Perplexity CEO explains RAG: Retrieval-Augmented Generation | Aravind Srinivas and Lex Fridman
Summary
TLDRThe transcript delves into the intricacies of the Retrieval Augmented Generation (RAG) framework, emphasizing its role in generating responses by retrieving relevant documents and paragraphs to answer queries. It underscores the importance of sticking to factual information from the retrieved documents to ensure accuracy and avoid 'hallucinations.' The discussion also touches on the challenges of model comprehension, index quality, and snippet detail, which can lead to confusion or irrelevant answers. Furthermore, it explores indexing complexities, including web crawling, respecting robots.txt policies, and the decision-making process behind which URLs to crawl and how frequently. The summary concludes by highlighting the necessity of a hybrid approach combining traditional retrieval methods and modern embeddings for effective search.
Takeaways
- π RAG (Retrieval-Augmented Generation) is a framework that retrieves relevant documents and uses them to generate answers to queries.
- π Perplexity, a more restrictive approach than RAG, ensures factual grounding by only allowing the model to use retrieved information to form responses.
- π The model's ability to accurately retrieve and understand information is crucial for providing truthful and relevant answers.
- π€ Hallucinations in AI responses can occur due to model skill limitations, poor or outdated snippets, too much detail, or irrelevant documents.
- π The indexing process involves crawling the web, respecting robots.txt policies, and deciding on the frequency of crawling and the content to index.
- π Modern web pages require headless rendering to capture JavaScript-rendered content, which adds complexity to the indexing process.
- π Indexing involves post-processing raw content into a format suitable for ranking, which can include machine learning and text extraction techniques.
- π Traditional retrieval methods like BM25 are still effective and sometimes outperform newer embedding techniques in ranking documents.
- π A hybrid approach combining traditional retrieval with modern embeddings and other ranking signals like domain authority and recency is necessary for effective search.
- π‘ The development of a high-quality search index requires significant domain knowledge and is a complex, time-consuming process.
Q & A
What is RAG and how does it work?
-RAG stands for Retrieval-Augmented Generation. It is a framework that, given a query, retrieves relevant documents and selects pertinent paragraphs from those documents to generate an answer. It ensures that the generated answer is grounded in the retrieved text, enhancing factual accuracy.
What is the principle behind Perplexity's approach to AI?
-Perplexity operates on the principle that the AI should not generate content beyond what is retrieved from documents. This ensures factual grounding and prevents the AI from producing nonsensical information or adding unverified details.
How does RAG ensure the accuracy of the information it retrieves?
-RAG ensures accuracy by sticking to the information found in human-written text on the internet. It uses this text as a source of truth and cites it, making the AI's responses more controllable and reliable.
What are the potential issues that can lead to 'hallucinations' in AI responses?
-Hallucinations in AI can occur due to several issues: 1) The model's inability to deeply understand the query or the retrieved text semantically, 2) Poor or outdated snippets in the index, and 3) Providing too much detail to the model, causing confusion and irrelevant information to be included in the response.
How can the quality of AI-generated answers be improved?
-The quality can be improved by enhancing the retrieval process, improving the freshness and detail of the index, refining the model's ability to handle documents, and ensuring the model is skillful enough to recognize when it lacks sufficient information to provide a good answer.
What is the role of indexing in the RAG framework?
-Indexing is a crucial part of the RAG framework. It involves building a searchable database of web content by crawling the internet, fetching and processing content, and converting it into a format suitable for retrieval and ranking.
How does the Perplexity bot decide what to crawl on the web?
-The Perplexity bot decides what to crawl based on factors like the URLs and domains to prioritize, how frequently to crawl them, and respecting the robots.txt file which indicates the politeness policy set by website owners.
What challenges does the Perplexity bot face while crawling and indexing web pages?
-The bot faces challenges like rendering modern websites that rely heavily on JavaScript, respecting the robots.txt file for politeness policies, and deciding the periodicity of recrawling to keep the index updated.
