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Exploring localized intelligence: technical insights, architecture decisions, and development progress for IntraMind.
IntraMind represents a paradigm shift in organizational knowledge management. Unlike cloud-based solutions that compromise data privacy, IntraMind operates entirely within your local network, ensuring complete data sovereignty while delivering enterprise-grade AI capabilities.
The system leverages state-of-the-art retrieval-augmented generation (RAG) techniques, combining dense vector embeddings with local large language models to provide accurate, contextual answers from your organization's document corpus.
Multi-format ingestion with OCR support for scanned documents
Efficient similarity search with ChromaDB
Ollama-powered inference with multiple model support
Context-aware retrieval and generation
Pluggable components for easy customization
RESTful API for integration
1from intramind import DocumentProcessor23# Initialize processor4processor = DocumentProcessor(5embedding_model="all-MiniLM-L6-v2",6chunk_size=5127)89# Process documents10documents = processor.ingest_folder("./documents")11print(f"Processed {len(documents)} documents")
1from intramind import QueryEngine23# Initialize engine4engine = QueryEngine(5vector_db="chromadb",6llm_model="llama3"7)89# Query documents10response = engine.query(11"What is our remote work policy?",12max_results=513)1415print(response.answer)16for source in response.sources:17print(f"Source: {source.document} (page {source.page})")
No releases found yet. Visit the repository: crux-ecosystem/IntraMind-Showcase
Exploring privacy-preserving RAG architectures for enterprise deployments
2025-11-01
Q1 2026