Cogwit Beta Feature Request Disable LLM Call In POST API Search

by StackCamp Team 64 views

Introduction

Hey guys! Today, we're diving into an exciting feature request for Cogwit's beta, focusing on enhancing control over the LLM processing within the search API. This suggestion comes directly from user feedback, highlighting a need for more flexibility when querying data. Let's break down the details and explore why this could be a game-changer for Cogwit users.

Background: The Cognee Discord Discussion

This feature request originated from a conversation on the Cognee Discord server. Specifically, the discussion took place in this thread. A user, @dexters1, raised an important point about the current behavior of the Cognee API and its interaction with Large Language Models (LLMs).

The Core Request: A skipLLMProcessing Flag

The main request is to introduce a boolean flag, skipLLMProcessing, to the POST /api/search payload. This flag would allow users to bypass the LLM processing step and receive only the raw database results for their queries. This would apply to all query types, including GRAPH_* queries and even RAG_COMPLETION, which sometimes leverages LLMs.

{
  "datasets": [
    "string"
  ],
  "datasetIds": ["string"],
  "runInBackground": false,
  "skipLLMProcessing": true
}

Why is this important? Understanding the User's Perspective

The rationale behind this request is pretty compelling. Currently, the LLM attempts to provide the best response it can, often drawing from its broader training dataset. While this can be helpful in many scenarios, it can also lead to responses that are unrelated to the specific data within the user's database. Imagine putting in your data and getting answers that are influenced by some other dataset – confusing, right?

This can result in:

  • Incorrect or Misinformed Responses: The LLM's understanding can overshadow the actual data being searched.
  • Masking Actual Data: It becomes difficult to discern the raw results returned by the database.
  • Confusion About API Functionality: Users may struggle to understand how the Cognee API is truly working if the responses don't directly reflect their data.

Essentially, the user wants more control over the output. They want to see the raw data first and then, if needed, format it using an LLM themselves. This approach offers a clearer understanding of the search results and allows for more precise data manipulation.

The Engineer's Perspective: Flexibility and Control

From an engineering standpoint, this makes a ton of sense. The user, in this case, an engineer, articulated that they could always process the raw database results through an LLM themselves if needed. This DIY approach gives them complete control over the formatting and presentation of the data.

There's an argument for having LLM processing baked in to save on processing costs, but the user rightly points out that having it as an option is far more valuable. This flexibility empowers users to choose the best approach for their specific use case.

Diving Deeper: Use Cases and Benefits of the skipLLMProcessing Flag

To really understand the value of this feature, let's explore some potential use cases and benefits. The skipLLMProcessing flag isn't just a small tweak; it could significantly impact how users interact with the Cogwit API.

Use Case 1: Data Validation and Debugging

Imagine you're building a complex application that relies on Cogwit's search capabilities. You need to ensure that the data you're feeding into Cogwit is being indexed and searched correctly. With the skipLLMProcessing flag, you can directly inspect the raw database results. This allows you to:

  • Verify Data Integrity: See exactly what data is being returned by your queries, ensuring it matches what you expect.
  • Debug Indexing Issues: Identify any discrepancies between your input data and the search results, helping you pinpoint indexing problems.
  • Isolate LLM Influence: Rule out any interference from the LLM, focusing solely on the database's behavior.

This is huge for development and testing. You can confidently validate your data pipeline without the added complexity of LLM interpretation.

Use Case 2: Custom Data Formatting and Presentation

Sometimes, the default formatting provided by the LLM might not suit your needs. You might have specific formatting requirements for your application or want to present the data in a unique way. The skipLLMProcessing flag gives you the freedom to:

  • Apply Custom Styles: Format the raw data according to your application's design and branding.
  • Integrate with Existing Systems: Easily integrate the search results into your existing data processing workflows.
  • Tailor Data Presentation: Present the data in a way that's most meaningful to your users, without being constrained by the LLM's output.

This level of customization is essential for creating a seamless and tailored user experience.

Use Case 3: Cost Optimization

While the user mentioned that baking in LLM processing could save on costs, there are scenarios where bypassing the LLM can actually be more cost-effective. For example, if you're running a high volume of simple queries, skipping the LLM processing can reduce the overall computational load and potentially lower your costs.

This flexibility allows you to optimize your usage of Cogwit based on the specific demands of your application.

Use Case 4: Preventing Data Leakage

In scenarios where data privacy is paramount, having the option to skip LLM processing provides an additional layer of security. By bypassing the LLM, you minimize the risk of sensitive data being inadvertently exposed or used for training purposes.

This is particularly important for industries dealing with confidential information, such as healthcare or finance.

Why This Request Is a High Priority

The user, @dexters1, has rightly flagged this as a very high priority feature request. The ability to control LLM processing directly impacts the accuracy, reliability, and usability of the Cogwit API. Without this control, users may struggle to:

  • Trust the Search Results: If responses are inconsistent or unrelated to the data, users will lose confidence in the API.
  • Effectively Debug Issues: Masked data makes it difficult to diagnose problems and ensure data integrity.
  • Customize the User Experience: Limited formatting options hinder the creation of tailored applications.

By implementing the skipLLMProcessing flag, Cogwit can empower its users, foster trust in the platform, and unlock a wider range of use cases. This isn't just about adding a feature; it's about enhancing the core value proposition of the API.

Proposed Solution: The Technical Details

So, how would this actually work? The proposed solution is straightforward and elegant. A boolean flag, skipLLMProcessing, is added to the JSON payload of the POST /api/search request. When set to true, this flag instructs the API to skip the LLM processing step and return the raw database results.

Let's revisit the example payload:

{
  "datasets": [
    "string"
  ],
  "datasetIds": ["string"],
  "runInBackground": false,
  "skipLLMProcessing": true
}

On the backend, this would require a simple conditional check. If skipLLMProcessing is true, the API would bypass the LLM processing and directly return the results from the database query. If it's false (or not present in the request), the API would continue with the default behavior of processing the results through the LLM.

This approach is:

  • Non-Breaking: It doesn't change the existing API behavior unless the flag is explicitly set.
  • Easy to Implement: The code changes required are relatively minimal.
  • Flexible: It provides a clear and simple way for users to control LLM processing.

Conclusion: Empowering Users with Control

In conclusion, the request to add a skipLLMProcessing flag to the Cogwit API is a significant one. It addresses a core need for greater control and flexibility in how users interact with the platform. By allowing users to bypass the LLM processing step, Cogwit can empower them to:

  • Validate Data Integrity
  • Customize Data Formatting
  • Optimize Costs
  • Enhance Data Privacy

This feature request is a prime example of how user feedback can drive meaningful improvements in software. By listening to its users and addressing their needs, Cogwit can continue to evolve and provide a truly valuable service. Let's hope the Cogwit team considers this high-priority request and implements it soon!