Merge pull request #4612 from colegottdank/main

Update Helicone Docs

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_API_KEY"] = "your-helicone-auth-key"
 os.environ["LANGFUSE_PUBLIC_KEY"] = ""
 os.environ["LANGFUSE_SECRET_KEY"] = ""
 os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
@@ -127,7 +128,7 @@ os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["lunary", "langfuse", "athina"] # log input/output to lunary, langfuse, supabase, athina etc
+litellm.success_callback = ["lunary", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

docs/my-website/docs/getting_started.md

@@ -87,13 +87,14 @@ 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"] = ""
 
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["lunary", "langfuse"] # log input/output to langfuse, lunary, supabase
+litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to langfuse, lunary, supabase, helicone
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

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"
@@ -317,7 +318,7 @@ os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["lunary", "langfuse"] # log input/output to lunary, langfuse, supabase
+litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to lunary, langfuse, supabase, helicone
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

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

@@ -1,64 +1,170 @@
-# Helicone Tutorial 
+# 🧠 Helicone - OSS LLM Observability Platform
 
 :::tip
 
-This is community maintained, Please make an issue if you run into a bug
+This is community maintained. Please make an issue if you run into a bug:
 https://github.com/BerriAI/litellm
 
 :::
 
+[Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more.
 
-[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.
+## Using Helicone with LiteLLM
 
-## Use Helicone to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
+LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses.
-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. 
+### Supported LLM Providers
+
+Helicone can log requests across [various LLM providers](https://docs.helicone.ai/getting-started/quick-start), including:
+
+- OpenAI
+- Azure
+- Anthropic
+- Gemini
+- Groq
+- Cohere
+- Replicate
+- And more
+
+### Integration Methods
+
+There are two main approaches to integrate Helicone with LiteLLM:
+
+1. Using callbacks
+2. Using Helicone as a proxy
+
+Let's explore each method in detail.
 
 ### Approach 1: Use Callbacks
-Use just 1 line of code, to instantly log your responses **across all providers** with helicone: 
+
+Use just 1 line of code to instantly log your responses **across all providers** with Helicone:
+
 ```python
-litellm.success_callback=["helicone"]
+litellm.success_callback = ["helicone"]
 ```
 
-Complete code
-```python
-from litellm import completion
-
-## set env variables
-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"}]) 
-
-#cohere call
-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 - 
-
-- Set helicone as your base url via: `litellm.api_url` 
-- Pass in helicone request headers via: `litellm.headers` 
-
 Complete Code
+
 ```python
-import litellm
+import os
 from litellm import completion
 
-litellm.api_base = "https://oai.hconeai.com/v1"
+## Set env variables
-litellm.headers = {"Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}"}
+os.environ["HELICONE_API_KEY"] = "your-helicone-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-key"
 
-response = litellm.completion(
+# Set callbacks
-    model="gpt-3.5-turbo",
+litellm.success_callback = ["helicone"]
-    messages=[{"role": "user", "content": "how does a court case get to the Supreme Court?"}]
+
+# OpenAI call
+response = completion(
+    model="gpt-4o",
+    messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}],
 )
 
 print(response)
 ```
+
+### Approach 2: Use Helicone as a proxy
+
+Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more.
+
+To use Helicone as a proxy for your LLM requests:
+
+1. Set Helicone as your base URL via: litellm.api_base
+2. Pass in Helicone request headers via: litellm.headers
+
+Complete Code:
+
+```python
+import os
+import litellm
+from litellm import completion
+
+litellm.api_base = "https://oai.hconeai.com/v1"
+litellm.headers = {
+    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",  # Authenticate to send requests to Helicone API
+}
+
+response = litellm.completion(
+    model="gpt-3.5-turbo",
+    messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}]
+)
+
+print(response)
+```
+
+### Advanced Usage
+
+You can add custom metadata and properties to your requests using Helicone headers. Here are some examples:
+
+```python
+litellm.headers = {
+    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",  # Authenticate to send requests to Helicone API
+    "Helicone-User-Id": "user-abc",  # Specify the user making the request
+    "Helicone-Property-App": "web",  # Custom property to add additional information
+    "Helicone-Property-Custom": "any-value",  # Add any custom property
+    "Helicone-Prompt-Id": "prompt-supreme-court",  # Assign an ID to associate this prompt with future versions
+    "Helicone-Cache-Enabled": "true",  # Enable caching of responses
+    "Cache-Control": "max-age=3600",  # Set cache limit to 1 hour
+    "Helicone-RateLimit-Policy": "10;w=60;s=user",  # Set rate limit policy
+    "Helicone-Retry-Enabled": "true",  # Enable retry mechanism
+    "helicone-retry-num": "3",  # Set number of retries
+    "helicone-retry-factor": "2",  # Set exponential backoff factor
+    "Helicone-Model-Override": "gpt-3.5-turbo-0613",  # Override the model used for cost calculation
+    "Helicone-Session-Id": "session-abc-123",  # Set session ID for tracking
+    "Helicone-Session-Path": "parent-trace/child-trace",  # Set session path for hierarchical tracking
+    "Helicone-Omit-Response": "false",  # Include response in logging (default behavior)
+    "Helicone-Omit-Request": "false",  # Include request in logging (default behavior)
+    "Helicone-LLM-Security-Enabled": "true",  # Enable LLM security features
+    "Helicone-Moderations-Enabled": "true",  # Enable content moderation
+    "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]',  # Set fallback models
+}
+```
+
+### Caching and Rate Limiting
+
+Enable caching and set up rate limiting policies:
+
+```python
+litellm.headers = {
+    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",  # Authenticate to send requests to Helicone API
+    "Helicone-Cache-Enabled": "true",  # Enable caching of responses
+    "Cache-Control": "max-age=3600",  # Set cache limit to 1 hour
+    "Helicone-RateLimit-Policy": "100;w=3600;s=user",  # Set rate limit policy
+}
+```
+
+### Session Tracking and Tracing
+
+Track multi-step and agentic LLM interactions using session IDs and paths:
+
+```python
+litellm.headers = {
+    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",  # Authenticate to send requests to Helicone API
+    "Helicone-Session-Id": "session-abc-123",  # The session ID you want to track
+    "Helicone-Session-Path": "parent-trace/child-trace",  # The path of the session
+}
+```
+
+- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together.
+- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace.
+
+By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows.
+
+### Retry and Fallback Mechanisms
+
+Set up retry mechanisms and fallback options:
+
+```python
+litellm.headers = {
+    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",  # Authenticate to send requests to Helicone API
+    "Helicone-Retry-Enabled": "true",  # Enable retry mechanism
+    "helicone-retry-num": "3",  # Set number of retries
+    "helicone-retry-factor": "2",  # Set exponential backoff factor
+    "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]',  # Set fallback models
+}
+```
+
+> **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start).
+> By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM.

docs/my-website/sidebars.js

@@ -188,6 +188,7 @@ const sidebars = {
       type: "category",
       label: "Logging & Observability",
       items: [
+        "observability/helicone_integration",
         "observability/langfuse_integration",
         "observability/logfire_integration",
         "debugging/local_debugging",
@@ -204,7 +205,6 @@ 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"