Add document scan API notes in API README.md
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@@ -17,6 +17,7 @@ git clone https://github.com/HKUDS/lightrag.git
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# Change to the repository directory
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# Change to the repository directory
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cd lightrag
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cd lightrag
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# create a Python virtual enviroment if neccesary
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# Install in editable mode with API support
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# Install in editable mode with API support
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pip install -e ".[api]"
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pip install -e ".[api]"
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```
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```
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@@ -309,6 +310,16 @@ curl -X POST "http://localhost:9621/documents/batch" \
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-F "files=@/path/to/doc2.txt"
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-F "files=@/path/to/doc2.txt"
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```
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```
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#### POST /documents/scan
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Trigger document scan for new files in the Input directory.
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```bash
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curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
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```
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> Ajust max-time according to the estimated index time for all new files.
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### Ollama Emulation Endpoints
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### Ollama Emulation Endpoints
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#### GET /api/version
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#### GET /api/version
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@@ -391,15 +402,15 @@ You can test the API endpoints using the provided curl commands or through the S
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2. Start the RAG server
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2. Start the RAG server
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3. Upload some documents using the document management endpoints
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3. Upload some documents using the document management endpoints
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4. Query the system using the query endpoints
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4. Query the system using the query endpoints
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5. Trigger document scan if new files is put into inputs directory
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### Important Features
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### Important Features
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#### Automatic Document Vectorization
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#### Automatic Document Vectorization
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When starting any of the servers with the `--input-dir` parameter, the system will automatically:
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When starting any of the servers with the `--input-dir` parameter, the system will automatically:
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1. Scan the specified directory for documents
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1. Check for existing vectorized content in the database
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2. Check for existing vectorized content in the database
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2. Only vectorize new documents that aren't already in the database
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3. Only vectorize new documents that aren't already in the database
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3. Make all content immediately available for RAG queries
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4. Make all content immediately available for RAG queries
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This intelligent caching mechanism:
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This intelligent caching mechanism:
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- Prevents unnecessary re-vectorization of existing documents
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- Prevents unnecessary re-vectorization of existing documents
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