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Merge branch 'main' into msabramo/fix-pydantic-warnings

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Marc Abramowitz 2024-05-13 11:34:06 -07:00
commit 1efa2e65ac
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102
docs/my-website/docs/proxy/user_keys.md
View file

@ -365,22 +365,113 @@ curl --location 'http://0.0.0.0:4000/moderations' \
## Advanced
### (BETA) Batch Completions - pass `model` as List
### (BETA) Batch Completions - pass multiple models
Use this when you want to send 1 request to N Models
#### Expected Request Format
Pass model as a string of comma separated value of models. Example `"model"="llama3,gpt-3.5-turbo"`
This same request will be sent to the following model groups on the [litellm proxy config.yaml](https://docs.litellm.ai/docs/proxy/configs)
- `model_name="llama3"`
- `model_name="gpt-3.5-turbo"`
<Tabs>
<TabItem value="openai-py" label="OpenAI Python SDK">
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
model="gpt-3.5-turbo,llama3",
messages=[
{"role": "user", "content": "this is a test request, write a short poem"}
],
)
print(response)
```
#### Expected Response Format
Get a list of responses when `model` is passed as a list
```python
[
ChatCompletion(
id='chatcmpl-9NoYhS2G0fswot0b6QpoQgmRQMaIf',
choices=[
Choice(
finish_reason='stop',
index=0,
logprobs=None,
message=ChatCompletionMessage(
content='In the depths of my soul, a spark ignites\nA light that shines so pure and bright\nIt dances and leaps, refusing to die\nA flame of hope that reaches the sky\n\nIt warms my heart and fills me with bliss\nA reminder that in darkness, there is light to kiss\nSo I hold onto this fire, this guiding light\nAnd let it lead me through the darkest night.',
role='assistant',
function_call=None,
tool_calls=None
)
)
],
created=1715462919,
model='gpt-3.5-turbo-0125',
object='chat.completion',
system_fingerprint=None,
usage=CompletionUsage(
completion_tokens=83,
prompt_tokens=17,
total_tokens=100
)
),
ChatCompletion(
id='chatcmpl-4ac3e982-da4e-486d-bddb-ed1d5cb9c03c',
choices=[
Choice(
finish_reason='stop',
index=0,
logprobs=None,
message=ChatCompletionMessage(
content="A test request, and I'm delighted!\nHere's a short poem, just for you:\n\nMoonbeams dance upon the sea,\nA path of light, for you to see.\nThe stars up high, a twinkling show,\nA night of wonder, for all to know.\n\nThe world is quiet, save the night,\nA peaceful hush, a gentle light.\nThe world is full, of beauty rare,\nA treasure trove, beyond compare.\n\nI hope you enjoyed this little test,\nA poem born, of whimsy and jest.\nLet me know, if there's anything else!",
role='assistant',
function_call=None,
tool_calls=None
)
)
],
created=1715462919,
model='groq/llama3-8b-8192',
object='chat.completion',
system_fingerprint='fp_a2c8d063cb',
usage=CompletionUsage(
completion_tokens=120,
prompt_tokens=20,
total_tokens=140
)
)
]
```
</TabItem>
<TabItem value="curl" label="Curl">
```shell
curl --location 'http://localhost:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": ["llama3", "gpt-3.5-turbo"],
"model": "llama3,gpt-3.5-turbo",
"max_tokens": 10,
"user": "litellm2",
"messages": [
@ -393,6 +484,8 @@ curl --location 'http://localhost:4000/chat/completions' \
```
#### Expected Response Format
Get a list of responses when `model` is passed as a list
@ -447,6 +540,11 @@ Get a list of responses when `model` is passed as a list
```
</TabItem>
</Tabs>
### Pass User LLM API Keys, Fallbacks

