> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abv.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom Scores

Custom Scores are the most flexible way to implement evaluation workflows using ABV. As any other evaluation method the purpose of custom scores is to assign evaluations metrics to `Traces`, `Observations`, `Sessions`, or `DatasetRuns` via the `Score` object (see [Scores Data Model](./scores-data-model)).

This is achieved by ingesting scores via the ABV SDKs or API.

# Common Use Cases

* **Collecting user feedback**: Capture in-app feedback from users on application quality or performance via the Browser SDK.
* **Custom evaluation data pipeline**: Continuously monitor quality by fetching traces from ABV, running custom evaluations, and ingesting scores back.
* **Custom internal workflow tooling**: build custom internal tooling that helps you manage human-in-the-loop workflows. Ingest scores back into ABV, optionally following your custom schema by referencing a config.
* **Custom run-time evaluations**: e.g. track whether the generated SQL code actually worked, or if the structured output was valid JSON.

# Ingesting Scores via API/SDKs

You can add scores via the ABV SDKs or API. Scores can take one of three data types: **Numeric**, **Categorical** or **Boolean**.

If a score is ingested manually using a `trace_id` to link the score to a trace, it is not necessary to wait until the trace has been created. The score will show up in the scores table and will be linked to the trace once the trace with the same `trace_id` is created.

Here are examples by `Score` data types

## Python SDK

Install package

```bash theme={null}
pip install abvdev
```

Numeric

Numeric score values must be provided as float.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    name="correctness",
    value=0.9,
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    
    data_type="NUMERIC", # optional, inferred if not provided
    comment="Factually correct", # optional
)

# Method 2: Score current span/generation (within context)
with abv.start_as_current_span(name="my-operation") as span:
    # Score the current span
    span.score(
        name="correctness",
        value=0.9,
        data_type="NUMERIC",
        comment="Factually correct"
    )

    # Score the trace
    span.score_trace(
        name="overall_quality",
        value=0.95,
        data_type="NUMERIC"
    )


# Method 3: Score via the current context
with abv.start_as_current_span(name="my-operation"):
    # Score the current span
    abv.score_current_span(
        name="correctness",
        value=0.9,
        data_type="NUMERIC",
        comment="Factually correct"
    )

    # Score the trace
    abv.score_current_trace(
        name="overall_quality",
        value=0.95,
        data_type="NUMERIC"
    )
```

### Categorical

Categorical score values must be provided as strings.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    name="accuracy",
    value="partially correct",
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    data_type="CATEGORICAL", # optional, inferred if not provided
    comment="Some factual errors", # optional
)

# Method 2: Score current span/generation (within context)
with abv.start_as_current_span(name="my-operation") as span:
    # Score the current span
    span.score(
        name="accuracy",
        value="partially correct",
        data_type="CATEGORICAL",
        comment="Some factual errors"
    )

    # Score the trace
    span.score_trace(
        name="overall_quality",
        value="partially correct",
        data_type="CATEGORICAL"
    )

# Method 3: Score via the current context
with abv.start_as_current_span(name="my-operation"):
    # Score the current span
    abv.score_current_span(
        name="accuracy",
        value="partially correct",
        data_type="CATEGORICAL",
        comment="Some factual errors"
    )

    # Score the trace
    abv.score_current_trace(
        name="overall_quality",
        value="partially correct",
        data_type="CATEGORICAL"
    )
```

### Boolean

Boolean scores must be provided as a float. The value's string equivalent will be automatically populated and is accessible on read.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    name="helpfulness",
    value=0, # 0 or 1
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    data_type="BOOLEAN", # required, numeric values 
    #without data type would be inferred as NUMERIC
    comment="Incorrect answer", # optional
)

# Method 2: Score current span/generation (within context)
with abv.start_as_current_span(name="my-operation") as span:
    # Score the current span
    span.score(
        name="helpfulness",
        value=1, # 0 or 1
        data_type="BOOLEAN",
        comment="Very helpful response"
    )

    # Score the trace
    span.score_trace(
        name="overall_quality",
        value=1, # 0 or 1
        data_type="BOOLEAN"
    )
# Method 3: Score via the current context
with abv.start_as_current_span(name="my-operation"):
    # Score the current span
    abv.score_current_span(
        name="helpfulness",
        value=1, # 0 or 1
        data_type="BOOLEAN",
        comment="Very helpful response"
    )

    # Score the trace
    abv.score_current_trace(
        name="overall_quality",
        value=1, # 0 or 1
        data_type="BOOLEAN"
    )
```

## JS/TS SDK

```bash theme={null}
npm i @abvdev/client 
```

**Environment variables**

Add your ABV credentials as environment variables, e.g. use .env file and dotenv package to load variable values.

