How RAG Turns AI Chatbots Into Something Practical
Summary
TLDRThe video explains the concept of Retrieval-Augmented Generation (RAG), a method that improves AI performance by retrieving accurate information from external documents instead of relying solely on pre-trained neural networks. It outlines the stages of RAG, including indexing, retrieval, and generation, and discusses its practical applications in tools like ThinkBuddy. RAG boosts AI's accuracy and cost-effectiveness without needing expensive fine-tuning. While considered a short-term workaround, its evolving complexity positions it as a significant field in AI research. The video also highlights tools and resources to implement RAG effectively.
Takeaways
- 🤖 Current AI chatbots are powerful but can be impractical in work settings due to hallucinations and inconsistencies.
- 📈 A short-term workaround called Retrieval-Augmented Generation (RAG) significantly improves chatbot accuracy and performance.
- 📚 RAG retrieves accurate information from external documents, allowing more cost-effective and reliable outputs without expensive AI training.
- 🧩 RAG is broken down into three stages: indexing documents, retrieving relevant data, and generating responses.
- 🧠 Vector databases and transformer models are used in RAG to encode and retrieve the most semantically relevant information.
- 🔍 Graph RAG is a promising new method that organizes information into knowledge graphs for better explainability and traceability.
- ⚙️ Hybrid search and input query rewriting help refine the retrieval process by improving accuracy and reducing irrelevant results.
- 💡 RAG pipelines can include re-ranking models and autocut functions to prioritize the most relevant responses and prevent hallucinations.
- 🚀 Think Buddy, an AI tool for Mac OS, leverages RAG principles to improve workflow with local storage and access to multiple models.
- 🛠️ Think Buddy offers deep integrations for screen capture, PDF processing, and code analysis, with voice input powered by Whisper.
Q & A
What is the main limitation of current AI chatbots according to the transcript?
-The main limitation of current AI chatbots is that they can hallucinate or generate incorrect information, making them unreliable for consistent use in professional settings.
What is RAG and how does it help mitigate the limitations of current AI models?
-RAG stands for Retrieval Augmented Generation. It improves AI performance by retrieving accurate information from external sources, such as a collection of documents, instead of relying solely on the AI's neural network. This reduces hallucination and enhances the usability of AI in tasks requiring specific and accurate data.
What are the three main stages of a naive RAG pipeline?
-The three main stages of a naive RAG pipeline are: 1) Indexing, where documents are divided into chunks and stored in a searchable vector database; 2) Retrieval, where relevant information is retrieved based on a query; and 3) Generation, where the AI generates a response using the retrieved content.
Why is RAG considered a 'short-term solution'?
-RAG is considered a short-term solution because it introduces additional moving parts like indexing, retrieval, and blending of data, which can introduce points of failure. It is seen as a workaround to the limitations of current AI models, rather than a long-term architectural solution.
What are some challenges associated with the RAG pipeline?
-Some challenges of the RAG pipeline include managing multiple components like document indexing, retrieval accuracy, and the AI's ability to blend and generate relevant responses. Any issue in these components can lead to poor output quality.
What is a 'knowledge graph' and how does it improve RAG?
-A knowledge graph is a structured representation of entities, relationships, and key claims extracted from documents. In RAG, it helps organize data more effectively, making retrieval more accurate and context-aware, and it improves the traceability and auditability of the AI's responses.
What role does 'reranking' play in the RAG pipeline?
-Reranking involves retrieving multiple top results and then passing them through a model to determine which results are the most relevant. This ensures that the most contextually appropriate response is used, reducing the chance of inaccurate results.
How can RAG help prevent AI hallucination?
-RAG can prevent hallucination by using a reranking model and auto-cut functions to remove unrelated retrieved results. If the AI cannot find a relevant match, it can be forced to admit that it does not know the answer, instead of generating an incorrect response.
What are some tools and libraries mentioned for building RAG systems?
-The transcript mentions several tools and libraries for building RAG systems, including Llama Index for general frameworks, Hugging Face for embedding models, Microsoft's GitHub for graph RAG implementation, and RAG-assess for evaluating RAG pipelines.
What is Think Buddy, and how does it enhance productivity for developers?
