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Show HN: PageIndex – Vectorless RAG
Not all improvements come from adding complexity — sometimes it's about removing it.
PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings.
As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively.
The broader implication is that retrieval doesn't need to scale endlessly in vectors to be powerful. By leaning on document structure and reasoning, it reminds us that efficiency and human-like logic can be just as transformative as raw horsepower.
- koakuma-chan 1y agoWhat about latency?
- marcodena 1y agoyeah vectors are way more efficient for this
- theshetty 1y agoCan you eloborate on this please?
- brap 1y agoTo put it in terms of data structures, a vector DB is more like a Map, this is more like a Tree
- neutronicus 1y agoFor the C++ programmers among us I think that means it's more like `unordered_map` than `map`
- page_index 1y agoLol you mean vector db is more like hash_map. map is more tree based
- Qwuke 1y agoThe approach used here for breaking down large documents into summarized chunks that can more easily be reasoned about is how a lot of AI systems deal with large documents that surpass effective context limits in-general, but in my experience this approach will only work up to a certain point and then the summaries will start to hide enough detail that you do need semantic search or another RAG approach like GraphRAG. I think the efficacy of this approach will really fall apart after a certain number of documents. Would've loved to seen the author run experiments about how they compare to other RAG approaches or what the limitations are to this one.
- mingtianzhang 1y agoThanks, that’s a great point! That’s why we use the tree structure, which can search layer by layer without putting the whole tree into the context (to compromise the summary quality). We’ll update with more examples and experiments on this. Thanks for the suggestion!
- mingtianzhang 1y agoIn this approach, the documents need to be pre-processed once to generate a tree structure, which is slower than the current vector-based method. However, during retrieval, this approach only requires conditioning on the context for the LLM and does not require an embedding model to convert the query into vectors. As a result, it can be efficient when the tree is small. When the tree is large, however, this approach may be slower than the vector-based method since it prioritizes accuracy. If you prioritize speed over accuracy, then I guess you should use Vector DB.
- mosselman 1y agoSo if I understand this correctly it goes over every possible document with an LLM each time someone performs a search? I might have misunderstood of course. If so, then the use cases for this would be fairly limited since you'd have to deal with lots of latency and costs. In some cases (legal documents, medical records, etc) it might be worth it though. An interesting alternative I've been meaning to try out is inverting this flow. Instead of using an LLM at time of searching to find relevant pieces to the query, you flip it around: at time of ingesting you let an LLM note all of the possible questions that you can answer with a given text and store those in an index. You could them use some traditional full-text search or other algorithms (BM25?) to search for relevant documents and pieces of text. You could even go for a hybrid approach with vectors on top or next to this. Maybe vectors first and then more ranking with something more traditional. What appeals to me with that setup is low latency and good debug-ability of the results. But as I said, maybe I've misunderstood the linked approach.
- Qwuke 1y ago>An interesting alternative I've been meaning to try out is inverting this flow. Instead of using an LLM at time of searching to find relevant pieces to the query, you flip it around: at time of ingesting you let an LLM note all of the possible questions that you can answer with a given text and store those in an index. You may already know of this one, but consider giving Google LangExtract a look. A lot of companies are doing what you described in production, too!
- summarity 1y agoThis is just a variation of index time HyDE (Hypothetical Document Embedding). I used a similar strategy when building the index and search engine for findsight.ai
- agentcoops 1y agoI’ve been working on RAG systems a lot this year and I think one thing people miss is that often for internal RAG efficiency/latency is not the main concern. You want predictable, linear pricing of course, but sometimes you want to simply be able to get a predictably better response by throwing a bit more money/compute time at it. It’s really hard to get to such a place with standard vector-based systems, even GraphRag. Because it relies on summaries of topic clusters that are pre-computed, if one of those summaries is inaccurate or none of the summaries deal with your exact question, that will never change during query processing. Moreover, GraphRag preprocessing is insanely expensive and precisely does not scale linearly with your dataset. TLDR all the trade-offs in RAG system design are still being explored, but in practice I’ve found the main desired property to be “predictably better answer with predictably scaling cost” and I can see how similar concerns got OP to this design.
