bedrock-Claude.ipynb

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   "id": "ab8ce8ee-b878-4c7c-b083-3f451f933736",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "!pip install --force-reinstall amazon-textract-textractor==1.7.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "403584fa-ccd0-405d-9b7a-1dae75c6ae03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import re\n",
    "import base64\n",
    "import json\n",
    "from textractor import Textractor\n",
    "from textractor.visualizers.entitylist import EntityList\n",
    "from textractor.data.constants import TextractFeatures\n",
    "import boto3\n",
    "from botocore.exceptions import ClientError\n",
    "# Create the bedrock runtime to invoke LLM\n",
    "from botocore.config import Config\n",
    "config = Config(\n",
    "    read_timeout=600, #this timeout determines the maximum time (secs) allowed for the client to wait for data to be received from the server. \n",
    "    retries = dict(\n",
    "        max_attempts = 5 ## maximum number of retry attempts that will be made on a single request\n",
    "    )\n",
    ")\n",
    "region=\"us-east-1\"\n",
    "bedrock_runtime = boto3.client(service_name='bedrock-runtime',region_name=region,config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5e26716-f03d-4fc2-82d6-a8d655eafe4c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def _invoke_bedrock_with_retries(system_message, prompt, model_id,image_path=None):\n",
    "    max_retries = 5\n",
    "    backoff_base = 2\n",
    "    max_backoff = 3  # Maximum backoff time in seconds\n",
    "    retries = 0\n",
    "    while True:\n",
    "        try:\n",
    "            response, input_token, output_token = bedrock_claude_(system_message, prompt,model_id,image_path)\n",
    "            return response, input_token, output_token\n",
    "        except ClientError as e:\n",
    "            if e.response['Error']['Code'] == 'ThrottlingException':\n",
    "                if retries < max_retries:\n",
    "                    # Throttling, exponential backoff\n",
    "                    sleep_time = min(max_backoff, backoff_base ** retries + random.uniform(0, 1))\n",
    "                    time.sleep(sleep_time)\n",
    "                    retries += 1\n",
    "                else:\n",
    "                    raise e\n",
    "            else:\n",
    "                # Some other API error, rethrow\n",
    "                raise\n",
    "\n",
    "def bedrock_streemer(response):\n",
    "    stream = response.get('body')\n",
    "    answer = \"\"\n",
    "    i = 1\n",
    "    if stream:\n",
    "        for event in stream:\n",
    "            chunk = event.get('chunk')\n",
    "            if  chunk:\n",
    "                chunk_obj = json.loads(chunk.get('bytes').decode())\n",
    "                if \"delta\" in chunk_obj:                    \n",
    "                    delta = chunk_obj['delta']\n",
    "                    if \"text\" in delta:\n",
    "                        text=delta['text'] \n",
    "                        print(text, end=\"\")\n",
    "                        answer+=str(text)       \n",
    "                        i+=1\n",
    "                if \"amazon-bedrock-invocationMetrics\" in chunk_obj:\n",
    "                    input_tokens= chunk_obj['amazon-bedrock-invocationMetrics']['inputTokenCount']\n",
    "                    output_tokens=chunk_obj['amazon-bedrock-invocationMetrics']['outputTokenCount']\n",
    "                    print(f\"\\nInput Tokens: {input_tokens}\\nOutput Tokens: {output_tokens}\")\n",
    "    return answer,input_tokens, output_tokens\n",
    "\n",
    "def bedrock_claude_(system_message, prompt,model_id,image_path=None):\n",
    "    content=[]\n",
    "    if image_path:       \n",
    "        if not isinstance(image_path, list):\n",
    "            image_path=[image_path]      \n",
    "        for img in image_path:\n",
    "            s3 = boto3.client('s3')\n",
    "            match = re.match(\"s3://(.+?)/(.+)\", img)\n",
    "            image_name=os.path.basename(img)\n",
    "            _,ext=os.path.splitext(image_name)\n",
    "            if \"jpg\" in ext: ext=\".jpeg\"                        \n",
    "            if match:\n",
    "                bucket_name = match.group(1)\n",
    "                key = match.group(2)    \n",
    "                obj = s3.get_object(Bucket=bucket_name, Key=key)\n",
    "                base_64_encoded_data = base64.b64encode(obj['Body'].read())\n",
    "                base64_string = base_64_encoded_data.decode('utf-8')\n",
    "            else:\n",
    "                with open(img, \"rb\") as image_file:\n",
    "                    binary_data = image_file.read()\n",
    "                base_64_encoded_data = base64.b64encode(binary_data)\n",
    "                base64_string = base_64_encoded_data.decode('utf-8')\n",
    "            content.extend([{\"type\":\"text\",\"text\":image_name},{\n",
    "              \"type\": \"image\",\n",
    "              \"source\": {\n",
    "                \"type\": \"base64\",\n",