126
litellm/__init__.py
View file

@ -406,69 +406,69 @@ replicate_models: List = [
]
clarifai_models: List = [
'clarifai/meta.Llama-3.Llama-3-8B-Instruct',
'clarifai/gcp.generate.gemma-1_1-7b-it',
'clarifai/mistralai.completion.mixtral-8x22B',
'clarifai/cohere.generate.command-r-plus',
'clarifai/databricks.drbx.dbrx-instruct',
'clarifai/mistralai.completion.mistral-large',
'clarifai/mistralai.completion.mistral-medium',
'clarifai/mistralai.completion.mistral-small',
'clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1',
'clarifai/gcp.generate.gemma-2b-it',
'clarifai/gcp.generate.gemma-7b-it',
'clarifai/deci.decilm.deciLM-7B-instruct',
'clarifai/mistralai.completion.mistral-7B-Instruct',
'clarifai/gcp.generate.gemini-pro',
'clarifai/anthropic.completion.claude-v1',
'clarifai/anthropic.completion.claude-instant-1_2',
'clarifai/anthropic.completion.claude-instant',
'clarifai/anthropic.completion.claude-v2',
'clarifai/anthropic.completion.claude-2_1',
'clarifai/meta.Llama-2.codeLlama-70b-Python',
'clarifai/meta.Llama-2.codeLlama-70b-Instruct',
'clarifai/openai.completion.gpt-3_5-turbo-instruct',
'clarifai/meta.Llama-2.llama2-7b-chat',
'clarifai/meta.Llama-2.llama2-13b-chat',
'clarifai/meta.Llama-2.llama2-70b-chat',
'clarifai/openai.chat-completion.gpt-4-turbo',
'clarifai/microsoft.text-generation.phi-2',
'clarifai/meta.Llama-2.llama2-7b-chat-vllm',
'clarifai/upstage.solar.solar-10_7b-instruct',
'clarifai/openchat.openchat.openchat-3_5-1210',
'clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B',
'clarifai/gcp.generate.text-bison',
'clarifai/meta.Llama-2.llamaGuard-7b',
'clarifai/fblgit.una-cybertron.una-cybertron-7b-v2',
'clarifai/openai.chat-completion.GPT-4',
'clarifai/openai.chat-completion.GPT-3_5-turbo',
'clarifai/ai21.complete.Jurassic2-Grande',
'clarifai/ai21.complete.Jurassic2-Grande-Instruct',
'clarifai/ai21.complete.Jurassic2-Jumbo-Instruct',
'clarifai/ai21.complete.Jurassic2-Jumbo',
'clarifai/ai21.complete.Jurassic2-Large',
'clarifai/cohere.generate.cohere-generate-command',
'clarifai/wizardlm.generate.wizardCoder-Python-34B',
'clarifai/wizardlm.generate.wizardLM-70B',
'clarifai/tiiuae.falcon.falcon-40b-instruct',
'clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat',
'clarifai/gcp.generate.code-gecko',
'clarifai/gcp.generate.code-bison',
'clarifai/mistralai.completion.mistral-7B-OpenOrca',
'clarifai/mistralai.completion.openHermes-2-mistral-7B',
'clarifai/wizardlm.generate.wizardLM-13B',
'clarifai/huggingface-research.zephyr.zephyr-7B-alpha',
'clarifai/wizardlm.generate.wizardCoder-15B',
'clarifai/microsoft.text-generation.phi-1_5',
'clarifai/databricks.Dolly-v2.dolly-v2-12b',
'clarifai/bigcode.code.StarCoder',
'clarifai/salesforce.xgen.xgen-7b-8k-instruct',
'clarifai/mosaicml.mpt.mpt-7b-instruct',
'clarifai/anthropic.completion.claude-3-opus',
'clarifai/anthropic.completion.claude-3-sonnet',
'clarifai/gcp.generate.gemini-1_5-pro',
'clarifai/gcp.generate.imagen-2',
