Project 2: Publication Assistant Multi-Agent System
This project implements a robust multi-agent system designed to optimize the documentation and metadata for AI/ML projects before public release. It demonstrates mastery of agentic concepts, including tool integration, multi-agent collaboration, and workflow orchestration.
Project Summary
The goal of the Publication Assistant is to take a raw project idea (simulated by a URL and a user goal) and transform its core documentation (like a README) into a professionally optimized summary. The final output provides a compelling title, relevant technical tags, and structural critiques necessary for high discoverability among specific audiences (e.g., senior data scientists, LLM experts).
1. Multi-Agent Collaboration and Roles
The system is governed by a sequential workflow that enforces collaboration between three specialized agents, ensuring the system output is more refined than a single-LLM pass.
Repo Analyzer :
Role : Data Extractor & Parser
Input : Project URL & Goal
Output : Raw project context, existing title, and file data.
Metadata Recommender :
Role : Discoverability Strategist (The "Marketing" Agent)
Input : Raw project context
Output : Optimized title, suggested tags, and trending keywords, grounded by simulated external search.
Content Improver :
Role : Documentation Compiler (The "Technical Editor")
Input : All previous context (Raw data + Suggestions)
Output : Final, formatted 'README.md' content and structural critique.
2. Tool Integration
The system integrates three specialized tools to extend the agents' capabilities beyond simple generation, fulfilling the core Tool Integration requirement.
RepoReaderTool (Custom I/O) : Used by the ' RepoAnalyzer ' to simulate RAG retrieval from repository file structures and extract raw project context.
GoogleSearchTool (Built-in Grounding) : Used by the ' MetadataRecommender ' to simulate web access for grounding suggestions and finding trending, relevant industry keywords (e.g., CVPR, SOTA, specific libraries).
MarkdownFixerTool(Custom Utility) : Used by the ' ContentImprover ' to perform the final structural check, ensuring the output is clean, correctly formatted, and ready for publication.
Example of Tool Usage (within the ' ContentImprover ') :
# The ContentImprover uses the MarkdownFixerTool to finalize the draft clean_draft = self.markdown_fixer_tool.use(raw_draft)
The workflow is managed by the ' PublicationAssistantOrchestrator 'class, simulating a framework like LangGraph. The orchestration enforces a rigid, step-by-step pipeline:
This sequential flow ensures that strategic suggestions are made only after the context is fully understood, and the final document is compiled only after the suggestions are generated.
4. Formal Evaluation Strategy
To ensure the system is reliable, two formal evaluation metrics are defined:
Metadata Relevance (Success Metric) :
Goal : Ensure the suggested tags and titles align with the user's specific goals and technical domain.
Metric : A pass/fail check where a human expert or a scoring LLM validates if the top 5 suggested tags are relevant (e.g., if the user asks for "computer vision," the tags must include 'tensorflow' or 'segmentation').
Structural Integrity (Safety Metric) :
Goal : Verify that the final Markdown output is correctly formatted and complete.
Metric : Automated checks to confirm the final output always starts with an H1 header ('' # ''), includes the "Core Dependencies" list, and correctly flags missing sections ('Installation/Setup', 'Evaluation Metrics').
Project Repository : [https://github.com/Pal17-cloud/Module2-Publication-Assistant]