Why would I use those models on your cloud instead of using Google's or Anthropic's models? I'm glad there are open models available and that they get better and better, but if I'm paying money to use a cloud API I might as well use the best commercial models, I think they will remain much better than the open alternatives for quite some time.
Fast forward to now, open models are quickly catching up, and at a significantly lower price point for most and can be customized for specific tasks instead of being general purpose. For general purpose models, absolutely the closed models are currently dominating.
Not particularly. Indexes are sort of like railroads. They're costly to build and maintain. They have significant external costs. (For railroads, in land use. For indexes, in crawler pressure on hosting costs.)
If you build an index, you should be entitled to a return on your investment. But you should also be required to share that investment with others (at a cost to them, of course).
Crucially, I want to understand the license that applies to the search results. Can I store them, can I re-publish them? Different providers have different rules about this.
Dead on arrival. Thanks for playing, Ollama, but you've already done the leg work in obsoleting yourself.
From where I'm standing, there's not enough money in B2C GPU hosting to make this sort of thing worthwhile. Features like paid search APIs this really hammer home how difficult it is to provide value around that proposition.
I like using ollama locally and I also index and query locally.
I would love to know how to hook ollama up to a traditional full-text-search system rather than learning how to 'fine tune' or convert my documents into embeddings or whatnot.
https://github.com/mjochum64/mcp-solr-search
A slightly heavier lift, but only slightly, would be to also use solr to also store a vectorized version of your docs and simultaneously do vector similarity search, solr has built in knn support fort it. Pretty good combo to get good quality with both semantic and full-text search.
Though I’m not sure if it would be relatively similar work to do solr w/ chromadb, for the vector portion, and marry the result stewards via llm pixie dust (“you are the helpful officiator of a semantic full-text matrimonial ceremony” etc). Also not sure the relative strengths of chromadb vs solr on that- maybe scales better for larger vector stores?
Is https://ollama.com/blog/tool-support not it?
Looking forward to try it with a few shell scripts (via the llm-ollama extension for the amazing Python ‘llm’) or Raycast (the lack of web search support for Ollama has been one of my biggest reasons for preferring cloud-hosted models).
I've been thinking about building a home-local "mini-Google" that indexes maybe 1,000 websites. In practice, I rarely need more than a handful of sites for my searches, so it seems like overkill to rely on full-scale search engines for my use case.
My rough idea for architecture:
- Crawler: A lightweight scraper that visits each site periodically.
- Indexer: Convert pages into text and create an inverted index for fast keyword search. Could use something like Whoosh.
- Storage: Store raw HTML and text locally, maybe compress older snapshots.
- Search Layer: Simple query parser to score results by relevance, maybe using TF-IDF or embeddings.
I would do periodic updates and build a small web UI to browse.
Anyone tried it or are there similar projects?
chungus42•1h ago
mchiang•1h ago
For smaller models, it can augment it with the latest data by fetching it from the web, solving the problem of smaller models lacking specific knowledge.
For larger models, it can start functioning as deep research.