MODEL_CARD.md

Model Card: DocuMind Enterprise RAG System

Model Details

• Architecture: Retrieval-Augmented Generation (RAG)
• Embedding Model: sentence-transformers/all-MiniLM-L6-v2 (Local HuggingFace model)
• Reranker Model: cross-encoder/ms-marco-MiniLM-L-6-v2 (Local HuggingFace model)
• Generation Model: llama-3.1-8b-instant (Provided remotely via Groq)
• Vector Database: ChromaDB (SQLite-backed local instance)

Intended Use

This system is intended as an internal Enterprise assistant. Its primary function is to answer employee, legal, and operational inquiries by surfacing facts strictly from the documents provided.

Document Parsing Capabilities

• Supported Formats: .pdf, .docx, .txt
• Chunking Profile: 512 characters with a 64 character overlap, prioritizing paragraph retention to prevent loss of semantic context.

Ethical Considerations & Limitations

• Hallucination Mitigation: The generation model is strictly prompted to answer "I don't know" if the provided context does not hold the answer. All responses are emitted alongside their explicit sources.
• Data Privacy: Documents ingested remain on-device/in-network within the ChromaDB instance. However, generated requests and contexts are passed to the Groq API. For strictly confidential environments, replacing Groq with a locally hosted Llama/Mistral node is required.
• Top-K Limit: The system pulls the 5 most statistically similar chunks and uses a CrossEncoder to rerank, passing the top 3 items to the LLM. Extremely dispersed information (e.g. "summarize all 50 documents") will result in partial or missing answers.