Why is it difficult to represent web page content using vector embeddings?
-Vector embeddings face challenges because they need to capture the multifaceted nature of web content, including individual entities, specific events, and deeper meanings that might apply across different contexts.
What are some traditional retrieval methods that can complement vector embeddings in search?
-Traditional retrieval methods like TF-IDF and BM25, which are based on term frequency and document relevance, can complement vector embeddings. These methods are still effective and can outperform pure embeddings in certain ranking tasks.
Why is a hybrid approach necessary for effective web search?
-A hybrid approach is necessary because no single method, whether it's traditional term-based retrieval or modern embedding-based retrieval, can fully address the complexity of web search. Combining these methods allows for more accurate and relevant search results.
Outlines
π€ Understanding Perplexity and RAG in AI
The paragraph introduces Perplexity, a framework for AI that adheres to the principle of not stating anything that hasn't been retrieved from documents. It contrasts with RAG (Retrieval Augmented Generation), which allows using additional context from retrieved documents to form answers. Perplexity ensures factual grounding by limiting the AI's responses to the exact information retrieved, aiming to avoid generating false or nonsensical information. The discussion touches on the challenges of model understanding at a semantic level and the potential for 'hallucinations' (erroneous information) due to model limitations, poor document quality, or excessive detail in the retrieved information.
π·οΈ The Intricate Process of Web Crawling for Indexing
This section delves into the technical aspects of how web crawlers, such as the hypothetical 'Perplexity bot', operate. It discusses the decision-making processes involved in selecting which web pages to crawl, the frequency of crawling, and the importance of respecting robots.txt files to avoid overloading servers. The paragraph also highlights the complexity of modern web pages, which often require JavaScript rendering to understand the true content. The discussion includes the need for bots to respect crawl directives and the challenges of determining the periodicity of re-crawling and the incorporation of new pages based on hyperlinks.
π The Complexity of Indexing and Ranking in Search
The final paragraph explores the complexities of indexing and ranking in search engines. It discusses the need for a hybrid approach that combines traditional term-based retrieval methods like BM25 with more modern vector space representations. The paragraph emphasizes the importance of domain knowledge in search and the various signals that contribute to ranking, such as page rank, domain authority, and recency. It also touches on the limitations of pure vector embeddings for text and the ongoing debate around the effectiveness of different retrieval algorithms in the context of large-scale web data.
Mindmap
Keywords
π‘Perplexity
π‘RAG (Retrieval-Augmented Generation)
π‘Factual Grounding
π‘Hallucination
π‘Indexing
π‘Search
π‘Snippets
π‘Machine Learning
π‘Ranking Algorithms
π‘Vector Space
π‘Domain Knowledge
Highlights
RAG is a framework that retrieves relevant documents and uses them to generate answers.
Perplexity ensures factual grounding by only allowing information from retrieved documents.
RAG enhances answers by adding context from retrieved documents.
Hallucinations can occur if the model doesn't understand the query or retrieved documents deeply.
Poor or outdated snippets can lead to model confusion and incorrect information.
Overloading the model with too much detail can result in irrelevant information confusing the answer.
Skillful models can identify when retrieved documents are irrelevant and state the lack of information.
Improving retrieval, index quality, and snippet detail can reduce model hallucinations.
Indexing involves crawling the web, respecting robots.txt, and deciding which URLs to crawl.
Modern websites require headless rendering to accurately crawl and index content.
Respecting the crawl-delay set by websites is crucial to avoid overloading their servers.
Deciding the periodicity of recrawling and adding new pages to the queue is part of the indexing process.
Post-processing raw URL content is necessary for creating an index suitable for ranking systems.
Vector embeddings are not the only solution; traditional retrieval methods like BM25 are still effective.
BM25 can outperform embeddings in many retrieval benchmarks due to its sophistication.
A hybrid approach combining traditional and modern retrieval methods is necessary for effective search.
Ranking signals beyond semantic or word-based, such as page rank and recency, are important for search.
