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docs(routing.md): add content policy fallbacks to docs

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Krrish Dholakia
2024-06-14 21:47:51 -07:00
parent 89a43c872a
commit 31a7328279
1 changed files with 101 additions and 69 deletions
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docs/my-website/docs/routing.md
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@@ -790,83 +790,115 @@ If the error is a context window exceeded error, fall back to a larger model gro
Fallbacks are done in-order - ["gpt-3.5-turbo, "gpt-4", "gpt-4-32k"], will do 'gpt-3.5-turbo' first, then 'gpt-4', etc.
You can also set 'default_fallbacks', in case a specific model group is misconfigured / bad.
You can also set `default_fallbacks`, in case a specific model group is misconfigured / bad.
There are 3 types of fallbacks:
- `content_policy_fallbacks`: For litellm.ContentPolicyViolationError - LiteLLM maps content policy violation errors across providers [**See Code**](https://github.com/BerriAI/litellm/blob/89a43c872a1e3084519fb9de159bf52f5447c6c4/litellm/utils.py#L8495C27-L8495C54)
- `context_window_fallbacks`: For litellm.ContextWindowExceededErrors - LiteLLM maps context window error messages across providers [**See Code**](https://github.com/BerriAI/litellm/blob/89a43c872a1e3084519fb9de159bf52f5447c6c4/litellm/utils.py#L8469)
- `fallbacks`: For all remaining errors - e.g. litellm.RateLimitError
**Content Policy Violation Fallback**
```python
from litellm import Router
router = Router(
model_list=[
{
"model_name": "claude-2",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": Exception("content filtering policy"),
},
},
{
"model_name": "my-fallback-model",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": "This works!",
},
},
],
content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}], # 👈 KEY CHANGE
# fallbacks=[..], # [OPTIONAL]
# context_window_fallbacks=[..], # [OPTIONAL]
)
response = router.completion(
model="claude-2",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
```
**Context Window Exceeded Fallback**
```python
from litellm import Router
from litellm import Router
model_list = [
{ # list of model deployments
"model_name": "azure/gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
router = Router(
model_list=[
{
"model_name": "claude-2",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": Exception("prompt is too long"),
},
},
"tpm": 240000,
"rpm": 1800
},
{ # list of model deployments
"model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
{
"model_name": "my-fallback-model",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": "This works!",
},
},
"tpm": 240000,
"rpm": 1800
},
{
"model_name": "azure/gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
],
context_window_fallbacks=[{"claude-2": ["my-fallback-model"]}], # 👈 KEY CHANGE
# fallbacks=[..], # [OPTIONAL]
# content_policy_fallbacks=[..], # [OPTIONAL]
)
response = router.completion(
model="claude-2",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
```
**Regular Fallbacks**
```python
from litellm import Router
router = Router(
model_list=[
{
"model_name": "claude-2",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": Exception("this is a rate limit error"),
},
},
"tpm": 240000,
"rpm": 1800
},
{
"model_name": "gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
{
"model_name": "my-fallback-model",
"litellm_params": {
"model": "claude-2",
"api_key": "",
"mock_response": "This works!",
},
},
"tpm": 1000000,
"rpm": 9000
},
{
"model_name": "gpt-3.5-turbo-16k", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo-16k",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
}
]
],
fallbacks=[{"claude-2": ["my-fallback-model"]}], # 👈 KEY CHANGE
# context_window_fallbacks=[..], # [OPTIONAL]
# content_policy_fallbacks=[..], # [OPTIONAL]
)
router = Router(model_list=model_list,
fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}],
default_fallbacks=["gpt-3.5-turbo-16k"],
context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}],
set_verbose=True)
user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]
# normal fallback call
response = router.completion(model="azure/gpt-3.5-turbo", messages=messages)
# context window fallback call
response = router.completion(model="azure/gpt-3.5-turbo-context-fallback", messages=messages)
print(f"response: {response}")
response = router.completion(
model="claude-2",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
```
### Caching