- guerby 1y agohttps://en.wikipedia.org/wiki/Retrieval-augmented_generation https://en.wikipedia.org/wiki/Retrieval-augmented_generation
- brap 1y agoVery cool. These days I’m building RAG over a large website, and when I look at the results being fed into the LLM, most of them are so silly it’s surprising the LLM even manages to extract something meaningful. Always makes me wonder if it’s just using prior knowledge even though it’s instructed not to do so (which is hacky). I like your approach because it seems like a very natural search process, like a human would navigate a website to find information. I imagine the tradeoff is performance of both indexing and search, but for some use cases (like mine) it’s a good sacrifice to make. I wonder if it’s useful to merge to two approaches. Like you could vectorize the nodes in the tree to give you a heuristic that guides the search. Could be useful in cases where information is hidden deep in a subtree, in a way that the document’s structure doesn’t give it away.
- page_index 1y ago[dead]
- mingtianzhang 1y agoStrongly agree! It is basically the Mone-Carlo tree search method used in Alpha Go! This is also mentioned in one of their toturials: PageIndex/blob/main/tutorials/doc-search/semantics.md. I believe it will make the method more scalable for large documents.
- neya 1y agoThis is good for applications where a background queue based RAG is acceptable. You upload a file, set the expectation to the user that you're processing it and needs more time for a few hours and then after X hours you deliver them. Great for manuals, documentation and larger content. But for on-demand, near instant RAG (like say in a chat application), this won't work. Speed vs accuracy vs cost. Cost will be a really big one.
- kruxigt 1y ago[dead]
- actionfromafar 1y agoIf you have a lot of time, cost on a local machine may be low.
- kruxigt 1y ago[dead]
- lewisjoe 1y agoThis will scale when you have a single/a small set of document(s) and want your questions answered. When you have a question and you don't know which of the million documents in your dataspace contains the answer - I'm not sure how this approach will perform. In that case we are looking at either feeding an enormously large tree as context to LLM or looping through potentially thousands of iterations between a tree & a LLM. That said, this really is a good idea for a small search space (like a single document).
- page_index 1y ago[dead]
- mikeve 1y agoNot sure if I fully understand it, but this seems highly inefficient? Instead of using embeddings which are easy to make a cheap to compare, you use summarized sections of documents and process them with an LLM? LLM's are slower and more expensive to run.
- falcor84 1y agoIf this is used as an important tool call for an AI agent that preforms many other calls, then it's likely that the added cost and latency would be negligible compared to the benefit of significantly improved retrieval. As an analogy, for a small task you're often ok with just going over the first few search results, but to prepare for a large project, you might want to spend an afternoon researching.
- page_index 1y agoIn specific domains, accuracy matters more than than speed. Document structure and reasoning bring better retrieval than semantic search which retrieves "similar" but not "relevant" results.
- page_index 1y ago[dead]
- mingtianzhang 1y agoI think it only needs to generate the tree once before retrieval, and it doesn’t require any external model at query time. The indexing may take some time upfront, but retrieval is then very fast and cost-free.
- CuriouslyC 1y agoThe idea this person is trying for is a LLM that explores the codebase using the source graph in the way a human might, by control+clicking in idea/vscode to go to definition, searching for usages of a function, etc. It actually does work, other systems use it as well, though they have the main agent performing the codebase walk rather than delegate to a "codebase walker" agent.
- dr_dshiv 1y agoUnrelated: why is chat search in Claude so bad?
- nikishuyi 1y agoMaybe lost in the context? I guess a tree method can be used to improve that?
- bigdickfounder 1y ago[dead]
- nathan_compton 1y agoI let a boot do a free text search over and indexed database. Works ok. I've also tried keyword based retrieval and vector search. I've found all leave something to be desired, sadly.