Search requires a significant amount of domain knowledge and is a complex problem to solve.
Transcripts
so can you speak to the technical
details of how perplexity Works you've
mentioned already rag retrieval
augmented generation what are the
different components here how does the
search happen first of all what is rag
yeah what does the llm do at at at a
high level how does the thing work yeah
so rag is retrieval augmented generation
simple
framework given a query always retrieve
relevant documents and pick relevant
paragraphs from each document and use
those documents and paragraphs to write
your answer for that query M the
principle and perplexity is you're not
supposed to say anything that you don't
retrieve which is even more powerful
than rag cuz rag just says okay use this
additional context and and and write an
answer but we say don't use anything
more than that too that way we ensure
factual grounding and if you don't have
enough information from documents you
retrieve just say we don't have enough
search results to can give you a good
answer yeah let's just Linger on that so
in general rag is doing the search part
with a query to add extra context yeah
to generate a uh a better answer I
suppose you're saying like you want to
really stick to the truth that is
represented by the human written text on
the internet and then cite it to that
text correct it's more controllable that
way yeah otherwise you can still end up
saying non ense or use the information
in the documents and add some stuff of
your own right despite this these things
still happen I'm not saying it's
foolproof so where is there room for
hallucination to seep in yeah there are
multiple ways it can happen one is you
have all the information you need for
the query the model is just not smart
enough to understand the query at a
deeply semantic level and the paragraphs
at a deeply semantic level and only pick
the relevant information and give you an
answer so that is a model skill issue
but that can be addressed as models get
better and they have been getting better
now the other place where hallucinations
can happen is you have uh poor
Snippets like your index is not good
enough ah yeah so you retrieve the right
documents or but but the information in
them was not up to date was stale or
are not detailed enough and then the
model had insufficient information or
conflicting information from multiple
sources and ended up like getting
confused and the third way it can happen
is you added too much detail to the
model like your index is so detailed
your Snippets are so you use the full
version of the
page and you threw all of it at the
model and ask it to arrive at the answer
and it's not able to discern clearly
what is needed and throws a lot of
irrelevant stuff to it and that
irrelevant stuff ended up confusing
it and made it like a bad answer so uh
all these three or the fourth way is
like you uh end up retrieving completely
irrelevant documents too but in such a
case if a model is skillful enough it
should just say I don't have enough
information so there are like multiple
Dimensions where you can improve a
product like this to reduce
hallucinations where you can improve the
retrieval you can improve the quality of
the index the fresh of the pages in the
index and you can include the level of
detail in the Snippets you can include
the uh improve the models uh ability to
handle all these documents really well
and uh if you do all these things well
you can keep making the product better
so it's kind of incredible I get to see
sort of directly because I've seen
answers uh in fact for for perplexity
page that youve posted about I've seen
ones that reference a transcript of this
podcast M and it's cool how it like gets
to the right snippet mhm like probably
some of the words I'm saying now and
you're saying now will end up in a
perplexing answer
possible it's crazy yeah it's very
meta including the Lex being uh smart
and handsome part that's out of your
mouth in a transcript forever now but if
the model is smart enough it'll know
that I set it as an example to say what
not to say
well not to say it's just a way to mess
with the model the model smart enough
it'll know that I specifically said
these are ways a model can go wrong and
it'll use that and say well the model
doesn't know that there's video
editing so the indexing is fascinating
so is there something you could say
about the some interesting aspects of
how the indexing is done yeah so
indexing is um you know multiple Parts
obviously you have to first build a um
cross
it's like you know Google has Google bot
we have flexity bot bang bot GPT bot
there's like a bunch of bots that crawl
the web how does perplexity bot work
like uh so that that's a that's a
beautiful little creature so it's
crawling the web like what are the
decisions it's making is it's crawling
the web Lots like even deciding like
what to put in the queue which we Pages
which domains and uh uh how frequently
all the domains need to get crawled and
um it's not just about like you know
knowing which
URLs this is like you know deciding what
URLs to crawl but um how you crawl them
