PDF Insight: RAG-Based Gen-AI Application
demo.mp4
Client: HTML, CSS, JS
Server: Flask
Database: Mongo-db, astra-db
- Adding PDFs seamlessly: Effortlessly incorporate PDF files into sessions.
- Smooth session switching: Easily transition between sessions with minimal hassle.
- Streamlined for faster inference: Designed for optimal performance, ensuring swift response times and minimal latency during inference.
-
LANGCHAIN_API_KEY
: Required for integrating with Langsmith (optional). -
LANGCHAIN_PROJECT
: Specifies the name of the Langsmith project (optional).- Example:
LANGCHAIN_PROJECT="MY_PROJECT_NAME"
- Example:
-
GROQ_API_KEY
: Required for interacting with the Groq inference engine. -
ASTRA_DB_APPLICATION_TOKEN
: Token used to connect with Astra DB. -
ASTRA_DB_ID
: Identifier for connecting with Astra Vector DB. -
MONGO_CONNECTION_STRING
: Connection string for MongoDB cluster.- Example:
MONGO_CONNECTION_STRING = "mongodb+srv://{username}:{password}@<cluster_name>.<value>.mongodb.net"
- Example:
-
MONGO_CLUSTER_USERNAME
: Username for accessing the MongoDB cluster. -
MONGO_CLUSTER_PASSWORD
: Password for accessing the MongoDB cluster. -
APP_SECRET_KEY
: Secret key for the Flask application.
Clone the project
git clone https://github.com/nis12ram/PDF-Insight.git
Create python virual environment
python -m venv <path>/venv
Install requirements & Add .env file
pip install -r requirements.txt
Change static path
approach2flaskServer.py
app.config['STATIC_FOLDER'] = <path_to_your_static_folder>
run
approach2flaskServer.py
python approach2flaskServer.py