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docs(routing.md): refactor docs to show how to use pre-call checks and fallback across model groups

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Krrish Dholakia 2024-04-01 11:21:27 -07:00
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71
docs/my-website/docs/proxy/load_balancing.md
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@ -1,4 +1,4 @@
# Load Balancing - Config Setup
# Multiple Instances
Load balance multiple instances of the same model
The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput**
@ -10,75 +10,6 @@ For more details on routing strategies / params, see [Routing](../routing.md)
:::
## Quick Start - Load Balancing
### Step 1 - Set deployments on config
**Example config below**. Here requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/<your-deployment-name>
api_base: <your-azure-endpoint>
api_key: <your-azure-api-key>
rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-small-ca
api_base: https://my-endpoint-canada-berri992.openai.azure.com/
api_key: <your-azure-api-key>
rpm: 6
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-large
api_base: https://openai-france-1234.openai.azure.com/
api_key: <your-azure-api-key>
rpm: 1440
```
### Step 2: Start Proxy with config
```shell
$ litellm --config /path/to/config.yaml
```
### Step 3: Use proxy - Call a model group [Load Balancing]
Curl Command
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
### Usage - Call a specific model deployment
If you want to call a specific model defined in the `config.yaml`, you can call the `litellm_params: model`
In this example it will call `azure/gpt-turbo-small-ca`. Defined in the config on Step 1
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "azure/gpt-turbo-small-ca",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
## Load Balancing using multiple litellm instances (Kubernetes, Auto Scaling)
LiteLLM Proxy supports sharing rpm/tpm shared across multiple litellm instances, pass `redis_host`, `redis_password` and `redis_port` to enable this. (LiteLLM will use Redis to track rpm/tpm usage )

215
docs/my-website/docs/proxy/reliability.md
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@ -2,7 +2,9 @@ import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Fallbacks, Retries, Timeouts, Cooldowns
# 🔥 Fallbacks, Retries, Timeouts, Load Balancing
Retry call with multiple instances of the same model.
If a call fails after num_retries, fall back to another model group.
@ -10,6 +12,77 @@ If the error is a context window exceeded error, fall back to a larger model gro
[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py)
## Quick Start - Load Balancing
### Step 1 - Set deployments on config
**Example config below**. Here requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/<your-deployment-name>
api_base: <your-azure-endpoint>
api_key: <your-azure-api-key>
rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-small-ca
api_base: https://my-endpoint-canada-berri992.openai.azure.com/
api_key: <your-azure-api-key>
rpm: 6
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-large
api_base: https://openai-france-1234.openai.azure.com/
api_key: <your-azure-api-key>
rpm: 1440
```
### Step 2: Start Proxy with config
```shell
$ litellm --config /path/to/config.yaml
```
### Step 3: Use proxy - Call a model group [Load Balancing]
Curl Command
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
### Usage - Call a specific model deployment
If you want to call a specific model defined in the `config.yaml`, you can call the `litellm_params: model`
In this example it will call `azure/gpt-turbo-small-ca`. Defined in the config on Step 1
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "azure/gpt-turbo-small-ca",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
## Fallbacks + Retries + Timeouts + Cooldowns
**Set via config**
```yaml
model_list:
@ -63,7 +136,143 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
'
```
## Custom Timeouts, Stream Timeouts - Per Model
## Advanced - Context Window Fallbacks
**Before call is made** check if a call is within model context window with **`enable_pre_call_checks: true`**.
[**See Code**](https://github.com/BerriAI/litellm/blob/c9e6b05cfb20dfb17272218e2555d6b496c47f6f/litellm/router.py#L2163)
**1. Setup config**
For azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with azure/.
<Tabs>
<TabItem value="same-group" label="Same Group">
Filter older instances of a model (e.g. gpt-3.5-turbo) with smaller context windows
```yaml
router_settings:
enable_pre_call_checks: true # 1. Enable pre-call checks
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo-1106
api_key: os.environ/OPENAI_API_KEY
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000
```
**3. Test it!**
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
text = "What is the meaning of 42?" * 5000
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{"role": "system", "content": text},
{"role": "user", "content": "Who was Alexander?"},
],
)
print(response)
```
</TabItem>
<TabItem value="different-group" label="Context Window Fallbacks (Different Groups)">
Fallback to larger models if current model is too small.
