Merge pull request #3512 from BerriAI/litellm_ui_fixes_6

Edit cost per input + cost per output token on UI
This commit is contained in:
Krish Dholakia 2024-05-07 22:01:11 -07:00 committed by GitHub
commit cec17d61a4
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32 changed files with 160 additions and 133 deletions

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@ -2528,7 +2528,10 @@ class ProxyConfig:
if model.model_info is not None and isinstance(model.model_info, dict):
if "id" not in model.model_info:
model.model_info["id"] = model.model_id
_model_info = RouterModelInfo(**model.model_info, db_model=db_model)
if "db_model" in model.model_info and model.model_info["db_model"] == False:
model.model_info["db_model"] = db_model
_model_info = RouterModelInfo(**model.model_info)
else:
_model_info = RouterModelInfo(id=model.model_id, db_model=db_model)
return _model_info
@ -2613,7 +2616,13 @@ class ProxyConfig:
for k, v in _litellm_params.items():
if isinstance(v, str):
# decode base64
decoded_b64 = base64.b64decode(v)
try:
decoded_b64 = base64.b64decode(v)
except Exception as e:
verbose_proxy_logger.error(
"Error decoding value - {}".format(v)
)
continue
# decrypt value
_value = decrypt_value(value=decoded_b64, master_key=master_key)
# sanity check if string > size 0
@ -2629,7 +2638,7 @@ class ProxyConfig:
model=m, db_model=True
) ## 👈 FLAG = True for db_models
added = llm_router.add_deployment(
added = llm_router.upsert_deployment(
deployment=Deployment(
model_name=m.model_name,
litellm_params=_litellm_params,
@ -7474,18 +7483,39 @@ async def update_model(
exclude_none=True
)
for key, value in _existing_litellm_params_dict.items():
if key in _new_litellm_params_dict:
_existing_litellm_params_dict[key] = _new_litellm_params_dict[key]
### ENCRYPT PARAMS ###
for k, v in _new_litellm_params_dict.items():
if isinstance(v, str):
encrypted_value = encrypt_value(value=v, master_key=master_key) # type: ignore
model_params.litellm_params[k] = base64.b64encode(
encrypted_value
).decode("utf-8")
### MERGE WITH EXISTING DATA ###
merged_dictionary = {}
_mp = model_params.litellm_params.dict()
for key, value in _mp.items():
if value is not None:
merged_dictionary[key] = value
elif (
key in _existing_litellm_params_dict
and _existing_litellm_params_dict[key] is not None
):
merged_dictionary[key] = _existing_litellm_params_dict[key]
else:
pass
_data: dict = {
"litellm_params": json.dumps(_existing_litellm_params_dict), # type: ignore
"litellm_params": json.dumps(merged_dictionary), # type: ignore
"updated_by": user_api_key_dict.user_id or litellm_proxy_admin_name,
}
model_response = await prisma_client.db.litellm_proxymodeltable.update(
where={"model_id": _model_id},
data=_data, # type: ignore
)
return model_response
except Exception as e:
traceback.print_exc()
if isinstance(e, HTTPException):

View file

@ -2572,6 +2572,38 @@ class Router:
self.model_names.append(deployment.model_name)
return deployment
def upsert_deployment(self, deployment: Deployment) -> Deployment:
"""
Add or update deployment
Parameters:
- deployment: Deployment - the deployment to be added to the Router
Returns:
- The added/updated deployment
"""
# check if deployment already exists
if deployment.model_info.id in self.get_model_ids():
# remove the previous deployment
removal_idx: Optional[int] = None
for idx, model in enumerate(self.model_list):
if model["model_info"]["id"] == deployment.model_info.id:
removal_idx = idx
if removal_idx is not None:
self.model_list.pop(removal_idx)
# add to model list
_deployment = deployment.to_json(exclude_none=True)
self.model_list.append(_deployment)
# initialize client
self._add_deployment(deployment=deployment)
# add to model names
self.model_names.append(deployment.model_name)
return deployment
def delete_deployment(self, id: str) -> Optional[Deployment]:
"""
Parameters:

View file

@ -140,6 +140,8 @@ async def test_add_existing_deployment():
deployment_2.to_json(exclude_none=True),
]
)
init_len_list = len(llm_router.model_list)
print(f"llm_router: {llm_router}")
master_key = "sk-1234"
setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
@ -164,7 +166,7 @@ async def test_add_existing_deployment():
db_models = [db_model]
num_added = pc._add_deployment(db_models=db_models)
assert num_added == 0
assert init_len_list == len(llm_router.model_list)
litellm_params = LiteLLM_Params(

