UpTrain Root Cause Analysis API
Performs root cause analysis (POST /perform_root_cause_analysis) on failing RAG or LLM responses, classifying why a response was poor - e.g. incomplete context, poor retrieval, or hallucination - to guide remediation.
OpenAPI Specification
openapi: 3.0.1
info:
title: UpTrain Managed Evaluation API
description: >-
Managed HTTP API for UpTrain, the open-source (Apache-2.0) LLM evaluation
platform. The API grades supplied LLM input / output / context rows against
a list of named checks (context relevance, factual accuracy, response
completeness, conciseness, tonality, prompt injection, hallucination and
more), logs results to a named project for dashboard monitoring, and
performs root cause analysis on failures. These paths correspond to the
public endpoints called by the uptrain Python package's APIClient
(uptrain/framework/remote.py), rooted at {server_url}/api/public. The
default managed server is https://demo.uptrain.ai. Requests are
authenticated with an uptrain-access-token header.
termsOfService: https://uptrain.ai/
contact:
name: UpTrain
url: https://uptrain.ai/
license:
name: Apache 2.0
url: https://www.apache.org/licenses/LICENSE-2.0.html
version: '0.7.1'
servers:
- url: https://demo.uptrain.ai/api/public
description: Default UpTrain managed evaluation service
security:
- UptrainAccessToken: []
paths:
/auth:
get:
operationId: checkAuth
tags:
- Auth
summary: Validate the UpTrain access token.
description: >-
Checks that the supplied uptrain-access-token is valid. Called by the
APIClient.check_auth() method on initialization.
responses:
'200':
description: Token is valid.
content:
application/json:
schema:
type: object
properties:
message:
type: string
'401':
description: Invalid or missing access token.
/evaluate:
post:
operationId: evaluate
tags:
- Evaluation
summary: Run evaluations on a set of LLM responses.
description: >-
Evaluates each row of input data against the supplied list of checks and
returns the original rows enriched with per-check scores and
explanations. Grading LLM calls are run server-side by UpTrain.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluateRequest'
responses:
'200':
description: Evaluation results, one object per input row.
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluationResults'
'401':
description: Invalid or missing access token.
'422':
description: Invalid request payload.
/log_and_evaluate:
post:
operationId: logAndEvaluate
tags:
- Evaluation
summary: Log data to a project and evaluate it.
description: >-
Logs the supplied data under a named project and runs the requested
checks, persisting results so they are visible on the UpTrain dashboard
with real-time monitoring. This endpoint also backs the SDK's
evaluate_experiments method, which adds experiment column names for A/B
comparison of prompt or model variants.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/LogAndEvaluateRequest'
responses:
'200':
description: Evaluation results, one object per input row.
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluationResults'
'401':
description: Invalid or missing access token.
'422':
description: Invalid request payload.
/perform_root_cause_analysis:
post:
operationId: performRootCauseAnalysis
tags:
- Root Cause Analysis
summary: Perform root cause analysis on failing responses.
description: >-
Analyzes failing RAG or LLM responses and classifies why each response
was poor (for example incomplete context, poor retrieval, or
hallucination) to guide remediation.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/RootCauseAnalysisRequest'
responses:
'200':
description: Root cause analysis results, one object per input row.
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluationResults'
'401':
description: Invalid or missing access token.
'422':
description: Invalid request payload.
/run:
post:
operationId: addRun
tags:
- Runs
summary: Create an evaluation run.
description: >-
Creates a run that pairs a previously uploaded dataset with a checkset
and executes the checks asynchronously. Returns a run identifier that
can be polled for status and results.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/RunRequest'
responses:
'200':
description: The created run.
content:
application/json:
schema:
$ref: '#/components/schemas/Run'
'401':
description: Invalid or missing access token.
/run/{run_id}:
get:
operationId: getRun
tags:
- Runs
summary: Get the status of a run.
parameters:
- name: run_id
in: path
required: true
schema:
type: string
responses:
'200':
description: The run object with its current status.
content:
application/json:
schema:
$ref: '#/components/schemas/Run'
'404':
description: Run not found.
/run/{run_id}/results:
get:
operationId: getRunResults
tags:
- Runs
summary: Download the results of a completed run.
parameters:
- name: run_id
in: path
required: true
schema:
type: string
responses:
'200':
description: Evaluation results for the run.
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluationResults'
'404':
description: Run not found.
