A modular Retrieval-Augmented Generation (RAG) ecosystem designed for large-scale codebase understanding, semantic retrieval, and intelligent context generation.

Built around three repositories:

• rag-cli
• rag-config
• rag-reranker

Modern LLMs struggle with large private codebases because:

• Context windows are limited
• Raw vector search retrieves noisy results
• Code is treated as plain text
• Monorepos become difficult to index and query
• Retrieval pipelines are often tightly coupled and hard to customize

This platform solves those issues through:

• Code-aware ingestion
• Config-driven pipelines
• Semantic retrieval
• Intelligent reranking
• Modular architecture

                ┌─────────────────┐
                │   rag-config    │
                │ Pipeline Config │
                └────────┬────────┘
                         │
                         ▼
                ┌─────────────────┐
                │    rag-cli      │
                │ Ingestion Layer │
                └────────┬────────┘
                         │
              Chunking / Embedding
                         │
                         ▼
                ┌─────────────────┐
                │ Vector Database │
                └────────┬────────┘
                         │
                  Top-K Retrieval
                         │
                         ▼
                ┌─────────────────┐
                │ rag-reranker    │
                │ Relevance Engine│
                └────────┬────────┘
                         │
                         ▼
                    Final Context
                         │
                         ▼
                        LLM

Core ingestion and orchestration engine.

• Repository scanning
• AST-aware chunking
• Embedding generation
• Vector indexing
• Query execution
• CLI workflows

• Large codebase ingestion
• Semantic chunking
• Multi-language parsing
• Local-first developer workflows

• Hybrid chunking strategies
• AST-based parsing
• Pluggable embedding providers
• Vector DB integration
• CLI automation
• Incremental ingestion support

Centralized configuration layer for the RAG pipeline.

• Chunking rules
• Include/exclude patterns
• Retrieval tuning
• Embedding configs
• Environment abstraction
• Shared presets

• Hardcoded pipelines
• Environment inconsistency
• Difficult customization
• Non-portable deployments

• JSON-driven configs
• Per-project customization
• Reusable presets
• Modular pipeline overrides
• Environment portability

Retrieval refinement and relevance optimization layer.

• Semantic reranking
• Relevance rescoring
• Noise reduction
• Context prioritization

Vector search alone often retrieves related but low-value chunks.

Reranking improves precision before context reaches the LLM.

• Cross-encoder reranking
• Top-K refinement
• Retrieval quality boosting
• Context compression
• Plug-and-play integration

Designed for production-scale workloads.

• Monorepos
• Multi-repository indexing
• Distributed vector databases
• Parallel chunk processing
• Incremental indexing
• High-volume retrieval pipelines

• Qdrant
• Pinecone
• Weaviate
• Ollama
• Local embedding models
• Cloud embedding providers

Every layer is replaceable.

Future expansion support includes:

• Hybrid search
• Knowledge graphs
• Agentic retrieval
• Multi-modal RAG
• Context caching
• Evaluation pipelines

The system separates responsibilities cleanly:

Benefits:

• Easier maintenance
• Independent scaling
• Faster iteration
• Cleaner integrations
• Better retrieval quality

• Codebase Q&A
• Internal developer copilots
• Documentation assistants
• Monorepo exploration
• Engineering onboarding
• Enterprise search
• Architecture understanding

• Modular over monolithic
• Config-driven over hardcoded
• Retrieval quality over brute-force context
• Local-first compatibility
• Provider-agnostic architecture

This suite provides a scalable and extensible RAG foundation focused on:

• Accurate retrieval
• Code-aware ingestion
• High-quality reranking
• Modular architecture
• Configurable pipelines
• Production-ready scalability

Instead of being just a chatbot over files, it acts as a structured retrieval platform for large engineering and knowledge systems.