Stop Watching YouTube Videos. Build Agents to Start Chatting With Them.

Based on the provided information and code snippets, here's a summary of how the project works for building a semantic search system over YouTube video transcripts:

Step 1: Preprocessing

Extracting Transcript Chunks

• metadata.py: Fetches metadata (title, thumbnail URL) from YouTube using their oEmbed API.
• transcript_extractor.py (not shown but implied): Likely extracts the transcript text from the YouTube video.

Processing and Embedding

• embedder.py: Handles embedding of the transcript chunks. It uses OpenAI's text-embedding-3-small model to create embeddings for each chunk, normalizes them, and stores these in a FAISS index.

  python
   
  1def build_index(chunks: list[dict], client: OpenAI) -> tuple[faiss.Index, list[dict]]:
  2    texts = [c["text"] for c in chunks]
  3    embeddings = get_embeddings(texts, client)
  4    
  5    # Normalize for cosine similarity
  6    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
  7    embeddings = embeddings / (norms + 1e-10)
  8
  9    dim = embeddings.shape[1]
  10    index = fa

Read the full article at Towards AI - Medium

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