Fix Extraction Failures: Rediscovering Deleted Records

by Alex Johnson 55 views

Introduction

It's incredibly frustrating when you expect a system to perform a certain way, only to have it fall short, especially when it comes to data management. Our recent test, aptly named extraction rediscovers physically deleted records, has been throwing a curveball. The core issue? When records are physically deleted from our database and we re-run the extraction process, those vanished records aren't making a comeback as expected. This article delves deep into why this is happening, explores the current implementation, analyzes the root cause, and proposes several solutions to get our extraction process back on track.

The Problem Scenario:

Let's paint a clear picture of what's going wrong. We start with an initial extraction that successfully pulls in 7 records, let's call them r1 through r7. Then, we simulate a scenario where 3 of these records – r1, r2, and r3 – are physically deleted from the database. This leaves us with 4 records (r4-r7) still intact. Logically, when we re-run the extraction, we'd anticipate it to be smart enough to notice the data discrepancy and rediscover those 3 deleted records, bringing our total back up to the original 7. However, the reality is starkly different. The test output shows that after re-running the extraction, we're still stuck with only 4 records. The system isn't detecting any new records, failing to account for the previously existing but now deleted ones. The test output clearly states: Initial: 7, After delete: 4, Final: 4, highlighting the exact point of failure.

This isn't just a minor bug; it points to a fundamental misunderstanding or flaw in how our extraction process handles data that has been removed and should, in theory, be re-identified. Understanding this behavior is crucial for maintaining data integrity and ensuring our extraction processes are robust and reliable. We need to ensure that deletions are not permanent blind spots in our data retrieval pipeline.

Root Cause Analysis: Why the Discrepancy?

To truly fix the extraction fails to rediscover physically deleted records issue, we need to understand why it's happening. The current implementation of our extraction system, while functional for many scenarios, has a critical flaw in its deduplication logic that prevents it from handling physically deleted records correctly. Let's break down how it currently operates and where it goes awry.

Current Implementation Explained

Our extraction system performs a multi-step process:

  1. Existing Records Passed to LLM: When the extraction process runs, it first identifies the records that are currently in the database. In our failing test scenario, this would be the 4 remaining records (r4-r7). Crucially, these records are passed to the Large Language Model (LLM) without their unique record_ids. This is handled by the build_existing_records_context() function in R/utils.R (lines 87-88). The absence of IDs is a key factor.

  2. Prompt Instructs LLM to Skip Duplicates: The prompt given to the LLM contains an instruction that seems straightforward: "Skip interactions already listed in the existing records section" (found in inst/prompts/extraction_prompt.md, line 67). The intention is for the LLM to compare the newly extracted data against what's already known and avoid re-adding duplicates.

  3. LLM Performs Semantic Matching: The LLM's task is to read through the provided document (the paper) and extract all relevant interactions. It then compares these extracted interactions against the existing records provided in the context. Based on its semantic understanding, it's supposed to return only those interactions it deems