```bash theme={null}
npm install dotenv
```

```bash title=".env" theme={null}
ABV_API_KEY="sk-abv-..."
ABV_BASEURL="https://app.abv.dev" # US region
# ABV_BASEURL="https://eu.app.abv.dev" # EU region
```

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
 
const abv = new ABVClient();
```

**alternatively use Constructor parameters**

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
 
const abv = new ABVClient({
  apiKey: "sk-abv-...",
  baseUrl: "https://app.abv.dev", // US region
  // baseUrl: "https://eu.app.abv.dev", // EU region
});
```

### Numeric

Numeric score values must be provided as float.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();
 
const abv = new ABVClient();
 
abv.score.create({
  id: "unique_id", // optional, can be used as an idempotency
  // key to update the score subsequently
  traceId: "target_trace_id_here",
  observationId: "target_observation_id_here", // optional
  name: "correctness",
  value: 0.9,
  dataType: "NUMERIC", // optional, inferred if not provided
  comment: "Factually correct", // optional
});
 
async function main() {
  // Flush the scores in short-lived environments
  await abv.flush();
}

main();
```

### Categorical

Categorical score values must be provided as strings.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();
 
const abv = new ABVClient();
 
abv.score.create({
  id: "unique_id", // optional, can be used 
  // as an idempotency key to update the score subsequently
  traceId: "target_trace_id_here",
  observationId: "target_observation_id_here", // optional
  name: "accuracy",
  value: "partially correct",
  dataType: "CATEGORICAL", // optional, inferred if not provided
  comment: "Factually correct", // optional
});
 
async function main() {
  // Flush the scores in short-lived environments
  await abv.flush();
}

main();
```

### Boolean

Boolean scores must be provided as a float. The value’s string equivalent will be automatically populated and is accessible on read. See API reference for more details on POST/GET scores endpoints.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();

const abv = new ABVClient();
 
abv.score.create({
  id: "unique_id", // optional, can be used as an 
  // idempotency key to update the score subsequently
  traceId: "target_trace_id_here",
  observationId: "target_observation_id_here", // optional
  name: "helpfulness",
  value: 0, // 0 or 1
  dataType: "BOOLEAN", // required, numeric values without
  // data type would be inferred as NUMERIC
  comment: "Incorrect answer", // optional
});
 
async function main() {
  // Flush the scores in short-lived environments
  await abv.flush();
}

main();
```

→ More details in [Python SDK docs ](/developer/sdks/python/overview) and [JS/TS SDK docs](/developer/sdks/js-ts/overview). See [API reference](/developer/platform/api-data-platform/public-api) for more details on POST/GET score configs endpoints.

## Preventing Duplicate Scores

By default, ABV allows for multiple scores of the same `name` on the same trace. This is useful if you'd like to track the evolution of a score over time or if e.g. you've received multiple user feedback scores on the same trace.

In some cases, you want to prevent this behavior or update an existing score. This can be achieved by creating an **idempotency key** on the score and add this as an `id` when creating the score, e.g. `<trace_id>-<score_name>`.

## Enforcing a Score Config

Score configs are helpful when you want to standardize your scores for future analysis.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/abv-2be93c70/images/evaluations-custom-scores-e25e946a.png" alt="" />

To enforce a score config, you can provide a `configId` when creating a score to reference a `ScoreConfig` that was previously created. `Score Configs` can be defined in the ABV UI or via our API. .

Whenever you provide a `ScoreConfig`, the score data will be validated against the config. The following rules apply:

* **Score Name**: Must equal the config's name
* **Score Data Type**: When provided, must match the config's data type
* **Score Value when Type is numeric**: Value must be within the min and max values defined in the config (if provided, min and max are optional and otherwise are assumed as -∞ and +∞ respectively)
* **Score Value when Type is categorical**: Value must map to one of the categories defined in the config
* **Score Value when Type is boolean**: Value must equal `0` or `1`

### Python SDK

**Numeric Scores**

When ingesting numeric scores, you can provide the value as a float. If you provide a configId, the score value will be validated against the config's numeric range, which might be defined by a minimum and/or maximum value.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    session_id="session_id_here", # optional, Id of the session the score relates to
    name="accuracy",
    value=0.9,
    comment="Factually correct", # optional
    score_id="unique_id", # optional, can be used 
    # as an idempotency key to update the score subsequently
    config_id="78545-6565-3453654-43543", # optional, 
    # to ensure that the score follows a specific min/max value range
    data_type="NUMERIC" # optional, possibly inferred
)

