Leveraging LLMs in your Obsidian Notes

2023-09-20 · Source: Ollama Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article details how to integrate Large Language Models (LLMs) with Obsidian notes to enable conversational interaction and enhanced search capabilities. It outlines a method for building an indexer using tools like Llama Index or LangChain to create conceptual embeddings of notes, moving beyond keyword-based search. The process involves initializing an in-memory data store, persisting the index, and reading Markdown files to generate embeddings. For searching, the article describes how to retrieve relevant note chunks based on a user prompt, using a retriever to select the top 5 matches. These chunks are then incorporated into a system prompt for an LLM, such as Llama 2 via Ollama, to generate answers. The author also suggests further applications like summarizing notes, extracting keywords, and generating Anki flashcards.

Key takeaway

For AI Engineers building knowledge management tools, integrating LLMs with note-taking applications like Obsidian offers a powerful way to enhance conceptual search and interaction. You should consider implementing an embedding-based indexing system to allow users to "talk to their notes" and generate insights. Focus on local LLM solutions like Ollama to ensure data privacy and enable customization of models and prompts for diverse tasks like summarization or Q&A.

Key insights

LLMs can transform note-taking by enabling conceptual search and conversational interaction with personal knowledge bases.

Principles

• Embeddings enable conceptual search beyond keywords.
• Index persistence avoids redundant processing.
• Local LLMs offer privacy and customization.

Method

Index notes using Llama Index/LangChain to create embeddings, retrieve top-k relevant chunks with a retriever, then feed these chunks into an LLM's system prompt to generate answers.

In practice

• Use Llama Index or LangChain for note indexing.
• Employ Ollama with local models like Llama 2.
• Experiment with different models and prompts.

Topics

• Large Language Models
• Obsidian Integration
• Vector Embeddings
• Information Retrieval
• Llama Index

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• LLM Wiki Obsidian Claude Tutorial — LLMs can build and maintain a compounding markdown wiki from documents to overcome forgetting and repeated processing.
• Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) — RAG combines vector search with LLMs to provide context-aware, fact-grounded responses from external data.
• How To Use AI for the Ancient Art of Close Reading — LLMs can significantly enhance close reading by providing interactive context, enabling deeper understanding and personalized learning.
• Feature Engineering with LLMs: Techniques & Python Examples — LLMs automate semantic feature engineering, transforming raw data into context-rich representations for improved ML performance.
• Building LLM-Powered Web Apps with Client-Side Technology — Client-side LLM applications offer cost, privacy, and speed benefits by processing data and models locally.
• AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents — Glossary-augmented LLM prompting significantly improves terminology accuracy in specialized translation without sacrificing overall quality.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Ollama Blog.