Knowledge — Engineering Context Layer (Native GitHub Bot)

Knowledge is an engineering context layer bot designed to run natively inside GitHub Issues and Pull Requests. When a contributor or maintainer comments @Knowledge <question> or /knowledge <question>, the engine classifies query intent, retrieves bounded evidence across repository files and conversation threads, enforces repository rules from KNOWLEDGE.md, and posts a structured engineering handoff directly back to GitHub.

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Features

• GitHub-Native Interaction: Triggered automatically by commenting @Knowledge <question> or /knowledge <question> on any Issue or PR.
• Multi-LLM Provider Architecture: Native REST adapters for Mistral AI, OpenAI, Anthropic Claude, Google Gemini, Groq, and local Ollama without heavy SDK dependencies.
• Intent-Driven Context Retrieval: Classifies queries across 7 intent categories (PR understanding, repo onboarding, architecture explanation, contribution guidance, feature flows, historical decisions, and issue onboarding) to collect high-signal evidence.
• Mandatory Guardrail Enforcement: Parses repository guidelines from KNOWLEDGE.md and injects them into system instructions.
• Hermetic Testing: 100% offline unit tests with mock fixtures and automated GitHub Actions CI matrix across Python 3.10, 3.11, and 3.12.
• Dual Deployment Options: Serverless GitHub Actions runner or standalone FastAPI webhook server with HMAC-SHA256 signature verification.
• Streamlit Web Dashboard: Interactive UI for exploring repository context graphs, testing questions, and visualizing evidence sets.

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1-Minute GitHub Action Setup

To add Knowledge Bot to any repository, copy these 4 files into your project:

Your-Repo/
├── .github/workflows/
│   └── knowledge.yml       # GitHub Action workflow
├── knowledge_engine.py     # Unified core engine
├── providers.py            # Multi-LLM provider adapters
└── KNOWLEDGE.md            # Repository rulebook & guidelines

GitHub Secrets Configuration

In your repository settings (Settings ➔ Secrets and variables ➔ Actions), add your LLM API key:

Secret Name	Description	Default Model
MISTRAL_API_KEY	Mistral AI API Key	mistral-small-2506
OPENAI_API_KEY	OpenAI API Key	gpt-4o-mini
ANTHROPIC_API_KEY	Anthropic Claude API Key	claude-3-5-haiku-20241022
GEMINI_API_KEY	Google Gemini API Key	gemini-1.5-flash
GROQ_API_KEY	Groq Ultra-fast API Key	llama-3.3-70b-versatile

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Multi-LLM Provider Configuration

Knowledge auto-detects configured provider keys from your environment. You can explicitly set the active provider using LLM_PROVIDER:

# Set active provider
export LLM_PROVIDER=openai  # mistral | openai | anthropic | gemini | groq | ollama

# Set provider-specific model override (optional)
export OPENAI_MODEL=gpt-4o
export ANTHROPIC_MODEL=claude-3-5-sonnet-20241022
export GEMINI_MODEL=gemini-1.5-pro
export GROQ_MODEL=llama-3.3-70b-versatile

Local / Air-Gapped Execution with Ollama

To run Knowledge without third-party API calls using local Ollama models:

export LLM_PROVIDER=ollama
export OLLAMA_HOST=http://localhost:11434
export OLLAMA_MODEL=llama3.2:latest

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Local Development & Testing

# Clone the repository
git clone https://github.com/PrithvijitBose/Knowledge-Agent.git
cd Knowledge-Agent

# Install dependencies
pip install -r requirements.txt

# Run all hermetic unit tests offline
python -m unittest discover -s . -p "test_*.py"

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Webhook Server Deployment

For real-time webhook deployments with custom domains:

# Start FastAPI Webhook Server on port 8000
python webhook_server.py

Set GITHUB_WEBHOOK_SECRET in your environment to automatically enforce HMAC-SHA256 signature verification on inbound webhooks.

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License

This project is licensed under the MIT License.