I never could get much beyond the basic search piece. I don't see how mixing in a black box AI model with probabilistic outcomes could add any value without having this working first.

It works pretty well. It might benefit from including some material on the page prior and after, but it mostly solves the "isolated chunk" problem.

If the model understands text/code and can generate text/code it should be able to talk to OpenSearch no problem.

The other problem is that embeddings search can miss things that a direct keyword match would have caught. If you have key terms that are specific to your corpus - product names for example - there's a risk that a vector match might not score those as highly as BM25 would have so you may miss the most relevant documents.

Finally, embeddings are much more black box and hard to debug and reason about. We have decades of experience tweaking and debugging and improving BM25-style FTS search - the whole field of "Information Retrieval". Throwing that all away in favour of weird new embedding vectors is suboptimal.

Why not have a similarity threshold? Say, if the distance is below 0.7, do not accept the search result.

That seems like a better stacking of the technologies even now

I did similar in 2019 but typically in reverse, FTS, and a dual tower model to rerank. Vector search was an additional capability but never augmented the FTS.

What is happening is that text is being embedded into a different space, and that format is an array of floats (a point in the embedding space). When we do retrieval, we embed the query and then find other points close to that query. The reason for Vector DB is (1) to optimize for this use-case, we have many specialized data stores / indexes (redis, elastic, dolt, RDBMS) (2) often to be memory based for faster retrieval. PgVector will be interesting to watch. I personally use Qdrant

Full-text search will never be able to do some of the things that are possible in the embedding space. The most capable systems will use both techniques

Vector DBs are cool, you want one handy (particularly for recommender tasks). I recommend FAISS as a solid baseline all these years later. If you’re on modern x86_64 then SVS is pretty shit hot.

A search engine that only uses a vector DB is a PoC.

For folks who want to go deeper on the topic, Lars basically invented the modern “news feed”, which looks a lot like a production RAG system would [1].

1. https://youtu.be/BuE3DIJGWOw

It makes sense for the hype, though. As we got LLM’s we also got wayyyy better embedding models, but they’re not dependencies.

That's interesting; do you then transform the question-as-prompt before embedding it at runtime, so that it "asks for" that metadata to be in the response? Because otherwise, it would seem to me that you're just making it harder for the prompt vector and the document vectors to match.

(I guess, if it's equally harder in all cases, then that might be fine. But if some of your documents have few tags or no title or something, they might be unfairly advantaged in a vector-distance-ranked search, because the formats of the documents more closely resemble the response format the question was expecting...)

But there’s no reason why they couldn’t — just capture the vectors of some of the earlier hidden layers during the RAG encoder’s inference run, and append these intermediate vectors to the final embedding vector of the output layer to become the vectors you throw into your vector DB. (And then do the same at runtime for embedding your query prompts.)

Probably you’d want to bias those internal-layer vectors, giving them an increasingly-high “artificial distance” coefficient for increasingly-early layers — so that a document closely matching in token space or word space or syntax-node space improves its retrieval rank a bit, but not nearly as much as if the document were a close match in concept space. (But maybe do something nonlinear instead of multiplication here — you might want near-identical token-wise or syntax-wise matches to show up despite different meanings, depending on your use-case.)

Come to think, you could probably build a pretty good source-code search RAG off of this approach.

(Also, it should hopefully be obvious here that if you fine-tuned an encoder-decoder LLM to label matches based on criteria where some of those criteria are only available in earlier layers, then you’d be training pass-through vector dimensions into the intermediate layers of the encoder — such that using such an encoder on its own for RAG embedding should produce the same effect as capturing + weighting the intermediate layers of a non-fine-tuned LLM.)

At my startup we also got pretty good results using 7B/8B models to generate meta information about chunks/parts of text.

RAG is a bit like having a pretty smart person take an open book test on a subject they are not an expert in. If your book has a good chapter layout and index, you probably do an ok job trying to find relevant information, quickly read it, and try to come up with an answer. But your not going to be able to test for a deep understanding of the material. This person is going to struggle if each chapter/concept builds on the previous concept, as you can't just look up something in Chapter 10 and be able to understand it without understanding Chapter 1-9.

Fine-tuning is a bit more like having someone go off and do a phd and specialize in a specific area. They get a much deeper understanding for the problem space and can conceptualize at a different level.

Fine tuning is just training. You can completely change the model if you want make learn anything you want.

But there are MANY challenges in doing so.

I regularly do fine tuning on a model with fine results and little damage to the base functionality.

It is possible, but it's too complex for the majority of users. It requires a lot of work per dataset you want trained on.

Models currently also have no way to update themselves with new info besides us putting data into their context window. They don’t learn after the initial training. It seems if they could, say, read documentation and internalize it, the need for RAG or even large context windows would decrease. Humans somehow are able to build understanding of extensive topics with what feels to be a much shorter context-window.

This is a foundational problem that requires your data. The way you search Etsy is different than the way you search Amazon. The queries these systems see are different and so are the desired results.

Trying to solve the problem with pretrained models is not currently realistic.

