Secure, flexible, and transparent AI coding

Secure, flexible, and transparent AI coding

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Why Choose Tabby?
Tabby is an open-source AI coding assistant, designed to bring the power of AI to your development workflow while keeping you in control. Whether you’re coding in the cloud or on-premises, Tabby offers a flexible, transparent, and highly configurable alternative to proprietary solutions.
Transparency & Security
As an open-source solution, Tabby ensures software supply chain safety, giving you peace of mind.
Flexible Deployment
Easily integrates with your existing infrastructure, including Cloud IDEs, with support for consumer-grade GPUs.
Configurability Control
Run Tabby in your way and on your terms with no need for external DBMS or cloud services.
Premier AI Quality
Enjoy cutting-edge AI capabilities for code completion, answering queries, inline chat, and more.
1  CREATE TABLE IF NOT EXISTS roles (    
2      id               INTEGER PRIMARY KEY AUTOINCREMENT,
3      name             VARCHAR(100) NOT NULL COLLATE NOCASE,
4      description      TEXT,
5      created_at       TIMESTAMP DEFAULT (DATETIME('now')),
6      updated_at       TIMESTAMP DEFAULT (DATETIME('now')),
7
8      CONSTRAINT  'idx_name' UNIQUE ('name')
9  );     
 
 50 def build_response(*, Client, response: httpx.Response) -> Response[Any]: 
51      return Response( 
52            status_code=HTTPStatus(response.status_code), 
53            content=response.content, 
54            headers=response.headers, 
55            parsed=_parse_response(client=client, response=response), 
56     ) 
57 def _delete_response( 
 
Code Completion: 
Accelerate your coding with intelligent, AI-powered suggestions.

Tabby’s code completion engine is designed to understand your coding context and provide real-time suggestions that are accurate and relevant. 
Efficient
Whether you’re writing a simple function or working on a complex project, Tabby predicts your next move, helping you code faster and with fewer errors. 
Intuitive
Experience the power of AI that adapts to your coding style and seamlessly integrates into your IDE.
Answer Engine: 
Get instant answers to your coding questions within your IDE.
When you’re deep in the development process, interruptions can break your flow. With Tabby’s Answer Engine, you can stay focused on your code while getting the answers you need, right within your IDE.
Instant Answers
Simply ask a question, and Tabby will provide clear, concise explanations or examples, helping you overcome roadblocks without missing a beat.
Faster Development
By leveraging TabbyML's Answer Engine, you can speed up your development workflow.
TabbyML/tabby Does this repo use 📚 Tailwind CSS library?
Sources
Display - Tailwind CSS
01002500100210200140
feat(ui): update dark mode...
docs: add blog post on orchest..
TAB-558
Yes, the repo TabbyML/tabby uses the Tailwind CSS library. This is evident from the usage of the Tailwind's utility classes in the HTML and CSS files, such as `inline-grid`, `grid-cols-3`, `gap-4`, `flex-1`, `contents`, `table`, `table-row`, and others 1 11 12. Additionally, the CSS files explicity import Tailwind's base, components, and utilities layers 11 12
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To generate a database migration based on the provided example, you can follow the structure and syntax used in the existing migrations. Here is an example of how you might create a new migration to add a 'roles' table.
sql
1  CREATE TABLE IF NOT EXISTS roles (    
2      id               INTEGER PRIMARY KEY AUTOINCREMENT,
3      name             VARCHAR(100) NOT NULL COLLATE NOCASE,
4      description      TEXT,
5      created_at       TIMESTAMP DEFAULT (DATETIME('now')),
6      updated_at       TIMESTAMP DEFAULT (DATETIME('now')),
7
8      CONSTRAINT  'idx_name' UNIQUE ('name')
9  );     
Inline Chat:
Collaborate and code more efficiently with AI-driven inline chat.
TabbyML's inline chat feature allows you to communicate with your AI coding assistant in real-time, making coding more efficient and collaborative.
Improved Efficiency
Enhance teamwork by keeping conversations contextually tied to the code, making collaboration more efficient and focused.
Seamless Collaboration
Engage in discussions, ask questions, and receive AI-driven suggestions, all without leaving your code editor.
Data Connectors:
Seamlessly connect to various data sources for enriched coding assistance.
Tabby’s Context Providers allow you to pull in data from multiple sources, enriching the AI’s ability to understand your project’s unique context.
Easy Integration
Whether it’s pulling documentation, reading configuration files, or accessing external APIs, Tabby uses this information to offer more relevant suggestions and insights.
Deeper Understanding
Create a smarter, more aware coding assistant that truly understands your project.
coming soon
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Tabby
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Happy to help! Based on what I learned from the docs in your repository, the solution you are looking for should be implemented as follows:
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