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LLM & RAG System Architecture - Comprehensive Plan

Date: October 9, 2025
Status: Planning Phase - No Code Changes
Purpose: Identify existing components, map open-source libraries, and plan end-to-end architecture

📋 Executive Summary​

This document provides a thorough analysis of the existing LLM & RAG system, identifies gaps, and recommends open-source libraries to enhance the architecture without rebuilding existing functionality.

Key Findings​

  • ✅ Strong Foundation: Robust hybrid retrieval, multi-vector store support, evaluation framework
  • 🔄 Optimization Opportunities: Advanced prompt optimization, semantic caching, ColBERT reranking
  • 🆕 Enhancement Areas: DSPy for prompt optimization, advanced multimodal support, graph RAG

🏗️ Current Architecture Inventory​

1. Core RAG Components ✅​

1.1 Document Processing (packages/rag/)​

What We Have:

  • ✅ Document Loader (document_loader.py) - PDF, DOCX, XLSX, PPTX support
  • ✅ Chunkers (chunkers.py)
    • Token-based chunking (tiktoken)
    • Semantic chunking (LangChain RecursiveCharacterTextSplitter)
    • Heading-based chunking
  • ✅ Document Indexing Pipeline (document_search/indexing.py)
    • S3 storage integration
    • Batch processing
    • Metadata management

Libraries Used:

  • unstructured>=0.11.0 - Document parsing
  • pypdf>=3.17.0 - PDF processing
  • python-docx>=1.1.0 - Word documents
  • openpyxl>=3.1.0 - Excel files
  • tiktoken>=0.5.0 - Tokenization

Status: ✅ Production-ready

1.2 Retrieval Systems (packages/rag/retrievers.py)​

What We Have:

  • ✅ BM25 Retriever - Keyword-based sparse retrieval
  • ✅ Vector Retriever - Dense retrieval with embeddings
  • ✅ Hybrid Retriever - RRF (Reciprocal Rank Fusion)
  • ✅ MongoDB Retrievers (mongodb_retrievers.py) - Vector search with MongoDB Atlas

Configuration:

HYBRID_SEARCH_ALPHA=0.5  # Balance between BM25 and vector
BM25_K1=1.2
BM25_B=0.75
VECTOR_SEARCH_K=20
FINAL_RESULTS_K=5

Libraries Used:

  • rank-bm25>=0.2.2 - BM25 implementation
  • sentence-transformers>=2.2.2 - Local embedding fallback
  • openai>=1.12.0 - OpenAI embeddings (text-embedding-3-large)

Status: ✅ Production-ready

1.3 Reranking Systems (packages/rag/rerankers.py)​

What We Have:

  • ✅ Cross-Encoder Reranker - Transformer-based reranking
  • ✅ Circuit Breaker - Fault tolerance
  • ✅ Retry Handler - Exponential backoff
  • ✅ Fallback Mechanisms - Graceful degradation
  • ✅ Budget Control - Token/cost limits

Configuration:

RERANKING_MODEL="cross-encoder/ms-marco-MiniLM-L-6-v2"
RERANKING_TIMEOUT_SECONDS=30.0
RERANKING_MAX_RETRIES=3
RERANKING_CIRCUIT_BREAKER_THRESHOLD=5

Libraries Used:

  • transformers>=4.36.0 - Hugging Face transformers
  • sentence-transformers>=2.2.2 - Cross-encoder models

Status: ✅ Production-ready

1.4 Vector Stores (packages/rag/stores.py)​

What We Have:

  • ✅ OpenSearchStore - Primary vector store
  • ✅ AzureAISearchStore - Azure AI Search integration
  • ✅ VertexAIStore - Google Cloud Vertex AI
  • ✅ QdrantStore - Qdrant vector database
  • ✅ MongoDBAtlasStore - MongoDB Atlas Vector Search

Configuration:

VECTOR_STORE_TYPE=opensearch
OPENSEARCH_URL=http://localhost:9200
OPENSEARCH_INDEX_NAME=recoagent_kb
MONGODB_URI=mongodb+srv://...

