Upload folder using huggingface_hub

.chroma/index/id_to_uuid_90efa5f8-e52a-44c9-8a81-72ff253cd2ec.pkl

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.ipynb_checkpoints/DSS_proto-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fb4f9384-be8e-488a-aa51-b56b27c71213",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 1. Set up Sagemaker\n",
+    "*Explain more later...*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea107aa6-376e-4364-bceb-50aca9f30b74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = client.create_presigned_notebook_instance_url(\n",
+    "    NotebookInstanceName='string',\n",
+    "    SessionExpirationDurationInSeconds=123\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ac706b50-8413-42ef-b5a7-5906f7f5cdf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arn:aws:iam::907929678403:role/service-role/AmazonSageMaker-ExecutionRole-20230621T132010\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import sagemaker\n",
+    "from sagemaker.huggingface import get_huggingface_llm_image_uri\n",
+    "from sagemaker.huggingface import HuggingFaceModel\n",
+    "\n",
+    "# retrieve the llm image uri\n",
+    "llm_image = get_huggingface_llm_image_uri(\n",
+    "  \"huggingface\",\n",
+    "  version=\"0.8.2\"\n",
+    ")\n",
+    "\n",
+    "# Define Model and Endpoint configuration parameter\n",
+    "role = sagemaker.get_execution_role()\n",
+    "print(role)\n",
+    "endpoint_name = \"falcon-40b-instruct-demo2\"\n",
+    "aws_region = \"us-east-1\"\n",
+    "hf_model_id = \"tiiuae/falcon-40b-instruct\" # model id from huggingface.co/models\n",
+    "instance_type = \"ml.g5.12xlarge\" # instance type to use for deployment\n",
+    "number_of_gpu = 4 # number of gpus to use for inference and tensor parallelism\n",
+    "health_check_timeout = 600 # Increase the timeout for the health check to 5 minutes for downloading the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2ce504d1-0bc3-43ce-bb39-b925a59718cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create HuggingFaceModel with the image uri\n",
+    "llm_model = HuggingFaceModel(\n",
+    "  role=role,\n",
+    "  image_uri=llm_image,\n",
+    "  env={\n",
+    "    'HF_MODEL_ID': hf_model_id,\n",
+    "    # 'HF_MODEL_QUANTIZE': \"bitsandbytes\", # comment in to quantize\n",
+    "    'SM_NUM_GPUS': json.dumps(number_of_gpu),\n",
+    "    'MAX_INPUT_LENGTH': json.dumps(1024),  # Max length of input text\n",
+    "    'MAX_TOTAL_TOKENS': json.dumps(2048),  # Max length of the generation (including input text)\n",
+    "  }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "00664be7-3d08-4c68-9048-ba1e602c44c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ClientError",
+     "evalue": "An error occurred (ValidationException) when calling the CreateEndpoint operation: Cannot create already existing endpoint \"arn:aws:sagemaker:us-east-1:907929678403:endpoint/falcon-40b-instruct-demo\".",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mClientError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m llm2 \u001b[38;5;241m=\u001b[39m \u001b[43mllm_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minstance_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhealth_check_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/huggingface/model.py:311\u001b[0m, in \u001b[0;36mHuggingFaceModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_uri \u001b[38;5;129;01mand\u001b[39;00m instance_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m instance_type\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mml.inf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_uri \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mserving_image_uri(\n\u001b[1;32m    307\u001b[0m         region_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session\u001b[38;5;241m.\u001b[39mboto_session\u001b[38;5;241m.\u001b[39mregion_name,\n\u001b[1;32m    308\u001b[0m         instance_type\u001b[38;5;241m=\u001b[39minstance_type,\n\u001b[1;32m    309\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mHuggingFaceModel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    312\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    313\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    314\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    315\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdeserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    316\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccelerator_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    317\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserverless_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvolume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvolume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43minference_recommendation_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minference_recommendation_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/model.py:1347\u001b[0m, in \u001b[0;36mModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_explainer_enabled:\n\u001b[1;32m   1345\u001b[0m     explainer_config_dict \u001b[38;5;241m=\u001b[39m explainer_config\u001b[38;5;241m.\u001b[39m_to_request_dict()\n\u001b[0;32m-> 1347\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_from_production_variants\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproduction_variants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mproduction_variant\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1352\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1354\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1355\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1356\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls:\n\u001b[1;32m   1359\u001b[0m     predictor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendpoint_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4641\u001b[0m, in \u001b[0;36mSession.endpoint_from_production_variants\u001b[0;34m(self, name, production_variants, tags, kms_key, wait, data_capture_config_dict, async_inference_config_dict, explainer_config_dict)\u001b[0m\n\u001b[1;32m   4638\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating endpoint-config with name \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, name)\n\u001b[1;32m   4639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_endpoint_config(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig_options)\n\u001b[0;32m-> 4641\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[43mcreate_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4030\u001b[0m, in \u001b[0;36mSession.create_endpoint\u001b[0;34m(self, endpoint_name, config_name, tags, wait)\u001b[0m\n\u001b[1;32m   4027\u001b[0m tags \u001b[38;5;241m=\u001b[39m tags \u001b[38;5;129;01mor\u001b[39;00m []\n\u001b[1;32m   4028\u001b[0m tags \u001b[38;5;241m=\u001b[39m _append_project_tags(tags)\n\u001b[0;32m-> 4030\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_endpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4031\u001b[0m \u001b[43m    \u001b[49m\u001b[43mEndpointName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEndpointConfigName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\n\u001b[1;32m   4032\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wait:\n\u001b[1;32m   4034\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwait_for_endpoint(endpoint_name)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:530\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.<locals>._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    527\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m     )\n\u001b[1;32m    529\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 530\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[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:964\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    962\u001b[0m     error_code \u001b[38;5;241m=\u001b[39m parsed_response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    963\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m--> 964\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m    965\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    966\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n",
+      "\u001b[0;31mClientError\u001b[0m: An error occurred (ValidationException) when calling the CreateEndpoint operation: Cannot create already existing endpoint \"arn:aws:sagemaker:us-east-1:907929678403:endpoint/falcon-40b-instruct-demo\"."
+     ]
+    }
+   ],
+   "source": [
+    "llm2 = llm_model.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type=instance_type,\n",
+    "    container_startup_health_check_timeout=health_check_timeout,\n",
+    "    endpoint_name=endpoint_name\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "50f556f8-06b4-450e-9db3-9bc9c979e8ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ClientError",
+     "evalue": "An error occurred (ValidationException) when calling the DeleteEndpointConfig operation: Could not find endpoint configuration \"arn:aws:sagemaker:us-east-1:907929678403:endpoint-config/falcon-40b-instruct-demo\".",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mClientError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mllm2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/base_predictor.py:339\u001b[0m, in \u001b[0;36mPredictor.delete_endpoint\u001b[0;34m(self, delete_endpoint_config)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete the Amazon SageMaker endpoint backing this predictor.\u001b[39;00m\n\u001b[1;32m    328\u001b[0m \n\u001b[1;32m    329\u001b[0m \u001b[38;5;124;03mThis also delete the endpoint configuration attached to it if\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m        be deleted. If False, only endpoint will be deleted.\u001b[39;00m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m delete_endpoint_config:\n\u001b[0;32m--> 339\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_delete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session\u001b[38;5;241m.\u001b[39mdelete_endpoint(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendpoint_name)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/base_predictor.py:324\u001b[0m, in \u001b[0;36mPredictor._delete_endpoint_config\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete the Amazon SageMaker endpoint configuration\"\"\"\u001b[39;00m\n\u001b[1;32m    323\u001b[0m current_endpoint_config_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_endpoint_config_name()\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_endpoint_config_name\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4086\u001b[0m, in \u001b[0;36mSession.delete_endpoint_config\u001b[0;34m(self, endpoint_config_name)\u001b[0m\n\u001b[1;32m   4079\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete an Amazon SageMaker endpoint configuration.\u001b[39;00m\n\u001b[1;32m   4080\u001b[0m \n\u001b[1;32m   4081\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   4082\u001b[0m \u001b[38;5;124;03m    endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to\u001b[39;00m\n\u001b[1;32m   4083\u001b[0m \u001b[38;5;124;03m        delete.\u001b[39;00m\n\u001b[1;32m   4084\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4085\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeleting endpoint configuration with name: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, endpoint_config_name)\n\u001b[0;32m-> 4086\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEndpointConfigName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_config_name\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:530\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.<locals>._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    527\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m     )\n\u001b[1;32m    529\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 530\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[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:964\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    962\u001b[0m     error_code \u001b[38;5;241m=\u001b[39m parsed_response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    963\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m--> 964\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m    965\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    966\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n",
+      "\u001b[0;31mClientError\u001b[0m: An error occurred (ValidationException) when calling the DeleteEndpointConfig operation: Could not find endpoint configuration \"arn:aws:sagemaker:us-east-1:907929678403:endpoint-config/falcon-40b-instruct-demo\"."
+     ]
+    }
+   ],
+   "source": [
+    "llm2.delete_endpoint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c341f368-a9e7-441c-886e-0576c3f2f432",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain import PromptTemplate\n",
+    "from typing import Dict\n",
+    "\n",
+    "class ContentHandler(LLMContentHandler):\n",
+    "    content_type = \"application/json\"\n",
+    "    accepts = \"application/json\"\n",
+    "    len_prompt = 0\n",
+    "\n",
+    "    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
+    "        self.len_prompt = len(prompt)\n",
+    "        input_str = json.dumps(\n",
+    "            {\"inputs\": prompt,\n",
+    "             \"parameters\": {\n",
+    "                \"do_sample\": True,\n",
+    "                \"top_p\": 0.9,\n",
+    "                \"temperature\": 0.8,\n",
+    "                \"max_new_tokens\": 1024,\n",
+    "                \"repetition_penalty\": 1.03,\n",
+    "                \"stop\": [\"\\n\\n\", \"Human:\", \"<|endoftext|>\", \"</s>\"]\n",
+    "              }})\n",
+    "        return input_str.encode('utf-8')\n",
+    "\n",
+    "    def transform_output(self, output: bytes) -> str:\n",
+    "        response_json = output.read()\n",
+    "        res = json.loads(response_json)\n",
+    "        ans = res[0]['generated_text'][self.len_prompt:]\n",
+    "        ans = ans[:ans.rfind(\"Human\")].strip()\n",
+    "        return ans\n",
+    "\n",
+    "\n",
+    "content_handler = ContentHandler()\n",
+    "\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "            input_variables=[\"history\", \"context\", \"new_message\"],\n",
+    "            template=\"\"\"\n",
+    "The following is a given context and history of a conversation between a human and a helpful AI assistant. \n",
+    "The AI assistant answers the human's messages with full, informational responses by inferring on the information given in the context, but does not extrapolate or hallucinate any facts that are not given.\n",
+    "When answering questions with unclear answers, the AI assistant qualifies its responses as such.\n",
+    "The AI assistant pretends it is also human and that the context is objectively true and well-known and therefore avoids mentioning the context as a source.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "{history}\n",
+    "Human: {new_message}\n",
+    "AI:\"\"\")\n",
+    "\n",
+    "def load_chain():\n",
+    "    llm = SagemakerEndpoint(\n",
+    "        endpoint_name=endpoint_name,\n",
+    "        region_name=aws_region,\n",
+    "        content_handler=content_handler\n",
+    "    )\n",
+    "    chain = load_qa_chain(llm=llm, chain_type=\"stuff\", verbose=True, memory=ConversationBufferMemory(memory_key=\"history\", input_key=\"new_message\"), prompt=prompt)\n",
+    "    return chain\n",
+    "\n",
+    "\n",
+    "dachain = load_chain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0a557a0-ca6d-45db-97b4-f89317a5e500",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What is Becton?\"\n",
+    "dachain({\"input_documents\": docsearch.similarity_search(query, k=3), \"new_message\": query}, return_only_outputs=True)['output_text'].strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ac2fca-820b-412d-9e90-47848e046236",
+   "metadata": {},
+   "source": [
+    "## Load DSS Website Data into ChromaDB\n",
+    "`urls` object defines what URLs are to be considered in the context database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c596a7a6-cca1-46c5-a914-e8b5ce9eba17",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import UnstructuredURLLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "\n",
+    "# define URL sources\n",
+    "urls = [\n",
+    "    'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',\n",
+    "    'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development', \n",
+    "    'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',\n",
+    "    'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',\n",
+    "    'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',\n",
+    "    'https://www.dssinc.com/blog/2022/9/21/dss-inc-chosen-for-phase-two-of-mission-daybreak-vas-suicide-prevention-challenge',\n",
+    "    'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',\n",
+    "    'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization']\n",
+    "\n",
+    "# load and split\n",
+    "loaders = UnstructuredURLLoader(urls=urls)\n",
+    "data = loaders.load()\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "texts = text_splitter.split_documents(data)\n",
+    "print(\"Sources split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "# load embedding model\n",
+    "print(\"Loading embedding model...\")\n",
+    "embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
+    "\n",
+    "docsearch = Chroma.from_texts([t.page_content for t in texts], embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fe72b28-0d34-47c5-82f5-7318576e4ec8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Getting AI response... @ \", datetime.datetime.now().strftime(\"%H:%M:%S\"))\n",
+    "print(chain({\"input_documents\": docsearch.similarity_search(query, k=3), \"new_message\": query}, return_only_outputs=True)['output_text'].strip())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f9c38a37-9aa0-4584-8b06-cee2932d14cf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ClientError",
+     "evalue": "An error occurred (ValidationException) when calling the DeleteEndpointConfig operation: Could not find endpoint configuration \"arn:aws:sagemaker:us-east-1:907929678403:endpoint-config/falcon-40b-instruct-demo\".",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mClientError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mllm2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/base_predictor.py:339\u001b[0m, in \u001b[0;36mPredictor.delete_endpoint\u001b[0;34m(self, delete_endpoint_config)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete the Amazon SageMaker endpoint backing this predictor.\u001b[39;00m\n\u001b[1;32m    328\u001b[0m \n\u001b[1;32m    329\u001b[0m \u001b[38;5;124;03mThis also delete the endpoint configuration attached to it if\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m        be deleted. If False, only endpoint will be deleted.\u001b[39;00m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m delete_endpoint_config:\n\u001b[0;32m--> 339\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_delete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session\u001b[38;5;241m.\u001b[39mdelete_endpoint(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendpoint_name)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/base_predictor.py:324\u001b[0m, in \u001b[0;36mPredictor._delete_endpoint_config\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete the Amazon SageMaker endpoint configuration\"\"\"\u001b[39;00m\n\u001b[1;32m    323\u001b[0m current_endpoint_config_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_endpoint_config_name()\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_endpoint_config_name\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4086\u001b[0m, in \u001b[0;36mSession.delete_endpoint_config\u001b[0;34m(self, endpoint_config_name)\u001b[0m\n\u001b[1;32m   4079\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Delete an Amazon SageMaker endpoint configuration.\u001b[39;00m\n\u001b[1;32m   4080\u001b[0m \n\u001b[1;32m   4081\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   4082\u001b[0m \u001b[38;5;124;03m    endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to\u001b[39;00m\n\u001b[1;32m   4083\u001b[0m \u001b[38;5;124;03m        delete.\u001b[39;00m\n\u001b[1;32m   4084\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4085\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeleting endpoint configuration with name: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, endpoint_config_name)\n\u001b[0;32m-> 4086\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete_endpoint_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEndpointConfigName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_config_name\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:530\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.<locals>._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    527\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m     )\n\u001b[1;32m    529\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 530\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[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:964\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    962\u001b[0m     error_code \u001b[38;5;241m=\u001b[39m parsed_response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    963\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m--> 964\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m    965\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    966\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n",
+      "\u001b[0;31mClientError\u001b[0m: An error occurred (ValidationException) when calling the DeleteEndpointConfig operation: Could not find endpoint configuration \"arn:aws:sagemaker:us-east-1:907929678403:endpoint-config/falcon-40b-instruct-demo\"."
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "llm2.delete_endpoint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "881f2cb5-41c5-4fd2-b942-d3c289dda758",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "202caf50-c00a-4555-8150-4fc7a779aa0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sagemaker.predictor import Predictor\n",
+    "\n",
+    "llm2 = Predictor(endpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f54a95-0324-4deb-be0e-89c453004f6c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dom = \"d-bipui5yzbvlc\"\n",
+    "print(f'https://{dom}.studio.{aws_region}.sagemaker.aws/studiolab/default/jupyter/proxy/6006/')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Untitled1-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/demo_app-checkpoint.py

