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Suggestion: replacing the default PDF parser with a more capable alternative #77

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Suggestion: replacing the default PDF parser with a more capable alternative#77

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@gwokhou
gwokhou
opened on May 29, 2026

I strongly recommend replacing the original PDF parser with Mineru (a better parser).

  • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.

  • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.

  • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.

  • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.

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Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
      Skip to content
      Suggestion: replacing the default PDF parser with a more capable alternative #77
      Open
      Open
      Suggestion: replacing the default PDF parser with a more capable alternative#77
      Description
      @gwokhou
      gwokhou
      opened on May 29, 2026
      I strongly recommend replacing the original PDF parser with Mineru (a better parser).
      • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
      • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
      • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
      • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
          Skip to content
          Suggestion: replacing the default PDF parser with a more capable alternative #77
          Open
          Open
          Suggestion: replacing the default PDF parser with a more capable alternative#77
          Description
          @gwokhou
          gwokhou
          opened on May 29, 2026
          I strongly recommend replacing the original PDF parser with Mineru (a better parser).
          • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
          • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
          • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
          • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
              Skip to content
              Suggestion: replacing the default PDF parser with a more capable alternative #77
              Open
              Open
              Suggestion: replacing the default PDF parser with a more capable alternative#77
              Description
              @gwokhou
              gwokhou
              opened on May 29, 2026
              I strongly recommend replacing the original PDF parser with Mineru (a better parser).
              • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
              • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
              • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
              • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
                  Skip to content
                  Suggestion: replacing the default PDF parser with a more capable alternative #77
                  Open
                  Open
                  Suggestion: replacing the default PDF parser with a more capable alternative#77
                  Description
                  @gwokhou
                  gwokhou
                  opened on May 29, 2026
                  I strongly recommend replacing the original PDF parser with Mineru (a better parser).
                  • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
                  • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
                  • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
                  • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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var __re = new RegExp('^' + ".*" + '
Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
                      Skip to content
                      Suggestion: replacing the default PDF parser with a more capable alternative #77
                      Open
                      Open
                      Suggestion: replacing the default PDF parser with a more capable alternative#77
                      Description
                      @gwokhou
                      gwokhou
                      opened on May 29, 2026
                      I strongly recommend replacing the original PDF parser with Mineru (a better parser).
                      • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
                      • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
                      • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
                      • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
                          Skip to content
                          Suggestion: replacing the default PDF parser with a more capable alternative #77
                          Open
                          Open
                          Suggestion: replacing the default PDF parser with a more capable alternative#77
                          Description
                          @gwokhou
                          gwokhou
                          opened on May 29, 2026
                          I strongly recommend replacing the original PDF parser with Mineru (a better parser).
                          • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
                          • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
                          • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
                          • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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, 'i');
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Suggestion: replacing the default PDF parser with a more capable alternative · Issue #77 · VectifyAI/OpenKB · GitHub
                              Skip to content
                              Suggestion: replacing the default PDF parser with a more capable alternative #77
                              Open
                              Open
                              Suggestion: replacing the default PDF parser with a more capable alternative#77
                              Description
                              @gwokhou
                              gwokhou
                              opened on May 29, 2026
                              I strongly recommend replacing the original PDF parser with Mineru (a better parser).
                              • My Use Case: Importing computer science papers from Zotero into OpenKB to build a research knowledge base.
                              • The Problem I Encountered: Initially, I used the deepseek-v4-flash model. During the Wiki compilation phase, the LLM frequently returned empty JSON responses, triggering numerous retry attempts; consequently, file processing throughput was low, and the failure rate was high. Later, I switched to the more advanced deepseek-v4-pro model, but I observed no improvement.
                              • My Solution and Results: I modified the local OpenKB codebase to replace the original PDF parser with Mineru. As a result, the frequency of empty JSON errors returned by the LLM during the Wiki compilation phase decreased significantly, while file processing throughput increased and the failure rate dropped.
                              • My Conclusion: Garbage in, garbage out. For complex, lengthy documents like research papers, the primary bottleneck lies in the quality of PDF parsing—a factor far more critical than the quality of the subsequent Wiki compilation. I recommend replacing the default parser with a more capable alternative, or at the very least, offering users a wider range of options.
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