Enterprise Knowledge Navigator using LLaMA 3.2 + LangChain

Description

An internal enterprise-grade Q&A system that allows employees to interact with company documents, SOPs, knowledge bases, and manuals using natural language. This project uses LLaMA 3.2 integrated with LangChain’s RAG pipeline to enable retrieval-augmented generation, offering contextual and reliable answers grounded in your company’s proprietary content. Designed for mid-to-large organizations, the solution significantly improves information accessibility across departments—without overloading internal support teams.

Key Features:

• Custom Data Ingestion Pipeline – Ingest and process internal documents in PDF, DOCX, XLSX, and Markdown formats using chunking and metadata tagging.
• Role-Based Access Control – Add fine-grained access permissions so employees only get answers from documents they’re authorized to view.
• Semantic Search with Vector Indexing – Enables retrieval of relevant document chunks using vector databases like FAISS or ChromaDB.
• LLM-Powered Conversational Interface – Employees can ask queries like “What’s the onboarding process in Germany?” or “Where is the 2024 compliance report?”

Ideal Use Cases or Scenarios:

• HR teams answering onboarding, leave, and compliance queries
• IT departments supporting internal tooling and system documentation
• Sales & operations teams accessing policy documents, guidelines, and handbooks
• Customer support units using internal knowledge for quick resolution

Deliverables:

• Complete backend codebase with RAG architecture using LLaMA 3.2
• Admin interface for uploading documents and setting access controls
• Integrated vector database setup with chunking strategy
• Responsive chatbot UI (Streamlit or React frontend)
• Project report with system architecture and deployment guide
• Optional Dockerized setup and walkthrough session