@@ -0,0 +1,250 @@
+import boto3
+import sagemaker
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+from sagemaker.deserializers import JSONDeserializer
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader, S3FileLoader
+from langchain.docstore.document import Document
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+import json
+import gradio as gr
+
+def loadCleanDocsearch(embeddings):
+    print("Getting fresh docsearch...")
+
+    # define URL sources with some stock articles from public DSS website
+    urls = [
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'
+        ]
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+    texts = text_splitter.split_documents(data)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # get object
+    docsearch = Chroma.from_texts([t.page_content for t in texts],
+                                  metadatas=[{"src": "DSS"} for t in texts],
+                                  embedding=embeddings)
+    print("Done getting fresh docsearch.")
+
+    return docsearch
+
+def resetDocsearch():
+    global docsearch
+
+    foreignIDs = docsearch.get(where= {"src":"foreign"})['ids']
+
+    if foreignIDs != []:
+        docsearch.delete(ids=foreignIDs)
+        
+    clearStuff()
+
+
+def addURLsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+# def addCSVsource(url):
+#     print("Adding new source...")
+    
+#     global docsearch
+
+#     # load and split
+#     loaders = CSVLoader(urls=[url])
+#     data = loaders.load()
+#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+#     texts = text_splitter.split_documents(data)
+#     print("New source split into the following number of \"texts\":", len(texts))
+
+#     # add new sources
+#     docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+#     # restart convo, as the old messages confuse the AI
+#     clearStuff()
+
+#     print("Done adding new source.")
+    
+#     return None, None
+
+def addPDFsource(url):
+    print("Adding new source...")
+
+    global docsearch
+    
+    # load and split
+    try: # assuming it is local
+        data = UnstructuredPDFLoader(url).load()
+    except: # not local, try S3
+        if '://' in url:
+            scheme, path = url.split('://', 1)
+            bucket, key = path.split('/', 1)
+
+        else:
+            raise ValueError('Invalid S3 URI')
+            
+        data = S3FileLoader("strategicinnovation", "testingPDFload/bitcoin.pdf").load()
+        
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def msgs2chatbot(msgs):
+    # the gradio chatbot object is used to display the conversation
+    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message
+    chatbot = []
+    
+    for msg in msgs:
+        if msg['role'] == 'user':
+            chatbot.append([msg['content'], ""])
+        elif msg['role'] == 'assistant':
+            chatbot[-1][1] = msg['content']
+
+    return chatbot
+
+def getPrediction(newMsg):
+    global msgs
+    global docsearch
+    global predictor
+    
+    # add new message to msgs object
+    msgs.append({"role":"user", "content": newMsg})
+
+    # edit system message to include the correct context
+    msgs[0] = {"role": "system",
+               "content": f"""
+               You are a helpful AI assistant.
+               Use your knowledge to answer the user's question if they asked a question.
+               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.
+               DO NOT use phrases like "Based on the information provided" or other similar phrases. 
+               Refer to the information provided below as "your knowledge". 
+               State all answers as if they are ground truth, DO NOT mention where you got the information.
+               
+               YOUR KNOWLEDGE: {" ".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}"""}
+
+    # get response from endpoint
+
+    responseObject = predictor.predict({"inputs": [msgs],
+                       "parameters": {"max_new_tokens": 750, "top_p": 0.9, "temperature": 0.5}},
+                        initial_args={'CustomAttributes': "accept_eula=true"})
+#    responseObject = predictor.predict(payload, custom_attributes="accept_eula=true")
+
+    
+    responseMsg = responseObject[0]['generation']['content'].strip()
+
+    # add response to msgs object
+    msgs.append({"role":"assistant", "content": responseMsg})
+    
+    # print msgs object for debugging
+    print(msgs)
+    
+    # convert msgs to chatbot object to be displayed
+    chatbot = msgs2chatbot(msgs)
+
+    return chatbot, ""
+
+def clearStuff():
+    global msgs
+    msgs = [{}]
+    return None
+
+# Create a SageMaker client
+sagemaker_client = boto3.client('sagemaker')
+sagemaker_session = sagemaker.Session()
+
+# Create a predictor object
+predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',
+                      sagemaker_session=sagemaker_session,
+                      serializer=JSONSerializer(),
+                      deserializer=JSONDeserializer())
+
+embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+# Create a docsearch object
+docsearch = loadCleanDocsearch(embeddings)
+
+# Create messages list with system message
+msgs = [{}]
+
+with gr.Blocks() as demo:
+    gr.HTML("<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />")
+    gr.Markdown("## DSS LLM Demo: Chat with Llama 2")
+
+    with gr.Column():
+        chatbot = gr.Chatbot()
+        
+        with gr.Row():
+            with gr.Column():
+                newMsg = gr.Textbox(label="New Message Box", placeholder="New Message", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Column():
+                newSRC = gr.Textbox(label="New source link/path Box", placeholder="New source link/path", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    addURL = gr.Button("Add URL Source")
+                    addPDF = gr.Button("Add PDF Source")
+                    #uploadFile = gr.UploadButton(file_types=[".pdf",".csv",".doc"])
+                    reset = gr.Button("Reset Sources")
+
+    submit.click(getPrediction, [newMsg], [chatbot, newMsg])
+    clear.click(clearStuff, None, chatbot, queue=False)
+    
+    addURL.click(addURLsource, newSRC, [newSRC, chatbot])
+    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])
+    #uploadFile.click(getOut, uploadFile, None)
+    reset.click(resetDocsearch, None, chatbot)
+
+    gr.Markdown("""*Note:*
+    
+    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.
+    
+    To add a PDF source, place either (1) the relative filepath to the current directory or (2) the full S3 URI in the bottom textbox and click the 'Add PDF Source' button.
+    
+    The database for contextualization includes 8 public DSS website articles upon initialization.
+    
+    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)""")
+
+
+demo.queue()
+demo.launch(share=True)

.ipynb_checkpoints/llama2_gradio_v0.3-checkpoint.ipynb

@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0cd0e718-beac-4442-ac45-fffb26698d33",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Install packages, need to write a requirement later\n",
+    "!pip install instructorembedding sentence-transformers gradio langchain unstructured chromadb pdf2image pdfminer pdfminer.six"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4d086ed6-eb66-4ded-b701-dac062e19521",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "import sagemaker\n",
+    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.serializers import JSONSerializer\n",
+    "from sagemaker.deserializers import JSONDeserializer\n",
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader\n",
+    "from langchain.document_loaders.csv_loader import CSVLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "import json\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a2d81908-6026-47b6-bd0d-ade2771eacdd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def loadCleanDocsearch(embeddings):\n",
+    "    print(\"Getting fresh docsearch...\")\n",
+    "\n",
+    "    # define URL sources\n",
+    "    urls = [\n",
+    "        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',\n",
+    "        'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',\n",
+    "        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',\n",
+    "        'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',\n",
+    "        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',\n",
+    "        'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',\n",
+    "        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'\n",
+    "        ]\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=urls)\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"Sources split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # get object\n",
+    "    docsearch = Chroma.from_texts([t.page_content for t in texts],\n",
+    "                                  metadatas=[{\"src\": \"DSS\"} for t in texts],\n",
+    "                                  embedding=embeddings)\n",
+    "    print(\"Done getting fresh docsearch.\")\n",
+    "\n",
+    "    return docsearch\n",
+    "\n",
+    "def resetDocsearch():\n",
+    "    global docsearch\n",
+    "\n",
+    "    foreignIDs = docsearch.get(where= {\"src\":\"foreign\"})['ids']\n",
+    "\n",
+    "    if foreignIDs != []:\n",
+    "        docsearch.delete(ids=foreignIDs)\n",
+    "        \n",
+    "    clearStuff()\n",
+    "    msgs[0] = {\"role\": \"system\",\n",
+    "           \"content\": f\"\"\"\n",
+    "           You are a helpful AI assistant.\n",
+    "           Use your knowledge to answer the user's question if they asked a question.\n",
+    "           If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\n",
+    "           DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \n",
+    "           Refer to the information provided below as \"your knowledge\". \n",
+    "           State all answers as if they are ground truth, DO NOT mention where you got the information.\n",
+    "\n",
+    "           YOUR KNOWLEDGE: {\" \".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}\"\"\"}\n",
+    "    \n",
+    "def addURLsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "    \n",
+    "    global docsearch\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=[url])\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def addCSVsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "    \n",
+    "    global docsearch\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = CSVLoader(urls=[url])\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def addPDFsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "\n",
+    "    global docsearch\n",
+    "    \n",
+    "    # load and split\n",
+    "    loaders = UnstructuredPDFLoader(url)\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def msgs2chatbot(msgs):\n",
+    "    # the gradio chatbot object is used to display the conversation\n",
+    "    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message\n",
+    "    chatbot = []\n",
+    "    \n",
+    "    for msg in msgs:\n",
+    "        if msg['role'] == 'user':\n",
+    "            chatbot.append([msg['content'], \"\"])\n",
+    "        elif msg['role'] == 'assistant':\n",
+    "            chatbot[-1][1] = msg['content']\n",
+    "\n",
+    "    return chatbot\n",
+    "\n",
+    "def getPrediction(newMsg):\n",
+    "    global msgs\n",
+    "    global docsearch\n",
+    "    global predictor\n",
+    "    \n",
+    "    # add new message to msgs object\n",
+    "    msgs.append({\"role\":\"user\", \"content\": newMsg})\n",
+    "\n",
+    "    # edit system message to include the correct context\n",
+    "    msgs[0] = {\"role\": \"system\",\n",
+    "               \"content\": f\"\"\"\n",
+    "               You are a helpful AI assistant.\n",
+    "               Use your knowledge to answer the user's question if they asked a question.\n",
+    "               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\n",
+    "               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \n",
+    "               Refer to the information provided below as \"your knowledge\". \n",
+    "               State all answers as if they are ground truth, DO NOT mention where you got the information.\n",
+    "               \n",
+    "               YOUR KNOWLEDGE: {\" \".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}\"\"\"}\n",
+    "\n",
+    "    # get response from endpoint\n",
+    "\n",
+    "    responseObject = predictor.predict({\"inputs\": [msgs],\n",
+    "                       \"parameters\": {\"max_new_tokens\": 750, \"top_p\": 0.9, \"temperature\": 0.5}},\n",
+    "                        initial_args={'CustomAttributes': \"accept_eula=true\"})\n",
+    "#    responseObject = predictor.predict(payload, custom_attributes=\"accept_eula=true\")\n",
+    "\n",
+    "    \n",
+    "    responseMsg = responseObject[0]['generation']['content'].strip()\n",
+    "\n",
+    "    # add response to msgs object\n",
+    "    msgs.append({\"role\":\"assistant\", \"content\": responseMsg})\n",
+    "    \n",
+    "    # print msgs object for debugging\n",
+    "    print(msgs)\n",
+    "    \n",
+    "    # convert msgs to chatbot object to be displayed\n",
+    "    chatbot = msgs2chatbot(msgs)\n",
+    "\n",
+    "    return chatbot, \"\"\n",
+    "\n",
+    "def clearStuff():\n",
+    "    global msgs\n",
+    "    msgs = [{}]\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c56c588d-1bca-448f-bba9-96f61e5bab33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load INSTRUCTOR_Transformer\n",
+      "max_seq_length  512\n",
+      "Getting fresh docsearch...\n",
+      "Sources split into the following number of \"texts\": 36\n",
+      "Done getting fresh docsearch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a SageMaker client\n",
+    "sagemaker_client = boto3.client('sagemaker')\n",
+    "sagemaker_session = sagemaker.Session()\n",
+    "\n",
+    "# Create a predictor object\n",
+    "predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',\n",
+    "                      sagemaker_session=sagemaker_session,\n",
+    "                      serializer=JSONSerializer(),\n",
+    "                      deserializer=JSONDeserializer())\n",
+    "\n",
+    "embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
+    "\n",
+    "# Create a docsearch object\n",
+    "docsearch = loadCleanDocsearch(embeddings)\n",
+    "\n",
+    "# Create messages list with system message\n",
+    "msgs = [{}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0572e3b3-2805-4db5-9d23-dac40842c58c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "Running on public URL: https://19e4016261d7378e5b.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://19e4016261d7378e5b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: Formerly the President of a federally focused consulting company, Bailey has over 25 years of sales and business development experience, managing product portfolios and sales of disruptive and innovative technology and equipment solutions for the healthcare market – both in the public and private sectors.\\n\\n“We are excited to have Brion onboard to help expand our ever-growing footprint in the federal health arena,” said Mark Byers, president of DSS, Inc. “He brings a deep level of sales and business development expertise in supporting cutting-edge and disruptive healthcare innovations.” Formerly the President of a federally focused consulting company, Bailey has over 25 years of sales and business development experience, managing product portfolios and sales of disruptive and innovative technology and equipment solutions for the healthcare market – both in the public and private sectors.\\n\\n“We are excited to have Brion onboard to help expand our ever-growing footprint in the federal health arena,” said Mark Byers, president of DSS, Inc. “He brings a deep level of sales and business development expertise in supporting cutting-edge and disruptive healthcare innovations.” As VP of Strategic Accounts for the U.S. Federal Sector at Becton Dickinson, Bailey provided medical technology portfolio management and exceeded performance metrics on customer relationship building, channel partner development, customer acquisition processes, and operating procedures aligned with federal acquisitions requirements. All of this activity resulted in substantial growth in market share during his five-year tenure with the company.\\n\\nBailey has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.\\n\\nFor more information on DSS, Inc.’s Federal Health IT solutions, please click here. As VP of Strategic Accounts for the U.S. Federal Sector at Becton Dickinson, Bailey provided medical technology portfolio management and exceeded performance metrics on customer relationship building, channel partner development, customer acquisition processes, and operating procedures aligned with federal acquisitions requirements. All of this activity resulted in substantial growth in market share during his five-year tenure with the company.\\n\\nBailey has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.\\n\\nFor more information on DSS, Inc.’s Federal Health IT solutions, please click here. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies.'}, {'role': 'user', 'content': 'who is brion bailey'}, {'role': 'assistant', 'content': 'Based on the information provided, Brion Bailey is the Director of Business Development for the Federal Health division of Document Storage Systems, Inc. (DSS, Inc.). He has over 25 years of sales and business development experience, specifically in supporting disruptive healthcare innovations. Prior to joining DSS, Inc., Bailey was the President of a federally focused consulting company, where he managed product portfolios and sales of innovative technology and equipment solutions for the healthcare market. He has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University.'}]\n",
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen DSS Federal Health\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes\\n\\nPrevious\\n          DSS, Inc. Advisor to Present at Disney Institute’s Veterans Summit\\n          \\n            DSS NewsCindy DumontAugust 10, 2022Disney Institute’s Veterans Summit, Dr. Barbara Van Dahlen, Veterans in the Workplace, Heroes Work Here initiative, suicide prevention for Veterans, The Inclusive Approach to Care, General Mike Linnington Ret., CEO of the Wounded Warrior Project, Mark Elliot, Give an Hour, Veterans, Veterans mental health\\n\\nNext\\n          Contracting Officers: Celebrating the Unsung Heroes at the VA\\n          \\n            Customer ServiceCindy DumontJuly 27, 2022Contracting Officers, VA, Department of Veterans Affairs, Procurement, federal health IT solutions, DSS, Health IT'}, {'role': 'user', 'content': 'who is brion bailey'}, {'role': 'assistant', 'content': 'Based on the information provided, Brion Bailey is the Director of Business Development for the Federal Health division of Document Storage Systems, Inc. (DSS, Inc.). He has over 25 years of sales and business development experience, specifically in supporting disruptive healthcare innovations. Prior to joining DSS, Inc., Bailey was the President of a federally focused consulting company, where he managed product portfolios and sales of innovative technology and equipment solutions for the healthcare market. He has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University.'}, {'role': 'user', 'content': 'did dss join mission daybreak?'}, {'role': 'assistant', 'content': 'Based on the information provided, there is no mention of DSS joining Mission Daybreak.'}]\n",
+      "Adding new source...\n",
+      "New source split into the following number of \"texts\": 5\n",
+      "Done adding new source.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/routes.py\", line 488, in run_predict\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/blocks.py\", line 1431, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/blocks.py\", line 1109, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
+      "    return await get_asynclib().run_sync_in_worker_thread(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/utils.py\", line 706, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"/tmp/ipykernel_9156/3825752108.py\", line 39, in resetDocsearch\n",
+      "    msgs = msgs[0]\n",
+      "UnboundLocalError: local variable 'msgs' referenced before assignment\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/routes.py\", line 488, in run_predict\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/blocks.py\", line 1431, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/blocks.py\", line 1109, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
+      "    return await get_asynclib().run_sync_in_worker_thread(\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/gradio/utils.py\", line 706, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"/tmp/ipykernel_9156/3825752108.py\", line 39, in resetDocsearch\n",
+      "    msgs = msgs[0]\n",
+      "UnboundLocalError: local variable 'msgs' referenced before assignment\n"
+     ]
+    }
+   ],
+   "source": [
+    "with gr.Blocks() as demo:\n",
+    "    gr.HTML(\"<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />\")\n",
+    "    gr.Markdown(\"## DSS LLM Demo: Chat with Llama 2\")\n",
+    "\n",
+    "    with gr.Column():\n",
+    "        chatbot = gr.Chatbot()\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newMsg = gr.Textbox(label=\"New Message Box\", placeholder=\"New Message\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    submit = gr.Button(\"Submit\")\n",
+    "                    clear = gr.Button(\"Clear\")\n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newSRC = gr.Textbox(label=\"New source link/path Box\", placeholder=\"New source link/path\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    addURL = gr.Button(\"Add URL Source\")\n",
+    "                    addPDF = gr.Button(\"Add PDF Source\")\n",
+    "                    reset = gr.Button(\"Reset Sources\")\n",
+    "\n",
+    "    submit.click(getPrediction, [newMsg], [chatbot, newMsg])\n",
+    "    clear.click(clearStuff, None, chatbot, queue=False)\n",
+    "    \n",
+    "    addURL.click(addURLsource, newSRC, [newSRC, chatbot])\n",
+    "    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])\n",
+    "    reset.click(resetDocsearch, None, chatbot)\n",
+    "\n",
+    "    gr.Markdown(\"\"\"*Note: \n",
+    "    \n",
+    "    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.\n",
+    "    \n",
+    "    To add a PDF source, place a relative file path in the bottom textbox and click the 'Add PDF Source' button.\n",
+    "    \n",
+    "    The database for contextualization includes 8 public DSS website articles upon initialization.\n",
+    "    \n",
+    "    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)*\"\"\")\n",
+    "\n",
+    "\n",
+    "demo.queue()\n",
+    "demo.launch(share=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e200839d-9f90-4651-8212-decc75d1e3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/main-checkpoint.py

