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

# Monitoring Dashboard

> Track retrieval quality trends over time

Make your retrieval performance measurable. Set SLOs, track trends over time, and catch regressions before they impact users, all with low-overhead sampling.

> **Prerequisites:** You've completed the [Quickstart](/quickstart) and understand the [Context & Event Schema](/context-and-event-schema).

***

## What You Get

* **Evaluator-defined Recall** on **unlabeled** traffic (flag queries with `recall < 1.0`)
* **Precision (proxy)**: Ratio of supporting passages to total context (detects context bloat)
* **F1 & nDCG** derived from recall + precision
* **P95 Latency** tracking for evaluation time
* **Trend charts** to catch drift over time
* **Environment filtering** to compare prod vs staging vs dev
* **Evaluator Accuracy** (when ground truth is provided)

***

## Enable Monitoring

Control volume with `sample_rate` to manage costs.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from seer import SeerClient
    import os

    client = SeerClient()

    def retrieve(query: str) -> list[dict]:
        # your real retriever here
        return [{"text": "...", "score": 0.9}]

    def handle_request(query: str):
        context = retrieve(query)

        client.log(
            task=query,
            context=context,
            metadata={
                "env": os.getenv("APP_ENV", "prod"),
                "feature_flag": "retrieval-v1",
            },
            sample_rate=0.10,  # 10% of calls are evaluated
        )
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    import { SeerClient } from '@seer/sdk';

    const client = new SeerClient();

    function retrieve(query: string) {
        // your real retriever here
        return [{ text: "...", score: 0.9 }];
    }

    function handleRequest(query: string) {
        const context = retrieve(query);

        client.log({
            task: query,
            context: context,
            metadata: {
                env: process.env.APP_ENV ?? "prod",
                feature_flag: "retrieval-v1",
            },
            sample_rate: 0.10,  // 10% of calls are evaluated
        });
    }
    ```
  </Tab>
</Tabs>

### Sampling Guidance

| Use Case                   | Recommended `sample_rate`     |
| -------------------------- | ----------------------------- |
| **Change testing**         | `1.0` (100%) for test queries |
| **High-volume production** | `0.05` - `0.10` (5-10%)       |
| **Low-volume or critical** | `0.25` - `0.50` (25-50%)      |
| **Debugging**              | `1.0` temporarily             |

***

## Smart Sampling with Decorator

Use the decorator with dynamic sampling based on metadata:

```python theme={null}
from seer import seer_trace
import os

@seer_trace(
    task_arg="query",
    context_from_return=True,
    metadata={"env": os.getenv("APP_ENV", "prod")},
    sample_rate=0.10,  # 10% sampling
)
def retrieve(query: str) -> list[dict]:
    return [{"text": "...", "score": 0.9}]

# Logs automatically when called
results = retrieve("user question...")
```

***

## The Monitoring Dashboard

<img src="https://mintcdn.com/seersearchinc/cM1syNm7U1TObVnY/images/monitoring/dashboard-overview.png?fit=max&auto=format&n=cM1syNm7U1TObVnY&q=85&s=edc8451561530a1290f138ef8cc23253" alt="Monitoring Dashboard" width="2048" height="1270" data-path="images/monitoring/dashboard-overview.png" />

### KPI Cards

At the top, you'll see summary metrics for the selected period:

| Metric                 | Description                                                |
| ---------------------- | ---------------------------------------------------------- |
| **Recall**             | Average fraction of requirements covered                   |
| **Precision**          | Average fraction of supporting documents                   |
| **F1**                 | Harmonic mean of recall and precision                      |
| **nDCG**               | Ranking quality (if scores provided)                       |
| **P95 Latency**        | 95th percentile evaluation time                            |
| **Evaluator Accuracy** | F1 against your ground truth (shown when gold data exists) |

Each card shows **delta vs. previous period** (e.g., +2.1% week-over-week).

### Trend Charts

The trend chart has three tabs:

| Tab           | Metrics                                             | Scale        |
| ------------- | --------------------------------------------------- | ------------ |
| **Quality**   | Recall, Precision, F1, nDCG, Subquery Effectiveness | 0-100%       |
| **Latency**   | P50, P95 evaluation latency                         | milliseconds |
| **Structure** | Trace Depth (multi-hop only)                        | count        |

Toggle individual metrics on/off to focus your analysis.

#### Latency Tab

<img src="https://mintcdn.com/seersearchinc/cM1syNm7U1TObVnY/images/monitoring/latency-tab.png?fit=max&auto=format&n=cM1syNm7U1TObVnY&q=85&s=2da88b2414766267d0834758d0464fa6" alt="Latency Tab" width="2048" height="882" data-path="images/monitoring/latency-tab.png" />

Track evaluation latency percentiles over time. Useful for detecting slow-downs or backend issues.

#### Structure Tab

<img src="https://mintcdn.com/seersearchinc/cM1syNm7U1TObVnY/images/monitoring/structure-tab.png?fit=max&auto=format&n=cM1syNm7U1TObVnY&q=85&s=611e20948c3c327aaee8b7000fa17d5e" alt="Structure Tab" width="2048" height="886" data-path="images/monitoring/structure-tab.png" />

For multi-hop retrieval, track trace depth to understand query complexity trends.

### Filtering

Filter your view using:

* **Environment**: Select a specific env (prod, staging, dev)
* **Period**: 24 hours, 7 days, or 30 days

<Tip title="Coming Soon">
  We're adding support for filtering by `feature_flag` and custom metadata fields directly in the Monitoring dashboard. For now, use the [A/B Testing](/change-testing) page to compare feature flags.
</Tip>

***

## Cost Management

* **Sampling** keeps evaluation cost predictable. Start at `0.05` and tune up if needed.
* **SDK batching** minimizes request overhead (events are queued and sent in batches).
* **Async mode** (default) — logging never blocks your request path.

### Estimated Costs

| Monthly Evaluations | Seer Cost       |
| ------------------- | --------------- |
| 100k                | \~\$16-20       |
| 1M                  | \~\$160-200     |
| 10M                 | \~\$1,600-2,000 |

***

## Privacy Considerations

* **You control what you send.** If passages are sensitive, include only what's needed for evaluation.
* Use `metadata` to tag records with access boundaries (e.g., `collection`, `tenant_id`) for future filtering.
* Consider truncating or summarizing very long passages.

***

## Example: Tier-Based Sampling

```python theme={null}
from seer import SeerClient

client = SeerClient()

def handle_request(query: str, user_tier: str):
    context = retrieve(query)

    # Higher sampling for enterprise customers
    rate = 0.25 if user_tier == "enterprise" else 0.05

    client.log(
        task=query,
        context=context,
        metadata={
            "env": "prod",
            "user_tier": user_tier,
            "feature_flag": "retrieval-v1",
        },
        sample_rate=rate,
    )
```

***

## See Also

* [Quickstart](/quickstart)
* [Context & Event Schema](/context-and-event-schema)
* [Metrics](/metrics)
* [Change Testing (A/B)](/change-testing)
* [Alerting](/alerts)