'clarifai/salesforce.blip.general-english-image-caption-blip-2',
"clarifai/meta.Llama-3.Llama-3-8B-Instruct",
"clarifai/gcp.generate.gemma-1_1-7b-it",
"clarifai/mistralai.completion.mixtral-8x22B",
"clarifai/cohere.generate.command-r-plus",
"clarifai/databricks.drbx.dbrx-instruct",
"clarifai/mistralai.completion.mistral-large",
"clarifai/mistralai.completion.mistral-medium",
"clarifai/mistralai.completion.mistral-small",
"clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
"clarifai/gcp.generate.gemma-2b-it",
"clarifai/gcp.generate.gemma-7b-it",
"clarifai/deci.decilm.deciLM-7B-instruct",
"clarifai/mistralai.completion.mistral-7B-Instruct",
"clarifai/gcp.generate.gemini-pro",
"clarifai/anthropic.completion.claude-v1",
"clarifai/anthropic.completion.claude-instant-1_2",
"clarifai/anthropic.completion.claude-instant",
"clarifai/anthropic.completion.claude-v2",
"clarifai/anthropic.completion.claude-2_1",
"clarifai/meta.Llama-2.codeLlama-70b-Python",
"clarifai/meta.Llama-2.codeLlama-70b-Instruct",
"clarifai/openai.completion.gpt-3_5-turbo-instruct",
"clarifai/meta.Llama-2.llama2-7b-chat",
"clarifai/meta.Llama-2.llama2-13b-chat",
"clarifai/meta.Llama-2.llama2-70b-chat",
"clarifai/openai.chat-completion.gpt-4-turbo",
"clarifai/microsoft.text-generation.phi-2",
"clarifai/meta.Llama-2.llama2-7b-chat-vllm",
"clarifai/upstage.solar.solar-10_7b-instruct",
"clarifai/openchat.openchat.openchat-3_5-1210",
"clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
"clarifai/gcp.generate.text-bison",
"clarifai/meta.Llama-2.llamaGuard-7b",
"clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
"clarifai/openai.chat-completion.GPT-4",
"clarifai/openai.chat-completion.GPT-3_5-turbo",
"clarifai/ai21.complete.Jurassic2-Grande",
"clarifai/ai21.complete.Jurassic2-Grande-Instruct",
"clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
"clarifai/ai21.complete.Jurassic2-Jumbo",
"clarifai/ai21.complete.Jurassic2-Large",
"clarifai/cohere.generate.cohere-generate-command",
"clarifai/wizardlm.generate.wizardCoder-Python-34B",
"clarifai/wizardlm.generate.wizardLM-70B",
"clarifai/tiiuae.falcon.falcon-40b-instruct",
"clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
"clarifai/gcp.generate.code-gecko",
"clarifai/gcp.generate.code-bison",
"clarifai/mistralai.completion.mistral-7B-OpenOrca",
"clarifai/mistralai.completion.openHermes-2-mistral-7B",
"clarifai/wizardlm.generate.wizardLM-13B",
"clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
"clarifai/wizardlm.generate.wizardCoder-15B",
"clarifai/microsoft.text-generation.phi-1_5",
"clarifai/databricks.Dolly-v2.dolly-v2-12b",
"clarifai/bigcode.code.StarCoder",
"clarifai/salesforce.xgen.xgen-7b-8k-instruct",
"clarifai/mosaicml.mpt.mpt-7b-instruct",
"clarifai/anthropic.completion.claude-3-opus",
"clarifai/anthropic.completion.claude-3-sonnet",
"clarifai/gcp.generate.gemini-1_5-pro",
"clarifai/gcp.generate.imagen-2",
"clarifai/salesforce.blip.general-english-image-caption-blip-2",
]

