Upload 15 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+vectore_storage/chroma/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+log/
+__pycache__

README.md

@@ -1,13 +1 @@
----
-title: GTA Multimodal RAG
-emoji: 🚀
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 4.32.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# GrandTheftAuto-multimodal-RAG-application

app.py

@@ -0,0 +1,51 @@
+from utils.utils import get_logger, initialization, get_result
+import gradio as gr
+import logging
+
+
+logger = get_logger()
+collection = None
+
+
+def main(query):
+    logger = logging.getLogger(__name__)
+    print("Starting search...")
+    logger.info("Starting search...")
+    print("-------------------------------------------------------")
+    logger.info("-------------------------------------------------------")
+    exit = False
+    while not exit:
+        # Collect user query
+        # query = input('Type your query, or "exit" if you want to exit: ')
+
+        if query == "exit":
+            exit = True
+            print("-------------------------------------------------------")
+            logger.info("-------------------------------------------------------")
+            print("Search terminated.")
+            logger.info("Search terminated.")
+            return None, "Search terminated."
+        else:
+            # Get search result including the original descriptions of the images
+            image, text = get_result(collection, data_set, query, model, n_results=2)
+
+            # Display the image, its caption, and user query
+            # show_image(image, text, query)
+            return image, text
+
+
+if __name__ == "__main__":
+    try:
+        if collection == None:
+            collection, data_set, model, logger = initialization(logger)
+        # main()
+        app = gr.Interface(
+            fn=main,
+            inputs=["text"],
+            outputs=["image", "text"],
+            title="Search for a scene in the world of GTA!"
+        )
+        app.launch(share=True)
+    except Exception as e:
+        logger.exception(e)
+        raise e

data/data_set.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3b259221c8f9df17cde27c4e0913b9ce110c768910ff81e40c3db443196b68c
+size 3229

