RAG retrieval system for the tutor (roadmap M4) #323

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RAG retrieval system for the tutor (roadmap M4)#323
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agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request

Description

@AndresL230
AndresL230
opened on Jul 7, 2026

Context

Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.

This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.

Scope (to refine)

  • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
  • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
  • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
  • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
  • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
  • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)

Relationships

Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.

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Metadata

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agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request

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Projects

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    Milestone

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    , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
     blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

    Skip to content
    RAG retrieval system for the tutor (roadmap M4) #323
    Closed
    Closed
    RAG retrieval system for the tutor (roadmap M4)#323
    Assignees
    Darkest-Teddy
    Labels
    agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
    Description
    @AndresL230
    AndresL230
    opened on Jul 7, 2026
    Context
    Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
    This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
    Scope (to refine)
    • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
    • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
    • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
    • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
    • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
    • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
    Relationships
    Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
    Metadata
    Metadata
    Assignees
    Labels
    agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
    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)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      RAG retrieval system for the tutor (roadmap M4) #323
      Closed
      Closed
      RAG retrieval system for the tutor (roadmap M4)#323
      Assignees
      Darkest-Teddy
      Labels
      agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
      Description
      @AndresL230
      AndresL230
      opened on Jul 7, 2026
      Context
      Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
      This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
      Scope (to refine)
      • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
      • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
      • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
      • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
      • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
      • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
      Relationships
      Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
      Metadata
      Metadata
      Assignees
      Labels
      agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
      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)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

        Skip to content
        RAG retrieval system for the tutor (roadmap M4) #323
        Closed
        Closed
        RAG retrieval system for the tutor (roadmap M4)#323
        Assignees
        Darkest-Teddy
        Labels
        agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
        Description
        @AndresL230
        AndresL230
        opened on Jul 7, 2026
        Context
        Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
        This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
        Scope (to refine)
        • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
        • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
        • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
        • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
        • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
        • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
        Relationships
        Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
        Metadata
        Metadata
        Assignees
        Labels
        agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
        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)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

          Skip to content
          RAG retrieval system for the tutor (roadmap M4) #323
          Closed
          Closed
          RAG retrieval system for the tutor (roadmap M4)#323
          Assignees
          Darkest-Teddy
          Labels
          agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
          Description
          @AndresL230
          AndresL230
          opened on Jul 7, 2026
          Context
          Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
          This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
          Scope (to refine)
          • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
          • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
          • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
          • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
          • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
          • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
          Relationships
          Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
          Metadata
          Metadata
          Assignees
          Labels
          agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
          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)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

            Skip to content
            RAG retrieval system for the tutor (roadmap M4) #323
            Closed
            Closed
            RAG retrieval system for the tutor (roadmap M4)#323
            Assignees
            Darkest-Teddy
            Labels
            agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
            Description
            @AndresL230
            AndresL230
            opened on Jul 7, 2026
            Context
            Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
            This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
            Scope (to refine)
            • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
            • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
            • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
            • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
            • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
            • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
            Relationships
            Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
            Metadata
            Metadata
            Assignees
            Labels
            agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
            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)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              RAG retrieval system for the tutor (roadmap M4) #323
              Closed
              Closed
              RAG retrieval system for the tutor (roadmap M4)#323
              Assignees
              Darkest-Teddy
              Labels
              agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
              Description
              @AndresL230
              AndresL230
              opened on Jul 7, 2026
              Context
              Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
              This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
              Scope (to refine)
              • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
              • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
              • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
              • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
              • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
              • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
              Relationships
              Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
              Metadata
              Metadata
              Assignees
              Labels
              agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
              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)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                Skip to content
                RAG retrieval system for the tutor (roadmap M4) #323
                Closed
                Closed
                RAG retrieval system for the tutor (roadmap M4)#323
                Assignees
                Darkest-Teddy
                Labels
                agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
                Description
                @AndresL230
                AndresL230
                opened on Jul 7, 2026
                Context
                Roadmap milestone M4 ("Graph-aware RAG tutor end to end") now explicitly names the RAG retrieval system as a deliverable, but there's no dedicated tracking issue. The closest existing issue is #149 (graph-grounded tutor retrieval + tool-use loop), which covers the retrieval + tool-use loop inside the tutor agent — not the RAG system as a whole.
                This issue tracks the RAG retrieval system that grounds the tutor: how student documents, notes, and knowledge-graph context get indexed and retrieved to build the tutor's prompt context.
                Scope (to refine)
                • Retrieval over student-supplied content (documents, notes) + knowledge-graph nodes/edges
                • Chunking / indexing strategy and where embeddings live (Supabase / pgvector vs. external store)
                • Retrieval interface consumed by the tutor agent — feeds the build_system_prompt seam in backend/routes/learn.py
                • Query-performance work on graph_nodes / graph_edges (shared with [P1] Agent migration: graph-grounded tutor retrieval + tool-use loop #149)
                • Retrieval-quality / relevance eval harness (relates to [P2] Agent migration: extraction-accuracy eval harness #148 extraction-accuracy eval harness)
                • Respect column-level encryption: decrypt document/note content at the read boundary before indexing/injecting (see services/encryption.py)
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                Filed as a tracking issue from a roadmap-planning session; scope to be refined by the backend owner.
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                agentsPydantic AI agents / LLM layerbackendBackend / APIenhancementNew feature or request
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