2. AI Chatbot serving with Django & LangCorn | Unveiling YouTube Insights w/ LLM

In part 2, we will develop a website that integrates sentiment analysis techniques and a Large Language Model to provide a comprehensive understanding of YouTube comments, enabling users to extract meaningful information effortlessly.
Feb 24, 2025 · Geraldus Wilsen

Part 2 Overview

In retrospect to Part 1, we've successfully developed a fully functional website capable of scraping, processing, and storing YouTube data into our database. As we culminate that endeavor, I left you a small challenge—to integrate Django REST Framework. Before delving into our exploration of Django REST Framework, it's crucial to understand the significance of APIs and why Django REST Framework holds such importance. APIs serve as conduits of seamless communication between software systems, enabling data and functionality exchange, a critical component that elevates our application's capabilities. Now, introducing Django REST Framework, a robust toolkit amplifying Django's prowess, simplifying Web API creation with features like serialization, authentication, and streamlined CRUD operations. This dynamic duo, API integration, and DRF, equips us to traverse the digital landscape with flexibility, scalability, and innovation, setting the stage for our exciting exploration in Part 2.

Setting up Django REST Framework

Begin by installing the Django REST Framework with a simple command:

pip install djangorestframework

Then, go to settings.py and add 'rest_framework' in INSTALLED_APPS

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
 
    'ypa',
    'rest_framework'
]

Create a new file named serializers.py and define a class named ResultSerializer inside it.

from rest_framework import serializers
from .models import Result
 
class ResultSerializer(serializers.ModelSerializer):
    class Meta:
        model = Result
        fields = '__all__'

Then, connect the serializers by importing them into views.py, and add some functionality from Django REST Framework

from .serializers import ResultSerializer
from rest_framework.decorators import api_view, permission_classes
from rest_framework.permissions import IsAuthenticated
from rest_framework.response import Response
from rest_framework import status

If you still remember, previously we store data directly to database, like this:

current_user = request.user
videoid = source['videoid']
 
Check if a record with the same videoid exists
try:
    result = Result.objects.get(user=current_user, videoid=videoid)
    print(result)
except Result.DoesNotExist:
    result = None
 
# If the record exists, update it; otherwise, create a new record
if result:
    result.videotitle = source['videotitle']
    result.view = source['view']
    result.like = source['like']
    result.comment = source['comment']
    result.total_positive_comment = source['total_positive_comment']
    result.positive_comment = source['positive_comment']
    result.total_negative_comment = source['total_negative_comment']
    result.negative_comment = source['negative_comment']
else:
    result = Result(
    user=current_user,
    videoid=videoid,
    videotitle= source['videotitle'],
    view = source['view'],
    like = source['like'],
    comment = source['comment'],
    total_positive_comment = source['total_positive_comment'],
    positive_comment = source['positive_comment'],
    total_negative_comment = source['total_negative_comment'],
    negative_comment = source['negative_comment']
    )
 
result.save()

Right now, we can simply delete it, and change it like this

serializer = ResultSerializer(data=source)
if serializer.is_valid():
    # Check if a record with the same videoid exists
    try:
        result = Result.objects.get(user=request.user, videoid=source['videoid'])
        serializer.update(result, serializer.validated_data)
    except Result.DoesNotExist:
        result = serializer.save()
 
    return redirect(reverse('chat') + f'?id={result.id}')
else:
    print(serializer.errors)

This code snippet is used to serialize data and interact with the Result model in Django using the ResultSerializer class. Let's break it down step by step:

serializer = ResultSerializer(data=source): This line initializes an instance of the ResultSerializer class and provides it with data from the source dictionary. The ResultSerializer is responsible for converting complex data types, like the source dictionary, into a format that can be stored in the Result model.
if serializer.is_valid():: This conditional statement checks if the data provided to the serializer is valid according to the validation rules defined in the ResultSerializer class.
try:: This marks the beginning of a try-except block, where the code attempts to perform certain operations, and if an error occurs, it is caught and handled in the except block.
result = Result.objects.get(user=request.user, videoid=source['videoid']): This line attempts to retrieve a Result object from the database that matches both the logged-in user and the videoid from the source dictionary. If a match is found, it means there's an existing record with the same videoid.
serializer.update(result, serializer.validated_data): If an existing record is found, this line updates the fields of the existing result object with the new validated data from the serializer. This is a way of updating the existing record with new information.
except Result.DoesNotExist:: If no matching record is found in the database, this block is executed, indicating that a new record needs to be created.
result = serializer.save(): This line saves the data from the serializer to the Result model, creating a new record.
return redirect(reverse('chat') + f'?id={result.id}'): After either updating an existing record or creating a new one, the code redirects the user to a view named 'chat' with a query parameter id that corresponds to the ID of the saved Result object.
else:: If the serializer data is not valid, this block is executed.
print(serializer.errors): This line prints out any validation errors that occurred during the serialization process. This can help in identifying and debugging issues with the data being processed.