log/20240530.log

@@ -0,0 +1,74 @@
+2024-05-30 15:47:21 INFO     Initializing...
+2024-05-30 15:47:21 INFO     -------------------------------------------------------
+2024-05-30 15:47:21 INFO     Importing functions...
+2024-05-30 15:47:29 INFO     Set directories...
+2024-05-30 15:47:29 INFO     Loading data...
+2024-05-30 15:47:30 INFO     Loading CLIP model...
+2024-05-30 15:47:30 INFO     Load pretrained SentenceTransformer: sentence-transformers/clip-ViT-L-14
+2024-05-30 15:47:34 INFO     Use pytorch device_name: cpu
+2024-05-30 15:47:34 INFO     Getting vector embeddings...
+2024-05-30 15:48:33 INFO     Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
+2024-05-30 15:48:33 INFO     Collection image_vectors is not created.
+2024-05-30 15:48:34 INFO     -------------------------------------------------------
+2024-05-30 15:48:34 INFO     Initialization completed! Ready for search.
+2024-05-30 15:48:35 INFO     HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
+2024-05-30 15:48:35 INFO     HTTP Request: GET http://127.0.0.1:7860/startup-events "HTTP/1.1 200 OK"
+2024-05-30 15:48:35 INFO     HTTP Request: HEAD http://127.0.0.1:7860/ "HTTP/1.1 200 OK"
+2024-05-30 15:48:36 INFO     HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
+2024-05-30 15:48:36 INFO     HTTP Request: GET https://api.gradio.app/v2/tunnel-request "HTTP/1.1 200 OK"
+2024-05-30 15:49:30 INFO     Starting search...
+2024-05-30 15:49:30 INFO     -------------------------------------------------------
+2024-05-30 15:52:06 INFO     Starting search...
+2024-05-30 15:52:06 INFO     -------------------------------------------------------
+2024-05-30 15:53:34 INFO     Starting search...
+2024-05-30 15:53:34 INFO     -------------------------------------------------------
+2024-05-30 15:53:34 INFO     -------------------------------------------------------
+2024-05-30 15:53:34 INFO     Search terminated.
+2024-05-30 15:54:41 INFO     Initializing...
+2024-05-30 15:54:41 INFO     -------------------------------------------------------
+2024-05-30 15:54:41 INFO     Importing functions...
+2024-05-30 15:54:50 INFO     Set directories...
+2024-05-30 15:54:50 INFO     Loading data...
+2024-05-30 15:54:51 INFO     Loading CLIP model...
+2024-05-30 15:54:51 INFO     Load pretrained SentenceTransformer: sentence-transformers/clip-ViT-L-14
+2024-05-30 15:54:55 INFO     Use pytorch device_name: cpu
+2024-05-30 15:54:55 INFO     Getting vector embeddings...
+2024-05-30 15:55:59 INFO     Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
+2024-05-30 15:56:00 INFO     Collection image_vectors is not created.
+2024-05-30 15:56:01 INFO     -------------------------------------------------------
+2024-05-30 15:56:01 INFO     Initialization completed! Ready for search.
+2024-05-30 15:56:02 INFO     HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
+2024-05-30 15:56:02 INFO     HTTP Request: GET http://127.0.0.1:7860/startup-events "HTTP/1.1 200 OK"
+2024-05-30 15:56:02 INFO     HTTP Request: HEAD http://127.0.0.1:7860/ "HTTP/1.1 200 OK"
+2024-05-30 15:56:02 INFO     HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
+2024-05-30 15:56:03 INFO     HTTP Request: GET https://api.gradio.app/v2/tunnel-request "HTTP/1.1 200 OK"
+2024-05-30 15:56:34 INFO     Starting search...
+2024-05-30 15:56:34 INFO     -------------------------------------------------------
+2024-05-30 15:57:55 INFO     Starting search...
+2024-05-30 15:57:55 INFO     -------------------------------------------------------
+2024-05-30 15:57:55 INFO     -------------------------------------------------------
+2024-05-30 15:57:55 INFO     Search terminated.
+2024-05-30 16:11:29 INFO     Initializing...
+2024-05-30 16:11:29 INFO     -------------------------------------------------------
+2024-05-30 16:11:29 INFO     Importing functions...
+2024-05-30 16:11:37 INFO     Set directories...
+2024-05-30 16:11:37 INFO     Loading data...
+2024-05-30 16:11:38 INFO     Loading CLIP model...
+2024-05-30 16:11:38 INFO     Load pretrained SentenceTransformer: sentence-transformers/clip-ViT-L-14
+2024-05-30 16:11:42 INFO     Use pytorch device_name: cpu
+2024-05-30 16:11:42 INFO     Getting vector embeddings...
+2024-05-30 16:12:38 INFO     Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
+2024-05-30 16:12:38 INFO     Collection image_vectors is not created.
+2024-05-30 16:12:39 INFO     -------------------------------------------------------
+2024-05-30 16:12:39 INFO     Initialization completed! Ready for search.
+2024-05-30 16:12:40 INFO     HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
+2024-05-30 16:12:40 INFO     HTTP Request: GET http://127.0.0.1:7860/startup-events "HTTP/1.1 200 OK"
+2024-05-30 16:12:40 INFO     HTTP Request: HEAD http://127.0.0.1:7860/ "HTTP/1.1 200 OK"
+2024-05-30 16:12:40 INFO     HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
+2024-05-30 16:12:41 INFO     HTTP Request: GET https://api.gradio.app/v2/tunnel-request "HTTP/1.1 200 OK"
+2024-05-30 16:14:12 INFO     Starting search...
+2024-05-30 16:14:12 INFO     -------------------------------------------------------
+2024-05-30 16:15:31 INFO     Starting search...
+2024-05-30 16:15:31 INFO     -------------------------------------------------------
+2024-05-30 16:15:31 INFO     -------------------------------------------------------
+2024-05-30 16:15:31 INFO     Search terminated.