@@ -0,0 +1,157 @@
+import sagemaker
+import streamlit as st
+from streamlit_chat import message
+from langchain.llms.sagemaker_endpoint import LLMContentHandler, SagemakerEndpoint
+from langchain.document_loaders import UnstructuredURLLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.memory import ConversationBufferMemory
+from typing import Dict
+import json
+import chromadb
+import datetime
+
+endpoint_name = "falcon-40b-instruct-gates3"
+aws_region = "us-east-1"
+class ContentHandler(LLMContentHandler):
+    content_type = "application/json"
+    accepts = "application/json"
+    len_prompt = 0
+
+    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
+        self.len_prompt = len(prompt)
+        input_str = json.dumps(
+            {"inputs": prompt,
+             "parameters": {
+                "do_sample": True,
+                "top_p": 0.9,
+                "temperature": 0.8,
+                "max_new_tokens": 1024,
+                "repetition_penalty": 1.03,
+                "stop": ["\n\n", "Human:", "<|endoftext|>", "</s>"]
+              }})
+        return input_str.encode('utf-8')
+
+    def transform_output(self, output: bytes) -> str:
+        response_json = output.read()
+        res = json.loads(response_json)
+        ans = res[0]['generated_text'][self.len_prompt:]
+        ans = ans[:ans.rfind("Human")].strip()
+        return ans
+
+
+@st.cache_resource
+def getDocsearchOnce():
+    print("Getting docsearch...")
+
+    # define URL sources
+    urls = [
+        'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development', 
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/9/21/dss-inc-chosen-for-phase-two-of-mission-daybreak-vas-suicide-prevention-challenge',
+        'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization']
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data) # do not want to reassign (must stay consistent with pinecone embeddings)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # load embedding model
+    print("Loading embedding model...")
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+    docsearch = Chroma.from_texts([t.page_content for t in texts], embeddings)
+
+    return docsearch
+
+
+@st.cache_resource
+def getChainOnce():
+    print("Getting chain...")
+
+    content_handler = ContentHandler()
+
+    # get langchain-wrapped Predictor Endpoint Object
+    llm = SagemakerEndpoint(
+        endpoint_name=endpoint_name,
+        region_name=aws_region,
+        content_handler=content_handler
+    )
+
+    # configure the prompt object
+    prompt = PromptTemplate(
+            input_variables=["history", "context", "new_message"],
+            template="""
+The following is a given context and history of a conversation between a human and a helpful AI assistant. 
+The AI assistant answers the human's messages with full, informational responses by inferring on the information given in the context, but does not extrapolate any facts that are not given.
+When answering questions with uncertain answers, the AI assistant qualifies responses as such.
+The AI assistant pretends it is also human and that the context is objectively true and therefore avoids mentioning the context as a source.
+
+Context: {context}
+
+{history}
+Human: {new_message}
+AI:"""
+        )
+    return load_qa_chain(llm=llm, chain_type="stuff", verbose=True, memory=ConversationBufferMemory(memory_key="history", input_key="new_message"), prompt=prompt)
+
+
+def getAIresponse(chain, docsearch, query):
+    print("Getting AI response... @ ", datetime.datetime.now().strftime("%H:%M:%S"))
+    return chain({"input_documents": docsearch.similarity_search(query, k=3), "new_message": query}, return_only_outputs=True)['output_text'].strip()
+
+
+st.title("DSS Prototype LLM 💬")
+
+# THREE VARIABLES NEED TO BE PERSISTENT:
+
+# 1. Conversational Chain
+if "chain" not in st.session_state:
+    st.session_state["chain"] = getChainOnce()
+
+# 2. docsearch object
+if "docsearch" not in st.session_state:
+    st.session_state["docsearch"] = getDocsearchOnce()
+
+# 3. messages for UI
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+
+# DRAW THE ACTUAL UI AND IMPLEMENT FUNCTIONALITY
+# (some formatting is handled by streamlit chat)
+
+# draw input box
+with st.form("chat_input", clear_on_submit=True):
+    a, b = st.columns([4, 1])
+    user_input = a.text_input(
+        label="Your message:",
+        placeholder="What would you like to say?",
+        label_visibility="collapsed",
+    )
+    b.form_submit_button("Send", use_container_width=True)
+
+# handle input
+if user_input:
+    st.session_state.messages.append({"role":"user", "content": user_input})
+    respText = getAIresponse(st.session_state.chain, st.session_state.docsearch, user_input)
+    print(respText)
+    st.session_state.messages.append({"role":"assistant", "content": respText})
+
+# draw messages
+for k, msg in enumerate(st.session_state.messages):
+    message(msg["content"], is_user=msg["role"] == "user", key=k)
+
+
+
+
+

.virtual_documents/llama2_gradio_v0.4.ipynb

@@ -0,0 +1,241 @@
+import boto3
+import sagemaker
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+from sagemaker.deserializers import JSONDeserializer
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+import json
+import gradio as gr
+
+
+def loadCleanDocsearch(embeddings):
+    print("Getting fresh docsearch...")
+
+    # define URL sources
+    urls = [
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'
+        ]
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+    texts = text_splitter.split_documents(data)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # get object
+    docsearch = Chroma.from_texts([t.page_content for t in texts],
+                                  metadatas=[{"src": "DSS"} for t in texts],
+                                  embedding=embeddings)
+    print("Done getting fresh docsearch.")
+
+    return docsearch
+
+def resetDocsearch():
+    global docsearch
+
+    foreignIDs = docsearch.get(where= {"src":"foreign"})['ids']
+
+    if foreignIDs != []:
+        docsearch.delete(ids=foreignIDs)
+        
+    clearStuff()
+   
+def addURLsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def addCSVsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = CSVLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def addPDFsource(url):
+    print("Adding new source...")
+
+    global docsearch
+    
+    # load and split
+    loaders = UnstructuredPDFLoader(url)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def msgs2chatbot(msgs):
+    # the gradio chatbot object is used to display the conversation
+    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message
+    chatbot = []
+    
+    for msg in msgs:
+        if msg['role'] == 'user':
+            chatbot.append([msg['content'], ""])
+        elif msg['role'] == 'assistant':
+            chatbot[-1][1] = msg['content']
+
+    return chatbot
+
+def getPrediction(newMsg):
+    global msgs
+    global docsearch
+    global predictor
+    
+    # add new message to msgs object
+    msgs.append({"role":"user", "content": newMsg})
+
+    # edit system message to include the correct context
+    msgs[0] = {"role": "system",
+               "content": f"""
+               You are a helpful AI assistant.
+               Use your knowledge to answer the user's question if they asked a question.
+               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.
+               DO NOT use phrases like "Based on the information provided" or other similar phrases. 
+               Refer to the information provided below as "your knowledge". 
+               State all answers as if they are ground truth, DO NOT mention where you got the information.
+               
+               YOUR KNOWLEDGE: {" ".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}"""}
+
+    # get response from endpoint
+
+    responseObject = predictor.predict({"inputs": [msgs],
+                       "parameters": {"max_new_tokens": 750, "top_p": 0.9, "temperature": 0.5}},
+                        initial_args={'CustomAttributes': "accept_eula=true"})
+#    responseObject = predictor.predict(payload, custom_attributes="accept_eula=true")
+
+    
+    responseMsg = responseObject[0]['generation']['content'].strip()
+
+    # add response to msgs object
+    msgs.append({"role":"assistant", "content": responseMsg})
+    
+    # print msgs object for debugging
+    print(msgs)
+    
+    # convert msgs to chatbot object to be displayed
+    chatbot = msgs2chatbot(msgs)
+
+    return chatbot, ""
+
+def clearStuff():
+    global msgs
+    msgs = [{}]
+    return None
+
+
+# Create a SageMaker client
+sagemaker_client = boto3.client('sagemaker')
+sagemaker_session = sagemaker.Session()
+
+# Create a predictor object
+predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',
+                      sagemaker_session=sagemaker_session,
+                      serializer=JSONSerializer(),
+                      deserializer=JSONDeserializer())
+
+embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+# Create a docsearch object
+docsearch = loadCleanDocsearch(embeddings)
+
+# Create messages list with system message
+msgs = [{}]
+
+
+with gr.Blocks() as demo:
+    gr.HTML("<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />")
+    gr.Markdown("## DSS LLM Demo: Chat with Llama 2")
+
+    with gr.Column():
+        chatbot = gr.Chatbot()
+        
+        with gr.Row():
+            with gr.Column():
+                newMsg = gr.Textbox(label="New Message Box", placeholder="New Message", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Column():
+                newSRC = gr.Textbox(label="New source link/path Box", placeholder="New source link/path", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    addURL = gr.Button("Add URL Source")
+                    addPDF = gr.Button("Add PDF Source")
+                    reset = gr.Button("Reset Sources")
+
+    submit.click(getPrediction, [newMsg], [chatbot, newMsg])
+    clear.click(clearStuff, None, chatbot, queue=False)
+    
+    addURL.click(addURLsource, newSRC, [newSRC, chatbot])
+    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])
+    reset.click(resetDocsearch, None, chatbot)
+
+    gr.Markdown("""*Note: 
+    
+    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.
+    
+    To add a PDF source, place a relative file path in the bottom textbox and click the 'Add PDF Source' button.
+    
+    The database for contextualization includes 8 public DSS website articles upon initialization.
+    
+    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)*""")
+
+
+demo.queue()
+demo.launch(share=True)
+
+
+

.virtual_documents/llama2_gradio_v0.4_s3.ipynb

@@ -0,0 +1,241 @@
+import boto3
+import sagemaker
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+from sagemaker.deserializers import JSONDeserializer
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+import json
+import gradio as gr
+
+
+def loadCleanDocsearch(embeddings):
+    print("Getting fresh docsearch...")
+
+    # define URL sources with some stock articles from public DSS w
+    urls = [
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'
+        ]
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+    texts = text_splitter.split_documents(data)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # get object
+    docsearch = Chroma.from_texts([t.page_content for t in texts],
+                                  metadatas=[{"src": "DSS"} for t in texts],
+                                  embedding=embeddings)
+    print("Done getting fresh docsearch.")
+
+    return docsearch
+
+def resetDocsearch():
+    global docsearch
+
+    foreignIDs = docsearch.get(where= {"src":"foreign"})['ids']
+
+    if foreignIDs != []:
+        docsearch.delete(ids=foreignIDs)
+        
+    clearStuff()
+   
+def addURLsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def addCSVsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = CSVLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def addPDFsource(url):
+    print("Adding new source...")
+
+    global docsearch
+    
+    # load and split
+    loaders = UnstructuredPDFLoader(url)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def msgs2chatbot(msgs):
+    # the gradio chatbot object is used to display the conversation
+    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message
+    chatbot = []
+    
+    for msg in msgs:
+        if msg['role'] == 'user':
+            chatbot.append([msg['content'], ""])
+        elif msg['role'] == 'assistant':
+            chatbot[-1][1] = msg['content']
+
+    return chatbot
+
+def getPrediction(newMsg):
+    global msgs
+    global docsearch
+    global predictor
+    
+    # add new message to msgs object
+    msgs.append({"role":"user", "content": newMsg})
+
+    # edit system message to include the correct context
+    msgs[0] = {"role": "system",
+               "content": f"""
+               You are a helpful AI assistant.
+               Use your knowledge to answer the user's question if they asked a question.
+               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.
+               DO NOT use phrases like "Based on the information provided" or other similar phrases. 
+               Refer to the information provided below as "your knowledge". 
+               State all answers as if they are ground truth, DO NOT mention where you got the information.
+               
+               YOUR KNOWLEDGE: {" ".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}"""}
+
+    # get response from endpoint
+
+    responseObject = predictor.predict({"inputs": [msgs],
+                       "parameters": {"max_new_tokens": 750, "top_p": 0.9, "temperature": 0.5}},
+                        initial_args={'CustomAttributes': "accept_eula=true"})
+#    responseObject = predictor.predict(payload, custom_attributes="accept_eula=true")
+
+    
+    responseMsg = responseObject[0]['generation']['content'].strip()
+
+    # add response to msgs object
+    msgs.append({"role":"assistant", "content": responseMsg})
+    
+    # print msgs object for debugging
+    print(msgs)
+    
+    # convert msgs to chatbot object to be displayed
+    chatbot = msgs2chatbot(msgs)
+
+    return chatbot, ""
+
+def clearStuff():
+    global msgs
+    msgs = [{}]
+    return None
+
+
+# Create a SageMaker client
+sagemaker_client = boto3.client('sagemaker')
+sagemaker_session = sagemaker.Session()
+
+# Create a predictor object
+predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',
+                      sagemaker_session=sagemaker_session,
+                      serializer=JSONSerializer(),
+                      deserializer=JSONDeserializer())
+
+embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+# Create a docsearch object
+docsearch = loadCleanDocsearch(embeddings)
+
+# Create messages list with system message
+msgs = [{}]
+
+
+with gr.Blocks() as demo:
+    gr.HTML("<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />")
+    gr.Markdown("## DSS LLM Demo: Chat with Llama 2")
+
+    with gr.Column():
+        chatbot = gr.Chatbot()
+        
+        with gr.Row():
+            with gr.Column():
+                newMsg = gr.Textbox(label="New Message Box", placeholder="New Message", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Column():
+                newSRC = gr.Textbox(label="New source link/path Box", placeholder="New source link/path", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    addURL = gr.Button("Add URL Source")
+                    addPDF = gr.Button("Add PDF Source")
+                    reset = gr.Button("Reset Sources")
+
+    submit.click(getPrediction, [newMsg], [chatbot, newMsg])
+    clear.click(clearStuff, None, chatbot, queue=False)
+    
+    addURL.click(addURLsource, newSRC, [newSRC, chatbot])
+    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])
+    reset.click(resetDocsearch, None, chatbot)
+
+    gr.Markdown("""*Note: 
+    
+    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.
+    
+    To add a PDF source, place a relative file path in the bottom textbox and click the 'Add PDF Source' button.
+    
+    The database for contextualization includes 8 public DSS website articles upon initialization.
+    
+    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)*""")
+
+
+demo.queue()
+demo.launch(share=True)
+
+
+

