LLM module
Source code in kili/llm/presentation/client/llm.py
class LlmClientMethods:
def __init__(self, kili_api_gateway: KiliAPIGateway):
self.kili_api_gateway = kili_api_gateway
def export(
self,
project_id: str,
disable_tqdm: Optional[bool] = False,
asset_ids: Optional[List[str]] = None,
external_ids: Optional[List[str]] = None,
) -> Optional[List[Dict[str, Union[List[str], str]]]]:
# pylint: disable=line-too-long
"""Returns an export of llm assets with valid labels.
Args:
project_id: Identifier of the project.
asset_ids: Optional list of the assets internal IDs from which to export the labels.
disable_tqdm: Disable the progress bar if True.
external_ids: Optional list of the assets external IDs from which to export the labels.
!!! Example
```python
kili.llm.export("your_project_id")
```
"""
if asset_ids and external_ids:
raise ValueError("You cannot provide both asset_ids and external_ids")
if external_ids is not None and asset_ids is None:
id_map = AssetUseCasesUtils(self.kili_api_gateway).infer_ids_from_external_ids(
asset_external_ids=cast(List[AssetExternalId], external_ids),
project_id=ProjectId(project_id),
)
resolved_asset_ids = [id_map[AssetExternalId(i)] for i in external_ids]
else:
resolved_asset_ids = (
list(AssetId(asset_id) for asset_id in asset_ids) if asset_ids else None
)
asset_filter = AssetFilters(
project_id=ProjectId(project_id), asset_id_in=resolved_asset_ids
)
try:
return export(
self.kili_api_gateway,
project_id=ProjectId(project_id),
asset_filter=asset_filter,
disable_tqdm=disable_tqdm,
)
except NoCompatibleJobError as excp:
warnings.warn(str(excp), stacklevel=2)
return None
def create_model(self, organization_id: str, model: dict) -> ModelDict:
# pylint: disable=line-too-long
"""Create a new model in an organization.
Args:
organization_id: Identifier of the organization.
model: A dictionary representing the model to create, containing:
- `name`: Name of the model.
- `type`: Type of the model, one of:
- `"AZURE_OPEN_AI"`
- `"OPEN_AI_SDK"`
- `credentials`: Credentials required for the model, depending on the type:
- For `"AZURE_OPEN_AI"` type:
- `api_key`: The API key for Azure OpenAI.
- `deployment_id`: The deployment ID within Azure.
- `endpoint`: The endpoint URL of the Azure OpenAI service.
- For `"OPEN_AI_SDK"` type:
- `api_key`: The API key for OpenAI SDK.
- `endpoint`: The endpoint URL of the OpenAI SDK service.
Returns:
A dictionary containing the created model's details.
Examples:
>>> # Example of creating an OpenAI SDK model
>>> model_data = {
... "name": "My OpenAI SDK Model",
... "type": "OPEN_AI_SDK",
... "credentials": {
... "api_key": "your_open_ai_api_key",
... "endpoint": "your_open_ai_endpoint"
... }
... }
>>> kili.llm.create_model(organization_id="your_organization_id", model=model_data)
"""
credentials_data = model["credentials"]
model_type = ModelType(model["type"])
if model_type == ModelType.AZURE_OPEN_AI:
credentials = AzureOpenAICredentials(**credentials_data)
elif model_type == ModelType.OPEN_AI_SDK:
credentials = OpenAISDKCredentials(**credentials_data)
else:
raise ValueError(f"Unsupported model type: {model['type']}")
model_input = ModelToCreateInput(
credentials=credentials,
name=model["name"],
type=model_type,
organization_id=organization_id,
)
return self.kili_api_gateway.create_model(model=model_input)
def models(self, organization_id: str, fields: Optional[List[str]] = None) -> List[ModelDict]:
# pylint: disable=line-too-long
"""List models in an organization.
Args:
organization_id: Identifier of the organization.
fields: All the fields to request among the possible fields for the models.
Defaults to ["id", "credentials", "name", "type"].
Returns:
A list of models.
Examples:
>>> kili.llm.models(organization_id="your_organization_id")
"""
converted_filters = OrganizationModelFilters(
organization_id=organization_id,
)
return list(self.kili_api_gateway.list_models(filters=converted_filters, fields=fields))
def model(self, model_id: str, fields: Optional[List[str]] = None) -> ModelDict:
# pylint: disable=line-too-long
"""Retrieve a specific model.
Args:
model_id: Identifier of the model.
fields: All the fields to request among the possible fields for the models.
Defaults to ["id", "credentials", "name", "type"].
Returns:
A dictionary representing the model.
Examples:
>>> kili.llm.model(model_id="your_model_id")
"""
return self.kili_api_gateway.get_model(
model_id=model_id,
fields=fields,
)
def update_properties_in_model(self, model_id: str, model: dict) -> ModelDict:
# pylint: disable=line-too-long
"""Update properties of an existing model.
Args:
model_id: Identifier of the model to update.
model: A dictionary containing the properties to update, which may include:
- `name`: New name of the model.
- `credentials`: Updated credentials for the model, depending on the type:
- For `"AZURE_OPEN_AI"` type:
- `api_key`: The API key for Azure OpenAI.
- `deployment_id`: The deployment ID within Azure.
- `endpoint`: The endpoint URL of the Azure OpenAI service.
- For `"OPEN_AI_SDK"` type:
- `api_key`: The API key for OpenAI SDK.
- `endpoint`: The endpoint URL of the OpenAI SDK service.
Returns:
A dictionary containing the updated model's details.
Examples:
>>> # Update the name of a model
>>> kili.llm.update_properties_in_model(
... model_id="your_model_id",
... model={"name": "Updated Model Name"}
... )
"""
credentials_data = model.get("credentials")
credentials = None
if credentials_data:
existing_model = self.kili_api_gateway.get_model(
model_id=model_id, fields=["id", "type"]
)
if not existing_model:
raise ValueError(f"Model with id {model_id} not found")
model_type = ModelType(existing_model["type"])
if model_type == ModelType.AZURE_OPEN_AI:
credentials = AzureOpenAICredentials(**credentials_data)
elif model_type == ModelType.OPEN_AI_SDK:
credentials = OpenAISDKCredentials(**credentials_data)
else:
raise ValueError(f"Unsupported model type: {model_type}")
model_input = ModelToUpdateInput(
credentials=credentials,
name=model.get("name"),
)
return self.kili_api_gateway.update_properties_in_model(
model_id=model_id, model=model_input
)
def delete_model(self, model_id: str) -> bool:
# pylint: disable=line-too-long
"""Delete a model from an organization.
Args:
model_id: Identifier of the model to delete.
Returns:
A dictionary indicating the result of the deletion.
Examples:
>>> kili.llm.delete_model(model_id="your_model_id")
"""
return self.kili_api_gateway.delete_model(model_id=model_id)
def create_project_model(
self, project_id: str, model_id: str, configuration: dict
) -> ProjectModelDict:
# pylint: disable=line-too-long
"""Associate a model with a project.
Args:
project_id: Identifier of the project.
model_id: Identifier of the model to associate.
configuration: Configuration parameters for the project model.
Returns:
A dictionary containing the created project model's details.
Examples:
>>> configuration = {
... # Configuration details specific to your use case
... }
>>> kili.llm.create_project_model(
... project_id="your_project_id",
... model_id="your_model_id",
... configuration={"temperature": 0.7}
... )
"""
project_model_input = ProjectModelToCreateInput(
project_id=project_id, model_id=model_id, configuration=configuration
)
return self.kili_api_gateway.create_project_model(project_model=project_model_input)
def project_models(
self, project_id: str, filters: Optional[Dict] = None, fields: Optional[List[str]] = None
) -> List[ProjectModelDict]:
"""List models associated with a project.
Args:
project_id: Identifier of the project.
filters: Optional filters to apply. Possible keys:
- `model_id`: Identifier of a specific model to filter by.
fields: All the fields to request among the possible fields for the project models.
