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Feature: Optional image understanding / vision for inline and referenced images #74

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Open
Feature: Optional image understanding / vision for inline and referenced images#74

Description

@jr2804
jr2804
opened on May 26, 2026

Feature Request

OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.

Current Behavior

Image path rewriting (images.py)

  • copy_relative_images() scans for ![alt](relative/path) references
  • Copies referenced image files into wiki/sources/images/<doc_name>/
  • Rewrites links to sources/images/<doc_name>/<filename>
  • Skips images not found on disk, http/https/data: URIs

During LLM compilation

  • The markdown with image references is sent to the LLM as plain text
  • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
  • No image bytes are ever sent to the LLM
  • No vision/multimodal capability is used

Why This Matters

For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:

  • Architecture diagrams: Show signal flow, system topology, protocol stacks
  • Tables rendered as images: Contain normative reference data that isn't in the text
  • Charts and plots: Performance benchmarks, measurement results
  • Schematics: Circuit diagrams, filter responses, encoder block diagrams

A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.

Proposed Solution (Optional / Configurable)

Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:

  1. Config flag: image_understanding: true (default: false)
  2. Detection: During compilation, identify ![]() references in the markdown
  3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
  4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
  5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference

This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.

Alternative (Minimal)

If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.

Environment

  • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
  • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content

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Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
      Skip to content
      Feature: Optional image understanding / vision for inline and referenced images #74
      Open
      Open
      Feature: Optional image understanding / vision for inline and referenced images#74
      Description
      @jr2804
      jr2804
      opened on May 26, 2026
      Feature Request
      OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
      Current Behavior
      Image path rewriting (images.py)
      • copy_relative_images() scans for ![alt](relative/path) references
      • Copies referenced image files into wiki/sources/images/<doc_name>/
      • Rewrites links to sources/images/<doc_name>/<filename>
      • Skips images not found on disk, http/https/data: URIs
      During LLM compilation
      • The markdown with image references is sent to the LLM as plain text
      • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
      • No image bytes are ever sent to the LLM
      • No vision/multimodal capability is used
      Why This Matters
      For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
      • Architecture diagrams: Show signal flow, system topology, protocol stacks
      • Tables rendered as images: Contain normative reference data that isn't in the text
      • Charts and plots: Performance benchmarks, measurement results
      • Schematics: Circuit diagrams, filter responses, encoder block diagrams
      A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
      Proposed Solution (Optional / Configurable)
      Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
      1. Config flag: image_understanding: true (default: false)
      2. Detection: During compilation, identify ![]() references in the markdown
      3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
      4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
      5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
      This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
      Alternative (Minimal)
      If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
      Environment
      • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
      • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
      Metadata
      Metadata
      Assignees
      No one assigned
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        No labels
        Type
        No type
        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, 'i');
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var __re = new RegExp('^' + ".*" + '
Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
          Skip to content
          Feature: Optional image understanding / vision for inline and referenced images #74
          Open
          Open
          Feature: Optional image understanding / vision for inline and referenced images#74
          Description
          @jr2804
          jr2804
          opened on May 26, 2026
          Feature Request
          OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
          Current Behavior
          Image path rewriting (images.py)
          • copy_relative_images() scans for ![alt](relative/path) references
          • Copies referenced image files into wiki/sources/images/<doc_name>/
          • Rewrites links to sources/images/<doc_name>/<filename>
          • Skips images not found on disk, http/https/data: URIs
          During LLM compilation
          • The markdown with image references is sent to the LLM as plain text
          • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
          • No image bytes are ever sent to the LLM
          • No vision/multimodal capability is used
          Why This Matters
          For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
          • Architecture diagrams: Show signal flow, system topology, protocol stacks
          • Tables rendered as images: Contain normative reference data that isn't in the text
          • Charts and plots: Performance benchmarks, measurement results
          • Schematics: Circuit diagrams, filter responses, encoder block diagrams
          A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
          Proposed Solution (Optional / Configurable)
          Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
          1. Config flag: image_understanding: true (default: false)
          2. Detection: During compilation, identify ![]() references in the markdown
          3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
          4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
          5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
          This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
          Alternative (Minimal)
          If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
          Environment
          • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
          • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            No labels
            No labels
            Type
            No type
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, 'i');
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
              Skip to content
              Feature: Optional image understanding / vision for inline and referenced images #74
              Open
              Open
              Feature: Optional image understanding / vision for inline and referenced images#74
              Description
              @jr2804
              jr2804
              opened on May 26, 2026
              Feature Request
              OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
              Current Behavior
              Image path rewriting (images.py)
              • copy_relative_images() scans for ![alt](relative/path) references
              • Copies referenced image files into wiki/sources/images/<doc_name>/
              • Rewrites links to sources/images/<doc_name>/<filename>
              • Skips images not found on disk, http/https/data: URIs
              During LLM compilation
              • The markdown with image references is sent to the LLM as plain text
              • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
              • No image bytes are ever sent to the LLM
              • No vision/multimodal capability is used
              Why This Matters
              For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
              • Architecture diagrams: Show signal flow, system topology, protocol stacks
              • Tables rendered as images: Contain normative reference data that isn't in the text
              • Charts and plots: Performance benchmarks, measurement results
              • Schematics: Circuit diagrams, filter responses, encoder block diagrams
              A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
              Proposed Solution (Optional / Configurable)
              Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
              1. Config flag: image_understanding: true (default: false)
              2. Detection: During compilation, identify ![]() references in the markdown
              3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
              4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
              5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
              This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
              Alternative (Minimal)
              If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
              Environment
              • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
              • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                No labels
                No labels
                Type
                No type
                Projects
                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions
                  
, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
                  Skip to content
                  Feature: Optional image understanding / vision for inline and referenced images #74
                  Open
                  Open
                  Feature: Optional image understanding / vision for inline and referenced images#74
                  Description
                  @jr2804
                  jr2804
                  opened on May 26, 2026
                  Feature Request
                  OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
                  Current Behavior
                  Image path rewriting (images.py)
                  • copy_relative_images() scans for ![alt](relative/path) references
                  • Copies referenced image files into wiki/sources/images/<doc_name>/
                  • Rewrites links to sources/images/<doc_name>/<filename>
                  • Skips images not found on disk, http/https/data: URIs
                  During LLM compilation
                  • The markdown with image references is sent to the LLM as plain text
                  • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
                  • No image bytes are ever sent to the LLM
                  • No vision/multimodal capability is used
                  Why This Matters
                  For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
                  • Architecture diagrams: Show signal flow, system topology, protocol stacks
                  • Tables rendered as images: Contain normative reference data that isn't in the text
                  • Charts and plots: Performance benchmarks, measurement results
                  • Schematics: Circuit diagrams, filter responses, encoder block diagrams
                  A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
                  Proposed Solution (Optional / Configurable)
                  Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
                  1. Config flag: image_understanding: true (default: false)
                  2. Detection: During compilation, identify ![]() references in the markdown
                  3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
                  4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
                  5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
                  This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
                  Alternative (Minimal)
                  If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
                  Environment
                  • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
                  • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    No labels
                    No labels
                    Type
                    No type
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
                      Skip to content
                      Feature: Optional image understanding / vision for inline and referenced images #74
                      Open
                      Open
                      Feature: Optional image understanding / vision for inline and referenced images#74
                      Description
                      @jr2804
                      jr2804
                      opened on May 26, 2026
                      Feature Request
                      OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
                      Current Behavior
                      Image path rewriting (images.py)
                      • copy_relative_images() scans for ![alt](relative/path) references
                      • Copies referenced image files into wiki/sources/images/<doc_name>/
                      • Rewrites links to sources/images/<doc_name>/<filename>
                      • Skips images not found on disk, http/https/data: URIs
                      During LLM compilation
                      • The markdown with image references is sent to the LLM as plain text
                      • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
                      • No image bytes are ever sent to the LLM
                      • No vision/multimodal capability is used
                      Why This Matters
                      For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
                      • Architecture diagrams: Show signal flow, system topology, protocol stacks
                      • Tables rendered as images: Contain normative reference data that isn't in the text
                      • Charts and plots: Performance benchmarks, measurement results
                      • Schematics: Circuit diagrams, filter responses, encoder block diagrams
                      A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
                      Proposed Solution (Optional / Configurable)
                      Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
                      1. Config flag: image_understanding: true (default: false)
                      2. Detection: During compilation, identify ![]() references in the markdown
                      3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
                      4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
                      5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
                      This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
                      Alternative (Minimal)
                      If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
                      Environment
                      • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
                      • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
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Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
                          Skip to content
                          Feature: Optional image understanding / vision for inline and referenced images #74
                          Open
                          Open
                          Feature: Optional image understanding / vision for inline and referenced images#74
                          Description
                          @jr2804
                          jr2804
                          opened on May 26, 2026
                          Feature Request
                          OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
                          Current Behavior
                          Image path rewriting (images.py)
                          • copy_relative_images() scans for ![alt](relative/path) references
                          • Copies referenced image files into wiki/sources/images/<doc_name>/
                          • Rewrites links to sources/images/<doc_name>/<filename>
                          • Skips images not found on disk, http/https/data: URIs
                          During LLM compilation
                          • The markdown with image references is sent to the LLM as plain text
                          • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
                          • No image bytes are ever sent to the LLM
                          • No vision/multimodal capability is used
                          Why This Matters
                          For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
                          • Architecture diagrams: Show signal flow, system topology, protocol stacks
                          • Tables rendered as images: Contain normative reference data that isn't in the text
                          • Charts and plots: Performance benchmarks, measurement results
