Personal Knowledgebase with DuckDuckGo + Qdrant

Create a domain-specific semantic index by pairing real-time DuckDuckGo search results with Qdrant storage, powered by LangDB.

This use case demonstrates how to build a private, self-updating knowledgebase using public web search (DuckDuckGo) and a vector database (Qdrant). By pairing search results with embeddings, you can create a local semantic index for recall, reasoning, or exploration later — all from a single prompt.

This Showcase Demonstrates:

• Querying DuckDuckGo for relevant articles and insights.

• Embedding the content into vector format using an LLM-powered embedding model.

• Storing the semantic vectors into Qdrant for future retrieval via semantic search.

Why It Matters

• Maintain your own always-updating, domain-specific research archive.

• Avoid reliance on vendor-controlled platforms or search engines.

• Enables AI-powered recall and insight generation from public sources.

MCP Server Setup

DuckDuckGo MCP Server

No configuration is required.

• Simply add the DuckDuckGo MCP server.

• Supports direct web search out of the box.

• No authentication or environment variables needed.

Qdrant MCP Server Setup

To store embeddings, Qdrant requires secure credential configuration:

Variable	Description
collection_name	Name of the Qdrant collection
qdrant_url	URL of your Qdrant instance
qdrant_api_key	API key for authentication

Checkout MCP Servers:

• DuckDuckGo: https://app.langdb.ai/mcp-servers/duck-duck-mcp

• QDrant: https://app.langdb.ai/mcp-servers/qdrant

Interaction Flow

Search DuckDuckGo for LangDB AI Gateway and embed the articles.

QDrant Output

Benefits

• Own your knowledgebase: No vendor lock-in, no third-party exposure.

• Fast recall: Ask contextual questions and retrieve relevant sources.

• Always current: Update your Qdrant index with new searches as the field evolves.