README.md

metadata

title: AI Technical Initiative Generator
emoji: 🧭
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false

AI-Powered Technical Initiative Generator

Turn a business objective into a pragmatic, categorized initiative plan for engineering leaders. Built for Hugging Face Spaces with a professional Gradio UI, modular agent, web research, and pluggable data sources.

Concept: The Engineering Leader's AI Toolkit — Pillar 2 (Strategy)

Features

• Initiative generation from a single objective into clear categories (Data & Analytics, Product, Platform Health, etc.)
• Optional grounding using your own data sources (CSV/JSON uploads, URLs with Basic/Bearer auth)
• Optional web research via DuckDuckGo search and content extraction
• Lightweight, configurable LLM stack: Hugging Face Inference API, local Transformers, or external providers
• Clean Gradio interface with debug prompt and details panel

Quick Start (Hugging Face Spaces)

1. Create a new Space (Gradio) and push the repo contents.
2. In the Space Settings, set the following (optional) secrets:
   • HUGGINGFACEHUB_API_TOKEN — recommended for Inference API
   • HF_INFERENCE_MODEL — default: Qwen/Qwen2.5-3B-Instruct
   • EMBEDDING_MODEL — default: sentence-transformers/all-MiniLM-L6-v2
   • LLM_PROVIDER — one of: hf_inference (default), local, openai, together, groq
   • Optionally: OPENAI_API_KEY, TOGETHER_API_KEY, GROQ_API_KEY
3. Hardware: CPU is OK. Enable Internet if you want web research and URL connectors to fetch content.
4. Space auto-detects app.py and launches the Gradio UI.

Local Run

pip install -r requirements.txt
python app.py

Usage

1. Generate Initiatives

   • Enter a business objective (e.g., “Reduce customer churn by 10%”).
   • Choose categories, set initiatives per category.
   • Toggle “Use Data Sources” and/or “Use Web Research”.
   • Click Generate to get a Markdown plan with Why/Impact/Effort for each item.

2. Data Sources

   • Upload: CSV/JSON/TXT are embedded and added to a lightweight vector store.
   • URL: Fetches public pages or API responses; supports none, basic, and bearer auth.
   • Multiple sources are supported; added content improves grounding and specificity.

Model Providers

• Hugging Face Inference API (default): set HUGGINGFACEHUB_API_TOKEN and HF_INFERENCE_MODEL.
• Local Transformers: set provider=local in code or env and ensure hardware is sufficient.
• External APIs: OpenAI, Together, Groq are supported if their API keys are set and the provider is selected in code.

The default model balances quality and weight: Qwen/Qwen2.5-3B-Instruct.

Architecture

• app.py — Space entry; launches the Gradio UI
• aitoolkit/ui.py — UI assembly and event wiring
• aitoolkit/agent.py — Initiative agent; retrieval + (optional) web research + LLM
• aitoolkit/models.py — Pluggable LLM client (HF Inference, local, external)
• aitoolkit/prompts.py — System + task prompt templates
• aitoolkit/embeddings.py — SentenceTransformer embeddings + cosine search
• aitoolkit/storage.py — Simple JSON/NumPy persistence for connections and vectors
• aitoolkit/connectors/ — Upload and HTTP connectors (basic/bearer)
• requirements.txt — Dependencies

Security Notes

• Uploaded file bytes are not stored verbatim; only text and metadata are embedded and saved. Still, do not upload sensitive data to public Spaces.
• URL connector supports Basic and Bearer token auth; secrets should be provided securely (e.g., Space Secrets or private Space).
• Runtime “connection script” execution is intentionally not supported for safety. To add custom connectors, implement a new module under aitoolkit/connectors/ and wire it in ui.py.

Roadmap

• Additional connectors (GitHub, Notion, Google Drive)
• Structured JSON output option and richer export
• Per-initiative cost/benefit scoring and timeline suggestions
• In-app provider selection and settings persistence

License

See LICENSE.