-Think Buddy is a macOS AI lab designed for developers. It combines strengths from multiple AI models like GPT-4, Claude, and Gemini Pro to generate responses by remixing the best parts of each. It integrates deeply with macOS, supports hotkeys for instant AI help, and handles various file formats like PDF and docx, making it a powerful tool for enhancing workflow.
Outlines
🤖 Limitations and Solutions for Current AI Chatbots
The current generation of AI chatbots excels in many areas but struggles with practicality in consistent work environments due to their tendency to hallucinate incorrect information. As companies take time to develop more advanced AI, a temporary solution called RAG (Retrieval-Augmented Generation) has emerged. RAG enhances chatbot performance by retrieving accurate information from uncompressed documents rather than relying solely on pre-trained data. This reduces costs and improves accuracy without requiring expensive fine-tuning. Additionally, the browsing function in some chatbots is an extension of RAG, useful when large amounts of reference data exceed the context window of language models (LLMs).
🔍 The Process of RAG: Indexing, Retrieval, and Generation
RAG operates in three key stages: indexing, retrieval, and generation. During the indexing phase, documents are divided into chunks and stored in a vector database. The retrieval stage involves analyzing user queries to pull relevant information from this database using semantic matching, often with a BERT model to find the most meaningful data. The final generation stage leverages this retrieved content to generate coherent, contextually appropriate responses. However, RAG’s complexity introduces points of failure, and it remains a short-term solution to overcome limitations in current AI systems while more refined architectures are developed.
📊 The Evolving Meta of RAG
RAG's methodology continues to evolve with new variations and techniques. One approach involves using trainable embedding models to improve document retrieval by encoding them into vectors, making semantic retrieval more precise. For example, in coding contexts, models fine-tuned for coding text can better understand code structure, like indentation. A promising development is 'Graph RAG,' which uses knowledge graphs to extract and organize key relationships and claims, making results more traceable and explainable. This structured approach enhances the accuracy and contextual relevance of retrievals, preventing the generation of irrelevant responses.
🧠 Refining Queries and Hybrid Search Techniques
To ensure accurate information retrieval, the input query is also encoded for search. However, irrelevant parts like greetings or notations should be excluded. Query rewriting models help streamline queries to focus on key information. Hybrid search techniques, combining nearest neighbor searches with word frequency analysis, improve retrieval accuracy. In some cases, web search integration ensures that time-sensitive information remains current. APIs can be integrated at this stage, providing flexibility for additional data sources.
🔄 Reranking and Improving Output Quality
RAG has introduced methods like reranking, where multiple results are retrieved and then ranked for relevance. Domain-specific fine-tuning can further improve accuracy. Additionally, methods like autocut remove irrelevant results, and reranking models set thresholds to prevent irrelevant or incorrect data from being returned. This approach helps prevent the hallucination problem common in LLMs, ensuring higher quality and more reliable responses.
🔧 Tools and Resources to Build RAG Systems
For those looking to implement RAG, there are several tools and models available. Llama Index and Llama Pars are popular frameworks for organizing documents. Many fine-tuned embedding models can be found on Hugging Face, and Cohere provides models optimized for RAG. Microsoft's GitHub has code for Graph RAG, while Llama Index also offers implementations. Additionally, tools like RAG-as-a-service help evaluate and optimize RAG pipelines. These resources enable developers to create tailored RAG systems for specific needs.
💡 ThinkBuddy: A Productivity-Boosting AI Lab for Developers
ThinkBuddy is a powerful AI tool designed for developers, offering seamless integration with macOS and access to over 10 leading models, such as GPT-4 and Claude. Its unique 'AI Remix' feature combines the strengths of different models to produce more accurate responses. ThinkBuddy supports various file formats (PDF, DOCX, XLSX), includes screen capture capabilities, and offers voice input powered by Whisper, supporting over 80 languages. Its local data storage ensures privacy, and it's working towards integrating local models for even faster, secure processing. ThinkBuddy offers both a free basic tier and a discounted lifetime deal for users.
🙏 Gratitude and Further Engagement
The speaker extends thanks to their supporters on Patreon and YouTube and encourages viewers to follow them on Twitter for more updates. They also invite viewers to subscribe to their newsletter for insights on the latest AI research papers, providing an additional way to stay informed about cutting-edge developments in the field.