Defaults to ["configuration", "id", "model.credentials", "model.name", "model.type"].
Returns:
A list of project models.
Examples:
>>> kili.llm.project_models(project_id="your_project_id")
"""
converted_filters = ProjectModelFilters(
project_id=project_id,
model_id=filters["model_id"] if filters and "model_id" in filters else None,
)
return list(
self.kili_api_gateway.list_project_models(
filters=converted_filters,
fields=fields,
)
)
def update_project_model(self, project_model_id: str, configuration: dict) -> ProjectModelDict:
"""Update the configuration of a project model.
Args:
project_model_id: Identifier of the project model to update.
configuration: New configuration parameters.
Returns:
A dictionary containing the updated project model's details.
Examples:
>>> configuration = {
... # Updated configuration details
... }
>>> kili.llm.update_project_model(
... project_model_id="your_project_model_id",
... configuration=configuration
... )
"""
project_model_input = ProjectModelToUpdateInput(configuration=configuration)
return self.kili_api_gateway.update_project_model(
project_model_id=project_model_id, project_model=project_model_input
)
def delete_project_model(self, project_model_id: str) -> bool:
"""Delete a project model.
Args:
project_model_id: Identifier of the project model to delete.
Returns:
A dictionary indicating the result of the deletion.
Examples:
>>> kili.llm.delete_project_model(project_model_id="your_project_model_id")
"""
return self.kili_api_gateway.delete_project_model(project_model_id)
def create_conversation(self, project_id: str, prompt: str) -> List[ChatItemDict]:
# pylint: disable=line-too-long
"""Create a new conversation in an LLM project starting with a user's prompt.
This method initiates a new conversation in the specified project by:
- Creating an LLM asset and label associated with the current user.
- Adding the user's prompt as the first chat item.
- Automatically generating assistant responses using the project's models.
Args:
project_id: The identifier of the project where the conversation will be created.
prompt: The initial prompt or message from the user to start the conversation.
Returns:
A list of chat items in the conversation, including the user's prompt and the assistant's responses.
Examples:
>>> PROMPT = "Hello, how can I improve my coding skills?"
>>> chat_items = kili.llm.create_conversation(project_id="your_project_id", prompt=PROMPT)
Notes:
- The first chat item corresponds to the user's prompt.
- The subsequent chat items are assistant responses generated by the project's models.
- An LLM asset and a label are created in the project with status "TODO" and labelType "PREDICTION".
"""
user_id = self.kili_api_gateway.get_current_user(["id"])["id"]
llm_asset = self.kili_api_gateway.create_llm_asset(
project_id=project_id,
author_id=user_id,
status="TODO",
label_type="PREDICTION",
)
asset_id = llm_asset["id"]
label_id = llm_asset["latestLabel"]["id"]
return self.kili_api_gateway.create_chat_item(
asset_id=asset_id, label_id=label_id, prompt=prompt
)
create_conversation(self, project_id, prompt)
Create a new conversation in an LLM project starting with a user's prompt.
This method initiates a new conversation in the specified project by: - Creating an LLM asset and label associated with the current user. - Adding the user's prompt as the first chat item. - Automatically generating assistant responses using the project's models.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_id |
str |
The identifier of the project where the conversation will be created. |
required |
prompt |
str |
The initial prompt or message from the user to start the conversation. |
required |
Returns:
Type | Description |
---|---|
List[kili.domain.llm.ChatItemDict] |
A list of chat items in the conversation, including the user's prompt and the assistant's responses. |
Examples:
>>> PROMPT = "Hello, how can I improve my coding skills?"
>>> chat_items = kili.llm.create_conversation(project_id="your_project_id", prompt=PROMPT)
Notes
- The first chat item corresponds to the user's prompt.
- The subsequent chat items are assistant responses generated by the project's models.
- An LLM asset and a label are created in the project with status "TODO" and labelType "PREDICTION".
