update docs'

docs/my-website/docs/proxy_server.md

@@ -1,25 +1,113 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # OpenAI Proxy Server
 
 Use this to spin up a proxy api to translate openai api calls to any non-openai model (e.g. Huggingface, TogetherAI, Ollama, etc.)
 
 This works for async + streaming as well. 
 
-**Requirements** Make sure relevant keys are set in the local .env. [Provider List](https://docs.litellm.ai/docs/providers)
-## usage
+Works with **ALL MODELS** supported by LiteLLM. To see supported providers check out this list - [Provider List](https://docs.litellm.ai/docs/providers).
+
+**Requirements** Make sure relevant keys are set in the local .env. 
+## quick start
+Call Huggingface models through your OpenAI proxy.
+
+Run this in your CLI.
 ```shell 
-pip install litellm
+$ pip install litellm
+```
+```shell
+$ export HUGGINGFACE_API_KEY=your-api-key # [OPTIONAL]
+
+$ litellm --model huggingface/stabilityai/stablecode-instruct-alpha-3b
 ```
 
+This will host a local proxy api at: **http://0.0.0.0:8000**
+
+Other supported models:
+<Tabs>
+<TabItem value="anthropic" label="Anthropic">
+
 ```shell
-litellm --model <your-model-name>
+$ export ANTHROPIC_API_KEY=my-api-key
+$ litellm --model claude-instant-1
 ```
 
-This will host a local proxy api at : **http://localhost:8000**
+</TabItem>
+
+<TabItem value="together_ai" label="TogetherAI">
+
+```shell
+$ export TOGETHERAI_API_KEY=my-api-key
+$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k
+```
+
+</TabItem>
+
+<TabItem value="replicate" label="Replicate">
+
+```shell
+$ export REPLICATE_API_KEY=my-api-key
+$ litellm \
+  --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3
+```
+
+</TabItem>
+
+<TabItem value="petals" label="Petals">
+
+```shell
+$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf
+```
+
+</TabItem>
+
+<TabItem value="palm" label="Palm">
+
+```shell
+$ export PALM_API_KEY=my-palm-key
+$ litellm --model palm/chat-bison
+```
+
+</TabItem>
+
+<TabItem value="azure" label="Azure OpenAI">
+
+```shell
+$ export AZURE_API_KEY=my-api-key
+$ export AZURE_API_BASE=my-api-base
+$ export AZURE_API_VERSION=my-api-version
+
+$ litellm --model azure/my-deployment-id
+```
+
+</TabItem>
+
+<TabItem value="ai21" label="AI21">
+
+```shell
+$ export AI21_API_KEY=my-api-key
+$ litellm --model j2-light
+```
+
+</TabItem>
+
+<TabItem value="cohere" label="Cohere">
+
+```shell
+$ export COHERE_API_KEY=my-api-key
+$ litellm --model command-nightly
+```
+
+</TabItem>
+
+</Tabs>
 
 [**Jump to Code**](https://github.com/BerriAI/litellm/blob/fef4146396d5d87006259e00095a62e3900d6bb4/litellm/proxy.py#L36)
 
-## [Advanced] setting api base
-If your model is running locally or on a custom endpoint
+## setting api base
+For **local model** or model on **custom endpoint**
 
 Pass in the api_base as well
 
@@ -27,13 +115,120 @@ Pass in the api_base as well
 litellm --model huggingface/meta-llama/llama2 --api_base https://my-endpoint.huggingface.cloud
 ```
 
+<Tabs>
+<TabItem value="openai" label="OpenAI">
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["OPENAI_API_KEY"] = "sk-litellm-7_NPZhMGxY2GoHC59LgbDw" # [OPTIONAL] replace with your openai key
+
+response = completion(
+  model="gpt-3.5-turbo", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+</TabItem>
+<TabItem value="anthropic" label="Anthropic">
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["ANTHROPIC_API_KEY"] = "sk-litellm-7_NPZhMGxY2GoHC59LgbDw" # [OPTIONAL] replace with your openai key
+
+response = completion(
+  model="claude-2", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+</TabItem>
+
+<TabItem value="vertex" label="VertexAI">
+
+```python
+from litellm import completion
+import os
+
+# auth: run 'gcloud auth application-default'
+os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
+os.environ["VERTEX_LOCATION"] = "us-central1"
+
+response = completion(
+  model="chat-bison", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+</TabItem>
+
+<TabItem value="hugging" label="HuggingFace">
+
+```python
+from litellm import completion 
+import os
+
+os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 
+
+# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
+response = completion(
+  model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
+  messages=[{ "content": "Hello, how are you?","role": "user"}], 
+  api_base="https://my-endpoint.huggingface.cloud"
+)
+
+print(response)
+```
+
+</TabItem>
+
+<TabItem value="azure" label="Azure OpenAI">
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["AZURE_API_KEY"] = ""
+os.environ["AZURE_API_BASE"] = ""
+os.environ["AZURE_API_VERSION"] = ""
+
+# azure call
+response = completion(
+  "azure/<your_deployment_id>", 
+  messages = [{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+</TabItem>
+
+
+<TabItem value="ollama" label="Ollama">
+
+```python
+from litellm import completion
+
+response = completion(
+            model="ollama/llama2", 
+            messages = [{ "content": "Hello, how are you?","role": "user"}], 
+            api_base="http://localhost:11434"
+)
+```
+</TabItem>
+
+</Tabs>
+
 ## test it 
 
 ```curl 
 curl --location 'http://0.0.0.0:8000/chat/completions' \
 --header 'Content-Type: application/json' \
 --data '{
-  "model": "gpt-3.5-turbo",
   "messages": [
     {
       "role": "user", 
@@ -43,3 +238,42 @@ curl --location 'http://0.0.0.0:8000/chat/completions' \
 }'
 ```
 
+## tutorial - using with aider 
+[Aider](https://github.com/paul-gauthier/aider) is an AI pair programming in your terminal.
+
+But it only accepts OpenAI API Calls. 
+
+In this tutorial we'll use Aider with WizardCoder (hosted on HF Inference Endpoints).
+
+[NOTE]: To learn how to deploy a model on Huggingface 
+
+### Step 1: Install aider and litellm
+```shell 
+$ pip install aider-chat litellm
+```
+
+### Step 2: Spin up local proxy
+Save your huggingface api key in your local environment (can also do this via .env)
+
+```shell
+$ export HUGGINGFACE_API_KEY=my-huggingface-api-key
+```
+
+Point your local proxy to your model endpoint
+
+```shell 
+$ litellm \
+  --model huggingface/WizardLM/WizardCoder-Python-34B-V1.0 \
+  --api_base https://my-endpoint.huggingface.com
+```
+This will host a local proxy api at: **http://0.0.0.0:8000**
+
+### Step 3: Replace openai api base in Aider
+Aider lets you set the openai api base. So lets point it to our proxy instead. 
+
+```shell
+$ aider --openai-api-base http://0.0.0.0:8000
+```
+
+
+

litellm/proxy/proxy_cli.py

@@ -7,8 +7,7 @@ load_dotenv()
 @click.option('--api_base', default=None, help='API base URL.')
 @click.option('--model', required=True, help='The model name to pass to litellm expects') 
 def run_server(port, api_base, model):
-    # from .proxy_server import app, initialize
-    from proxy_server import app, initialize
+    from .proxy_server import app, initialize
     initialize(model, api_base)
     try:
         import uvicorn

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "0.1.793"
+version = "0.1.794"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT License"