For instance I have a policy that I try hard not to say anything like "most people think that..." without providing links because I work at an archive of public opinion data and if it gets out that one of our people was spouting false information about our domain, even if we weren't advertising the affiliation, that would look bad.

Those are being worked on and RAG is the ducktape solution until they become available

Some kind of incremental fine tuning is probably necessary to keep a model like ChatGPT up to date but I can't picture it happening each time something happens in the news.

I think you’d get a close approximation of speaking with someone who was watching the game with you.

To reach their potential LLMs need to know how to use external sources.

Update: After some more thinking - if you required it to know information about itself - then this would lead to some paradox - I am sure.

Source: built a few products using RAG+LLM products.

My instinct at this point is, these algos look attractive because we are constrained to giving a user a wow moment where they upload something and get to chat with the doc/dataset within minutes. As attractive as that is, it is a distinct second priority to building a system that works 99% of the time, even if takes a day or two to set up. You get a feel of the data, have a feel of type of questions that may be asked, and create an algo that works for a specific type of dataset-usecase combo (assuming any more data you add in this system would be similar and work pretty well). There is no silver bullet that we seem to be searching for.

... and then you have situations where people ask complex questions with multiple logical steps, or knowledge gathering requirements, and using some sort of hierarchical RAG strategy works better.

I think a lot of solutions (including this post) abstract to building knowledge graphs of some sort... But knowledge graphs still require an ontology associated to the problem you're solving and will fail outside of those domains.

I found Azure Document Intelligence specifically with the Layout Model to be fantastic for this because it can identify headers. All the better if you write a parser for the output JSON to track depth and stuff multiple headers from the path into the chunk.

If we think about what this is about, it is basically entity augmentation & lexical linking / citations.

Ex: A patient document may be all about patient id 123. That won't be spelled out in every paragraph, but by carrying along the patient ID (semantic entity) and the document (citation), the combined model gets access to them. A naive one-shot retrieval over a naive chunked vector index would want it at the text/embedding, while a smarter one also in the entry metadata. And as others write, this helps move reasoning from the symbolic domain to the semantic domain, so less of a hack.

We are working on some fun 'pure-vector' graph RAG work here to tackle production problems around scale, quality, & always-on scenarios like alerting - happy to chat!

A better solution I had thought about its "local RAG". I came across this while processing embeddings from chunks parsed from Azure Document Intelligence JSON. The realization is that relevant topics are often localized within a document. Even across a corpus of documents, relevant passages are localized.

Because the chunks are processed sequentially, one needs only to keep track o the sequence number of the chunk. Assume that the embedding matches with a chunk n, then it would follow that the most important context are the chunks localized at n - m and n + p. So find the top x chunks via hybrid embedding + full text match and expand outwards from each of the chunks to grab the chunks around it.

While a chunk may represent just a few sentences of a larger block of text, this strategy will grab possibly the whole section or page of text localized around the chunk with the highest match.

Go for chunk n, n - m, n + p and n' where n' are closest chunks to n semantically.

Moreover you can give this traversal possibility to your LLM to use itself as a tool or w/e whenever it is missing crucial information to answer the question. Thanks to that you don't always retrieve thousands of tokens even when not needed.

    > positionally related chunks but also semantically related ones

I think where GRAG yields more relevancy is when the referenced content is not semantically similar nor even semantically adjacent to the entry concept but is semantically similar to some sub fragment of a matched chunk. Depending on the corpus, this can either be common (no familiarity with financial documents) or rare. I've primarily worked with clinical trial protocols and at least in that space, the concepts are what I would consider "snowflake-shaped" in that it branches out pretty cleanly and rarely cross-references (because it is more common that it repeats the relevant reference).

All that said, I think that as a matter of practicality, most teams will probably get much bigger yield with much less effort doing local expansion based on matching for semantic similarity first since it addresses two core problems with embeddings (text chunk size vs embedding accuracy, relevancy or embeddings matched below a given threshold). Experiment with GRAG depending on the type of questions you're trying to answer and the nature of the underlying content. Don't get me wrong; I'm not saying GRAG has no benefit, but that most teams can explore other ways of using RAG before trying GRAG.

GRAG in the direction of the MSR paper adds some important areas:

- summary indexes that can be lexical (document hierarchy) or not (topic, patient ID, etc), esp via careful entity extraction & linking

- domain-optimized summarization templates, both automated & manual

- + as mentioned, wider context around these at retrieval

- introducing a more generalized framework for handling different kinds of concept relations, summary indexing, and retrieval around these

Ex: The same patient over time & docz, and seperately, similar kinds of patients across documents

Note that I'm not actually a big fan of how the MSR paper indirects the work through KG extraction, as that exits the semantic domain, and we don't do it that way

Fundamentally, that both moves away from paltry retrieval result sets that are small/gaps/etc, and enables cleaner input to the runtime query

I agree it is a quick win if quality can be low and you have low budget/time. Like combine a few out of the box index types and do rank retrieval. But a lot of the power gets lost. We are working on infra (+ OSSing it) because that is an unfortunate and unnecessary state of affairs. Right now llamaindex/langchain and raw vector DBs feel like adhoc and unprincipled ways to build these pipelines in a software engineering and AI perspective, so from an investment side, moving away from hacks and to more semantic, composable, & scalable pipelines is important IMO.