Source code in kili/llm/presentation/client/llm.py
def create_conversation(self, project_id: str, prompt: str) -> List[ChatItemDict]:
# pylint: disable=line-too-long
"""Create a new conversation in an LLM project starting with a user's prompt.
This method initiates a new conversation in the specified project by:
- Creating an LLM asset and label associated with the current user.
- Adding the user's prompt as the first chat item.
- Automatically generating assistant responses using the project's models.
Args:
project_id: The identifier of the project where the conversation will be created.
prompt: The initial prompt or message from the user to start the conversation.
Returns:
A list of chat items in the conversation, including the user's prompt and the assistant's responses.
Examples:
>>> PROMPT = "Hello, how can I improve my coding skills?"
>>> chat_items = kili.llm.create_conversation(project_id="your_project_id", prompt=PROMPT)
Notes:
- The first chat item corresponds to the user's prompt.
- The subsequent chat items are assistant responses generated by the project's models.
- An LLM asset and a label are created in the project with status "TODO" and labelType "PREDICTION".
"""
user_id = self.kili_api_gateway.get_current_user(["id"])["id"]
llm_asset = self.kili_api_gateway.create_llm_asset(
project_id=project_id,
author_id=user_id,
status="TODO",
label_type="PREDICTION",
)
asset_id = llm_asset["id"]
label_id = llm_asset["latestLabel"]["id"]
return self.kili_api_gateway.create_chat_item(
asset_id=asset_id, label_id=label_id, prompt=prompt
)
create_model(self, organization_id, model)
Create a new model in an organization.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
organization_id |
str |
Identifier of the organization. |
required |
model |
dict |
A dictionary representing the model to create, containing:
- |
required |
Returns:
Type | Description |
---|---|
ModelDict |
A dictionary containing the created model's details. |
Examples:
>>> # Example of creating an OpenAI SDK model
>>> model_data = {
... "name": "My OpenAI SDK Model",
... "type": "OPEN_AI_SDK",
... "credentials": {
... "api_key": "your_open_ai_api_key",
... "endpoint": "your_open_ai_endpoint"
... }
... }
>>> kili.llm.create_model(organization_id="your_organization_id", model=model_data)
Source code in kili/llm/presentation/client/llm.py
def create_model(self, organization_id: str, model: dict) -> ModelDict:
# pylint: disable=line-too-long
"""Create a new model in an organization.
Args:
organization_id: Identifier of the organization.
model: A dictionary representing the model to create, containing:
- `name`: Name of the model.
- `type`: Type of the model, one of:
- `"AZURE_OPEN_AI"`
- `"OPEN_AI_SDK"`
- `credentials`: Credentials required for the model, depending on the type:
- For `"AZURE_OPEN_AI"` type:
- `api_key`: The API key for Azure OpenAI.
- `deployment_id`: The deployment ID within Azure.
- `endpoint`: The endpoint URL of the Azure OpenAI service.
- For `"OPEN_AI_SDK"` type:
- `api_key`: The API key for OpenAI SDK.
- `endpoint`: The endpoint URL of the OpenAI SDK service.
Returns:
A dictionary containing the created model's details.
Examples:
>>> # Example of creating an OpenAI SDK model
>>> model_data = {
... "name": "My OpenAI SDK Model",
... "type": "OPEN_AI_SDK",
... "credentials": {
... "api_key": "your_open_ai_api_key",
... "endpoint": "your_open_ai_endpoint"
... }
... }
>>> kili.llm.create_model(organization_id="your_organization_id", model=model_data)
"""
credentials_data = model["credentials"]
model_type = ModelType(model["type"])
if model_type == ModelType.AZURE_OPEN_AI:
credentials = AzureOpenAICredentials(**credentials_data)
elif model_type == ModelType.OPEN_AI_SDK:
credentials = OpenAISDKCredentials(**credentials_data)
else:
raise ValueError(f"Unsupported model type: {model['type']}")
model_input = ModelToCreateInput(
credentials=credentials,
name=model["name"],
type=model_type,
organization_id=organization_id,
)
return self.kili_api_gateway.create_model(model=model_input)
create_project_model(self, project_id, model_id, configuration)
Associate a model with a project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_id |
str |
Identifier of the project. |
required |
model_id |
str |
Identifier of the model to associate. |
required |
configuration |
dict |
Configuration parameters for the project model. |
required |
Returns:
Type | Description |
---|---|
ProjectModelDict |
A dictionary containing the created project model's details. |
Examples:
>>> configuration = {
... # Configuration details specific to your use case
... }
>>> kili.llm.create_project_model(
... project_id="your_project_id",
... model_id="your_model_id",
... configuration={"temperature": 0.7}
... )
Source code in kili/llm/presentation/client/llm.py
def create_project_model(
self, project_id: str, model_id: str, configuration: dict
) -> ProjectModelDict:
# pylint: disable=line-too-long
"""Associate a model with a project.
