Knowledge Gap Agent

An AI-powered agent that analyzes knowledge bases, support data, and user queries to identify documentation gaps, auto-generate FAQs, and optimize content coverage. Built with LangChain, MCP (Model Context Protocol) integrations, and Next.js 15.

Problem Statement

Organizations struggle with knowledge management:

• Incomplete Documentation: Critical topics missing from knowledge bases
• Outdated Content: Information becomes stale as products evolve
• User Frustration: Customers can't find answers to common questions
• Reactive Approach: Gaps only discovered after customer complaints
• Manual Analysis: Time-consuming to identify what's missing

Solution

Knowledge Gap Agent provides intelligent knowledge base optimization:

1. Gap Detection: Analyzes search queries, support tickets, and user behavior to find missing content
2. FAQ Generation: Automatically creates FAQs from common questions and their answers
3. Coverage Analysis: Maps existing content against user needs
4. Content Suggestions: Recommends new articles and topics to address gaps
5. Quality Scoring: Evaluates content freshness, completeness, and relevance

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        Next.js 15 Frontend                       │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────────┐ │
│  │   Gap    │  │   FAQ    │  │ Coverage │  │     Content      │ │
│  │ Analysis │  │Generator │  │   Map    │  │   Suggestions    │ │
│  └──────────┘  └──────────┘  └──────────┘  └──────────────────┘ │
└─────────────────────────────┬───────────────────────────────────┘
                              │ REST API
┌─────────────────────────────▼───────────────────────────────────┐
│                     FastAPI Backend                              │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │                   LangChain Agent Core                       ││
│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  ││
│  │  │    Gap      │  │    FAQ      │  │     Content         │  ││
│  │  │  Detector   │  │  Generator  │  │     Analyzer        │  ││
│  │  └─────────────┘  └─────────────┘  └─────────────────────┘  ││
│  └─────────────────────────────────────────────────────────────┘│
│  ┌─────────────────────────────────────────────────────────────┐│
│  │                    MCP Integrations                          ││
│  │  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────────────────┐ ││
│  │  │  CMS   │  │ Search │  │Support │  │      Database      │ ││
│  │  │  MCP   │  │  MCP   │  │  MCP   │  │        MCP         │ ││
│  │  └────────┘  └────────┘  └────────┘  └────────────────────┘ ││
│  └─────────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        ▼                     ▼                     ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Remote LLM │    │  Local LLM   │    │   Vector DB  │
│ (OpenAI/etc) │    │   (Ollama)   │    │  (ChromaDB)  │
└──────────────┘    └──────────────┘    └──────────────┘

Tech Stack

Backend

• Python 3.11+
• FastAPI - High-performance async API framework
• LangChain 0.3+ - Agent orchestration and NLP
• LangGraph - Multi-agent workflow coordination
• ChromaDB - Vector storage for semantic search
• Pydantic - Data validation

Frontend

• Next.js 15 - React framework with App Router
• TypeScript - Type-safe development
• TailwindCSS - Utility-first styling
• Shadcn/UI - Accessible components
• React Query - Server state management

MCP Integrations

• CMS MCP - Content management system access
• Search MCP - Search analytics integration
• Support MCP - Support ticket data
• Database MCP - Gap and FAQ storage

Quick Start

Prerequisites

• Python 3.11+
• Node.js 20+
• Docker (optional)

1. Clone the repository

git clone https://github.com/VaibhavJeet/knowledge-gap-agent.git
cd knowledge-gap-agent

2. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
uvicorn app.main:app --reload

3. Frontend Setup

cd frontend
npm install
cp .env.example .env.local
npm run dev

4. Access the Application

• Frontend: http://localhost:3000
• API Docs: http://localhost:8000/docs

Configuration

LLM Configuration

# Remote LLM
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-your-key
OPENAI_MODEL=gpt-4-turbo-preview

# Local LLM (Ollama)
LLM_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.2

MCP Integrations

Configure in config/mcp.yaml:

integrations:
  cms:
    enabled: true
    provider: contentful  # or strapi, notion
    api_key: ${CMS_API_KEY}

  search:
    enabled: true
    provider: algolia  # or elasticsearch
    app_id: ${ALGOLIA_APP_ID}
    api_key: ${ALGOLIA_API_KEY}

  support:
    enabled: true
    provider: zendesk  # or freshdesk, intercom
    api_key: ${SUPPORT_API_KEY}

Features

Gap Detection

• Search query analysis
• Zero-result query tracking
• Support ticket pattern mining
• User feedback analysis
• Competitive content comparison

FAQ Generation

• Automatic Q&A extraction from tickets
• Similar question clustering
• Answer quality scoring
• Multi-language support
• Approval workflow

Coverage Analysis

• Topic taxonomy mapping
• Content completeness scoring
• User journey coverage
• Search-to-content matching
• Freshness tracking

Content Suggestions

• Priority-ranked recommendations
• Outline generation
• Source material compilation
• SEO optimization hints
• Related content linking

API Reference

Gaps

• GET /api/gaps - List identified gaps
• GET /api/gaps/{id} - Get gap details
• POST /api/gaps/analyze - Run gap analysis
• PUT /api/gaps/{id}/status - Update gap status

FAQs

• GET /api/faqs - List generated FAQs
• POST /api/faqs/generate - Generate FAQs from data
• PUT /api/faqs/{id} - Update FAQ
• POST /api/faqs/{id}/publish - Publish FAQ

Content

• GET /api/content - List knowledge base content
• GET /api/content/coverage - Get coverage report
• POST /api/content/suggestions - Get content suggestions

Analysis

• POST /api/analysis/run - Run full analysis
• GET /api/analysis/reports - List analysis reports
• GET /api/analysis/reports/{id} - Get report details

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.