I must be missing something, this says it can be self-hosted. But the first page of the self-hosting docs say you need accounts with no less than 6 (!) other third-party hosted services.
We have very different ideas about the meaning of self-hosted.
That was my observation as well. To be fair their business is to sell a hosted version, they’re under no obligation to release a truly self hosted version.
I’ve never worked in such a space where the deployed environment had unfettered internet access, no access at all actually.
I’ve probably missed a huge wave of programming technology because of this, and I’ve figured out a way to make it work for a consistent paycheck over these past 20 years.
I’m also not a great example, I think I’ve watched 7 whole hours of YouTube videos ever, and those were all for car repair help.
I shy away from tech that needs to be online/connected/whatever.
The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do parallel search, and then use reciprocal rank fusion to combine the list into a set of broadly strong performers. For the searches we use hybrid dense + sparse bm25, since dense doesn't work well for technical words.
This, combined with a subsequent reranker, basically eliminated any of our issues on search.
Yep- that's all best practice. I want to know if we could push performance further- routing the query to different embedding models or scoring strategies, or using multiple re-rankers- still feels like the process is missing something.
Boy, that should not be the concern of the end user (developer) but those implementing RAG solutions as a service at Amazon, Microsoft, Openai and so on.
At Microsoft, that's all baked into Azure AI Search - hybrid search does BM25, vector search, and re-ranking, just with setting booleans to true.
It also has a new Agentic retrieval feature that does the query rewriting and parallel search execution.
So few developers realize that you need more than just vector search, so I still spend many of my talks emphasizing the FULL retrieval stack for RAG.
It's also possible to do it on top of other DBs like Postgres, but takes more effort.
> For the searches we use hybrid dense + sparse bm25, since dense doesn't work well for technical words.
One thing I’m always curious about is if you could simplify this and get good/better results using SPLADE. The v3 models look really good and seem to provide a good balance of semantic and lexical retrieval.
The big LLM-based rerankers (e.g. Qwen3-reranker) are what you always wanted your cross-encoder to be, and I highly recommend giving them a try. Unfortunately they're also quite computationally expensive.
Your metadata/tabular data often contains basic facts that a human takes for granted, but which aren't repeated in every text chunk - injecting it can help a lot in making the end model seem less clueless.
The point about queries that don't work with simple RAG (like "summarize the most recent twenty documents") is very important to keep in mind. We made our UI very search-oriented and deemphasized the chat, to try to communicate to users that search is what's happening under the hood - the model only sees what you see.
I agree completely with your point, especially the difficulty of developing the user's mental model for what's going on with context and the need to move away from chat UX. It's interesting that there are still few public examples of non-chat UIs that make context management explicit. It's possible that the big names tried this and decided it wasn't worth it -- but from comments here it seems like everyone that has built a production RAG system has come to the opposite conclusion. I'm guessing the real reason is otherwise: likely for the consumer apps controlling context (especially for free users) and inference time is one of the main levers for cost management at scale. Private RAGs, on the other hand, are more concerned with maximizing result quality and minimizing time spent by employee on a particular problem with cost per query much less of a concern --- that's been my experience at least.
I wish there was more info on the article about actual customer usage - particularly whether it improved process efficiency. It's great to focus on the technical aspects of system optimization but unless this translates to tangible business value it's all just hype.
Not here to schlep for AWS but S3 Vectors is hands down the SOTA here. That combined with a Bedrock Knowledge Base to handle Discovery/Rebalance tasks makes for the simplest implementation on the Market.
Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.
Embedding based RAG will always just be OK at best. It is useful for little parts of a chain or tech demos, but in real life use it will always falter.
The difference is this feature explicitly isn't designed to do a whole lot, which is still the best way to build most LLM-based products and sandwich it between non-LLM stuff.
rag will be pronounced differently ad again and again. it has its use cases. we moved to agentic search having rag as a tool while other retrieval strategies we added use real time search in the sources. often skipping ingested and chunked soueces. large changes next windows allow for putting almost whole documents into one request.
Most of my ChatGPT queries use RAG (based on the query ChatGPT will decide if it needs to search the web) to get up to date information about the world. In reality life it's effective and it's why every large provider supports it.
OP here. We've been working on agentset.ai full-time for 2 months. The product now gets you something working quite well out of the box. Better than most people with no experience in RAG (I'd say so with confidence).
Ingestion + Agentic Search are two areas that we're focused on in the short term.
I'm not sure there is a chunker in this repo. The file you linked certainly doesn't seem to perform any chunking, it just defines a data model for chunks.
The only place I see that actually operates on chunks does so by fetching them from Redis, and AFAICT nothing in the repo actually writes to Redis, so I assume the chunker is elsewhere.
I find it interesting that so many services and tools were investigated except for embedding models. I would have thought that's one of the biggest levers.