- thatjoeoverthr 1y agoThere's good reasons to do this. Embedding similarity is _not_ a reliable method of determining relevance. I did some measurements and found you can't even really tell if two documents are "similar" or not. Here: https://joecooper.me/blog/redundancy/ https://joecooper.me/blog/redundancy/ One common way is to mix approaches. e.g. take a large top-K from ANN on embeddings as a preliminary shortlist, then run a tuned LLM or cross encoder to evaluate relevance. I'll link here these guys' paper which you might find fun: https://arxiv.org/pdf/2310.08319 https://arxiv.org/pdf/2310.08319 At the end of the day you just want a way to shortlist and focus information that's cheaper, computationally, and more reliable, than dumping your entire corpus into a very large context window. So what we're doing is fitting the technique to the situation. Price of RAM; GPU price; size of dataset; etc. The "ideal" setup will evolve as the cost structure and model quality evolves, and will always depend on your activity. But for sure, ANN-on-embedding as your RAG pipeline is a very blunt instrument and if you can afford to do better you can usually think of a way.
- page_index 1y ago[dead]
- tomomomo 1y agoThe "redundacy" experiment is very interesting! Strongly agree, we just need to do something better than "dumping your entire corpus into a very large context window", maybe using this table-of-contents methods would be very useful?
- monster_truck 1y agovectorless rag? I think I have one of those in my kitchen
- nikishuyi 1y agoLoll you also need one in your computer.
- page_index 1y agoI have page index in my book :)
- Koaisu 1y agoSounds a bit like generative retrieval (e.g. this Google paper here: https://arxiv.org/abs/2202.06991 https://arxiv.org/abs/2202.06991)
- thatjoeoverthr 1y agoI love it
- mingtianzhang 1y agoYeah, they share a similar intuition. I found that the difference is that PageIndex is more of a learning-free approach, more like how a human would do retrieval?
- jdthedisciple 1y agoLooks like this should scale spectacularly poorly. Might be useful for a few hundred documents max though.
- bjornsing 1y agoIt scales as log(N), right? So if you can tolerate it for a few hundred docs you can probably tolerate it for a lot more.
- deleted 1y ago[deleted]
- mingtianzhang 1y agoA good thing about tree representation compared to a 'list' representation is that you can search hierarchically, layer by layer, in a large tree. For example, AlphaGo performs search in a large tree. Since the scale of retrieval is smaller than that of the Go game, I guess this framework can scale very well.
- CuriouslyC 1y agoThis design isn't new, Codanna MCP uses it, and it definitely works (at least when run by the main agent, a dumb subagent might biff it).
- geedzmo 1y ago"Human-like Retrieval: Simulates how human experts navigate and extract knowledge from complex documents." - pretty sure I use control-f when I look for stuff
- scotty79 1y agoI think it's about how you decide where to press Ctrl+F next.
- mingtianzhang 1y agoBut different people may have different ways. For example, I use command+f in macbook.
- rejojer 1y ago[dead]
- page_index 1y agoLOL ctrl-f feels like bm25 vector search
- raytang 1y agothis is how I as a human retrieve on a computer :)
- huqedato 1y agoI have a RAG built on 10000+ docs knowledge base. On vector store, of course (Qdrant - hybrid search). It work smoothly and quite reliable. I wonder how this "vectorless" engine would deal with this. Simply, I can't see this tech scalable.
- mingtianzhang 1y agoA good thing about tree representation compared to a 'list' representation is that you can search hierarchically, layer by layer, in a large tree. For example, AlphaGo performs search in a large tree. Since the scale of retrieval is smaller than that of the Go game, I guess this framework can scale very well.
- huqedato 1y agoA proof/real-world example would be needed to validate your claim(s). I think the technology is promising but I don't believe in all those "advantages" that they advertise on the website.
