datus-agent-clickzetta 0.2.1

Dashscope-powered Datus agent with Clickzetta integrations

🎯 Overview

Datus is an open-source data engineering agent that builds evolvable context for your data system.

Data engineering needs a shift from "building tables and pipelines" to "delivering scoped, domain-aware agents for analysts and business users.

• Datus-CLI: An AI-powered command-line interface for data engineers—think "Claude Code for data engineers." Write SQL, build subagents, and construct context interactively.
• Datus-Chat: A web chatbot providing multi-turn conversations with built-in feedback mechanisms (upvotes, issue reports, success stories) for data analysts.
• Datus-API: APIs for other agents or applications that need stable, accurate data services.

🚀 Key Features

🧩 Contextual Data Engineering

Automatically builds a living semantic map of your company’s data — combining metadata, metrics, SQL history, and external knowledge — so engineers and analysts collaborate through context instead of raw SQL.

💬 Agentic Chat

A Claude-Code-like CLI for data engineers.
Chat with your data, recall tables or metrics instantly, and run agentic actions — all in one terminal.

🧠 Subagents for Every Domain

Turn data domains into domain-aware chatbots.
Each subagent encapsulates the right context, tools, and rules — making data access accurate, reusable, and safe.

🔁 Continuous Learning Loop

Every query and feedback improves the model.
Datus learns from success stories and user corrections to evolve reasoning accuracy over time.

🛠️ Developer Quickstart

Set up a local environment that uses Dashscope for LLM calls and Clickzetta as the data source:

1. Clone and install dependencies

   git clone https://github.com/<your-org>/Datus-agent-clickzetta.git
   cd Datus-agent-clickzetta
   python3.11 -m venv .venv
   source .venv/bin/activate  # Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   

2. Create a .env file at the project root to store secrets:

   DASHSCOPE_API_KEY=your_dashscope_key
   DEEPSEEK_API_KEY=your_deepseek_key
   CLICKZETTA_SERVICE=your_clickzetta_service
   CLICKZETTA_USERNAME=your_clickzetta_username
   CLICKZETTA_PASSWORD=your_clickzetta_password
   CLICKZETTA_INSTANCE=your_clickzetta_instance
   CLICKZETTA_WORKSPACE=your_clickzetta_workspace
   CLICKZETTA_SCHEMA=your_clickzetta_schema
   CLICKZETTA_VCLUSTER=your_clickzetta_vcluster
   

   The entry points (datus-cli, python -m datus.main, datus/api/server.py) automatically load this file via python-dotenv, so no manual export is required. For shell-based workflows you can still run export $(grep -v '^#' .env | xargs) before launching the CLI.

3. Copy the Clickzetta configuration

   cp conf/agent.clickzetta.yml.example conf/agent.clickzetta.yml
   

   The example file ships with Dashscope/DeepSeek models and a clickzetta namespace. Edit the copy if your workspace or model choices differ.

4. Start the CLI (or API)

   mkdir -p .datus_home
   DATUS_HOME=$(pwd)/.datus_home python -m datus.cli.main --config conf/agent.clickzetta.yml --namespace clickzetta
   # optionally launch the API server
   DATUS_HOME=$(pwd)/.datus_home python -m datus.api.server --config conf/agent.clickzetta.yml --namespace clickzetta
   

   The CLI will automatically connect to Clickzetta, retrieve schema metadata, and use Dashscope for SQL generation.

---

🧰 Installation

Requirements: Python >= 3.9 and Python <= 3.11, 3.11 is verified.

pip install datus-agent-clickzetta==0.2.1

datus-agent-clickzetta init  # 或使用 datus-agent init 兼容命令

For detailed installation instructions, see the Quickstart Guide.

🧭 User Journey

1️⃣ Initial Exploration

A Data Engineer (DE) starts by chatting with the database using /chat. They run simple questions, test joins, and refine prompts using @table or @file. Each round of feedback (e.g., "Join table1 and table2 by PK") helps the model improve accuracy. datus-cli --namespace demo /Check the top 10 bank by assets lost @Table duckdb-demo.main.bank_failures

Learn more: CLI Introduction

2️⃣ Building Context

The DE imports SQL history and generates summaries or semantic models:

/gen_semantic_model xxx @subject They edit or refine models in @subject, combining AI-generated drafts with human corrections. Now, /chat can reason using both SQL history and semantic context.

Learn more: Knowledge Base Introduction

3️⃣ Creating a Subagent

When the context matures, the DE defines a domain-specific chatbot (Subagent):

.subagent add mychatbot

They describe its purpose, add rules, choose tools, and limit scope (e.g., 5 tables). Each subagent becomes a reusable, scoped assistant for a specific business area.

Learn more: Subagent Introduction

4️⃣ Delivering to Analysts

The Subagent is deployed to a web interface: http://localhost:8501/?subagent=mychatbot

Analysts chat directly, upvote correct answers, or report issues for feedback. Results can be saved via !export.

Learn more: Web Chatbot Introduction

5️⃣ Refinement & Iteration

Feedback from analysts loops back to improve the subagent: engineers fix SQL, add rules, and update context. Over time, the chatbot becomes more accurate, self-evolving, and domain-aware.

For detailed guidance, please follow our tutorial.