notebook_2.ipynb

@@ -0,0 +1,1148 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data set already exists in the local drive. Loading it.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import pickle\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "curr_dir = Path(os.getcwd())\n",
+    "data_dir = curr_dir / 'data'\n",
+    "if not os.path.exists(data_dir):\n",
+    "    os.mkdir(data_dir)\n",
+    "data_pickle_path = data_dir / 'data_set.pkl'\n",
+    "\n",
+    "if not os.path.exists(data_pickle_path):\n",
+    "    print(f\"Data set hasn't been loaded. Loading from the datasets library and save it as a pickle.\")\n",
+    "    data_set = load_dataset(\"vipulmaheshwari/GTA-Image-Captioning-Dataset\")\n",
+    "    with open(data_pickle_path, 'wb') as outfile:\n",
+    "        pickle.dump(data_set, outfile)\n",
+    "else:\n",
+    "    print(f\"Data set already exists in the local drive. Loading it.\")\n",
+    "    with open(data_pickle_path, 'rb') as infile:\n",
+    "        data_set = pickle.load(infile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(data_set)\n",
+    "# len(data_set['train']['image']), len(data_set['train']['text'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Source: https://huggingface.co/sentence-transformers/clip-ViT-L-14\n",
+    "\n",
+    "from sentence_transformers import SentenceTransformer, util\n",
+    "# from PIL import Image\n",
+    "\n",
+    "#Load CLIP model\n",
+    "model = SentenceTransformer(\"sentence-transformers/clip-ViT-L-14\") # SentenceTransformer('clip-ViT-L-14')\n",
+    "\n",
+    "#Encode an image:\n",
+    "# img_emb = model.encode(image) # Image.open('two_dogs_in_snow.jpg')\n",
+    "\n",
+    "# #Encode text descriptions\n",
+    "# text_emb = model.encode(text) # ['Two dogs in the snow', 'A cat on a table', 'A picture of London at night']\n",
+    "\n",
+    "# #Compute cosine similarities \n",
+    "# cos_scores = util.cos_sim(img_emb, text_emb)\n",
+    "# print(cos_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_embeddings = []\n",
+    "for image in tqdm(data_set['train']['image'][:2]):\n",
+    "    img_embedding = model.encode(image)\n",
+    "    img_embeddings.append(img_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# try FAISS. Chroma, Pinecone (check the GAFS project)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyarrow as pa\n",
+    "import lancedb\n",
+    "\n",
+    "db = lancedb.connect('./data/tables')\n",
+    "schema = pa.schema(\n",
+    "  [\n",
+    "      pa.field(\"vector\", pa.list_(pa.float32())),\n",
+    "      # pa.field(\"text\", pa.string()),\n",
+    "      # pa.field(\"id\", pa.int32())\n",
+    "  ])\n",
+    "# tbl = db.create_table(\"gta_data\", schema=schema, mode=\"overwrite\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2/2 [00:15<00:00,  7.65s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "\n",
+    "img_embeddings = []\n",
+    "for image in tqdm(data_set['train']['image'][:2]):\n",
+    "    img_embedding = model.encode(image)\n",
+    "    img_embeddings.append(img_embedding)\n",
+    "\n",
+    "tbl_data = pa.Table.from_arrays([pa.array(img_embeddings)], [\"vector\"])\n",
+    "tbl = db.create_table(\"gta_data\", tbl_data, schema=schema, mode=\"overwrite\")\n",
+    "\n",
+    "# tbl.add(img_embeddings)\n",
+    "# tbl.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Query column vector must be a vector. Got list<item: float>.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mtbl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ma road with a stop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvector_column_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvector\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pandas\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m res\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lancedb\\query.py:262\u001b[0m, in \u001b[0;36mLanceQueryBuilder.to_pandas\u001b[1;34m(self, flatten)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_pandas\u001b[39m(\u001b[38;5;28mself\u001b[39m, flatten: Optional[Union[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mbool\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpd.DataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    248\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;124;03m    Execute the query and return the results as a pandas DataFrame.\u001b[39;00m\n\u001b[0;32m    250\u001b[0m \u001b[38;5;124;03m    In addition to the selected columns, LanceDB also returns a vector\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[38;5;124;03m        If unspecified, do not flatten the nested columns.