AdvanceRAG and Eval: Enhanced Document Retrieval and Evaluation

Overview

"AdvanceRAG and Eval" is an advanced retrieval augmented generation project that utilizes the Llama_Index library for efficient document indexing and retrieval, coupled with TruLens for in-depth evaluation and performance tracking. This project targets the enhancement of information retrieval systems by implementing state-of-the-art technologies in natural language processing and machine learning.

Project Description

This project is divided into two core components:

Document Indexing and Retrieval: Using Llama_Index, the project creates an automated merging index from document collections, facilitating rapid and accurate retrieval of information.

Performance Evaluation: Leveraging TruLens, the system evaluates the retrieval performance, providing insights and metrics that help refine and optimize the retrieval process.

Key Features

Automated Merging Index: Constructs a hierarchical node structure from documents, enabling efficient query handling and data retrieval.

Embedding Models: Utilizes embedding models such as BAAI's bge-small-en-v1.5 to transform text data into high-dimensional space, improving the semantic search capabilities.

Dynamic Retrieval: Implements an AutoMerging Retriever that adjusts query processing based on the contextual relevance and node hierarchy.

Evaluation with TruLens: Integrates TruLens to assess the quality of retrieval outputs, track system performance, and provide a leaderboard and dashboard for monitoring.

Workflow

Data Processing: Documents are loaded and processed using a hierarchical node parser that segments the text into manageable chunks.

Index Building: An index is built using the processed nodes, allowing for quick retrieval of information through vector space mapping.

Query Handling: Incorporates a query engine that leverages the index to retrieve and rank the most relevant documents based on the user's query.

Performance Tracking: Uses TruLens to record and evaluate the retrieval results, giving feedback on the effectiveness of the retrieval strategies implemented.

Tools and Libraries Used

Llama_Index: For building and managing the automated merging index.

TruLens: For evaluating and visualizing the performance of the retrieval system.

OpenAI's GPT Models: For generating embeddings and processing queries.

Python Libraries: Including pandas, numpy, and matplotlib for data manipulation and visualization.

Conclusion

"AdvanceRAG and Eval" showcases a robust approach to document retrieval and evaluation, combining advanced indexing techniques with thorough performance analysis. This project sets the stage for further innovations in retrieval systems, making it a valuable tool for researchers and professionals in information management and retrieval.