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@@ -72,16 +72,6 @@ Now, we are ready to try our vector db pipeline:
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[Notebook for Step 3](./notebooks/Part_3_RAG_Setup_and_Validation.ipynb) and [Final Demo Script](./scripts/label_script.py)
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-For running the script:
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-```
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-python final_demo.py \
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- --images_folder "../MM-Demo/compressed_images" \
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- --csv_path "../MM-Demo/final-captions.csv" \
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- --table_path "~/.lancedb" \
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- --api_key "your_together_api_key" \
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- --default_model "BAAI/bge-large-en-v1.5" \
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- --use_existing_table
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-```
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With the cleaned descriptions and dataset, we can now store these in a vector-db
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@@ -93,11 +83,19 @@ You will note that we are not using the categorization from our model-this is by
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We try the approach with different retrieval methods.
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-
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-### Step 4: Gradio App using Together API for Llama-3.2-11B and Lance-db for RAG
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-
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Finally, we can bring this all together in a Gradio App.
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+For running the script:
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+```
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+python scripts/final_demo.py \
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+ --images_folder "../MM-Demo/compressed_images" \
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+ --csv_path "../MM-Demo/final-captions.csv" \
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+ --table_path "~/.lancedb" \
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+ --api_key "your_together_api_key" \
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+ --default_model "BAAI/bge-large-en-v1.5" \
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+ --use_existing_table
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+```
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+
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Task: We can further improve the description prompt. You will notice sometimes the description starts with the title of the cloth which causes in retrieval of "similar" clothes instead of "complementary" items
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- Upload an image
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