5-Langchain Series-Advanced RAG Q&A Chatbot With Chain And Retrievers Using Langchain
Summary
TLDRIn this video, Krishak walks viewers through building an advanced RAG (Retrieval-Augmented Generation) pipeline using LangChain's Retriever and Chain concepts. He shares a personal anecdote before diving into the technical aspects, explaining how to retrieve documents and utilize large language models (LLMs) to generate responses based on context. Krishak demonstrates step-by-step how to set up vector stores, split documents, create a Q&A chatbot, and integrate LLMs like LLaMA 2, showcasing the practical application of chains and retrievers for building an intelligent pipeline.
Takeaways
- 🏸 Krishak starts by sharing a funny incident from his badminton routine, where a neighbor recognized him by his shoes but noticed his new look.
- 💡 The video continues the Langchain series, focusing on developing an advanced RAG (Retriever-Augmented Generation) pipeline using retrievers and chain concepts.
- 📄 The initial tutorial discussed creating a simple RAG pipeline with data sources like PDFs and websites, loading and transforming the data into vector stores.
- 🧩 The next step involves improving query efficiency by incorporating large language models (LLMs) and chaining techniques to retrieve more accurate results.
- 🔗 Krishak explains the concept of chains, particularly the 'stuff document chain,' which formats documents into a prompt and passes them to the LLM.
- 🛠️ The practical example demonstrates how to split a document into chunks, convert it into vectors, and store it in a vector store using OpenAI embeddings.
- 🔍 The 'retriever' is introduced as an interface for extracting relevant documents from the vector store based on a user query.
- 🤖 Krishak integrates the retriever with the LLM and the document chain to create a Q&A chatbot that can generate context-based answers.
- 📝 He emphasizes the customization potential of the system, allowing it to work with open-source LLMs like Llama 2 for users without paid access.
- 🚀 The tutorial concludes by showing how combining retrievers, chains, and LLMs creates a more advanced RAG pipeline for efficient document retrieval and query answering.
Q & A
What is the primary focus of this video?
-The primary focus of this video is on developing an advanced RAG (Retrieval-Augmented Generation) pipeline using LangChain, specifically employing Retriever and Chain concepts along with LLMs (Large Language Models).
What was the funny incident that the speaker shared?
-The speaker shared a funny incident about how one of his neighbors identified him just by his shoes after a badminton session, noting that his new low-maintenance look had completely changed his appearance.
What are the main steps in developing the RAG pipeline as described in the video?
-The steps include: 1) Loading documents like PDFs and websites, 2) Breaking down large documents into chunks, 3) Converting those chunks into vectors and storing them in a vector store, 4) Using an LLM with Retriever and Chain concepts to retrieve information and generate a response based on a prompt.
What models can be used in this RAG pipeline?
-The video discusses using both open-source and paid LLM models, such as OpenAI embeddings and LLaMA 2 from Meta. Users can choose between these models depending on their needs and resources.
What is the purpose of using a prompt in this pipeline?
-The prompt is used to guide the LLM to answer a specific question based on the provided context. In the pipeline, the prompt helps format the documents from the vector store into a query that the LLM can use to generate a relevant response.
What is a 'stuff document chain' and how is it used?
-A 'stuff document chain' is a sequence of operations that formats a list of documents into a prompt and passes it to the LLM. It helps combine the documents and the user's query into a format that the LLM can process, allowing for a more coherent response.
How does the retriever function within the pipeline?
-The retriever is an interface connected to the vector store. When the user inputs a query, the retriever fetches the relevant documents from the vector store and passes them to the LLM to generate a response.
What is the role of the Vector Store in the pipeline?
-The Vector Store holds the vectorized representations of the document chunks. It allows for similarity-based searching when queries are made, and the retriever fetches data from it to provide relevant documents to the LLM for processing.
What is the advantage of using LLMs in this pipeline?
-LLMs enhance the RAG pipeline by providing more accurate and context-aware responses based on the documents retrieved from the vector store. LLMs can handle complex queries and generate more refined results compared to simple vector searches.
How can users customize their RAG pipeline based on their needs?
-Users can customize their RAG pipeline by choosing between different LLMs (open-source or paid), adjusting document chunk sizes, tweaking prompts, and using various LangChain functions like retrievers, stuff document chains, and vector stores.
Outlines
😀 Introduction and Personal Anecdote
Krishak introduces himself and welcomes viewers to his YouTube channel, where he continues his series on LangChain. Before diving into the content, he shares a humorous story about a neighbor recognizing him by his shoes after a badminton game, commenting on his new low-maintenance look. He invites viewers to comment on his appearance before transitioning to the main content.