Really solid write-up — it’s rare to see someone break down the real tradeoffs of scaling RAG beyond the toy examples. The bit about reranking and chunking actually saving more than fancy LLM tricks hits home to me.
> Reranking: the highest value 5 lines of code you'll add. The chunk ranking shifted a lot. More than you'd expect. Reranking can many times make up for a bad setup if you pass in enough chunks. We found the ideal reranker set-up to be 50 chunk input -> 15 output.
What is re-ranking in the context of RAG? Why not just show the code if it’s only 5 lines?
OP. Reranking is a specialized LLM that takes the user query, and a list of candidate results, then re-sets the order based on which ones are more relevant to the query.
If you generate embeddings (of the query, and of the candidate documents) and compare them for similarity, you're essentially asking whether the documents "look like the question."
If you get an LLM to evaluate how well each candidate document follows from the query, you're asking whether the documents "look like an answer to the question."
An ideal candidate chunk/document from a cosine-similarity perspective, would be one that perfectly restates what the user said — whether or not that document actually helps the user. Which can be made to work, if you're e.g. indexing a knowledge base where every KB document is SEO-optimized to embed all pertinent questions a user might ask that "should lead" to that KB document. But for such documents, even matching the user's query text against a "dumb" tf-idf index will surface them. LLMs aren't gaining you any ground here. (As is evident by the fact that webpages SEO-optimized in this way could already be easily surfaced by old-school search engines if you typed such a query into them.)
An ideal candidate chunk/document from a re-ranking LLM's perspective, would be one that an instruction-following LLM (with the whole corpus in its context) would spit out as a response, if it were prompted with the user's query. E.g. if the user asks a question that could be answered with data, a document containing that data would rank highly. And that's exactly the kind of documents we'd like "semantic search" to surface.
I have a RAG setup that doesn't work on documents but other data points that we use for generation (the original data is call recordings but it is heavily processed to just a few text chunks).
Instead of a reranker model we do vector search and then simply ask GPT-5 in an extra call which of the results is the most relevant to the input question. Is there an advantage to actual reranker models rather than using a generic LLM?
Chunking strategy is a big issue. I found acceptable results by shoving large texts to to gemini flash and have it summarize and extract chunks instead of whatever text splitter I tried. I use the method published by Anthropic https://www.anthropic.com/engineering/contextual-retrieval i.e. include full summary along with chunks for each embedding.
I also created a tool to enable the LLM to do vector search on its own .
I do not use Langchain or python.. I use Clojure+ LLMs' REST APIs.
It's described in the remainder of the point - they use an LLM to generate additional search queries, either rephrasings of the user's query or bringing additional context from the chat history.
The article raises several interesting points, but I find its claim that Claude Code relies primarily on grep for code search unconvincing. It's clear that Claude Code can parse and reason about code structure, employing techniques far beyond simple regex matching. Since this assumption underpins much of the article's argument, it makes me question the overall reliability of its conclusions a bit.
Or am I completely misunderstanding how Claude Code works?
I must be missing something, this says it can be self-hosted. But the first page of the self-hosting docs say you need accounts with no less than 6 (!) other third-party hosted services.
We have very different ideas about the meaning of self-hosted.
That was my observation as well. To be fair their business is to sell a hosted version, they’re under no obligation to release a truly self hosted version.
I’ve never worked in such a space where the deployed environment had unfettered internet access, no access at all actually.
I’ve probably missed a huge wave of programming technology because of this, and I’ve figured out a way to make it work for a consistent paycheck over these past 20 years.
I’m also not a great example, I think I’ve watched 7 whole hours of YouTube videos ever, and those were all for car repair help.
I shy away from tech that needs to be online/connected/whatever.
I consider this to be good open source and I'm a happy user of their OSS offering. Want no hosted dependencies? Then go write it all in Rust.
The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do parallel search, and then use reciprocal rank fusion to combine the list into a set of broadly strong performers. For the searches we use hybrid dense + sparse bm25, since dense doesn't work well for technical words.
This, combined with a subsequent reranker, basically eliminated any of our issues on search.
Yep- that's all best practice. I want to know if we could push performance further- routing the query to different embedding models or scoring strategies, or using multiple re-rankers- still feels like the process is missing something.
Boy, that should not be the concern of the end user (developer) but those implementing RAG solutions as a service at Amazon, Microsoft, Openai and so on.
At Microsoft, that's all baked into Azure AI Search - hybrid search does BM25, vector search, and re-ranking, just with setting booleans to true. It also has a new Agentic retrieval feature that does the query rewriting and parallel search execution.
