Integrating Local LLMs with Obsidian Notes via Ollama

Ollama has outlined a method for integrating Large Language Models (LLMs) into Obsidian notes to enable conversational interaction and conceptual search. By combining local LLMs with indexing tools, users can move beyond exact-word searching to query their notes based on concepts and meanings.

Implementing Conceptual Search with Embeddings

To enable a model to converse with a large volume of notes, the system must identify and provide only the most relevant sections of text, as most models cannot process an entire knowledge base at once. This is achieved through the creation of an index using embeddings, which allow the system to search for concepts rather than exact phrases.

Building the Indexer

An Obsidian plugin can be configured to index notes upon loading and persist that progress to avoid redundant regeneration. The following technical approach uses Llama Index for the indexing process:

• Data Store: An in-memory data store is initialized, with options such as Chroma DB or the default Llama Index store.
• Persistence: Storage contexts are defined to ensure the index is saved to a local directory.
• Reading: A MarkdownReader is used to load and process Markdown files, which Llama Index handles by understanding the meanings of words and their relationships.

While OpenAI services can be used for embedding, Ollama provides an embed function, and LangChain offers local alternatives for those preferring a fully self-hosted environment.

Querying Notes via Retrieval Augmented Generation (RAG)

Once an index is established, the system can retrieve relevant note fragments to construct a system prompt for the LLM. This process follows a Retrieval Augmented Generation (RAG) workflow:

1. Initialization: The index is initialized from the persisted storage.
2. Retrieval: A retriever is configured (e.g., setting similarityTopK to 5) to find the top matches for a given user prompt.
3. Prompt Construction: The retrieved text chunks are joined and inserted into a system prompt, instructing the model to use that specific text to answer the prompt.
4. Generation: The local model (such as llama2) is executed via the ollama-node library to generate the final response.

To maintain speed and ensure the retrieved text fits within the model's input context window, the use of smaller models is recommended. In the provided example, five chunks of 256 tokens each are used.

Extended Use Cases for Note Integration

Beyond simple Q&A, integrating LLMs into note-taking tools allows for several advanced productivity workflows:

• Automated Summarization: Generating concise summaries of long-form notes.
• Metadata Enhancement: Identifying optimal keywords to add to note front matter to improve connectivity between documents.
• ** Obsidian-to-Anki Pipeline**: Generating question-and-answer pairs from notes to be exported to Anki for spaced repetition learning.

These workflows can be optimized by experimenting with different model weights and prompt engineering to suit the specific task.