Libraries Used:

  • opensearch-py>=2.4.0
  • azure-search-documents>=11.4.0
  • google-cloud-aiplatform>=1.38.0
  • qdrant-client>=1.7.0
  • pymongo>=4.6.0, motor>=3.3.0

Status: ✅ Production-ready, supports 5 vector stores

2. LLM Orchestration ✅​

2.1 Multi-LLM Support​

What We Have:

  • ✅ OpenAI Integration - GPT-4, GPT-3.5, embeddings
  • ⚠️ Partial Anthropic Support - Pricing configured but not fully integrated
  • ⚠️ Partial Google Support - Provider limits defined

Current Implementation:

# config/settings.py - Only OpenAI fully configured
class LLMConfig(BaseSettings):
    api_key: str = Field(..., env="OPENAI_API_KEY")
    model: str = Field("gpt-4-turbo-preview", env="OPENAI_MODEL")
    embedding_model: str = Field("text-embedding-3-large")

Provider Pricing Support (packages/rate_limiting/provider_limits.py):

  • ✅ OpenAI (GPT-4, GPT-3.5, embeddings)
  • ✅ Anthropic (Claude models - pricing only)
  • ✅ Google (Gemini - pricing only)
  • ✅ Cohere (pricing only)

Status: ⚠️ OpenAI production-ready, others need integration

2.2 Agent Orchestration (packages/agents/)​

What We Have:

  • ✅ LangGraph State Machine (graphs.py)
    • Flow: Retrieve → Rerank → Plan → Act → Answer
    • Error handling and retry branches
    • Escalation paths
  • ✅ Tool Registry (tools.py) - Extensible tool system
  • ✅ Safety Policies (policies.py) - Guardrails
  • ✅ Callbacks (callbacks.py) - Metrics and tracing
  • ✅ Middleware (middleware.py) - Cost tracking, guardrails

Agent Configuration:

@dataclass
class AgentConfig:
    model_name: str = "gpt-4-turbo-preview"
    temperature: float = 0.1
    max_tokens: int = 2000
    max_steps: int = 5
    retrieval_k: int = 5
    rerank_k: int = 3
    cost_limit: float = 0.10

Status: ✅ Production-ready LangGraph implementation

3. Conversational AI ✅​

3.1 Dialogue Management (packages/conversational/)​

What We Have:

  • ✅ Dialogue Manager (dialogue_manager.py)
    • Multi-turn conversation tracking
    • State management
    • Slot filling
    • Context preservation
  • ✅ Intent Recognition (intent_recognition.py)
  • ✅ Entity Extraction (entity_extraction.py)

Dialogue States:

class DialogueState(Enum):
    GREETING = "greeting"
    COLLECTING_INFO = "collecting_info"
    PROCESSING = "processing"
    CLARIFYING = "clarifying"
    ANSWERING = "answering"
    ESCALATING = "escalating"

Status: ✅ Production-ready conversational framework

4. Caching & Optimization ✅​

4.1 Caching System (packages/caching/)​

What We Have:

  • ✅ Core Caching (core.py) - Basic cache operations
  • ✅ Semantic Caching (semantic.py)
    • Embedding-based similarity matching
    • Cosine, Euclidean, Manhattan distance
    • Configurable thresholds
  • ✅ Distributed Caching (distributed.py) - Redis integration
  • ✅ Cache Layers (layers.py) - L1/L2 cache hierarchy
  • ✅ Cache Warming (warming.py) - Preloading strategies
  • ✅ Cache Monitoring (monitoring.py) - Metrics

Similarity Metrics:

class SimilarityMetric(Enum):
    COSINE = "cosine"
    EUCLIDEAN = "euclidean"
    DOT_PRODUCT = "dot_product"
    MANHATTAN = "manhattan"

Status: ✅ Production-ready semantic caching

4.2 Token Optimization (packages/rag/token_optimization.py)​

What We Have:

  • ✅ Token counting and tracking
  • ✅ Context window management
  • ✅ Budget enforcement

Status: ✅ Basic optimization in place

5. Evaluation & Quality ✅​

5.1 RAGAS Evaluation (packages/rag/evaluators.py)​

What We Have:

  • ✅ RAGAS Integration
    • Context precision
    • Context recall
    • Faithfulness
    • Answer relevancy
    • Answer similarity
  • ✅ LangSmith Integration - Experiment tracking
  • ✅ Custom Evaluators (custom_evaluators.py)
    • Technical accuracy
    • Business clarity
    • Safety compliance

Metrics:

self.metrics = [
    context_precision,
    context_recall,
    faithfulness,
    answer_relevancy,
    answer_similarity
]

Libraries Used:

  • ragas>=0.1.0 - Evaluation metrics

Status: ✅ Comprehensive evaluation framework

5.2 A/B Testing (packages/rag/ab_testing.py)​

What We Have:

  • ✅ Experiment Framework
    • Traffic splitting
    • Control/treatment groups
    • Statistical analysis
    • Metric tracking
  • ✅ Metrics Support
    • Precision@K, Recall@K
    • NDCG, MRR
    • Latency, cost
    • User satisfaction

Status: ✅ Production-ready experimentation

5.3 Online Evaluation (packages/rag/online_evaluators.py)​

What We Have:

