PythonRAGAgentic AIPatent AnalysisEmbeddingsSemantic SearchDockerData PipelineInnovation Forecasting
- A Python-based platform that automates patent data retrieval, embedding generation, and innovation forecasting using an agentic Retrieval-Augmented Generation (RAG) system.
- The Patent Innovation Researcher is designed to assist researchers, IP professionals, and innovators in analyzing large patent datasets efficiently. It combines automated data ingestion, semantic search, and multi-agent RAG orchestration to provide actionable insights and predictive analysis.
- Key features: - Patent Search & Retrieval: Tools for collecting and organizing patent data efficiently. - Embedding Generation: Converts patent documents into vector representations for semantic search. - Agentic RAG Architecture: Specialized agents (Research Director, Patent Retriever, Data Analyst, Innovation Forecaster) collaborate to process, analyze, and provide predictions. - Dockerized Deployment: Ready-to-run environment with `docker-compose` for easy setup and scaling. - Interactive Exploration: Supports Jupyter Notebook (`agent.ipynb`) for experimentation and deeper analysis.
- System architecture is structured into User Interface, Agent Orchestration, Knowledge Processing, and Data Storage layers, ensuring modularity and scalability. The backend leverages Python scripts for ingestion, embedding, analysis, and RAG operations to streamline patent research workflows.