8.2. RAG Blueprint

Retrieval-Augmented Generation (RAG) is an architecture that combines large language models (LLMs) with external data sources to provide accurate, up-to-date, and context-aware answers. Setting up a RAG infrastructure with open-source and ready-to-use tools involves several key steps:

General Approach

  1. Data Ingestion & Indexing - Collect and preprocess documents (PDFs, web pages, databases, etc.). - Use open-source tools for document parsing and chunking (e.g., Haystack, LlamaIndex, LangChain). - Store embeddings in a vector database such as Qdrant, Weaviate, Milvus, or Chroma.

  2. Embedding Generation - Generate vector representations using open-source embedding models (e.g., sentence-transformers, InstructorXL, or OpenAI-compatible models). - Batch process documents and store their embeddings in your vector database.

  3. Retrieval Pipeline - Implement semantic search using your vector database. - Use retrievers from frameworks like Haystack or LlamaIndex to fetch relevant chunks based on user queries.

  4. LLM Integration - Connect to an open-source LLM (e.g., Llama 3, Mistral, Mixtral, Phi-3) using APIs or local inference servers (vLLM, Ollama, LM Studio). - Use orchestration frameworks (LangChain, Haystack) to combine retrieval and generation steps.

  5. Orchestration & API Layer - Expose your RAG pipeline via a REST or gRPC API (e.g., FastAPI, Haystack server). - Add authentication, logging, and monitoring as needed.

  6. Evaluation & Monitoring - Use open-source tools for RAG evaluation (e.g., Ragas, Trulens) to measure answer quality and retrieval relevance. - Monitor latency, throughput, and cost.

Example Open-Source Stack

  • Document Processing: Haystack, LlamaIndex, LangChain

  • Vector Database: Qdrant, Weaviate, Milvus, Chroma

  • Embeddings: Sentence Transformers, InstructorXL, OpenAI-compatible models

  • LLM: Llama 3, Mistral, Mixtral, Phi-3 (via vLLM, Ollama, LM Studio)

  • API/Orchestration: FastAPI, Haystack, LangChain

  • Evaluation: Ragas, Trulens

This approach enables rapid prototyping and production deployment of RAG systems using open, modular, and scalable components.