ollama-classifier 0.4.1

Text classification with constrained output and confidence scoring across multiple LLM backends

ollama-classifier

A Python library for LLM-based text classification with constrained output and confidence scoring. Supports multiple inference backends: Ollama (≥0.12), vLLM, SGLang, and llama.cpp.

Every backend works through a single unified LLMClassifier with two scoring methods — adaptive constrained generation (generate()) and exact multi-call completion scoring (classify()).

Features

• Two Scoring Methods: generate() for adaptive budget-controlled scoring, classify() for exact gold-standard confidence
• Constrained Output: Output is guaranteed to be one of your labels (JSON enum, guided_choice, regex, or GBNF — depending on backend)
• Calibrated Confidence: Probability distribution over all choices with geometric-mean normalization (no token-count bias)
• Sync & Async: Full support for both synchronous and asynchronous operations
• Batch Processing: Classify multiple texts efficiently with parallel execution
• Flexible Choices: Support for simple labels or labels with descriptions
• Custom Prompts: Override the default system prompt for specialized tasks
• Multiple Backends: Ollama, vLLM, SGLang, or llama.cpp — all behind one API

Installation

pip install ollama-classifier

Or with uv:

uv add ollama-classifier

Ollama runtime support (optional)

To use the Ollama backend, install the ollama SDK as an extra:

pip install "ollama-classifier[ollama]"

Note: httpx and pydantic are required dependencies and installed automatically. The ollama Python SDK is optional — it is only needed when using OllamaBackend.

Prerequisites

You need at least one running inference backend:

• Ollama backend: Ollama ≥0.12 installed and running, with a model pulled (e.g., ollama pull llama3.2). Logprobs support requires v0.12 or later.
• vLLM backend: A running vLLM server
• SGLang backend: A running SGLang server
• llama.cpp backend: A running llama.cpp server

Quick Start

All backends follow the same pattern: create a backend, wrap it in an LLMClassifier, and call generate() or classify().

Ollama

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import OllamaBackend

backend = OllamaBackend(model="llama3.2")
classifier = LLMClassifier(backend)

result = classifier.classify(
    text="I love this product!",
    choices=["positive", "negative", "neutral"],
)

print(f"Prediction: {result.prediction}")
print(f"Confidence: {result.confidence:.2%}")
print(f"Method: {result.method}")

vLLM

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import VLLMBackend

backend = VLLMBackend(
    model="meta-llama/Llama-3.2-3B-Instruct",
    base_url="http://localhost:8000/v1",
)
classifier = LLMClassifier(backend)

result = classifier.classify(
    text="I love this product!",
    choices=["positive", "negative", "neutral"],
)

SGLang

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import SGLangBackend

backend = SGLangBackend(
    model="meta-llama/Llama-3.2-3B-Instruct",
    base_url="http://localhost:30000/v1",
)
classifier = LLMClassifier(backend)

llama.cpp

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import LlamaCppBackend

backend = LlamaCppBackend(
    model="model",
    base_url="http://localhost:8080/v1",
)
classifier = LLMClassifier(backend)

Choosing a Scoring Method

LLMClassifier provides two scoring methods. Each returns a ClassificationResult with a full probability distribution.

	generate()	classify()
How it works	Adaptive constrained generation over a prefix trie of label tokens. Per-label logprobs are reconstructed from the winning generation path and any unresolved clusters.	Multi-call completion scoring: each label is scored independently as a completion of the prompt.
API calls	1 to max_calls (adaptive)	N calls for N labels
Confidence	Divergence-aware (may be partial for labels that diverge early)	Exact (geometric-mean normalization)
Approximate?	result.approximate is True when any label has partial coverage	Always False — fully resolved
result.method	"adaptive_generate"	"multi_call"
Best for	Speed, large label sets, when a single call suffices	Gold-standard accuracy, small-to-medium label sets

generate() — adaptive, budget-controlled

The max_calls parameter controls the accuracy/cost tradeoff:

max_calls	Behavior	Calls
1 (default)	Single constrained call. Labels are scored up to their divergence point from the winning path. Fast but approximate — set result.approximate=True.	1
K	Adaptive resolution. After each call, unresolved label clusters trigger supplementary constrained calls until the budget is exhausted.	≤ K
None	Fully recursive resolution. Every cluster is resolved to completion. Equivalent to exact scoring.	≤ N

