Unleash Your Logseq Knowledge: Conversational Search with Local LLMs

Ever feel like your Logseq is a treasure trove of information, but buried deep within lies the nugget you desperately need? Daily tasks, web clippings, and personal notes - it’s a wealth of knowledge, but searching effectively can be a challenge.

In this post, we’ll explore the exciting potential of local Large Language Models (LLMs) to transform your Logseq experience. Imagine a conversational interface where you can chat with your personal knowledge base, retrieving the most relevant information with ease. All while keeping your data private and secure!

The Challenge: Information Overload

Let’s face it, life is busy. We juggle tasks, encounter interesting web pages, and jot down personal reflections, all within Logseq. Over time, this creates a vast collection of journals. But when it comes time to recall that specific website you bookmarked months ago, or that insightful note you wrote during a brainstorming session, searching through endless entries can be frustrating.

The Solution: Local LLM + Conversational Search

Here’s where local LLMs come in. Unlike traditional cloud-based LLMs, local LLMs operate entirely on your device. This means your data stays private, while you still harness the power of advanced language processing.

By integrating a local LLM with Logseq, we can create a conversational search interface. Instead of clunky keyword searches, you simply talk to your knowledge base! Ask questions like:

• “Hey Logseq, remind me of that website I saved about sustainable gardening?”
• “Can you summarize my key takeaways from last month’s brainstorming session on project X?”
• “Show me all my notes related to productivity hacks.”

The LLM, trained on your Logseq data, understands the context of your queries and delivers the most relevant information in a natural, conversational manner.

Benefits of Local LLM Integration

• Effortless Search: No more struggling with keywords or complex search strings. Just ask your questions in natural language.
• Enhanced Recall: The LLM can connect seemingly unrelated notes and journals, surfacing insights you might have missed.
• Deeper Knowledge Exploration: Conversational search encourages a more fluid exploration of your knowledge base, fostering new connections and ideas.
• Privacy by Design: Local LLMs keep your data secure on your own device, eliminating privacy concerns associated with cloud-based solutions.

Getting Started with Local LLM and Logseq

As a proof of concept, I have created a Git repository with a simple implementation of a conversational search interface using Ollama and Logseq.

• Use Langchain to connect to local Ollama with llama3 Model.
• Directly access the Logseq data using the file system.
• For simplicity, use the Memory Vector Store to store the data.
• For simplicity of Typescript support and speed, I use bun instead of node.

This version is a simple proof of concept. Future versions will include more advanced features and optimizations:

• Test with other LLM models.
• Implement a more sophisticated memory store.
• Experiment with different loading and parsing strategies.
• Enable chat memory.
• Interface with WhatsApp, Telegram, or other messaging platforms.

The Future of Personal Knowledge Management

Integrating local LLMs with Logseq promises to revolutionize the way we interact with our personal knowledge bases. Imagine a future where you have a seamless conversation with your own data, effortlessly retrieving the information you need, all while safeguarding your privacy.