Mindmap
Keywords
💡RAG (Retrieval Augmented Generation)
💡Indexing
💡Vector Database
💡Semantic Similarity
💡Embedding Model
💡Knowledge Graph
💡Re-ranking
💡Hybrid Search
💡Hallucination
💡Fine-tuning
Highlights
RAG (Retrieval-Augmented Generation) drastically improves the performance and usability of LLMs by retrieving accurate information from uncompressed documents instead of relying solely on the LLM's neural network.
RAG serves as a short-term solution to overcome LLM hallucinations by introducing document retrieval mechanisms that are cost-effective and accurate without needing to fine-tune the model.
RAG's process involves three key stages: indexing, retrieval, and generation, which together enhance LLM performance using external documents.
Indexing is the first stage in RAG, where documents are divided into meaningful chunks and stored as vectors in a database for easy retrieval.
In the retrieval stage, a bi-encoder model is used to capture semantic similarities between the input query and the indexed documents by measuring vector distances.
The final generation stage uses the LLM to combine the input and retrieved content to produce coherent, contextually relevant responses.
RAG can fail due to several variables, including how information is indexed, retrieved, and integrated, which introduces instability in the pipeline.
Graph RAG is an emerging method that uses knowledge graphs to organize and retrieve information, offering better traceability and explainability compared to traditional vector databases.
Hybrid retrieval techniques, like combining nearest-neighbor search with word frequency matching, can improve the accuracy of retrieved information.
Query rewriting with an LLM is used in RAG to remove irrelevant tokens like greetings, ensuring the input query is focused on key information before retrieval.
Reranking retrieved results using a domain-specific model helps ensure the most relevant documents are used in response generation.
Autocut is a technique to remove unrelated retrieved results based on similarity thresholds, preventing LLM hallucinations and forcing it to admit when it doesn't know the answer.
LlamaIndex is a popular RAG framework for general use, offering tools for organizing documents and retrieving information efficiently.
Graph RAG is supported by Microsoft, with open-source implementations available on GitHub for further experimentation and application.
ThinkBuddy is an AI-powered tool for MacOS that integrates with RAG, providing access to multiple LLMs and allowing users to combine strengths from different models into one response.
Transcripts
the current AI chap Bots are good at
everything except for being practical
and it's really frustrating that it
cannot be easily utilized consistently
in work settings because it'll just
hallucinate the most out- of pocket
thing you can imagine and since we don't
have the patience to wait for the Mega
corpse to train an even more powerful AI
we need ways to utilize the chat Bots we
have right now to get ahead of the curve
before AGI replaces us to write email
SLO so there's this short-term
workaround called rag which stands for
retrieval augmented generation in all of
the latest research and even in services
like CH gbt or claw rag has drastically
improved the performance and usability
for these L because instead of spawning
the key information in a neural network
that has compressed the data with rag it
retrieves accurate information from a
collection of uncompressed documents
separately stored that even the LM may
not have been trained on this method
would provide results that are both cost
effective and accurate without needing
to train or fine-tune the L that would
have cost tens of thousands and remember
the function we browsing that a lot of
chat Bots have this function is also an
extension of rag which makes rag perfect
when you need to use a large amount of
documents as reference that cannot fit
within the context window of an llm so
with these insane benefits why would it
be a short-term solution well before we
can answer that we first need to
understand how it generally Works let's
take a naive rag as an example oh yeah
keep in mind the field is still a bit
new so the process can be differentiated
a bit differently from place to place
but I would decompose them into three
main stages index ret retrieval and
generation in the index stage you will
be indexing your documents for the AI to
easily retrieve it later this index
process can vary but the most common way
is to divide the documents into
meaningful chunks and store it in a
vector form that can be searched easily
this stage usually doesn't repeat once
all your documents has been indexed and
stored within a vector database this
then brings us to the retrieval stage
where we will need to retrieve the
information for the LM to use to know
what information the LM needs to
retrieve we need to First Look at the
user input to see what the query is
about so we can bring out the most
relevant data from the vector database
for the LM to work with while there is
the classic word frequency matching
between the input query and the
documents to retrieve the ideal
information it still can capture the
semantic information between the words
so a bir model that is an encoder only
Transformer is used to encode and
provide measurements for semantic
similarities between the documents and
the input query so by measuring their
Vector distances we can find the most
semantic relevant information from the
doc doent and provide it to the llm for
further processing which brings us to
the last stage the generation stage this
stage relies on D llm to utilize the
retrieve content and the input content
to formulate coherent and contextually
relevant responses it needs to strike a
balance between following the
information on the reference document
