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dosubot avatar dosubot commented on June 20, 2024 2

To resolve the 500 Internal Server Error when calling the API interface with multiple Azure OpenAI modules, ensure the following configurations are correctly set:

  1. API Key: Verify that the API key is valid and correctly set.
  2. Azure Endpoint: Ensure the Azure endpoint URL is correctly formatted and includes the resource.
  3. Deployment Name: Confirm the deployment name is accurate.
  4. API Version: Use a valid API version from the supported list.
  5. Model Name: Ensure you are using a valid model name from the AZURE_OPENAI_MODELS list.
  6. Temperature and Max Tokens: Set valid values for temperature and max tokens.

Here is an example of how you might configure these parameters in your Java project:

import java.net.HttpURLConnection;
import java.net.URL;
import java.io.OutputStream;

public class AzureOpenAIClient {
    private static final String API_URL = "https://example-resource.azure.openai.com/";
    private static final String DEPLOYMENT_NAME = "your-deployment-name";
    private static final String API_VERSION = "2023-09-01-preview";
    private static final String API_KEY = "your-api-key";
    private static final String MODEL = "gpt-4";
    private static final double TEMPERATURE = 0.7;
    private static final int MAX_TOKENS = 1000;

    public static void main(String[] args) {
        try {
            URL url = new URL(API_URL + "openai/deployments/" + DEPLOYMENT_NAME + "/completions?api-version=" + API_VERSION);
            HttpURLConnection connection = (HttpURLConnection) url.openConnection();
            connection.setRequestMethod("POST");
            connection.setRequestProperty("Content-Type", "application/json");
            connection.setRequestProperty("Authorization", "Bearer " + API_KEY);
            connection.setDoOutput(true);

            String inputJson = "{"
                + "\"model\": \"" + MODEL + "\","
                + "\"temperature\": " + TEMPERATURE + ","
                + "\"max_tokens\": " + MAX_TOKENS + ","
                + "\"prompt\": \"Your input prompt here\""
                + "}";

            try (OutputStream os = connection.getOutputStream()) {
                byte[] input = inputJson.getBytes("utf-8");
                os.write(input, 0, input.length);
            }

            int responseCode = connection.getResponseCode();
            if (responseCode == 500) {
                System.out.println("Internal Server Error: Check your configuration and API key.");
            } else {
                System.out.println("Response Code: " + responseCode);
                // Handle the response
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Ensure all parameters are correctly set and the API key is valid. If the error persists, check the Azure OpenAI service status or contact Azure support for further assistance [1].

To continue talking to Dosu, mention @dosu.

from langflow.

dosubot avatar dosubot commented on June 20, 2024 2

To resolve the 500 Internal Server Error when making an API request with multiple Azure OpenAI components, ensure that all required parameters are correctly configured and valid. Specifically, check the following:

  1. Azure Endpoint: Ensure that the azure_endpoint is correctly formatted and points to a valid Azure resource. Example: https://example-resource.azure.openai.com/.
  2. Deployment Name: Verify that the azure_deployment is correct and exists in your Azure resource.
  3. API Version: Make sure the api_version is one of the supported versions listed in the API_VERSION_OPTIONS.
  4. API Key: Ensure that the api_key is valid and has the necessary permissions.
  5. Model Name: Confirm that the model is one of the supported models listed in the AZURE_OPENAI_MODELS.

Here is an example of how to configure these parameters in Python:

from langflow.base.models.model import LCModelComponent
from langchain_openai import AzureChatOpenAI
from pydantic.v1 import SecretStr
from langflow.field_typing import Text
from typing import Optional

class AzureChatOpenAIComponent(LCModelComponent):
    display_name: str = "Azure OpenAI"
    description: str = "Generate text using Azure OpenAI LLMs."
    documentation: str = "https://python.langchain.com/docs/integrations/llms/azure_openai"
    beta = False
    icon = "Azure"

    field_order = [
        "model",
        "azure_endpoint",
        "azure_deployment",
        "api_version",
        "api_key",
        "temperature",
        "max_tokens",
        "input_value",
        "system_message",
        "stream",
    ]

    AZURE_OPENAI_MODELS = [
        "gpt-35-turbo",
        "gpt-35-turbo-16k",
        "gpt-35-turbo-instruct",
        "gpt-4",
        "gpt-4-32k",
        "gpt-4-vision",
    ]