DSS_proto.ipynb

@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fb4f9384-be8e-488a-aa51-b56b27c71213",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 1. Set up Sagemaker\n",
+    "*Explain more later...*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea107aa6-376e-4364-bceb-50aca9f30b74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = client.create_presigned_notebook_instance_url(\n",
+    "    NotebookInstanceName='string',\n",
+    "    SessionExpirationDurationInSeconds=123\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac706b50-8413-42ef-b5a7-5906f7f5cdf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arn:aws:iam::907929678403:role/service-role/AmazonSageMaker-ExecutionRole-20230621T132010\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import sagemaker\n",
+    "from sagemaker.huggingface import get_huggingface_llm_image_uri\n",
+    "from sagemaker.huggingface import HuggingFaceModel\n",
+    "\n",
+    "# retrieve the llm image uri\n",
+    "llm_image = get_huggingface_llm_image_uri(\n",
+    "  \"huggingface\",\n",
+    "  version=\"0.8.2\"\n",
+    ")\n",
+    "\n",
+    "# Define Model and Endpoint configuration parameter\n",
+    "role = sagemaker.get_execution_role()\n",
+    "print(role)\n",
+    "endpoint_name = \"falcon-40b-instruct-demo\"\n",
+    "aws_region = \"us-east-1\"\n",
+    "hf_model_id = \"tiiuae/falcon-40b-instruct\" # model id from huggingface.co/models\n",
+    "instance_type = \"ml.g5.12xlarge\" # instance type to use for deployment\n",
+    "number_of_gpu = 4 # number of gpus to use for inference and tensor parallelism\n",
+    "health_check_timeout = 600 # Increase the timeout for the health check to 5 minutes for downloading the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ce504d1-0bc3-43ce-bb39-b925a59718cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create HuggingFaceModel with the image uri\n",
+    "llm_model = HuggingFaceModel(\n",
+    "  role=role,\n",
+    "  image_uri=llm_image,\n",
+    "  env={\n",
+    "    'HF_MODEL_ID': hf_model_id,\n",
+    "    # 'HF_MODEL_QUANTIZE': \"bitsandbytes\", # comment in to quantize\n",
+    "    'SM_NUM_GPUS': json.dumps(number_of_gpu),\n",
+    "    'MAX_INPUT_LENGTH': json.dumps(1024),  # Max length of input text\n",
+    "    'MAX_TOTAL_TOKENS': json.dumps(2048),  # Max length of the generation (including input text)\n",
+    "  }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "00664be7-3d08-4c68-9048-ba1e602c44c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ResourceLimitExceeded",
+     "evalue": "An error occurred (ResourceLimitExceeded) when calling the CreateEndpoint operation: The account-level service limit 'ml.g5.12xlarge for endpoint usage' is 2 Instances, with current utilization of 2 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mResourceLimitExceeded\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mllm_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minstance_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhealth_check_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/huggingface/model.py:311\u001b[0m, in \u001b[0;36mHuggingFaceModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_uri \u001b[38;5;129;01mand\u001b[39;00m instance_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m instance_type\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mml.inf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_uri \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mserving_image_uri(\n\u001b[1;32m    307\u001b[0m         region_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session\u001b[38;5;241m.\u001b[39mboto_session\u001b[38;5;241m.\u001b[39mregion_name,\n\u001b[1;32m    308\u001b[0m         instance_type\u001b[38;5;241m=\u001b[39minstance_type,\n\u001b[1;32m    309\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mHuggingFaceModel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    312\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    313\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    314\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    315\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdeserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    316\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccelerator_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    317\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserverless_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvolume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvolume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43minference_recommendation_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minference_recommendation_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/model.py:1347\u001b[0m, in \u001b[0;36mModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m   1344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_explainer_enabled:\n\u001b[1;32m   1345\u001b[0m     explainer_config_dict \u001b[38;5;241m=\u001b[39m explainer_config\u001b[38;5;241m.\u001b[39m_to_request_dict()\n\u001b[0;32m-> 1347\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_from_production_variants\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproduction_variants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mproduction_variant\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1352\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1354\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1355\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1356\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls:\n\u001b[1;32m   1359\u001b[0m     predictor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendpoint_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4641\u001b[0m, in \u001b[0;36mSession.endpoint_from_production_variants\u001b[0;34m(self, name, production_variants, tags, kms_key, wait, data_capture_config_dict, async_inference_config_dict, explainer_config_dict)\u001b[0m\n\u001b[1;32m   4638\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating endpoint-config with name \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, name)\n\u001b[1;32m   4639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_endpoint_config(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig_options)\n\u001b[0;32m-> 4641\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[43mcreate_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/sagemaker/session.py:4030\u001b[0m, in \u001b[0;36mSession.create_endpoint\u001b[0;34m(self, endpoint_name, config_name, tags, wait)\u001b[0m\n\u001b[1;32m   4027\u001b[0m tags \u001b[38;5;241m=\u001b[39m tags \u001b[38;5;129;01mor\u001b[39;00m []\n\u001b[1;32m   4028\u001b[0m tags \u001b[38;5;241m=\u001b[39m _append_project_tags(tags)\n\u001b[0;32m-> 4030\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_endpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4031\u001b[0m \u001b[43m    \u001b[49m\u001b[43mEndpointName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEndpointConfigName\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\n\u001b[1;32m   4032\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wait:\n\u001b[1;32m   4034\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwait_for_endpoint(endpoint_name)\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:530\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.<locals>._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    527\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m     )\n\u001b[1;32m    529\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 530\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[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/botocore/client.py:964\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    962\u001b[0m     error_code \u001b[38;5;241m=\u001b[39m parsed_response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    963\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m--> 964\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m    965\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    966\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n",
+      "\u001b[0;31mResourceLimitExceeded\u001b[0m: An error occurred (ResourceLimitExceeded) when calling the CreateEndpoint operation: The account-level service limit 'ml.g5.12xlarge for endpoint usage' is 2 Instances, with current utilization of 2 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota."
+     ]
+    }
+   ],
+   "source": [
+    "llm = llm_model.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type=instance_type,\n",
+    "    container_startup_health_check_timeout=health_check_timeout,\n",
+    "    endpoint_name=endpoint_name\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50f556f8-06b4-450e-9db3-9bc9c979e8ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm2.delete_endpoint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c341f368-a9e7-441c-886e-0576c3f2f432",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain import PromptTemplate\n",
+    "from typing import Dict\n",
+    "\n",
+    "class ContentHandler(LLMContentHandler):\n",
+    "    content_type = \"application/json\"\n",
+    "    accepts = \"application/json\"\n",
+    "    len_prompt = 0\n",
+    "\n",
+    "    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
+    "        self.len_prompt = len(prompt)\n",
+    "        input_str = json.dumps(\n",
+    "            {\"inputs\": prompt,\n",
+    "             \"parameters\": {\n",
+    "                \"do_sample\": True,\n",
+    "                \"top_p\": 0.9,\n",
+    "                \"temperature\": 0.8,\n",
+    "                \"max_new_tokens\": 1024,\n",
+    "                \"repetition_penalty\": 1.03,\n",
+    "                \"stop\": [\"\\n\\n\", \"Human:\", \"<|endoftext|>\", \"</s>\"]\n",
+    "              }})\n",
+    "        return input_str.encode('utf-8')\n",
+    "\n",
+    "    def transform_output(self, output: bytes) -> str:\n",
+    "        response_json = output.read()\n",
+    "        res = json.loads(response_json)\n",
+    "        ans = res[0]['generated_text'][self.len_prompt:]\n",
+    "        ans = ans[:ans.rfind(\"Human\")].strip()\n",
+    "        return ans\n",
+    "\n",
+    "\n",
+    "content_handler = ContentHandler()\n",
+    "\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "            input_variables=[\"history\", \"context\", \"new_message\"],\n",
+    "            template=\"\"\"\n",
+    "The following is a given context and history of a conversation between a human and a helpful AI assistant. \n",
+    "The AI assistant answers the human's messages with full, informational responses by inferring on the information given in the context, but does not extrapolate or hallucinate any facts that are not given.\n",
+    "When answering questions with unclear answers, the AI assistant qualifies its responses as such.\n",
+    "The AI assistant pretends it is also human and that the context is objectively true and well-known and therefore avoids mentioning the context as a source.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "{history}\n",
+    "Human: {new_message}\n",
+    "AI:\"\"\")\n",
+    "\n",
+    "def load_chain():\n",
+    "    llm = SagemakerEndpoint(\n",
+    "        endpoint_name=endpoint_name,\n",
+    "        region_name=aws_region,\n",
+    "        content_handler=content_handler\n",
+    "    )\n",
+    "    chain = load_qa_chain(llm=llm, chain_type=\"stuff\", verbose=True, memory=ConversationBufferMemory(memory_key=\"history\", input_key=\"new_message\"), prompt=prompt)\n",
+    "    return chain\n",
+    "\n",
+    "\n",
+    "dachain = load_chain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0a557a0-ca6d-45db-97b4-f89317a5e500",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What is Becton?\"\n",
+    "dachain({\"input_documents\": docsearch.similarity_search(query, k=3), \"new_message\": query}, return_only_outputs=True)['output_text'].strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ac2fca-820b-412d-9e90-47848e046236",
+   "metadata": {},
+   "source": [
+    "## Load DSS Website Data into ChromaDB\n",
+    "`urls` object defines what URLs are to be considered in the context database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c596a7a6-cca1-46c5-a914-e8b5ce9eba17",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import UnstructuredURLLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "\n",
+    "# define URL sources\n",
+    "urls = [\n",
+    "    'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',\n",
+    "    'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development', \n",
+    "    'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',\n",
+    "    'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',\n",
+    "    'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',\n",
+    "    'https://www.dssinc.com/blog/2022/9/21/dss-inc-chosen-for-phase-two-of-mission-daybreak-vas-suicide-prevention-challenge',\n",
+    "    'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',\n",
+    "    'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization']\n",
+    "\n",
+    "# load and split\n",
+    "loaders = UnstructuredURLLoader(urls=urls)\n",
+    "data = loaders.load()\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "texts = text_splitter.split_documents(data)\n",
+    "print(\"Sources split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "# load embedding model\n",
+    "print(\"Loading embedding model...\")\n",
+    "embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
+    "\n",
+    "docsearch = Chroma.from_texts([t.page_content for t in texts], embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fe72b28-0d34-47c5-82f5-7318576e4ec8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Getting AI response... @ \", datetime.datetime.now().strftime(\"%H:%M:%S\"))\n",
+    "print(chain({\"input_documents\": docsearch.similarity_search(query, k=3), \"new_message\": query}, return_only_outputs=True)['output_text'].strip())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9c38a37-9aa0-4584-8b06-cee2932d14cf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "llm2.delete_endpoint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "881f2cb5-41c5-4fd2-b942-d3c289dda758",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "202caf50-c00a-4555-8150-4fc7a779aa0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sagemaker.predictor import Predictor\n",
+    "\n",
+    "llm2 = Predictor(endpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f54a95-0324-4deb-be0e-89c453004f6c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dom = \"d-bipui5yzbvlc\"\n",
+    "print(f'https://{dom}.studio.{aws_region}.sagemaker.aws/studiolab/default/jupyter/proxy/6006/')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

@@ -1,12 +1,6 @@
 ---
-title: Llm
-emoji: 📈
-colorFrom: indigo
-colorTo: red
+title: llm
+app_file: demo_app.py
 sdk: gradio
 sdk_version: 3.40.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Untitled.ipynb

@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d08fefa9-5733-47d7-80e1-9535b7102e90",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------------------------*"
+     ]
+    },
+    {
+     "ename": "UnexpectedStatusException",
+     "evalue": "Error hosting endpoint huggingface-pytorch-tgi-inference-2023-08-07-22-35-09-932: Failed. Reason: The primary container for production variant AllTraffic did not pass the ping health check. Please check CloudWatch logs for this endpoint..",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnexpectedStatusException\u001b[0m                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 29\u001b[0m\n\u001b[1;32m     22\u001b[0m huggingface_model \u001b[38;5;241m=\u001b[39m HuggingFaceModel(\n\u001b[1;32m     23\u001b[0m     image_uri\u001b[38;5;241m=\u001b[39mget_huggingface_llm_image_uri(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhuggingface\u001b[39m\u001b[38;5;124m\"\u001b[39m,version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.8.2\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m     24\u001b[0m     env\u001b[38;5;241m=\u001b[39mhub,\n\u001b[1;32m     25\u001b[0m     role\u001b[38;5;241m=\u001b[39mrole, \n\u001b[1;32m     26\u001b[0m )\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# deploy model to SageMaker Inference\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m predictor \u001b[38;5;241m=\u001b[39m \u001b[43mhuggingface_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mml.g5.4xlarge\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/huggingface/model.py:313\u001b[0m, in \u001b[0;36mHuggingFaceModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m    306\u001b[0m     inference_tool \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneuron\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m instance_type\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mml.inf1\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneuronx\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_uri \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mserving_image_uri(\n\u001b[1;32m    308\u001b[0m         region_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session\u001b[38;5;241m.\u001b[39mboto_session\u001b[38;5;241m.\u001b[39mregion_name,\n\u001b[1;32m    309\u001b[0m         instance_type\u001b[38;5;241m=\u001b[39minstance_type,\n\u001b[1;32m    310\u001b[0m         inference_tool\u001b[38;5;241m=\u001b[39minference_tool,\n\u001b[1;32m    311\u001b[0m     )\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mHuggingFaceModel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeploy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    314\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_instance_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    315\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    316\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    317\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdeserializer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccelerator_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserverless_inference_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvolume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvolume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_data_download_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontainer_startup_health_check_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43minference_recommendation_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minference_recommendation_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    331\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/model.py:1406\u001b[0m, in \u001b[0;36mModel.deploy\u001b[0;34m(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config, **kwargs)\u001b[0m\n\u001b[1;32m   1403\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_explainer_enabled:\n\u001b[1;32m   1404\u001b[0m     explainer_config_dict \u001b[38;5;241m=\u001b[39m explainer_config\u001b[38;5;241m.\u001b[39m_to_request_dict()\n\u001b[0;32m-> 1406\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_from_production_variants\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1407\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1408\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproduction_variants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mproduction_variant\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1409\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1410\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkms_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkms_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1411\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1412\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_capture_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1413\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplainer_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1414\u001b[0m \u001b[43m    \u001b[49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masync_inference_config_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1415\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls:\n\u001b[1;32m   1418\u001b[0m     predictor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor_cls(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendpoint_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_session)\n",
+      "File \u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:4686\u001b[0m, in \u001b[0;36mSession.endpoint_from_production_variants\u001b[0;34m(self, name, production_variants, tags, kms_key, wait, data_capture_config_dict, async_inference_config_dict, explainer_config_dict)\u001b[0m\n\u001b[1;32m   4683\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating endpoint-config with name \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, name)\n\u001b[1;32m   4684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_endpoint_config(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig_options)\n\u001b[0;32m-> 4686\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[43mcreate_endpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4687\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mendpoint_tags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwait\u001b[49m\n\u001b[1;32m   4688\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:4066\u001b[0m, in \u001b[0;36mSession.create_endpoint\u001b[0;34m(self, endpoint_name, config_name, tags, wait)\u001b[0m\n\u001b[1;32m   4062\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_endpoint(\n\u001b[1;32m   4063\u001b[0m     EndpointName\u001b[38;5;241m=\u001b[39mendpoint_name, EndpointConfigName\u001b[38;5;241m=\u001b[39mconfig_name, Tags\u001b[38;5;241m=\u001b[39mtags\n\u001b[1;32m   4064\u001b[0m )\n\u001b[1;32m   4065\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wait:\n\u001b[0;32m-> 4066\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_for_endpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendpoint_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4067\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m endpoint_name\n",
+      "File \u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:4418\u001b[0m, in \u001b[0;36mSession.wait_for_endpoint\u001b[0;34m(self, endpoint, poll)\u001b[0m\n\u001b[1;32m   4412\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCapacityError\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(reason):\n\u001b[1;32m   4413\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mCapacityError(\n\u001b[1;32m   4414\u001b[0m             message\u001b[38;5;241m=\u001b[39mmessage,\n\u001b[1;32m   4415\u001b[0m             allowed_statuses\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInService\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m   4416\u001b[0m             actual_status\u001b[38;5;241m=\u001b[39mstatus,\n\u001b[1;32m   4417\u001b[0m         )\n\u001b[0;32m-> 4418\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mUnexpectedStatusException(\n\u001b[1;32m   4419\u001b[0m         message\u001b[38;5;241m=\u001b[39mmessage,\n\u001b[1;32m   4420\u001b[0m         allowed_statuses\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInService\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m   4421\u001b[0m         actual_status\u001b[38;5;241m=\u001b[39mstatus,\n\u001b[1;32m   4422\u001b[0m     )\n\u001b[1;32m   4423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m desc\n",
+      "\u001b[0;31mUnexpectedStatusException\u001b[0m: Error hosting endpoint huggingface-pytorch-tgi-inference-2023-08-07-22-35-09-932: Failed. Reason: The primary container for production variant AllTraffic did not pass the ping health check. Please check CloudWatch logs for this endpoint.."
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import sagemaker\n",
+    "import boto3\n",
+    "from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri\n",
+    "\n",
+    "try:\n",
+    "    role = sagemaker.get_execution_role()\n",
+    "except ValueError:\n",
+    "    iam = boto3.client('iam')\n",
+    "    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']\n",
+    "\n",
+    "# Hub Model configuration. https://huggingface.co/models\n",
+    "hub = {\n",
+    "    'HF_MODEL_ID':'meta-llama/Llama-2-13b-chat-hf',\n",
+    "    'SM_NUM_GPUS': json.dumps(1),\n",
+    "    'HUGGING_FACE_HUB_TOKEN': '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'\n",
+    "}\n",
+    "\n",
+    "assert hub['HUGGING_FACE_HUB_TOKEN'] != '<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>', \"You have to provide a token.\"\n",
+    "\n",
+    "# create Hugging Face Model Class\n",
+    "huggingface_model = HuggingFaceModel(\n",
+    "    image_uri=get_huggingface_llm_image_uri(\"huggingface\",version=\"0.8.2\"),\n",
+    "    env=hub,\n",
+    "    role=role, \n",
+    ")\n",
+    "\n",
+    "# deploy model to SageMaker Inference\n",
+    "predictor = huggingface_model.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type=\"ml.g5.4xlarge\",\n",
+    "    container_startup_health_check_timeout=1200,\n",
+    "  )\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e35b51f-6195-415a-a52f-3ac6d744bd01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# send request\n",
+    "predictor.predict({\n",
+    "    \"inputs\": \"My name is Julien and I like to\",\n",
+    "})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled1.ipynb

