From Idea to AI: Building “Chat with any YouTube Video”

Hey everyone! I’m excited to share a project I’ve been working on: Chat with any YouTube Video. It’s a tool that lets you talk to a YouTube video, asking questions and getting answers based on its content.

The inspiration for this project came from a common frustration: having to scrub through a long tutorial or a lecture just to find one specific piece of information. I wanted to build something that could instantly surface the answer I was looking for.

Instead of building a complex, large-scale application, I wanted to see how far I could get with a minimal tech stack, leveraging the power of modern AI tools. The result is a simple, yet surprisingly effective, RAG pipeline built right on my local machine.

Why RAG?

Initially, I considered just feeding the entire video transcript to a language model. But anyone who has worked with LLMs knows that context windows are limited and can be expensive. The real breakthrough came when I decided to implement a Retrieval-Augmented Generation (RAG) pipeline.

The idea is simple: don’t give the LLM the entire transcript. Instead, give it only the most relevant parts. This makes the process much more efficient and ensures the answers are directly tied to the video’s content, preventing the model from “hallucinating” or making stuff up.

Here’s a quick breakdown of my thought process for each step:

Getting the Text: The first challenge was getting a transcript. I chose yt-dlp to download the audio and whisper to handle the transcription. yt-dlp is a fantastic, battle-tested tool, and whisper is known for its high accuracy, even on low-quality audio. The code snippet for this looks like:

ydl_opts = {
    'format': 'bestaudio/best',
    'postprocessors': [...],
    'outtmpl': 'downloaded_audio.%(ext)s',
    'quiet': True,
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    ydl.download([url])
result = whisper_model.transcribe("downloaded_audio.mp3")

Making it Searchable: The raw transcript isn’t useful for searching. I needed to break it down and convert it into a format a computer could understand semantically. I decided to split the transcript into overlapping chunks of text (around 500 words with a 50-word overlap). The “overlapping” part is key.It ensures that important context isn’t lost at the boundaries of a chunk. I then used Sentence-Transformers (all-MiniLM-L6-v2 specifically) to create vector embeddings for each chunk. These embeddings are essentially numerical representations of the text’s meaning. The Python for chunking and embedding:
```
def split_into_chunks(text: str, chunk_size: int = 500, chunk_overlap: int = 50):
    # ... splitting logic ...
    return chunks

embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embedding_model.encode(text_chunks, convert_to_tensor=False)
```
The In-Memory Database: I needed a way to store and quickly search these embeddings. For a local application, an in-memory vector store like FAISS was the perfect solution. It’s incredibly fast and doesn’t require a separate database server, keeping the project lightweight. I just load the embeddings into the FAISS index using faiss.IndexFlatL2(dimension) and index.add(embeddings), and it’s ready to go.
Connecting it All with an LLM: Finally, I needed a powerful language model to generate the final answer. I chose Llama 3 8B via the Groq API. Groq’s API is incredibly fast, which was crucial for making the chat experience feel responsive. The prompt I designed is straightforward:
```
You are a helpful YouTube assistant. Your task is to answer the user's question, you can use the provided video transcript context"

CONTEXT FROM THE VIDEO:
---
{context}
---

USER'S QUESTION: {user_query}
```
This simple instruction, combined with the context retrieved using a cosine similarity search in FAISS, works wonders. The actual API call with Groq looks something like:
```
client = Groq(api_key=groq_api_key)
stream = client.chat.completions.create(
    model="llama3-8b-8192",
    messages=[{"role": "system", "content": prompt}],
    temperature=0,
    stream=True,
)
```

The User Interface: Streamlit was a Game-Changer

Building the front end was a breeze thanks to Streamlit. It allowed me to create a clean, interactive web app with minimal Python code. I could focus on the core logic of the RAG pipeline without getting bogged down in HTML, CSS, or JavaScript. Handling user input, displaying chat messages, and showing the video processing status was surprisingly easy with Streamlit’s intuitive API. For example, the chat interface uses st.chat_input and st.chat_message to manage the conversation flow.

If you’re a developer looking to build a quick, data-focused web app, I can’t recommend Streamlit enough.

Next Steps & Learnings

This project taught me a lot about the practical implementation of RAG pipelines and the power of combining different tools in the AI ecosystem. I also learned the importance of choosing the right tools for the job like yt-dlp for robust downloading, whisper for accurate transcription, Sentence-Transformers for effective embeddings, FAISS for fast local search, and Groq for rapid inference with Llama 3. It’s a simple idea, but it has a ton of potential for further development. I’m excited to see what the community thinks and am open to suggestions for improvements!

Feel free to check out the code on my GitHub and try it out for yourself. Happy coding!