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README.md

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-# Transaction Reconciliation API
-
-A FastAPI-based service for reconciling bank and credit card transactions using intelligent matching algorithms.
-
-## Features
-
-- **Fuzzy Description Matching**: Uses similarity algorithms with configurable threshold (default 70%)
-- **Amount Tolerance**: Matches transactions with small amount differences (configurable)
-- **Date Proximity**: Considers transactions within a date range (default 7 days)
-- **Type Mapping**: Intelligent mapping between bank and credit card transaction types
-- **Reference Number Matching**: Exact matching on reference numbers when available
-- **Custom Parameters**: Endpoint for custom matching parameters
-
-## API Endpoints
-
-### `POST /reconcile`
-Main reconciliation endpoint with default parameters.
-
-### `POST /reconcile/custom`
-Reconciliation with custom matching parameters:
-- `description_threshold`: Fuzzy match threshold (0-1, default 0.7)
-- `amount_tolerance`: Amount difference tolerance (default 0.01)
-- `max_date_diff_days`: Maximum date difference in days (default 7)
-
-### `GET /` and `GET /health`
-Health check endpoints.
-
-## Request Format
-
-```json
-{
-  "bank_transactions": [
-    {
-      "id": "B001",
-      "date": "2025-05-01",
-      "amount": -120.50,
-      "description": "Uber Trip - Office Commute",
-      "type": "debit",
-      "reference_number": "TXN123456"
-    }
-  ],
-  "credit_card_transactions": [
-    {
-      "id": "C001",
-      "date": "2025-05-01",
-      "amount": -120.50,
-      "description": "UBER *Trip ID 987654",
-      "type": "payment",
-      "reference_number": "TXN123456"
-    }
-  ]
-}
-```
-
-## Response Format
-
-```json
-{
-  "matched_transactions": [
-    {
-      "bank_id": "B001",
-      "credit_card_id": "C001",
-      "match_score": 0.95,
-      "match_reason": "amounts match, reference numbers match, descriptions match"
-    }
-  ],
-  "unmatched_bank_transactions": [],
-  "unmatched_credit_card_transactions": []
-}
-```
-
-## Matching Algorithm
-
-The service uses a weighted scoring system:
-
-1. **Amount Matching (40% weight)**: Exact or near-exact amount matching
-2. **Reference Number (30% weight)**: Exact reference number matching
-3. **Description Similarity (20% weight)**: Fuzzy string matching with pattern recognition
-4. **Date Proximity (10% weight)**: Transactions within acceptable date range
-
-**Minimum match threshold**: 60% overall score required for a match.
-
-## Transaction Type Mappings
-
-- `debit` ↔ `payment`
-- `credit` ↔ `receipt`
-- `withdrawal` ↔ `payment`
-- `deposit` ↔ `receipt`
-
-## Deployment to Hugging Face Spaces
-
-1. Create a new Space on Hugging Face Hub
-2. Choose "Docker" as the SDK
-3. Upload the following files:
-   - `main.py`
-   - `requirements.txt`
-   - `Dockerfile`
-   - `README.md`
-
-4. Your Space will be available at: `https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME`
-
-## Local Testing
-
-```bash
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the server
-uvicorn main:app --reload
-
-# API will be available at http://localhost:8000
-# Interactive docs at http://localhost:8000/docs
-```
-
-## Example Test Request
-
-```bash
-curl -X POST "http://localhost:8000/reconcile" \
-     -H "Content-Type: application/json" \
-     -d '{
-       "bank_transactions": [
-         {
-           "id": "B001",
-           "date": "2025-05-01",
-           "amount": -120.50,
-           "description": "Uber Trip - Office Commute",
-           "type": "debit",
-           "reference_number": "TXN123456"
-         }
-       ],
-       "credit_card_transactions": [
-         {
-           "id": "C001",
-           "date": "2025-05-01",
-           "amount": -120.50,
-           "description": "UBER *Trip ID 987654",
-           "type": "payment",
-           "reference_number": "TXN123456"
-         }
-       ]
-     }'
-```
-
-## Error Handling
-
-The API includes comprehensive error handling and returns appropriate HTTP status codes:
-- `200`: Successful reconciliation
-- `422`: Invalid input data
-- `500`: Internal server error
-
-## Performance
-
-- Optimized for datasets with hundreds of transactions
-- O(n*m) complexity where n and m are the number of bank and credit card transactions
-- Efficient fuzzy matching with pattern recognition optimization