identity-rag-customer-insights-chatbot

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Basic Information

This repository provides a demonstrator application for IdentityRAG, a retrieval-augmented generation system that integrates identity resolution to give context-aware answers about specific customers. It unifies customer records from multiple, disparate sources, builds a deduplicated golden record for each customer, and then uses that unified view as grounding for responses generated by any supported large language model. The project includes a conversational demo built with Chainlit and a Streamlit live demo, and it shows how to connect to a Tilores customer data instance. The repo includes instructions to install dependencies, configure environment variables for Tilores and an LLM provider, and run a local chat server to query customer data and retrieve actionable insights.

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App Details

Features
The README lists core capabilities focused on customer data resolution and retrieval-augmented generation. It can unify data across multiple sources, perform fuzzy search to find relevant records, consolidate information into a golden record, disambiguate conflicting matches, and deduplicate redundant entries. The project integrates with LangChain and supports multiple LLM providers including OpenAI and AWS Bedrock models. It includes configuration steps for Tilores API credentials and LLM keys, a runnable Chainlit chat demo, a Streamlit live demo, and an optional PDF link lookup tool that requires poppler utilities. The repo provides example environment variables and guidance for uploading CSV data into a Tilores instance to use as the knowledge base.
Use Cases
This project helps teams and developers build or evaluate a customer insights chatbot that answers questions with context-aware, consolidated customer information. By resolving identities and creating a golden record before generation, it reduces misinformation that can arise from fragmented or duplicate records. The ability to connect any LLM lets organizations reuse existing models or try alternatives without changing the data plumbing. The included demo and step-by-step configuration accelerate experimentation and validation with real customer data uploaded to a Tilores instance. It is useful for customer support, sales, analytics, and any use case that requires reliable, single-view answers about individual customers.

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