and transforming into a response that
answers the input query so without any
fine-tuning the llm would be able to
respond to questions about your
documents with Rag and now you would
realize that with that many components
rag could have have a single point of
failure there are now various moving
Parts like how you index information how
you retrieve them and how good the model
is at blending and presenting the output
that have the power to affect the
quality of rag so it's kind of
reasonable to call it a short-term
solution because it is certainly a hacky
way to bypass L's limitations by
introducing more unstable variables but
being hacky about it kind of transforms
rack into a whole new field of research
with a more applied mentality rather
than a theoretical one as a better AR
chitectural model would be more of a
long-term solution so naturally this
simple pipeline has evolved into
something even more complex which brings
us to the million dooll question what is
the current meta for rag okay there are
way too many variants right now and
sometimes it boils down to what works
the best with your own data but here's
how The Meta roughly looks like starting
from the indexing stage other than
chunking the document semantically using
LM to better organize the information
retrieved a trainable embedding model
which is to convert text to vect vectors
can be used to better connect the input
query and the relevant documents when
storing and comparing them within a
vector database so for things like
indentation where it holds a more
significant meaning in coding compared
to your typical writing an embedding
model that is fine-tuned on coding would
be much more mindful about this detail
when converting the text into Vector
encoding so later on when the AI is
retrieving the influence of indentations
is much more respected another really
new and promising method is something
called graph rag this technique uses a
knowledge know graph and utilizes L to
extract entities relationships and key
claims from your document then the
hierarchical clustering with the lien
technique is used to organize the graph
which can be easily visualized for
better traceability and is much more
explainable and auditable than looking
at a vector database which brings us to
the retrieval stage where the model now
can not only retrieve the most relevant
information with the input query but
also obtain the context of the retrieved
content thanks to the knowledge graph by
using the structured data previous ly
generated this makes mistakes much more
traceable and preventable as answers
that might be contextually irrelevant to
the input query can be ignored but for
the model to retrieve information
accurately the previously mentioned
embedding model would need to encode the
input query too right however not all
the input query is needed for search
like the greetings in the inputs the
next line notation or end of sentence
token should be completely left out so
an input query rewriting LM would be
here to help to condense or even
transform the query to its key
information that is then encoded into
Vector form to be compared and searched
within the vector database or the
knowledge graph to retrieve more
accurate information additionally a
hybrid search can be used in the
meantime like the FIS SS nearest
neighbor plus word frequency to increase
the chance of getting the desired
retrieval optionally web search can be
done at this stage too which is really
useful to ensure any time sensitive
information or citations are correct and
this is also the part where you can
literally insert any apis then in the
final stage which is Generation
something called reranking is often used
now where instead of retrieving only
once you would instead retrieve topk
results in the retrieving stage and pass
the topk results into a reranking model
to see which results are actually the
most relevant that would be able to
enter the input query and the rerank
model can also be fine-tuned to be
domain specific another function code
autocut would also be used to remove
unrelated retrieved results based on
similarity distance gaps and sometimes
the content relevant score measured by
the rerank model would have a threshold
in place so if the retrieved information
is not as relevant it'll Force the model
to say they don't know anything about
the input query instead of hallucinating
or providing bad results so yeah that's
roughly the current mattera of rag but
since I've only been talking about the
conceptual ideas here are some relevant
sources you can use to build your own
rag for a more General rag framework
llama index is a more popular one it
also has Library like llama pars which
is good for organizing your documents
for retrievals for the embedding models
there are a lot of fine-tuned ones on
hugging face which are free download so
pick one at your own cost for rack model
commend R models from cooh here are some
of the best rack optimized models and
they also offer some really easy to ous
rerank and embedding models but of
course they're not free for graph rag
you can check out Microsoft's official
GitHub and yoink their codes from there
and I think llama index also has an
implementation so you can check that out
you can also check out rag ass which is
a framework that helps you evaluate your
rag pipelines so on the topic of rag
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for sponsoring this video if you like
what I talked about today you should
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there I will be breaking down the latest
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even if I am late to the news or don't
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stuff on there but anyways a big shout
out to andul lelas chrisad do Alex J
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