Args:
project_id: Identifier of the project.
model_id: Identifier of the model to associate.
configuration: Configuration parameters for the project model.
Returns:
A dictionary containing the created project model's details.
Examples:
>>> configuration = {
... # Configuration details specific to your use case
... }
>>> kili.llm.create_project_model(
... project_id="your_project_id",
... model_id="your_model_id",
... configuration={"temperature": 0.7}
... )
"""
project_model_input = ProjectModelToCreateInput(
project_id=project_id, model_id=model_id, configuration=configuration
)
return self.kili_api_gateway.create_project_model(project_model=project_model_input)
delete_model(self, model_id)
Delete a model from an organization.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_id |
str |
Identifier of the model to delete. |
required |
Returns:
Type | Description |
---|---|
bool |
A dictionary indicating the result of the deletion. |
Examples:
>>> kili.llm.delete_model(model_id="your_model_id")
Source code in kili/llm/presentation/client/llm.py
def delete_model(self, model_id: str) -> bool:
# pylint: disable=line-too-long
"""Delete a model from an organization.
Args:
model_id: Identifier of the model to delete.
Returns:
A dictionary indicating the result of the deletion.
Examples:
>>> kili.llm.delete_model(model_id="your_model_id")
"""
return self.kili_api_gateway.delete_model(model_id=model_id)
delete_project_model(self, project_model_id)
Delete a project model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_model_id |
str |
Identifier of the project model to delete. |
required |
Returns:
Type | Description |
---|---|
bool |
A dictionary indicating the result of the deletion. |
Examples:
>>> kili.llm.delete_project_model(project_model_id="your_project_model_id")
Source code in kili/llm/presentation/client/llm.py
def delete_project_model(self, project_model_id: str) -> bool:
"""Delete a project model.
Args:
project_model_id: Identifier of the project model to delete.
Returns:
A dictionary indicating the result of the deletion.
Examples:
>>> kili.llm.delete_project_model(project_model_id="your_project_model_id")
"""
return self.kili_api_gateway.delete_project_model(project_model_id)
export(self, project_id, disable_tqdm=False, asset_ids=None, external_ids=None)
Returns an export of llm assets with valid labels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_id |
str |
Identifier of the project. |
required |
asset_ids |
Optional[List[str]] |
Optional list of the assets internal IDs from which to export the labels. |
None |
disable_tqdm |
Optional[bool] |
Disable the progress bar if True. |
False |
external_ids |
Optional[List[str]] |
Optional list of the assets external IDs from which to export the labels. |
None |
Example
kili.llm.export("your_project_id")
Source code in kili/llm/presentation/client/llm.py
def export(
self,
project_id: str,
disable_tqdm: Optional[bool] = False,
asset_ids: Optional[List[str]] = None,
external_ids: Optional[List[str]] = None,
) -> Optional[List[Dict[str, Union[List[str], str]]]]:
# pylint: disable=line-too-long
"""Returns an export of llm assets with valid labels.
Args:
project_id: Identifier of the project.
asset_ids: Optional list of the assets internal IDs from which to export the labels.
disable_tqdm: Disable the progress bar if True.
external_ids: Optional list of the assets external IDs from which to export the labels.
