Invoke LLM to Generate String Response

Oct 13, 2025 by thesweetdesigns.com 119 views

Introduction

The generate_structured approach has been a subject of concern due to its severe issues with losing detail from the generate_str call. This article aims to provide a comprehensive analysis of the current implementation and propose a revised approach to address the existing problems.

Current Implementation

The current implementation of generate_structured involves a two-step process:

Invoke LLM to Generate String Response: The first step involves invoking the Large Language Model (LLM) to generate a string response using the generate_str method.
Pass Text through Instructor to Extract Structured Data: The second step involves passing the generated text through the Instructor to extract structured data.

Code Snippet

async def generate_structured(
    self,
    message,
    response_model: Type[ModelT],
    request_params: RequestParams | None = None,
) -> ModelT:
    # First we invoke the LLM to generate a string response
    # We need to do this in a two-step process because Instructor doesn't
    # know how to invoke MCP tools via call_tool, so we'll handle all the
    # processing first and then pass the final response through Instructor
    response = await self.generate_str(
        message=message,
        request_params=request_params,
    )

    # Next we pass the text through instructor to extract structured data
    client = instructor.from_openai(
        OpenAI(api_key=self.context.config.openai.api_key, base_url=self.context.config.openai.base_url),
        mode=instructor.Mode.TOOLS_STRICT,
    )

    params = self.get_request_params(request_params)
    model = await self.select_model(params)

    # Extract structured data from natural language
    structured_response = client.chat.completions.create(
        model=model or "gpt-4o",
        response_model=response_model,
        messages=[
            {"role": "user", "content": response},
        ],
    )

    return structured_response

Problems with the Current Implementation

The current implementation has several issues that need to be addressed:

Loss of Detail: The generate_str method loses detail from the original message, which can lead to inaccurate structured data extraction.
Inefficient Processing: The two-step process involves invoking the LLM and then passing the text through the Instructor, which can be inefficient and time-consuming.

Revised Approach

To address the existing problems, a revised approach can be proposed:

Integrate LLM and Instructor: Instead of invoking the LLM and then passing the text through the Instructor, the LLM can be integrated with the Instructor to extract structured data directly from the natural language input.
Use a Single-Step Process: The revised approach can use a single-step process to generate structured data, eliminating the need for a two-step process.

Code Snippet

async def generate_structured(
    self,
    message,
    response_model: Type[ModelT],
    request_params: RequestParams | None = None,
) -> ModelT:
    # Integrate LLM and Instructor to extract structured data
    client = instructor.from_openai(
        OpenAI(api_key=self.context.config.openai.api_key, base_url=self.context.config.openai.base_url),
        mode=instructor.Mode.TOOLS_STRICT,
    )

    params = self.get_request_params(request_params)
    model = await self.select_model(params)

    # Extract structured data from natural language using a single-step process
    structured_response = client.chat.completions.create(
        model=model or "gpt-4o",
        response_model=response_model,
        messages=[
            {"role": "user", "content": message},
        ],
    )

    return structured_response

Benefits of the Revised Approach

The revised approach offers several benefits:

Improved Accuracy: The integrated LLM and Instructor can extract structured data more accurately, reducing the loss of detail from the original message.
Increased Efficiency: The single-step process can reduce processing time and improve overall efficiency.
Simplified Code: The revised approach can simplify the code, making it easier to maintain and update.

Conclusion

Q: What are the main issues with the current implementation of `generate_structured`?

A: The current implementation of generate_structured has several issues, including:

Loss of detail: The generate_str method loses detail from the original message, which can lead to inaccurate structured data extraction.
Inefficient processing: The two-step process involves invoking the LLM and then passing the text through the Instructor, which can be inefficient and time-consuming.

Q: What is the proposed revised approach to `generate_structured`?

A: The proposed revised approach involves integrating the LLM and Instructor to extract structured data directly from the natural language input. This approach uses a single-step process, eliminating the need for a two-step process.

Q: How does the revised approach improve accuracy and efficiency?

A: The revised approach improves accuracy and efficiency in several ways:

Improved accuracy: The integrated LLM and Instructor can extract structured data more accurately, reducing the loss of detail from the original message.
Increased efficiency: The single-step process can reduce processing time and improve overall efficiency.
Simplified code: The revised approach can simplify the code, making it easier to maintain and update.

Q: What are the benefits of using a single-step process in `generate_structured`?

A: The benefits of using a single-step process in generate_structured include:

Reduced processing time: The single-step process can reduce processing time and improve overall efficiency.
Improved accuracy: The single-step process can improve accuracy by reducing the loss of detail from the original message.
Simplified code: The single-step process can simplify the code, making it easier to maintain and update.

Q: How can developers implement the revised approach to `generate_structured`?

A: Developers can implement the revised approach to generate_structured by:

Integrating the LLM and Instructor: Developers can integrate the LLM and Instructor to extract structured data directly from the natural language input.
Using a single-step process: Developers can use a single-step process to generate structured data, eliminating the need for a two-step process.
Simplifying the code: Developers can simplify the code by removing unnecessary steps and improving the overall efficiency of the process.

Q: What are the potential challenges of implementing the revised approach to `generate_structured`?

A: The potential challenges of implementing the revised approach to generate_structured include:

Integration complexity: Integrating the LLM and Instructor can be complex and require significant development effort.
Performance optimization: Optimizing the performance of the single-step process can be challenging and require significant testing and iteration.
Code maintenance: Maintaining the simplified code can be challenging and require significant effort to ensure that the code remains accurate and efficient.

Q: How can developers overcome the potential challenges of implementing the revised approach to `generate_structured`?

A: Developers can overcome the potential challenges of implementing the revised approach to generate_structured by:

Seeking expert guidance: Developers can seek expert guidance from experienced developers and technical leaders to ensure that the implementation is accurate and efficient.
Conducting thorough testing: Developers can conduct thorough testing to ensure that the implementation is accurate and efficient.
Continuously monitoring and maintaining the code: Developers can continuously monitor and maintain the code to ensure that it remains accurate and efficient.

Conclusion

The revised approach to generate_structured offers several benefits, including improved accuracy and efficiency. By integrating the LLM and Instructor and using a single-step process, developers can create more accurate and efficient structured data extraction systems. However, implementing the revised approach can be challenging and require significant development effort. By seeking expert guidance, conducting thorough testing, and continuously monitoring and maintaining the code, developers can overcome the potential challenges and create a more accurate and efficient generate_structured system.