DolphinDB × Obsidian: Turn Your Data into an AI-Ready Knowledge Base

DolphinDB
2026-07-20

Every day, you run analysis in DolphinDB, review the results, and move on.

The data is there, but the insights stay locked inside your database. AI can't easily access them, understand their context, or use them to answer questions.

That's why we built the Obsidian module. With a single function call, DolphinDB can write analysis results directly to your Obsidian knowledge base as structured Markdown notes—making them immediately available for AI to search, reason over, and build on.

What the Obsidian Module Can Do

Run your analysis in DolphinDB, then save the results directly to Obsidian with a single line of code.

The module provides the following capabilities:

  • Read and write Obsidian notes
  • Perform full-text search across your entire Vault
  • Query note metadata using Dataview or JsonLogic
  • Manage records in Obsidian Bases
  • Read from and append to periodic notes (daily, weekly, monthly, etc.)
  • Execute Obsidian commands and custom API requests

Installation takes only a few commands:

login("admin", "123456")
listRemoteModules()
installModule("obsidian")
use obsidian

Real-World Examples

Financial Research

Imagine the daily workflow of a quantitative researcher: After the market closes, DolphinDB calculates factor performance metrics such as IR, turnover rate, and trading signals. Normally, these results stay in your database or analysis scripts. With one function call, DolphinDB writes a structured summary directly to your Obsidian daily note.

def appendFactorSummary(client, factorName, ir, turnover, signal){
    summary = "- " + factorName + " | IR: " + string(ir) +
              " | Turnover: " + string(turnover) +
              " | Signal: " + signal
    obsidian::appendPeriodicNote(client, summary, "daily")
}

// Call once after each factor analysis
appendFactorSummary(c, "Momentum", 0.82, 0.15, "Hold")
appendFactorSummary(c, "Value", 0.45, 0.08, "Watch")

Over time, your daily notes build a searchable history of factor analysis. When you need a quarterly review, AI can summarize key trends and generate reports—without rerunning queries or searching through old records.

Industrial IoT

Now consider an equipment maintenance engineer: At the end of each day, DolphinDB analyzes sensor data, calculating metrics such as maximum temperature and alarm counts to evaluate equipment health.

Previously, these conclusions were scattered across monitoring dashboards and maintenance tickets. With the Obsidian module, one function call automatically appends the day's equipment summary to your daily note.

def appendDeviceSummary(client, deviceId, maxTemp, alertCount, suggestion){
    summary = "- " + deviceId + " | Max Temp: " + string(maxTemp) +
              "°C | Alerts: " + string(alertCount) +
              " | Recommendation: " + suggestion
    obsidian::appendPeriodicNote(client, summary, "daily")
}

// Call once after daily analysis
appendDeviceSummary(c, "PLC-2024-01", 85.3, 3, "Inspect cooling system")
appendDeviceSummary(c, "PLC-2024-02", 62.1, 0, "Operating normally")

Over time, your daily notes build a searchable history of device health. AI can use them to generate weekly reports, summarize key events, and spot operational trends—without searching through ticketing systems.

Beyond Archiving: Building a Knowledge Foundation for AI

In reality, every note written to Obsidian is structured, contextualized knowledge that AI can immediately understand.

In a traditional workflow, analysis results stay in the database. Before AI can use them, you typically need to query the data, extract the relevant tables, and transform them into a format the model can understand.

With the Obsidian module, DolphinDB writes analysis results directly to your knowledge base as Markdown while the analysis is running. The output is immediately searchable by both humans and AI—no extra processing required.

You're not just storing analysis results. You're building a knowledge base that AI can search, reason over, and build on over time.

Join the DolphinDB Plugin Ecosystem

The Obsidian module is only the beginning. The DolphinDB Plugin Marketplace supports modular development, allowing developers to package integrations as reusable plugins. Whether you're working in finance, industrial IoT, or another domain, you can contribute your own integrations and extend DolphinDB's connectivity.

If there's a tool or platform you'd like DolphinDB to connect with, we'd love to hear your ideas or invite you to build the integration yourself.

Learn more or get started by contacting info@dolphindb.com.