base

A discriminated union of question types accepted by decision models.

Ensure that a config is a dict with all keys present.

Run a function in an executor.

Complete input for one decision model invocation.

Keeping the state and questions in the Runnable input ensures both values participate in composition, batching, and tracing.

Typed answers and metadata returned from one classification request.

Access every answer through answers, or use nouls, choices, scores, and boxes for views filtered by answer type. Each mapping preserves the question IDs supplied to ClassifierRequest.questions.

Runnable that can be serialized to JSON.

Root state accepted by decision models.

State is a string, JSON object, or JSON array. LangChain BaseMessage objects and message sequences can appear at the root or at any depth inside objects and arrays. LangChain standard media blocks (for example {"type": "image", "base64": ..., "mime_type": ...}) may appear anywhere in the state. Each provider documents which media it supports and how it sends state its API cannot accept natively.

Configuration for a Runnable.

# Parent sets tags
chain.invoke(input, config={"tags": ["parent"]})
# Child automatically inherits and can add:
# ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}

Answer typed questions about text, messages, or structured state.

A decision model evaluates shared state against one or more questions and returns typed answers: a probability for a Noul, a selected label for a Choice, an expected level for a Score, and a location for a Box. Answers keep their probability distributions and confidence so applications can choose their own thresholds.

Subclasses implement _classify, and optionally _aclassify, to call a provider. This class handles callbacks, tracing metadata, and token-usage reporting.

from langchain_core.decisions import BaseDecisionModel, ClassifierResponse

class MyDecisionModel(BaseDecisionModel):
    model: str = "my-model"

    @property
    def _provider(self) -> str:
        return "my_provider"

    def _classify(self, state, questions):
        body = my_client.classify(state=state, questions=questions)
        return ClassifierResponse.model_validate(body)