```yaml
router_settings:
enable_pre_call_checks: true # 1. Enable pre-call checks
model_list:
- model_name: gpt-3.5-turbo-small
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL
- model_name: gpt-3.5-turbo-large
litellm_params:
model: gpt-3.5-turbo-1106
api_key: os.environ/OPENAI_API_KEY
- model_name: claude-opus
litellm_params:
model: claude-3-opus-20240229
api_key: os.environ/ANTHROPIC_API_KEY
litellm_settings:
context_window_fallbacks: [{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}]
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000
```
**3. Test it!**
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
text = "What is the meaning of 42?" * 5000
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{"role": "system", "content": text},
{"role": "user", "content": "Who was Alexander?"},
],
)
print(response)
```
</TabItem>
</Tabs>
## Advanced - Custom Timeouts, Stream Timeouts - Per Model
For each model you can set `timeout` & `stream_timeout` under `litellm_params`
```yaml
model_list:
@ -92,7 +301,7 @@ $ litellm --config /path/to/config.yaml
```
## Setting Dynamic Timeouts - Per Request
## Advanced - Setting Dynamic Timeouts - Per Request
LiteLLM Proxy supports setting a `timeout` per request

108
docs/my-website/docs/routing.md
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@ -567,10 +567,14 @@ from litellm import Router
router = Router(model_list=model_list, enable_pre_call_checks=True) # 👈 Set to True
```
**2. (Azure-only) Set base model**
**2. Set Model List**
For azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with `azure/`.
<Tabs>
<TabItem value="same-group" label="Same Group">
```python
model_list = [
{
@ -582,7 +586,7 @@ model_list = [
"api_base": os.getenv("AZURE_API_BASE"),
},
"model_info": {
"base_model": "azure/gpt-35-turbo", # 👈 SET BASE MODEL
"base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL
}
},
{
@ -593,8 +597,51 @@ model_list = [
},
},
]
router = Router(model_list=model_list, enable_pre_call_checks=True)
```
</TabItem>
<TabItem value="different-group" label="Context Window Fallbacks (Different Groups)">
```python
model_list = [
{
"model_name": "gpt-3.5-turbo-small", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
},
"model_info": {
"base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL
}
},
{
"model_name": "gpt-3.5-turbo-large", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo-1106",
"api_key": os.getenv("OPENAI_API_KEY"),
},
},
{
"model_name": "claude-opus",
"litellm_params": { call
"model": "claude-3-opus-20240229",
"api_key": os.getenv("ANTHROPIC_API_KEY"),
},
},
]
router = Router(model_list=model_list, enable_pre_call_checks=True, context_window_fallbacks=[{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}])
```
</TabItem>
</Tabs>
**3. Test it!**
```python
@ -646,60 +693,9 @@ print(f"response: {response}")
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Setup config**
For azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with azure/.
```yaml
router_settings:
enable_pre_call_checks: true # 1. Enable pre-call checks
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo-1106
api_key: os.environ/OPENAI_API_KEY
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000
```
**3. Test it!**
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
text = "What is the meaning of 42?" * 5000
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{"role": "system", "content": text},
{"role": "user", "content": "Who was Alexander?"},
],
)
print(response)
```
:::info
Go [here](./proxy/reliability.md#advanced---context-window-fallbacks) for how to do this on the proxy
:::
</TabItem>
</Tabs>

15
docs/my-website/sidebars.js
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@ -31,24 +31,25 @@ const sidebars = {
"proxy/quick_start",
"proxy/deploy",
"proxy/prod",
"proxy/configs",
{
type: "link",
label: "📖 All Endpoints",
label: "📖 All Endpoints (Swagger)",
href: "https://litellm-api.up.railway.app/",
},
"proxy/enterprise",
"proxy/user_keys",
"proxy/virtual_keys",
"proxy/configs",
"proxy/reliability",
"proxy/users",
"proxy/user_keys",
"proxy/enterprise",
"proxy/virtual_keys",
"proxy/team_based_routing",
"proxy/ui",
"proxy/cost_tracking",
"proxy/token_auth",
{
type: "category",
label: "🔥 Load Balancing",
items: ["proxy/load_balancing", "proxy/reliability"],
label: "Extra Load Balancing",
items: ["proxy/load_balancing"],
},
"proxy/model_management",
"proxy/health",

2
litellm/router.py
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@ -2170,7 +2170,7 @@ class Router:
Filter out model in model group, if:
- model context window < message length
- function call and model doesn't support function calling
- [TODO] function call and model doesn't support function calling
"""
verbose_router_logger.debug(
f"Starting Pre-call checks for deployments in model={model}"