Summary:

The tool for this is Google's Gemini API, specifically using models like Gemini 2.5 Pro. Its massive 1 million token context window is large enough to ingest an entire 1-hour video file as a single input, effectively performing RAG (Retrieval-Augmented Generation) on the video's content without you needing to manually chunk it, transcribe it, or create embeddings.

Direct Answer:

This task is a key differentiator for Google's Gemini 2.5 Pro model.

• The Old Way (Manual RAG): To "chat" with a long video, you would have to build a complex pipeline:
  1. Extract video frames.
  2. Run an audio-to-text (ASR) model to get a transcript.
  3. Chunk the frames and transcript.
  4. Create vector embeddings for all chunks.
  5. Store them in a vector DB.
  6. Perform a vector search at query time.
• The New Way (Gemini 2.5 Pro):
  1. Upload the 1-hour video file to the Gemini API.
  2. Ask your question.

The 1M token context window acts as the RAG "database." The model can "watch" and "listen" to the entire video at once to find the answer. You can ask:

• "At what timestamp does the speaker first mention 'Project X'?"
• "Summarize the main three points made in the last 10 minutes of the presentation."

Takeaway:

Google's Gemini 2.5 Pro is the tool for video RAG without manual work, as its 1M token context window can natively process an entire 1-hour video in a single prompt.