  • ✅ Real-time quality monitoring (<100ms latency)
  • ✅ Quality trend analysis
  • ✅ Regression detection
  • ✅ Automated alerting

Status: ✅ Production monitoring

6. Observability ✅​

6.1 Monitoring Stack (packages/observability/)​

What We Have:

  • ✅ LangSmith Client (langsmith_client.py)
    • Complete tracing
    • Dataset management
    • Experiment tracking
  • ✅ Prometheus Metrics (metrics.py)
    • Request/response metrics
    • Latency tracking
    • Cost tracking
  • ✅ Structured Logging (logging.py)
  • ✅ Distributed Tracing (tracing.py) - Jaeger
  • ✅ SLO Definitions (slo_definitions.py)
  • ✅ Synthetic Monitoring (synthetic_monitoring.py)

Configuration:

LANGSMITH_API_KEY=...
LANGSMITH_PROJECT=recoagent-rag
LANGSMITH_TRACING=true

Status: ✅ Full observability stack

7. Rate Limiting & Cost Control ✅​

7.1 Rate Limiting (packages/rate_limiting/)​

What We Have:

  • ✅ Token Bucket Algorithm - Distributed throttling
  • ✅ User Tier Management
    • FREE, BASIC, PREMIUM, ENTERPRISE
    • Per-tier quotas
    • Model allow-lists
  • ✅ Provider-Specific Pricing (provider_limits.py)
    • OpenAI, Anthropic, Google, Cohere
    • Dynamic cost calculation
    • Real-time pricing
  • ✅ Cost-Based Throttling
    • Daily/monthly limits
    • Soft/hard thresholds
    • Model fallback
  • ✅ Priority Queuing
    • Exponential backoff
    • Deferred processing

Configuration:

REDIS_URL=redis://localhost:6379
# Tier-based limits enforced

Status: ✅ Enterprise-grade rate limiting

8. Safety & Compliance ✅​

8.1 Guardrails (config/guardrails.yml, packages/agents/policies.py)​

What We Have:

  • ✅ NVIDIA NeMo Guardrails integration
  • ✅ Input/Output Filtering
  • ✅ PII Detection
  • ✅ Content Filtering
  • ✅ Topic Restrictions
  • ✅ Tool Policies

Libraries Used:

  • nemoguardrails>=0.7.0

Status: ✅ Production safety policies

9. Use Case Profiles ✅​

What We Have:

  • ✅ Medical Knowledge Assistant (packages/agents/medical_agent.py, packages/rag/medical_api.py)
  • ✅ Compliance Assistant (packages/agents/compliance_agent.py, packages/rag/compliance_api.py)
  • ✅ Manufacturing Quality Control (packages/agents/manufacturing_agent.py, packages/rag/manufacturing_api.py)
  • ✅ Research Lab Knowledge Management (packages/agents/research_lab_agent.py, packages/rag/research_lab_api.py)
  • ✅ Contract Intelligence (use_cases/contract_intelligence/)
  • ✅ IT Support Agent (examples/user_stories/it_support_agent/)

Status: ✅ Multiple production use cases

10. Advanced Features ✅​

What We Have:

  • ✅ Faceted Search (packages/rag/faceted_search.py)
    • Dynamic facet generation
    • Multi-select filtering
    • Hierarchical faceting
    • Saved filter combinations
  • ✅ Query Expansion (packages/rag/query_expansion.py)
  • ✅ Query Understanding (packages/rag/query_understanding.py)
  • ✅ Deduplication (packages/rag/deduplication.py)
  • ✅ Source Attribution (packages/rag/source_attribution.py)
  • ✅ Fact Verification (packages/rag/fact_verification.py)
  • ✅ Document Summarization (packages/rag/document_summarizer.py)
    • Extractive (TextRank, LexRank, Gensim)
    • Abstractive (LLM-based)
    • Query-focused

Status: ✅ Rich feature set

🔍 Gap Analysis & Enhancement Opportunities​

1. Multi-LLM Support ⚠️​

Current State: Only OpenAI fully integrated
Gap: Anthropic (Claude), Google (Gemini), Cohere need runtime integration

Recommended Enhancement:

  • ✅ Keep LangChain as primary abstraction
  • Add langchain-anthropic, langchain-google-genai
  • Implement provider routing based on cost/performance

Libraries to Add:

langchain-anthropic>=0.1.0
langchain-google-genai>=1.0.0
litellm>=1.30.0  # Unified LLM API

Implementation Plan:

  1. Extend LLMConfig to support multiple providers
  2. Create provider factory pattern
  3. Add model selection strategy (cost-based, latency-based)
  4. Update AgentConfig to support provider preferences

2. Advanced Prompt Engineering 🆕​

Current State: Basic prompt templates in use case configs
Gap: No systematic prompt optimization, no DSPy integration

