| Current Version of this application features: |
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| 1. dual mode with embedding and llm mode |
| 2. data preprocessing retrieving from csv data |
| 3. Pincode Logic has been updated |
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| Objective: |
| This repository contains the implementation of a **GenAI-based Entity Matching** system. It supports a dual‑mode architecture with a Fastapi backend, a Streamlit frontend, and a collection of services for data processing and model interaction. |
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| Features: |
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| - **Flexible matching service** implemented in `backend/matching_service.py`. |
| - **Modular data models** defined in `backend/models.py`. |
| - **Streamlit frontend** for quick experimentation (`frontend/app_streamlit.py`). |
| - **Configurable rules and LLM model integration** under `services/`. |
| - **Extensive test suite** located in `tests/`. |
| - **Configuration files** and property management in `backend/config` and `services/config.py`. |
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| Active endpoints : |
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| POST /backend/v1/match – Match a single pair of records |
| POST /backend/v1/match/batch – Match multiple pairs # multithread implementation |
| GET /backend/v1/health – Full health check (CSV data, models, LLM) |
| GET /backend/v1/health/llm – LLM server health check only |
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| To Run the application : |
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| for embedding mode: |
| models will be loaded when we initiate the server |
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| for llm mode: |
| we have to paste the llm up url in the common.properties , base-url: |
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| for frontend : |
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| python -m streamlit run frontend/app_streamlit.py |
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| for backend: |
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| python -m uvicorn backend.server:app |