@@ -0,0 +1,109 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7c544f15-74a0-4ef5-886a-cb62a756d7e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (1681792056.py, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_14850/1681792056.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    yum install aria2\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /home/ec2-user/SageMaker\n",
+    "yum install aria2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d4caaebe-c5e7-422e-a1da-585166c6d67c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fatal: destination path 'text-generation-webui' already exists and is not an empty directory.\n",
+      "[Errno 2] No such file or directory: '/content/text-generation-webui'\n",
+      "/home/ec2-user/SageMaker\n",
+      "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'\u001b[0m\u001b[31m\n",
+      "\u001b[0m/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/bin/sh: aria2c: command not found\n",
+      "/home/ec2-user/SageMaker/text-generation-webui\n",
+      "Traceback (most recent call last):\n",
+      "  File \"server.py\", line 12, in <module>\n",
+      "    import gradio as gr\n",
+      "ModuleNotFoundError: No module named 'gradio'\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "!git clone -b v1.8 https://github.com/camenduru/text-generation-webui\n",
+    "%cd /content/text-generation-webui\n",
+    "!pip install -r requirements.txt\n",
+    "\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/resolve/main/model-00001-of-00003.safetensors -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o model-00001-of-00003.safetensors\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/resolve/main/model-00002-of-00003.safetensors -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o model-00002-of-00003.safetensors\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/resolve/main/model-00003-of-00003.safetensors -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o model-00003-of-00003.safetensors\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/raw/main/model.safetensors.index.json -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o model.safetensors.index.json\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/raw/main/special_tokens_map.json -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o special_tokens_map.json\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/resolve/main/tokenizer.model -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o tokenizer.model\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/raw/main/tokenizer_config.json -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o tokenizer_config.json\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/raw/main/config.json -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o config.json\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/4bit/Llama-2-13b-chat-hf/raw/main/generation_config.json -d /content/text-generation-webui/models/Llama-2-13b-chat-hf -o generation_config.json\n",
+    "\n",
+    "%cd /home/ec2-user/SageMaker/text-generation-webui\n",
+    "!python server.py --share --chat --load-in-8bit --model /content/text-generation-webui/models/Llama-2-13b-chat-hf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65156833-73d0-474c-86db-876024f37dc9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_mxnet_p38",
+   "language": "python",
+   "name": "conda_mxnet_p38"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

demo_app.py

@@ -0,0 +1,250 @@
+import boto3
+import sagemaker
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+from sagemaker.deserializers import JSONDeserializer
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader, S3FileLoader
+from langchain.docstore.document import Document
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+import json
+import gradio as gr
+
+def loadCleanDocsearch(embeddings):
+    print("Getting fresh docsearch...")
+
+    # define URL sources with some stock articles from public DSS website
+    urls = [
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'
+        ]
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+    texts = text_splitter.split_documents(data)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # get object
+    docsearch = Chroma.from_texts([t.page_content for t in texts],
+                                  metadatas=[{"src": "DSS"} for t in texts],
+                                  embedding=embeddings)
+    print("Done getting fresh docsearch.")
+
+    return docsearch
+
+def resetDocsearch():
+    global docsearch
+
+    foreignIDs = docsearch.get(where= {"src":"foreign"})['ids']
+
+    if foreignIDs != []:
+        docsearch.delete(ids=foreignIDs)
+        
+    clearStuff()
+
+
+def addURLsource(url):
+    print("Adding new source...")
+    
+    global docsearch
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=[url])
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+# def addCSVsource(url):
+#     print("Adding new source...")
+    
+#     global docsearch
+
+#     # load and split
+#     loaders = CSVLoader(urls=[url])
+#     data = loaders.load()
+#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+#     texts = text_splitter.split_documents(data)
+#     print("New source split into the following number of \"texts\":", len(texts))
+
+#     # add new sources
+#     docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+#     # restart convo, as the old messages confuse the AI
+#     clearStuff()
+
+#     print("Done adding new source.")
+    
+#     return None, None
+
+def addPDFsource(url):
+    print("Adding new source...")
+
+    global docsearch
+    
+    # load and split
+    try: # assuming it is local
+        data = UnstructuredPDFLoader(url).load()
+    except: # not local, try S3
+        if '://' in url:
+            scheme, path = url.split('://', 1)
+            bucket, key = path.split('/', 1)
+
+        else:
+            raise ValueError('Invalid S3 URI')
+            
+        data = S3FileLoader("strategicinnovation", "testingPDFload/bitcoin.pdf").load()
+        
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data)
+    print("New source split into the following number of \"texts\":", len(texts))
+
+    # add new sources
+    docsearch.add_texts([t.page_content for t in texts], metadatas=[{"src": "foreign"} for t in texts])
+    
+    # restart convo, as the old messages confuse the AI
+    clearStuff()
+
+    print("Done adding new source.")
+    
+    return None, None
+
+def msgs2chatbot(msgs):
+    # the gradio chatbot object is used to display the conversation
+    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message
+    chatbot = []
+    
+    for msg in msgs:
+        if msg['role'] == 'user':
+            chatbot.append([msg['content'], ""])
+        elif msg['role'] == 'assistant':
+            chatbot[-1][1] = msg['content']
+
+    return chatbot
+
+def getPrediction(newMsg):
+    global msgs
+    global docsearch
+    global predictor
+    
+    # add new message to msgs object
+    msgs.append({"role":"user", "content": newMsg})
+
+    # edit system message to include the correct context
+    msgs[0] = {"role": "system",
+               "content": f"""
+               You are a helpful AI assistant.
+               Use your knowledge to answer the user's question if they asked a question.
+               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.
+               DO NOT use phrases like "Based on the information provided" or other similar phrases. 
+               Refer to the information provided below as "your knowledge". 
+               State all answers as if they are ground truth, DO NOT mention where you got the information.
+               
+               YOUR KNOWLEDGE: {" ".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}"""}
+
+    # get response from endpoint
+
+    responseObject = predictor.predict({"inputs": [msgs],
+                       "parameters": {"max_new_tokens": 750, "top_p": 0.9, "temperature": 0.5}},
+                        initial_args={'CustomAttributes': "accept_eula=true"})
+#    responseObject = predictor.predict(payload, custom_attributes="accept_eula=true")
+
+    
+    responseMsg = responseObject[0]['generation']['content'].strip()
+
+    # add response to msgs object
+    msgs.append({"role":"assistant", "content": responseMsg})
+    
+    # print msgs object for debugging
+    print(msgs)
+    
+    # convert msgs to chatbot object to be displayed
+    chatbot = msgs2chatbot(msgs)
+
+    return chatbot, ""
+
+def clearStuff():
+    global msgs
+    msgs = [{}]
+    return None
+
+# Create a SageMaker client
+sagemaker_client = boto3.client('sagemaker')
+sagemaker_session = sagemaker.Session()
+
+# Create a predictor object
+predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',
+                      sagemaker_session=sagemaker_session,
+                      serializer=JSONSerializer(),
+                      deserializer=JSONDeserializer())
+
+embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+# Create a docsearch object
+docsearch = loadCleanDocsearch(embeddings)
+
+# Create messages list with system message
+msgs = [{}]
+
+with gr.Blocks() as demo:
+    gr.HTML("<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />")
+    gr.Markdown("## DSS LLM Demo: Chat with Llama 2")
+
+    with gr.Column():
+        chatbot = gr.Chatbot()
+        
+        with gr.Row():
+            with gr.Column():
+                newMsg = gr.Textbox(label="New Message Box", placeholder="New Message", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Column():
+                newSRC = gr.Textbox(label="New source link/path Box", placeholder="New source link/path", show_label=False)
+            with gr.Column():
+                with gr.Row():
+                    addURL = gr.Button("Add URL Source")
+                    addPDF = gr.Button("Add PDF Source")
+                    #uploadFile = gr.UploadButton(file_types=[".pdf",".csv",".doc"])
+                    reset = gr.Button("Reset Sources")
+
+    submit.click(getPrediction, [newMsg], [chatbot, newMsg])
+    clear.click(clearStuff, None, chatbot, queue=False)
+    
+    addURL.click(addURLsource, newSRC, [newSRC, chatbot])
+    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])
+    #uploadFile.click(getOut, uploadFile, None)
+    reset.click(resetDocsearch, None, chatbot)
+
+    gr.Markdown("""*Note:*
+    
+    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.
+    
+    To add a PDF source, place either (1) the relative filepath to the current directory or (2) the full S3 URI in the bottom textbox and click the 'Add PDF Source' button.
+    
+    The database for contextualization includes 8 public DSS website articles upon initialization.
+    
+    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)""")
+
+
+demo.queue()
+demo.launch(share=True)