    "                \"media_type\": f\"image/{ext.lower().replace('.','')}\",\n",
    "                \"data\": base64_string\n",
    "              }\n",
    "            }])\n",
    "\n",
    "    content.append({\n",
    "        \"type\": \"text\",\n",
    "        \"text\": prompt\n",
    "            })\n",
    "    prompt = {\n",
    "        \"anthropic_version\": \"bedrock-2023-05-31\",\n",
    "        \"max_tokens\": 2500,\n",
    "        \"temperature\": 0.5,\n",
    "        \"system\":system_message,\n",
    "        \"messages\": [    \n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": content\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "    answer = \"\"\n",
    "    prompt = json.dumps(prompt)\n",
    "    response = bedrock_runtime.invoke_model_with_response_stream(body=prompt, modelId=model_id, accept=\"application/json\", contentType=\"application/json\")\n",
    "    answer,input_tokens,output_tokens=bedrock_streemer(response) \n",
    "    return answer, input_tokens, output_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b17649-5d41-4872-957e-df551a5e52ac",
   "metadata": {
    "tags": []
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   "source": [
    "## TEXTARCT TO EXTRACT PDF\n",
    "**`(optional)`**\n",
    "If you want to run summary or extract information from a pdf you can use Azazon Textract to extract the pdf and pass as context to the LLM call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7460309e-4c9c-43c7-a797-8b46745cd4b9",
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   "source": [
    "file=\"LOCAL OR S# FILE PATH\"\n",
    "output_bucket=\"BUCKET NAME\"\n",
    "extractor = Textractor(region_name=\"us-east-1\")\n",
    "\n",
    "if file.startswith(\"s3://\"):\n",
    "    document = extractor.start_document_analysis(\n",
    "        file_source=file,\n",
    "        features=[TextractFeatures.LAYOUT,TextractFeatures.FORMS, TextractFeatures.TABLES],   \n",
    "        save_image=False,\n",
    "        s3_output_path=f\"s3://{output_bucket}/textract-output/\"\n",
    "    )\n",
    "else:\n",
    "    document = extractor.start_document_analysis(\n",
    "        file_source=file,\n",
    "        features=[TextractFeatures.LAYOUT,TextractFeatures.FORMS, TextractFeatures.TABLES],   \n",
    "        save_image=False,\n",
    "        s3_upload_path=f\"s3://{output_bucket}/uploaded_document/\",\n",
    "        s3_output_path=f\"s3://{output_bucket}/textract-output/\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4c508382-7705-456f-a35f-ef67ce7f852f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from textractor.data.text_linearization_config import TextLinearizationConfig\n",
    "configs = TextLinearizationConfig(\n",
    "    hide_figure_layout=True,\n",
    "    hide_header_layout=False,\n",
    "    table_prefix=\"<table>\",\n",
    "    table_suffix=\"</table>\",\n",
    "    hide_footer_layout=True,\n",
    "    hide_page_num_layout=True,\n",
    ")\n",
    "extracted_content=document.get_text(config=configs)\n",
    "print(extracted_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc07a75-355e-4975-8dbf-4195768f278d",
   "metadata": {},
   "source": [
    "## GENERATE\n",
    "\n",
    "Anthropic claude is able to process images, you can pass a list of images as a paramter to the api call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "530b5e9a-b94b-4a76-b988-f9eb7d0248c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The recommended amount of sleep per day varies by age group, but for most healthy adults, the general recommendation is:\n",
      "\n",
      "7-9 hours per night\n",
      "\n",
      "More specifically, the sleep recommendations from the National Sleep Foundation are:\n",
      "\n",
      "- Newborns (0-3 months): 14-17 hours\n",
      "- Infants (4-11 months): 12-15 hours  \n",
      "- Toddlers (1-2 years): 11-14 hours\n",
      "- Preschoolers (3-5 years): 10-13 hours\n",
      "- School-age children (6-13 years): 9-11 hours\n",
      "- Teens (14-17 years): 8-10 hours\n",
      "- Adults (18-64 years): 7-9 hours\n",
      "- Older Adults (65+ years): 7-8 hours\n",
      "\n",
      "Getting sufficient quality sleep on a regular basis is important for physical and mental health. Individual sleep needs can vary somewhat based on factors like activity levels and genetic differences. But 7-9 hours is the general target for most adults to be well-rested.\n",
      "Input Tokens: 25\n",
      "Output Tokens: 247\n"
     ]
    }
   ],
   "source": [
    "model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\"#\"anthropic.claude-v2\",\"anthropic.claude-3-haiku-20240307-v1:0\",\"anthropic.claude-3-sonnet-20240229-v1:0\"\n",
    "system_prompt=\"You are a an AI assistant.\"\n",
    "prompt=\"What is the average time of sleep per day recommended?\"\n",
    "image_path=[] # local or s3 path to images. Images must be in jpeg, png or webp format (maximum of 20 images per api call)\n",
    "summary=_invoke_bedrock_with_retries(system_prompt,prompt,model_id,image_path if \"claude-3\" in model_id else None)"
   ]
  }
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