    API_VERSION_OPTIONS = [
        "2022-12-01",
        "2023-03-15-preview",
        "2023-05-15",
        "2023-06-01-preview",
        "2023-07-01-preview",
        "2023-08-01-preview",
    ]

    def build_config(self):
        return {
            "model": {
                "display_name": "Model Name",
                "value": self.AZURE_OPENAI_MODELS[0],
                "options": self.AZURE_OPENAI_MODELS,
            },
            "azure_endpoint": {
                "display_name": "Azure Endpoint",
                "info": "Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`",
            },
            "azure_deployment": {
                "display_name": "Deployment Name",
            },
            "api_version": {
                "display_name": "API Version",
                "options": self.API_VERSION_OPTIONS,
                "value": self.API_VERSION_OPTIONS[-1],
                "advanced": True,
            },
            "api_key": {"display_name": "API Key", "password": True},
            "temperature": {
                "display_name": "Temperature",
                "value": 0.7,
            },
            "max_tokens": {
                "display_name": "Max Tokens",
                "advanced": True,
                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
            },
            "code": {"show": False},
            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
            "stream": {
                "display_name": "Stream",
                "info": STREAM_INFO_TEXT,
                "advanced": True,
            },
            "system_message": {
                "display_name": "System Message",
                "info": "System message to pass to the model.",
                "advanced": True,
            },
        }

    def build(
        self,
        model: str,
        azure_endpoint: str,
        input_value: Text,
        azure_deployment: str,
        api_version: str,
        api_key: str,
        temperature: float,
        system_message: Optional[str] = None,
        max_tokens: Optional[int] = 1000,
        stream: bool = False,
    ) -> Text:
        if api_key:
            secret_api_key = SecretStr(api_key)
        else:
            secret_api_key = None
        try:
            output = AzureChatOpenAI(
                model=model,
                azure_endpoint=azure_endpoint,
                azure_deployment=azure_deployment,
                api_version=api_version,
                api_key=secret_api_key,
                temperature=temperature,
                max_tokens=max_tokens or None,
            )
        except Exception as e:
            raise ValueError("Could not connect to AzureOpenAI API.") from e

        return self.get_chat_result(output, stream, input_value, system_message)

Ensure that the values for model, azure_endpoint, azure_deployment, api_version, and api_key are correctly set before making the API request [1][2].

To continue talking to Dosu, mention @dosu.

from langflow.

alex-ge123 avatar alex-ge123 commented on June 20, 2024

this is my api curl

curl -X POST
"http://192.168.0.29:7860/api/v1/run/a912a0a3-a1b3-4dd8-80df-835016ea2dae?stream=false"
-H 'Content-Type: application/json'
-H 'x-api-key: '
-d '{"input_value": "message",
"output_type": "chat",
"input_type": "chat",
"tweaks": {
"ChatInput-S3vqS": {},
"ChatOutput-ZIsW5": {},
"MemoryComponent-OfrPd": {},
"IDGenerator-iGWjc": {},
"Prompt-XzfNj": {},
"Prompt-e7qUH": {},
"TextOutput-no6Nj": {},
"TextOperator-s3kmU": {},
"TextOperator-zskCY": {},
"Pass-CP7JO": {},
"CombineText-CEx4H": {},
"TextInput-dgFKO": {},
"CombineText-Qr2bX": {},
"APIRequest-jEuXi": {},
"RecordsOutput-LgcE9": {},
"RecordsToText-Ryeud": {},
"Prompt-bum9a": {},
"AzureOpenAIModel-Ae4b9": {},
"TextInput-nmKuw": {},
"TextInput-9Inko": {},
"TextInput-d26Mx": {},
"AzureOpenAIModel-4sLz7": {},
"AzureOpenAIModel-xnHhY": {},
"AzureOpenAIModel-SmZ8l": {},
"TextInput-pmxV5": {},
"Prompt-5i7dg": {},
"CombineText-CJX2Q": {},
"CreateRecord-YQSRu": {},
"CreateRecord-4mspE": {},
"TextInput-Wm08d": {},
"TextInput-HxJ1a": {},
"TextInput-AIu7U": {},
"AzureOpenAIModel-YZawU": {},
"Prompt-jcR0T": {},
"TextInput-E07ke": {},
"APIRequest-f1LJz": {},
"TextOutput-FPSMu": {},
"RecordsOutput-rfPmB": {}
}}'

My project has multiple Azure OpenAI components

from langflow.

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