This project focuses on developing a multi-document Large Language Model (LLM) agent, specifically tailored to handle technical documentation of Tesla models. The goal is to provide a sophisticated tool for parsing, summarizing, and comparing intricate technical details across various Tesla models.

• Note: You need to add 'openai_api_key' to .env file to run the code.

• Document Agents and Top Agent: The architecture for building document-specific agents and a coordinating top agent is adapted from this project.
• Composable Graph Engine: The implementation of a composable graph engine, which enables comparative analysis of different models, is based on methodologies outlined in LLAMA Index Documentation.

Data-Driven Performance Enhancement

• The performance and capability of the top agent are directly proportional to the quality and diversity of the input data. By injecting more varied and well-structured data into the LLM model, we can significantly enhance the quality of the responses.

• Cross-Brand Technical Documentation: An exciting extension could be the inclusion of technical documentation from various automotive brands, aiding customers in discerning the best brand for their needs.
• Sales Data Integration: Incorporating detailed sales data, including different brand sales and other customers preferences, will allow the agent to assist customers in finding their ideal car model based on specific preferences.

• Document Summarization: In case of performance issues, employing a document summary agent to condense texts can be effective. This approach reduces the computational load and response time, as the final agents process these summarized texts instead of raw data.