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Prompt

What about a more advanced approach. Is it possible to use a local LLM for search? For instance, the LLM could look through my vault and try to find the right file for me?

Output

Yes, using a local LLM for search is a more advanced approach that can significantly enhance your ability to retrieve relevant documents from your vault. Here’s how this can be implemented and some considerations to keep in mind:

  1. Context Understanding: A local LLM can understand the context and nuances of your queries better than traditional keyword-based search methods. It can interpret user intent and provide more relevant results based on semantic understanding.
  2. Search Process:
  3. The LLM can be trained or fine-tuned to work with the specific content in your vault.
  4. It can index the contents of your markdown files, allowing it to recall information when prompted.

Implementation Steps

Step 1: Set Up a Local LLM

  1. Choose an LLM: You can use a smaller model that can run locally, such as:
  2. GPT-Neo or GPT-J: Open-source models that can be hosted locally.
  3. LLaMA or other fine-tuned models: Depending on your hardware capabilities, you may want to experiment with various models.
  4. Installation:
  5. Use libraries like Hugging Face’s transformers to load and interact with the LLM.
  6. Ensure your environment is set up to handle the computational requirements (e.g., GPU or sufficient CPU resources).

Step 2: Index Your Vault

  1. Preprocess Your Files: Create a script to read all markdown files in your Obsidian vault and extract relevant content.
  2. You can structure the data into a format that the LLM can easily process.
  3. Create Embeddings: Generate embeddings for the content of each file. You can use models like Sentence Transformers for this.
  4. Store these embeddings in a way that allows for quick retrieval.

Step 3: Implement the Search Functionality

  1. Query Handling: When a user submits a search query:
  2. The LLM can be used to interpret the query and identify the most relevant documents based on their embeddings.
  3. For instance, you can generate embeddings for the query and calculate similarity scores against the stored embeddings of your documents.
  4. Retrieve and Display Results: The system can return the titles and snippets of the most relevant documents, allowing the user to select the one they need.

Step 4: User Interface

  1. Create a Simple UI: Develop a user interface (CLI or GUI) that allows users to input their queries.
  2. Display the retrieved results clearly, perhaps with a preview of the content.
  3. Integration with Obsidian: Optionally, you can integrate the search results with Obsidian, opening relevant documents directly from the interface.

Considerations

  1. Resource Requirements: Running an LLM locally can be resource-intensive, particularly in terms of RAM and processing power. Ensure your hardware can handle it.
  2. Model Training: If you have specific terminology or context within your vault, consider fine-tuning the LLM on your dataset for better accuracy in understanding queries.
  3. Performance: Depending on the size of your vault and the LLM used, retrieval times may vary. Optimizing how you handle embeddings and queries will be key to performance.
  4. Fallback Options: Implement a fallback mechanism using traditional search methods if the LLM does not yield satisfactory results.

Summary

Using a local LLM for search can provide a more intuitive and effective way to retrieve information from your vault compared to traditional search methods. By combining indexing with embeddings and semantic search capabilities, you can enhance your ability to find relevant documents based on meaning rather than just keywords.

If you’d like further details on specific implementation steps, tools, or have other questions, feel free to ask!

Prompt

Isn't this essentially building a RAG!?

Output

Yes, you're absolutely right! Using a local LLM to enhance search capabilities within your vault does resemble the core principles of a Retrieval-Augmented Generation (RAG) system. Here’s how it aligns with the RAG framework:

Similarities to RAG

  1. Retrieval Component:
  2. In your proposed setup, the local LLM acts as the retrieval component by understanding user queries and finding the most relevant documents based on their content.
  3. It indexes your markdown files and retrieves information semantically, which is a key aspect of RAG systems.
  4. Augmentation with Generation:
  5. While traditional RAG systems focus on generating responses based on retrieved documents, your setup can also include this element by allowing the LLM to summarize or contextualize the retrieved information.
  6. You could extend the functionality so that after retrieving relevant context, the LLM generates a cohesive summary or response based on those documents.
  7. Embedding Use:
  8. RAG systems often utilize embeddings to facilitate efficient retrieval. Your approach would involve creating embeddings for both the documents in your vault and the queries, which is essential for semantic searching.
  9. Improved Search Performance:
  10. The goal of a RAG system is to improve information retrieval and user experience, and your implementation aims to do just that by making it easier to find relevant documents.

Differences from Traditional RAG

  1. Local Implementation:
  2. Traditional RAG systems often rely on cloud-based resources or powerful server setups. Your approach is tailored for local use, which could limit some capabilities but enhances privacy and control over data.
  3. Simplified Architecture:
  4. While RAG systems can be complex with multiple components, your proposed solution is a more streamlined approach focused specifically on search functionality.

Summary

In essence, your idea for using a local LLM for search within your Obsidian vault embodies many principles of RAG, such as retrieval and augmentation. It’s a great way to leverage the power of LLMs to enhance your document management and retrieval capabilities while still keeping things relatively simple.

If you’re interested in diving deeper into any specific aspect of this RAG-like system or have further questions, feel free to ask!

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.