Simple experiment for question answering on YouTube videos using embeddings and the top n YouTube search result transcripts.
The function will take a question and optionally a YouTube search query (otherwise an LLM will auto-generate one), will compile transcripts for each video result, generate an embedding index using the transcripts and then answer the question using the relevant embeddings.
The function will return both a string response and a list of sources that were used for the answer.
The package can be installed from PyPI with pip install youtube-qa. Make sure to set your OPENAI_API_KEY
environment variable before using.
from youtube_qa.youtube_video_index import VideoIndexQueryResponse, YouTubeVideoIndex
video_index = YouTubeVideoIndex()
video_index.build_index(
search_term="huberman motivation",
video_results=3,
)
response: VideoIndexQueryResponse = video_index.answer_question(
question="what are the best researched supplements to help with exercise motivation",
)
print(response.answer) # The answer to the question.
print(response.sources) # Video links and other metadata.You can also generate the search query given the question:
from youtube_qa.youtube_video_index import VideoIndexQueryResponse, YouTubeVideoIndex
question = "what are the best researched supplements to help with exercise motivation"
video_index = YouTubeVideoIndex()
search_term = video_index.generate_search_query(question)
video_index.build_index(
search_term=search_term,
video_results=3,
)
response: VideoIndexQueryResponse = video_index.answer_question(
question=question,
)
print(response.answer) # The answer to the question.
print(response.sources) # Video links and other metadata.
