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Biomedical LLM Chatbot interacting with Knowledge Graphs

This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed. Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f

Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.

Installation

conda create -n llm_chatbot python=3.9.7
pip install requirements.txt

How to run?

streamlit run streamlit_app.py

How to use?

Enter a query in the text box and press enter to receive a response.

Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."

Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.

How does it look like?

Prototype

About

Web application that allows you to interact with biomedical knowledge graphs.

Resources

Readme
Activity

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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" + '

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Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Repository files navigation
Biomedical LLM Chatbot interacting with Knowledge Graphs
This is a web application that allows you to interact with GPT, augmented with a Neo4j graph database based on PubMed.
Base code and idea from: https://medium.com/towards-artificial-intelligence/using-large-language-models-to-build-a-biomedical-chatbot-and-deploying-it-af7818a0ab7f
Note that the Neo4j graph database (connection info) and how to fill it is not part of the repository.
Installation
conda create -n llm_chatbot python=3.9.7
pip install requirements.txt
How to run?
streamlit run streamlit_app.py
How to use?
Enter a query in the text box and press enter to receive a response.
Ex query: "Give me an article from 2023 published in The lancet. Gastroenterology & hepatology."
Note that the database is essential for this query as chatGPT has no knowledge beyond September 2021.
How does it look like?
Prototype
About
Web application that allows you to interact with biomedical knowledge graphs.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages