Enterprise RAG Implementation - 15K Legal Documents Architecture 🍄 Review 🏢

🚀 Project Overview: Designing a contract analysis system for a multi-portfolio family office with 15,000+ vendor agreements spanning 200+ companies. Building an intelligent RAG system that needs to:

Auto-process contracts from existing Google Drive infrastructure
Stream new documents via N8N automation pipelines
Support complex legal queries across the full document corpus
Enable secure client access to the knowledge base

🛠️ Current Tech Stack:

Document Storage: Google Drive (client's existing setup)
Workflow Engine: N8N for document processing automation
Vector Database: Pinecone for semantic search capabilities
Parsing Engine: Evaluating Llama Index Cloud vs Dockling (on-premise)

⚖️ Architecture Decision: Favoring Llama Index Cloud due to superior legal document understanding and managed infrastructure, though client security policies may require on-premise parsing with Dockling.

📈 Scale Considerations: At 15K+ legal documents, I'm prioritizing architecture validation over rapid prototyping to avoid performance bottlenecks during production.

🎯 Seeking Community Input:

Pinecone Performance: Real-world experience with 10K+ document collections? Latency/cost insights?
Document Pipeline: Google Drive → N8N → Vector DB optimization strategies?
Legal Parsing: Comparative experiences with Llama Index vs Dockling on contract documents?
Access Control: Implementing secure multi-client access patterns with Pinecone?

🔧 Implementation Scope: Beyond vendor contracts, integrating internal policies and corporate agreements for comprehensive legal intelligence. Target use case: "Find all contracts with auto-renewal clauses expiring in Q3."

🧠 Learning from Veterans: If you were architecting a legal document RAG system today, what would be your non-negotiable design principles? Where did previous implementations hit unexpected complexity?

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