26
litellm/integrations/slack_alerting.py
View file

@ -76,16 +76,14 @@ class SlackAlerting(CustomLogger):
internal_usage_cache: Optional[DualCache] = None,
alerting_threshold: float = 300, # threshold for slow / hanging llm responses (in seconds)
alerting: Optional[List] = [],
alert_types: Optional[
List[
Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
]
alert_types: List[
Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
]
] = [
"llm_exceptions",
@ -812,14 +810,6 @@ Model Info:
updated_at=litellm.utils.get_utc_datetime(),
)
)
if "llm_exceptions" in self.alert_types:
original_exception = kwargs.get("exception", None)
await self.send_alert(
message="LLM API Failure - " + str(original_exception),
level="High",
alert_type="llm_exceptions",
)
async def _run_scheduler_helper(self, llm_router) -> bool:
"""

20
litellm/llms/vertex_ai.py
View file

@ -867,6 +867,8 @@ async def async_completion(
Add support for acompletion calls for gemini-pro
"""
try:
import proto # type: ignore
if mode == "vision":
print_verbose("\nMaking VertexAI Gemini Pro/Vision Call")
print_verbose(f"\nProcessing input messages = {messages}")
@ -901,9 +903,21 @@ async def async_completion(
):
function_call = response.candidates[0].content.parts[0].function_call
args_dict = {}
for k, v in function_call.args.items():
args_dict[k] = v
args_str = json.dumps(args_dict)
# Check if it's a RepeatedComposite instance
for key, val in function_call.args.items():
if isinstance(
val, proto.marshal.collections.repeated.RepeatedComposite
):
# If so, convert to list
args_dict[key] = [v for v in val]
else:
args_dict[key] = val
try:
args_str = json.dumps(args_dict)
except Exception as e:
raise VertexAIError(status_code=422, message=str(e))
message = litellm.Message(
content=None,
tool_calls=[

16
litellm/proxy/_super_secret_config.yaml
View file

@ -8,15 +8,26 @@ model_list:
base_model: text-embedding-ada-002
mode: embedding
model_name: text-embedding-ada-002
- model_name: gpt-3.5-turbo-012
litellm_params:
model: gpt-3.5-turbo
api_base: http://0.0.0.0:8080
api_key: ""
- model_name: gpt-3.5-turbo-0125-preview
litellm_params:
model: azure/chatgpt-v-2
api_key: os.environ/AZURE_API_KEY
api_base: os.environ/AZURE_API_BASE
router_settings:
redis_host: redis
# redis_password: <your redis password>
redis_port: 6379
enable_pre_call_checks: true
litellm_settings:
set_verbose: True
enable_preview_features: true
fallbacks: [{"gpt-3.5-turbo-012": ["gpt-3.5-turbo-0125-preview"]}]
# service_callback: ["prometheus_system"]
# success_callback: ["prometheus"]
# failure_callback: ["prometheus"]
@ -25,4 +36,5 @@ general_settings:
enable_jwt_auth: True
disable_reset_budget: True
proxy_batch_write_at: 60 # 👈 Frequency of batch writing logs to server (in seconds)
routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
alerting: ["slack"]