Disclosure: I work at MS and help maintain our most popular open-source RAG template, so I follow the best practices closely: https://github.com/Azure-Samples/azure-search-openai-demo/
So few developers realize that you need more than just vector search, so I still spend many of my talks emphasizing the FULL retrieval stack for RAG. It's also possible to do it on top of other DBs like Postgres, but takes more effort.
That is concerning given that pure vector search is terrible outside of abstractions
I'd love to work with Azure search but because copilot with external items has been made so cheap it's hard to justify...
> For the searches we use hybrid dense + sparse bm25, since dense doesn't work well for technical words.
One thing I’m always curious about is if you could simplify this and get good/better results using SPLADE. The v3 models look really good and seem to provide a good balance of semantic and lexical retrieval.
final tip is to also feed the interpretation of the user search to the user on the other side, so he can check if the llm understanding was correct.
I concur:
The big LLM-based rerankers (e.g. Qwen3-reranker) are what you always wanted your cross-encoder to be, and I highly recommend giving them a try. Unfortunately they're also quite computationally expensive.
Your metadata/tabular data often contains basic facts that a human takes for granted, but which aren't repeated in every text chunk - injecting it can help a lot in making the end model seem less clueless.
The point about queries that don't work with simple RAG (like "summarize the most recent twenty documents") is very important to keep in mind. We made our UI very search-oriented and deemphasized the chat, to try to communicate to users that search is what's happening under the hood - the model only sees what you see.
I agree completely with your point, especially the difficulty of developing the user's mental model for what's going on with context and the need to move away from chat UX. It's interesting that there are still few public examples of non-chat UIs that make context management explicit. It's possible that the big names tried this and decided it wasn't worth it -- but from comments here it seems like everyone that has built a production RAG system has come to the opposite conclusion. I'm guessing the real reason is otherwise: likely for the consumer apps controlling context (especially for free users) and inference time is one of the main levers for cost management at scale. Private RAGs, on the other hand, are more concerned with maximizing result quality and minimizing time spent by employee on a particular problem with cost per query much less of a concern --- that's been my experience at least.
I wish there was more info on the article about actual customer usage - particularly whether it improved process efficiency. It's great to focus on the technical aspects of system optimization but unless this translates to tangible business value it's all just hype.
Similar writeup I did about 1.5 years ago for processing millions of (technical) pages for RAG. Lots has stayed the same it seems
https://jakobs.dev/learnings-ingesting-millions-pages-rag-az...
I also built a RAG system about a year back for technical search, everything seems the same!
Not here to schlep for AWS but S3 Vectors is hands down the SOTA here. That combined with a Bedrock Knowledge Base to handle Discovery/Rebalance tasks makes for the simplest implementation on the Market.
Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.
Shill, not schlep.
I'm correcting you less out of pedantry, and more because I find the correct term to be funny.
I feel like I'm schelpin' through these comments, it's all mishigas
You feel like a schlemiel, perhaps?
more a schlimazel, Charles Schultzie, Lucy's everywhere
Embedding based RAG will always just be OK at best. It is useful for little parts of a chain or tech demos, but in real life use it will always falter.
Not necessarily? It's been the basis of one of the major ways people would query their data since 2023 on a product I worked on: https://www.honeycomb.io/blog/introducing-query-assistant
The difference is this feature explicitly isn't designed to do a whole lot, which is still the best way to build most LLM-based products and sandwich it between non-LLM stuff.
rag will be pronounced differently ad again and again. it has its use cases. we moved to agentic search having rag as a tool while other retrieval strategies we added use real time search in the sources. often skipping ingested and chunked soueces. large changes next windows allow for putting almost whole documents into one request.
What do you recommend? Query generation?
Compared with what?
Full text agentic retrieval. Instead of cosine similarity on vectors, parsing metadata through an agentic loop.
To give a real world example, the way Claude Code works versus how Cursor's embedded database works.
How do you do that on 5 million documents?
Most of my ChatGPT queries use RAG (based on the query ChatGPT will decide if it needs to search the web) to get up to date information about the world. In reality life it's effective and it's why every large provider supports it.
They say the chunker is the most important part, but theirs looks rudimentary: https://github.com/agentset-ai/agentset/blob/main/packages/e...
That is, there is nothing here that one could not easily write without a library.
OP here. We've been working on agentset.ai full-time for 2 months. The product now gets you something working quite well out of the box. Better than most people with no experience in RAG (I'd say so with confidence).
Ingestion + Agentic Search are two areas that we're focused on in the short term.
I'm not sure there is a chunker in this repo. The file you linked certainly doesn't seem to perform any chunking, it just defines a data model for chunks.
The only place I see that actually operates on chunks does so by fetching them from Redis, and AFAICT nothing in the repo actually writes to Redis, so I assume the chunker is elsewhere.
https://github.com/agentset-ai/agentset/blob/main/packages/j...