- rco8786 1y agoThis seems really interesting but I can't quite figure out if this is like a SaaS product or an OSS library? The code sample seems to indicate that it uses some sort of "client" to send the document somewhere and then wait to retrieve it later. But the home page doesn't indicate any sort of sign up or pricing. So I'm a little confused. edit Ok I found a sign up flow, but the verification email never came :(
- dmezzetti 1y agoContext and prompt engineering is the most important of AI, hands down. There are plenty of lightweight retrieval options that don't require a separate vector database (I'm the author of txtai [https://github.com/neuml/txtai https://github.com/neuml/txtai], which is one of them). It can be as simple this in Python: you pass an index operation a data generator and save the index to a local folder. Then use that for RAG.
- mingtianzhang 1y agoStrongly agree, I also found txtai is super interesting! Thank you for your open-source effort!
- dmezzetti 1y agoYou got it!
- CuriouslyC 1y agoContext and prompt engineering are super automatable. DSPy can automate prompt generation that massively outperforms human prompts, and instead of hand packing context, you can use IR/ML algorithms to intelligently select the optimal context bundle to produce the desired output. Context and prompt engineering are going to be replaced by algorithms, 100%.
- dmezzetti 1y agoYep, context, however you build it.
- dcre 1y agoMy approach in "LLM-only RAG for small corpora" [0] was to mechanically make an outline version of all the documents _without_ an LLM, feed that to an LLM with the prompt to tell which docs are likely relevant, and then feed the entirety of those relevant docs to a second LLM call to answer the prompt. It only works with markdown and asciidoc files, but it's surprisingly solid for, for example, searching a local copy of the jj or helix docs. And if the corpus is small enough and your model is on the cheap side (like Gemini 2.5 Flash), you can of course skip the retrieval step and just send the entire thing every time. [0]: https://crespo.business/posts/llm-only-rag/ https://crespo.business/posts/llm-only-rag/
- mingtianzhang 1y agoLLM-only RAG for small corpora looks super interesting!
- raytang 1y agothis rocks! will definitely check
- cantor_S_drug 1y agoThis is like semantic version of B+ trees.
- page_index 1y ago[dead]
- nikishuyi 1y agoYeah, I strongly agree. I also found in AI coding tools, tree search has replaced vector search. I’m wondering if in generic RAG systems, tree search will replace vector databases?
- CuriouslyC 1y agoTree search hasn't replaced vector search, you can use them synergistically, it's just that vector search is "fiddly" as you have to set up a bunch of stuff to index your repos, manage embeddings, etc and it can use a lot of disk space if you don't use graph representations for your embeddings like LEANN.
- rejojer 1y ago[dead]
- mvieira38 1y ago> It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively. Wasn't this a feature of RAGs, though? That they could match semantics instead of structure, while us mere balls of flesh need to rely on indexes. I'd be interested in benchmarks of this versus traditional vector-based RAGs, is something to that effect planned?
- mingtianzhang 1y agoIn their gitHub repo’s readme, they show a benchmark on FinanceBench and found that PageIndex-based retrieval significantly outperforms vector-based methods. I’ve noticed that in domain-specific documents, where all the text has similar “semantic vibes,” non-vector methods like PageIndex can be more useful. In contrast, for use cases like recommendation systems, you might actually need a semantic-vibe search.
- leetharris 1y agoRAG is just finding the right context for your question. Embedding based RAG is fast and conceptually accurate, but very poor for high complexity tasks. Agentic RAG is higher quality, but much higher compute and latency cost. But often worth it for complex situations.
- ineedasername 1y ago>"Retrieval based on reasoning — say goodbye to approximate semantic search ("vibe retrieval" How is this not precisely "vibe retrieval" and much more approximate, where approximate in this case is uncertainty over the precise reasoning? Similarity with conversion to high-dimensional vectors and then something like kNN seems significantly less approximate, less "vibe" based, than this. This also appears to be completely predicated on pre-enrichment of the documents by adding structure through API calls to, in the example, openAI. It doesn't at all seem accurate to: 1: Toss out mathematical similarity calculations 2: Add structure with LLMs 3: Use LLMs to traverse the structure 4: Label this as less vibe-ish Also for any sufficiently large set of documents, or granularity on smaller sets of documents, scaling will become problematic as the doc structure approaches the context limit of the LLM doing the retrieval.