21
litellm/proxy/proxy_server.py
View file

@ -3698,8 +3698,9 @@ async def chat_completion(
# skip router if user passed their key
if "api_key" in data:
tasks.append(litellm.acompletion(**data))
elif isinstance(data["model"], list) and llm_router is not None:
_models = data.pop("model")
elif "," in data["model"] and llm_router is not None:
_models_csv_string = data.pop("model")
_models = _models_csv_string.split(",")
tasks.append(llm_router.abatch_completion(models=_models, **data))
elif "user_config" in data:
# initialize a new router instance. make request using this Router
@ -3761,6 +3762,7 @@ async def chat_completion(
"x-litellm-cache-key": cache_key,
"x-litellm-model-api-base": api_base,
"x-litellm-version": version,
"x-litellm-model-region": user_api_key_dict.allowed_model_region or "",
}
selected_data_generator = select_data_generator(
response=response,
@ -3777,6 +3779,9 @@ async def chat_completion(
fastapi_response.headers["x-litellm-cache-key"] = cache_key
fastapi_response.headers["x-litellm-model-api-base"] = api_base
fastapi_response.headers["x-litellm-version"] = version
fastapi_response.headers["x-litellm-model-region"] = (
user_api_key_dict.allowed_model_region or ""
)
### CALL HOOKS ### - modify outgoing data
response = await proxy_logging_obj.post_call_success_hook(
@ -4161,6 +4166,9 @@ async def embeddings(
fastapi_response.headers["x-litellm-cache-key"] = cache_key
fastapi_response.headers["x-litellm-model-api-base"] = api_base
fastapi_response.headers["x-litellm-version"] = version
fastapi_response.headers["x-litellm-model-region"] = (
user_api_key_dict.allowed_model_region or ""
)
return response
except Exception as e:
@ -4330,6 +4338,9 @@ async def image_generation(
fastapi_response.headers["x-litellm-cache-key"] = cache_key
fastapi_response.headers["x-litellm-model-api-base"] = api_base
fastapi_response.headers["x-litellm-version"] = version
fastapi_response.headers["x-litellm-model-region"] = (
user_api_key_dict.allowed_model_region or ""
)
return response
except Exception as e:
@ -4523,6 +4534,9 @@ async def audio_transcriptions(
fastapi_response.headers["x-litellm-cache-key"] = cache_key
fastapi_response.headers["x-litellm-model-api-base"] = api_base
fastapi_response.headers["x-litellm-version"] = version
fastapi_response.headers["x-litellm-model-region"] = (
user_api_key_dict.allowed_model_region or ""
)
return response
except Exception as e:
@ -4698,6 +4712,9 @@ async def moderations(
fastapi_response.headers["x-litellm-cache-key"] = cache_key
fastapi_response.headers["x-litellm-model-api-base"] = api_base
fastapi_response.headers["x-litellm-version"] = version
fastapi_response.headers["x-litellm-model-region"] = (
user_api_key_dict.allowed_model_region or ""
)
return response
except Exception as e:

25
litellm/router.py
View file

@ -1413,7 +1413,7 @@ class Router:
verbose_router_logger.debug(f"Trying to fallback b/w models")
if (
hasattr(e, "status_code")
and e.status_code == 400
and e.status_code == 400 # type: ignore
and not isinstance(e, litellm.ContextWindowExceededError)
): # don't retry a malformed request
raise e
@ -1444,6 +1444,9 @@ class Router:
response = await self.async_function_with_retries(
*args, **kwargs
)
verbose_router_logger.info(
"Successful fallback b/w models."
)
return response
except Exception as e:
pass
@ -1478,6 +1481,9 @@ class Router:
response = await self.async_function_with_fallbacks(
*args, **kwargs
)
verbose_router_logger.info(
"Successful fallback b/w models."
)
return response
except Exception as e:
raise e
@ -3259,13 +3265,12 @@ class Router:
healthy_deployments.remove(deployment)
# filter pre-call checks
_allowed_model_region = (
request_kwargs.get("allowed_model_region")
if request_kwargs is not None
else None
)
if self.enable_pre_call_checks and messages is not None:
_allowed_model_region = (
request_kwargs.get("allowed_model_region")
if request_kwargs is not None
else None
)
if _allowed_model_region == "eu":
healthy_deployments = self._pre_call_checks(
model=model,
@ -3286,8 +3291,10 @@ class Router:
)
if len(healthy_deployments) == 0:
if _allowed_model_region is None:
_allowed_model_region = "n/a"
raise ValueError(
f"{RouterErrors.no_deployments_available.value}, passed model={model}"
f"{RouterErrors.no_deployments_available.value}, passed model={model}. Enable pre-call-checks={self.enable_pre_call_checks}, allowed_model_region={_allowed_model_region}"
)
if (
@ -3647,7 +3654,7 @@ class Router:
)
asyncio.create_task(
proxy_logging_obj.slack_alerting_instance.send_alert(
message=f"Router: Cooling down deployment: {_api_base}, for {self.cooldown_time} seconds. Got exception: {str(exception_status)}",
message=f"Router: Cooling down deployment: {_api_base}, for {self.cooldown_time} seconds. Got exception: {str(exception_status)}. Change 'cooldown_time' + 'allowed_failes' under 'Router Settings' on proxy UI, or via config - https://docs.litellm.ai/docs/proxy/reliability#fallbacks--retries--timeouts--cooldowns",
alert_type="cooldown_deployment",
level="Low",
)