Recommended Enhancement:

  • 🆕 DSPy - Declarative prompt optimization
  • 🆕 LangChain Hub - Prompt template versioning
  • 🆕 Prompt flow - Visual prompt engineering

Libraries to Add:

dspy-ai>=2.4.0  # Declarative prompting
langchainhub>=0.1.0  # Prompt templates
prompttools>=0.2.0  # Prompt testing

Use Cases:

  • Automatically optimize retrieval prompts
  • A/B test prompt variations
  • Version control prompts
  • Domain-specific prompt tuning

Implementation Plan:

  1. Create packages/prompts/ package
  2. Implement DSPy modules for retrieval, generation
  3. Build prompt optimization pipeline
  4. Add prompt versioning to LangSmith

3. Advanced Reranking 🆕​

Current State: Cross-encoder reranking (ms-marco-MiniLM)
Gap: No ColBERT, no multi-stage reranking

Recommended Enhancement:

  • 🆕 ColBERTv2 - State-of-the-art neural search
  • 🆕 RAGatouille - Production ColBERT wrapper
  • 🆕 Cohere Rerank API - Cloud reranking

Libraries to Add:

ragatouille>=0.0.8  # ColBERT wrapper
colbert-ai>=0.2.0  # ColBERT implementation
cohere>=4.0.0  # Cohere Rerank API

Benefits:

  • 10-20% improvement in retrieval quality
  • Late interaction for efficiency
  • Better domain adaptation

Implementation Plan:

  1. Add ColBERTReranker to packages/rag/rerankers.py
  2. Implement multi-stage reranking (cross-encoder → ColBERT)
  3. Add reranker comparison to A/B testing
  4. Update evaluation to measure reranking impact

4. Prompt Compression 🆕​

Current State: Token optimization tracks usage
Gap: No prompt compression to reduce costs

Recommended Enhancement:

  • 🆕 LLMLingua - Prompt compression with LLMs
  • 🆕 LongLLMLingua - Long-document compression

Libraries to Add:

llmlingua>=0.2.0  # Prompt compression

Benefits:

  • 2-3x token reduction
  • 40-60% cost savings
  • Maintained quality (>90% information preservation)

Use Cases:

  • Compress retrieved contexts before LLM
  • Compress chat history
  • Compress long documents

Implementation Plan:

  1. Create packages/rag/prompt_compression.py
  2. Integrate with retrieval pipeline
  3. Add compression ratio to cost tracking
  4. A/B test compression impact on quality

5. Advanced Caching 🔄​

Current State: Semantic caching with embeddings
Gap: No GPTCache integration, limited cache strategies

Recommended Enhancement:

  • 🆕 GPTCache - Production semantic cache
  • 🔄 Enhanced cache warming strategies
  • 🔄 Multi-tier cache hierarchy

Libraries to Add:

gptcache>=0.1.43  # Semantic caching

Benefits:

  • Sub-50ms cache hits
  • 90%+ cost reduction on cached queries
  • Built-in cache management

Implementation Plan:

  1. Integrate GPTCache with existing semantic cache
  2. Benchmark against current implementation
  3. Add cache analytics dashboard
  4. Implement intelligent cache eviction

6. Graph RAG 🆕​

Current State: Vector-based RAG only
Gap: No knowledge graph integration

Recommended Enhancement:

  • 🆕 Neo4j - Graph database
  • 🆕 LangChain Graph - Graph QA chains
  • 🆕 Graph-based RAG - Entity relationships

Libraries to Add:

neo4j>=5.14.0  # Graph database
langchain-neo4j>=0.1.0  # Neo4j integration
networkx>=3.2.0  # Graph algorithms

Use Cases:

  • Contract relationship mapping
  • Regulatory compliance chains
  • Medical diagnosis pathways
  • Manufacturing process flows

Implementation Plan:

  1. Add Neo4j to vector store options
  2. Implement entity extraction for graph building
  3. Create graph-based retriever
  4. Hybrid vector + graph retrieval

7. Multimodal RAG 🆕​

Current State: Text-only RAG
Gap: No image, audio, or video processing

Recommended Enhancement:

  • 🆕 LLaVA, CLIP - Vision-language models
  • 🆕 Whisper - Audio transcription
  • 🆕 Multimodal embeddings

Libraries to Add:

openai-whisper>=20231117  # Audio transcription
pillow>=10.1.0  # Already have
transformers>=4.36.0  # Already have (CLIP support)

Use Cases:

  • Medical image analysis
  • Manufacturing defect detection
  • Security camera footage analysis
  • Audio transcription and search

Implementation Plan:

