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config
config
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LICENSE
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README.md
README.md
main.py
main.py
requirements.txt
requirements.txt

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sql-interpreter

SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.

sample output

Functionalities

  • Automatic PostgresSQL code generation and execution in postgresSQL server
  • Automatic database understanding before initiating SQL development
  • Automatic Exit condition handled by chatgpt
  • Support for Azure OpenAI
  • Add logging
  • Handling repeated answers
  • Adding function calling support
  • Better Exit condition
  • Using simpler LLMs
  • UI for better engagement
  • Many more bug fixes

Requriements

  1. python 3.8.10
  2. pip

Setup

  1. Setup Virtual Environment

    i. Ubuntu

    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate

    ii.Windows

    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate

  2. Install required libraries

pip install -r requirements.txt

  1. Maintain .env as per your configurations

    i. Ubuntu

    cp config/.env.example config/.env

    ii. Windows

    copy config\.env.example config\.env

    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.

  2. Run the code and have fun

python main.py

Disclaimer

This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.

About

SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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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README.md
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main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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('^' + ".*" + '

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config
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.gitignore
LICENSE
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README.md
README.md
main.py
main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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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main.py
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requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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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11 Commits
Folders and files
NameName
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config
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.gitignore
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README.md
main.py
main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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
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11 Commits
Folders and files
NameName
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config
config
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
main.py
main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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
Latest commit
History
11 Commits
Folders and files
NameName
Last commit message
Last commit date
config
config
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
main.py
main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
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); }
})();
})();

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.gitignore
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README.md
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main.py
requirements.txt
requirements.txt
Repository files navigation
sql-interpreter
SQL interpreter based on Auto-GPT to autonomously generate and run postgressql code based on natural language input.
sample output
Functionalities
  •  Automatic PostgresSQL code generation and execution in postgresSQL server
  •  Automatic database understanding before initiating SQL development
  •  Automatic Exit condition handled by chatgpt
  •  Support for Azure OpenAI
  •  Add logging
  •  Handling repeated answers
  •  Adding function calling support
  •  Better Exit condition
  •  Using simpler LLMs
  •  UI for better engagement
  •  Many more bug fixes
Requriements
  1. python 3.8.10
  2. pip
Setup
  1. Setup Virtual Environment
    i. Ubuntu
    pip3 install virtualenvpython3 -m virtualenv venvsource venv/bin/activate
    ii.Windows
    pip install virtualenvpython -m virtualenv venv.\venv\Scripts\activate
  2. Install required libraries
pip install -r requirements.txt
  1. Maintain .env as per your configurations
    i. Ubuntu
    cp config/.env.example config/.env
    ii. Windows
    copy config\.env.example config\.env
    iii. Maintain PostgresSQL DB credentials and OpenAI API credentials in the .env file.
  2. Run the code and have fun
python main.py
Disclaimer
This repository is a basic version of an SQL Interpreter for PostgresSQL databases. This can generate SQL queries and uses LLMs to do so. Currently it only supports ChatGPT and OpenAI API calls. Would love to see the LLM community to build more on this public repo to make it more robust and useful.
About
SQL interpreter based on Auto-GPT to autonomously generate and run sql code based on natural language input.
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