diff --git a/docs/my-website/docs/providers/azure_ai.md b/docs/my-website/docs/providers/azure_ai.md
index ed13c56641b..87b8041ef57 100644
--- a/docs/my-website/docs/providers/azure_ai.md
+++ b/docs/my-website/docs/providers/azure_ai.md
@@ -3,53 +3,155 @@ import TabItem from '@theme/TabItem';
# Azure AI Studio
-**Ensure the following:**
-1. The API Base passed ends in the `/v1/` prefix
- example:
- ```python
- api_base = "https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/"
- ```
+LiteLLM supports all models on Azure AI Studio
-2. The `model` passed is listed in [supported models](#supported-models). You **DO NOT** Need to pass your deployment name to litellm. Example `model=azure/Mistral-large-nmefg`
## Usage
+### ENV VAR
```python
-import litellm
-response = litellm.completion(
- model="azure/command-r-plus",
- api_base="/v1/"
- api_key="eskk******"
- messages=[{"role": "user", "content": "What is the meaning of life?"}],
+import os
+os.environ["AZURE_API_API_KEY"] = ""
+os.environ["AZURE_AI_API_BASE"] = ""
+```
+
+### Example Call
+
+```python
+from litellm import completion
+import os
+## set ENV variables
+os.environ["AZURE_API_API_KEY"] = "azure ai key"
+os.environ["AZURE_AI_API_BASE"] = "azure ai base url" # e.g.: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/
+
+# predibase llama-3 call
+response = completion(
+ model="azure_ai/command-r-plus",
+ messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```
-## Sample Usage - LiteLLM Proxy
-
1. Add models to your config.yaml
```yaml
model_list:
- - model_name: mistral
- litellm_params:
- model: azure/mistral-large-latest
- api_base: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/v1/
- api_key: JGbKodRcTp****
- model_name: command-r-plus
litellm_params:
- model: azure/command-r-plus
- api_key: os.environ/AZURE_COHERE_API_KEY
- api_base: os.environ/AZURE_COHERE_API_BASE
+ model: azure_ai/command-r-plus
+ api_key: os.environ/AZURE_AI_API_KEY
+ api_base: os.environ/AZURE_AI_API_BASE
```
+2. Start the proxy
+
+ ```bash
+ $ litellm --config /path/to/config.yaml --debug
+ ```
+
+3. Send Request to LiteLLM Proxy Server
+
+
+
+
+
+ ```python
+ import openai
+ client = openai.OpenAI(
+ api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys
+ base_url="http://0.0.0.0:4000" # litellm-proxy-base url
+ )
+
+ response = client.chat.completions.create(
+ model="command-r-plus",
+ messages = [
+ {
+ "role": "system",
+ "content": "Be a good human!"
+ },
+ {
+ "role": "user",
+ "content": "What do you know about earth?"
+ }
+ ]
+ )
+
+ print(response)
+ ```
+
+
+
+
+
+ ```shell
+ curl --location 'http://0.0.0.0:4000/chat/completions' \
+ --header 'Authorization: Bearer sk-1234' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "model": "command-r-plus",
+ "messages": [
+ {
+ "role": "system",
+ "content": "Be a good human!"
+ },
+ {
+ "role": "user",
+ "content": "What do you know about earth?"
+ }
+ ],
+ }'
+ ```
+
+
+
+
+
+
+
+
+
+## Passing additional params - max_tokens, temperature
+See all litellm.completion supported params [here](../completion/input.md#translated-openai-params)
+
+```python
+# !pip install litellm
+from litellm import completion
+import os
+## set ENV variables
+os.environ["AZURE_AI_API_KEY"] = "azure ai api key"
+os.environ["AZURE_AI_API_BASE"] = "azure ai api base"
+
+# command r plus call
+response = completion(
+ model="azure_ai/command-r-plus",
+ messages = [{ "content": "Hello, how are you?","role": "user"}],
+ max_tokens=20,
+ temperature=0.5
+)
+```
+
+**proxy**
+
+```yaml
+ model_list:
+ - model_name: command-r-plus
+ litellm_params:
+ model: azure_ai/command-r-plus
+ api_key: os.environ/AZURE_AI_API_KEY
+ api_base: os.environ/AZURE_AI_API_BASE
+ max_tokens: 20
+ temperature: 0.5
+```
+
+
+
2. Start the proxy
```bash
@@ -103,9 +205,6 @@ response = litellm.completion(
-
-
-
## Function Calling
@@ -115,8 +214,8 @@ response = litellm.completion(
from litellm import completion
# set env
-os.environ["AZURE_MISTRAL_API_KEY"] = "your-api-key"
-os.environ["AZURE_MISTRAL_API_BASE"] = "your-api-base"
+os.environ["AZURE_AI_API_KEY"] = "your-api-key"
+os.environ["AZURE_AI_API_BASE"] = "your-api-base"
tools = [
{
@@ -141,9 +240,7 @@ tools = [
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
response = completion(
- model="azure/mistral-large-latest",
- api_base=os.getenv("AZURE_MISTRAL_API_BASE")
- api_key=os.getenv("AZURE_MISTRAL_API_KEY")
+ model="azure_ai/mistral-large-latest",
messages=messages,
tools=tools,
tool_choice="auto",
@@ -206,10 +303,12 @@ curl http://0.0.0.0:4000/v1/chat/completions \
## Supported Models
+LiteLLM supports **ALL** azure ai models. Here's a few examples:
+
| Model Name | Function Call |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Cohere command-r-plus | `completion(model="azure/command-r-plus", messages)` |
-| Cohere ommand-r | `completion(model="azure/command-r", messages)` |
+| Cohere command-r | `completion(model="azure/command-r", messages)` |
| mistral-large-latest | `completion(model="azure/mistral-large-latest", messages)` |
diff --git a/docs/my-website/docs/providers/clarifai.md b/docs/my-website/docs/providers/clarifai.md
index 85ee8fa26a4..085ab8ed9eb 100644
--- a/docs/my-website/docs/providers/clarifai.md
+++ b/docs/my-website/docs/providers/clarifai.md
@@ -1,4 +1,4 @@
-# 🆕 Clarifai
+# Clarifai
Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai.
## Pre-Requisites
diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md
index 08a3e4f7630..24c7c40cff9 100644
--- a/docs/my-website/docs/providers/databricks.md
+++ b/docs/my-website/docs/providers/databricks.md
@@ -125,11 +125,12 @@ See all litellm.completion supported params [here](../completion/input.md#transl
from litellm import completion
import os
## set ENV variables
-os.environ["PREDIBASE_API_KEY"] = "predibase key"
+os.environ["DATABRICKS_API_KEY"] = "databricks key"
+os.environ["DATABRICKS_API_BASE"] = "databricks api base"
-# predibae llama-3 call
+# databricks dbrx call
response = completion(
- model="predibase/llama3-8b-instruct",
+ model="databricks/databricks-dbrx-instruct",
messages = [{ "content": "Hello, how are you?","role": "user"}],
max_tokens=20,
temperature=0.5