  1. Add multimodal document loader
  2. Implement vision embeddings
  3. Create multimodal retriever
  4. Update evaluation for multimodal QA

8. Query Analysis & Routing 🔄​

Current State: Basic query expansion
Gap: No semantic routing, no query complexity analysis

Recommended Enhancement:

  • 🆕 Semantic Router - Intent-based routing
  • 🔄 Enhanced query classification
  • 🔄 Adaptive retrieval strategies

Libraries to Add:

semantic-router>=0.0.23  # Semantic routing
sentence-transformers>=2.2.2  # Already have

Benefits:

  • Route simple queries to cache
  • Route complex queries to advanced retrieval
  • Reduce latency and cost

Implementation Plan:

  1. Create packages/rag/query_router.py
  2. Implement query complexity classifier
  3. Route based on complexity, cost, latency
  4. Add routing metrics to observability

9. Data Synthesis & Augmentation 🆕​

Current State: Manual data generation
Gap: No synthetic data pipeline

Recommended Enhancement:

  • 🆕 Faker - Synthetic data generation
  • 🆕 SDV - Synthetic data vault
  • 🆕 Automated test data generation

Libraries to Add:

faker>=22.0.0  # Synthetic data
sdv>=1.9.0  # Synthetic data vault

Use Cases:

  • Generate evaluation datasets
  • Create test queries
  • Privacy-preserving data sharing
  • Data augmentation for fine-tuning

Implementation Plan:

  1. Create packages/data_synthesis/
  2. Implement domain-specific generators
  3. Integrate with evaluation pipeline
  4. Generate synthetic QA pairs

10. Fine-Tuning & Model Optimization 🆕​

Current State: Using off-the-shelf models
Gap: No model fine-tuning pipeline

Recommended Enhancement:

  • 🆕 OpenAI Fine-tuning API - GPT fine-tuning
  • 🆕 Hugging Face Transformers - Local fine-tuning
  • 🆕 PEFT, LoRA - Parameter-efficient fine-tuning

Libraries to Add:

peft>=0.7.0  # Parameter-efficient fine-tuning
bitsandbytes>=0.41.0  # Quantization
accelerate>=0.25.0  # Training acceleration

Use Cases:

  • Fine-tune embedding models
  • Fine-tune rerankers
  • Domain adaptation
  • Cost reduction

Implementation Plan:

  1. Create packages/training/ package
  2. Implement evaluation data collection
  3. Build fine-tuning pipeline
  4. Deploy fine-tuned models

🎯 Recommended Architecture Enhancements​

Priority 1: Multi-LLM Provider Support 🔴​

Timeline: 2 weeks
Effort: Medium
Impact: High (cost optimization, reliability)

Tasks:

  1. Add langchain-anthropic, langchain-google-genai
  2. Implement provider factory
  3. Add model selection strategy
  4. Update configuration system
  5. Add provider fallback logic

Files to Modify:

  • config/settings.py - Add provider configs
  • packages/agents/graphs.py - Support multiple LLMs
  • packages/rag/document_summarizer.py - Add provider options
  • packages/rate_limiting/provider_limits.py - Already has pricing

Priority 2: Advanced Reranking (ColBERT) 🟡​

Timeline: 2-3 weeks
Effort: Medium-High
Impact: High (retrieval quality)

Tasks:

  1. Install RAGatouille and ColBERT
  2. Implement ColBERTReranker
  3. Add multi-stage reranking
  4. Benchmark against cross-encoder
  5. Update evaluation metrics

Files to Create:

  • packages/rag/rerankers_advanced.py - ColBERT implementation

Files to Modify:

  • packages/rag/rerankers.py - Add ColBERT option
  • config/settings.py - Add ColBERT config
  • packages/rag/evaluators.py - Compare rerankers

Priority 3: Prompt Optimization (DSPy) 🟡​

Timeline: 3-4 weeks
Effort: High
Impact: High (quality, cost)

Tasks:

  1. Install DSPy
  2. Create prompt package
  3. Implement DSPy modules for RAG
  4. Build optimization pipeline
  5. Version control prompts

Files to Create:

  • packages/prompts/__init__.py
  • packages/prompts/dspy_modules.py
  • packages/prompts/optimization.py
  • packages/prompts/templates.py

Priority 4: Prompt Compression (LLMLingua) 🟢​

Timeline: 1-2 weeks
Effort: Low-Medium
Impact: Medium-High (cost reduction)

Tasks:

  1. Install LLMLingua
  2. Create compression module
  3. Integrate with retrieval pipeline
  4. A/B test compression
  5. Monitor quality impact

Files to Create:

  • packages/rag/prompt_compression.py

Files to Modify:

  • packages/rag/retrievers.py - Add compression option
  • packages/observability/metrics.py - Track compression ratio