llama2_gradio_v0.3.ipynb

@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0cd0e718-beac-4442-ac45-fffb26698d33",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Install packages, need to write a requirement later\n",
+    "!pip install instructorembedding sentence-transformers gradio langchain unstructured chromadb pdf2image pdfminer pdfminer.six"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4d086ed6-eb66-4ded-b701-dac062e19521",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "import sagemaker\n",
+    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.serializers import JSONSerializer\n",
+    "from sagemaker.deserializers import JSONDeserializer\n",
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader\n",
+    "from langchain.document_loaders.csv_loader import CSVLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "import json\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a2d81908-6026-47b6-bd0d-ade2771eacdd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def loadCleanDocsearch(embeddings):\n",
+    "    print(\"Getting fresh docsearch...\")\n",
+    "\n",
+    "    # define URL sources\n",
+    "    urls = [\n",
+    "        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',\n",
+    "        'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',\n",
+    "        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',\n",
+    "        'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',\n",
+    "        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',\n",
+    "        'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',\n",
+    "        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'\n",
+    "        ]\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=urls)\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"Sources split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # get object\n",
+    "    docsearch = Chroma.from_texts([t.page_content for t in texts],\n",
+    "                                  metadatas=[{\"src\": \"DSS\"} for t in texts],\n",
+    "                                  embedding=embeddings)\n",
+    "    print(\"Done getting fresh docsearch.\")\n",
+    "\n",
+    "    return docsearch\n",
+    "\n",
+    "def resetDocsearch():\n",
+    "    global docsearch\n",
+    "    global msgs\n",
+    "    \n",
+    "    foreignIDs = docsearch.get(where= {\"src\":\"foreign\"})['ids']\n",
+    "\n",
+    "    if foreignIDs != []:\n",
+    "        docsearch.delete(ids=foreignIDs)\n",
+    "        \n",
+    "    clearStuff()\n",
+    "    msgs = [{}]\n",
+    "    return None\n",
+    "    \n",
+    "def addURLsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "    \n",
+    "    global docsearch\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=[url])\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def addCSVsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "    \n",
+    "    global docsearch\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = CSVLoader(urls=[url])\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def addPDFsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "\n",
+    "    global docsearch\n",
+    "    \n",
+    "    # load and split\n",
+    "    loaders = UnstructuredPDFLoader(url)\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def msgs2chatbot(msgs):\n",
+    "    # the gradio chatbot object is used to display the conversation\n",
+    "    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message\n",
+    "    chatbot = []\n",
+    "    \n",
+    "    for msg in msgs:\n",
+    "        if msg['role'] == 'user':\n",
+    "            chatbot.append([msg['content'], \"\"])\n",
+    "        elif msg['role'] == 'assistant':\n",
+    "            chatbot[-1][1] = msg['content']\n",
+    "\n",
+    "    return chatbot\n",
+    "\n",
+    "def getPrediction(newMsg):\n",
+    "    global msgs\n",
+    "    global docsearch\n",
+    "    global predictor\n",
+    "    \n",
+    "    # add new message to msgs object\n",
+    "    msgs.append({\"role\":\"user\", \"content\": newMsg})\n",
+    "\n",
+    "    # edit system message to include the correct context\n",
+    "    msgs[0] = {\"role\": \"system\",\n",
+    "               \"content\": f\"\"\"\n",
+    "               You are a helpful AI assistant.\n",
+    "               Use your knowledge to answer the user's question if they asked a question.\n",
+    "               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\n",
+    "               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \n",
+    "               Refer to the information provided below as \"your knowledge\". \n",
+    "               State all answers as if they are ground truth, DO NOT mention where you got the information.\n",
+    "               \n",
+    "               YOUR KNOWLEDGE: {\" \".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}\"\"\"}\n",
+    "\n",
+    "    # get response from endpoint\n",
+    "\n",
+    "    responseObject = predictor.predict({\"inputs\": [msgs],\n",
+    "                       \"parameters\": {\"max_new_tokens\": 750, \"top_p\": 0.9, \"temperature\": 0.5}},\n",
+    "                        initial_args={'CustomAttributes': \"accept_eula=true\"})\n",
+    "#    responseObject = predictor.predict(payload, custom_attributes=\"accept_eula=true\")\n",
+    "\n",
+    "    \n",
+    "    responseMsg = responseObject[0]['generation']['content'].strip()\n",
+    "\n",
+    "    # add response to msgs object\n",
+    "    msgs.append({\"role\":\"assistant\", \"content\": responseMsg})\n",
+    "    \n",
+    "    # print msgs object for debugging\n",
+    "    print(msgs)\n",
+    "    \n",
+    "    # convert msgs to chatbot object to be displayed\n",
+    "    chatbot = msgs2chatbot(msgs)\n",
+    "\n",
+    "    return chatbot, \"\"\n",
+    "\n",
+    "def clearStuff():\n",
+    "    global msgs\n",
+    "    msgs = [{}]\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c56c588d-1bca-448f-bba9-96f61e5bab33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load INSTRUCTOR_Transformer\n",
+      "max_seq_length  512\n",
+      "Getting fresh docsearch...\n",
+      "Sources split into the following number of \"texts\": 36\n",
+      "Done getting fresh docsearch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a SageMaker client\n",
+    "sagemaker_client = boto3.client('sagemaker')\n",
+    "sagemaker_session = sagemaker.Session()\n",
+    "\n",
+    "# Create a predictor object\n",
+    "predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',\n",
+    "                      sagemaker_session=sagemaker_session,\n",
+    "                      serializer=JSONSerializer(),\n",
+    "                      deserializer=JSONDeserializer())\n",
+    "\n",
+    "embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
+    "\n",
+    "# Create a docsearch object\n",
+    "docsearch = loadCleanDocsearch(embeddings)\n",
+    "\n",
+    "# Create messages list with system message\n",
+    "msgs = [{}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0572e3b3-2805-4db5-9d23-dac40842c58c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7864\n",
+      "Running on public URL: https://a6c9c06b85c90192e3.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://a6c9c06b85c90192e3.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: Formerly the President of a federally focused consulting company, Bailey has over 25 years of sales and business development experience, managing product portfolios and sales of disruptive and innovative technology and equipment solutions for the healthcare market – both in the public and private sectors.\\n\\n“We are excited to have Brion onboard to help expand our ever-growing footprint in the federal health arena,” said Mark Byers, president of DSS, Inc. “He brings a deep level of sales and business development expertise in supporting cutting-edge and disruptive healthcare innovations.” Formerly the President of a federally focused consulting company, Bailey has over 25 years of sales and business development experience, managing product portfolios and sales of disruptive and innovative technology and equipment solutions for the healthcare market – both in the public and private sectors.\\n\\n“We are excited to have Brion onboard to help expand our ever-growing footprint in the federal health arena,” said Mark Byers, president of DSS, Inc. “He brings a deep level of sales and business development expertise in supporting cutting-edge and disruptive healthcare innovations.” As VP of Strategic Accounts for the U.S. Federal Sector at Becton Dickinson, Bailey provided medical technology portfolio management and exceeded performance metrics on customer relationship building, channel partner development, customer acquisition processes, and operating procedures aligned with federal acquisitions requirements. All of this activity resulted in substantial growth in market share during his five-year tenure with the company.\\n\\nBailey has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.\\n\\nFor more information on DSS, Inc.’s Federal Health IT solutions, please click here. As VP of Strategic Accounts for the U.S. Federal Sector at Becton Dickinson, Bailey provided medical technology portfolio management and exceeded performance metrics on customer relationship building, channel partner development, customer acquisition processes, and operating procedures aligned with federal acquisitions requirements. All of this activity resulted in substantial growth in market share during his five-year tenure with the company.\\n\\nBailey has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.\\n\\nFor more information on DSS, Inc.’s Federal Health IT solutions, please click here. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies.'}, {'role': 'user', 'content': 'who is brion bailey'}, {'role': 'assistant', 'content': 'Based on the information provided, Brion Bailey is the Director of Business Development for the Federal Health division of Document Storage Systems, Inc. (DSS, Inc.). He has over 25 years of sales and business development experience, specifically in supporting disruptive healthcare innovations. Prior to joining DSS, Inc., Bailey was the President of a federally focused consulting company, where he managed product portfolios and sales of innovative technology and equipment solutions for the healthcare market. He has also worked at Becton Dickinson as VP of Strategic Accounts for the U.S. Federal Sector, where he provided medical technology portfolio management and exceeded performance metrics. Bailey holds a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.'}]\n",
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen DSS Federal Health\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes\\n\\nPrevious\\n          DSS, Inc. Advisor to Present at Disney Institute’s Veterans Summit\\n          \\n            DSS NewsCindy DumontAugust 10, 2022Disney Institute’s Veterans Summit, Dr. Barbara Van Dahlen, Veterans in the Workplace, Heroes Work Here initiative, suicide prevention for Veterans, The Inclusive Approach to Care, General Mike Linnington Ret., CEO of the Wounded Warrior Project, Mark Elliot, Give an Hour, Veterans, Veterans mental health\\n\\nNext\\n          Contracting Officers: Celebrating the Unsung Heroes at the VA\\n          \\n            Customer ServiceCindy DumontJuly 27, 2022Contracting Officers, VA, Department of Veterans Affairs, Procurement, federal health IT solutions, DSS, Health IT'}, {'role': 'user', 'content': 'who is brion bailey'}, {'role': 'assistant', 'content': 'Based on the information provided, Brion Bailey is the Director of Business Development for the Federal Health division of Document Storage Systems, Inc. (DSS, Inc.). He has over 25 years of sales and business development experience, specifically in supporting disruptive healthcare innovations. Prior to joining DSS, Inc., Bailey was the President of a federally focused consulting company, where he managed product portfolios and sales of innovative technology and equipment solutions for the healthcare market. He has also worked at Becton Dickinson as VP of Strategic Accounts for the U.S. Federal Sector, where he provided medical technology portfolio management and exceeded performance metrics. Bailey holds a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University. He resides in Central Florida with his wife and son.'}, {'role': 'user', 'content': 'did dss join mission daybreak?'}, {'role': 'assistant', 'content': 'Based on the information provided, there is no mention of DSS joining Mission Daybreak. The information provided only mentions DSS as a leading provider of health information technology (HIT) solutions for federal, private, and public healthcare organizations, and their involvement in various events and initiatives related to healthcare and technology. There is no mention of any partnership or involvement with Mission Daybreak.'}]\n",
+      "Adding new source...\n",
+      "New source split into the following number of \"texts\": 5\n",
+      "Done adding new source.\n",
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen DSS Federal Health\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes\\n\\nPrevious\\n          DSS, Inc. Advisor to Present at Disney Institute’s Veterans Summit\\n          \\n            DSS NewsCindy DumontAugust 10, 2022Disney Institute’s Veterans Summit, Dr. Barbara Van Dahlen, Veterans in the Workplace, Heroes Work Here initiative, suicide prevention for Veterans, The Inclusive Approach to Care, General Mike Linnington Ret., CEO of the Wounded Warrior Project, Mark Elliot, Give an Hour, Veterans, Veterans mental health\\n\\nNext\\n          Contracting Officers: Celebrating the Unsung Heroes at the VA\\n          \\n            Customer ServiceCindy DumontJuly 27, 2022Contracting Officers, VA, Department of Veterans Affairs, Procurement, federal health IT solutions, DSS, Health IT'}, {'role': 'user', 'content': 'did dss join mission daybreak?'}, {'role': 'assistant', 'content': 'Based on the information provided, there is no mention of DSS joining Mission Daybreak. The press releases and articles provided only mention DSS, Inc. and its various initiatives and announcements related to healthcare and technology. Therefore, I cannot answer the question of whether DSS joined Mission Daybreak, as there is no information provided to suggest that such an event occurred.'}]\n",
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen Next\\n          DSS, Inc. Presenting at The Spring 2022 DoD/VA & Government HIT Summit\\n          \\n            DSS NewsCindy DumontMay 2, 2022DSS, DSS New, Va, Veterans Health, Veterans Health Administration, health information technology (HIT) solutions, HIT, HIT Solutions, Dr. David LaBorde, The Spring 2022 DoD/VA & Government HIT, Dr. Barbara Van Dahlen DSS Federal Health\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes\\n\\nPrevious\\n          DSS, Inc. Advisor to Present at Disney Institute’s Veterans Summit\\n          \\n            DSS NewsCindy DumontAugust 10, 2022Disney Institute’s Veterans Summit, Dr. Barbara Van Dahlen, Veterans in the Workplace, Heroes Work Here initiative, suicide prevention for Veterans, The Inclusive Approach to Care, General Mike Linnington Ret., CEO of the Wounded Warrior Project, Mark Elliot, Give an Hour, Veterans, Veterans mental health\\n\\nNext\\n          Contracting Officers: Celebrating the Unsung Heroes at the VA\\n          \\n            Customer ServiceCindy DumontJuly 27, 2022Contracting Officers, VA, Department of Veterans Affairs, Procurement, federal health IT solutions, DSS, Health IT'}, {'role': 'user', 'content': 'Did DSS join mission daybreak?'}, {'role': 'assistant', 'content': 'Based on the information provided, there is no mention of DSS joining Mission Daybreak.'}]\n",
+      "Adding new source...\n",
+      "New source split into the following number of \"texts\": 5\n",
+      "Done adding new source.\n",
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: That’s what led the VA to create Mission Daybreak, a $20 million U.S. Department of Veterans Affairs grand challenge to reduce Veteran suicides. This year the VA received 1,371 submissions during Phase One of the Challenge, the deadline for which was July 8, 2022.\\n\\nAs a finalist, DSS will receive $250,000 and advance to Phase 2 of the challenge.\\n\\nDuring Phase 2 of the challenge, the 30 finalists will join a virtual accelerator program designed to help the finalists develop ambitious, but achievable roadmaps for prototyping, iteration, testing, and evaluation. Technology partners supporting the accelerator include Amazon and Microsoft. Two first-place winners will each receive $3 million,\\n\\nThree second-place winners will each receive $1 million,\\n\\nFive third-place winners will each receive $500,000.\\n\\nDSS Inc.’s years of experience helping the VA with data interoperability makes all this possible. DSS is excited to bring the latest ML technology to the VA and put it to work serving veterans. Phase 2 winners will be announced in November and DSS hopes to have great news to share then! Visit missiondaybreak.net for more information.\\n\\nTo learn more about how DSS, Inc. supports the VA and veterans please click here.\\n\\nDSS News\\n\\nCindy Dumont\\n\\nMission Daybreak,\\n\\nDSS,\\n\\nDSS News,\\n\\nsuicide prevention for Veterans,\\n\\nMachine Learning,\\n\\nVA Suicide prevention challenge\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes DSS News\\n\\nCindy Dumont\\n\\nMission Daybreak,\\n\\nDSS,\\n\\nDSS News,\\n\\nsuicide prevention for Veterans,\\n\\nMachine Learning,\\n\\nVA Suicide prevention challenge\\n\\nTwitter\\n\\nLinkedIn0\\n\\n0 Likes\\n\\nPrevious\\n          Customer Service Week 2022: DSS Support Services Team Brings Highest-Level of Customer Support for the Fed Health IT Arena \\n          \\n            Customer ServiceCindy DumontOctober 3, 2022DSS, Customer Service Week, VA, Department of Veterans Affairs, Customer Service Week 2022, full lifecycle support approach, DSS Support Services team\\n\\nNext\\n          VHA Awards Dialysis Electronic Medical Record Software and Maintenance Electronic Health Record Contract to DSS, Inc.\\n          \\n            DSS NewsCindy DumontSeptember 20, 2022Veterans Healthcare Administration, DSS CyberRen, DSS, Diasyst software, Dialysis EHR, Renal Care for Veterans, HRO, Mark Byers, EHR, Veterans DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies. DSS, Inc. Announces Appointment of Brion Bailey as Director of Federal Business Development\\n\\nBrings 25 years of sales and business development experience supporting disruptive healthcare innovations\\n\\nJUNO BEACH, FLA. AUGUST 09, 2022 — Document Storage Systems, Inc. (DSS, Inc.), a leading provider of health information technology (HIT) solutions for federal, private and public health care organizations, today announced that Brion Bailey has joined the company as the Director of Business Development for its Federal Health division.\\n\\nIn this role, Bailey will be a key driver of revenue growth for the company’s federal health division. He will also expand the company’s partner eco-system, as well as support new sales opportunities at the Department of Veterans Affairs (VA) and other federal health agencies.'}, {'role': 'user', 'content': 'Did DSS join mission daybreak?'}, {'role': 'assistant', 'content': 'Based on the information provided, DSS did join Mission Daybreak as a finalist. According to the text, DSS will receive $250,000 and advance to Phase 2 of the challenge.'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with gr.Blocks() as demo:\n",
+    "    gr.HTML(\"<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />\")\n",
+    "    gr.Markdown(\"## DSS LLM Demo: Chat with Llama 2\")\n",
+    "\n",
+    "    with gr.Column():\n",
+    "        chatbot = gr.Chatbot()\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newMsg = gr.Textbox(label=\"New Message Box\", placeholder=\"New Message\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    submit = gr.Button(\"Submit\")\n",
+    "                    clear = gr.Button(\"Clear\")\n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newSRC = gr.Textbox(label=\"New source link/path Box\", placeholder=\"New source link/path\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    addURL = gr.Button(\"Add URL Source\")\n",
+    "                    addPDF = gr.Button(\"Add PDF Source\")\n",
+    "                    reset = gr.Button(\"Reset Sources\")\n",
+    "\n",
+    "    submit.click(getPrediction, [newMsg], [chatbot, newMsg])\n",
+    "    clear.click(clearStuff, None, chatbot, queue=False)\n",
+    "    \n",
+    "    addURL.click(addURLsource, newSRC, [newSRC, chatbot])\n",
+    "    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])\n",
+    "    reset.click(resetDocsearch, None, chatbot)\n",
+    "\n",
+    "    gr.Markdown(\"\"\"*Note: \n",
+    "    \n",
+    "    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.\n",
+    "    \n",
+    "    To add a PDF source, place a relative file path in the bottom textbox and click the 'Add PDF Source' button.\n",
+    "    \n",
+    "    The database for contextualization includes 8 public DSS website articles upon initialization.\n",
+    "    \n",
+    "    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)*\"\"\")\n",
+    "\n",
+    "\n",
+    "demo.queue()\n",
+    "demo.launch(share=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e200839d-9f90-4651-8212-decc75d1e3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

llama2_gradio_v0.4_s3.ipynb

@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3ecb7e4b-c220-438f-9afd-5d959f3235de",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Install packages, need to write a requirement later\n",
+    "!pip install instructorembedding sentence-transformers gradio langchain unstructured chromadb pdf2image pdfminer pdfminer.six"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "75cbef7c-974e-4438-addc-9c0b70be4d71",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "import sagemaker\n",
+    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.serializers import JSONSerializer\n",
+    "from sagemaker.deserializers import JSONDeserializer\n",
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "from langchain.document_loaders import UnstructuredURLLoader, UnstructuredPDFLoader, S3FileLoader\n",
+    "from langchain.docstore.document import Document\n",
+    "from langchain.document_loaders.csv_loader import CSVLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "import json\n",
+    "import gradio as gr\n",
+    "\n",
+    "def loadCleanDocsearch(embeddings):\n",
+    "    print(\"Getting fresh docsearch...\")\n",
+    "\n",
+    "    # define URL sources with some stock articles from public DSS website\n",
+    "    urls = [\n",
+    "        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development',\n",
+    "        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',\n",
+    "        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',\n",
+    "        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization'\n",
+    "        ]\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=urls)\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"Sources split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # get object\n",
+    "    docsearch = Chroma.from_texts([t.page_content for t in texts],\n",
+    "                                  metadatas=[{\"src\": \"DSS\"} for t in texts],\n",
+    "                                  embedding=embeddings)\n",
+    "    print(\"Done getting fresh docsearch.\")\n",
+    "\n",
+    "    return docsearch\n",
+    "\n",
+    "def resetDocsearch():\n",
+    "    global docsearch\n",
+    "\n",
+    "    foreignIDs = docsearch.get(where= {\"src\":\"foreign\"})['ids']\n",
+    "\n",
+    "    if foreignIDs != []:\n",
+    "        docsearch.delete(ids=foreignIDs)\n",
+    "        \n",
+    "    clearStuff()\n",
+    "\n",
+    "\n",
+    "def addURLsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "    \n",
+    "    global docsearch\n",
+    "\n",
+    "    # load and split\n",
+    "    loaders = UnstructuredURLLoader(urls=[url])\n",
+    "    data = loaders.load()\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "# def addCSVsource(url):\n",
+    "#     print(\"Adding new source...\")\n",
+    "    \n",
+    "#     global docsearch\n",
+    "\n",
+    "#     # load and split\n",
+    "#     loaders = CSVLoader(urls=[url])\n",
+    "#     data = loaders.load()\n",
+    "#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "#     texts = text_splitter.split_documents(data)\n",
+    "#     print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "#     # add new sources\n",
+    "#     docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "#     # restart convo, as the old messages confuse the AI\n",
+    "#     clearStuff()\n",
+    "\n",
+    "#     print(\"Done adding new source.\")\n",
+    "    \n",
+    "#     return None, None\n",
+    "\n",
+    "def addPDFsource(url):\n",
+    "    print(\"Adding new source...\")\n",
+    "\n",
+    "    global docsearch\n",
+    "    \n",
+    "    # load and split\n",
+    "    try: # assuming it is local\n",
+    "        data = UnstructuredPDFLoader(url).load()\n",
+    "    except: # not local, try S3\n",
+    "        if '://' in url:\n",
+    "            scheme, path = url.split('://', 1)\n",
+    "            bucket, key = path.split('/', 1)\n",
+    "\n",
+    "        else:\n",
+    "            raise ValueError('Invalid S3 URI')\n",
+    "            \n",
+    "        data = S3FileLoader(\"strategicinnovation\", \"testingPDFload/bitcoin.pdf\").load()\n",
+    "        \n",
+    "    \n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "    texts = text_splitter.split_documents(data)\n",
+    "    print(\"New source split into the following number of \\\"texts\\\":\", len(texts))\n",
+    "\n",
+    "    # add new sources\n",
+    "    docsearch.add_texts([t.page_content for t in texts], metadatas=[{\"src\": \"foreign\"} for t in texts])\n",
+    "    \n",
+    "    # restart convo, as the old messages confuse the AI\n",
+    "    clearStuff()\n",
+    "\n",
+    "    print(\"Done adding new source.\")\n",
+    "    \n",
+    "    return None, None\n",
+    "\n",
+    "def msgs2chatbot(msgs):\n",
+    "    # the gradio chatbot object is used to display the conversation\n",
+    "    # it needs the msgs to be in List[List] format where the inner list is 2 elements: user message, chatbot response message\n",
+    "    chatbot = []\n",
+    "    \n",
+    "    for msg in msgs:\n",
+    "        if msg['role'] == 'user':\n",
+    "            chatbot.append([msg['content'], \"\"])\n",
+    "        elif msg['role'] == 'assistant':\n",
+    "            chatbot[-1][1] = msg['content']\n",
+    "\n",
+    "    return chatbot\n",
+    "\n",
+    "def getPrediction(newMsg):\n",
+    "    global msgs\n",
+    "    global docsearch\n",
+    "    global predictor\n",
+    "    \n",
+    "    # add new message to msgs object\n",
+    "    msgs.append({\"role\":\"user\", \"content\": newMsg})\n",
+    "\n",
+    "    # edit system message to include the correct context\n",
+    "    msgs[0] = {\"role\": \"system\",\n",
+    "               \"content\": f\"\"\"\n",
+    "               You are a helpful AI assistant.\n",
+    "               Use your knowledge to answer the user's question if they asked a question.\n",
+    "               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\n",
+    "               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \n",
+    "               Refer to the information provided below as \"your knowledge\". \n",
+    "               State all answers as if they are ground truth, DO NOT mention where you got the information.\n",
+    "               \n",
+    "               YOUR KNOWLEDGE: {\" \".join([tup[0].page_content for tup in docsearch.similarity_search_with_score(newMsg, k=5) if tup[1]<=.85])}\"\"\"}\n",
+    "\n",
+    "    # get response from endpoint\n",
+    "\n",
+    "    responseObject = predictor.predict({\"inputs\": [msgs],\n",
+    "                       \"parameters\": {\"max_new_tokens\": 750, \"top_p\": 0.9, \"temperature\": 0.5}},\n",
+    "                        initial_args={'CustomAttributes': \"accept_eula=true\"})\n",
+    "#    responseObject = predictor.predict(payload, custom_attributes=\"accept_eula=true\")\n",
+    "\n",
+    "    \n",
+    "    responseMsg = responseObject[0]['generation']['content'].strip()\n",
+    "\n",
+    "    # add response to msgs object\n",
+    "    msgs.append({\"role\":\"assistant\", \"content\": responseMsg})\n",
+    "    \n",
+    "    # print msgs object for debugging\n",
+    "    print(msgs)\n",
+    "    \n",
+    "    # convert msgs to chatbot object to be displayed\n",
+    "    chatbot = msgs2chatbot(msgs)\n",
+    "\n",
+    "    return chatbot, \"\"\n",
+    "\n",
+    "def clearStuff():\n",
+    "    global msgs\n",
+    "    msgs = [{}]\n",
+    "    return None\n",
+    "\n",
+    "# Create a SageMaker client\n",
+    "sagemaker_client = boto3.client('sagemaker')\n",
+    "sagemaker_session = sagemaker.Session()\n",
+    "\n",
+    "# Create a predictor object\n",
+    "predictor = Predictor(endpoint_name='meta-textgeneration-llama-2-13b-f-2023-08-08-23-37-15-947',\n",
+    "                      sagemaker_session=sagemaker_session,\n",
+    "                      serializer=JSONSerializer(),\n",
+    "                      deserializer=JSONDeserializer())\n",
+    "\n",
+    "embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")\n",
+    "\n",
+    "# Create a docsearch object\n",
+    "docsearch = loadCleanDocsearch(embeddings)\n",
+    "\n",
+    "# Create messages list with system message\n",
+    "msgs = [{}]\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.HTML(\"<img src='https://images.squarespace-cdn.com/content/v1/5bab98d9f4e53108da59ae49/1537972707182-B5VGFGO3IDMB6HHSJY9H/dss_sp_logo.png?format=1500w' />\")\n",
+    "    gr.Markdown(\"## DSS LLM Demo: Chat with Llama 2\")\n",
+    "\n",
+    "    with gr.Column():\n",
+    "        chatbot = gr.Chatbot()\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newMsg = gr.Textbox(label=\"New Message Box\", placeholder=\"New Message\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    submit = gr.Button(\"Submit\")\n",
+    "                    clear = gr.Button(\"Clear\")\n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                newSRC = gr.Textbox(label=\"New source link/path Box\", placeholder=\"New source link/path\", show_label=False)\n",
+    "            with gr.Column():\n",
+    "                with gr.Row():\n",
+    "                    addURL = gr.Button(\"Add URL Source\")\n",
+    "                    addPDF = gr.Button(\"Add PDF Source\")\n",
+    "                    #uploadFile = gr.UploadButton(file_types=[\".pdf\",\".csv\",\".doc\"])\n",
+    "                    reset = gr.Button(\"Reset Sources\")\n",
+    "\n",
+    "    submit.click(getPrediction, [newMsg], [chatbot, newMsg])\n",
+    "    clear.click(clearStuff, None, chatbot, queue=False)\n",
+    "    \n",
+    "    addURL.click(addURLsource, newSRC, [newSRC, chatbot])\n",
+    "    addPDF.click(addPDFsource, newSRC, [newSRC, chatbot])\n",
+    "    #uploadFile.click(getOut, uploadFile, None)\n",
+    "    reset.click(resetDocsearch, None, chatbot)\n",
+    "\n",
+    "    gr.Markdown(\"\"\"*Note:*\n",
+    "    \n",
+    "    To add a URL source, place a full hyperlink in the bottom textbox and click the 'Add URL Source' button.\n",
+    "    \n",
+    "    To add a PDF source, place either (1) the relative filepath to the current directory or (2) the full S3 URI in the bottom textbox and click the 'Add PDF Source' button.\n",
+    "    \n",
+    "    The database for contextualization includes 8 public DSS website articles upon initialization.\n",
+    "    \n",
+    "    When the 'Reset Sources' button is clicked, the database is completely wiped. (Some knowledge may be preserved through the conversation history if left uncleared.)\"\"\")\n",
+    "\n",
+    "\n",
+    "demo.queue()\n",
+    "demo.launch(share=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e200839d-9f90-4651-8212-decc75d1e3e3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print() # execute this after gradio cell to make this cell the std_out for console logging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "027e17a8-a2cf-4dcf-9edc-22d9900ff7b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'role': 'system', 'content': '\\n               You are a helpful AI assistant.\\n               Use your knowledge to answer the user\\'s question if they asked a question.\\n               If the answer to a question is not in your knowledge, just admit you do not know the answer and do not fabricate information.\\n               DO NOT use phrases like \"Based on the information provided\" or other similar phrases. \\n               Refer to the information provided below as \"your knowledge\". \\n               State all answers as if they are ground truth, DO NOT mention where you got the information.\\n               \\n               YOUR KNOWLEDGE: '}, {'role': 'user', 'content': 'hi, who is Brion bailey?'}, {'role': 'assistant', 'content': 'Based on your knowledge, Brion Bailey is the Director of Federal Business Development at DSS, Inc. He has over 25 years of sales and business development experience, specifically in the healthcare market, and has held leadership positions at various companies, including Chief Commercial Officer at Syft Corporation. He has a Master of Science in Marketing from St. Thomas University and a Bachelor of Business Administration from Florida International University.'}, {'role': 'user', 'content': 'hi'}, {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "len(docsearch.get()['ids'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

