Comment by LASR
5 days ago
So I've done a ton of work in this area.
Few learnings I've collected:
1. Lexical search with BM25 alone gives you very relevant results if you can do some work during ingestion time with an LLM.
2. Embeddings work well only when the size of the query is roughly on the same order of what you're actually storing in the embedding store.
3. Hypothetical answer generation from a query using an LLM, and then using that hypothetical answer to query for embeddings works really well.
So combining all 3 learnings, we landed on a knowledge decomposition and extraction step very similar to yours. But we stick a metaprompter to essentially auto-generate the domain / entity types.
LLMs are naively bad at identifying the correct level of granularity for the decomposed knowledge. One trick we found is to ask the LLM to output a mermaid.js mindmap to hierarchically break down the input into a tree. At the end of that output, ask the LLM to state which level is the appropriate root for a knowledge node.
Then the node is used to generate questions that could be answered from the knowledge contained in this node. We then index the text of these questions and also embed them.
You can directly match the user's query from these questions using purely BM25 and get good outputs. But a hybrid approach works even better, though not by that much.
Not using LLMs are query time also means we can hierarchically walk down the root into deeper and deeper nodes, using the embedding similiarity as a cost function for the traversal.
> LLMs are naively bad at identifying the correct level of granularity for the decomposed knowledge. One trick we found is to ask the LLM to output a mermaid.js mindmap to hierarchically break down the input into a tree. At the end of that output, ask the LLM to state which level is the appropriate root for a knowledge node. > Then the node is used to generate questions that could be answered from the knowledge contained in this node. We then index the text of these questions and also embed them.
Ha, that's brilliant. Thanks for sharing this!
Thanks for sharing this! It sounds very interesting. We experimented with a similar tree setup some time ago and it was giving good results. We eventually decided to move towards graphs as a general case of trees. I think the notion of using embeddings similarity for "walking" the graph is key, and we're actively integrating it in FastGraphRAG too by weighting the edges by the query. It's very nice to see so many solutions landing on similar designs!
> 1. Lexical search with BM25 alone gives you very relevant results if you can do some work during ingestion time with an LLM
Can you expand on what the LLM work here is and it’s purpose?
> 3. Hypothetical answer generation from a query using an LLM, and then using that hypothetical answer to query for embeddings works really well.
Interesting idea, going to add to our experiments. Thanks.
It seems to come down to keyword expansion, though I'd be curious if there's more to it than just asking "please generate relevant keywords".
Something that I'm working on is making it easy to fix spelling and grammatical errors in documents that can affect BM25 and embeddings. So in addition to generating keyword/metadata with LLM, you could also ask it to clean the document; however, based on what I've learned so far, fixing spelling and grammatical errors should involve humans in the process, so you really can't automate this.
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> 3. Hypothetical answer generation from a query using an LLM, and then using that hypothetical answer to query for embeddings works really well.
I've been wondering about that and am glad to hear it's working in the wild.
I'm now wondering if using a fine-tuned LLM (on the corpus) to gen the hypothetical answers and then use those for the rag flow would work even better.
The technique of generating hypothetical answers (or documents) from the query was first described in the "HyDE (Hypothetical Document Expansion) paper". [1]
Interestingly, going both ways: generate hypothetical answers for the query, and also generate hypothetical questions for the text chunk at ingestion both increase RAG performance in my experience.
Though LLM-based query-processing is not always suitable for chat applications if inference time is a concer (like near-real time customer support RAG), so ingestion-time hypothetical answer generation is more apt there.
1. https://aclanthology.org/2023.acl-long.99/
We do this as well with a lot of success. It’s cool to see others kinda independently coalescing around this solution.
What we find really effective is at content ingestion time, we prepend “decorator text” to the document or chunk. This incorporates various metadata about the document (title, author(s), publication date, etc).
Then at query time, we generate a contextual hypothetical document that matches the format of the decorator text.
We add hybrid search (BM25 and rerank) to that, also add filters (documents published between these dates, by this author, this type of content, etc). We have an LLM parameterize those filters and use them as part of our retrieval step.
This process works incredibly for end users.
but what about the chunk size, if we have a small chunks like 1 sentence and the hyde embeddings are most of the time larger, the results are not so good
Very interesting. Thank you getting into the details. Do you chunk the text that goes into the BM25 index? For the hypothetical answer, do you also prompt for "chunk size" responses?
Very cool and relatable I faced a similar issue for my content categorization engine for local events: http://drophere.co/presence/where (code: https://github.com/itissid/drop_webdemo). Finding the right category for a local event is difficult, an event could be "Outdoorsy" but also "Family Fun" and "Urban Exploration".
Initially I generated categories by asking an LLM with a long prompt(https://github.com/itissid/Drop-PoT/blob/main/src/drop_backe...) But I like your idea better!
My next iteration to solve this problem – I never got to it – was gonna be to generate the most appropriate categories based on user's personal interest, weather, time of day and non PII data and fine-tune a retrieval and a ranking engine to generate categories for each content piece personalized to them.
I organize community calls for Memgraph community and recently a community member presented how he uses hypothetical answer generation as a crucial component to enhancing the effectiveness and reliability of the system, allowing for more accurate and contextually appropriate responses to user queries. Here's more about it: https://memgraph.com/blog/precina-health-memgraph-graphrag-t...
My experience matches your's, but related to
>3. Hypothetical answer generation from a query using an LLM, and then using that hypothetical answer to query for embeddings works really well.
What sort of performance are you getting in production with this one? The other two are basically solved for performance and RAG in general if it is related to a known and pre-processed corpus but I am having trouble thinking of how you don't get a hit with #3.
It's slow. So we use hypothetical mostly for async experiences.
For live experiences like chat, we solved it with UX. As soon as you start typing the words of a question into the chat box, it does the FTS search and retrieves a set of documents that have word-matches, scored just using ES heuristics (eg: counting matching words etc)
These are presented as cards that expand when clicked. The user can see it's doing something.
While that's happening, also issue a full hyde flow in the background with a placeholder loading shimmer that loads in the full answer.
So there is some dead-time of about 10 seconds or so while it generates the hypothetical answers. After that, a short ~1 sec interval to load up the knowledge nodes, and then it starts streaming the answer.
This approach tested well with UXR participants and maintains acceptable accuracy.
A lot of the times, when looking for specific facts from a knowledge base, just the card UX gets an answer immediately. Eg: "What's the email for product support?"
> Hypothetical answer generation from a query using an LLM, and then using that hypothetical answer to query for embeddings works really well.
This is honestly wear I think LLM really shines. This also gives you a very good idea if your documentation is deficient or not.
Thanks for sharing! These are all very helpful insights! We'll keep this in mind :)