View file

@ -117,6 +117,7 @@ def test_embedding(client):
"input_cost_per_token": 0.002,
"mode": "embedding",
"id": "hello",
"db_model": False,
}
result = response.json()
print(f"Received response: {result}")
@ -191,6 +192,7 @@ def test_chat_completion(client):
"id": "gm",
"input_cost_per_token": 0.0002,
"mode": "chat",
"db_model": False,
}
assert proxy_server_request_object == {
"url": "http://testserver/chat/completions",

View file

@ -127,6 +127,11 @@ class GenericLiteLLMParams(BaseModel):
aws_access_key_id: Optional[str] = None
aws_secret_access_key: Optional[str] = None
aws_region_name: Optional[str] = None
## CUSTOM PRICING ##
input_cost_per_token: Optional[float] = None
output_cost_per_token: Optional[float] = None
input_cost_per_second: Optional[float] = None
output_cost_per_second: Optional[float] = None
def __init__(
self,
@ -149,6 +154,10 @@ class GenericLiteLLMParams(BaseModel):
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_region_name: Optional[str] = None,
input_cost_per_token: Optional[float] = None,
output_cost_per_token: Optional[float] = None,
input_cost_per_second: Optional[float] = None,
output_cost_per_second: Optional[float] = None,
**params
):
args = locals()
@ -242,28 +251,10 @@ class LiteLLM_Params(GenericLiteLLMParams):
setattr(self, key, value)
class updateLiteLLMParams(BaseModel):
class updateLiteLLMParams(GenericLiteLLMParams):
# This class is used to update the LiteLLM_Params
# only differece is model is optional
model: Optional[str] = None
tpm: Optional[int] = None
rpm: Optional[int] = None
api_key: Optional[str] = None
api_base: Optional[str] = None
api_version: Optional[str] = None
timeout: Optional[Union[float, str]] = None # if str, pass in as os.environ/
stream_timeout: Optional[Union[float, str]] = (
None # timeout when making stream=True calls, if str, pass in as os.environ/
)
max_retries: int = 2 # follows openai default of 2
organization: Optional[str] = None # for openai orgs
## VERTEX AI ##
vertex_project: Optional[str] = None
vertex_location: Optional[str] = None
## AWS BEDROCK / SAGEMAKER ##
aws_access_key_id: Optional[str] = None
aws_secret_access_key: Optional[str] = None
aws_region_name: Optional[str] = None
class updateDeployment(BaseModel):

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@ -332,7 +332,24 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
<InputNumber min={0} step={1} />
</Form.Item>
<Form.Item
label="input_cost_per_token"
name="input_cost_per_token"
tooltip="float (optional) - Input cost per token"
>
<InputNumber min={0} step={0.0001} />
</Form.Item>
<Form.Item
label="output_cost_per_token"
name="output_cost_per_token"
tooltip="float (optional) - Output cost per token"
>
<InputNumber min={0} step={0.0001} />
</Form.Item>
<Form.Item
@ -394,19 +411,16 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
console.log("handleEditSubmit payload:", payload);
let newModelValue = await modelUpdateCall(accessToken, payload);
try {
let newModelValue = await modelUpdateCall(accessToken, payload);
message.success("Model updated successfully, restart server to see updates");
// Update the teams state with the updated team data
// if (teams) {
// const updatedTeams = teams.map((team) =>
// team.team_id === teamId ? newTeamValues.data : team
// );
// setTeams(updatedTeams);
// }
message.success("Model updated successfully, restart server to see updates");
setEditModalVisible(false);
setSelectedModel(null);
} catch (error) {
console.log(`Error occurred`)
}
setEditModalVisible(false);
setSelectedModel(null);
};
@ -956,9 +970,8 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
</Accordion>
</TableCell>
<TableCell>{model.input_cost}</TableCell>
<TableCell>{model.output_cost}</TableCell>
<TableCell>{model.input_cost || model.litellm_params.input_cost_per_token || null}</TableCell>
<TableCell>{model.output_cost || model.litellm_params.output_cost_per_token || null}</TableCell>
<TableCell>{model.max_tokens}</TableCell>
<TableCell>
{