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Y.C Lee
Y.C Lee

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Task:Implement RAG engine with semantic search

  • [ ] 4.2 Implement RAG engine with semantic search
    • Create query understanding and intent classification modules
    • Implement semantic search across vector database
    • Write context ranking and selection algorithms
    • Create response generation with source attribution
    • Requirements: 1.3, 3.6, 3.7, 3.8

Here is a clear, organized summary of the completed Task 4.2 on the RAG Engine with Semantic Search, including its core components, API features, capabilities, and architecture:

✅ Task 4.2 Complete: RAG Engine with Semantic Search

Core Components Created

  • RAG Manager (rag_manager.py)

    • Implements the full RAG pipeline: query processing, semantic search, and response generation.
    • Includes intelligent query intent classification for troubleshooting, optimization, analysis, and general queries.
    • Query expansion and rewriting to improve search relevance.
    • Builds context with source attribution and relevance ranking.
    • Confidence scoring to assess response reliability.
    • Integrates seamlessly with vector database and LLM services.

  • RAG Service (rag_service.py)

    • FastAPI REST service providing RAG operations with streaming query processing.
    • Chat completion interface compatible with OpenAI format.
    • Semiconductor-specific endpoints for manufacturing data analysis, troubleshooting, and process optimization.
    • Supports knowledge base search without invoking LLM generation.
    • Health check and service monitoring endpoints included.

  • Configuration (rag_config.yaml)

    • Comprehensive configuration for query processing including intent classification, expansion, rewriting.
    • Context building and response generation parameters customizable by collection and intent type.
    • Caching for query results and performance optimization.

  • Infrastructure (docker-compose.yml)

    • Complete containerized RAG stack including Redis caching, Elasticsearch, Kibana, Neo4j knowledge graph, Apache Tika for document processing, Prometheus and Grafana for monitoring.

  • Testing (test_rag_manager.py)

    • Extensive unit tests covering query processing, intent classification, context building, and response generation.
    • Integration testing with mock services to validate end-to-end workflows.

Here is a comprehensive and organized file mapping summary for Task 4.2 RAG Engine with Semantic Search, detailing components, configuration, infrastructure, and testing:

📋 Task 4.2: RAG Engine with Semantic Search - File Mapping & Content

Component File Path Content Description
Core RAG Manager services/ai-ml/rag-engine/src/rag_manager.py Complete RAG pipeline including intelligent query processing (intent classification, entity extraction, query rewriting), semantic search across multiple knowledge collections, context building with source attribution, confidence scoring, and integration with vector DB and LLM services.
REST API Service services/ai-ml/rag-engine/src/rag_service.py FastAPI-based service exposing core query processing, streaming chat completion interface, semiconductor-specific endpoints (analyze, troubleshoot, optimize), knowledge base search, authentication, and health monitoring.
Configuration services/ai-ml/rag-engine/config/rag_config.yaml Comprehensive YAML configuration covering query processing parameters, intent classification patterns, collection mapping by intent, context building parameters, confidence calculation, caching, and environment overrides.
Dependencies services/ai-ml/rag-engine/requirements.txt Python libraries including FastAPI, HTTPX/AIOHTTP for async service communication, NLP libraries (NLTK, spaCy), sentence-transformers, Redis for caching, and monitoring tools.
Container Setup services/ai-ml/rag-engine/Dockerfile Docker container setup with Python 3.11, NLP libraries, spaCy model downloads, NLTK data, optimized for RAG processing workloads.
Infrastructure services/ai-ml/rag-engine/docker-compose.yml Complete RAG stack including Redis caching, Elasticsearch and Kibana for search analytics, Neo4j knowledge graph, Apache Tika for document processing, Jupyter for query analytics, and Prometheus/Grafana monitoring.
Logging Utilities services/ai-ml/rag-engine/utils/logging_utils.py Structured JSON logging with Prometheus metrics tracking query durations per intent, confidence scores, sources used, intent counts, and active queries.
Unit Tests services/ai-ml/rag-engine/tests/test_rag_manager.py Comprehensive test coverage for query pipeline, intent classification, semantic search, context building, response generation, confidence scoring, and integration with mocks.
Documentation services/ai-ml/rag-engine/README.md Complete documentation detailing RAG pipeline architecture, API reference, semiconductor domain knowledge, query processing flow, performance tuning, integration examples, and deployment instructions.

Key Features Implemented

  • Intelligent query processing with intent classification, entity extraction, and query rewriting.
  • Semantic search across multiple vector database collections with relevance filtering and result diversification.
  • Context-aware LLM response generation enhanced with relevant technical documentation.
  • Semiconductor domain knowledge integration covering equipment, processes, and standards.
  • Multi-modal analysis supporting process data, test results, and defect inspection.
  • Confidence scoring for multi-factor reliability in generated responses.
  • Real-time processing with caching and performance optimizations.
  • Full REST API supporting RAG operations, semiconductor analytical endpoints, and monitoring.
  • Containerized deployment ensuring scalable and resilient system operation.

Semiconductor-Specific Capabilities

  • Expertise across semiconductor process modules such as lithography, etch, deposition, CMP, implant, anneal.
  • Knowledge of equipment from Applied Materials, KLA, LAM Research, ASML.
  • Standards integration including SEMI E10, E30, E40, E90, E94, and JEDEC specifications.
  • Measurement support including CD, overlay, thickness, resistivity, and defect classification.
  • Advanced analysis types including trend, correlation, anomaly detection.
  • Troubleshooting with root cause analysis and systematic problem solving.
  • Optimization of process parameters and yield improvement strategies.

API Endpoints Summary

Category Endpoint Method Description
Core RAG /query POST Process query with RAG pipeline
Core RAG /chat POST Chat completion with RAG context
Analysis /analyze POST Analyze semiconductor data
Troubleshooting /troubleshoot POST Troubleshoot process issues
Optimization /optimize POST Optimize process parameters
Knowledge /knowledge/search GET Search knowledge base
Health /health GET Service health check
Monitoring /metrics GET Prometheus metrics

RAG Pipeline Flow

  1. Query Processing: Intent classification → Entity extraction → Query expansion/rewriting.
  2. Semantic Search: Multi-collection vector search → Relevance filtering → Result diversification.
  3. Context Building: Source attribution → Length optimization → Relevance ranking.
  4. Response Generation: Intent-specific prompting → Context integration → LLM generation.
  5. Quality Assessment: Confidence scoring → Source validation → Response optimization.

Requirements Satisfied

Requirement Description Status
1.3 RAG with vector embeddings & semantic search ✅
3.6 Document processing and automated indexing ✅
3.7 Knowledge graph relationships and entity linking ✅
3.8 Similarity search and retrieval algorithms ✅

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