# Fast: single call, approximate confidence
result = classifier.generate(
    text="The team won the championship!",
    choices=["sports", "finance", "politics"],
    max_calls=1,
)
print(result.prediction)        # "sports"
print(result.approximate)       # True (if any label had partial coverage)
print(result.coverage)          # {"sports": 1.0, "finance": 1.0, "politics": 1.0}

# Adaptive: allow up to 3 calls for better resolution
result = classifier.generate(text="...", choices=[...], max_calls=3)

# Exact: resolve everything (unlimited calls)
result = classifier.generate(text="...", choices=[...], max_calls=None)

classify() — exact, N calls

Makes one completion-scoring call per label. Each label's per-token logprobs are extracted without generation, then normalized via geometric mean to eliminate token-count bias. This is the gold-standard confidence method.

result = classifier.classify(
    text="The movie was fantastic!",
    choices=["positive", "negative", "neutral"],
)
print(result.method)        # "multi_call"
print(result.approximate)   # False
print(result.n_calls)       # 3

Usage

Basic Classification

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import OllamaBackend

backend = OllamaBackend(model="llama3.2")
classifier = LLMClassifier(backend)

result = classifier.classify(
    text="The goalkeeper made an incredible save!",
    choices=["sports", "politics", "technology", "entertainment"],
)

Classification with Label Descriptions

Providing descriptions helps the model understand each category better:

choices = {
    "positive": "Text expresses happiness, satisfaction, or approval",
    "negative": "Text expresses anger, disappointment, or disapproval",
    "mixed": "Text contains both positive and negative sentiments",
    "neutral": "Text is factual without strong emotional content",
}

result = classifier.classify(
    text="The food was amazing but the service was terrible.",
    choices=choices,
)

Custom System Prompt

result = classifier.classify(
    text="The quarterly earnings exceeded analyst expectations.",
    choices=["bullish", "bearish", "neutral"],
    system_prompt="You are a financial sentiment analyzer. "
                  "Classify financial news based on market sentiment.",
)

Adaptive Generation

# Single call, approximate (fastest)
result = classifier.generate(
    text="The team won the championship!",
    choices=["sports", "finance", "politics"],
)

print(result.prediction)
print(result.confidence)
print(result.coverage)  # per-label fraction of tokens scored

Batch Processing

texts = [
    "The goalkeeper made an incredible save!",
    "The central bank raised interest rates.",
    "The new smartphone features a revolutionary camera.",
]

results = classifier.batch_classify(
    texts=texts,
    choices=["sports", "finance", "technology"],
)

for text, result in zip(texts, results):
    print(f"{text} -> {result.prediction} ({result.confidence:.2%})")

Async Usage

import asyncio
from ollama_classifier import LLMClassifier
from ollama_classifier.backends import OllamaBackend

async def main():
    backend = OllamaBackend(model="llama3.2")
    classifier = LLMClassifier(backend)

    # Single classification
    result = await classifier.aclassify(
        text="The concert was amazing!",
        choices=["positive", "negative", "neutral"],
    )

    # Batch classification (concurrent)
    results = await classifier.abatch_classify(
        texts=["Text 1", "Text 2", "Text 3"],
        choices=["positive", "negative", "neutral"],
    )

asyncio.run(main())

Inference Backends

Ollama

Uses the Ollama Python SDK. Constraint mechanism: JSON Schema enum via the format parameter (the model generates {"label": "<chosen>"}). Requires Ollama runtime ≥0.12 for logprobs.

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import OllamaBackend

# Local
backend = OllamaBackend(model="llama3.2")

# Remote
backend = OllamaBackend(model="llama3.2", host="http://remote-host:11434")

classifier = LLMClassifier(backend)

vLLM

High-throughput serving engine. Constraint mechanism: guided_choice (generates bare label text).

Local server:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.2-3B-Instruct \
    --host 0.0.0.0 --port 8000

Connect:

from ollama_classifier.backends import VLLMBackend

backend = VLLMBackend(
    model="meta-llama/Llama-3.2-3B-Instruct",
    base_url="http://localhost:8000/v1",
)

Remote server:

backend = VLLMBackend(
    model="your-model",
    base_url="https://your-vllm-server.com/v1",
    api_key="your-api-key",  # if authentication is required
)

SGLang

Fast serving system for large language models. Constraint mechanism: regex (generates bare label text).

