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Intelligent Document Retrieval System

Objective:

To create a system that retrieves documents based on semantic similarity, improving the efficiency and accuracy of information retrieval.

Technology Stack:

  • Generative AI Model: OpenAI's GPT-3

  • Vectorization Techniques: Word2Vec, GloVe

  • Vector Database: Pinecone

  • Search Interface: Elasticsearch

Approach:

  • Data Preparation: Curated and preprocessed a diverse dataset of financial documents, including reports, news articles, and market analyses.

  • Model Selection and Vectorization: Used GPT-3 to understand document content and generate high-dimensional vector embeddings using Word2Vec and GloVe.

  • Database Integration: Stored the embeddings in Pinecone, a scalable vector database optimized for similarity searches.

  • Semantic Search Development: Implemented a search interface using Elasticsearch, enabling users to input queries and retrieve semantically relevant documents based on vector similarity.

Outcome:

The system achieved a 30% improvement in search relevance, significantly reducing the time required for analysts to find pertinent information. The enhanced search capability led to more informed decision-making and improved operational efficiency.

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