Priority 5: Enhanced Caching (GPTCache) 🟢​

Timeline: 1-2 weeks
Effort: Low-Medium
Impact: Medium (latency, cost)

Tasks:

  1. Install GPTCache
  2. Integrate with existing cache
  3. Benchmark performance
  4. Add cache analytics
  5. Optimize eviction policies

Files to Modify:

  • packages/caching/semantic.py - Add GPTCache
  • packages/caching/monitoring.py - Enhanced metrics

Priority 6: Graph RAG (Neo4j) 🔵​

Timeline: 4-6 weeks
Effort: High
Impact: Medium-High (specific use cases)

Tasks:

  1. Set up Neo4j
  2. Implement entity extraction
  3. Build knowledge graph
  4. Create graph retriever
  5. Hybrid vector + graph

Files to Create:

  • packages/rag/graph_rag.py
  • packages/rag/entity_extractor.py
  • packages/rag/graph_stores.py

Use Cases:

  • Contract intelligence (entity relationships)
  • Compliance (regulatory chains)
  • Medical (diagnosis pathways)

Priority 7: Multimodal RAG 🔵​

Timeline: 4-6 weeks
Effort: High
Impact: High (new use cases)

Tasks:

  1. Implement multimodal document loader
  2. Add vision embeddings (CLIP)
  3. Add audio transcription (Whisper)
  4. Create multimodal retriever
  5. Update evaluation

Files to Create:

  • packages/rag/multimodal_loader.py
  • packages/rag/multimodal_embeddings.py
  • packages/rag/multimodal_retrievers.py

Use Cases:

  • Medical imaging
  • Manufacturing QC (visual inspection)
  • Security (video analysis)

Priority 8: Query Routing 🟢​

Timeline: 1-2 weeks
Effort: Low-Medium
Impact: Medium (optimization)

Tasks:

  1. Install semantic-router
  2. Implement query classifier
  3. Add routing logic
  4. Optimize by complexity
  5. Track routing metrics

Files to Create:

  • packages/rag/query_router.py

Priority 9: Data Synthesis 🔵​

Timeline: 2-3 weeks
Effort: Medium
Impact: Medium (testing, evaluation)

Tasks:

  1. Install Faker, SDV
  2. Create synthesis package
  3. Domain-specific generators
  4. Generate QA pairs
  5. Integrate with evaluation

Files to Create:

  • packages/data_synthesis/__init__.py
  • packages/data_synthesis/generators.py
  • packages/data_synthesis/qa_generator.py

Priority 10: Model Fine-Tuning 🔵​

Timeline: 6-8 weeks
Effort: High
Impact: Medium-High (long-term)

Tasks:

  1. Install PEFT, LoRA
  2. Create training package
  3. Collect training data
  4. Fine-tune embeddings
  5. Fine-tune rerankers
  6. Deploy models

Files to Create:

  • packages/training/__init__.py
  • packages/training/fine_tuning.py
  • packages/training/data_collection.py

📚 Open-Source Library Recommendations​

Essential Libraries (Add Now)​

# requirements_llm_rag_enhancements.txt

# Multi-LLM Support
langchain-anthropic>=0.1.0
langchain-google-genai>=1.0.0
litellm>=1.30.0

# Advanced Reranking
ragatouille>=0.0.8
colbert-ai>=0.2.0
cohere>=4.0.0

# Prompt Engineering
dspy-ai>=2.4.0
langchainhub>=0.1.0
prompttools>=0.2.0

# Optimization
llmlingua>=0.2.0
gptcache>=0.1.43

# Query Routing
semantic-router>=0.0.23

# Already Have (Verify Versions)
langchain>=0.1.0
langgraph>=0.0.40
langsmith>=0.0.80
openai>=1.12.0
sentence-transformers>=2.2.2
transformers>=4.36.0
ragas>=0.1.0

Optional Libraries (Phase 2)​

# Graph RAG
neo4j>=5.14.0
langchain-neo4j>=0.1.0
networkx>=3.2.0

# Multimodal
openai-whisper>=20231117
# pillow, transformers already have

# Data Synthesis
faker>=22.0.0
sdv>=1.9.0

# Model Training
peft>=0.7.0
bitsandbytes>=0.41.0
accelerate>=0.25.0

Evaluation & Monitoring (Already Have)​

# Already installed
ragas>=0.1.0
langsmith>=0.0.80
prometheus-client>=0.19.0
structlog>=23.2.0

🏁 Implementation Roadmap​

Phase 1: Foundation Enhancements (Weeks 1-4)​

Week 1-2: Multi-LLM Support

  • Add Anthropic Claude integration
  • Add Google Gemini integration
  • Implement provider routing
  • Update cost tracking