# Method 2: Score within context
with abv.start_as_current_span(name="my-operation") as span:
    span.score(
        name="accuracy",
        value=0.9,
        comment="Factually correct",
        config_id="78545-6565-3453654-43543",
        data_type="NUMERIC"
    )
```

**Categorical Scores**

Categorical scores are used to evaluate data that falls into specific categories. When ingesting categorical scores, you can provide the value as a string. If you provide a configId, the score value will be validated against the config’s categories.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    name="correctness",
    value="correct",
    comment="Factually correct", # optional
    score_id="unique_id", # optional, can be used as an idempotency 
    # key to update the score subsequently
    config_id="12345-6565-3453654-43543", # optional, to ensure that 
    # the score maps to a specific category defined in a score config
    data_type="CATEGORICAL" # optional, possibly inferred
)

# Method 2: Score within context
with abv.start_as_current_span(name="my-operation") as span:
    span.score(
        name="correctness",
        value="correct",
        comment="Factually correct",
        config_id="12345-6565-3453654-43543",
        data_type="CATEGORICAL"
    )
```

**Boolean Scores**

When ingesting boolean scores, you can provide the value as a float. If you provide a configId, the score’s name and config’s name must match as well as their data types.

```python theme={null}
from abvdev import ABV

abv = ABV(
    api_key="sk-abv-...", # your api key here
    host="https://app.abv.dev", # host="https://eu.app.abv.dev", for EU region
)

# Method 1: Score via low-level method
abv.create_score(
    trace_id="trace_id_here",
    observation_id="observation_id_here", # optional
    name="helpfulness",
    value=1,
    comment="Factually correct", # optional
    score_id="unique_id", # optional, can be used as an 
    # idempotency key to update the score subsequently
    config_id="93547-6565-3453654-43543", # optional, can 
    # be used to infer the score data type and validate the score value
    data_type="BOOLEAN" # optional, possibly inferred
)

# Method 2: Score within context
with abv.start_as_current_span(name="my-operation") as span:
    span.score(
        name="helpfulness",
        value=1,
        comment="Factually correct",
        config_id="93547-6565-3453654-43543",
        data_type="BOOLEAN"
    )
```

### **JS/TS SDK**

**Numeric Scores**

When ingesting numeric scores, you can provide the value as a float. If you provide a configId, the score value will be validated against the config's numeric range, which might be defined by a minimum and/or maximum value.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();
 
const abv = new ABVClient();
 
abv.score.create({
  traceId: "target_trace_id_here",
  observationId: "target_observation_id_here", // optional
  name: "accuracy",
  value: 7,
  comment: "Factually correct", // optional
  id: "unique_id", // optional, can be used as an 
  // idempotency key to update the score subsequently
  configId: "config-id-here", // optional, 
  // to ensure that the score follows a specific min/max value range
  dataType: "NUMERIC", // optional, possibly inferred
});
 
async function main() {
    // Flush the scores in short-lived environments
    await abv.flush();
}

main();
```

**Categorical Scores**

Categorical scores are used to evaluate data that falls into specific categories. When ingesting categorical scores, you can provide the value as a string. If you provide a configId, the score value will be validated against the config’s categories.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();
 
const abv = new ABVClient();
 
abv.score.create({
  id: "unique_id", // optional, can be used 
  // as an idempotency key to update the score subsequently
  traceId: "target_trace_id_here",
  observationId: "target_observation_id_here", // optional
  name: "accuracy",
  value: "partially correct",
  // key to update the score subsequently
  configId: "config-id-here", // optional, to ensure that 
  // a score maps to a specific category defined in a score config
  dataType: "CATEGORICAL", // optional, inferred if not provided
  comment: "Factually correct", // optional
});
 
async function main() {
    // Flush the scores in short-lived environments
    await abv.flush();
}

```

**Boolean Scores**

When ingesting boolean scores, you can provide the value as a float. If you provide a configId, the score’s name and config’s name must match as well as their data types.

```typescript theme={null}
import { ABVClient } from "@abvdev/client";
import dotenv from "dotenv";
dotenv.config();

const abv = new ABVClient();
 
abv.score.create({
  id: "unique_id", // optional, can be used as an 
  // idempotency key to update the score subsequently
  traceId: "cb35f468686ad95603029f404004d456",
  observationId: "f7145d410802f3fe", // optional
  name: "helpfulness",
  value: 0, // 0 or 1
  configId: "config-id-here", // optional, 
  // can be used to infer the score data type and validate the score value
  dataType: "BOOLEAN", // required, numeric values without
  // data type would be inferred as NUMERIC
  comment: "Incorrect answer", // optional
});
 
async function main() {
  // Flush the scores in short-lived environments
  await abv.flush();
}

main();
```

→ More details in [Python SDK docs ](/developer/sdks/python/overview) and [JS/TS SDK docs](/developer/sdks/js-ts/overview). See [API reference](/developer/platform/api-data-platform/public-api) for more details on POST/GET score configs endpoints.

