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Graph RAG

graph-rag

This repo provides an implementation of GraphRAG which does the following:

  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph

Getting Started

There are two components here:

  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o

Neo4J Docker Setup

Download the following JAR to your $/user/plugins directory - APOC

Now run the following docker command (assuming you have Docker Desktop installed)

docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.

Run App

Important - Add you OpenAI API key before proceeding.

First ensure you have installed the dependencies. I use Poetry for deps management.

poetry install

Next start up the streamlit app

streamlit run .\graph_rag\app.py

Setup the Knowledge Graph

graph-rag

The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.

You can now chat with the LLM which has a graph based knowledge base to feed from.

Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
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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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Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, '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('^' + ".*" + '

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Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 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('^' + ".*" + '

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poetry.lock
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Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 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" + '

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pyproject.toml
Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, '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('^' + ".*" + '

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poetry.lock
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pyproject.toml
Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 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('^' + ".*" + '

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.gitignore
.gitignore
README.md
README.md
poetry.lock
poetry.lock
pyproject.toml
pyproject.toml
Repository files navigation
Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
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, 'i');
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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Graph RAG
graph-rag
This repo provides an implementation of GraphRAG which does the following:
  • Extracts content from a series of YouTube videos, Wikipedia articles & text files. It is initially setup with resources on Garmin watches but you can swap this out for anything you like
  • Once extracted it will build a knowledge graph for all the extracted entities in the content
  • A custom retriever will combine a hybrid approach using both vector nearest neighbor & cipher queries to extract relevant content to the users query
  • Finally,, we will bring this all together in a basic Stramlit chat interface so you can talk with the knowledge graph
Getting Started
There are two components here:
  1. A Neo4J instance running locally within a docker container
  2. The Python app which is managing graph construction and query via GPT-4o
Neo4J Docker Setup
Download the following JAR to your $/user/plugins directory - APOC
Now run the following docker command (assuming you have Docker Desktop installed)
docker run `
-p 7474:7474 -p 7687:7687 `
-v ${PWD}/data:/data -v ${PWD}/plugins:/plugins `
--name neo4j-v5-apoc `
-e NEO4J_apoc_export_file_enabled=true `
-e NEO4J_apoc_import_file_enabled=true `
-e NEO4J_apoc_import_file_use_neo4j_config=true `
-e NEO4J_PLUGINS='["apoc"]' `
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*" `
neo4j:5.20.0

Navigate to the URL exposed by the container and set the password, ensure the app has this before starting it up.
Run App
Important - Add you OpenAI API key before proceeding.
First ensure you have installed the dependencies. I use Poetry for deps management.
poetry install
Next start up the streamlit app
streamlit run .\graph_rag\app.py
Setup the Knowledge Graph
graph-rag
The first time you run the app you'll need to build the graph. This can be achieved with the default resources right away by hitting the "Populate Graph" button. This will take a few minutes but will result in a fully formed graph that you can visualize in Neo4j.
You can now chat with the LLM which has a graph based knowledge base to feed from.
Resetting the graph will remove all nodes and edges as well as any metadata, useful if you want to start again.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages