IdentityRAG
Customer Insights Chatbot

Connect to your customer data using any LLM and gain actionable insights.

| IdentityRAG | Live Demo | LinkedIn |

IdentityRAGDemo.mp4

What is IdentityRAG?

IdentityRAG is a retrieval-augmented generation system that integrates identity resolution capabilities to provide accurate, context-aware responses about specific customers. It retrieves unified customer data across disparate sources to create a comprehensive golden record before generating LLM responses, ensuring answers are based on an accurate, deduplicated view of the customer.

LangChain Integration

Key Features

• Unify - bring data together from various sources.
• Search - find and retrieve all relevant customer data with fuzzy matching.
• Consolidate - combine it meaningfully by creating a golden record.
• Disambiguate - resolve conflicts/unclear matches.
• Deduplicate - remove redundancies where repeated with no extra value.

Multiple Customer Data Sources / Knowledge Bases

Live Demo

If you don't want to use your own LLM keys then give it a try on the following live demo.

Live Demo

Getting Started

1. Clone this repository
2. Install dependencies: pip install -r requirements.txt
3. Set up your environment variables (see Configuration section)
4. Run the demo server: chainlit run chat.py -w
5. Open http://localhost:8000 in your browser
6. Try asking "search for Sophie Muller"

Configuration

1. Configure Customer Data Access:

export TILORES_API_URL='https://8edvhd7rqb.execute-api.eu-central-1.amazonaws.com'
export TILORES_TOKEN_URL='https://saas-umgegwho-tilores.auth.eu-central-1.amazoncognito.com/oauth2/token'
export TILORES_CLIENT_ID='3l3i0ifjurnr58u4lgf0eaeqa3'
export TILORES_CLIENT_SECRET='1c0g3v0u7pf1bvb7v65pauqt6s0h3vkkcf9u232u92ov3lm4aun2'

2. Configure LLM Access:

Option 1: ChatGPT using OpenAI keys

export OPENAI_API_KEY='your openAI key'
export OPENAI_MODEL_NAME='gpt-4o-mini'

Option 2: Anthropic Claude using AWS Bedrock keys

export LLM_PROVIDER='Bedrock'
export BEDROCK_CREDENTIALS_PROFILE_NAME='your configured AWS profile name with bedrock access'
export BEDROCK_REGION='us-east-1'
export BEDROCK_MODEL_ID='anthropic.claude-3-5-sonnet-20240620-v1:0'

Important

The aws profile needs to have access to the model with action InvokeModelWithResponseStream. Also make sure the model is enabled in bedrock console and in the correct region.

Using Your Own Data

To use your own data you will need to create a Tilores instance and get your free Tilores API credentials, Here's how to do that:

• Visit app.tilores.io and sign up for free.
• Click on "Switch to Instance View" on the bottom right.
• Select "Upload Data File" option and proceed. It is recommended to use csv file format.
• If the file has well named headers the matching will be automatically configured and you can proceed with the instance creation without any further changes. The deployment will take around 3-5 minutes.
• Once the deployment is done, navigate to "Manage Instance" -> "Integration" -> "GraphQL API"
• The first URL is the TILORES_GRAPHQL_API, and the second is TILORES_TOKEN_URL you will need to export these two values as shown in Configuration section.
• Then click CREATE NEW CREDENTIALS and store both values. Then export each one into its corresponding environment value TILORES_CLIENT_ID and TILORES_CLIENT_SECRET.
• Now run chainlit run chat.py -w and ask to search for one of the records in your data.

PDF Link Lookup Tool

If you want to test the automatic lookup from the PDFs, you also must have the poppler-utils installed:

sudo apt-get install poppler-utils