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fix(router.py): fix linting issues
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Krrish Dholakia
parent
65917815d5
commit
78fb8cf941
3 changed files with 100 additions and 102 deletions
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@ -1,5 +1,10 @@
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import Image from '@theme/IdealImage';
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# Reliability - Fallbacks, Azure Deployments, etc.
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<Image img={require('../img/multiple_deployment.png')} alt="HF_Dashboard" style={{ maxWidth: '100%', height: 'auto' }}/>
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# Reliability
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LiteLLM helps prevent failed requests in 3 ways:
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@ -14,6 +19,99 @@ LiteLLM supports the following functions for reliability:
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* `completion()` with fallbacks: switch between models/keys/api bases in case of errors.
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* `router()`: An abstraction on top of completion + embeddings to route the request to a deployment with capacity (available tpm/rpm).
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## Manage Multiple Deployments
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Use this if you're trying to load-balance across multiple deployments (e.g. Azure/OpenAI).
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`Router` prevents failed requests, by picking the deployment which is below rate-limit and has the least amount of tokens used.
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In production, [Router connects to a Redis Cache](#redis-queue) to track usage across multiple deployments.
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### Quick Start
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```python
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from litellm import Router
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model_list = [{ # list of model deployments
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "azure/chatgpt-v-2",
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"api_key": os.getenv("AZURE_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_API_BASE")
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}
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}, {
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "azure/chatgpt-functioncalling",
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"api_key": os.getenv("AZURE_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_API_BASE")
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}
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}, {
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "gpt-3.5-turbo",
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"api_key": os.getenv("OPENAI_API_KEY"),
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}
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}]
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router = Router(model_list=model_list)
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# openai.ChatCompletion.create replacement
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response = router.completion(model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}]
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print(response)
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```
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### Redis Queue
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In production, we use Redis to track usage across multiple Azure deployments.
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```python
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router = Router(model_list=model_list,
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redis_host=os.getenv("REDIS_HOST"),
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redis_password=os.getenv("REDIS_PASSWORD"),
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redis_port=os.getenv("REDIS_PORT"))
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print(response)
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```
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### Deploy Router
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1. Clone repo
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```shell
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git clone https://github.com/BerriAI/litellm
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```
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2. Create + Modify router_config.yaml (save your azure/openai/etc. deployment info)
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```shell
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cp ./router_config_template.yaml ./router_config.yaml
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```
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3. Build + Run docker image
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```shell
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docker build -t litellm-proxy . --build-arg CONFIG_FILE=./router_config.yaml
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```
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```shell
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docker run --name litellm-proxy -e PORT=8000 -p 8000:8000 litellm-proxy
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```
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### Test
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```curl
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curl 'http://0.0.0.0:8000/router/completions' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hey"}]
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}'
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```
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## Retry failed requests
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Call it in completion like this `completion(..num_retries=2)`.
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@ -73,106 +171,6 @@ response = completion(model="azure/gpt-4", messages=messages, api_key=api_key,
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[Check out this section for implementation details](#fallbacks-1)
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## Manage Multiple Deployments
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Use this if you're trying to load-balance across multiple deployments (e.g. Azure/OpenAI).
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`Router` prevents failed requests, by picking the deployment which is below rate-limit and has the least amount of tokens used.
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In production, [Router connects to a Redis Cache](#redis-queue) to track usage across multiple deployments.
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### Quick Start
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```python
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from litellm import Router
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model_list = [{ # list of model deployments
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "azure/chatgpt-v-2",
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"api_key": os.getenv("AZURE_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_API_BASE")
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},
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"tpm": 240000,
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"rpm": 1800
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}, {
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "azure/chatgpt-functioncalling",
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"api_key": os.getenv("AZURE_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_API_BASE")
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},
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"tpm": 240000,
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"rpm": 1800
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}, {
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"model_name": "gpt-3.5-turbo", # openai model name
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"litellm_params": { # params for litellm completion/embedding call
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"model": "gpt-3.5-turbo",
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"api_key": os.getenv("OPENAI_API_KEY"),
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},
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"tpm": 1000000,
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"rpm": 9000
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}]
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router = Router(model_list=model_list)
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# openai.ChatCompletion.create replacement
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response = router.completion(model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}]
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print(response)
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```
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### Redis Queue
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In production, we use Redis to track usage across multiple Azure deployments.
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```python
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router = Router(model_list=model_list,
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redis_host=os.getenv("REDIS_HOST"),
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redis_password=os.getenv("REDIS_PASSWORD"),
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redis_port=os.getenv("REDIS_PORT"))
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print(response)
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```
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### Deploy Router
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1. Clone repo
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```shell
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git clone https://github.com/BerriAI/litellm
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```
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2. Create + Modify router_config.yaml (save your azure/openai/etc. deployment info)
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```shell
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cp ./router_config_template.yaml ./router_config.yaml
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```
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3. Build + Run docker image
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```shell
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docker build -t litellm-proxy . --build-arg CONFIG_FILE=./router_config.yaml
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```
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```shell
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docker run --name litellm-proxy -e PORT=8000 -p 8000:8000 litellm-proxy
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```
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### Test
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```curl
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curl 'http://0.0.0.0:8000/router/completions' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "Hey"}]
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}'
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```
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## Implementation Details
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### Fallbacks
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BIN
docs/my-website/img/multiple_deployment.png
Normal file
BIN
docs/my-website/img/multiple_deployment.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 135 KiB |
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@ -32,7 +32,7 @@ class Router:
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cache_responses: bool = False) -> None:
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if model_list:
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self.set_model_list(model_list)
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self.healthy_deployments = []
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self.healthy_deployments: List = []
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### HEALTH CHECK THREAD ### - commenting out as further testing required
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self._start_health_check_thread()
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@ -168,7 +168,7 @@ class Router:
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data = deployment["litellm_params"]
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# call via litellm.completion()
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return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs})
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return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore
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def embedding(self,
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model: str,
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