Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tilotech/identity-rag-customer-insights-chatbot
Connect to your customer data using any LLM and gain actionable insights. IdentityRAG creates a single comprehensive customer 360 view (golden record) by unifying, consolidating, disambiguating and deduplicating data across multiple sources through identity resolution.
https://github.com/tilotech/identity-rag-customer-insights-chatbot
agentic-rag identity-resolution langchain
Last synced: 4 months ago
JSON representation
Connect to your customer data using any LLM and gain actionable insights. IdentityRAG creates a single comprehensive customer 360 view (golden record) by unifying, consolidating, disambiguating and deduplicating data across multiple sources through identity resolution.
- Host: GitHub
- URL: https://github.com/tilotech/identity-rag-customer-insights-chatbot
- Owner: tilotech
- License: mit
- Created: 2024-10-22T10:25:07.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T14:36:32.000Z (4 months ago)
- Last Synced: 2024-10-23T16:16:02.235Z (4 months ago)
- Topics: agentic-rag, identity-resolution, langchain
- Language: Python
- Homepage: https://identityrag.com
- Size: 3.29 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - tilotech/identity-rag-customer-insights-chatbot
README
IdentityRAG
Customer Insights Chatbot
Connect to your customer data using any LLM and gain actionable insights.
| IdentityRAG | Live Demo | LinkedIn |
https://github.com/user-attachments/assets/b6639333-54ae-4d20-a585-c8cf604036d6
## 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 |
|--|
|[](https://identity-rag.streamlit.app)|
## 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](http://localhost:8000) in your browser
6. Try asking "search for Sophie Muller"
## Configuration
### 1. Configure Customer Data Access:
```bash
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
```bash
export OPENAI_API_KEY='your openAI key'
export OPENAI_MODEL_NAME='gpt-4o-mini'
```
#### Option 2: Anthropic Claude using AWS Bedrock keys
```bash
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](https://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