# Personal Knowledge Brain – User-Scoped RAG Assistant

Overview

Personal Knowledge Brain (PKB) is a user-scoped Retrieval-Augmented Generation (RAG) assistant designed to answer questions grounded strictly in user-provided documents. Unlike generic chatbots, PKB focuses on personal knowledge management with persistent memory and clean architectural separation.

Each user has an isolated knowledge base and conversation memory, making the system suitable for multi-user and future multi-tenant deployments.

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Key Capabilities

• Retrieval-Augmented Generation (RAG)
• User-scoped document ingestion and isolation
• Semantic search using vector embeddings
• Persistent conversation memory
• Modular and extensible backend architecture

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System Architecture

The system follows a clean backend-first design:

• Conversation Memory: Stored persistently using SQLite
• Knowledge Store: Vector embeddings stored in ChromaDB
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• LLM: Groq-hosted large language model
• User Isolation: Separate data directories per user

Each user operates within their own logical “knowledge brain”.

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RAG Workflow

1. User adds documents (text or markdown)
2. Documents are chunked into overlapping segments
3. Chunks are embedded into vector representations
4. Embeddings are stored persistently per user
5. User queries are matched using semantic similarity
6. Retrieved context is injected into the LLM prompt
7. Grounded responses are generated

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Knowledge vs Conversation Memory

The system intentionally separates:

• Conversation Memory
  Used for conversational continuity and stored automatically.

• Knowledge Base
  Represents factual or long-term information and is updated explicitly to avoid accidental ingestion.

This design improves accuracy, traceability, and explainability.

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Usage Summary

• Add documents to the user-specific documents directory
• Run the ingestion pipeline to index knowledge
• Query the assistant via the backend interface
• Responses are generated using retrieved document context

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Limitations

• Minimal interface (backend-focused)
• Manual ingestion step
• Limited document formats (text/markdown)

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Future Improvements

• Web-based chat interface
• Document upload via UI
• Knowledge editing and deletion
• Advanced memory summarization
• Multi-agent reasoning workflows

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Conclusion

Personal Knowledge Brain demonstrates a practical and extensible implementation of a user-scoped RAG system with persistent memory. The project emphasizes correctness, modularity, and real-world applicability over superficial features.