In summary, this code segment serializes data using ResultSerializer, updates existing records or creates new ones based on the validity of the data, and handles redirection and error reporting. It's a key part of the logic for managing and interacting with Result objects in the application.

Our final getoutput function in views.py will be like this:

@api_view(['POST'])
@permission_classes([IsAuthenticated])
def getoutput(request):
 
    context = {
        'user_id': request.user.pk
    }
        
    if request.method == "POST":
        url = request.POST["videoid"]
 
        try:
            key = ApiKey.objects.get(user=request.user)
            youtubeapikey = key.youtube_api_key
            if youtubeapikey is None:
                youtubeapikey = os.environ.get('youtubeapikey')
        except:
            youtubeapikey = os.environ.get('youtubeapikey')
 
        username = request.user.username
        recipient = User.objects.get(username=username)
        recipient_email = recipient.email
 
        async def run_async():
            stats, df, videoid, positive, negative, neutral = await get_result(url, youtubeapikey, username, recipient_email)
 
            source = {
                # if using API 'user' should be added
                'user': request.user.pk,
                'videoid': videoid,
                'videotitle': stats['title'],
                'view': stats['viewCount'],
                'like': stats['likeCount'],
                'comment': stats['commentCount'],
                'total_positive_comment': len(df[df['sentiment'] == 'positive']),
                'total_negative_comment': len(df[df['sentiment'] == 'negative']),
                'total_neutral_comment': len(df[df['sentiment'] == 'neutral']),
                'positive_comment': positive,
                'negative_comment': negative,
                'neutral_comment': neutral,
            }
 
            return source
 
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        source = loop.run_until_complete(run_async())
        loop.close()
 
        serializer = ResultSerializer(data=source)
        if serializer.is_valid():
            # Check if a record with the same videoid exists
            try:
                result = Result.objects.get(user=request.user, videoid=source['videoid'])
                serializer.update(result, serializer.validated_data)
            except Result.DoesNotExist:
                result = serializer.save()
 
 
            return redirect(reverse('chat') + f'?id={result.id}')
        else:
            print(serializer.errors) 
 
 
    else:
        return render(request, "home.html", {'context':context})

Creating API Endpoints in Django Rest Framework

Before we jump to the next step, if you look at the top of the code, we utilize a decorator (@) provided by Django REST Framework (DRF) to define the behavior and permissions associated with a specific API view. Let's break down each line:

@api_view(['POST']): This decorator, @api_view, is used to specify which HTTP methods are allowed for this API view. In this case, it's set to ['POST'], meaning this view will only respond to HTTP POST requests. This decorator ensures that the view is properly configured for handling POST data. It's not only limited to POST method, we can use DELETE, POST, etc.
@permission_classes([IsAuthenticated]): This decorator, @permission_classes, is used to define the permissions required to access the view. In this case, [IsAuthenticated] is specified, which means that only authenticated users are allowed to access this view. The IsAuthenticated permission class is a built-in DRF permission class that restricts access to authenticated users.

Our setup of Django REST Framework is nearly complete. Up to this point, we've primarily utilized serializers to populate our model with data. However, if we intend to retrieve data from our model through an API endpoint, we need to take an additional step. To begin, we must establish a function that provides access to our API, followed by seamlessly incorporating this new pathway into our urls.py configuration.

@api_view(['GET', 'PUT', 'DELETE'])
def result_list_by_user(request,user):
 
    try:
        result = Result.objects.filter(user=user)
    except Result.DoesNotExist:
        return Response(status=status.HTTP_404_NOT_FOUND)
 
    if request.method == 'GET':
        serializer = ResultSerializer(result, many = True)
        return Response(serializer.data)

This function defines an API endpoint that retrieves a list of Result objects associated with a specific user. The user parameter is received from the URL. The code attempts to retrieve Result objects filtered by the provided user. If no results are found, a 404 Not Found response is returned. If the HTTP method is GET, the ResultSerializer is used to serialize the results and return them as a response.