you basically have to render headless
render and then websites are more modern
these days it's not just the
HTML um there's a lot of JavaScript
rendering uh you have to decide like
what's what's the real thing you want
from a page and obviously uh people have
robots the text file um and that's like
a politeness policy where you you should
respect the delay time time mhm so that
you don't like overload their servers by
continually crawling them and then
there's like stuff that they say is not
supposed to be crawled and stuff that
they allow to be crawl and you have to
respect that and uh the bot needs to be
aware of all these things and
appropriately craw stuff but most most
of the details of how a page works
especially with JavaScript is not
provided to the bot like has to figure
all that out yeah it depends if some
some Publishers allow that so that you
know they think it'll benefit their
ranking more mhm some Publishers don't
allow that and uh um you need to
like keep track of all these things per
domains and subdomains and it's crazy
and then you also need to decide the
periodicity yeah with which you
recrawl and you also need to decide what
new pages to add to this queue based on
like
hyperlinks so that's the crawling and
then there's a part of like building
fetching the content from each URL and
like once you did that through the
Headless render you have to actually
build the index now uh and you have to
reprocess you have to post-process all
the content you fetched which is the raw
dump into something that's inestable for
a ranking system so that requires some
machine learning text extraction Google
has this whole system called now boost
that extracts relevant metadata and like
relevant content from each uh raw URL
content is that a fuling machine
learning system is like embedding into
some kind of vector space it's not
purely Vector space it's not like once
the content is fetched there is some uh
Bird model that runs on all of it and uh
puts it into a big gigantic Vector
database which you R from it's not like
that uh because packing all the
knowledge about a web page into one
vector space representation is very very
difficult there's like first of all
Vector embeddings are not magically
working for text
it's very hard to like understand what's
a relevant document to a particular
query should it be about the individual
in the query or should it be about the
specific event in the query or should it
be at a deeper level about the meaning
of that query such that the same meaning
applying to different individual should
also be retrieved you can keep arguing
right like what should an representation
really capture and it's very hard to
make these Vector embeddings have
different dimensions be disentangled
from each other and capturing different
sem
so uh what retrieval typically this is
the ranking part by the way there's
indexing part assuming you have like a
post process version per URL and then
there's a ranking part that uh depending
on the query you ask FES the relevant
documents from the
index and some kind of score and that's
where like when you have like billions
of pages in your index and you only want
the top K you have to rely on
approximate algorithms to get you the
top okay so that's that's the ranking
but you also I mean that step of
converting a page into something that
could be stored in a vector
database it just seems really difficult
it doesn't always have to be stored
entirely in Vector databases there are
other data structures you can use sure
uh and other forms of uh traditional
retrieval that you can use uh there is
an algorithm called bm25 precisely for
this which is a more sophisticated
version of uh tfidf tfidf is term
frequency times inverse document
frequency a very uh uh old school
information retrieval system that just
works actually really well even today uh
and uh bm25 is a more uh sophisticated
version of that is still you know
beating most embeding on ranking wow
like when open AI released their
embeddings there was some controversy
around it because it wasn't even beating
bm2 on many many retrievable benchmarks
not because they didn't do a good job
bm25 is so good so this is why like just
pure embeddings and Vector spaces are
not going to solve the search problem
you need the
traditional uh termbase retrieval you
need some kind of engram based retrieval
so for the for the
unrestricted web data you can't just uh
you need a combination of all a hybrid
and you also need other ranking signals
outside of the semantic or word-based
this is like page ranks like signals
that score domain Authority and uh
recency right so you have to put some
extra positive weight on the recency but
not so it overwhelms and this really
depends on the query category and that's
why search is a hard lot of domain
knowledge invol problem yeah that's why
we chose to work on like everybody talks
about rappers competition models there
insane amount of domain knowledge you
need to work on this and it takes a lot
of time to build up towards like uh
highly really good
index with like really good ranking all
these signals
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