Feat/llm provider abstraction architecture#63
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Add comprehensive architecture design and implementation guide for migrating task-worker from hardcoded OpenAI integration to provider-agnostic LLM system. This enables: - Vendor flexibility (swap providers without code changes) - Cost optimization (route to cheaper providers) - Local inference support (AMD GPU friendly) - Multi-provider fallback (reliability) - Future model routing optimization
…guration and request handling
… JSON handling and error normalization
…rchitecture with metrics and recommendations
… configuration - Updated LLM provider factory to support new timeout environment variable. - Introduced fallback logic in OpenAI provider for transient errors and improved logging. - Refined LLM reflection and planning prompts for clarity and execution context. - Added signal and idempotency key support to various tool execution contexts. - Enhanced tests for OpenAI provider to cover auth failures and transient errors. - Documented AMD production environment configuration for better deployment guidance.
- Introduced `judge-verification.sh` for comprehensive submission artifact checks. - Added `validate-demo.sh` to ensure system readiness before demo presentations. - Created `validate-submission.sh` for final validation of submission requirements. - Enhanced error handling in `openai-provider.ts` for better clarity on response issues.
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- Changed the startup script in task-worker to use 'tsx' for better development experience. - Updated import paths in various API routes and services to use the new alias for improved clarity and consistency. - Introduced a new utility file for tool normalization, enhancing email parameter handling and validation.
- Bumped versions for chat-app (2.0.5), @chat/socket (3.0.2), @chat/web (4.0.2), and @chat/auth (2.3.2). - Updated fast-xml-builder to 1.2.0 and ip-address to 10.2.0. - Added new dependency xml-naming (0.1.0) and updated path-expression-matcher to 1.5.0.
…UI styles across components - Improved error handling in the emitInternal function to provide detailed feedback on failed internal emits. - Updated CSS variables for better theming support and added new styles for UI components. - Refactored chat placeholder and conversation components for improved readability and consistency in styling. - Adjusted button and text styles to align with the new design system, enhancing overall user experience.
refactor: update import paths and improve task worker startup script
Add comprehensive architecture design and implementation guide for migrating task-worker from hardcoded OpenAI integration to provider-agnostic LLM system. This enables: - Vendor flexibility (swap providers without code changes) - Cost optimization (route to cheaper providers) - Local inference support (AMD GPU friendly) - Multi-provider fallback (reliability) - Future model routing optimization
…guration and request handling
… JSON handling and error normalization
…rchitecture with metrics and recommendations
… configuration - Updated LLM provider factory to support new timeout environment variable. - Introduced fallback logic in OpenAI provider for transient errors and improved logging. - Refined LLM reflection and planning prompts for clarity and execution context. - Added signal and idempotency key support to various tool execution contexts. - Enhanced tests for OpenAI provider to cover auth failures and transient errors. - Documented AMD production environment configuration for better deployment guidance.
- Introduced `judge-verification.sh` for comprehensive submission artifact checks. - Added `validate-demo.sh` to ensure system readiness before demo presentations. - Created `validate-submission.sh` for final validation of submission requirements. - Enhanced error handling in `openai-provider.ts` for better clarity on response issues.
…github.com/HarshdeepsinghLocham/chat-app into feat/llm-provider-abstraction-architecture
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This pull request adds comprehensive documentation and verification resources to support deployment, demo reliability, and final submission validation for the Autonomous Task Agent project. The changes introduce detailed guides for demo hardening, deployment, and a final verification report script, ensuring that both technical and non-technical reviewers can confidently evaluate, deploy, and operate the system.
The most important changes are:
Documentation for Deployment and Demo Reliability:
DEMO_HARDENING.md, a detailed guide covering demo environment setup, stability principles, logging strategy, demo scenarios, known edge cases, and success criteria to ensure predictable and reliable judge demonstrations.DEPLOYMENT_CHECKLIST.md, a step-by-step checklist for pre-deployment verification, provider and tool configuration, startup validation, deployment steps, post-deployment monitoring, rollback procedures, and production tuning, tailored for various environments and providers.