@api_view(['GET', 'PUT', 'DELETE'])
def result_details(request,user,id):
 
    try:
        result = Result.objects.filter(user=user,id= id)
    except Result.DoesNotExist:
        return Response(status=status.HTTP_404_NOT_FOUND)
 
    if request.method == 'GET':
        serializer = ResultSerializer(result, many = True)
        return Response(serializer.data)
 
    elif request.method == 'DELETE':
        result.delete()
        return Response(status=status.HTTP_204_NO_CONTENT)

This function defines an API endpoint to retrieve, update, or delete a specific Result object identified by both the user and the id parameter from the URL. Similar to the previous function, it tries to retrieve a Result object based on the user and id. If the object is not found, a 404 Not Found response is returned. For GET requests, the serialized data of the result is returned. For DELETE requests, the result is deleted and a 204 No Content response is returned.

Overall, these views provide API endpoints to retrieve lists of Result objects for a specific user and to perform CRUD operations (GET, PUT, DELETE) on individual Result objects based on the user and id. The use of DRF decorators and serializers simplifies the process of building these API endpoints while adhering to best practices for API design. Next step is registering a new path in urls.py

from django.urls import path
from . import views
 
urlpatterns = [
    path('', views.home, name="home"),
    path('getoutput',views.getoutput, name = "getoutput"),
    path('chat',views.chat,name = "chat"),
    path('login/', views.loginpage, name='login'),
    path('signup/', views.signuppage, name='signup'),
    path('logout/', views.logoutpage, name='logout'),
    path('result/<int:user>', views.result_list_by_user),
    path('result/<int:user>/<int:id>', views.result_details)
]

We have successfully integrated Django REST Framework. Now, upon entering http://127.0.0.1:8000/result/1 into your browser, the result_list_by_user view corresponding to the result/<int:user> path pattern will be executed. Ultimately, you will be presented with a Django Rest Framework template showcasing your data, as follows:

django rest framework example

Develop an AI Chatbot

Now we're all set to construct a cutting-edge chatbot that will enable our users to engage in dynamic interactions and seek information pertinent to their YouTube videos.

If you recall the progression laid out in Part 1, we will replicate those steps for this endeavor as well. Our initial stride involves crafting a foundational code that will serve as the essence of our chatbot, leveraging the capabilities of Langchain and Large Language Model. Following this, we'll proceed to elevate our Django views.py by incorporating a new view aptly named 'chat'. Let's get started!

First, let's revisit our maincodes.py, where we previously composed our scraping code. As a swift reminder, if you have completed Part 1, you will encounter crucial components such as the imported libraries and our prominent large language model, depicted as follows:

from langchain import HuggingFaceHub
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain import PromptTemplate,  LLMChain
import textwrap
from transformers import pipeline
 
 
load_dotenv(find_dotenv())
HUGGINGFACEHUB_API_TOKEN = os.environ["huggingfacehub_api_token"]
 
 
repo_id = "tiiuae/falcon-7b-instruct"  
falcon_llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_new_tokens": 425}
)

Secondly, create a new function named answer_question with a set of parameters, including question, videoid, videotitle, view, like, comment, total_positive_comment, positive_comment, total_negative_comment, negative_comment, total_neutral_comment, and neutral_comment.

def answer_question(question,videoid, videotitle, view, like, comment, total_positive_comment, positive_comment, total_negative_comment, negative_comment, total_neutral_comment, neutral_comment):

Next, let's proceed by crafting a template—a command that will guide our Large Language Model. Here's the format you can follow:

    template = """
    You are an intelligent chatbot that act as a senior data consultant. 
    Here is report of youtube video performance from your client:
    The video title is {videotitle} with video id is {videoid}. 
 