💻 Recap of Previous Video and Introduction to Advanced RAG Pipeline
Krishak recaps the previous video where he discussed creating a basic RAG (Retrieve and Generate) pipeline using LangChain. He explains how data from PDFs or websites was loaded, broken into chunks, converted into vectors, and stored in a vector store. He outlines the limitations of query vectors and introduces the concept of using LLMs (Large Language Models) and prompts to enhance retrieval. This marks the beginning of a more advanced pipeline, leveraging chains and retrievers for improved results.
🔗 Understanding Chains and Retrievers in LangChain
Krishak dives into the technical details of LangChain, explaining how chains and retrievers work together. He highlights the 'stuff document chain' and its importance in taking documents, formatting them into prompts, and feeding them to an LLM. The LLM then generates responses based on these prompts. He reiterates the importance of using chains and retrievers for building advanced RAG pipelines and explains that different LLM models (such as open-source Lama 2) can be used in this process.
🤖 Implementing the Chain with LangChain
Krishak discusses implementing a chain in LangChain. He outlines the steps to create a 'stuff document chain' by importing the required functions from LangChain and explains how to combine the LLM and the prompt to create the chain. He emphasizes the need for a retriever, which acts as an interface to fetch relevant documents from a vector store. He also explains how to use the 'DB.as_retriever' method to link the vector store to the retriever.
🔄 Combining Chains and Retrievers
Krishak explains how to combine the retriever and document chain to form a retriever chain. He describes the flow of information: user queries go through the retriever to fetch relevant documents from the vector store, which are then passed to the LLM along with the prompt. The LLM generates a response based on the provided context. This integrated setup forms the backbone of the advanced RAG pipeline.
🛠 Practical Implementation of the Retrieval Chain
In this section, Krishak demonstrates the practical implementation of the retriever chain in LangChain. He explains how to use the 'create_retrieval_chain' method to link the retriever and the document chain. He shows how to input a query, invoke the retriever chain, and get the response. Using an open-source LLM like Lama 2, he retrieves the context from the vector store and generates the output.
🎯 Building a Q&A System with the RAG Pipeline
Krishak concludes by showcasing how the RAG pipeline can be used to build a Q&A system. He walks through examples of inputting different queries and generating accurate answers from the vector store using the retriever chain. He highlights the effectiveness of combining the chain, retriever, and LLM to build a sophisticated question-answering system. He encourages viewers to try the implementation and invites them to stay tuned for more advanced topics.
Mindmap
Keywords
💡Retriever
💡Chain
💡LLM (Large Language Model)
💡Vector Store
💡Prompt
💡LangChain
💡Document Chain
💡Similarity Search
💡Embedding
💡Stuff Document Chain
Highlights
Introduction to the LangChain series and the concept of developing an advanced RAG (Retrieval-Augmented Generation) pipeline using Retriever and chain concepts.
Discussion of a personal story about being identified by shoes after a change in appearance.
Overview of the previous tutorial where a simple RAG pipeline was created using a data source like PDF or website and vector store for storing chunks of data.
Explanation of how query vectors aren't efficient for retrieving complete results and the need to integrate LLM models with Retriever and chain.
Introduction of the advanced RAG pipeline implementation using LLM models, chain, and Retriever to improve retrieval and customization of prompts.
Overview of converting documents into chunks using RecursiveCharacterTextSplitter and storing them in a vector store.
Use of OpenAI or AMA embeddings for converting document chunks into vectors and saving them in a vector store for retrieval.
Key difference between vector store search (similarity search) and combining it with LLM models using chains for more context-aware responses.
Demonstration of creating a custom prompt template for a Q&A chatbot that fetches answers from context using both a Retriever and LLM model.
Detailed breakdown of the create_stuff_document_chain function, which formats a list of documents into a prompt for the LLM model to generate answers.
Description of the Retriever interface, which serves as a more general way to access data compared to a direct vector store query.
Explanation of how the retriever works as an interface to query the vector store, retrieve documents, and pass them to the LLM model for response generation.
Step-by-step process of implementing a retriever chain that combines the Retriever and document chain to handle both querying and formatting for the LLM.
Flowchart explanation of how user inquiries pass through the retriever, vector store, and LLM to generate context-driven responses.
Demonstration of how to invoke the retrieval chain and use the LLM model (e.g., LLaMA 2) to provide answers based on context from a PDF file.
Transcripts
hello all my name is krishak and welcome
to my YouTube channel so guys we are
going to continue the langin series and
now we will be developing Advanced rag
pipeline using Retriever and chain
Concepts that are available in Lang
chain now before I go ahead I really
want to talk about a funny incident that
really happened uh recently just today
itself so what I did what I do is that
every day morning I usually go and play
some badminton you know I play for 1
hours one and a half hour and usually a
lot of my friends and neighbors usually
come and play you know so today what
happened after playing I went today in
this look okay I played around three to
four games and then one of my neighbors
said hey Kish your Chris right you just
got identified right now just by seeing
your shoes I identified you but your
look has completely changed so let me
know if this is true in the comment
section of this particular video but I
am liking this look uh you know it's
like so much of less maintenance you
don't have to maintain your beard your
mustach or your hair also right it looks
super cool now uh let's go ahead and uh
work towards this specific project in
our previous tutorial what we had
actually done is that we had created
this simple rack pipeline okay we had a
data data source like we took PDF we
took website then we uh load that
particular data set using different
different data injection techniques uh
that were available in Lang chain then
we did transformation where in we broke
down our bigger PDFs into chunks and
then we converted all all these
particular chunks into vectors and
stored it in a vector store and then
with the help of query we are able to
probably retrieve some of the data that
is available in the vector store now
this is one step now the further step
after this particular query Vector Now
understand query Vector are not that
efficient you know when in terms of
retrieving the entire results here we
will also specifically use llm models
okay so now what we will try to do is
that using some prompts
okay and we will take this specific
prompts we will take this particular
data using the concept of chain and
retriever okay and understand this topic
is very important because this is where
your advanc rack pipeline implementation
will start using chain and retriever we
will also use and in this chain and
retriever what we do is that we
specifically use llm models it can be
open source it can be paid whatever
model you want so so we will
specifically use this llm model and
based on this prompt we will try to get
the
response on what we are specifically
looking right so there will be a lot of
customization that will be added once we
implement this specific part in the
first part we discussed about this and
this is the second part that we are
going to discuss okay how we can use
chain how we can use retriever what
exactly is chain how you can integrate
in this particular llm model there is a
concept of something called a stuff
document chain what exactly it it is so
we will discuss everything all about it
and here we also going to do a practical
implementation so please make sure that
you watch this video till the end and we
are going to learn a lot of things okay
so here the first step what we had done
on already uh we have implemented this
in our previous tutorial also so here
you'll be able to see that I trying to
read attention. PDF which is present in
a folder and then we just write loader.
load and we get the documents okay so
these are all the documents that will be
available in this specific PDF okay then
what we are specifically doing next step
is that from lin. text plat we will be
using recursive character text splitter
wherein we convert the entire document
into chunks right and then we are
probably using this text splitter and we
are using a chunk size of thousand
overlap of 20 so this everything is
implemented in my previous videos right
so we are going to split this entire
document and save it in this particular
documents in the previous tutorial you
have implemented all these things now
here what we are going to take do now
we'll take all these documents and then
we will convert it into a vector store
right so Vector store for that we are
using this F okay so here we are going
we can use AMA embedding or open AI
embedding right as I said you open AI
embedding is very much Advanced and it
will perform better than AMA embedding
if you don't have opening a API key use
AMA embedding instead of open AI
embedding over here you can just write
AMA embedding right so from here I'll be
using fires which is is again a kind of
vector store and it has been developed
by meta so fest. from documents document
of 20 so I'm just taking the first 20
documents and I'm just writing open AI
Bings let's make it to 30 so that it
will have some amount of data right now
we are specifically using open IM
bidding and this DB that you
specifically see is a my Vector store
okay so here you can see Vector store f
f of type okay perfect
now any question that I ask attention
function can be described as a mapping
query and then we can take this Vector
store and just write do similarity
search on this query and we will get the
result over here okay so this all things
we have actually done in our previous
video now is the most important thing
how I can combine prompt along with
chains and Retriever and then probably
get a response based on the prompt okay
so since many people have the use of
only open source uh they have the exess
of Open Source model llm model so I'm
going to use AMA from lin. community.
llms import olama then I'm going to use
AMA over here model will be Lama 2 if
you don't have Lama 2 just do go to
command prompt after downloading o Lama
I hope everybody knows how to download
it if you're seeing my series of videos
here you can just write AMA run Lama 2
right so once you write like this then
the model will get downloaded right if
it is already downloaded it will be
coming something like this okay so this
is the first step that you really need
to do then I have written from lin.com
community. LMS AMA load olama so
whatever AMA model we are specifically
using that is Lama 2 so this is my open
source model so if you see Lama llm it
is nothing but AMA now is the time we
will start designing our prompt template
now in order to design the chat prompt
template I will be using chat linore
core. prompts import chat prompt
template okay and then from chat pron
template from template I'm just writing
like this so I'm saying answer the
following question based only on the
provided context now see we are trying
to develop a Q&A Q&A chatboard based on
the context it should provide me the
response
previously what we are doing using
Vector store we used to if you see the
code over here we used to query the
vector score right Vector store by using
similarity search algorithm but here
what we are doing here we are defining
our own prompt and we saying hey answer
the following question based on the
provided context right I will and simply
I'm writing away I'll tip you $1,000 if
you find the answer helpful okay if the
user find the answer helpful okay just
at least by seeing money the AI May
perform well and then we are giving our
context and then question will be input
okay how why I'm writing in this
specific way because this chain and
retriever right you'll be understanding
this context will be autofilled and this
input will also get autofilled okay how
it will get autofilled I'll let you know
so now what I will do I will execute
this now I will go ahead and Implement
about chain uh it is always a good idea
okay to probably go to your browser and
check about each and every topic that
I'm explaining so what does chain refer
to chain referred to a sequence of calls
whether to an llm tool or data
pre-processing step the primary
supported way to do this is LCL okay now
if you talk about chain over here there
are multiple functions with respect to
chain one of the function that I'm going
to use is create stuff document chain
now what this exactly does this chain
takes a list of documents and formats
them into all into a prompt then passes
that prompt to an llm okay see this this
chains take a list of documents and
formats them based on the prompt formats
them into a prompt sorry not based on
the prompt into the prompt that
basically means over here if I go ahead
and open my
browser here in the context I definitely
require my document s self right based
on that context and based on this input
I will be able to give the answer right
so based on this context and based on
the input right context basically means
all the documents that are there
available in the vector store right
inputs are what question I'm asking
right so with the help of this create
stuff document chain what is basically
happening this chain takes a list of
documents and formats them all into a
prompt then passes that prompt to an llm
it passes all the documents show that
you should make sure that it fits within
the context window the llm you are using
so what it is exactly doing it'll take
up all the documents from the vector
store it will put that put inside that
particular prom template and then it
will send the to the llm and then we
finally get the response okay and that
is what we'll be using similarly there
are different different uh things also
over here like create stuff document
chain is there create SQL query chain if
you are working with respect to SQL
database for natural language and this
is one of the very important project
that I'll also do in the future one or
the other way I'll try to use one or the
other functionalities to just make you
understand how we can use all these
functionalities itself right but it is
always good that we have a specific use
case okay now if I open this okay let's
go ahead and create my chain how do I
create my chain over here again it's
very simple so I will write from Lang
chain Lang chain uncore community so it
is present inside Community itself or
not Community sorry chains Lang chore
chains dot combine
documents
import create stuff document chain now
how do I know this okay I did not create
this I have already seen the
documentation okay that is the reason
I'm writing then I will go ahead and
create my document chain now inside this
document chain as I said I'll be using
create stuff document chain okay that we
have already seen now inside this chain
two things are basically required one is
the llm model and the second one is the
prompt that I have created because
inside this prompt itself the list of
documents will be added right whatever
documents is basically coming from here
right from my Vector store that will be
added over here okay so once I create
this so This basically becomes my
document chain okay very much simple now
after this we also have to learn about
one very important thing which is called
as
retrievers okay so I will go ahead and
write something called as
retri retriever Lang chain okay now what
exactly retriever Lang chain is it is an
interface that Recs document given an
unstructured query it is more General
than a vector store right a retriever
does not need to be able to store the
documents only to return or retrieve
them Vector store can be based as the
back backbone of the retriever now see
there is a vector store which is having
some information some Vector stored in
it right if I want to take out any data
from there right I can actually do a
similarity search which we have already
seen okay but langen what it did is that
since we usually do a lot of programming
in a way right where in classes are used
interfaces are used so it created a
separate interface which is called as
Retriever and that interface has a
backend source to that particular Vector
store to retrieve any information right
whenever a query is given right that
entire Vector store will be passing the