19
litellm/tests/test_amazing_vertex_completion.py
View file

@ -590,19 +590,20 @@ def test_gemini_pro_vision_base64():
pytest.fail(f"An exception occurred - {str(e)}")
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
def test_gemini_pro_function_calling():
async def test_gemini_pro_function_calling(sync_mode):
try:
load_vertex_ai_credentials()
response = litellm.completion(
model="vertex_ai/gemini-pro",
messages=[
data = {
"model": "vertex_ai/gemini-pro",
"messages": [
{
"role": "user",
"content": "Call the submit_cities function with San Francisco and New York",
}
],
tools=[
"tools": [
{
"type": "function",
"function": {
@ -618,11 +619,13 @@ def test_gemini_pro_function_calling():
},
}
],
)
}
if sync_mode:
response = litellm.completion(**data)
else:
response = await litellm.acompletion(**data)
print(f"response: {response}")
except litellm.APIError as e:
pass
except litellm.RateLimitError as e:
pass
except Exception as e:

1
litellm/tests/test_router_debug_logs.py
View file

@ -85,6 +85,7 @@ def test_async_fallbacks(caplog):
"litellm.acompletion(model=gpt-3.5-turbo)\x1b[31m Exception OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: bad-key. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}} \nModel: gpt-3.5-turbo\nAPI Base: https://api.openai.com\nMessages: [{'content': 'Hello, how are you?', 'role': 'user'}]\nmodel_group: gpt-3.5-turbo\n\ndeployment: gpt-3.5-turbo\n\x1b[0m",
"Falling back to model_group = azure/gpt-3.5-turbo",
"litellm.acompletion(model=azure/chatgpt-v-2)\x1b[32m 200 OK\x1b[0m",
"Successful fallback b/w models.",
]
# Assert that the captured logs match the expected log messages

46
litellm/tests/test_router_fallbacks.py
View file

@ -961,3 +961,49 @@ def test_custom_cooldown_times():
except Exception as e:
print(e)
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_service_unavailable_fallbacks(sync_mode):
"""
Initial model - openai
Fallback - azure
Error - 503, service unavailable
"""
router = Router(
model_list=[
{
"model_name": "gpt-3.5-turbo-012",
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "anything",
"api_base": "http://0.0.0.0:8080",
},
},
{
"model_name": "gpt-3.5-turbo-0125-preview",
"litellm_params": {
"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"),
},
},
],
fallbacks=[{"gpt-3.5-turbo-012": ["gpt-3.5-turbo-0125-preview"]}],
)
if sync_mode:
response = router.completion(
model="gpt-3.5-turbo-012",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
else:
response = await router.acompletion(
model="gpt-3.5-turbo-012",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
assert response.model == "gpt-35-turbo"

4
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
version = "1.37.5"
version = "1.37.6"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT"
@ -80,7 +80,7 @@ requires = ["poetry-core", "wheel"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
version = "1.37.5"
version = "1.37.6"
version_files = [
"pyproject.toml:^version"
]

5
tests/test_openai_endpoints.py
View file

@ -424,10 +424,7 @@ async def test_batch_chat_completions():
response = await chat_completion(
session=session,
key="sk-1234",
model=[
"gpt-3.5-turbo",
"fake-openai-endpoint",
],
model="gpt-3.5-turbo,fake-openai-endpoint",
)
print(f"response: {response}")