    Here are some statistic of the video performance:
    Total view (people viewing client's video): {view}
    Total like (people liking client's video): {like}
    Total comment (people commenting client's video): {comment}
    Total negative comment (people giving negative comments to client's video): {total_negative_comment}
    Total positive comment (people giving positive comments to client's video): {total_positive_comment}
    Total neutral comment (people giving neutral comments to client's video): {total_neutral_comment}
 
    Positive comments: {positive_comment}
    Negative comments: {negative_comment} 
    Neutral comments: {neutral_comment} 
 
    Answer the following question with the fact based on the report provided above. Don't hallucinating! Be concise and don't repeating the same thing
    Question: {question}
    Answer: your answer here be concise and specific
    
    """

With the template in place, the subsequent step involves integrating it into a PromptTemplate from Langchain and storing it within a variable named prompt. The PromptTemplate constructor accepts two crucial parameters: template and input_variables. We'll provide our formulated template for the template parameter, while the input_variables parameter encompasses the variables employed within our template.

    prompt = PromptTemplate(
        template=template, 
        input_variables=[
            "question","videoid","videotitle","view","like","comment",
            "total_positive_comment", "positive_comment", "total_negative_comment",
            "negative_comment", "total_neutral_comment", "neutral_comment"
            ]
        )

Subsequently, it becomes imperative to establish a chain model using LLMChain, requiring two pivotal parameters. Initially, the prompt - the template we've devised - assumes the forefront. Meanwhile, the second parameter necessitates specifying the Large Language Model of our choice. Given that we've stored our LLM under the variable falcon_llm, we can readily reference it within this context.

    llm_chain = LLMChain(prompt=prompt, llm=falcon_llm)

Lastly, to get the result, we can use llm_chain.run command with adding some parameter as below:

    answer = llm_chain.run(
        question=question, videoid = videoid, 
        videotitle = videotitle, view = view, like = like, 
        comment = comment, total_positive_comment = total_positive_comment, 
        positive_comment = positive_comment, total_negative_comment = total_negative_comment,
        negative_comment = negative_comment, total_neutral_comment = total_neutral_comment,
        neutral_comment = neutral_comment)
    
    return answer

Our final answer_question function would look like this:

def answer_question(question,videoid, videotitle, view, like, comment, total_positive_comment, positive_comment, total_negative_comment, negative_comment, total_neutral_comment, neutral_comment):
 
    template = """
    You are an intelligent chatbot that act as a senior data consultant. 
    Here is report of youtube video performance from your client:
    The video title is {videotitle} with video id is {videoid}. 
 
    Here are some statistic of the video performance:
    Total view (people viewing client's video): {view}
    Total like (people liking client's video): {like}
    Total comment (people commenting client's video): {comment}
    Total negative comment (people giving negative comments to client's video): {total_negative_comment}
    Total positive comment (people giving positive comments to client's video): {total_positive_comment}
    Total neutral comment (people giving neutral comments to client's video): {total_neutral_comment}
 
    Positive comments: {positive_comment}
    Negative comments: {negative_comment} 
    Neutral comments: {neutral_comment} 
 
    Answer the following question with the fact based on the report provided above. Don't hallucinating! Be concise and don't repeating the same thing
    Question: {question}
    Answer: your answer here be concise and specific
    
    """
 
    prompt = PromptTemplate(
        template=template, 
        input_variables=[
            "question","videoid","videotitle","view","like","comment",
            "total_positive_comment", "positive_comment", "total_negative_comment",
            "negative_comment", "total_neutral_comment", "neutral_comment"
            ]
        )
 
    llm_chain = LLMChain(prompt=prompt, llm=falcon_llm)
 
    print("start working")
 
    answer = llm_chain.run(
        question=question, videoid = videoid, 
        videotitle = videotitle, view = view, like = like, 
        comment = comment, total_positive_comment = total_positive_comment, 
        positive_comment = positive_comment, total_negative_comment = total_negative_comment,
        negative_comment = negative_comment, total_neutral_comment = total_neutral_comment,
        neutral_comment = neutral_comment)
    
    print("answer ready")
    
    return answer

One last thing is to update our views.py. Since we already implement Django REST Framework and created API endpoints, let's utilize it in our chat function.