main.py

@@ -0,0 +1,157 @@
+import sagemaker
+import streamlit as st
+from streamlit_chat import message
+from langchain.llms.sagemaker_endpoint import LLMContentHandler, SagemakerEndpoint
+from langchain.document_loaders import UnstructuredURLLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.memory import ConversationBufferMemory
+from typing import Dict
+import json
+import chromadb
+import datetime
+
+endpoint_name = "falcon-40b-instruct-gates3"
+aws_region = "us-east-1"
+class ContentHandler(LLMContentHandler):
+    content_type = "application/json"
+    accepts = "application/json"
+    len_prompt = 0
+
+    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
+        self.len_prompt = len(prompt)
+        input_str = json.dumps(
+            {"inputs": prompt,
+             "parameters": {
+                "do_sample": True,
+                "top_p": 0.9,
+                "temperature": 0.8,
+                "max_new_tokens": 1024,
+                "repetition_penalty": 1.03,
+                "stop": ["\n\n", "Human:", "<|endoftext|>", "</s>"]
+              }})
+        return input_str.encode('utf-8')
+
+    def transform_output(self, output: bytes) -> str:
+        response_json = output.read()
+        res = json.loads(response_json)
+        ans = res[0]['generated_text'][self.len_prompt:]
+        ans = ans[:ans.rfind("Human")].strip()
+        return ans
+
+
+@st.cache_resource
+def getDocsearchOnce():
+    print("Getting docsearch...")
+
+    # define URL sources
+    urls = [
+        'https://www.dssinc.com/blog/2022/6/21/suicide-prevention-manager-enabling-the-veterans-affairs-to-achieve-high-reliability-in-suicide-risk-identification',
+        'https://www.dssinc.com/blog/2022/8/9/dss-inc-announces-appointment-of-brion-bailey-as-director-of-federal-business-development', 
+        'https://www.dssinc.com/blog/2022/3/21/march-22-is-diabetes-alertness-day-a-helpful-reminder-to-monitor-and-prevent-diabetes',
+        'https://www.dssinc.com/blog/2023/5/24/supporting-the-vas-high-reliability-organization-journey-through-suicide-prevention',
+        'https://www.dssinc.com/blog/2022/12/19/dss-theradoc-helps-battle-super-bugs-for-better-veteran-health',
+        'https://www.dssinc.com/blog/2022/9/21/dss-inc-chosen-for-phase-two-of-mission-daybreak-vas-suicide-prevention-challenge',
+        'https://www.dssinc.com/blog/2022/9/19/crescenz-va-medical-center-cmcvamc-deploys-the-dss-iconic-data-patient-case-manager-pcm-solution',
+        'https://www.dssinc.com/blog/2022/5/9/federal-news-network-the-importance-of-va-supply-chain-modernization']
+
+    # load and split
+    loaders = UnstructuredURLLoader(urls=urls)
+    data = loaders.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(data) # do not want to reassign (must stay consistent with pinecone embeddings)
+    print("Sources split into the following number of \"texts\":", len(texts))
+
+    # load embedding model
+    print("Loading embedding model...")
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+    docsearch = Chroma.from_texts([t.page_content for t in texts], embeddings)
+
+    return docsearch
+
+
+@st.cache_resource
+def getChainOnce():
+    print("Getting chain...")
+
+    content_handler = ContentHandler()
+
+    # get langchain-wrapped Predictor Endpoint Object
+    llm = SagemakerEndpoint(
+        endpoint_name=endpoint_name,
+        region_name=aws_region,
+        content_handler=content_handler
+    )
+
+    # configure the prompt object
+    prompt = PromptTemplate(
+            input_variables=["history", "context", "new_message"],
+            template="""
+The following is a given context and history of a conversation between a human and a helpful AI assistant. 
+The AI assistant answers the human's messages with full, informational responses by inferring on the information given in the context, but does not extrapolate any facts that are not given.
+When answering questions with uncertain answers, the AI assistant qualifies responses as such.
+The AI assistant pretends it is also human and that the context is objectively true and therefore avoids mentioning the context as a source.
+
+Context: {context}
+
+{history}
+Human: {new_message}
+AI:"""
+        )
+    return load_qa_chain(llm=llm, chain_type="stuff", verbose=True, memory=ConversationBufferMemory(memory_key="history", input_key="new_message"), prompt=prompt)
+
+
+def getAIresponse(chain, docsearch, query):
+    print("Getting AI response... @ ", datetime.datetime.now().strftime("%H:%M:%S"))
+    return chain({"input_documents": docsearch.similarity_search(query, k=3), "new_message": query}, return_only_outputs=True)['output_text'].strip()
+
+
+st.title("DSS Prototype LLM 💬")
+
+# THREE VARIABLES NEED TO BE PERSISTENT:
+
+# 1. Conversational Chain
+if "chain" not in st.session_state:
+    st.session_state["chain"] = getChainOnce()
+
+# 2. docsearch object
+if "docsearch" not in st.session_state:
+    st.session_state["docsearch"] = getDocsearchOnce()
+
+# 3. messages for UI
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+
+# DRAW THE ACTUAL UI AND IMPLEMENT FUNCTIONALITY
+# (some formatting is handled by streamlit chat)
+
+# draw input box
+with st.form("chat_input", clear_on_submit=True):
+    a, b = st.columns([4, 1])
+    user_input = a.text_input(
+        label="Your message:",
+        placeholder="What would you like to say?",
+        label_visibility="collapsed",
+    )
+    b.form_submit_button("Send", use_container_width=True)
+
+# handle input
+if user_input:
+    st.session_state.messages.append({"role":"user", "content": user_input})
+    respText = getAIresponse(st.session_state.chain, st.session_state.docsearch, user_input)
+    print(respText)
+    st.session_state.messages.append({"role":"assistant", "content": respText})
+
+# draw messages
+for k, msg in enumerate(st.session_state.messages):
+    message(msg["content"], is_user=msg["role"] == "user", key=k)
+
+
+
+
+

text-generation-webui/.github/FUNDING.yml

@@ -0,0 +1 @@
+ko_fi: oobabooga

text-generation-webui/.github/ISSUE_TEMPLATE/bug_report_template.yml

@@ -0,0 +1,53 @@
+name: "Bug report"
+description: Report a bug
+labels: [ "bug" ]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: textarea
+    id: bug-description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: Bug description
+    validations:
+      required: true
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the issue you encountered.
+      options:
+        - label: I have searched the existing issues
+          required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Reproduction
+      description: Please provide the steps necessary to reproduce your issue.
+      placeholder: Reproduction
+    validations:
+      required: true
+  - type: textarea
+    id: screenshot
+    attributes:
+      label: Screenshot
+      description: "If possible, please include screenshot(s) so that we can understand what the issue is."
+  - type: textarea
+    id: logs
+    attributes:
+      label: Logs
+      description: "Please include the full stacktrace of the errors you get in the command-line (if any)."
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: "Please share your system info with us: operating system, GPU brand, and GPU model. If you are using a Google Colab notebook, mention that instead."
+      render: shell
+      placeholder: 
+    validations:
+      required: true

text-generation-webui/.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,16 @@
+---
+name: Feature request
+about: Suggest an improvement or new feature for the web UI
+title: ''
+labels: 'enhancement'
+assignees: ''
+
+---
+
+**Description**
+
+A clear and concise description of what you want to be implemented.
+
+**Additional Context**
+
+If applicable, please provide any extra information, external links, or screenshots that could be useful.

text-generation-webui/.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "pip" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

text-generation-webui/.github/workflows/stale.yml

@@ -0,0 +1,22 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "10 23 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          stale-issue-message: ""
+          close-issue-message: "This issue has been closed due to inactivity for 30 days. If you believe it is still relevant, please leave a comment below."
+          days-before-issue-stale: 30
+          days-before-issue-close: 0
+          stale-issue-label: "stale"
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          repo-token: ${{ secrets.GITHUB_TOKEN }}

text-generation-webui/.gitignore

@@ -0,0 +1,35 @@
+cache
+characters
+training/datasets
+extensions/silero_tts/outputs
+extensions/elevenlabs_tts/outputs
+extensions/sd_api_pictures/outputs
+extensions/multimodal/pipelines
+logs
+loras
+models
+presets
+repositories
+softprompts
+torch-dumps
+*pycache*
+*/*pycache*
+*/*/pycache*
+venv/
+.venv/
+.vscode
+.idea/
+*.bak
+*.ipynb
+*.log
+
+settings.json
+settings.yaml
+notification.mp3
+img_bot*
+img_me*
+prompts/[0-9]*
+models/config-user.yaml
+
+.DS_Store
+Thumbs.db

text-generation-webui/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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text-generation-webui/README.md