\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 262\u001b[0m     tbl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_arrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    263\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    264\u001b[0m         \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lancedb\\query.py:527\u001b[0m, in \u001b[0;36mLanceVectorQueryBuilder.to_arrow\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_arrow\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pa\u001b[38;5;241m.\u001b[39mTable:\n\u001b[0;32m    519\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    520\u001b[0m \u001b[38;5;124;03m    Execute the query and return the results as an\u001b[39;00m\n\u001b[0;32m    521\u001b[0m \u001b[38;5;124;03m    [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[38;5;124;03m    vector and the returned vectors.\u001b[39;00m\n\u001b[0;32m    526\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_batches\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mread_all()\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lancedb\\query.py:557\u001b[0m, in \u001b[0;36mLanceVectorQueryBuilder.to_batches\u001b[1;34m(self, batch_size)\u001b[0m\n\u001b[0;32m    544\u001b[0m     vector \u001b[38;5;241m=\u001b[39m [v\u001b[38;5;241m.\u001b[39mtolist() \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m vector]\n\u001b[0;32m    545\u001b[0m query \u001b[38;5;241m=\u001b[39m Query(\n\u001b[0;32m    546\u001b[0m     vector\u001b[38;5;241m=\u001b[39mvector,\n\u001b[0;32m    547\u001b[0m     \u001b[38;5;28mfilter\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_where,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    555\u001b[0m     with_row_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_with_row_id,\n\u001b[0;32m    556\u001b[0m )\n\u001b[1;32m--> 557\u001b[0m result_set \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_table\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    558\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reranker \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    559\u001b[0m     rs_table \u001b[38;5;241m=\u001b[39m result_set\u001b[38;5;241m.\u001b[39mread_all()\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lancedb\\table.py:1616\u001b[0m, in \u001b[0;36mLanceTable._execute_query\u001b[1;34m(self, query, batch_size)\u001b[0m\n\u001b[0;32m   1612\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_execute_query\u001b[39m(\n\u001b[0;32m   1613\u001b[0m     \u001b[38;5;28mself\u001b[39m, query: Query, batch_size: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1614\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pa\u001b[38;5;241m.\u001b[39mRecordBatchReader:\n\u001b[0;32m   1615\u001b[0m     ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_lance()\n\u001b[1;32m-> 1616\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscanner\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1617\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1618\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1619\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprefilter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprefilter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1620\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnearest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[0;32m   1621\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvector_column\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1622\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mq\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvector\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1623\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mk\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1624\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetric\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1625\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnprobes\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnprobes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1626\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrefine_factor\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrefine_factor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1627\u001b[0m \u001b[43m        \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1628\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwith_row_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwith_row_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1629\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1630\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto_reader()\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lance\\dataset.py:321\u001b[0m, in \u001b[0;36mLanceDataset.scanner\u001b[1;34m(self, columns, filter, limit, offset, nearest, batch_size, batch_readahead, fragment_readahead, scan_in_order, fragments, prefilter, with_row_id, use_stats)\u001b[0m\n\u001b[0;32m    305\u001b[0m builder \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    306\u001b[0m     ScannerBuilder(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    307\u001b[0m     \u001b[38;5;241m.\u001b[39mcolumns(columns)\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[38;5;241m.\u001b[39muse_stats(use_stats)\n\u001b[0;32m    319\u001b[0m )\n\u001b[0;32m    320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nearest \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 321\u001b[0m     builder \u001b[38;5;241m=\u001b[39m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnearest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnearest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m builder\u001b[38;5;241m.\u001b[39mto_scanner()\n",