last_id = None
@login_required(login_url='login')
def chat(request):
 
    context = {
        'user_id': request.user.pk
    }
 
    global last_id
 
    user = request.user.pk
    username = request.user
 
    button_id = request.POST.get('button_id')
    print(button_id)
 
    id = request.GET.get('id')
    print(id)
 
 
    if button_id is not None and button_id != '':
        print('y')
        last_id = button_id 
        print(button_id)
        api_url = f'http://127.0.0.1:8000/result/{user}/{button_id}'
        print(api_url)
    else:
        print('n')
        if id != None:
            api_url = f'http://127.0.0.1:8000/result/{user}/{id}'
            print(api_url)
            last_id = id
        else:
            api_url = f'http://127.0.0.1:8000/result/{user}/{last_id}'
            print(api_url)
 
    response = requests.get(api_url)
 
    if response.status_code == 200:
        data = response.json()
        source = data[0]
        print(source)
    else:
        print('Error:', response.status_code)
 
    videoid = source['videoid'], 
    videotitle= source['videotitle'],
    view = source['view'],
    like = source['like'],
    comment = source['comment'],
    total_positive_comment = source['total_positive_comment'],
    positive_comment = source['positive_comment'],
    total_negative_comment = source['total_negative_comment'],
    negative_comment = source['negative_comment']
    total_neutral_comment = source['total_neutral_comment'],
    neutral_comment = source['neutral_comment']
 
    if request.method == 'POST' or request.method == 'GET':
        user_input = request.POST.get('user_input')
        print(user_input)
 
        if user_input is not None and user_input != '':
 
            answer = answer_question(
              user_input, videoid, videotitle, view, like, comment,
              total_positive_comment, positive_comment,
              total_negative_comment, negative_comment,
              total_neutral_comment, neutral_comment)
 
            print(answer)
        
        else:
            answer = f'''Hey there, {username}! Let's dive deep into your report together. 
                    Feel free to ask me anything you'd like, whether it's seeking advice, 
                    summarizing the comments on your video, or exploring other fascinating insights! 
                    We're here to make your experience as engaging and informative as possible!'''
            print(answer)
 
    return(render(request, "home.html", {"response":answer, "source":source, "context":context}))

A quick recap: What does `chat` do?

To gain a better understanding, let's break down its functionality:

@login_required(login_url='login'): This decorator ensures that only authenticated users can access the chat view. If a user is not authenticated, they will be redirected to the specified login page ('login').
context: A dictionary containing the current user's ID is created, which will be passed to the template for rendering.
global last_id: Declares a global variable named last_id, presumably intended to keep track of a specific ID for user interactions.
Various variables are assigned using the request's POST and GET data, such as button_id and id, both of which are print-debugged for monitoring.
The code checks whether button_id is provided and updates last_id accordingly, forming an API URL to fetch data.
If no button_id is provided, the code checks for the existence of id, and if present, constructs an API URL with id. If neither button_id nor id is available, the code uses last_id to generate the API URL.
The application sends a GET request to the API URL and retrieves data, storing it in the source dictionary.
Key data elements (e.g., videoid, videotitle, etc.) are extracted from the source dictionary.
The view handles both POST and GET requests. In case of a user input, the function calls the answer_question function to generate a response. If no user input is provided, a default response is generated.
The view renders the home.html template, passing the generated response, source data, and context for rendering.

In summary, this view function handles user interactions with the web application's chat feature. It manages user input, communicates with an API to retrieve data, generates responses using the answer_question function, and ultimately renders the response along with other relevant data in the home.html template.

In reference to our previous discussion on APIs, during the journey of developing this project, I came across a highly beneficial approach: implementing the Large Language Model as a separate API. The decision to compartmentalize the model has proven to have significant advantages, particularly when dealing with large-sized models. By segregating the model into an API, we have effectively optimized scalability, security, and the ease of model updates. This strategic separation has proven to be an immensely advantageous choice in our project's development journey.

The way we transform a Large Language Model into an API endpoint is by using Langcorn and FASTAPI. I found a great tutorial on using Langcorn and FASTAPI in this video by Assembly AI. Langcorn is also well-documented; please take a look at this Langcorn Github Repository. The process is quite straightforward, so please watch the tutorial and follow the steps one by one. While I won't include the entire sequence in this article, I will provide you with some essential code snippets that were not covered in the video. These snippets explain how to call the API endpoint and integrate it into our chat view. Before proceeding with the modification of our chat function in views.py, ensure that when you enter your API endpoint in your browser, you receive a result similar to the following:

ra_langcorn

If you haven't encountered this view, there's no need to worry. I have also provided a code for you. Please kindly check my Github Repository. One last step of this project, let's update our chat function in views.py become like this:

last_id = None
@login_required(login_url='login')
def chat(request):
 
    context = {
        'user_id': request.user.pk
    }
 
    global last_id
 
    user = request.user.pk
    username = request.user
 
    button_id = request.POST.get('button_id')
    print(button_id)
 
    id = request.GET.get('id')
    print(id)
 
 
    if button_id is not None and button_id != '':
        print('y')
        last_id = button_id 
        print(button_id)
        api_url = f'http://127.0.0.1:8000/result/{user}/{button_id}'
        print(api_url)
    else:
        print('n')
        if id != None:
            api_url = f'http://127.0.0.1:8000/result/{user}/{id}'
            print(api_url)
            last_id = id
        else:
            api_url = f'http://127.0.0.1:8000/result/{user}/{last_id}'
            print(api_url)
 
    response = requests.get(api_url)
 
    if response.status_code == 200:
        data = response.json()
        source = data[0]
        print(source)
    else:
        print('Error:', response.status_code)
 
 
    if request.method == 'POST' or request.method == 'GET':
        user_input = request.POST.get('user_input')
        print(user_input)
 
        if user_input is not None and user_input != '':
            
            
            headers = {
                "accept":"application/json",
                "Content-Type":"application/json"
            }
 
            json_data = {
                "question": user_input,
                "videoid" : source['videoid'],
                "videotitle": source['videotitle'], 
                "view" : source['view'],
                "like" : source['like'],
                "comment" : source['comment'],
                "total_positive_comment" : source['total_positive_comment'],
                "positive_comment" : source['positive_comment'],
                "total_negative_comment" : source['total_negative_comment'],
                "negative_comment" : source['negative_comment'],
                "total_neutral_comment" : source['total_neutral_comment'],
                "neutral_comment" : source['neutral_comment']
            }
 
 
            response = requests.post(
                "your_langchain_model_api_endpoint_here", headers = headers, json = json_data
            )
 
            data = response.text
            parsed_data = json.loads(data)
            print(parsed_data)
            answer = parsed_data['output']
 
            print(answer)
        
        else:
            answer = f'''Hey there, {username}! Let's dive deep into your report together. 
                    Feel free to ask me anything you'd like, whether it's seeking advice, 
                    summarizing the comments on your video, or exploring other fascinating insights! 
                    We're here to make your experience as engaging and informative as possible!'''
            print(answer)
 
    return(render(request, "home.html", {"response":answer, "source":source, "context":context}))

Once again, don't forget to change your_langchain_model_api_endpoint_here in

    response = requests.post(
        "your_langchain_model_api_endpoint_here", headers = headers, json = json_data
   )

with your api endpoint. Right after we change the chat function, we can delete or comment the answer_question function in maincodes.py since we don't need it anymore.

Conclusion

As we conclude our project journey, we recognize that our implementation merely scratches the surface of the expansive capabilities offered by Large Language Models (LLMs). I encourage you to delve deeper into the myriad features that LLMs, especially those harnessed through Langchain, have to offer. While certain aspects remained unexplored within this project, you can broaden your horizons by exploring a playlist titled LangChain & LLM tutorials (ft. gpt3, chatgpt, llamaindex, chroma) explained by Samuel Chan. I hope that this project has laid a strong foundation for your endeavors, and I eagerly anticipate witnessing the innovative AI creations that you will undoubtedly develop.

Until our next article, stay inspired and keep innovating!

2. AI Chatbot serving with Django & LangCorn | Unveiling YouTube Insights w/ LLM

Setting up Django REST Framework

Creating API Endpoints in Django Rest Framework

Develop an AI Chatbot

A quick recap: What does `chat` do?

Conclusion

Further reading

Unveiling YouTube insights with LLM: Part 1

Read More

1. Data Collection & Processing, Database | Unveiling YouTube Insights w/ LLM

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2. AI Chatbot serving with Django & LangCorn | Unveiling YouTube Insights w/ LLM

Setting up Django REST Framework

Creating API Endpoints in Django Rest Framework

Develop an AI Chatbot

A quick recap: What does chat do?

Conclusion

Further reading

Unveiling YouTube insights with LLM: Part 1

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1. Data Collection & Processing, Database | Unveiling YouTube Insights w/ LLM

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A quick recap: What does `chat` do?