PIKE-RAG - A specialized framework for complex industrial knowledge understanding and reasoning.

## PIKE-RAG Overview PIKE-RAG (sPecIalized KnowledgE and Rationale Augmented Generation) is an open-source framework developed by Microsoft. It is designed to enhance the performance of large language models (LLMs) in industrial applications by focusing on understanding and reasoning with complex private knowledge. The framework is particularly suited for industries such as manufacturing, mining, and pharmaceuticals, where it can process multimodal data like technical documents and operation manuals. ## Key Features of PIKE-RAG The key features of PIKE-RAG include: - **Multimodal Data Processing**: Ability to handle text, tables, and images, organizing information into structured knowledge networks. - **Knowledge Extraction and Organization**: Techniques like context-aware segmentation, automatic term-label alignment, and multi-granularity knowledge extraction to improve accuracy. - **Benchmark Performance**: Achieves high accuracy in benchmark tests such as HotpotQA (87.6%), 2WikiMultiHopQA (82.0%), and MuSiQue (59.6%). - **Industrial Applications**: Tested and improved in industries like manufacturing, mining, and pharmaceuticals. - **Online Demo**: An interactive online demo is available to showcase its capabilities. ## Enhancing LLM Performance with PIKE-RAG PIKE-RAG improves LLM performance in industrial applications by: - **Extracting Domain-Specific Knowledge**: It extracts and applies specialized knowledge from industrial contexts. - **Building Coherent Reasoning Chains**: It constructs logical reasoning chains to guide LLMs in generating accurate responses. - **Handling Complex Data**: It processes multimodal data like technical documents and operation manuals, which are common in industrial settings. - **Task Decomposition**: It breaks down complex problems into manageable tasks, supporting multi-hop reasoning and prediction. ## Functional Modules of PIKE-RAG The functional modules of PIKE-RAG include: - **Document Parsing**: Processes multimodal data such as text, tables, and images. - **Knowledge Extraction**: Extracts domain-specific knowledge from professional corpora. - **Knowledge Storage**: Builds a hierarchical heterogeneous knowledge base for efficient retrieval. - **Knowledge Retrieval**: Uses multi-granularity retrieval techniques to ensure precise information extraction. - **Knowledge Organization**: Organizes knowledge into structured networks using automatic labeling mechanisms. - **Knowledge-Centric Reasoning**: Constructs coherent reasoning chains to guide LLMs in generating accurate responses. - **Task Decomposition and Coordination**: Decomposes complex problems and supports multi-hop reasoning and prediction. ## Accessing and Using PIKE-RAG Users can access and use PIKE-RAG by following these steps: 1. **Clone the Repository**: Clone the [PIKE-RAG Repository](https://github.com/Microsoft/PIKE-RAG) from GitHub. 2. **Set Up the Environment**: Install the necessary Python dependencies as per the documentation. 3. **Create Environment File**: Create a `.env` file to store endpoint information and other necessary environment variables. 4. **Run Examples**: Modify the YAML configuration file and run example scripts to experience basic functionalities. 5. **Build Pipelines**: Construct custom pipelines or add custom components based on specific needs. Additionally, users can explore the [Online Demo](https://pike-rag-azurewebsites.net/) for an interactive experience. ## Benchmark Performances of PIKE-RAG PIKE-RAG has demonstrated the following benchmark performances: - **HotpotQA**: 87.6% accuracy - **2WikiMultiHopQA**: 82.0% accuracy - **MuSiQue**: 59.6% accuracy These results highlight PIKE-RAG's capability in handling multi-hop question answering and complex reasoning tasks. ## Industrial Applications of PIKE-RAG PIKE-RAG is particularly suited for industries such as: - **Industrial Manufacturing**: Helps in processing technical documents and operation manuals. - **Mining**: Assists in analyzing complex data related to mining operations. - **Pharmaceuticals**: Supports the interpretation of technical and regulatory documents. The framework's ability to handle multimodal data and complex reasoning makes it valuable in these specialized fields. ### Citation sources: - [PIKE-RAG](https://github.com/Microsoft/PIKE-RAG) - Official URL Updated: 2025-03-31