!!! Example
```python
kili.llm.export("your_project_id")
```
"""
if asset_ids and external_ids:
raise ValueError("You cannot provide both asset_ids and external_ids")
if external_ids is not None and asset_ids is None:
id_map = AssetUseCasesUtils(self.kili_api_gateway).infer_ids_from_external_ids(
asset_external_ids=cast(List[AssetExternalId], external_ids),
project_id=ProjectId(project_id),
)
resolved_asset_ids = [id_map[AssetExternalId(i)] for i in external_ids]
else:
resolved_asset_ids = (
list(AssetId(asset_id) for asset_id in asset_ids) if asset_ids else None
)
asset_filter = AssetFilters(
project_id=ProjectId(project_id), asset_id_in=resolved_asset_ids
)
try:
return export(
self.kili_api_gateway,
project_id=ProjectId(project_id),
asset_filter=asset_filter,
disable_tqdm=disable_tqdm,
)
except NoCompatibleJobError as excp:
warnings.warn(str(excp), stacklevel=2)
return None
model(self, model_id, fields=None)
Retrieve a specific model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_id |
str |
Identifier of the model. |
required |
fields |
Optional[List[str]] |
All the fields to request among the possible fields for the models. Defaults to ["id", "credentials", "name", "type"]. |
None |
Returns:
Type | Description |
---|---|
ModelDict |
A dictionary representing the model. |
Examples:
>>> kili.llm.model(model_id="your_model_id")
Source code in kili/llm/presentation/client/llm.py
def model(self, model_id: str, fields: Optional[List[str]] = None) -> ModelDict:
# pylint: disable=line-too-long
"""Retrieve a specific model.
Args:
model_id: Identifier of the model.
fields: All the fields to request among the possible fields for the models.
Defaults to ["id", "credentials", "name", "type"].
Returns:
A dictionary representing the model.
Examples:
>>> kili.llm.model(model_id="your_model_id")
"""
return self.kili_api_gateway.get_model(
model_id=model_id,
fields=fields,
)
models(self, organization_id, fields=None)
List models in an organization.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
organization_id |
str |
Identifier of the organization. |
required |
fields |
Optional[List[str]] |
All the fields to request among the possible fields for the models. Defaults to ["id", "credentials", "name", "type"]. |
None |
Returns:
Type | Description |
---|---|
List[kili.domain.llm.ModelDict] |
A list of models. |
Examples:
>>> kili.llm.models(organization_id="your_organization_id")
Source code in kili/llm/presentation/client/llm.py
def models(self, organization_id: str, fields: Optional[List[str]] = None) -> List[ModelDict]:
# pylint: disable=line-too-long
"""List models in an organization.
Args:
organization_id: Identifier of the organization.
fields: All the fields to request among the possible fields for the models.
Defaults to ["id", "credentials", "name", "type"].
Returns:
A list of models.
Examples:
>>> kili.llm.models(organization_id="your_organization_id")
"""
converted_filters = OrganizationModelFilters(
organization_id=organization_id,
)
return list(self.kili_api_gateway.list_models(filters=converted_filters, fields=fields))
project_models(self, project_id, filters=None, fields=None)
List models associated with a project.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_id |
str |
Identifier of the project. |
required |
filters |
Optional[Dict] |
Optional filters to apply. Possible keys:
- |
None |
fields |
Optional[List[str]] |
All the fields to request among the possible fields for the project models. Defaults to ["configuration", "id", "model.credentials", "model.name", "model.type"]. |
None |
Returns:
Type | Description |
---|---|
List[kili.domain.llm.ProjectModelDict] |
A list of project models. |
Examples:
>>> kili.llm.project_models(project_id="your_project_id")
Source code in kili/llm/presentation/client/llm.py
def project_models(
self, project_id: str, filters: Optional[Dict] = None, fields: Optional[List[str]] = None
) -> List[ProjectModelDict]:
"""List models associated with a project.
Args:
project_id: Identifier of the project.
filters: Optional filters to apply. Possible keys:
- `model_id`: Identifier of a specific model to filter by.
fields: All the fields to request among the possible fields for the project models.