                          • Schematics: Circuit diagrams, filter responses, encoder block diagrams
                          A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
                          Proposed Solution (Optional / Configurable)
                          Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
                          1. Config flag: image_understanding: true (default: false)
                          2. Detection: During compilation, identify ![]() references in the markdown
                          3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
                          4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
                          5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
                          This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
                          Alternative (Minimal)
                          If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
                          Environment
                          • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
                          • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
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                            No labels
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                            No type
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                            No projects
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                              No milestone
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                              None yet
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                              No branches or pull requests
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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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})();
Feature: Optional image understanding / vision for inline and referenced images · Issue #74 · VectifyAI/OpenKB · GitHub
                              Skip to content
                              Feature: Optional image understanding / vision for inline and referenced images #74
                              Open
                              Open
                              Feature: Optional image understanding / vision for inline and referenced images#74
                              Description
                              @jr2804
                              jr2804
                              opened on May 26, 2026
                              Feature Request
                              OpenKB currently treats images in documents as text-only syntax — the LLM sees ![alt](path/to/image.png) but never the actual image content. This significantly reduces knowledge base quality for technical and scientific documents where figures, diagrams, and charts carry essential information.
                              Current Behavior
                              Image path rewriting (images.py)
                              • copy_relative_images() scans for ![alt](relative/path) references
                              • Copies referenced image files into wiki/sources/images/<doc_name>/
                              • Rewrites links to sources/images/<doc_name>/<filename>
                              • Skips images not found on disk, http/https/data: URIs
                              During LLM compilation
                              • The markdown with image references is sent to the LLM as plain text
                              • The LLM sees ![Figure 3: SBA rendering pipeline](sources/images/doc_name/fig3.png) but cannot see the actual image
                              • No image bytes are ever sent to the LLM
                              • No vision/multimodal capability is used
                              Why This Matters
                              For technical and scientific documents — the kind that benefit most from a knowledge base — figures are often irreplaceable:
                              • Architecture diagrams: Show signal flow, system topology, protocol stacks
                              • Tables rendered as images: Contain normative reference data that isn't in the text
                              • Charts and plots: Performance benchmarks, measurement results
                              • Schematics: Circuit diagrams, filter responses, encoder block diagrams
                              A knowledge base that ignores all of this produces summaries and concept articles that are missing critical information. For example, a 3GPP spec document on "Immersive Audio Rendering" might have 15+ figures showing rendering pipelines, binaural processing chains, and speaker layouts — none of which would be captured.
                              Proposed Solution (Optional / Configurable)
                              Since not all users need image understanding (and it requires a vision-capable model), this should be opt-in:
                              1. Config flag: image_understanding: true (default: false)
                              2. Detection: During compilation, identify ![]() references in the markdown
                              3. Vision pass: For each referenced image file found on disk, send the image to a vision-capable LLM with a prompt like: "Describe this figure from document {doc_name}. Include: caption, what it depicts, key information conveyed, visible text/labels, related concepts."
                              4. Injection: Prepend the vision-generated description as a text block before the image reference in the prompt sent to the summarization LLM
                              5. Wiki output: Include the description in the generated summary/concept pages alongside the image reference
                              This approach is framework-agnostic — it works with any vision-capable model (GPT-4o, Claude 3.5+, Gemini, LLaVA via local Ollama, etc.) and doesn't require changes to the wiki output format.
                              Alternative (Minimal)
                              If full vision integration is too complex, a simpler approach: add an image_caption_step config that lets the user provide pre-generated captions in a sidecar file (e.g., doc_name.images.yaml), which get injected into the LLM prompt. This avoids the vision dependency entirely while still giving the LLM access to image content descriptions.
                              Environment
                              • Document corpus: 3GPP ATIAS technical specifications (converted PDF → markdown with inline image references)
                              • Many documents contain critical figures (protocol diagrams, test setups, signal flow charts) that are essential for understanding the content
                              Metadata
                              Metadata
                              Assignees
                              No one assigned
                                Labels
                                No labels
                                No labels
                                Type
                                No type
                                Projects
                                No projects
                                  Milestone
                                  No milestone
                                  Relationships
                                  None yet
                                  Development
                                  No branches or pull requests
                                  Issue actions