PLANNING.md

General RAG Platform Plan

Overview

The goal is to transform the existing RAG system into a versatile, modular platform for building various RAG applications.

Architecture

%%{init: {'theme': 'neutral', 'themeVariables': { 'primaryColor': '#e3f2fd', 'edgeLabelBackground':'#fffde7'}}}%%
graph TD
    A[General RAG Platform Architecture]
    
    subgraph DataIngestion
        A1[Universal Data Loader\n<- Files\n<- Databases\n<- APIs\n<- Cloud Storage]
        A2[Smart Document Processor\n<- Format detection\n<- Metadata extraction\n<- Content normalization]
        A3[Chunking Strategies\n<- Semantic\n<- Structural\n<- Domain-specific]
    end

    subgraph CoreServices
        B1[Embedding Service\n<- Multi-model support\n<- Batch processing\n<- Cache layer]
        B2[VectorDB Orchestrator\n<- Chroma\n<- Pinecone\n<- Weaviate\n<- FAISS]
        B3[LLM Gateway\n<- OpenAI\n<- Anthropic\n<- Mistral\n<- Custom models]
    end

    subgraph QueryEngine
        C1[Query Analyzer\n<- Intent detection\n<- Query expansion\n<- Filter generation]
        C2[Hybrid Retriever\n<- Vector search\n<- Keyword\n<- Hybrid ranking]
        C3[Response Generator\n<- Citation\n<- Formatting\n<- Guardrails]
    end

    subgraph Management
        D1[Config Manager\n<- Tenant isolation\n<- Model configs\n<- Access controls]
        D2[Monitoring\n<- Metrics\n<- Logging\n<- Alerting]
        D3[API Gateway\n<- REST\n<- GraphQL\n<- gRPC]
    end

    subgraph Extensibility
        E1[Plugin System\n<- Custom loaders\n<- Chunkers\n<- Post-processors]
        E2[Workflow Engine\n<- Pipeline designer\n<- Versioning\n<- A/B testing]
    end

    A --> DataIngestion
    A --> CoreServices
    A --> QueryEngine
    A --> Management
    A --> Extensibility
    
    DataIngestion -->|Processed Chunks| CoreServices
    CoreServices -->|Vector Index| QueryEngine
    QueryEngine -->|Formatted Response| Management
    Management -->|APIs| ExternalSystems

Implementation Plan

1. Core Abstraction Layer

   • Unified interfaces for:
   • Document Loaders (File, DB, API)
   • Chunking Strategies
   • Embedding Providers
   • VectorDB Adapters
   • LLM Gateways

2. Multi-tenancy Features

   • Tenant isolation
   • Resource quotas
   • Custom pipeline configurations
   • Role-based access control

3. Advanced Retrieval

   • Hybrid search (vector + keyword + custom)
   • Query understanding module
   • Result reranking layer
   • Cache mechanisms

4. Operational Excellence

   • Observability stack
   • Auto-scaling
   • CI/CD pipelines
   • Health checks

5. Security

   • Data encryption
   • Audit trails
   • Content moderation
   • PII detection

6. Developer Ecosystem

   • SDKs (Python/JS)
   • CLI tools
   • Template repository
   • Testing framework