Spring AI - openAi Error extracting response of type OpenAiApi$EmbeddingList<......OpenAiApi$Embedding>

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04-10-2024

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Demystifying Spring AI's OpenAI Embedding Errors: A Practical Guide

Are you struggling with errors related to extracting OpenAI embeddings using Spring AI? This article will break down the common "Error extracting response of type OpenAiApi$EmbeddingList..." problem, providing clear explanations, solutions, and practical tips for successful integration.

The Scenario: OpenAI Embeddings and Spring AI

Spring AI is a powerful framework for building AI-powered applications. When working with OpenAI's embedding capabilities, you might encounter an error message like "Error extracting response of type OpenAiApi$EmbeddingList<...OpenAiApi$Embedding>". This error usually signals an issue with how Spring AI handles the response structure returned by OpenAI's API.

Here's a typical example:

@GetMapping("/embeddings")
public List<OpenAiApi.Embedding> getEmbeddings(@RequestParam("text") String text) {
    return openAiService.getEmbeddings(text); 
}

// Inside your OpenAI service:
public List<OpenAiApi.Embedding> getEmbeddings(String text) {
    OpenAiApi.EmbeddingRequest request = OpenAiApi.EmbeddingRequest.builder()
        .model("text-embedding-ada-002")
        .input(text)
        .build();
    OpenAiApi.EmbeddingResponse response = openAi.createEmbeddings(request);
    // This is where the error occurs:
    return response.getData(); // Expected: List<OpenAiApi.Embedding>
}

This code snippet demonstrates a common scenario where the OpenAI API call returns a response object, and we aim to extract the embedding data (response.getData()). However, the error indicates a mismatch between the expected type (List<OpenAiApi.Embedding>) and the actual response structure.

Understanding the Root Cause: Deserialization and Type Mismatch

The core problem lies in the deserialization process. Spring AI relies on libraries like Jackson to convert the JSON response from OpenAI's API into Java objects. When a type mismatch occurs, the deserialization fails, leading to the error.

The key insights:

• Nested Data Structure: OpenAI's response isn't a simple list of embeddings. It typically includes additional metadata, creating a nested JSON structure.
• Type Mismatch: The OpenAiApi.EmbeddingList class might not accurately reflect the nested structure of the OpenAI response, causing the deserialization to fail.

Resolving the Error: Adapting your Code for Correct Deserialization

Here's how to address the problem:

1. Inspect the JSON Response: First, carefully analyze the JSON structure returned by OpenAI's API using tools like Postman or curl. This will reveal the exact nesting of the data field and any associated metadata.

2. Adjust Data Model: Update your OpenAiApi.EmbeddingList class to accurately represent the response structure. Consider using annotations like @JsonProperty to map JSON fields to Java properties, ensuring a correct mapping.

Example:

public class OpenAiApi {
    public static class EmbeddingList {
        @JsonProperty("data")
        private List<Embedding> data; 

        // Getters and setters for the data field
    }

    public static class Embedding {
        private String object;
        private List<Double> embedding;

        // Getters and setters for object and embedding fields
    }
}

3. Utilize Spring's Type Conversion: If necessary, leverage Spring's type conversion capabilities to handle any complex mapping or transformation during deserialization. This can involve custom converters or the use of Spring's @Convert annotation.

4. Employ a Helper Method: Create a helper method within your OpenAI service to extract the data field from the response object and return a properly formatted list of OpenAiApi.Embedding.

Example:

public List<OpenAiApi.Embedding> getEmbeddings(String text) {
    OpenAiApi.EmbeddingRequest request = OpenAiApi.EmbeddingRequest.builder()
        .model("text-embedding-ada-002")
        .input(text)
        .build();
    OpenAiApi.EmbeddingResponse response = openAi.createEmbeddings(request);
    return extractEmbeddings(response); 
}

private List<OpenAiApi.Embedding> extractEmbeddings(OpenAiApi.EmbeddingResponse response) {
    return response.getData(); 
}

Additional Tips for Smooth Integration:

• Thorough Documentation: Refer to OpenAI's API documentation for detailed specifications of the response structure, including field names and their data types.
• Testing and Debugging: Implement robust unit tests to validate your code and ensure correct deserialization. Utilize debugging tools to track down any unexpected behavior.
• Version Compatibility: Ensure that your Spring AI library and OpenAI client libraries are compatible with the specific versions of the OpenAI API you are using.

Conclusion

Understanding the nuances of deserialization and the JSON structure returned by OpenAI's API is crucial for smooth integration with Spring AI. By carefully inspecting the response, adapting your data models, and leveraging Spring's type conversion features, you can effectively tackle the "Error extracting response of type OpenAiApi$EmbeddingList..." problem and unlock the power of OpenAI embeddings within your applications.