Semantic-Search-with-Qdrant

This project provides a base setup for experimenting with Qdrant, an open-source vector database for AI and semantic search applications.

Features

• Local development environment for Qdrant
• Example scripts for basic operations (insert, search, delete)
• Easy-to-extend structure for custom experiments

Getting Started

1. Clone the repository:

   git clone https://github.com/Moetez-Fradi/Semantic-Search-with-Qdrant.git
   cd qdrant_testing

2. Install dependencies:

   pip install -r requirements.txt

3. Run Qdrant (Docker recommended):

   docker pull qdrant/qdrant
   docker run -p 6333:6333 -p 6334:6334 \
   -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
   qdrant/qdrant

4. Test example scripts: Exemple PDF: Prompt Engineering by Lee Boonstra

   First, embed the pdf into the vector databse.

   python index_docs.py

   Then Try to ask a question.

   $ python main.py 
       type your query: 
       is a short prompt good ?
       best prompt, optimizing prompt length, and evaluating a prompt’s writing style and structure in relation to the task. In the context of natural language processing and LLMs, a prompt is an input provided to the model to generate a response
       
       in a situation where you have to try to come up with a good prompt, you might want to find multiple people to make an attempt. When everyone follows the best practices (as listed in this chapter) you are going
       
       from code, so it’s easier to maintain. Finally, ideally your prompts are part of an operationalized system, and as a prompt engineer you should rely on automated tests and evaluation procedures to understand how well your prompt generalizes to a

Resources

• Qdrant Documentation
• Qdrant GitHub