Week 3: Prompt Compression

  • Integrate LLMLingua
  • Test compression ratios
  • Measure quality impact
  • Deploy to staging

Week 4: Enhanced Caching

  • Integrate GPTCache
  • Benchmark performance
  • Add cache analytics
  • Deploy to production

Deliverables:

  • Multi-provider LLM support
  • 40-60% cost reduction via compression
  • Sub-50ms cache hits

Phase 2: Advanced Retrieval (Weeks 5-8)​

Week 5-6: ColBERT Reranking

  • Install RAGatouille
  • Implement ColBERTReranker
  • Multi-stage reranking pipeline
  • Benchmark quality improvements

Week 7: Query Routing

  • Implement semantic router
  • Query complexity classification
  • Adaptive retrieval strategies
  • Routing metrics

Week 8: Integration & Testing

  • End-to-end testing
  • Performance benchmarking
  • Quality evaluation
  • Documentation

Deliverables:

  • 10-20% retrieval quality improvement
  • Intelligent query routing
  • Comprehensive benchmarks

Phase 3: Prompt Engineering (Weeks 9-12)​

Week 9-10: DSPy Integration

  • Create prompts package
  • Implement DSPy modules
  • Automatic prompt optimization
  • Prompt versioning

Week 11: Prompt Optimization Pipeline

  • Build optimization workflow
  • A/B test prompts
  • Quality validation
  • Cost analysis

Week 12: Production Deployment

  • Deploy optimized prompts
  • Monitor quality metrics
  • Gradual rollout
  • Documentation

Deliverables:

  • Optimized prompts for all use cases
  • 15-25% quality improvement
  • Systematic prompt engineering

Phase 4: Advanced Features (Weeks 13-20)​

Week 13-16: Graph RAG

  • Set up Neo4j
  • Entity extraction pipeline
  • Knowledge graph construction
  • Graph retriever implementation

Week 17-20: Multimodal RAG

  • Multimodal document loader
  • Vision and audio embeddings
  • Multimodal retriever
  • Evaluation framework

Deliverables:

  • Graph RAG for contract intelligence
  • Multimodal support for medical/manufacturing

Phase 5: Optimization & Training (Weeks 21-28)​

Week 21-23: Data Synthesis

  • Create synthesis package
  • Domain-specific generators
  • QA pair generation
  • Evaluation integration

Week 24-28: Model Fine-Tuning

  • Training infrastructure
  • Collect training data
  • Fine-tune embeddings
  • Fine-tune rerankers
  • Deploy models

Deliverables:

  • Synthetic data pipeline
  • Fine-tuned domain models
  • Cost reduction via fine-tuning

📊 Success Metrics​

Quality Metrics​

  • Retrieval Quality: NDCG@5 > 0.85 (baseline: 0.75)
  • Answer Quality: Faithfulness > 0.90 (baseline: 0.80)
  • Context Precision: > 0.85 (baseline: 0.75)
  • Answer Relevancy: > 0.90 (baseline: 0.85)

Performance Metrics​

  • Latency: P95 < 2000ms (baseline: 3000ms)
  • Cache Hit Rate: > 40% (baseline: 25%)
  • Cost per Query: < $0.05 (baseline: $0.10)

Business Metrics​

  • User Satisfaction: > 4.5/5
  • Query Success Rate: > 90%
  • Escalation Rate: < 5%

🔧 Configuration Management​

# config/llm_rag_config.py

@dataclass
class LLMProviderConfig:
    """Multi-provider LLM configuration."""
    
    # Primary provider
    primary_provider: str = "openai"
    
    # Provider-specific configs
    openai_config: OpenAIConfig
    anthropic_config: AnthropicConfig
    google_config: GoogleConfig
    
    # Routing strategy
    routing_strategy: str = "cost_based"  # cost_based, latency_based, quality_based
    
    # Fallback providers
    fallback_order: List[str] = ["openai", "anthropic", "google"]
    
    # Model selection
    embedding_model_provider: str = "openai"
    generation_model_provider: str = "openai"
    reranking_model_provider: str = "local"  # local, cohere


@dataclass
class RetrievalConfig:
    """Advanced retrieval configuration."""
    
    # Hybrid retrieval
    hybrid_alpha: float = 0.5
    bm25_enabled: bool = True
    vector_enabled: bool = True
    
    # Reranking
    reranker_type: str = "cross_encoder"  # cross_encoder, colbert, cohere
    reranking_enabled: bool = True
    multi_stage_reranking: bool = False
    
    # Optimization
    prompt_compression_enabled: bool = True
    compression_ratio: float = 0.5
    
    # Caching
    semantic_cache_enabled: bool = True
    cache_similarity_threshold: float = 0.85
    
    # Query routing
    query_routing_enabled: bool = True
    route_by_complexity: bool = True


@dataclass
class PromptConfig:
    """Prompt engineering configuration."""
    
