llm-ner 0.1.0

Schema-driven Named Entity Recognition powered by local LLMs via Ollama

llm-ner

Schema-driven Named Entity Recognition powered by local LLMs via Ollama.

llm-ner lets you define arbitrary extraction schemas as plain Pydantic models and extract structured entities from free text – without training a custom model. Every extracted value is paired with a short verbatim evidence quote from the source, making results auditable and explainable.

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Features

• Schema-first – define what to extract with pure Python + Pydantic; the library builds the LLM prompt automatically.
• Evidence tracking – every field carries an evidence quote that must appear verbatim in the source text.
• Smart retries – automatically re-runs extraction and merges results when fields are missing.
• Tolerant parsing – invalid enum values, malformed numbers, bad dates, etc. become None instead of crashing.
• Fully typed – ships with a py.typed marker and complete type annotations.
• No cloud required – runs entirely on a local Ollama instance.

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Installation

With uv (recommended)

# Install uv if you don't have it
pip install uv

# Clone the repository
git clone https://github.com/ManuelMunozBer/llm-ner.git
cd llm-ner

# Create a virtual environment and install the package
uv venv
uv pip install -e .

# With test dependencies
uv pip install -e ".[test]"

# With all development dependencies
uv pip install -e ".[dev]"

With pip

pip install llm-ner

Prerequisites

A running Ollama instance with your chosen model:

ollama serve
ollama pull qwen2.5:7b-instruct   # or any instruction-following model

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Quick Start

from llmner import NERBaseModel, NERExtractor, SchemaRegistry

# 1. Create a registry – one per schema
registry = SchemaRegistry()

# 2. Define typed field annotations
GenderType = registry.categorical(
    "gender",
    options=["male", "female"],
    instruction="Extract the subject's gender.",
)
AgeType = registry.int_range(
    "age",
    "Extract the subject's age as an integer or range (e.g. '25-30').",
)
NameType = registry.generic(
    "name",
    "Extract the subject's full name.",
)

# 3. Define your Pydantic extraction schema
class PersonSchema(NERBaseModel):
    name:   NameType   | None = None  # type: ignore[valid-type]
    gender: GenderType | None = None  # type: ignore[valid-type]
    age:    AgeType    | None = None  # type: ignore[valid-type]

# 4. Create the extractor
extractor = NERExtractor(
    schema_class=PersonSchema,
    system_role="You are an expert information extractor.",
    system_task=(
        "Extract the requested fields from the text. "
        "Return null for any field not mentioned."
    ),
    rules_registry=registry.rules,
)

# 5. Extract
result = extractor.extract_one(
    "Detective John Smith, 42, was assigned to the case."
)

print(result.name.value)     # "John Smith"
print(result.name.evidence)  # "John Smith"
print(result.age.value)      # "42"
print(result.gender.value)   # "male"

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Concepts

SchemaRegistry

A SchemaRegistry instance is used to create self-documenting Pydantic field types. Each factory call registers a rule that will be injected into the LLM prompt.

registry = SchemaRegistry()

# Categorical field – only values from the allowed list are accepted
StatusType = registry.categorical(
    "status",
    options={"active": "currently employed", "inactive": "no longer employed"},
    instruction="Extract the person's employment status.",
)

# Integer / range field
SalaryType = registry.int_range(
    "salary",
    "Extract the annual salary in thousands of euros.",
)

# Free-text field
AddressType = registry.generic(
    "address",
    "Extract the full postal address.",
)

# Datetime field – normalised to YYYY-MM-DD HH:MM:SS
DateType = registry.datetime_format(
    "date",
    "Extract the contract signing date.",
)

EvidenceField

Every factory produces Annotated[EvidenceField, ...] types. An EvidenceField has two attributes:

Attribute	Type	Description
value	str | None	The normalised extracted value.
evidence	str | None	Verbatim quote from the source text that justifies value.

field: EvidenceField = result.name
print(field.value)     # "John Smith"
print(field.evidence)  # "John Smith, 42"

Evidence is validated: if the quote does not appear verbatim in the source text it is set to None.

