How to let user choose one model to anwser question via python api? #3240
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To ensure that only one model is used to answer the question in your Python API setup, you need to explicitly specify the model in the configuration and ensure that the API call is directed to only that model. Here is an example of how you can modify the def build(
self,
model: str,
provider: str,
api_key: Optional[str] = None,
streaming: bool = True,
temperature: Optional[float] = 0.7,
model_kwargs: Optional[Dict[str, Any]] = {},
top_p: Optional[float] = None,
top_k: Optional[int] = None,
n: int = 1,
max_tokens: int = 256,
max_retries: int = 6,
verbose: bool = False,
) -> LanguageModel:
try:
import litellm # type: ignore
litellm.drop_params = True
litellm.set_verbose = verbose
except ImportError:
raise ChatLiteLLMException(
"Could not import litellm python package. " "Please install it with `pip install litellm`"
)
provider_map = {
"OpenAI": "openai_api_key",
"Azure": "azure_api_key",
"Anthropic": "anthropic_api_key",
"Replicate": "replicate_api_key",
"Cohere": "cohere_api_key",
"OpenRouter": "openrouter_api_key",
}
# Set the API key based on the provider
api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
if variable_name := provider_map.get(provider):
api_keys[variable_name] = api_key
else:
raise ChatLiteLLMException(
f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
)
# Ensure only the specified model is used
if model not in ["gpt-3.5-turbo", "llama"]:
raise ValueError(f"Unsupported model: {model}")
LLM = ChatLiteLLM(
model=model,
client=None,
streaming=streaming,
temperature=temperature,
model_kwargs=model_kwargs if model_kwargs is not None else {},
top_p=top_p,
top_k=top_k,
n=n,
max_tokens=max_tokens,
max_retries=max_retries,
openai_api_key=api_keys["openai_api_key"],
azure_api_key=api_keys["azure_api_key"],
anthropic_api_key=api_keys["anthropic_api_key"],
replicate_api_key=api_keys["replicate_api_key"],
cohere_api_key=api_keys["cohere_api_key"],
openrouter_api_key=api_keys["openrouter_api_key"],
)
return LLMIn this example, the |
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Yes, Langflow provides a feature called "Component Freeze" that allows you to lock a component's previous output state to prevent it from re-running. This feature is useful for avoiding extra token usage and removing repetition when the output should remain constant. Using the
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@dosu |
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To freeze a component named Programmatically Freezing the Component# Assuming you have access to the component object
component = get_component_by_id("OpenAIModel-1pAyL") # Replace with the actual method to get the component
component['node']['frozen'] = True
# Save the updated component back to the project
save_component(component) # Replace with the actual method to save the componentUsing an API to Freeze the ComponentIf you are using an API, you would need to send a request to update the component's import requests
component_id = "OpenAIModel-1pAyL"
api_url = f"http://your-langflow-api.com/components/{component_id}"
# Payload to update the frozen attribute
payload = {
"node": {
"frozen": True
}
}
# Send the request to update the component
response = requests.patch(api_url, json=payload)
if response.status_code == 200:
print("Component frozen successfully.")
else:
print("Failed to freeze the component.")Notes
By setting the |
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@dosu |
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A Correct HTTP Method for Freezing a ComponentTo freeze a component, you should use the import requests
component_id = "OpenAIModel-1pAyL"
api_url = f"http://your-langflow-api.com/components/{component_id}"
# Payload to update the frozen attribute
payload = {
"node": {
"frozen": True
}
}
# Headers (if authentication is required)
headers = {
"Authorization": "Bearer your_api_token",
"Content-Type": "application/json"
}
# Send the PATCH request to update the component
response = requests.patch(api_url, json=payload, headers=headers)
if response.status_code == 200:
print("Component frozen successfully.")
else:
print(f"Failed to freeze the component: {response.status_code} - {response.text}")Ensure Correct Endpoint and Method
Example of Correct API UsageHere is a complete example assuming the API endpoint and authentication are correctly set up: import requests
component_id = "OpenAIModel-1pAyL"
api_url = f"http://your-langflow-api.com/api/v1/components/{component_id}"
# Payload to update the frozen attribute
payload = {
"node": {
"frozen": True
}
}
# Headers (if authentication is required)
headers = {
"Authorization": "Bearer your_api_token",
"Content-Type": "application/json"
}
# Send the PATCH request to update the component
response = requests.patch(api_url, json=payload, headers=headers)
if response.status_code == 200:
print("Component frozen successfully.")
else:
print(f"Failed to freeze the component: {response.status_code} - {response.text}")By ensuring you are using the correct HTTP method ( |
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Hi,
I'd like to ask how to choose just one model to anwser my question. In the langflow, I put the question to two model(GPT and llama), and then combine their output.
The code below is tried to remove LLM from the flow. But I found that it will still put the question to both model
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