This commit is contained in:
colegottdank 2024-07-08 13:56:53 -07:00
parent cb52c59481
commit a3d9e34b26
6 changed files with 29 additions and 13 deletions

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@ -120,6 +120,7 @@ from litellm import completion
## set env variables for logging tools
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_AUTH"] = "your-helicone-auth-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"

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@ -87,6 +87,7 @@ from litellm import completion
## set env variables for logging tools
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_API_KEY"] = "your-helicone-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""

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@ -310,6 +310,7 @@ LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone
from litellm import completion
## set env variables for logging tools
os.environ["HELICONE_API_KEY"] = "your-helicone-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"

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@ -1,4 +1,4 @@
# Helicone Tutorial
# 🧠 Helicone - OSS LLM Observability Platform
:::tip
@ -7,21 +7,24 @@ https://github.com/BerriAI/litellm
:::
[Helicone](https://helicone.ai/) is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
## Use Helicone to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses.
In this case, we want to log requests to Helicone when a request succeeds.
### Approach 1: Use Callbacks
Use just 1 line of code, to instantly log your responses **across all providers** with helicone:
```python
litellm.success_callback=["helicone"]
```
Complete code
```python
from litellm import completion
@ -40,6 +43,7 @@ response = completion(model="command-nightly", messages=[{"role": "user", "conte
```
### Approach 2: [OpenAI + Azure only] Use Helicone as a proxy
Helicone provides advanced functionality like caching, etc. Helicone currently supports this for Azure and OpenAI.
If you want to use Helicone to proxy your OpenAI/Azure requests, then you can -
@ -48,6 +52,7 @@ If you want to use Helicone to proxy your OpenAI/Azure requests, then you can -
- Pass in helicone request headers via: `litellm.headers`
Complete Code
```python
import litellm
from litellm import completion
@ -62,3 +67,10 @@ response = litellm.completion(
print(response)
```
### Group and visualize multi-step LLM interactions.
Track request flows across multiple traces and gain insights into complex AI workflows by adding only 2 simple headers.
- `Helicone-Session-Id` - The session id you want to track
- `Helicone-Session-Path` - The path of the session

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@ -186,6 +186,7 @@ const sidebars = {
type: "category",
label: "Logging & Observability",
items: [
"observability/helicone_integration",
"observability/langfuse_integration",
"observability/logfire_integration",
"debugging/local_debugging",
@ -202,7 +203,6 @@ const sidebars = {
"observability/athina_integration",
"observability/lunary_integration",
"observability/greenscale_integration",
"observability/helicone_integration",
"observability/supabase_integration",
`observability/telemetry`,
],

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@ -304,6 +304,7 @@ LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone
from litellm import completion
## set env variables for logging tools
os.environ["HELICONE_API_KEY"] = "your-helicone-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"