Show ingestion logs in readme
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							|  | @ -56,6 +56,19 @@ Run the following command to ingest all the data. | ||||||
| python ingest.py | python ingest.py | ||||||
| ``` | ``` | ||||||
| 
 | 
 | ||||||
|  | Output should look like this: | ||||||
|  | 
 | ||||||
|  | ```shell | ||||||
|  | Creating new vectorstore | ||||||
|  | Loading documents from source_documents | ||||||
|  | Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00,  1.73s/it] | ||||||
|  | Loaded 1 new documents from source_documents | ||||||
|  | Split into 90 chunks of text (max. 500 tokens each) | ||||||
|  | Creating embeddings. May take some minutes... | ||||||
|  | Using embedded DuckDB with persistence: data will be stored in: db | ||||||
|  | Ingestion complete! You can now run privateGPT.py to query your documents | ||||||
|  | ``` | ||||||
|  | 
 | ||||||
| It will create a `db` folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. | It will create a `db` folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. | ||||||
| You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. | You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. | ||||||
| If you want to start from an empty database, delete the `db` folder. | If you want to start from an empty database, delete the `db` folder. | ||||||
|  |  | ||||||
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