+      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\grandtheftauto-multimodal-rag-application-ufxwo2j--py3.11\\Lib\\site-packages\\lance\\dataset.py:2049\u001b[0m, in \u001b[0;36mScannerBuilder.nearest\u001b[1;34m(self, column, q, k, metric, nprobes, refine_factor, use_index)\u001b[0m\n\u001b[0;32m   2047\u001b[0m     column_type \u001b[38;5;241m=\u001b[39m column_type\u001b[38;5;241m.\u001b[39mstorage_type\n\u001b[0;32m   2048\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_fixed_size_list(column_type):\n\u001b[1;32m-> 2049\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   2050\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery column \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolumn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be a vector. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolumn_field\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2051\u001b[0m     )\n\u001b[0;32m   2052\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(q) \u001b[38;5;241m!=\u001b[39m column_type\u001b[38;5;241m.\u001b[39mlist_size:\n\u001b[0;32m   2053\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2054\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery vector size \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(q)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not match index column size\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2055\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolumn_type\u001b[38;5;241m.\u001b[39mlist_size\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2056\u001b[0m     )\n",
+      "\u001b[1;31mTypeError\u001b[0m: Query column vector must be a vector. Got list<item: float>."
+     ]
+    }
+   ],
+   "source": [
+    "res = tbl.search(model.encode(\"a road with a stop\"), vector_column_name=\"vector\").limit(3).to_pandas()\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://huggingface.co/openai/clip-vit-large-patch14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clip\n",
+    "import torch\n",
+    "import os\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# ds = load_dataset(\"vipulmaheshwari/GTA-Image-Captioning-Dataset\")\n",
+    "# device = torch.device(\"mps\")\n",
+    "model, preprocess = clip.load(\"ViT-L/14\") # , device=device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "768"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def embed_txt(txt):\n",
+    "    tokenized_text = clip.tokenize([txt])\n",
+    "    embeddings = model.encode_text(tokenized_text)\n",
+    "    \n",
+    "    # Detach, move to CPU, convert to numpy array, and extract the first element as a list\n",
+    "    result = embeddings.detach().numpy()[0].tolist()\n",
+    "    return result\n",
+    "\n",
+    "len(embed_txt(\"a road with a stop\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
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+       " -0.08006825298070908]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# https://vipul-maheshwari.github.io/2024/03/03/multimodal-rag-application\n",
+    "\n",
+    "def embed_image(img):\n",
+    "    processed_image = preprocess(img)\n",
+    "    unsqueezed_image = processed_image.unsqueeze(0)\n",
+    "    embeddings = model.encode_image(unsqueezed_image)\n",
+    "    \n",
+    "    # Detach, move to CPU, convert to numpy array, and extract the first element as a list\n",
+    "    result = embeddings.detach().numpy()[0].tolist()\n",
+    "    return result\n",
+    "\n",
+    "len(embed_image(image))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def embed_txt(txt):\n",
+    "    tokenized_text = clip.tokenize([txt]).to(device)\n",
+    "    embeddings = model.encode_text(tokenized_text)\n",
+    "    \n",
+    "    # Detach, move to CPU, convert to numpy array, and extract the first element as a list\n",
+    "    result = embeddings.detach().cpu().numpy()[0].tolist()\n",
+    "    return result\n",
+    "\n",
+    "res = tbl.search(embed_txt(\"a road with a stop\")).limit(3).to_pandas()\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "https://blog.lancedb.com/lancedb-polars-2d5eb32a8aa3/\n",
+    "\n",
+    "https://github.com/lancedb/lancedb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pyproject.toml

