Enhance DSPy Evals With Langfuse For Better Dashboards

by SLV Team 55 views

Hey everyone! 👋 We're diving into how we can supercharge DSPy's evaluation capabilities by integrating it with Langfuse's awesome evaluation dashboards. This is all about making it easier to track and understand your evaluation results, especially when you're using optimizers like GEPA or MIPRO. Let's break down the plan, the benefits, and how we're going to get it done.

The Problem: Evals and the Need for Better Dashboards

Currently, DSPy::Evals logs evaluation results using events like DSPy.event('evals.example.complete', ...) and DSPy.event('evals.batch.complete', ...) (check out lib/dspy/evals.rb:641 and lib/dspy/evals.rb:653 if you're curious). These events show up as spans in Langfuse, which is super helpful for tracing what's going on. However, these spans don’t quite give us the full picture when it comes to evaluation dashboards. We want rollups, filtering, and a clear history of our eval metrics. Langfuse’s evaluation dashboards are designed to provide exactly that, but they need a specific format of data – score objects created through their Scores API (POST /scores). Think of it like this: spans are great for seeing individual steps, but dashboards are where we get the big-picture view and trends. This integration will make our lives easier, especially when optimizing our DSPy programs.

Why This Matters

Centralized evaluation dashboards are a game-changer. They let us easily see aggregate metrics, filter data to find specific issues, and track performance over time. This is super important when you’re using optimizers because you want to see how your changes are actually improving your program's performance. By getting our evaluation results into Langfuse’s dashboards, we're not just improving visibility; we’re aligning with the bigger goal of consolidating observability.

The Solution: Integrating DSPy::Evals with Langfuse Scores

So, how do we make this happen? We're going to create a Langfuse score exporter inside DSPy::Evals. Here's the gist:

  1. Hook into emit_example_observation/emit_batch_observation: We’ll modify these functions to enqueue score payloads when Langfuse keys are present. This means whenever an evaluation happens, we'll also send the relevant data to Langfuse. Think of it as an extra step that automatically sends your eval results to the right place.
  2. Use existing Langfuse config: We'll reuse the existing Langfuse authentication setup (because DSPy::Observability already takes care of the host and credentials). This keeps things simple and consistent. No need to set up anything new, just ensure your Langfuse keys are in place.
  3. Config Flags: We'll add some configuration flags so users can easily enable or disable Langfuse score export. By default, it will be turned off to prevent any unexpected API calls, but when you want to use the dashboards, you can easily switch it on.
  4. Map Evaluation Metrics: We will map the evaluation metrics to the right format.
    • Example Level: For individual examples, the name will be the metric name (e.g., eval.score), the value will be the normalized score, and the metadata will include things like pass/fail status, the dataset used, and the program's details.
    • Batch Level: For aggregated metrics (like score_avg or pass_rate), we'll either make separate score entries or add them as metadata. This lets us have a comprehensive view of the results.
  5. Docs Update: We'll update the documentation (docs/src/optimization/evaluation.md) to clearly explain how to enable the dashboards and what kind of data you can expect to see in Langfuse.
  6. Tests: We will add integration tests or VCR-backed specs (similar to the existing Langfuse spans tests) to confirm score payloads are emitted when the credentials are set. This helps ensure everything works correctly.

Open Questions We Need to Answer

  • Payloads and Rate Limits: What’s the minimal amount of data needed for scores.create? We need to know about traceId, any observation IDs, a timestamp, and any optional metadata. And of course, we need to understand any rate limits to make sure we don't accidentally get throttled.
  • Syncing Scores: Should we send scores for each example, for batches, or both? Sending example-level scores gives us richer dashboards, but it means more API calls. We need to find the right balance.
  • Trace IDs: How do we get the traceId for the evaluations? Can we reuse the current span context, or will callers need to pass a trace identifier? This is important for linking the scores back to the original traces.
  • Offline Mode/Retries: What happens when Langfuse isn't configured, or there are connection issues? We need a plan for retries and offline mode to make sure we don't lose any data.

Benefits of the Integration

Alright, let’s talk about why this is going to be so beneficial. First off, it dramatically enhances the observability of your DSPy programs. You'll gain a centralized view of your evaluation metrics, complete with filtering and historical trends, which is critical for understanding performance over time. This will give you much better insight into how your programs are doing. Next up, it streamlines the optimization process. With better dashboards, you’ll be able to quickly spot areas for improvement, track the impact of your changes, and iterate faster. Finally, this integration aligns with existing infrastructure. By using Langfuse, we benefit from the same infrastructure, which provides stability and reliability.

Step-by-Step Implementation

  1. Prototype a Langfuse score exporter inside DSPy::Evals: This involves hooking into the existing emit_example_observation and emit_batch_observation functions to enqueue score payloads when Langfuse keys are present. This ensures that the score payloads are created whenever the evaluation results are available. This will include the essential data for the Langfuse Scores API, such as trace IDs, metric names, and scores. This ensures that the evaluation results are sent to Langfuse for dashboarding.
  2. Add Configuration Flags: Introduce configuration flags that enable users to turn the Langfuse score export on or off. By default, this feature will be disabled to prevent unnecessary API writes. This configuration gives users control over whether they want to use the Langfuse integration and ensures that they can enable it only when needed.
  3. Map Evaluation Metrics: Establish a clear mapping between DSPy's evaluation metrics and Langfuse's score fields. This involves defining how example-level and batch-level metrics are translated into name, value, and metadata. Example-level scores would contain information such as the metric name and normalized score, which allows for detailed analysis of individual examples. Batch-level metrics, like average scores or pass rates, can be represented as separate score entries or metadata, providing aggregated insights.
  4. Update Documentation: Update the documentation to explain how to enable and use the Langfuse dashboards, including what data will be displayed in Langfuse. This ensures users know how to use the integration and understand the data they'll see in Langfuse.
  5. Add Integration Tests: Add integration tests or VCR-backed specs to verify that score payloads are correctly emitted when credentials are set. This includes testing that the right data is sent to Langfuse. This ensures that the integration is robust and reliable, providing confidence in its functionality.

Conclusion

Integrating DSPy::Evals with Langfuse’s evaluation dashboards is a big win for everyone. It will provide better visibility into your evaluation results and make optimizing your DSPy programs a lot easier. We're excited to get this done and see how it helps you all! Stay tuned for updates and let us know if you have any questions. Cheers!