I find it interesting that so many services and tools were investigated except for embedding models. I would have thought that's one of the biggest levers.
they just grabbed the better one (3-large) right off the bat. 6x cost to 3-small, but it's still tiny.
Really solid write-up — it’s rare to see someone break down the real tradeoffs of scaling RAG beyond the toy examples. The bit about reranking and chunking actually saving more than fancy LLM tricks hits home to me.
> Reranking: the highest value 5 lines of code you'll add. The chunk ranking shifted a lot. More than you'd expect. Reranking can many times make up for a bad setup if you pass in enough chunks. We found the ideal reranker set-up to be 50 chunk input -> 15 output.
What is re-ranking in the context of RAG? Why not just show the code if it’s only 5 lines?
OP. Reranking is a specialized LLM that takes the user query, and a list of candidate results, then re-sets the order based on which ones are more relevant to the query.
Here's sample code: https://docs.cohere.com/reference/rerank
What is the difference between reranking versus generating text embeddings and comparing with cosine similarity?
My understanding:
If you generate embeddings (of the query, and of the candidate documents) and compare them for similarity, you're essentially asking whether the documents "look like the question."
If you get an LLM to evaluate how well each candidate document follows from the query, you're asking whether the documents "look like an answer to the question."
An ideal candidate chunk/document from a cosine-similarity perspective, would be one that perfectly restates what the user said — whether or not that document actually helps the user. Which can be made to work, if you're e.g. indexing a knowledge base where every KB document is SEO-optimized to embed all pertinent questions a user might ask that "should lead" to that KB document. But for such documents, even matching the user's query text against a "dumb" tf-idf index will surface them. LLMs aren't gaining you any ground here. (As is evident by the fact that webpages SEO-optimized in this way could already be easily surfaced by old-school search engines if you typed such a query into them.)
An ideal candidate chunk/document from a re-ranking LLM's perspective, would be one that an instruction-following LLM (with the whole corpus in its context) would spit out as a response, if it were prompted with the user's query. E.g. if the user asks a question that could be answered with data, a document containing that data would rank highly. And that's exactly the kind of documents we'd like "semantic search" to surface.
text similarity finds items that closely match. Reranking my select items that are less semantically "similar" but are more relevant to the query.
I have a RAG setup that doesn't work on documents but other data points that we use for generation (the original data is call recordings but it is heavily processed to just a few text chunks). Instead of a reranker model we do vector search and then simply ask GPT-5 in an extra call which of the results is the most relevant to the input question. Is there an advantage to actual reranker models rather than using a generic LLM?
OP here. rerankers are finetuned small models, they're cheap and very fast compared to an additional GPT-5 call.
It's an async process in my case (custom deep research like) so speed is not that critical
Thanks for sharing. TIL about rerankers.
Chunking strategy is a big issue. I found acceptable results by shoving large texts to to gemini flash and have it summarize and extract chunks instead of whatever text splitter I tried. I use the method published by Anthropic https://www.anthropic.com/engineering/contextual-retrieval i.e. include full summary along with chunks for each embedding.
I also created a tool to enable the LLM to do vector search on its own .
I do not use Langchain or python.. I use Clojure+ LLMs' REST APIs.
Have you measured your latency, and how sensitive are you to it?
>> Have you measured your latency, and how sensitive are you to it?
Not sensitive to latency at all. My users would rather have well researched answers than poor answers.
Also, I use batch mode APIs for chunking .. it is so much cheaper.
They should've tested other embedding models, there are better ones than openai's (and cheaper)
Which do you suggest?
https://huggingface.co/spaces/mteb/leaderboard
yep
The Qwen3 600M and 4B embedding models are near state of the art and aren't too computationally intensive.
> What moved the needle: Query Generation
What does query generation mean in this context, it’s probably not SQL queries right?
I think they mean query expansion: https://en.wikipedia.org/wiki/Query_expansion
It's described in the remainder of the point - they use an LLM to generate additional search queries, either rephrasings of the user's query or bringing additional context from the chat history.
Here's an interesting read on the evolution beyond RAG: https://www.nicolasbustamante.com/p/the-rag-obituary-killed-...
One of the key features in Claude Code is "Agentic Search" aka using (rip)grep/ls to search a codebase without any of the overhead of RAG.
Sounds like even RAG approaches use a similar approach (Query Generation).
The article raises several interesting points, but I find its claim that Claude Code relies primarily on grep for code search unconvincing. It's clear that Claude Code can parse and reason about code structure, employing techniques far beyond simple regex matching. Since this assumption underpins much of the article's argument, it makes me question the overall reliability of its conclusions a bit.
Or am I completely misunderstanding how Claude Code works?
do you still use langchain/llamaindex for other agents/AI use cases?
Exactly what kind of processing was done? Your pipeline is a function of the use case, lest you overengineer…