### 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

```mermaid
%%{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