/dataset:
post:
operationId: addDataset
tags:
- Datasets
summary: Upload an evaluation dataset.
requestBody:
required: true
content:
multipart/form-data:
schema:
type: object
properties:
name:
type: string
description: Name to register the dataset under.
file:
type: string
format: binary
description: JSONL / CSV file of evaluation rows.
responses:
'200':
description: The registered dataset.
content:
application/json:
schema:
$ref: '#/components/schemas/Dataset'
'401':
description: Invalid or missing access token.
get:
operationId: getDataset
tags:
- Datasets
summary: Get a dataset by name.
parameters:
- name: name
in: query
required: true
schema:
type: string
responses:
'200':
description: The dataset.
content:
application/json:
schema:
$ref: '#/components/schemas/Dataset'
'404':
description: Dataset not found.
/checkset:
post:
operationId: addCheckset
tags:
- Checksets
summary: Create a reusable checkset.
description: >-
Registers a named bundle of checks that can be paired with a dataset in
a run. This endpoint also backs add_experiment in the SDK.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/Checkset'
responses:
'200':
description: The registered checkset.
content:
application/json:
schema:
$ref: '#/components/schemas/Checkset'
'401':
description: Invalid or missing access token.
/evaluation_results/{project_name}:
get:
operationId: downloadProjectEvalResults
tags:
- Evaluation
summary: Download evaluation results for a project.
parameters:
- name: project_name
in: path
required: true
schema:
type: string
responses:
'200':
description: All evaluation results logged under the project.
content:
application/json:
schema:
$ref: '#/components/schemas/EvaluationResults'
'404':
description: Project not found.
components:
securitySchemes:
UptrainAccessToken:
type: apiKey
in: header
name: uptrain-access-token
description: >-
UpTrain managed-service access token. Obtained from the UpTrain
dashboard and supplied on every request as the uptrain-access-token
header.
schemas:
EvalRow:
type: object
description: >-
A single evaluation row. The exact keys depend on the checks requested;
common keys are question, response and context.
properties:
question:
type: string
description: The user query / prompt sent to the LLM.
response:
type: string
description: The LLM-generated response to grade.
context:
type: string
description: Retrieved context provided to the LLM (for RAG checks).
ground_truth:
type: string
description: Optional reference answer for accuracy checks.
additionalProperties: true
EvaluateRequest:
type: object
required:
- data
- checks
properties:
data:
type: array
description: List of rows to evaluate.
items:
$ref: '#/components/schemas/EvalRow'
checks:
type: array
description: >-
List of checks to run. Each item is a preconfigured check name (for
example context_relevance, factual_accuracy, response_completeness,
response_conciseness, response_relevance, valid_response, tonality,
prompt_injection, jailbreak_detection) or a check object.
items:
type: string
example:
- context_relevance
- factual_accuracy
- response_completeness
metadata:
type: object
description: Optional metadata to attach to the evaluation.
additionalProperties: true
LogAndEvaluateRequest:
type: object
required:
- project_name
- data
- checks
properties:
project_name:
type: string
description: Project to log the data and results under.
data:
type: array
items:
$ref: '#/components/schemas/EvalRow'
checks:
type: array
items:
type: string
example:
- context_relevance
- factual_accuracy
metadata:
type: object
additionalProperties: true
RootCauseAnalysisRequest:
type: object
required:
- project_name
- data
- rca_template
properties:
project_name:
type: string
data:
type: array
items:
$ref: '#/components/schemas/EvalRow'
rca_template:
type: string
description: >-
Root cause analysis template to apply, for example
rag_with_citation.
example: rag_with_citation
EvaluationResults:
type: array
description: >-
The input rows echoed back, each enriched with score_<check> and
explanation_<check> fields for every requested check.
items:
type: object
additionalProperties: true
RunRequest:
type: object
required:
- dataset
- checkset
properties:
name:
type: string
dataset:
type: string
description: Name of a previously uploaded dataset.
checkset:
type: string
description: Name of a previously created checkset.
Run:
type: object
properties:
run_id:
type: string
status:
type: string
description: Run status, for example queued, running, or done.
created_at:
type: string
format: date-time
Dataset:
type: object
properties:
name:
type: string
rows:
type: integer
created_at:
type: string
format: date-time
Checkset:
type: object
required:
- name
- checks
properties:
name:
type: string
checks:
type: array
items:
type: string