I had no idea they had their own cloud offering, I thought the whole point of Ollama was local models? Why would I pay $20/month to use small inferior models instead of using one of the usual AI companies like OpenAI or even Mistral? I'm not going to make an account to use models on my own computer.
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.
I was hoping for more details about their implementation, I saw ollama as the open source // platform agnostic tool but I worry their recent posturing is going against that
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.
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.
Perhaps not quite solving your problem, but I have a handful of domain-specific Google CSE (Custom Search Engine) that limit the results to predefined websites. I summon them from Alfred with short keywords when I'm doing interest-specific searches.
https://blog.gingerbeardman.com/2021/04/20/interest-specific...
Reminds me of building a Obsidian vault with all the content in markdown form. There's also plugins to show vault results when doing a Google search, making notes within your vault show up before external websites.
Drew DeVault tried building something similar to this under the name SearchHut, but the project was abandoned [1]. I tried hacking on it a while ago (since it's built on Postgres and a bit of Go), but I ran out of steam trying to understand the Postgres RUM extension.
This is a nice first step - web search makes sense, and it’s easy to imagine other tools being added next: filesystem, browser, maybe even full desktop control. Could turn Ollama into more than just a model runner. Curious if they’ll open up a broader tool API for third-party stuff too
I think because Google knows traditional search is gonna die, they will be aggressively pushing ads on traditional search to extract as much money as possible till they figure out newer ways of making money.
There are very few recently launched pure open source projects these days (most are at least running donation-ware models or funded by corporate backers), none in the AI space that I'm aware of.
I am just working on a tool using websearch and iterating over different providers.
openAI, xAI, gemini all suffer from not being allowed on respective competitor sites.
this searched works for me with some quick tests well on YT videos, which OpenAI web search can't access. It kind of failed on X but sometimes returned ok relevant results. Definitely hit and miss but on average good
I added search to my LLMs years ago with the python DuckDuckGo package.
However I found that Google gives better results, so I switched to that. (I forget exactly but I had to set up something in a Google dev console for that.)
I think the DDG one is unofficial, and the Google one has limits (so it probably wouldn't work well for deep research type stuff).
I mostly just pipe it into LLM apis. I found that "shove the first few Google results into GPT, followed by my question" gave me very good results most of the time.
It of course also works with Ollama, but I don't have a very good GPU, so it gets really slow for me on long contexts.
Your regular reminder that you don't need ollama to get a quick chat engine on the command line, you can just do this with pretty much any major model on huggingface:
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[ 4.1 ms ] story [ 62.4 ms ] threadCrucially, 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.
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.
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?
Crawling was tricky. Something like stackoverflow will stop returning pages when it detects that you're crawling, much sooner than you'd expect.
[1]: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
Like a full search engine that can visit pages on your behalf. Is anyone building this?
I wonder how they plan to monetize their users. Doesn't sound promising.
openAI, xAI, gemini all suffer from not being allowed on respective competitor sites.
this searched works for me with some quick tests well on YT videos, which OpenAI web search can't access. It kind of failed on X but sometimes returned ok relevant results. Definitely hit and miss but on average good
However I found that Google gives better results, so I switched to that. (I forget exactly but I had to set up something in a Google dev console for that.)
I think the DDG one is unofficial, and the Google one has limits (so it probably wouldn't work well for deep research type stuff).
I mostly just pipe it into LLM apis. I found that "shove the first few Google results into GPT, followed by my question" gave me very good results most of the time.
It of course also works with Ollama, but I don't have a very good GPU, so it gets really slow for me on long contexts.
pip install transformers
transformers chat Qwen/Qwen2.5-0.5B-Instruct