Difference in skin feel between vector search and RAG
That's because Scrapbox is a collaborative editing tool.
Not if it's a RAG or not.
The other side of the chat doesn't afford editing of the other side's statements either.
8/29
kazunori_279 I guess instead of messing around with fine tuning and RAG, we can simply do a vector search with emb and display the results. I thought, "Why not? I thought so. nishio I have always been of the "just do a vector search" school of thought, but I feel that RAG is better when used as an intellectual production assistant rather than just a question and answer tool. I feel that RAG is better for use as an assistant for intellectual production, not just for answering questions. The generation part functions as "summarizing the search results to suit your purpose". This assumes that the purpose is given separately from the query. kazunori_279 Indeed, RAG is useful if the main service is to interact and assist users. __BELOW_IS_AI_GENERATED__
ベクトル検索とRAGの肌感の違い 2023-10-05 16:53 omni.icon
digest.
Discusses the difference between using "vector search" and "RAG," arguing that Scrapbox is a collaborative editing tool and affords editing; AI output must also afford editing. Also introduced a discussion on Twitter about the difference between vector search and RAG.
Relation to Fragment.
The "next study group" fragment describes the advantages and disadvantages of vector search and relates to the difference in usability between the note "vector search" and "RAG." The fragment "cross vector search experiment memo 2023-09-20" describes an experiment in vector search and is related to the usability of the note "vector search. The fragment "RAG" describes the concept of RAG and relates to the usability of RAG in the notebook.
deep thinking
The difference in usability between vector search and RAG may be due to the different "editorial affords" that each provides. Vector searches display results as they are, while RAGs summarize the search results and display them for a specific purpose. In this way, RAGs provide an environment in which users can easily edit information.
summary of thoughts
The difference in the usability of vector searches and RAGs is due to the different "editorial affords" that each provides.
Title.
"Vector Search vs. RAG Usability Differences: an Editorial Afford's Perspective."
extra info
titles: ["next study group"], "Cross-sectional vector search experiment memo 2023-09-20", "RAG", "In the end, what we need is to improve search accuracy", "pVectorSearch2023-04-29~05-31", "The reality of collaborative work with AI and S-curves"]
code:fragments
### next study group
Vector searches are good for vague searches in natural language conversations because they produce a flurry of hits, but they also produce hits for "the name of the company you visited on a sales trip" or "the product model number" because they produce hits for "different products that look very similar," which places a high cognitive burden on the user.
The following is a summary of the information enteredgpt.icon
7/29: AI writes to Scrapbox, introducing the concept of Scrapbox Agents.
Early to mid-August: Omoikane study group, discussion on working with AI, writing research notes with AI, evolution of vector search, and topics on handling AI-generated pages.
Mid to end of August: topics related to updating and search, taking into account multi-head, page memory, and user cognitive load.
Beginning of September: Explore AI-user interaction, note management, and relevance between different content.
Mid-September: various topics related to multi-head thinking, the intellectual production techniques of engineers, and working with AI.
Late September: LLM vs. other models, discussion on human concepts, optimal use of Scrapbox, and feedback on non-public tools.
Overall, this period seems to focus on Scrapbox and AI integration, particularly the concepts of vector search and multi-heading, and AI thinking and interaction with the user.
### Cross-sectional vector search experiment memo 2023-09-20
Transverse Vector Search Experiment Memo 2023-09-20
We conducted an experiment to increase the number of data to be searched for in Extending the Red Link with AI, which we have been running publicly on this Scrapbox. Searches include other people's Scrapbox data, books and papers
relevance
Since there might be some problems with publishing the generated results as they are without review, the output destination is a private project that is not open to the public.
Subsequent updates changed the search hits to output directly to Scrapbox, making it clearly unpublishable.
https://gyazo.com/bb1a0bd6cb151fa1b6ffa2d7c498744c
The page is automatically generated by AI at the destination of the red link created like this
mounting
Load from local pickle
It takes about 5 seconds to read 100,000 records
About 7 seconds to perform a local vector search
It's slow when you think of it as a web app response, but with the recent "throw a keyword or page that comes to mind, work on something else, and look at it after a while" style, it's not a problem.
There's about 10-25 minutes between when you throw the query and when I come back to check the results (I don't measure it).
### RAG
RAG
### In the end, what we need is to improve the accuracy of search
nishio Generation AI is effective when used properly for generation, but what do customers want to generate in the first place? When I think about it, most customers' needs are "to find the important parts among a lot of things that are hard to read", and even if they finally generate, there is "to find materials for generation" before the generation, so search is necessary after all. nishio The story is slightly different and I don't think "conventional technology is better" at all. I already use neither conventional search nor my own vector search almost exclusively RAG. I think that the domain of competition is to combine and re-rank sparse and dens search when increasing the usefulness of it. ### pVectorSearch2023-04-29~05-31
This looks interesting.
For example, loading a request with "false dichotomy" will return https://gyazo.com/80d45ec33ca8cbb138108d71ad7eec02 in the response.
誤った二分法.icon
blind spot card can be drawn in a vector search in the sentence I'm writing, rather than randomly. This is possible because of the accumulation of "image" and "text" pairs in the form of "pages" in Scrapbox
2023-05-04
Examples of vector searching and looking at images
2023-05-11
2023/5/16
A system that recognizes speech when you are speaking and automatically queries it to search this Scrapbox and display matches.
Related Pages" for speaking
At first I thought "search only images", but then I thought I could just use Scrapbox's related page format to display pages without images.
2023/5/31
Maybe it would be good to be able to do a "vector search including things other than yourself" based on this area.
I can't really relate to the "Scrapbox by Yasukazu Nishio" vector search function, because it makes me see things differently from the way other people see them, and I have no sympathy for what they should be.
### Actual conditions of collaborative work with AI and S-curve
It beats the experience of "writing it down to the last word and handing it over to the AI."
__BELOW_IS_AI_GENERATED__
AIとの協働作業の実態とS字カーブ 2023-09-18 00:24 omni.icon
digest.
Collaborative work with AI detects periods of stagnation in thinking and throws the content to AI to move on to another task. Human thinking follows an S-curve, and AI can be helpful in times of stagnation; working with AI can smooth the transition from Task A to Task B. Also, collaborative work with AI can avoid stagnant periods in thinking by sensing when thinking is stagnant and throwing content to AI to move on to another task.
Relation to Fragment.
The fragment "Intelligent Collaboration Between AI and Humans" provides a specific example of AI-human collaborative work, which is directly related to the note. Specifically, the AI and human become team members and create a new form of teamwork, which is consistent with the collaborative work with AI described in the note.
deep thinking
Collaborative work between AI and humans can avoid periods of stagnant thinking by sensing periods of stagnant thinking and throwing the content to AI to move on to another task. This is based on the characteristic of an S-shaped curve in human thinking, and demonstrates the effectiveness of collaborative work between AI and humans.
generated: 2023-10-05 16:53
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