- raytang 1y ago[dead]
- SV_BubbleTime 1y ago> This also appears to be completely predicated on pre-enrichment of the documents by adding structure through API calls to, in the example, openAI. That was my immediate take. [Look at the summary and answer based on where you expect the data to be found] maybe works well for reliably structured data.
- jimmytucson 1y agoIt is just as "vibe-ish" as vector search and notably does require chunking (document chunks are fed to the indexer to build the table of contents). That said, I don't find vector search any less "vibey". While "mathematical similarity" is a structured operation, the "conversion to high-dimensional vectors" part is predicated on the encoder, which can be trained towards any objective. > scaling will become problematic as the doc structure approaches the context limit of the LLM doing the retrieval IIUC, retrieval is based on traversing a tree structure, so only the root nodes have to fit in the context window. I find that kinda cool about this approach. But yes, still "vibe retrieval".
- mritchie712 1y agoan effective "vectorless RAG" is to have an LLM write search queries against the documents. e.g. if you store your documents in postgres, allow the LLM to construct a regex string that will find relevant matches. If you were searching for “Martin Luther King Jr.”, it might write something like: SELECT id, body FROM docs WHERE body ~* E'(?x) -- x = allow whitespace/comments (?:\\m(?:dr|rev(?:erend)?)\\.?\\M[\\s.]+)? -- optional title: Dr., Rev., Reverend ( -- name forms (?:\\mmartin\\M[\\s.]+(?:\\mluther\\M[\\s.]+)?\\mking\\M) -- "Martin (Luther)? King" | (?:\\mm\\.?\\M[\\s.]+(?:\\ml\\.?\\M[\\s.]+)?\\mking\\M) -- "M. (L.)? King" / "M L King" | (?:\\mmlk\\M) -- "MLK" ) (?:[\\s.,-]*\\m(?:jr|junior)\\M\\.?)* -- optional suffix(es): Jr, Jr., Junior ';
- sgt 1y agoWon't that be slower than vector DB's by an order of magnitude or more?
- ahonn 1y agoFaster is not always better. In certain situations, we may choose to sacrifice speed for increased accuracy.
- page_index 1y agoI guess the major foucs in certain uses cases is not speed but accuracy and retrieval quality.
- esafak 1y agoI don't see this scaling: https://deepwiki.com/search/how-is-the-tree-formed-and-tra_91bcc006-ed40-481e-9f3b-336832c55703 https://deepwiki.com/search/how-is-the-tree-formed-and-tra_9... I'd do some large scale benchmarks before doubling down on this approach.
- mingtianzhang 1y agoA good thing about tree representation compared to a 'list' representation is that you can search hierarchically, layer by layer, in a large tree. For example, AlphaGo performs search in a large tree. Since the scale of retrieval is smaller than that of the Go game, I guess this framework can scale very well.
- raytang 1y ago[dead]
- malshe 1y agoThe folks who are using RAG, what's the SOTA for extracting text from pdf documents? I have been following discussions on HN and I have seen a few promising solutions that involve converting pdf to png and then doing extraction. However, for my application this looks a bit risky because my pdfs have tons of tables and I can't afford to get in return incorrect of made up numbers. The original documents are in HTML format and although I don't have access to them I can obtain them if I want. Is it better to just use these HTML documents instead? Previously I tried converting HTML to markdown and then use these for RAG. I wasn't too happy with the result although I fear I might be doing something wrong.
- JJax7 1y agoIf accuracy is a major concern, then it's probably guaranteed better to go with the HTML documents. Otherwise, I've heard Docling is pretty good from a few co-workers.
- malshe 1y agoSo you suggest working directly with HTML or going HTML -> Markdown first?
