---
title: "Api Scores List"
method: GET
path: "/api/scores/"
tags: ["logs"]
---

# Api Scores List

`GET /api/scores/`

Create view for evaluation results

Synced to clickhouse automatically via evaluation.signals

Endpoint:
    /api/scores/

Args:
    - evaluator_id: The ID of the Evaluator instance to associate with
    - numerical_value: The numerical score of this result
    - string_value: The string score of this result
    - boolean_value: The boolean score of this result
    - categorical_value (Optional): The categorical score values (list of strings)
    - log_id (Optional): The ID of the CHLogV3 instance to associate with
    - prompt_id (Optional): The ID of the Prompt instance to associate with
    - prompt_version_number (Optional): The version number of the Prompt instance to associate with
    - dataset_id (Optional): The ID of the Dataset instance to associate with
Return:
    {
        "id": "xxxx",
        "created_at": "2025-09-07T08:35:16.770817Z",
        "type": "llm",
        "environment": "test",
        "numerical_value": null,
        "string_value": null,
        "boolean_value": null,
        "categorical_value": [],
        "is_passed": false,
        "cost": 0.0,
        "evaluator_id": "9b589384-574a-429c-8996-58419f514871",
        "log_id": "some_log_id",
        "dataset_id": null
    }

## Query parameters

- `page` integer
- `page_size` integer

## Headers

- `Authorization` string, required

## Response `200`

- PaginatedCHEvalResultListList
  - `count` integer, required
  - `next` string, uri, nullable
  - `previous` string, uri, nullable
  - `total_count` integer
  - `current_filters` FilterParamDictPydantic — Pydantic model for FilterParamDict. A dictionary that maps metric names to their filter parameters. Each key is a metric name (str), and each value can be: - A single MetricFilterParamPydantic (one condition) - A List[MetricFilterParamPydantic] (multiple conditions for same metric) - A FilterBundlePydantic (nested filter bundle with connector) Note: Uses extra="allow" for dynamic metric name fields. The __pydantic_extra__ annotation tells Pydantic what types to expect for extra fields, and generates typed additionalProperties in JSON Schema.
  - `filters_data` PaginatedChEvalResultListListFiltersData
  - `results` CHEvalResultList[], required
    - `id` string, required
    - `passed` string, required
    - `eval_result_unique_id` string
    - `created_at` string, date-time
    - `updated_at` string, date-time
    - `run_at` string, date-time
    - `log_timestamp` string, date-time, nullable
    - `unique_organization_id` string
    - `environment` string
    - `type` 'llm' | 'code' | 'human' | 'function' | 'human_numerical' | 'human_categorical' | 'human_boolean' | 'human_text' | 'custom' — * `llm` - Llm * `code` - Code * `human` - Human * `function` - Function * `human_numerical` - Human Numerical * `human_categorical` - Human Categorical * `human_boolean` - Human Boolean * `human_text` - Human Text * `custom` - Custom
    - `prompt_id` string
    - `prompt_version_number` integer
    - `eval_set_id` string
    - `evaluator_id` string
    - `workflow_version_id` string
    - `automation_id` string
    - `source` string
    - `eval_class` union
      - 'ragas_faithfulness' | 'ragas_noise_sensitivity' | 'ragas_response_relevancy' | 'ragas_answer_relevancy' | 'ragas_context_precision' | 'ragas_context_recall' | 'ragas_context_entity_recall' | 'ragas_factual_correctness' | 'ragas_semantic_similarity' | 'ragas_non_llm_string_similarity' | 'ragas_non_llm_string_presence' | 'ragas_non_llm_exact_match' | 'relari_llm_based_custom_metric' | 'relari_llm_based_answer_correctness' | 'keywordsai_custom_evaluator' | 'keywordsai_custom_llm' | 'output_char_count' | 'output_word_count' | 'custom_code' — * `ragas_faithfulness` - ragas_faithfulness * `ragas_noise_sensitivity` - ragas_noise_sensitivity * `ragas_response_relevancy` - ragas_response_relevancy * `ragas_answer_relevancy` - ragas_answer_relevancy * `ragas_context_precision` - ragas_context_precision * `ragas_context_recall` - ragas_context_recall * `ragas_context_entity_recall` - ragas_context_entity_recall * `ragas_factual_correctness` - ragas_factual_correctness * `ragas_semantic_similarity` - ragas_semantic_similarity * `ragas_non_llm_string_similarity` - ragas_non_llm_string_similarity * `ragas_non_llm_string_presence` - ragas_non_llm_string_presence * `ragas_non_llm_exact_match` - ragas_non_llm_exact_match * `relari_llm_based_custom_metric` - relari_llm_based_custom_metric * `relari_llm_based_answer_correctness` - relari_llm_based_answer_correctness * `keywordsai_custom_evaluator` - keywordsai_custom_evaluator * `keywordsai_custom_llm` - keywordsai_custom_llm * `output_char_count` - output_char_count * `output_word_count` - output_word_count * `custom_code` - custom_code
      - ''
    - `evaluator_slug` string
    - `evaluator_name` string
    - `scorer` string
    - `log_unique_id` string
    - `customer_identifier` string
    - `pipeline_run_id` string
    - `primary_score` number, double
    - `string_value` string, nullable
    - `json_value` string
    - `boolean_value` integer
    - `cost` number, double
    - `status` string
    - `error_message` string
    - `storage_object_key` string

---

[API](https://skmtc.net/keywordsai/apis/api-reference.md) · [All operations](https://skmtc.net/keywordsai/apis/api-reference/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/keywordsai/api-reference/versions/c26d550029f8/schema)