@@ -0,0 +1,29 @@
+[tool.poetry]
+name = "grandtheftauto-multimodal-rag-application"
+version = "0.1.0"
+description = ""
+authors = ["henryhyunwookim <[email protected]>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+pillow = "^10.3.0"
+datasets = "^2.19.0"
+ipykernel = "^6.29.4"
+jupyter = "^1.0.0"
+ipywidgets = "^8.1.2"
+matplotlib = "^3.8.4"
+sentence-transformers = "^2.7.0"
+lancedb = "^0.6.11"
+torch = "^2.3.0"
+clip = {git = "https://github.com/openai/CLIP.git"}
+chromadb = "^0.5.0"
+gradio = "^4.32.0"
+
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.4"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -0,0 +1,15 @@
+pillow==10.3.0
+datasets==2.19.0
+ipykernel==6.29.4
+jupyter==1.0.0
+ipywidgets==8.1.2
+matplotlib==3.8.4
+sentence-transformers==2.7.0
+lancedb==0.6.11
+torch==2.3.0
+clip @ git+https://github.com/openai/CLIP.git
+chromadb==0.5.0
+gradio==4.32.0
+
+# Development dependencies
+ipykernel==6.29.4

utils/utils.py

@@ -0,0 +1,154 @@
+import os
+import logging
+from datetime import datetime
+from pathlib import Path
+import pickle
+from tqdm import tqdm
+from datasets import load_dataset
+import chromadb
+import matplotlib.pyplot as plt
+
+
+def set_directories():
+    curr_dir = Path(os.getcwd())
+    
+    data_dir = curr_dir / 'data'
+    data_pickle_path = data_dir / 'data_set.pkl'
+
+    vectordb_dir = curr_dir / 'vectore_storage'
+    chroma_dir = vectordb_dir / 'chroma'
+    
+    for dir in [data_dir, vectordb_dir, chroma_dir]:
+        if not os.path.exists(dir):
+            os.mkdir(dir)
+
+    return data_pickle_path, chroma_dir
+
+
+def load_data(data_pickle_path, dataset="vipulmaheshwari/GTA-Image-Captioning-Dataset"):
+    if not os.path.exists(data_pickle_path):
+        print(f"Data set hasn't been loaded. Loading from the datasets library and save it as a pickle.")
+        data_set = load_dataset(dataset)
+        with open(data_pickle_path, 'wb') as outfile:
+            pickle.dump(data_set, outfile)
+    else:
+        print(f"Data set already exists in the local drive. Loading it.")
+        with open(data_pickle_path, 'rb') as infile:
+            data_set = pickle.load(infile)
+
+    return data_set
+
+
+def get_embeddings(data, model):
+    # Get the id and embedding of each data/image
+    ids = []
+    embeddings = []
+    for id, image in tqdm(zip(list(range(len(data))), data)):
+        ids.append("image "+str(id))
+
+        embedding = model.encode(image)
+        embeddings.append(embedding.tolist())
+
+    return ids, embeddings
+
+
+def get_collection(chroma_dir, model, collection_name, data):
+    client = chromadb.PersistentClient(path=chroma_dir.__str__())
+    collection = client.get_or_create_collection(name=collection_name)
+
+    if collection.count() != len(data):
+        print("Adding embeddings to the collection.")
+        ids, embeddings = get_embeddings(data, model)
+        collection.add(
+            ids=ids,
+            embeddings=embeddings
+        )
+    else:
+        print("Embeddings are already added to the collection.")
+
+    return collection
+
+
+def get_result(collection, data_set, query, model, n_results=2):
+    # Query the vector store and get results
+    results = collection.query(
+        query_embeddings=model.encode([query]),
+        n_results=2
+    )
+
+    # Get the id of the most relevant image
+    img_id = int(results['ids'][0][0].split('image ')[-1])
+    
+    # Get the image and its caption
+    image = data_set['train']['image'][img_id]
+    text = data_set['train']['text'][img_id]
+
+    return image, text
+
+
+def show_image(image, text, query):
+    plt.ion()
+    plt.axis("off")
+    plt.imshow(image)
+    plt.show()
+    print(f"User query: {query}")
+    print(f"Original description: {text}\n")
+    
+
+def get_logger():
+    log_path = "./log/"
+    if not os.path.exists(log_path):
+        os.mkdir(log_path)
+
+    cur_date = datetime.utcnow().strftime("%Y%m%d")
+    log_filename = f"{log_path}{cur_date}.log"
+
+    logging.basicConfig(
+        filename=log_filename,
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)-8s %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S")
+    
+    logger = logging.getLogger(__name__)
+    
+    return logger
+
+
+def initialization(logger):
+    print("Initializing...")
+    logger.info("Initializing...")
+    print("-------------------------------------------------------")
+    logger.info("-------------------------------------------------------")
+
+    print("Importing functions...")
+    logger.info("Importing functions...")
+    # Import module, classes, and functions
+    from sentence_transformers import SentenceTransformer
+    from utils.utils import set_directories, load_data, get_collection, get_result, show_image
+
+    print("Set directories...")
+    logger.info("Set directories...")
+    # Set directories
+    data_pickle_path, chroma_dir = set_directories()
+
+    print("Loading data...")
+    logger.info("Loading data...")
+    # Load dataset
+    data_set = load_data(data_pickle_path)
+
+    print("Loading CLIP model...")
+    logger.info("Loading CLIP model...")
+    # Load CLIP model
+    model = SentenceTransformer("sentence-transformers/clip-ViT-L-14")
+
+    print("Getting vector embeddings...")
+    logger.info("Getting vector embeddings...")
+    # Get vector embeddings
+    collection = get_collection(chroma_dir, model, collection_name='image_vectors', data=data_set['train']['image'])
+
+    print("-------------------------------------------------------")
+    logger.info("-------------------------------------------------------")
+    print("Initialization completed! Ready for search.")
+    logger.info("Initialization completed! Ready for search.")
+
+    return collection, data_set, model, logger

vectore_storage/chroma/chroma.sqlite3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34a5e8e1ac1cff55f102ec9eeb2fb556494f2d1d5c496e76641d5f4aab4feda5
+size 3473408