consensus-pipeline

项目详情摘要

🧠 Consensus Pipeline **Multi-agent debate framework for academic research.** Instead of one AI writing a literature review for you — an AI team interviews you, debates each claim, reaches consensus with per-claim confidence scores, and verifies every citation against the source abstracts. 📖 中文文档 · 📦 GitHub Releases ⚡ Quick Start Pick one of three paths (start with 1 or 2): 🚀 1. One-shot installer (fastest)** One command clones + installs deps + prints your MCP config. 🤖 2. DSH plugin (AI-driven, recommended)** Then tell DSH "共识管线开始需求调研" — it runs the requirement interview → department config → multi-round debate → confidence-annotated report. The 📊 控制台 floating button (bottom-right) shows live progress, atomic verification, and full-text upload. 🖥️ 3. Streamlit / CLI (manual)** A full run is an offline batch job — start it and let it run in the background, no need to watch. Full details on all three paths (MCP config, full-text upload, custom endpoints) → 📖 Usage ❓ Why Not Just Ask ChatGPT? A single LLM produces confident-sounding answers with no cross-validation — hallucinations slip through, conflicting perspectives get flattened, and you can't tell which conclusions are solid vs. speculative. Consensus Pipeline replaces one-shot generation with **structured multi-agent debate as a quality gate**: every claim is challenged by independent "departments," contradictions are surfaced explicitly, and final conclusions carry **confidence annotations** (e.g., "42/77 papers, high confidence"). Think of it as built-in peer review — not a single author, but an adversarial committee. 📸 What It Looks Like Step 1: Requirement Interview The pipeline starts by interviewing you — an AI agent asks clarifying questions to understand your research scope, constraints, and goals. Step 2: