5-Langchain Series-Advanced RAG Q&A Chatbot With Chain And Retrievers Using Langchain

Summary

In 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

00:00

😀 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.

05:01

💻 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.

10:02

🔗 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.

15:05

🤖 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.

Keywords

💡Retriever

A retriever in the context of LangChain refers to an interface that extracts documents based on an unstructured query. It is more general than a vector store and is used to return relevant documents for a user query. In the video, the retriever is connected to the vector store, fetching documents that will then be processed by the LLM model to generate responses.

💡Chain

A 'chain' in LangChain is a sequence of calls made to either an LLM, a tool, or a data pre-processing step. Chains help structure multiple operations in a logical order, making the overall process more organized and efficient. In the video, the speaker uses 'create stuff document chain,' which formats documents into a prompt that is sent to the LLM for processing.

💡LLM (Large Language Model)

LLM stands for Large Language Model, a type of AI model designed to understand and generate human-like text based on the input it receives. In the video, the LLM (Llama 2) is used in combination with prompts to generate responses to queries based on the provided context from the vector store.

💡Vector Store

A vector store is a storage system that holds text or documents converted into vectors, which are mathematical representations of the data. In the video, the speaker explains how documents are transformed into vectors and stored in a vector store, allowing for efficient retrieval of information based on query similarity.

💡Prompt

A prompt is a structured input given to an LLM to generate a response. In this video, prompts are used to structure queries such that the model can retrieve relevant information from the vector store and return answers based on context. The speaker even demonstrates how to design a prompt template in LangChain.

💡LangChain

LangChain is a framework used for developing applications powered by large language models. It provides tools for chaining different components, such as LLMs, retrievers, and vector stores, into coherent pipelines. The video showcases how LangChain can be used to develop an advanced RAG (Retrieve-then-Answer Generation) pipeline.

💡Document Chain

A document chain is a process in LangChain where a list of documents is formatted into a prompt and then passed to an LLM for processing. This chain helps combine context from multiple documents into a single query. The video explains how to create a document chain using LangChain's 'create stuff document chain' method.

💡Similarity Search

Similarity search is a method used to retrieve information that is semantically or contextually similar to a query. In the video, the speaker explains how to perform a similarity search on the vector store, allowing the retriever to find the most relevant chunks of information based on the user's input.

💡Embedding

Embeddings are numerical representations of text, which capture the semantic meaning of words, phrases, or documents. These embeddings are used to store and retrieve data in a vector store. The speaker explains how embeddings, such as OpenAI's embeddings or Meta's AMA embeddings, are used to transform documents into vectors for more efficient retrieval.

💡Stuff Document Chain

The 'stuff document chain' in LangChain refers to a method where documents are collected, formatted into a prompt, and passed to an LLM for response generation. It helps ensure that the relevant documents are used within the context window of the LLM, making sure that the response is accurate. The video illustrates how this chain is essential for combining retrievers and LLMs.

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