information through this retriever okay
so what we will do is that here I will
quickly open this I have also written
some amount of description so that it
will be helpful for you whenever you
probably go ahead and check it this
entire materials okay so now what I will
do I will just go ahead and write DB Dot
DB dot as retriever now once I do like
this DB do as retriever this basically
has become my retriever right so what we
have done DB is our Vector store already
it is there we have connected to an
interface which is basically this
particular variable now okay so if you
go ahead and ex uh probably display this
what is retriever it is nothing but it
is a vector store retriever internally
you'll be also able to see that what all
it is implemented F and open AI Bings
and all all the information are there
now retriever is done chain is also done
okay now is the time that what I will do
I will try to use this Retriever and
document chain both together to probably
see when we combine both of them then
only we'll be able to get the response
right so with respect to this now let's
go ahead and create my retriever chain
okay so the next step is what but since
I need to combine both of them one is
Retriever and one is the document chain
right this document chain is responsible
for putting the information in the
context when we combine both of them
then it becomes a retriever chain now
what is the definition this chain takes
an input as a user inquiry which is then
passed to the retriever to fetch the
relevant documents so it passes through
the retriever it is connected to the
vector store then those documents are
then passed to an llm to generate the
response and this l M that we are
basically getting it is basically coming
from what this document chain understand
the flow okay so let me just go ahead
and mention this flow again so that it
becomes very much easy for you so
whenever the
users this is my user okay whenever the
user asks for any
inquiry okay any inquiry so first what
it is going it is going to this
retriever okay very much important okay
so this this is my retriever this
retriever is an interface to what Vector
store which has all the information
right so once we basically get the
retriever then the next step what it
happens it goes to what it goes to my
llm model with some prompt right there
will be some prompt involved to this and
how this is basically
happening with the help of stuff
document
chain so this stuff document chain has
has already both these things combined
llm and prompt right and then finally we
get our
response I hope you're able to
understand this right and this is what
we have basically implemented over here
now how to basically create a retriever
chain so first for for all of this again
I will be using a library um with
respect to chains okay so form Lang
chain Lang
chain Dot
chains
import create retrieval chain right and
there are lot of things create Q with
Source chain retrieval chain this chain
that chain I will try to explain you all
of things don't worry okay I will cast
the right kind of use case and I will be
showcasing you all these things don't
worry about that okay then what I will
do I will I will take this entire create
retrieval chain and then I will create
my uh retrieval chain
so here I will write
retrieval chain is equal
to create retrieval chain and here I'm
going to use first parameter that I'm
going to use is retriever then the
second parameter is nothing but document
chain so once I have this chain right
now I will be able to invoke any queries
so I will go ahead and write retrieval
chain dot invoke okay and now what are
the parameters that I have to to give
nothing I have to give my input so the
input will be given over here colon and
whatever input that I can give uh let's
say from the PDF I have put some input
over here let me just copy and paste it
okay so this is one of the text that is
available in the PDF that is attention.
PDF and now if I invoke this you'll be
able to see that I will be able to get
the entire response okay so retrieval do
chain. invoke and again we are using
open source llm model that is Lama 2
okay yeah I've used openi bidding so
here you can see this is my input this
was the context right all the context
information is there and finally I get
the answer and this answer I will just
try to save it over here something like
response uh res
response okay and then I will execute
response response of answer so this
finally becomes my output that is
probably coming over here okay so this
if I execute it the answer to the
question is right and all the
information you can see the answer to
the question and it is retrieving all
the details right so here you'll be able
to see that how beautifully with the
help of llm we have constructed this
entire thing and we have used this
chains Retriever and this is the first
step towards developing a advanced rack
pipeline right so whatever question you
ask let let me just open this and
probably show you some more examples
okay so what I will do I will just open
my download
page let's see my download page I'll ask
for any statement okay just a second um
attention Okay so this is my
thing uh let me just go ahead and search
for
anything the decoder is the this and
this okay so I'll be searching from here
to here okay now let me go ahead and
change my
input and search for it so chain. invoke
I've done and here I've got the response
now let me just go and everything uh the
answer to this question is
six um the decoder is also composed of
stack of n6 oh it has basically taken
this no worries okay let's take some
more
thing I'll write scaled do product
attention some more
examples oops uh
error okay just a second no I think it
should not
be okay it is taking that question and
it is trying to form some answers out of
it
okay got it got it it is not a mistake I
thought it was a mistake mistake out
there scale is a type of this this this
see I'm getting all the answers over
here so now it has become a perfect Q&A
um Q&A with rack pipeline along with
this Retriever and chain with Lang chain
so I hope you like this particular video
this was it from my side I'll see you in
the next video have a great day thank
you one all take care bye-bye
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