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A step by step implementation of a complex RAG pipeline to solve real world situations
| Date | Stars |
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| 2026-07-31 | 498 |
| 2026-08-01 | 499 |
| 2026-08-06 | 499 |
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<!-- omit in toc --> # Building a Complex, Production-Ready RAG System You can follow me on medium: [Fareed Khan](https://medium.com/@fareedkhandev) Building a production-ready RAG system involves a series of thoughtful and iterative steps. 1. It all starts with **cleaning** and **preparing** thse data, followed by testing different chunking strategies both **logical** and **traditional** to find what works best for your use case. 2. Next comes **anonymization**, which helps reduce hallucinations by stripping away sensitive or irrelevant details. 3. To further improve retriever performance, creating subgraphs can help focus retrieval on the most relevant information while filtering out noise. 4. On top of the retrieval layer, we introduce a **planning and execution** system powered by **LLMs**. This acts like an agent that learns from previous steps, decides what to do next. 5. Finally, once the RAG system generates responses, we **evaluate** its performance using a range of metrics. > In this blog, we will walk through how to build this full-stack RAG system Using **LangChain**, **LangGraph**, and **RAGAS** (Evaluation), simulating real-world challenges and showcasing practical solutions that developers face while building RAG bots. This is created on top of the version of [nirDiamant](https://github.com/NirDiamant) guide. Thanks to him for the foundational work. <!-- omit in toc --> # Table of Contents - [Understanding our RAG Pipeline](#understanding-our-rag-pipeline) - [Setting up the Environment](#setting-up-the-environment) - [Breaking our Data (Traditional / Logical) Forms](#breaking-our-data-traditional--logical-forms) - [Cleaning Our Data](#cleaning-our-data) - [Restructuring the Data](#restructuring-the-data) - [Vectorizing the Data](#vectorizing-the-data) - [Creating a Retriever for Context](#creating-a-retriever-for-context) - [A Filter for Irrelevant Information](#a-filter-for-irrelevant-information) - [Query Rewriter](#query-rewriter) - [Chain-of-Though (COT) Reasoning](#chain-of-though-cot-reasoning) - [Relevancy Check and Grounded on Facts](#relevancy-check-and-grounded-on-facts) - [Testing our RAG Pipeline](#testing-our-rag-pipeline) - [Visualizing our RAG Pipeline using LangGraph](#visualizing-our-rag-pipeline-using-langgraph) - [Sub Graph Approach and Distillation Grounding](#sub-graph-approach-and-distillation-grounding) - [Creating Sub Graph for Retrieval and Distillation](#creating-sub-graph-for-retrieval-and-distillation) - [Creating Sub Graph to solve Hallucinations](#creating-sub-graph-to-solve-hallucinations) - [Creating and Testing Plan Executor](#creating-and-testing-plan-executor) - [Re-Planner Thinking Logic](#re-planner-thinking-logic) - [Creating Task Handler](#creating-task-handler) - [Anonymize/De-Anonymize the Input Question](#anonymizede-anonymize-the-input-question) - [Compiling and Visualizing the RAG Pipeline](#compiling-and-visualizing-the-rag-pipeline) - [Testing our Finalized Pipeline](#testing-our-finalized-pipeline) - [Evaluation using RAGAS](#evaluation-using-ragas) - [Summarizing Everything](#summarizing-everything) # Understanding our RAG Pipeline Before we start coding its better to visually see how our rag pipeline looks, when we move further we will visualize each of the components of it.  First, we call `anonymize_question`. This replaces specific names (e.g., "Harry Potter", "Voldemort") with placeholders (`Person X`, `Villain Y`) to avoid bias from the LLM pre-trained knowledge. Next, the `planner` builds a high-level strategy. For a question like "How did `X` defeat `Y`?", it might plan: 1. Identify `X` and `Y` 2. Locate their final confrontation 3. Analyze `X` actions 4. Draft an answer We then run `de_anonymize_plan`, restoring original names to make the plan specific and usable. The updated plan goes to `break_down_plan`, which turns each high-level step in
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