Python SDK

pip install classer

import classer

# Uses CLASSER_API_KEY environment variable
result = classer.classify(
    source="I can't log in",
    labels=["billing", "technical", "sales"]
)

print(result.label)      # "technical"
print(result.confidence) # 0.94

export CLASSER_API_KEY=your-api-key

from classer import ClasserClient

client = ClasserClient(
    api_key="your-api-key",
    base_url="https://api.classer.ai",  # optional
    timeout=30.0  # optional, in seconds
)

result = classer.classify(
    source="Text to classify",
    labels=["label1", "label2", "label3"],
    descriptions={"label1": "Description"},  # optional
    model="model-name"  # optional
)

# Response
result.label        # str
result.confidence   # float
result.latency_ms   # float
result.usage        # Usage | None

result = classer.tag(
    source="Text to tag",
    labels=["label1", "label2", "label3"],
    descriptions={"label1": "Description"},  # optional
    threshold=0.3,  # optional, default 0.3
    model="model-name"  # optional
)

# Response
result.tags         # list[str]
result.confidences  # list[float]
result.latency_ms   # float
result.usage        # Usage | None

result = classer.match(
    source="Document text",
    query="Search query",
    model="model-name"  # optional
)

# Response
result.score        # float
result.latency_ms   # float
result.usage        # Usage | None

result = classer.score(
    source="Text to score",
    attribute="urgency",
    description="How urgent is this",  # optional
    model="model-name"  # optional
)

# Response
result.score        # float
result.latency_ms   # float
result.usage        # Usage | None

from classer import ClasserClient, ClasserError

client = ClasserClient(api_key="your-key")

try:
    result = client.classify(
        source="Hello",
        labels=["greeting", "question"]
    )
except ClasserError as e:
    print(f"Error: {e.message}")
    print(f"Status: {e.status}")
    print(f"Detail: {e.detail}")

@dataclass
class ClassifyResponse:
    label: str
    confidence: float
    latency_ms: float
    usage: Optional[Usage] = None

@dataclass
class TagResponse:
    tags: list[str]
    confidences: list[float]
    latency_ms: float
    usage: Optional[Usage] = None

@dataclass
class MatchResponse:
    score: float
    latency_ms: float
    usage: Optional[Usage] = None

@dataclass
class ScoreResponse:
    score: float
    latency_ms: float
    usage: Optional[Usage] = None

@dataclass
class Usage:
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int