RAG Complaint Chatbot
Evidence-grounded AI analysis of 1.37 million consumer complaints
Problem & Architectural Solution
Core business challenges, domain requirements, and technical strategy
The Problem & Bottlenecks
The Consumer Financial Protection Bureau (CFPB) receives an immense volume of consumer complaints about financial products and services. Manually sifting through this dataset, which includes 1.37 million records, to find relevant information is extremely challenging due to its sheer scale, the unstructured nature of complaint narratives, and the diverse range of financial issues covered. Traditional keyword search often falls short, missing semantically related complaints and leading to slow, inefficient manual investigations.
The Implemented Solution
Developed a comprehensive RAG pipeline to automate and enhance the analysis of CFPB consumer complaints. The solution involves cleaning and chunking complaint narratives, converting them into 384-dimensional embeddings using Sentence Transformers, and storing these efficiently in a FAISS vector index. Analyst queries are semantically matched against this index to retrieve the most relevant complaint excerpts, which are then used to ground the answers generated by a FLAN-T5 language model. A Gradio interface provides an interactive way to query the system and view both the generated answers and their supporting evidence.
Detailed Scope & Objectives
The RAG Complaint Chatbot is an AI/ML engineering project focused on developing an evidence-grounded system for analyzing large volumes of consumer complaints from the CFPB database. This project demonstrates the full lifecycle of a Retrieval-Augmented Generation (RAG) pipeline, from data preparation and semantic indexing to efficient retrieval and grounded language generation. It aims to empower analysts to quickly extract insights and evidence from unstructured complaint narratives.
System Architecture & Data Flow
Component boundaries, async pipeline execution, and transaction lifecycle
Data Preparation
The CFPB consumer complaint dataset undergoes rigorous cleaning and filtering to extract useful complaint narratives, removing noise and standardizing formats.
Semantic Representation
Each cleaned complaint text chunk is transformed into a 384-dimensional numerical vector (embedding) using sentence-transformers.
Vector Indexing
The generated embeddings are efficiently stored and indexed using FAISS, enabling rapid similarity queries across the large dataset.
Retrieval
When an analyst poses a question, it is first converted into an embedding and matched against the FAISS index to semantically retrieve relevant excerpts.
Grounded Generation
The retrieved complaint evidence is fed into a language model to generate a concise answer grounded in the evidence, eliminating hallucinations.
flowchart TD
Data[CFPB Complaint Dataset] --> Clean[Preprocessing & Filtering]
Clean --> Chunk[Text Chunking]
Chunk --> Embed[Sentence Transformer Embeddings]
Embed --> FAISS[(FAISS Vector Store)]
Query[Analyst Query] --> Retrieve[Semantic Retrieval]
Retrieve --> FAISS
FAISS --> Context[Relevant Complaint Evidence]
Context --> LLM[FLAN-T5 Generation]
Query --> LLM
LLM --> Answer[Grounded Answer]
Answer --> UI[Gradio Interface]Technology Stack Breakdown
Explicit technical responsibilities and tooling justification per layer
Backend & Systems
Primary programming language for the entire RAG pipeline.
Data manipulation and numerical computation.
AI & Model Pipelines
Converts text into 384-dimensional dense vector embeddings.
Efficient library for similarity search and clustering of dense vectors.
Transformer-based language model for generating grounded answers.
Frontend & Client
Python library for creating customizable UI components for ML models.
Key Technical Capabilities
Production-grade features, system subsystems, and upcoming roadmap items
Large-Scale Complaint Indexing
CompletedProcessed and indexed 1.37 million CFPB consumer complaints, making them semantically searchable.
Semantic Retrieval
CompletedUses dense sentence embeddings and FAISS similarity search to retrieve semantically relevant complaint evidence.
Grounded Answer Generation
CompletedGenerates concise answers from retrieved complaint context rather than relying solely on internal model weights.
Architecture Decisions & Trade-Offs
Technical context, decision rationales, and verified system outcomes
1Sentence Transformers for Embeddings
Analyzing complaint narratives required semantic matching, as simple keyword search missed relevant documents due to varied phrasing.
Employed all-MiniLM-L6-v2 from Sentence Transformers to generate compact and semantically rich 384-dimensional vector representations.
Enabled highly effective semantic retrieval across a large corpus of diverse complaint narratives.
Engineering Challenges & Solutions
Real architectural bottlenecks encountered and the engineering rationale behind their resolution
#1Large Dataset Scale & Indexing Efficiency
Processing and indexing 1.37 million unstructured complaint records efficiently into a searchable format.
Developed an optimized preprocessing pipeline for data cleaning and text chunking, followed by the creation of a persisted FAISS vector store.
Security, Integrity & Reliability
Production safeguards, boundary enforcement, and fault-tolerance patterns
Persisted FAISS Index
The FAISS vector index is persisted to disk, ensuring data durability and rapid reloading for continuous operation.
Verified Results & Status
Factual metrics, operational milestones, and current production state
- An AI-powered RAG application successfully processing and indexing 1.37 million CFPB consumer complaints.
- Demonstrated 100% retrieval rate on the documented benchmark.
- Developed an evidence-based Gradio interface for interactive querying and transparent display.
Lessons Learned & Retrospective
Key technical takeaways that inform future platform architecture decisions
Building large-scale RAG systems necessitates meticulous data preprocessing and chunking before any meaningful evaluation of retrieval quality can be performed.
ETBooking Solutions
Full-Stack Web Platform
Christian Digital Content Platform - Backend API
Backend & Admin System
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