Jev decision model

Use TypeSafe AI’s Jev for classification, filtering, reranking, cluster labeling and hierarchical cluster search

Jev is an evaluation model from TypeSafe AI. It answers typed questions about a piece of text and returns calibrated probabilities. It does not generate text. Use it for steps that are a decision over a fixed set of options: keep or discard, pick a label, rate on a scale, route to a branch.

Question type	Jev returns	Example
Yes/no	Probability that the answer is yes	Does this document mention side effects?
Choice	The chosen option and a probability for each option	Which category fits: footwear, apparel or electronics?
Score	A value across ordered levels you define	How severe is the complaint, 1 to 5?

Jev reads text only, up to 32,000 tokens per request. Media documents are judged on their text fields, such as descriptions, transcripts and metadata. TypeSafe prices Jev at $0.042 per million input tokens, and output is free.

Jev in Mixpeek

Surface	How to select Jev	What Jev decides
Text extractor	llm_provider: "typesafe"	Each response_shape field, at ingestion
Cluster labeling	llm_labeling.provider: "typesafe" + candidate_labels	The label for each cluster, from your vocabulary
LLM Filter	model_name: "jev-latest"	Keep or discard each document
LLM Enrich	model_name: "jev-latest" + output_schema	Each schema field for each document
Rerank	inference_name: "typesafe__jev"	Relevance probability for each query and document pair
Classify	inference_name: "typesafe__jev" + labels	Probability of each label for each document
Agent Search	strategy: "cluster_navigation" + model_name: "jev-latest"	Which child cluster to descend into, at every level
Inference API	inference_name: "typesafe__jev"	Any of the question types, called directly

Schema fields and the questions they become

Extraction, cluster labeling and LLM Enrich describe their output as a JSON schema. Jev answers each field as one question, and all questions for one document go in one request.

Schema field	Question
boolean	Yes/no. The value is true when the probability is at least 0.5
number with minimum: 0 and maximum: 1	Yes/no. The value is the probability
string with enum (2 to 255 values)	Choice. Add enumDescriptions to describe each option
integer with minimum and maximum (2 to 10 levels)	Score
array of enum strings	One yes/no per value. The value lists each option at 0.5 or above
object	Its properties, answered the same way

A string field named reason, reasoning, explanation, rationale or justification receives the probabilities behind the answer. Any other field without a closed set of values is rejected before a request is sent, and the error names the field. Use a generative model such as gemini-2.5-flash-lite for free-text fields.

Call Jev through the inference API

typesafe__jev accepts four input shapes.

Inputs	Parameters	Returns
pairs: list of [query, document]	relevance_criteria (optional)	scores: one probability per pair
text	labels: list, or map of label to description	labels: every label with its probability, highest first
text + schema	none	data shaped like the schema, and answers with the probabilities
state + questions	none	TypeSafe’s response, unchanged

cURL

curl -sS -X POST "$MP_API_URL/v1/inference" \
  -H "Authorization: Bearer $MP_API_KEY" \
  -H "X-Namespace: $MP_NAMESPACE" \
  -H "Content-Type: application/json" \
  -d '{
    "inference_name": "typesafe__jev",
    "inputs": {
      "text": "Kickflip down the eight stair at the city skatepark",
      "schema": {
        "type": "object",
        "properties": {
          "sport": {"type": "string", "enum": ["skateboarding", "surfing", "basketball"]},
          "is_tutorial": {"type": "boolean"}
        }
      }
    }
  }'

Writing questions Jev answers well

• State the exact condition. Jev reads criteria word for word, so “The document states the refund policy for damaged items” works better than “relevant to refunds”.
• Keep arithmetic, counting and date comparison in code. Ask Jev for the parts, then compute.
• Send only the text the question needs. Unrelated content lowers accuracy.
• Use the probabilities. Route answers below a threshold you choose to review or to a generative model.

Bring your own key

The inference API accepts parameters.api_key to call Jev with your own TypeSafe key.