NERBaseModel

Your extraction schemas must subclass NERBaseModel. It adds four reflection- based utilities:

Method	Description
prompt_schema()	Generate the JSON skeleton injected into the LLM prompt.
has_missing_fields()	Return True if any nested EvidenceField.value is None.
merge(e1, e2)	Fill None values in e1 with values from e2.
safe_parse(data)	Tolerantly parse LLM output, isolating per-field errors.

NERExtractor

The main orchestrator. Key parameters:

Parameter	Default	Description
schema_class	–	Your NERBaseModel subclass.
system_role	–	LLM persona / expertise description.
system_task	–	Extraction task and constraints.
rules_registry	–	registry.rules from your SchemaRegistry.
llm_model	"qwen2.5:7b-instruct"	Ollama model tag.
llm_base_url	"http://localhost:11434"	Ollama server URL.
max_retries	1	Extra calls on incomplete extraction.

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Nested Schemas

class Address(NERBaseModel):
    street: registry.generic("street", "Street name and number.") | None = None  # type: ignore[valid-type]
    city:   registry.generic("city",   "City name.")              | None = None  # type: ignore[valid-type]

class PersonSchema(NERBaseModel):
    name:    NameType    | None = None   # type: ignore[valid-type]
    address: Address     | None = None
    suspects: list[SuspectSchema] = []

prompt_schema() and safe_parse() handle arbitrary nesting and lists of sub-models automatically.

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Advanced Usage

Custom LLM client

Implement BaseLLMClient to use a different inference backend:

from llmner.llm_client import BaseLLMClient

class MyClient(BaseLLMClient):
    def generate(self, prompt: str) -> dict | None:
        # Call your backend here
        ...

extractor = NERExtractor(
    ...,
    llm_client=MyClient(),
)

Custom prompt template

from llmner import DEFAULT_PROMPT_TEMPLATE

MY_TEMPLATE = """\
[INST] {system_role}

{system_task}

Rules:
{rules_text}

Schema:
{schema_json}

Text: {input_text} [/INST]
"""

extractor = NERExtractor(
    ...,
    prompt_template=MY_TEMPLATE,
)

Controlling retries

# Disable retries
result = extractor.extract_one(text, retry_on_null=False)

# Configure at extractor level
extractor = NERExtractor(..., max_retries=3)

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Running the Examples

# Make sure Ollama is running and the model is available
ollama pull qwen2.5:7b-instruct

# Run the crime extraction example
python examples/crime_extraction/run.py

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Running the Tests

Integration tests require a live Ollama instance. Mark them accordingly:

# Run only unit tests (no Ollama needed)
pytest tests/ -m "not integration"

# Run all tests including integration
pytest tests/ -m integration -v

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Project Structure

llm-ner/
├── src/
│   └── llmner/
│       ├── __init__.py        # Public API
│       ├── base_model.py      # NERBaseModel
│       ├── factories.py       # SchemaRegistry + EvidenceField
│       ├── extractor.py       # NERExtractor
│       ├── llm_client.py      # OllamaClient
│       └── prompt.py          # PromptBuilder
├── tests/
│   ├── conftest.py            # Pytest configuration
│   ├── schema/
│   │   └── crime_schema.py    # Crime-specific schema (integration test)
│   ├── data/
│   │   ├── complaints.csv
│   │   ├── crimes_perceived_detailed.csv
│   │   └── perceived_suspects.csv
│   └── test_ner_accuracy.py   # End-to-end accuracy test
├── examples/
│   └── crime_extraction/
│       ├── schema.py          # English crime schema example
│       └── run.py             # Runnable example script
├── pyproject.toml
├── LICENSE
└── README.md

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Contributing

1. Fork the repository and create a feature branch.
2. Install development dependencies: uv pip install -e ".[dev]".
3. Run linting: ruff check src/.
4. Run type checking: mypy src/llmner.
5. Open a pull request with a clear description of your changes.

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License

MIT – see LICENSE.