MeetBot

Online meetings often lack effective question-and-answer dynamics, hampering professional engagement. This project introduces an interactive conversational agent, using Large Language Models, to enhance real-time questioning and fosters a more interactive and productive virtual meeting environment for working professionals. The goal is to streamline information retrieval and elevate the quality of online professional interactions.

Project Highlights

• LLM based question and answer system that will use following:
  • Google Gemini-Pro LLM
  • FAISS embeddings
  • Streamlit for UI
  • Langchain framework
  • FAISS LOCAL STORE as a vector store
  • Few/One shot learning
• In the UI, the meeting participant will ask their questions in natural language, and it will produce the answers accordingly.

Installation

1. Clone this repository to your local machine using:

  git clone https://github.com/Skriller18/MeetBot.git

2. Navigate to the project directory:

  cd MeetBot

3. Create Environment:

  conda create -n "myenv" python=3.10.0

4. Install the required dependencies using pip:

  pip install -r requirements.txt

5. Install and setup Kafka

  wget https://downloads.apache.org/kafka/3.9.0/kafka-3.9.0-src.tgz
  tar -xzf kafka-3.9.0-src.tgz
  cd kafka-3.9.0-src.tgz

  bin/zookeeper-server-start.sh config/zookeeper.properties
  bin/kafka-server-start.sh config/server.properties

6. Acquire an api key through makersuite.google.com or Google AI studio and put it in a .env file:

  GOOGLE_API_KEY="your_api_key_here"
  LAXIS_API_KEY ="your_api_key_here"

7. Create Kafka topics and stream pipeline

  bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --topic realtime_transcripts --partitions 1 --replication-factor 1
  bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --topic processing_status --partitions 1 --replication-factor 1
  bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --topic transcript_analytics --partitions 1 --replication-factor 1
  bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --topic transcript_uploads --partitions 1 --replication-factor 1

  bin/kafka-topics.sh --list --bootstrap-server localhost:9092

Usage

1. Run the Kafka Consumer client

python consumer_service.py

2. In a paralell terminal, Run the Streamlit app by executing:

streamlit run main.py

2. The web app will open in your browser where you can ask questions related to the meet:s

Architecture

Sample Output