Local server:

python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.2-3B-Instruct \
    --host 0.0.0.0 --port 30000

Connect:

from ollama_classifier.backends import SGLangBackend

backend = SGLangBackend(
    model="meta-llama/Llama-3.2-3B-Instruct",
    base_url="http://localhost:30000/v1",
)

llama.cpp

Lightweight inference via llama-server. Ideal for CPU or mixed CPU/GPU environments. Constraint mechanism: GBNF grammar (generates bare label text).

Local server:

./llama-server -m model.gguf --host 0.0.0.0 --port 8080 -c 4096

Connect:

from ollama_classifier.backends import LlamaCppBackend

backend = LlamaCppBackend(
    model="model",
    base_url="http://localhost:8080/v1",
)

Note: Logprobs and grammar constraints require llama.cpp to be compiled with the appropriate flags.

Backend Configuration

All backends share common configuration options:

Parameter	Default	Description
model	(required)	Model identifier
base_url	Engine-specific	Base URL of the inference server
api_key	"not-needed"	API key for authentication (not used by OllamaBackend)
timeout	120.0	Request timeout in seconds
max_tokens	256	Maximum tokens to generate
extra_body	{}	Extra parameters merged into every request

OllamaBackend additionally accepts a host parameter (defaults to http://localhost:11434) and accepts pre-initialized sync_client / async_client instances.

API Reference

ClassificationResult

from pydantic import BaseModel

class ClassificationResult(BaseModel):
    prediction: str                          # The predicted class label
    confidence: float                        # Confidence score (0.0 to 1.0)
    probabilities: Dict[str, float]          # Probability distribution over all choices
    method: str = "multi_call"               # "adaptive_generate" or "multi_call"
    approximate: bool = False                # True if any label has partial coverage
    coverage: Dict[str, float] = {}          # Per-label fraction of tokens scored (0.0–1.0)
    n_calls: int = 1                         # Number of API calls made
    raw_response: Dict[str, Any] = {}        # Raw data for debugging

LLMClassifier Methods

Method	Async	Description
generate(text, choices, system_prompt, *, max_calls)	agenerate	Adaptive constrained generation (1 to max_calls calls)
classify(text, choices, system_prompt)	aclassify	Exact multi-call completion scoring (N calls for N labels)
batch_generate(texts, choices, system_prompt, *, max_calls)	abatch_generate	Batch adaptive generation (parallelized)
batch_classify(texts, choices, system_prompt)	abatch_classify	Batch multi-call classification (parallelized)

Parameters

• text (str): The text to classify
• texts (List[str]): List of texts to classify (batch methods)
• choices (Union[List[str], Dict[str, str]]): Either a list of choice labels, or a dict mapping labels to descriptions
• system_prompt (str | None): Optional custom system prompt
• max_calls (int | None): Maximum number of API calls for generate(). 1 (default) = fast/approximate, K = adaptive, None = exact

Sample Data

The package ships with ready-to-use sample datasets under examples/sample_data.py so you can test the classifier immediately without preparing your own data.

Two datasets are provided, both containing 20 short customer-support ticket texts across 4 labels (billing, technical_support, account, general):

• DATASET_WITHOUT_DESCRIPTIONS — labels as a plain list.
• DATASET_WITH_DESCRIPTIONS — labels as a dict mapping each label to a human-readable description, which improves classification accuracy.

Import the datasets

from examples.sample_data import DATASET_WITHOUT_DESCRIPTIONS, DATASET_WITH_DESCRIPTIONS

Each dataset is a SampleDataset dataclass with these fields:

Attribute	Type	Description
texts	List[str]	Short texts to classify
choices	List[str] or Dict[str, str]	Label list or label→description dict
expected_labels	List[str]	Expected correct label for each text
description	str	Human-readable description of the dataset

Run a quick test

from ollama_classifier import LLMClassifier
from ollama_classifier.backends import OllamaBackend
from examples.sample_data import DATASET_WITHOUT_DESCRIPTIONS

backend = OllamaBackend(model="llama3.2")
classifier = LLMClassifier(backend)

results = classifier.batch_generate(
    texts=DATASET_WITHOUT_DESCRIPTIONS.texts,
    choices=DATASET_WITHOUT_DESCRIPTIONS.choices,
)

# Verify accuracy
correct = sum(r.prediction == e for r, e in
              zip(results, DATASET_WITHOUT_DESCRIPTIONS.expected_labels))
print(f"Accuracy: {correct}/{len(results)}")

Run the bundled example script

python -m examples.run_sample_data

License

MIT License

Development

This project just started! Looking forward to suggestions, issues, and pull requests!