Yael R.

Responsibilities:

• Finding the most efficient and accurate method for obtaining the most appropriate documents for the requested topic through trial and error research

• Retrieving documents that match a query using embedding - Converting each title to a vector upon receipt of the database and upon entering a query, converting it to a vector as well and finding the documents with the closest Euclidean distance.

• Running the model on the server with various queries that address different data sources to test the efficiency and accuracy of the model.

• BM25 model experiment (Smart Search Model) following the disqualification of embedding Due to slow response speed and relatively large memory footprint .

• Open search( open source for efficient searching in a large database using indexes)

  Testing the model with various queries and parameters to find the formula to obtain a quick and highly accurate answer. Comparing accuracy and efficiency against previous models, and implementing the model in the project after it has proven to be highly accurate and efficient.

• Researching the way to get the most correct answers – a broad study examining how the most correct sources of information will be reached in the shortest time using the HotpotQA dataset.

  Checking the number of sources returned and trying to use a threshold that limits the minimum match score.

• Data minimization to prevent server crashes after production. The minimization is performed before the data is entered into the LLM model to obtain the answer.

• Improved user experience in React UI such as user access to the information sources from which the answer was drawn

  The sources were saved and returned to the UI to be displayed to the user

• Responsibility for server maintenance, user management, and resource distribution between teams - and limited storage space for two AI teams, as well as smart use of the available memory.