CortexRAG: An End-to-End AI Research Assistant Integrating Multi-Query Retrieval and Local LLM Inference

CortexRAG is an end-to-end AI research assistant based on the RAG architecture, supporting academic paper processing, semantic vector search, multi-query retrieval, and local LLM inference. It provides researchers with a privatized, customizable intelligent literature analysis tool.

• Original author/maintainer: ritugupta8898-cloud
• Source platform: github
• Original link: https://github.com/ritugupta8898-cloud/CortexRAG
• Update time: 2026-05-25T18:42:17Z

In academic research, researchers need to process large volumes of literature. Traditional keyword search struggles to capture deep semantics, and generic AI assistants lack domain-specific depth. CortexRAG aims to address this pain point by providing an intelligent literature analysis platform based on the RAG architecture, integrating semantic vector search, multi-query retrieval, and local LLM inference technologies.

FastAPI Backend Service

Adopting the modern high-performance asynchronous web framework FastAPI, suitable for handling I/O-intensive AI tasks, supporting multi-user concurrent queries, and automatically generating OpenAPI documentation for easy integration testing.

ChromaDB Vector Database

An open-source lightweight vector database that stores paper vector embeddings and performs efficient similarity searches. It is easy to deploy and meets the needs of research scenarios.

Multi-Query Retrieval Strategy

Automatically generates multiple query variants, synthesizes retrieval results to improve recall rate, and avoids missing important literature fragments.

Local LLM Inference

Supports local deployment, protects data privacy, eliminates network dependencies, and reduces usage costs.

Literature Ingestion and Processing

1. Document parsing: Extract text content while preserving chapter structure
2. Intelligent chunking: Semantically complete segmentation of long documents, using an overlap strategy to ensure context coherence
3. Embedding generation: Convert text chunks into vectors using pre-trained models
4. Metadata indexing: Attach metadata such as source and page number for traceability

Retrieval-Augmented Generation Flow

1. Query expansion: Generate multiple semantically related query variants
2. Parallel retrieval: Perform similarity searches on the vector database using multiple query variants
3. Result fusion: Merge and deduplicate results, then sort by relevance
4. Context construction: Select the most relevant text chunks to organize structured context
5. Answer generation: Local LLM generates answers based on retrieved content
6. Citation annotation: Annotate information sources for easy verification

• Semantic search capability: Captures semantic associations between concepts, enabling discovery of relevant content even with different terminology
• Context-aware answers: Generated based on the user's literature library, free of hallucinations and accompanied by source citations
• Multi-document comprehensive analysis: Retrieves and analyzes multiple papers, identifies correlations and differences, and grasps the current research status
• Privatized deployment: Local storage and inference to meet sensitive data management needs

• Literature review assistance: Quickly understand the current research status of a field, identify gaps and hotspots
• Experimental design reference: Query experimental setups and method details of similar studies
• Interdisciplinary research support: Quickly master basic knowledge and terminology of adjacent fields
• Team collaboration knowledge base: Share literature libraries and improve collaboration efficiency through Q&A

• Multi-modal support: Expand processing of non-text content such as charts and formula images
• Knowledge graph integration: Build literature citation networks and concept graphs
• Collaboration features: Add annotations, highlighting, sharing, and other features
• Automated updates: Integrate literature database APIs to obtain the latest papers
• Visualization analysis: Provide literature statistics, trend analysis, topic clustering, and other functions

CortexRAG provides a practical and advanced AI-assisted tool for academic research. Combining the RAG architecture, semantic search, and local LLM inference, it ensures answer accuracy and traceability while meeting data privacy requirements. For researchers and teams looking to improve literature processing efficiency and build privatized knowledge bases, it is an open-source project worth trying, representing a pragmatic application model of AI in the academic assistance field.