Note: This repository assume you have all the prerequisite software

• Ollama with gemma2 installed, if not follow
  • https://ollama.com/library/gemma2
• host your qdrant cloud by following below
  • https://qdrant.tech/cloud/

instructions to run the code

standalone

• git clone [email protected]:pavanjava/Conversational_Media_Platform.git
• pip install -r requirements.txt
• create a file with .env and keep two key-value pairs named qdrant_api_key, qdrant_url
• open main.py and change the value of youtube_url
• then run main.py

Expose as API

• install ngrok (mandatory)
• git clone [email protected]:pavanjava/Conversational_Media_Platform.git
• pip install -r requirements.txt
• create a file with .env and keep two key-value pairs named `qdrant_api
• run python transcribe_api.py (runs on localhost:8000)
• expose this service as public with ngrok http http://localhost:8000

---

you can start conversation with your media content in standalone mode

• Enter your query [type 'bye' to 'exit']: how is search performed on vector embeddings ?
• bot: Each piece of data is represented as a vector, and your query is also converted into a vector representation. Quadrant then calculates how similar the query vector is to every data vector, surfacing the closest matches in the entire dataset.