Fix 2

README.md

@@ -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"

docs/my-website/docs/getting_started.md

@@ -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"] = ""
 

docs/my-website/docs/index.md

@@ -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"

docs/my-website/docs/observability/helicone_integration.md

@@ -1,4 +1,4 @@
-# Helicone Tutorial 
+# 🧠 Helicone - OSS LLM Observability Platform
 
 :::tip
 
@@ -7,47 +7,52 @@ 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. 
+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:
 
-### 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
 
 ## set env variables
-os.environ["HELICONE_API_KEY"] = "your-helicone-key" 
+os.environ["HELICONE_API_KEY"] = "your-helicone-key"
 os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", ""
 
 # set callbacks
 litellm.success_callback=["helicone"]
 
 #openai call
-response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) 
+response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
 
 #cohere call
-response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}]) 
+response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}])
 ```
 
 ### 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 - 
+If you want to use Helicone to proxy your OpenAI/Azure requests, then you can -
 
-- Set helicone as your base url via: `litellm.api_url` 
+- Set helicone as your base url via: `litellm.api_url`
-- Pass in helicone request headers via: `litellm.headers` 
+- 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

docs/my-website/sidebars.js

@@ -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`,
       ],

docs/my-website/src/pages/index.md

@@ -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"