11–20 of 28 posts
I think the magic of Grok's implementation of this is that they already have most of the websites cached (guessing via their twitter crawler) so it all feels very snappy. Bing/Brave search don't seem to offer that in their search apis. Does such a thing exist as a service?
https://milvus.io/blog/i-built-a-deep-research-with-open-sou...
https://milvus.io/blog/introduce-deepsearcher-a-local-open-s...
Is there a deep searcher that can also use local LLMs like those hosted by Ollama and LM Studio?
I think the magic of Grok's implementation of this is that they already have most of the websites cached (guessing via their twitter crawler) so it all feels very snappy. Bing/Brave search don't seem to offer that in their search apis. Does such a thing exist as a service?
I wrote my own implementation using various web search APIs and a puppeteer service to download individual documents as needed. It wasn't that hard but I do get blocked by some sites (reddit for example).
This doesn't seem to use local LLMs... so it's not really local. :-\ Is there a deep searcher that can also use local LLMs like those hosted by Ollama and LM Studio?
From a quick glance, this project doesn't seem to use any tool/function calling or streaming or format enforcement or any other "fancy" API features, so all chances are that it may just work, although I have some reservations about the quality, especially with smaller models.
This doesn't seem to use local LLMs... so it's not really local. :-\ Is there a deep searcher that can also use local LLMs like those hosted by Ollama and LM Studio?
Looking at the code ( https://github.com/zilliztech/deep-searcher/blob/master/deep... ), I think it probably may work at least with Ollama without any additional tweaks if you run it with `OPENAI_BASE_URL= http://localhost:11434/v1 ` or define `provide_settings.llm.base_url` in `config.yaml` ( https://github.com/zilliztech/deep-searcher/blob/6c77b1e5597... ) and tweak the model appropriately. From a quick glance, thi…
Earlier quoted context omitted.
Looking at the code ( https://github.com/zilliztech/deep-searcher/blob/master/deep... ), I think it probably may work at least with Ollama without any additional tweaks if you run it with `OPENAI_BASE_URL= http://localhost:11434/v1 ` or define `provide_settings.llm.base_url` in `config.yaml` ( https://github.com/zilliztech/deep-searcher/blob/6c77b1e5597... ) and tweak the model appropriately. From a quick glance, thi…
I’ve been having issues parsing the LLM responses using Ollama and llama3.2, deepseek-r1:7b and mistral-small. I think the lack of structured output/schema is hurting it here
If you're feeling adventurous, you can probably refactor the prompt functions in https://github.com/zilliztech/deep-searcher/blob/master/deep... to return additional metadata (required output structure) together with the prompt itself, update all `llm.chat()` calls throughout the codebase to account for this (probably changing the `chat` method API by adding an extra `format` argument and not just `messages`) and implement a custom Ollama-specific handler class that would pass this to the LLM runner. Or maybe task some of those new agentic coding tools to do this, since it looks like a mostly mechanical refactoring that doesn't require a lot of thinking past figuring out the new API contract.
I’m curious how this compares to the open-source version made by HuggingFace [1]. As I can tell, the HF version uses reasoning LLMs to search/traverse and parse the web and gather results, then evaluates the results before eventually synthesizing a result. This version appears to show off a vector store for documents generated from a web crawl (the writer is a vector-store-aaS company) [1] https://github.com/huggingf…
I think the biggest one is the goal: HF is to replicate the performance of Deep Research on the GAIA benchmark whereas ours is to teach agentic concepts and show how to build research agents with open-source.
Also, we go into the design in a lot more detail than HF's blog post. On the design side, HF uses code writing and execution as a tool, whereas we use prompt writing and calling as a tool. We do an explicit break down of the query into sub-queries, and sub-sub-queries, etc. whereas HF uses a chain of reasoning to decide what to do next.
I think ours is a better approach for producing a detailed report on an open-ended question, whereas HFs is better for answering a specific, challenging question in short form.
I think the magic of Grok's implementation of this is that they already have most of the websites cached (guessing via their twitter crawler) so it all feels very snappy. Bing/Brave search don't seem to offer that in their search apis. Does such a thing exist as a service?
Web search APIs can't present the full document due to copyright. They can only present the snippet contextual to the query. I wrote my own implementation using various web search APIs and a puppeteer service to download individual documents as needed. It wasn't that hard but I do get blocked by some sites (reddit for example).