrible, worst purchase ever.         0
Great value for money, highly recommend!         1

Multi-Field Extraction

Define Complex Output Schemas

Extract multiple pieces of structured data simultaneously:

# Create LLM instance
feedback_llm = llm.create_llm(
    llm_platform="ollama", model_name="qwen2.5:7b", temperature=0.0
)

# Schema for analyzing customer feedback using Literal types for constraints
from typing import Literal

schema = {
    "sentiment": Literal["positive", "negative", "neutral"],
    "urgency": Literal[1, 2, 3, 4, 5],  # 1-5 scale
    "department": Literal["IT", "Support", "Product", "Sales", "Other"],
    "action_required": Literal["yes", "no"],
}

descriptions = {
    "sentiment": "Customer's emotional tone",
    "urgency": "How quickly this needs attention (1=low, 5=urgent)",
    "department": "Which department should handle this",
    "action_required": "Whether immediate action is needed",
}

# Build prompt with automatic schema integration
system_message = prompts.create_system_message(
    base_message="Analyze customer feedback and extract structured information.",
    keywords=["urgent", "complaint", "praise", "bug", "feature"],
)

# Schema automatically added to prompt - no manual JSON formatting needed!
extraction_prompt = prompts.create_prompt(
    system_message=system_message,
    examples=None,  # We'll add examples in the next section
    example_human_template=None,
    example_assistant_template=None,
    final_query="Feedback: {text}",
    schema=schema,  # Automatically shows allowed values and types
    descriptions=descriptions,  # Provides field context to LLM
)

FeedbackModel = pydantic_models.make_dynamic_example_model(
    schema=schema,
    descriptions=descriptions,
    model_name="CustomerFeedbackAnalysis",
)

Improve Accuracy with Examples

Add few-shot examples to guide the model:

# Training examples for better extraction - JSON format must match schema
few_shot_examples = [
    {
        "text": "System is down, can't access anything!",
        "sentiment": "negative",
        "urgency": 5,
        "department": "IT",
        "action_required": "yes",
    },
    {
        "text": "Love the new interface design",
        "sentiment": "positive",
        "urgency": 1,
        "department": "Product",
        "action_required": "yes",
    },
]

extraction_prompt = prompts.create_prompt(
    system_message=system_message,
    examples=few_shot_examples,
    example_human_template="Feedback: {text}",
    example_assistant_template="""{{
        "sentiment": "{sentiment}",
        "urgency": {urgency},
        "department": "{department}",
        "action_required": "{action_required}"
    }}""",
    final_query="Feedback: {text}",
    schema=schema,  # Schema formatting still included with examples
    descriptions=descriptions,
)

# Create extractor and process sample data
parser = PydanticOutputParser(pydantic_object=FeedbackModel)
extractor = extract.BatchExtractor(
    llm=feedback_llm,  # your LLM instance
    prompt=extraction_prompt,
    parser=parser,
)

# Sample customer feedback data
feedback_data = pd.DataFrame(
    {
        "text": [
            "The login system crashed and I lost all my work!",
            "Really appreciate the new dark mode feature",
            "Can we get a mobile app version soon?",
            "Billing charged me twice this month, need help",
        ]
    }
)

results = extractor.process_chunk(feedback_data, text_column="text")
print(results.to_string(index=False))

This will output something like:

                                            text sentiment  urgency department action_required
The login system crashed and I lost all my work!  negative        5         IT             yes
     Really appreciate the new dark mode feature  positive        2    Product              no
           Can we get a mobile app version soon?   neutral        3    Product             yes
  Billing charged me twice this month, need help  negative        3    Support             yes

Notebooks

The notebooks/ directory contains a combination of Jupyter and marimo notebooks for exploring Laurium. We recommend starting with the Jupyter notebooks, especially if unfamiliar with marimo.

To run one of the marimo notebooks:

1. Clone the Laurium repo

2. Sync dependencies with uv (uv sync)

3. Run the notebook of your choosing with the command

   uv run marimo run notebooks/[name of notebook].py
   

4. (For more advanced users) To get a deeper look at the code, you can open the notebook in "edit" mode, which allows you to view the code being run alongside the notebook itself.

   uv run marimo edit notebooks/[name of notebook].py
   

For more information about using marimo, check out their documentation.

Prompt engineering notebook

The prompt engineering notebook provides a walkthrough of using Laurium's decoder-only methods to extract custom information from the Rotten Tomatoes dataset of movie reviews. Starting from configuring the LLM, the notebook steps through writing a prompt, defining output fields and evaluating the results on this labelled dataset.

Fine-tuning notebook

The fine-tuning notebook illustrates a couple of different ways of fine-tuning transformer models using Laurium's encoder-only methods. This notebook is best run in marimo's edit mode, allowing the user to view both the code and the output at the same time.

Supported Models

Ollama (Local)

Use any model available in Ollama.

AWS Bedrock (Cloud)

Supported Bedrock models:

• claude-3-sonnet - Best for complex extraction tasks
• claude-3-haiku - Faster, cost-effective option

Modules Reference

Module	Sub-module	Description
decoder_models	llm	Create and manage LLM instances from Ollama and AWS Bedrock
	prompts	Create and manage prompt templates with optional few-shot examples
	extract	Efficient batch processing of text using LLMs
	pydantic_models	Dynamic Pydantic models for structured LLM output
components	extract_context	Extract keyword mentions with configurable context windows
	evaluate	Compute evaluation metrics for model predictions
	load	Load and chunk data from various sources (CSV, SQL, etc.)
encoder_models	nli	Natural Language Inference models for text analysis
	fine_tune	Fine-tune transformer models for custom tasks

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Why 'Laurium'

Laurium was an ancient Greek mine, famed for its rich silver veins that fueled the rise of Athens as a Mediterranean powerhouse.

Just as Laurium’s silver generated immense wealth for ancient Athens, so modern text mining (based on LLMs) holds the potential to unlock huge untapped value from unstructured information.

Contact Us

Please reach out to the AI for Linked Data team at [e-mail] or [e-mail].