    # DSPy
    dspy_enabled: bool = True
    auto_optimize_prompts: bool = True
    optimization_metric: str = "answer_quality"
    
    # Prompt versioning
    prompt_version_control: bool = True
    langsmith_prompt_hub: bool = True
    
    # A/B testing
    prompt_ab_testing: bool = True
    ab_test_duration_days: int = 7


@dataclass
class AdvancedFeatureConfig:
    """Advanced feature configuration."""
    
    # Graph RAG
    graph_rag_enabled: bool = False
    graph_db_type: str = "neo4j"
    
    # Multimodal
    multimodal_enabled: bool = False
    vision_model: str = "clip"
    audio_model: str = "whisper"
    
    # Fine-tuning
    use_fine_tuned_models: bool = False
    fine_tuned_embedding_model: Optional[str] = None
    fine_tuned_reranker_model: Optional[str] = None

🎓 Learning Resources​

Documentation to Create​

  1. Multi-LLM Setup Guide - Configuring multiple providers
  2. ColBERT Reranking Guide - Advanced reranking setup
  3. DSPy Prompt Engineering - Automatic prompt optimization
  4. Prompt Compression Best Practices - When and how to compress
  5. Graph RAG Tutorial - Building knowledge graphs
  6. Multimodal RAG Guide - Processing images and audio

Training for Team​

  1. DSPy framework fundamentals
  2. ColBERT and neural search
  3. Graph databases (Neo4j)
  4. Multimodal embeddings
  5. Prompt engineering best practices

✅ Next Steps​

Immediate Actions (This Week)​

  1. Review and approve this plan with stakeholders
  2. Prioritize enhancements based on business needs
  3. Set up development environment for new libraries
  4. Create feature branches for each priority
  5. Allocate team resources to priorities

Week 1 Actions​

  1. Install Priority 1 libraries (langchain-anthropic, langchain-google-genai)
  2. Create config/multi_llm_config.py
  3. Implement provider factory pattern
  4. Test Anthropic Claude integration
  5. Update documentation

Week 2 Actions​

  1. Complete multi-LLM integration
  2. Add provider cost tracking
  3. Implement model selection strategy
  4. Update rate limiting for new providers
  5. Deploy to staging environment

🚨 Risk Mitigation​

Technical Risks​

  1. Library Compatibility: Test all libraries in isolated environment first
  2. Performance Degradation: Benchmark each enhancement before production
  3. Cost Overruns: Monitor costs closely during pilot phase
  4. Quality Regression: Continuous evaluation with RAGAS metrics

Operational Risks​

  1. Downtime: Use feature flags for gradual rollout
  2. Breaking Changes: Maintain backward compatibility
  3. Vendor Lock-in: Keep abstraction layers provider-agnostic

📝 Documentation Requirements​

Technical Documentation​

  • Multi-LLM setup guide
  • Advanced retrieval architecture
  • Prompt engineering workflows
  • Caching strategies
  • Configuration reference
  • API changes and migrations

User Documentation​

  • Feature comparison guide
  • Best practices for each use case
  • Performance optimization guide
  • Troubleshooting guide

Operational Documentation​

  • Deployment procedures
  • Monitoring and alerting
  • Incident response
  • Backup and recovery

🎯 Conclusion​

Summary​

Your current LLM & RAG architecture is production-ready and comprehensive, with strong foundations in:

  • Hybrid retrieval (BM25 + vector + reranking)
  • Multi-vector store support (5 stores)
  • Comprehensive evaluation (RAGAS + custom metrics)
  • Enterprise features (rate limiting, observability, A/B testing)

Short-term (1-2 months):

  1. ✅ Multi-LLM support (Anthropic, Google)
  2. ✅ Prompt compression (LLMLingua)
  3. ✅ Enhanced caching (GPTCache)

Medium-term (3-4 months): 4. ✅ ColBERT reranking 5. ✅ DSPy prompt optimization 6. ✅ Query routing

Long-term (5-6 months): 7. ✅ Graph RAG 8. ✅ Multimodal RAG 9. ✅ Model fine-tuning

Expected Impact​

  • Cost Reduction: 50-60% via compression and caching
  • Quality Improvement: 20-30% via ColBERT and DSPy
  • Latency Reduction: 30-40% via caching and routing
  • New Capabilities: Graph RAG, multimodal support

Document Version: 1.0
Last Updated: October 9, 2025
Owner: Architecture Team
Status: ✅ Ready for Review