- giamma 1y agoHow about using something like Apache Tika for extracting text from multiple documents? It's a subproject of Lucene and consists of a proxy parser + delegates for a number of document formats. If a document, e.g. PDF, comes from a scanner, Tika can optionally shell-out a Tesseract invocation and perform OCR for you.
- huqedato 1y agoThe Tika's documentation is abysmal. Maybe it is a great product but we had to scrap it because of this.
- davidajackson 1y agoCan you explain why to png? why not to markdown?
- gillesjacobs 1y agoA suspicious lack of any performance metrics on the many standard RAG/QA benchmarks out there, except for their highly fine-tuned and dataset-specific MAFIN2.5 system. I would love the see this approach vs. a similarly well-tuned structured hybrid retriever (vector similarity + text matching) which is the common way of building domain-specific RAG. The FinanceBench GPT4o+Search system never mentions what the retrieval approach is [1,2], so I will have to assume it is the dumbest retriever possible to oversell the improvement. PageIndex does not state to what degree the semantic structuring is rule-based (document structure) or also inferred by an ML model, in any case structuring chunks using semantic document structure is nothing new and pretty common, as is adding generated titles and summaries to the chunk nodes. But I find it dubious that prompt-based retrieval on structured chunk metadata works robustly, and if it does perform well it is because of the extra work in prompt-engineering done on chunk metadata generation and retrieval. This introduces two LLM-based components that can lead to highly variable output versus a traditional vector chunker and retriever. There are many more knobs to tune in a text prompt and an LLM-based chunker than in a sentence/paragraph chunker and a vector+text similarity hybrid retriever. You will have to test retrieval and generation performance for your application regardless, but with so many LLM-based components this will lead to increased iteration time and cost vs. embeddings. Advantage of PageIndex is you can make it really domain-specific probably. Claims of improved retrieval time are dubious, vector databases (even with hybrid search) are highly efficient, definitely more efficient that prompting an LLM to select relevant nodes. 1. https://pageindex.ai/blog/Mafin2.5 https://pageindex.ai/blog/Mafin2.5 2. https://github.com/VectifyAI/Mafin2.5-FinanceBench https://github.com/VectifyAI/Mafin2.5-FinanceBench
- CuriouslyC 1y agoSo, this has already been done plenty, Serena MCP and Codanna MCP both do this with AST source graphs, Codanna even gives hints in the MCP response to guide the agent to walk up/down the graph. There might be some small efficiency gain in having a separate agent walk the graph in terms of context savings, but you also lose solution fidelity, so I'm not sure it's a win. Also, it's not a replacement for RAG, it's just another piece in the pipeline that you merge over (rerank+cut or llm distillate).
- tomomomo 1y agoYeah, I agree it’s not something new, since humans also do this kind of retrieval. It’s just a way to generate a table of contents for an LLM. I’m wondering, when LLMs become stronger, will we still need vector-based retrieval? Or will we need a retrieval method that’s more like how humans do it?
- sdesol 1y ago> will we still need vector-based retrieval I think for most use cases, it doesn't make much sense to use vector DBs. When I started to design my AI Search feature, I researched chunking a lot and the general consensus was, you can can lose context if you don't chunk in the right way and there wasn't really a right way to chunk. This was why I decided to take the approach that I am using today, which I talk about in another comment. With input cost for very good models ($0.30/1M) for Gemini 2.5 Flash (bulk rates would be $0.15/1M), feeding the llm thousands of documents to generate summaries would probably cost 5 dollars or less if using bulk rate pricing. With input cost and with most SOTA LLMs being able to handle 50k tokens in context window with no apparent lost in reasoning, I really don't see the reason for vector DBs anymore, especially if it means potentially less accurate results.
- CuriouslyC 1y agoActually, chunking isn't such a bad problem with code, it chunks itself, and code embeddings produce better results. The problem is that RAG is fiddly, and people try to just copy a basic template or use a batteries included lib that's tuned to QA, which isn't gonna produce good results.
- petesergeant 1y agoSecond attempt to get away from vectors and embeddings I’ve seen here recently. Are people really struggling that much with their RAG systems?