Defaults to ["configuration", "id", "model.credentials", "model.name", "model.type"].
Returns:
A list of project models.
Examples:
>>> kili.llm.project_models(project_id="your_project_id")
"""
converted_filters = ProjectModelFilters(
project_id=project_id,
model_id=filters["model_id"] if filters and "model_id" in filters else None,
)
return list(
self.kili_api_gateway.list_project_models(
filters=converted_filters,
fields=fields,
)
)
update_project_model(self, project_model_id, configuration)
Update the configuration of a project model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
project_model_id |
str |
Identifier of the project model to update. |
required |
configuration |
dict |
New configuration parameters. |
required |
Returns:
Type | Description |
---|---|
ProjectModelDict |
A dictionary containing the updated project model's details. |
Examples:
>>> configuration = {
... # Updated configuration details
... }
>>> kili.llm.update_project_model(
... project_model_id="your_project_model_id",
... configuration=configuration
... )
Source code in kili/llm/presentation/client/llm.py
def update_project_model(self, project_model_id: str, configuration: dict) -> ProjectModelDict:
"""Update the configuration of a project model.
Args:
project_model_id: Identifier of the project model to update.
configuration: New configuration parameters.
Returns:
A dictionary containing the updated project model's details.
Examples:
>>> configuration = {
... # Updated configuration details
... }
>>> kili.llm.update_project_model(
... project_model_id="your_project_model_id",
... configuration=configuration
... )
"""
project_model_input = ProjectModelToUpdateInput(configuration=configuration)
return self.kili_api_gateway.update_project_model(
project_model_id=project_model_id, project_model=project_model_input
)
update_properties_in_model(self, model_id, model)
Update properties of an existing model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_id |
str |
Identifier of the model to update. |
required |
model |
dict |
A dictionary containing the properties to update, which may include:
- |
required |
Returns:
Type | Description |
---|---|
ModelDict |
A dictionary containing the updated model's details. |
Examples:
>>> # Update the name of a model
>>> kili.llm.update_properties_in_model(
... model_id="your_model_id",
... model={"name": "Updated Model Name"}
... )
Source code in kili/llm/presentation/client/llm.py
def update_properties_in_model(self, model_id: str, model: dict) -> ModelDict:
# pylint: disable=line-too-long
"""Update properties of an existing model.
Args:
model_id: Identifier of the model to update.
model: A dictionary containing the properties to update, which may include:
- `name`: New name of the model.
- `credentials`: Updated credentials for the model, depending on the type:
- For `"AZURE_OPEN_AI"` type:
- `api_key`: The API key for Azure OpenAI.
- `deployment_id`: The deployment ID within Azure.
- `endpoint`: The endpoint URL of the Azure OpenAI service.
- For `"OPEN_AI_SDK"` type:
- `api_key`: The API key for OpenAI SDK.
- `endpoint`: The endpoint URL of the OpenAI SDK service.
Returns:
A dictionary containing the updated model's details.
Examples:
>>> # Update the name of a model
>>> kili.llm.update_properties_in_model(
... model_id="your_model_id",
... model={"name": "Updated Model Name"}
... )
"""
credentials_data = model.get("credentials")
credentials = None
if credentials_data:
existing_model = self.kili_api_gateway.get_model(
model_id=model_id, fields=["id", "type"]
)
if not existing_model:
raise ValueError(f"Model with id {model_id} not found")
model_type = ModelType(existing_model["type"])
if model_type == ModelType.AZURE_OPEN_AI:
credentials = AzureOpenAICredentials(**credentials_data)
elif model_type == ModelType.OPEN_AI_SDK:
credentials = OpenAISDKCredentials(**credentials_data)
else:
raise ValueError(f"Unsupported model type: {model_type}")
model_input = ModelToUpdateInput(
credentials=credentials,
name=model.get("name"),
)
return self.kili_api_gateway.update_properties_in_model(
model_id=model_id, model=model_input
)