## Inferred Score Properties

Certain score properties might be inferred based on your input:

* **If you don't provide a score data type** it will always be inferred. See tables below for details.
* **For boolean and categorical scores**, we will provide the score value in both numerical and string format where possible. The score value format that is not provided as input, i.e. the translated value is referred to as the inferred value in the tables below.
* **On read for boolean scores both** numerical and string representations of the score value will be returned, e.g. both 1 and True.
* **For categorical scores**, the string representation is always provided and a numerical mapping of the category will be produced only if a `ScoreConfig` was provided.

Detailed Examples:

### Numeric Scores

For example, let's assume you'd like to ingest a numeric score to measure **accuracy**. We have included a table of possible score ingestion scenarios below.

| Value   | Data Type | Config Id | Description                                                 | Inferred Data Type | Valid                            |
| ------- | --------- | --------- | ----------------------------------------------------------- | ------------------ | -------------------------------- |
| `0.9`   | `Null`    | `Null`    | Data type is inferred                                       | `NUMERIC`          | Yes                              |
| `0.9`   | `NUMERIC` | `Null`    | No properties inferred                                      |                    | Yes                              |
| `depth` | `NUMERIC` | `Null`    | Error: data type of value does not match provided data type |                    | No                               |
| `0.9`   | `NUMERIC` | `78545`   | No properties inferred                                      |                    | Conditional on config validation |
| `0.9`   | `Null`    | `78545`   | Data type inferred                                          | `NUMERIC`          | Conditional on config validation |
| `depth` | `NUMERIC` | `78545`   | Error: data type of value does not match provided data type |                    | No                               |

### Categorical Scores

For example, let’s assume you’d like to ingest a categorical score to measure **correctness**. We have included a table of possible score ingestion scenarios below.

| Value     | Data Type     | Config Id | Description                                                 | Inferred Data Type | Inferred Value representation       | Valid                            |
| --------- | ------------- | --------- | ----------------------------------------------------------- | ------------------ | ----------------------------------- | -------------------------------- |
| `correct` | `Null`        | `Null`    | Data type is inferred                                       | `CATEGORICAL`      |                                     | Yes                              |
| `correct` | `CATEGORICAL` | `Null`    | No properties inferred                                      |                    |                                     | Yes                              |
| `1`       | `CATEGORICAL` | `Null`    | Error: data type of value does not match provided data type |                    |                                     | No                               |
| `correct` | `CATEGORICAL` | `12345`   | Numeric value inferred                                      |                    | `4` numeric config category mapping | Conditional on config validation |
| `correct` | `NULL`        | `12345`   | Data type inferred                                          | `CATEGORICAL`      |                                     | Conditional on config validation |
| `1`       | `CATEGORICAL` | `12345`   | Error: data type of value does not match provided data type |                    |                                     | No                               |

### **Boolean Scores**

For example, let’s assume you’d like to ingest a boolean score to measure **helpfulness**. We have included a table of possible score ingestion scenarios below.

| Value   | Data Type | Config Id | Description                                                 | Inferred Data Type | Inferred Value representation | Valid                            |
| ------- | --------- | --------- | ----------------------------------------------------------- | ------------------ | ----------------------------- | -------------------------------- |
| `1`     | `BOOLEAN` | `Null`    | Value's string equivalent inferred                          |                    | `True`                        | Yes                              |
| `true`  | `BOOLEAN` | `Null`    | Error: data type of value does not match provided data type |                    |                               | No                               |
| `3`     | `BOOLEAN` | `Null`    | Error: boolean data type expects `0` or `1` as input value  |                    |                               | No                               |
| `0.9`   | `Null`    | `93547`   | Data type and value's string equivalent inferred            | `BOOLEAN`          | `True`                        | Conditional on config validation |
| `depth` | `BOOLEAN` | `93547`   | Error: data type of value does not match provided data type |                    |                               | No                               |

# Update Existing Scores via API/SDKs

When creating a score, you can provide an optional `id` parameter. This will update the score if it already exists within your project.

If you want to update a score without needing to fetch the list of existing scores from ABV, you can set your own `id` parameter as an idempotency key when initially creating the score.