- page_index 1y agocurious about the other attempt you see
- petesergeant 1y agohttps://news.ycombinator.com/item?id=44969622 https://news.ycombinator.com/item?id=44969622
- gogeta_99999 1y ago>Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. So are we are creating create for each document on the fly ? even if its a batch process then dont you think we are pointing back to something which is graph (approximation vs latency sort of framework) Looks like you are talking more in line of LLM driven outcome where "semantic" part is replaced with LLM intelligence. I tried similar approaches few months back but those often results in poor scalablity, predictiablity and quality.
- deleted 1y ago[deleted]
- joshua_s_penman 1y agoThe thing is — for very long documents, it's actually pretty hard for humans to find things, even with a hierarchical structure. This is why we made indexes — the original indexes! — on paper. What you're saying makes pretty hard assumptions about document content, and of course doesn't start to touch multiple documents. My feeling is that what you're getting at is actually the fact that it's hard to get semantic chunks and when embedding them, it's hard to have those chunks retain context/meaning, and then when retrieving, the cosine similarity of query/document is too vibes-y and not strictly logical. These are all extremely real problems with the current paradigm of vector search. However, my belief is that one can fix each of these problems vs abandoning the fundamental technology. I think that we've only seen the first generation of vector search technology and there is a lot more to be built. At Vectorsmith, we have some novel takes on both the comptuation and storage architecture for vector search. We have been working on this for the last 6 months and have seen some very promising resutls. Fundamentally my belief is that the system is smarter when it mostly stays latent. All the steps of discretization that are implied in a search system like the above lose information in a way that likely hampers retrieval.
- zan2434 1y agointeresting, so you think the issue with the above approach is the graph structure being too rigid / lossy (in terms of losing semantics)? And embeddings are also too lossy (in terms of losing context and structure)? But you guys are working on something less lossy for both semantics and context?
- joshua_s_penman 1y ago> interesting, so you think the issue with the above approach is the graph structure being too rigid / lossy (in terms of losing semantics)? Yeah, exactly. >And embeddings are also too lossy (in terms of losing context and structure) Interestingly, it appears that the problem is not embeddings but rather retrieval. It appears that embeddings can contain a lot more information than we're currently able to pull out. Like, obviously they are lossy, but... less than maybe I thought before I started this project? Or at least can be made to be that way? > But you guys are working on something less lossy for both semantics and context? Yes! :) We're getting there! It's currently at the good-but-not-great like GPT-2ish kind of stage. It's a model-toddler - it can't get a job yet, but it's already doing pretty interesting stuff (i.e. it does much better than SOTA on some complex tasks). I feel pretty optimistic that we're going to be able to get it to work at a usable commercial level for at least some verticals — maybe at an alpha/design partner level — before the end of the year. We'll definitely launch the semantic part before the context part, so this probably means things like people search etc. first — and then the contextual chunking for big docs for legal etc... ideally sometime next year?
- mingtianzhang 1y agoI just realized that the whole Hacker News discussion is formalized as a tree, and I am using my eyes to tree search through the tree to retrieve ideas from the insightful comments.
- joshua_s_penman 1y agothis is fundamentally organized by popularity, though
- raytang 1y agothinking if this is related to llms.txt?
- visarga 1y agoI did something like this myself. Take a large PDF, summarize each page. Make sure to have the titles of previous 3 pages, it helps with consistency and detecting transitions from one part to another. Then you take all page summaries in a list, and do another call to generate the table of contents. When you want to use it you add the TOC in the prompt and use a tool to retrieve sections on demand. This works better than embeddings which are blind to relations and larger context. It was for a complex scenario of QA on long documents, like 200 page earning reports.
- deleted 1y ago[deleted]
- vasa_ 1y agoWe've done this for a while with cognee, where we have graph completition retrieval that does that + many other things like weighting, self improving feedback and more https://github.com/topoteretes/cognee https://github.com/topoteretes/cognee