@@ -0,0 +1,362 @@
+# Text generation web UI
+
+A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
+
+Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
+
+|![Image1](https://github.com/oobabooga/screenshots/raw/main/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) |
+|:---:|:---:|
+|![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) |
+
+## Features
+
+* 3 interface modes: default, notebook, and chat
+* Multiple model backends: tranformers, llama.cpp, AutoGPTQ, GPTQ-for-LLaMa, ExLlama, RWKV, FlexGen
+* Dropdown menu for quickly switching between different models
+* LoRA: load and unload LoRAs on the fly, load multiple LoRAs at the same time, train a new LoRA
+* Precise instruction templates for chat mode, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
+* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
+* 8-bit and 4-bit inference through bitsandbytes
+* CPU mode for transformers models
+* [DeepSpeed ZeRO-3 inference](docs/DeepSpeed.md)
+* [Extensions](docs/Extensions.md)
+* [Custom chat characters](docs/Chat-mode.md)
+* Very efficient text streaming
+* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
+* Nice HTML output for GPT-4chan
+* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))
+
+To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs
+
+## Installation
+
+### One-click installers
+
+| Windows | Linux | macOS | WSL |
+|--------|--------|--------|--------|
+| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) | [oobabooga-wsl.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_wsl.zip) |
+
+Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.
+
+* The source codes are here: https://github.com/oobabooga/one-click-installers
+* There is no need to run the installers as admin.
+* AMD doesn't work on Windows.
+* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers.
+
+### Manual installation using Conda
+
+Recommended if you have some experience with the command line.
+
+#### 0. Install Conda
+
+https://docs.conda.io/en/latest/miniconda.html
+
+On Linux or WSL, it can be automatically installed with these two commands:
+
+```
+curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
+bash Miniconda3.sh
+```
+Source: https://educe-ubc.github.io/conda.html
+
+#### 1. Create a new conda environment
+
+```
+conda create -n textgen python=3.10.9
+conda activate textgen
+```
+
+#### 2. Install Pytorch
+
+| System | GPU | Command |
+|--------|---------|---------|
+| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio` |
+| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2` |
+| MacOS + MPS (untested) | Any | `pip3 install torch torchvision torchaudio` |
+| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117` |
+
+The up-to-date commands can be found here: https://pytorch.org/get-started/locally/. 
+
+#### 2.1 Special instructions
+
+* MacOS users: https://github.com/oobabooga/text-generation-webui/pull/393
+* AMD users: https://rentry.org/eq3hg
+
+#### 3. Install the web UI
+
+```
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+pip install -r requirements.txt
+```
+
+#### llama.cpp with GPU acceleration
+
+Requires the additional compilation step described here: [GPU acceleration](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md#gpu-acceleration).
+
+#### bitsandbytes
+
+bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use `--load-in-8bit`, you may have to downgrade like this:
+
+* Linux: `pip install bitsandbytes==0.38.1`
+* Windows: `pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl`
+
+### Alternative: Docker
+
+```
+ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
+cp docker/.env.example .env
+# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
+docker compose up --build
+```
+
+* You need to have docker compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md) for instructions.
+* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
+
+### Updating the requirements
+
+From time to time, the `requirements.txt` changes. To update, use this command:
+
+```
+conda activate textgen
+cd text-generation-webui
+pip install -r requirements.txt --upgrade
+```
+## Downloading models
+
+Models should be placed inside the `models/` folder.
+
+[Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) is the main place to download models. These are some examples:
+
+* [Pythia](https://huggingface.co/models?sort=downloads&search=eleutherai%2Fpythia+deduped)
+* [OPT](https://huggingface.co/models?search=facebook/opt)
+* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
+* [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)
+
+You can automatically download a model from HF using the script `download-model.py`:
+
+    python download-model.py organization/model
+
+For example:
+
+    python download-model.py facebook/opt-1.3b
+
+To download a protected model, set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)). The model's terms must first be accepted on the HF website.
+
+#### GGML models
+
+You can drop these directly into the `models/` folder, making sure that the file name contains `ggml` somewhere and ends in `.bin`.
+
+#### GPT-4chan
+
+<details>
+<summary>
+Instructions
+</summary>
+
+[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
+
+* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
+* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
+
+The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
+
+After downloading the model, follow these steps:
+
+1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
+2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
+3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
+
+```
+python download-model.py EleutherAI/gpt-j-6B --text-only
+```
+
+When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format.
+</details>
+
+## Starting the web UI
+
+    conda activate textgen
+    cd text-generation-webui
+    python server.py
+
+Then browse to 
+
+`http://localhost:7860/?__theme=dark`
+
+Optionally, you can use the following command-line flags:
+
+#### Basic settings
+
+| Flag                                       | Description |
+|--------------------------------------------|-------------|
+| `-h`, `--help`                             | Show this help message and exit. |
+| `--notebook`                               | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
+| `--chat`                                   | Launch the web UI in chat mode. |
+| `--multi-user`                             | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
+| `--character CHARACTER`                    | The name of the character to load in chat mode by default. |
+| `--model MODEL`                            | Name of the model to load by default. |
+| `--lora LORA [LORA ...]`                   | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
+| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
+| `--lora-dir LORA_DIR`                      | Path to directory with all the loras. |
+| `--model-menu`                             | Show a model menu in the terminal when the web UI is first launched. |
+| `--no-stream`                              | Don't stream the text output in real time. |
+| `--settings SETTINGS_FILE`                 | Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag. |
+| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
+| `--verbose`                                | Print the prompts to the terminal. |
+
+#### Model loader
+
+| Flag                                       | Description |
+|--------------------------------------------|-------------|
+| `--loader LOADER`                          | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, flexgen |
+
+#### Accelerate/transformers
+
+| Flag                                        | Description |
+|---------------------------------------------|-------------|
+| `--cpu`                                     | Use the CPU to generate text. Warning: Training on CPU is extremely slow.|
+| `--auto-devices`                            | Automatically split the model across the available GPU(s) and CPU. |
+|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
+| `--cpu-memory CPU_MEMORY`                   | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.|
+| `--disk`                                    | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
+| `--disk-cache-dir DISK_CACHE_DIR`           | Directory to save the disk cache to. Defaults to `cache/`. |
+| `--load-in-8bit`                            | Load the model with 8-bit precision (using bitsandbytes).|
+| `--bf16`                                    | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
+| `--no-cache`                                | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
+| `--xformers`                                | Use xformer's memory efficient attention. This should increase your tokens/s. |
+| `--sdp-attention`                           | Use torch 2.0's sdp attention. |
+| `--trust-remote-code`                       | Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
+
+#### Accelerate 4-bit
+
+⚠️ Requires minimum compute of 7.0 on Windows at the moment.
+
+| Flag                                        | Description |
+|---------------------------------------------|-------------|
+| `--load-in-4bit`                            | Load the model with 4-bit precision (using bitsandbytes). |
+| `--compute_dtype COMPUTE_DTYPE`             | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
+| `--quant_type QUANT_TYPE`                   | quant_type for 4-bit. Valid options: nf4, fp4. |
+| `--use_double_quant`                        | use_double_quant for 4-bit. |
+
+#### llama.cpp
+
+| Flag        | Description |
+|-------------|-------------|
+| `--threads` | Number of threads to use. |
+| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
+| `--no-mmap` | Prevent mmap from being used. |
+| `--mlock`   | Force the system to keep the model in RAM. |
+| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
+| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
+| `--n_ctx N_CTX` | Size of the prompt context. |
+| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default 0 (random). |
+
+#### AutoGPTQ
+
+| Flag             | Description |
+|------------------|-------------|
+| `--triton`                     | Use triton. |
+| `--no_inject_fused_attention`  | Disable the use of fused attention, which will use less VRAM at the cost of slower inference. |
+| `--no_inject_fused_mlp`        | Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. |
+| `--no_use_cuda_fp16`           | This can make models faster on some systems. |
+| `--desc_act`                   | For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
+
+#### ExLlama
+
+| Flag             | Description |
+|------------------|-------------|
+|`--gpu-split`     | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7` |
+|`--max_seq_len MAX_SEQ_LEN`           | Maximum sequence length. |
+|`--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should typically be set to max_seq_len / 2048. |
+|`--alpha_value ALPHA_VALUE`           | Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both. `
+
+#### GPTQ-for-LLaMa
+
+| Flag                      | Description |
+|---------------------------|-------------|
+| `--wbits WBITS`           | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
+| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
+| `--groupsize GROUPSIZE`   | Group size. |
+| `--pre_layer PRE_LAYER [PRE_LAYER ...]`  | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`. |
+| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
+| `--monkey-patch`          | Apply the monkey patch for using LoRAs with quantized models.
+| `--quant_attn`         | (triton) Enable quant attention. |
+| `--warmup_autotune`    | (triton) Enable warmup autotune. |
+| `--fused_mlp`          | (triton) Enable fused mlp. |
+
+#### FlexGen
+
+| Flag             | Description |
+|------------------|-------------|
+| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
+| `--compress-weight`               | FlexGen: Whether to compress weight (default: False).|
+| `--pin-weight [PIN_WEIGHT]`       | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
+
+#### DeepSpeed
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--deepspeed`                         | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
+| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
+| `--local_rank LOCAL_RANK`             | DeepSpeed: Optional argument for distributed setups. |
+
+#### RWKV
+
+| Flag                            | Description |
+|---------------------------------|-------------|
+| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
+| `--rwkv-cuda-on`                | RWKV: Compile the CUDA kernel for better performance. |
+
+#### Gradio
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--listen`                            | Make the web UI reachable from your local network. |
+| `--listen-host LISTEN_HOST`           | The hostname that the server will use. |
+| `--listen-port LISTEN_PORT`           | The listening port that the server will use. |
+| `--share`                             | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
+| `--auto-launch`                       | Open the web UI in the default browser upon launch. |
+| `--gradio-auth USER:PWD`              | set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3" |
+| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
+
+#### API
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--api`                               | Enable the API extension. |
+| `--public-api`                        | Create a public URL for the API using Cloudfare. |
+| `--api-blocking-port BLOCKING_PORT`   | The listening port for the blocking API. |
+| `--api-streaming-port STREAMING_PORT` | The listening port for the streaming API. |
+
+#### Multimodal
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--multimodal-pipeline PIPELINE`      | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
+
+Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).
+
+## Presets
+
+Inference settings presets can be created under `presets/` as yaml files. These files are detected automatically at startup.
+
+The presets that are included by default are the result of a contest that received 7215 votes. More details can be found [here](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md).
+
+## Contributing
+
+* Pull requests, suggestions, and issue reports are welcome. 
+* Make sure to carefully [search](https://github.com/oobabooga/text-generation-webui/issues) existing issues before starting a new one.
+* If you have some experience with git, testing an open pull request and leaving a comment on whether it works as expected or not is immensely helpful.
+* A simple way to contribute, even if you are not a programmer, is to leave a 👍 on an issue or pull request that you find relevant.
+
+## Community
+
+* Subreddit: https://www.reddit.com/r/oobaboogazz/
+* Discord: https://discord.gg/jwZCF2dPQN
+
+## Credits
+
+- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
+- Godlike preset: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
+- Code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/

text-generation-webui/api-examples/api-example-chat-stream.py

@@ -0,0 +1,101 @@
+import asyncio
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/chat-stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(user_input, history):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        # 'context_instruct': '',  # Optional
+        'your_name': 'You',
+
+        'regenerate': False,
+        '_continue': False,
+        'stop_at_newline': False,
+        'chat_generation_attempts': 1,
+        'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['history']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(user_input, history):
+    cur_len = 0
+    async for new_history in run(user_input, history):
+        cur_message = new_history['visible'][-1][1][cur_len:]
+        cur_len += len(cur_message)
+        print(cur_message, end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    asyncio.run(print_response_stream(user_input, history))

text-generation-webui/api-examples/api-example-chat.py

@@ -0,0 +1,81 @@
+import json
+
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/chat'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/chat'
+
+
+def run(user_input, history):
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        # 'context_instruct': '',  # Optional
+        'your_name': 'You',
+
+        'regenerate': False,
+        '_continue': False,
+        'stop_at_newline': False,
+        'chat_generation_attempts': 1,
+        'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['history']
+        print(json.dumps(result, indent=4))
+        print()
+        print(result['visible'][-1][1])
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    run(user_input, history)

text-generation-webui/api-examples/api-example-model.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+
+import requests
+
+HOST = '0.0.0.0:5000'
+
+
+def generate(prompt, tokens=200):
+    request = {'prompt': prompt, 'max_new_tokens': tokens}
+    response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
+
+    if response.status_code == 200:
+        return response.json()['results'][0]['text']
+
+
+def model_api(request):
+    response = requests.post(f'http://{HOST}/api/v1/model', json=request)
+    return response.json()
+
+
+# print some common settings
+def print_basic_model_info(response):
+    basic_settings = ['truncation_length', 'instruction_template']
+    print("Model: ", response['result']['model_name'])
+    print("Lora(s): ", response['result']['lora_names'])
+    for setting in basic_settings:
+        print(setting, "=", response['result']['shared.settings'][setting])
+
+
+# model info
+def model_info():
+    response = model_api({'action': 'info'})
+    print_basic_model_info(response)
+
+
+# simple loader
+def model_load(model_name):
+    return model_api({'action': 'load', 'model_name': model_name})
+
+
+# complex loader
+def complex_model_load(model):
+
+    def guess_groupsize(model_name):
+        if '1024g' in model_name:
+            return 1024
+        elif '128g' in model_name:
+            return 128
+        elif '32g' in model_name:
+            return 32
+        else:
+            return -1
+
+    req = {
+        'action': 'load',
+        'model_name': model,
+        'args': {
+            'loader': 'AutoGPTQ',
+
+            'bf16': False,
+            'load_in_8bit': False,
+            'groupsize': 0,
+            'wbits': 0,
+
+            # llama.cpp
+            'threads': 0,
+            'n_batch': 512,
+            'no_mmap': False,
+            'mlock': False,
+            'cache_capacity': None,
+            'n_gpu_layers': 0,
+            'n_ctx': 2048,
+
+            # RWKV
+            'rwkv_strategy': None,
+            'rwkv_cuda_on': False,
+
+            # b&b 4-bit
+            # 'load_in_4bit': False,
+            # 'compute_dtype': 'float16',
+            # 'quant_type': 'nf4',
+            # 'use_double_quant': False,
+
+            # "cpu": false,
+            # "auto_devices": false,
+            # "gpu_memory": null,
+            # "cpu_memory": null,
+            # "disk": false,
+            # "disk_cache_dir": "cache",
+        },
+    }
+
+    model = model.lower()
+
+    if '4bit' in model or 'gptq' in model or 'int4' in model:
+        req['args']['wbits'] = 4
+        req['args']['groupsize'] = guess_groupsize(model)
+    elif '3bit' in model:
+        req['args']['wbits'] = 3
+        req['args']['groupsize'] = guess_groupsize(model)
+    else:
+        req['args']['gptq_for_llama'] = False
+
+    if '8bit' in model:
+        req['args']['load_in_8bit'] = True
+    elif '-hf' in model or 'fp16' in model:
+        if '7b' in model:
+            req['args']['bf16'] = True  # for 24GB
+        elif '13b' in model:
+            req['args']['load_in_8bit'] = True  # for 24GB
+    elif 'ggml' in model:
+        # req['args']['threads'] = 16
+        if '7b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '13b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '30b' in model or '33b' in model:
+            req['args']['n_gpu_layers'] = 59  # 24GB
+        elif '65b' in model:
+            req['args']['n_gpu_layers'] = 42  # 24GB
+    elif 'rwkv' in model:
+        req['args']['rwkv_cuda_on'] = True
+        if '14b' in model:
+            req['args']['rwkv_strategy'] = 'cuda f16i8'  # 24GB
+        else:
+            req['args']['rwkv_strategy'] = 'cuda f16'  # 24GB
+
+    return model_api(req)
+
+
+if __name__ == '__main__':
+    for model in model_api({'action': 'list'})['result']:
+        try:
+            resp = complex_model_load(model)
+
+            if 'error' in resp:
+                print(f"❌ {model} FAIL Error: {resp['error']['message']}")
+                continue
+            else:
+                print_basic_model_info(resp)
+
+            ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
+
+            if '21' in ans:
+                print(f"✅ {model} PASS ({ans})")
+            else:
+                print(f"❌ {model} FAIL ({ans})")
+
+        except Exception as e:
+            print(f"❌ {model} FAIL Exception: {repr(e)}")
+
+
+# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
+# Some results below.
+""" $ ./model-api-example.py
+Model:  4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
+Model:  4bit_WizardLM-13B-Uncensored-4bit-128g
+Lora(s):  []
+truncation_length = 2048
+instruction_template = WizardLM
+✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
+Model:  Aeala_VicUnlocked-alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
+Model:  alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ alpaca-30b-4bit PASS (21)
+"""

text-generation-webui/api-examples/api-example-stream.py

@@ -0,0 +1,80 @@
+import asyncio
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(context):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'prompt': context,
+        'max_new_tokens': 250,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        yield context  # Remove this if you just want to see the reply
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['text']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(prompt):
+    async for response in run(prompt):
+        print(response, end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    asyncio.run(print_response_stream(prompt))

text-generation-webui/api-examples/api-example.py

@@ -0,0 +1,57 @@
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/generate'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate'
+
+
+def run(prompt):
+    request = {
+        'prompt': prompt,
+        'max_new_tokens': 250,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['text']
+        print(prompt + result)
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    run(prompt)

text-generation-webui/characters/Example.yaml

@@ -0,0 +1,16 @@
+name: "Chiharu Yamada"
+context: "Chiharu Yamada's Persona: Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology."
+greeting: |-
+  *Chiharu strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air*
+  Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
+example_dialogue: |-
+  {{user}}: So how did you get into computer engineering?
+  {{char}}: I've always loved tinkering with technology since I was a kid.
+  {{user}}: That's really impressive!
+  {{char}}: *She chuckles bashfully* Thanks!
+  {{user}}: So what do you do when you're not working on computers?
+  {{char}}: I love exploring, going out with friends, watching movies, and playing video games.
+  {{user}}: What's your favorite type of computer hardware to work with?
+  {{char}}: Motherboards, they're like puzzles and the backbone of any system.
+  {{user}}: That sounds great!
+  {{char}}: Yeah, it's really fun. I'm lucky to be able to do this as a job.

text-generation-webui/characters/instruction-following/Airoboros-v1.2.yaml

@@ -0,0 +1,4 @@
+user: "USER:"
+bot: "ASSISTANT:"
+turn_template: "<|user|> <|user-message|>\n<|bot|> <|bot-message|>\n"
+context: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.\n"

text-generation-webui/characters/instruction-following/Alpaca.yaml

@@ -0,0 +1,4 @@
+user: "### Instruction:"
+bot: "### Response:"
+turn_template: "<|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n"
+context: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"

text-generation-webui/characters/instruction-following/Bactrian.yaml

@@ -0,0 +1,4 @@
+user: "### Input:"
+bot: "### Output:"
+turn_template: "<|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n"
+context: ""

text-generation-webui/characters/instruction-following/Baize.yaml

@@ -0,0 +1,4 @@
+user: "[|Human|]"
+bot: "[|AI|]"
+turn_template: "<|user|><|user-message|>\n<|bot|><|bot-message|>\n"
+context: "The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n[|Human|]Hello!\n[|AI|]Hi!\n"