I've been using Langfuse to track my OpenAI calls for a few months now, and it's been fantastic for visibility into latency, costs, and token usage. Now I'm starting to integrate other providers—specifically Google Gemini and Cohere—for some specific workloads where they make more sense.
I checked the docs and saw that Langfuse supports various SDKs and the OpenAI integration is very smooth. But for these other providers, what's the best practice? My stack is primarily Python, and I'm using the official client libraries from Google AI and Cohere.
Do I need to use the generic Langfuse SDK and manually instrument each call? Or is there a more integrated way, perhaps using the Langfuse OpenAI wrapper as a base? I'm particularly interested in tracking the same metrics: generation details, latency, and of course, cost (though I know cost calculation might need manual mapping).
Here's a snippet of how I'm currently calling Gemini, for example:
```python
import google.generativeai as genai
genai.configure(api_key="...")
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Explain quantum computing")
```
Should I wrap this in a `langfuse.trace()`? Any examples or patterns from the community would be super helpful. I also want to make sure the traces connect properly within my larger application traces.
-- Amy
Cloud cost nerd. No, I don't use Reserved Instances.
Yes, you'll need to instrument each call manually. The Langfuse OpenAI wrapper won't work.
Wrap your calls in `langfuse.span()` or `langfuse.generation()` inside a trace. Capture the raw request and response, then map the token counts to the standard `input`, `output`, and `total` fields.
For cost, you'll have to calculate it yourself. You can attach it as a span `input_cost` and `output_cost` or use the `total_cost` field. Set up your own mapping from model name to your provider's pricing sheet.
Your snippet becomes:
```python
with langfuse.trace(name="gemini-call"):
genai.configure(api_key="...")
model = genai.GenerativeModel('gemini-pro')
with langfuse.generation(
name="gemini-pro",
input="Explain quantum computing",
model="gemini-pro"
):
response = model.generate_content("Explain quantum computing")
# Parse response for token counts and set them here
```
It's more boilerplate, but it's the only way to get the same metrics. Keep your cost mapping in a config file.
cost per transaction is the only metric
Yes, that snippet gets you started, but you'll hit issues parsing token counts. Google's Gemini doesn't return them in the standard response, you have to use the `count_tokens` method first. That's an extra API call per generation, so factor in the latency.
Also, for Cohere, the token counts are in `response.meta` which is easier. Your cost mapping config should also handle versioned model names, like `command-r-plus-08-2024`.
One more tip: wrap your provider client calls in a small utility function. That way you can centralize the Langfuse instrumentation and cost calculation without cluttering your business logic.
sub-100ms or bust
That's exactly what I did. I ended up creating a shared wrapper function to handle the instrumentation for all providers. For Gemini, the extra count_tokens call does add overhead, so you might consider logging token counts separately in a lower-volume trace if latency is critical.
How are you planning to map costs? I found that keeping a simple dictionary model -> (input_cost_per_token, output_cost_per_token) in a config file works, but it needs regular updates when providers change pricing.
Benchmarking my way to better decisions
Yes, you'll need to manually instrument. The OpenAI wrapper won't help you here. Your snippet is the exact place to start.
Wrap it in a `langfuse.trace()`, but you need a `generation()` inside it to capture the specifics. The key problem you'll immediately face is that Gemini doesn't return token usage in the `generate_content` response. You have to make a separate `count_tokens` API call, which doubles your latency for that operation. You'll need to decide if tracking token counts is worth that penalty for your workload.
For cost, you're right about manual mapping. You'll need to maintain your own lookup table from model name to the provider's current pricing, then calculate and attach `input_cost`, `output_cost`, and `total_cost` to the generation. It's a maintenance burden, but it's the only way until Langfuse or the providers offer a native integration.
Benchmarks or bust
That latency penalty for the extra Gemini API call is a real concern. If you're doing high volume, maybe track token usage in a sample of calls instead of every single one? I haven't seen a good rule of thumb for that sampling rate.
Maintaining the cost mapping file sounds like it could get messy. Does anyone know if there's a community-maintained list for this, or are we all just building our own spreadsheets?
Great starting snippet! Since you're already using the official Gemini library, you'll indeed wrap it manually. A `langfuse.trace()` is perfect for the overall operation, and you'll want a `generation()` inside to capture the specific call details.
But heads up: that `generate_content` response won't have token counts. To get them, you need a separate `model.count_tokens()` call before your generation, which adds latency and cost. For some workloads, I just log the prompt tokens and skip counting the output for speed.
For cost tracking, I keep a simple YAML config mapping model names to input/output prices. It's a bit manual, but you can hook it into a cron job that checks the provider pricing pages weekly. Have you thought about sampling token counts for high-volume calls instead of doing it every time?
Try everything, keep what works.
Yeah, that extra API call for Gemini token counts is a real trade-off. For a recent project, I ended up skipping it entirely for latency-critical paths and just logging the model name and prompt length as a rough proxy.
> until Langfuse or the providers offer a native integration.
Has anyone tried pinging Langfuse support about this? It feels like a common enough pain point that they might have a roadmap item for it.