    > Neo4j graph rag is typically not graph rag

But why Neo4j? Neo4j has some nice amenities for building GRAG on top of. In particular, it has packages to support community partitioning including Leiden[0] (also used by Microsoft's GraphRAG[1]) and Louvain[2] as well as several other community detection algorithms. The built-in support for node embeddings[3] as well as external AI APIs[4] make the DX -- in so far as building the underlying storage for complex retrieval -- quite good, IMO.

The approach that we are taking is that we are importing a corpus of information into Neo4j and performing ETL on the way in to create additional relationships; effectively connecting individual chunks by some related "facet". Then we plan to run community detection over it to identify communities of interest and use a combination of communities, locality, and embedding match to retrieve chunks.

I just started exploring it over the past week and I would say that if your team is going to end up doing some more complex GRAG, then Neo4j feels like it has the right tooling to be the underlying storage layer and you could even feasibly implement other parts of your workflow in there as well, but entity extraction and such feels like it belongs one layer up in the application layer. Most notably, having direct query access to the underlying graph with a graph query language (Cypher) means that you will have more control and different ways to experiment with retrieval. However; as I mentioned, I would encourage most teams to be more clever with embedding RAG before adding more infrastructure like Neo4j.

[0] https://neo4j.com/docs/graph-data-science/current/algorithms...

[1] https://microsoft.github.io/graphrag/

[2] https://neo4j.com/docs/graph-data-science/current/algorithms...

[3] https://neo4j.com/docs/graph-data-science/current/machine-le...

[4] https://neo4j.com/labs/apoc/5/ml/openai/

Separately, we're still unsure about vector search inside vs outside the graph DB during retrieval, both in the graph RAG scenario and the more general intelligence work domains. I'm more optimistic there for keeping in these graph DB, especially for small cases (< 10M node+edges) we do in notebooks.

And agreed, it's unfortunate neo4j uses graph RAG to market a variety of mostly bad quality solutions and conflate it with graph db storage, and the MSR researchers used it for a more specific and more notable technique (in AI circles) that doesn't need a graph DB and IMO, fundamentally, not even a KG. It's especially confusing that both groups are 'winning' on the term... in different circles.

Instead, completely transforming the text into a dense set of denormalized “notes” that cover every concept present in the text seems like it would be easier to mine for answers to user questions.

Essentially, it would be like taking comprehensive notes from a book and handing them to a friend who didn’t take the class for a test. What would they need to be effective?

Longer term, the sequence would likely be “get question”, hand it to research assistant who has full access to source material and can run a variety of AI / retrieval strategies to customize the notes, and then hand those notes back for answers. By spending more time on the note gathering step, it will be more likely the LLM will be able to answer the question.

If you generate and then "stuff" more text into this, my hunch is that accuracy drops off as the token count increases and it becomes "muddy". GRAG or even normal RAG can solve this to an extent because -- as you propose -- you can generate a congruent "note" and then embed that and link them together.

I'd propose something more flexible: expand on the input query instead and basically multiplex it to the related topics and ideas instead and perform cheap embedding search using more than 1 input vector.

The idea here is to add in higher-level context to the chunk by prepending a chunk header. This chunk header could be as simple as just the document title, or it could use a combination of document title, a concise document summary, and the full hierarchy of section and sub-section titles.

That is from the article. Is this different from your suggested approach?

This wasn't focused on RAG, but there seems to be a lot of crossover to me. Using the LLM to make "episodes" is a similar problem to chunking, and letting the LLM decide the boundary might also yield good results.

But it is only natural that different QA use cases require different strategies. I built 3 production RAG systems / virtual assistant now, and 4 that didn't make it past PoC and what advanced techniques works really depends on document type, text content and genre, use case, source knowledgebase structure and metadata to exploit etc.

Current go-to is semantic similarity chunking (with overlap) + title or question generation > retriever with fusion on bienc vector sim + classic bm25 + condensed question reformulated QA agent. If you don't get some decent results with that setup there is no hope.

For every project we start the creation of a use-case eval set immediately in parallel with the actual RAG agent, but sometimes the client doesn't think this is priority. We convinced them all it's highly important though, because it is.

Having an evaluation set is doubly important in GenAI projects: a generative system will do unexpected things and an objective measure is needed. Your client will run into weird behaviour when testing and they will get hung up on a 1-in-100 undesirable generation.

I think it's a useful term.

The pure vector approach of in-chunk text augmentation is much simpler of course, but my hypothesis is that the resulting vector will cause too much false positives in retrieval.

In my experience retrieval precision is most commonly the problem not recall with vector similarity. This method will indeed improve recall for out-of-context chunks, but for me recall has not been a problem very often.