From 5adde109841541597b9f5c6eaa876d61bb58567e Mon Sep 17 00:00:00 2001 From: Chris Jarrett Date: Thu, 3 Oct 2024 15:19:00 -0700 Subject: [PATCH 1/6] Modifies example notebooks to use nvidia endoints and milvus --- examples/langchain_multimodal_rag.ipynb | 184 +++++++++++++++++--- examples/llama_index_multimodal_rag.ipynb | 198 ++++++++++++---------- 2 files changed, 270 insertions(+), 112 deletions(-) diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index 70416c65..436c01a3 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -29,7 +29,7 @@ "id": "2dcc96cf-f8e1-4baa-b1ca-b6521d23dec8", "metadata": {}, "source": [ - "pip install langchain langchain_community langchain_chroma langchain-openai" + "pip install -qU langchain langchain_community langchain_chroma langchain-nvidia-ai-endpoints" ] }, { @@ -48,9 +48,11 @@ "outputs": [], "source": [ "from nv_ingest_client.client import NvIngestClient\n", + "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", - "from nv_ingest_client.primitives.tasks import SplitTask\n", + "\n", + "\n", "from nv_ingest_client.util.file_processing.extract import extract_file_content\n", "import logging, time\n", "\n", @@ -64,7 +66,12 @@ " payload=file_content,\n", " source_id=file_name,\n", " source_name=file_name,\n", - " extended_options={\"tracing_options\": {\"trace\": True, \"ts_send\": time.time_ns()}},\n", + " extended_options={\n", + " \"tracing_options\": {\n", + " \"trace\": True,\n", + " \"ts_send\": time.time_ns()\n", + " }\n", + " },\n", ")" ] }, @@ -92,7 +99,12 @@ "\n", "\n", "job_spec.add_task(extract_task)\n", - "client = NvIngestClient()\n", + "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", "job_id = client.add_job(job_spec)\n", "\n", "client.submit_job(job_id, \"morpheus_task_queue\")\n", @@ -131,8 +143,8 @@ " 'raise_on_failure': False,\n", " 'source_metadata': {'access_level': 1,\n", " 'collection_id': '',\n", - " 'date_created': '2024-09-04T15:55:15.103673',\n", - " 'last_modified': '2024-09-04T15:55:15.103335',\n", + " 'date_created': '2024-10-03T22:15:35.818603',\n", + " 'last_modified': '2024-10-03T22:15:35.818339',\n", " 'partition_id': -1,\n", " 'source_id': '../data/multimodal_test.pdf',\n", " 'source_location': '',\n", @@ -153,7 +165,7 @@ } ], "source": [ - "result[0][0][0]" + "result[0][0]" ] }, { @@ -175,7 +187,7 @@ "\n", "texts = []\n", "tables = []\n", - "for element in result[0][0]:\n", + "for element in result[0]:\n", " if element['document_type'] == 'text':\n", " texts.append(Document(element['metadata']['content']))\n", " elif element['document_type'] == 'structured':\n", @@ -191,7 +203,7 @@ { "data": { "text/plain": [ - "[Document(page_content='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.')]" + "[Document(metadata={}, page_content='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.')]" ] }, "execution_count": 5, @@ -212,10 +224,10 @@ { "data": { "text/plain": [ - "[Document(page_content='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard'),\n", - " Document(page_content='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk'),\n", - " Document(page_content='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray'),\n", - " Document(page_content='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000')]" + "[Document(metadata={}, page_content='locations. Animal Activity Place Giraffe Driving a car. At the beach Lion Putting on sunscreen At the park. Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard'),\n", + " Document(metadata={}, page_content='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost Chart 1 - Gadgets and their cost'),\n", + " Document(metadata={}, page_content='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray'),\n", + " Document(metadata={}, page_content='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000 Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 10 - 100 - 1000 - 10000 - 100000 Frequency Range Start (Hz) - Frequency Range End (Hz) This chart shows some average frequency ranges for speaker drivers - Frequency Ranges of Speaker Drivers')]" ] }, "execution_count": 6, @@ -244,12 +256,13 @@ "source": [ "import os\n", "from langchain_chroma import Chroma\n", - "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n", "\n", - "# TODO: Add your OpenAI API key here\n", - "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "# TODO: Add your NVIDIA API key here\n", + "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", "\n", - "vectorstore = Chroma.from_documents(documents=(texts+tables), embedding=OpenAIEmbeddings())" + "embedding = NVIDIAEmbeddings()\n", + "vectorstore = Chroma.from_documents(documents=(texts+tables), embedding=embedding)" ] }, { @@ -267,11 +280,11 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_openai import ChatOpenAI\n", + "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n", "\n", "retriever = vectorstore.as_retriever()\n", "\n", - "llm = ChatOpenAI(model=\"gpt-4o-mini\")" + "llm = ChatNVIDIA(model=\"meta/llama-3.1-405b-instruct\")" ] }, { @@ -334,10 +347,141 @@ "rag_chain.invoke(\"What is the dog doing and where?\")" ] }, + { + "cell_type": "markdown", + "id": "1860f415-d281-4d31-95fb-fcd37ce68ee3", + "metadata": {}, + "source": [ + "Alternatively, we can use the milvus vector database packaged with NV-Ingest. This requires pymilvus and the milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ee4c94a-5f20-4608-aee1-fed664eb4e10", + "metadata": {}, + "outputs": [], + "source": [ + "pip install -qU pymilvus langchain_milvus" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "87867fb8-e56d-4d49-97af-9c7edf5117ad", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_milvus import Milvus\n", + "\n", + "vector_store = Milvus(\n", + " embedding_function=embedding,\n", + " connection_args={\"uri\": \"http://localhost:19530\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cdca2679-94b4-4636-af44-3716bdf92556", + "metadata": {}, + "source": [ + "And then we'll load our documents into our new store" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ae6380ba-a369-48d3-891d-a58c891ae906", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2cfd802a-c655-495b-acc8-a51ec098bc95',\n", + " '4c4fc62d-ae33-4f89-b0e0-667f702d5dbe',\n", + " '8b9e6af9-7c48-4165-b9ab-116ed8a834e6',\n", + " '45e6cd4e-0bee-47e9-bec1-3bacc7cb37f2',\n", + " '71fe6278-99bf-48bb-8aae-85bad4370466']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from uuid import uuid4\n", + "\n", + "uuids = [str(uuid4()) for _ in range(len(texts+tables))]\n", + "vector_store.add_documents(documents=texts+tables, ids=uuids)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a35efa2-20f0-49f7-9d23-bc0dfdcc97e7", + "metadata": {}, + "outputs": [], + "source": [ + "retriever = vectorstore.as_retriever()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f3f78d40-f7dd-4bf7-9068-f8a743c71c12", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "\n", + "template = (\n", + " \"You are an assistant for question-answering tasks. \"\n", + " \"Use the following pieces of retrieved context to answer \"\n", + " \"the question. If you don't know the answer, say that you \"\n", + " \"don't know. Keep the answer concise.\"\n", + " \"\\n\\n\"\n", + " \"{context}\"\n", + " \"Question: {question}\"\n", + ")\n", + "\n", + "prompt = PromptTemplate.from_template(template)\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f1200811-b4ac-468e-bb32-77c102aa9e30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The dog is chasing a squirrel in the front yard.'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rag_chain.invoke(\"What is the dog doing and where?\")" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "dcbcfcb7-c6c6-4112-86c4-39e6975d325b", + "id": "9d5ea762-70cc-4eef-8bb8-b1347cd43392", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index 925009fc..01a53c89 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -31,7 +31,7 @@ "metadata": {}, "outputs": [], "source": [ - "pip install llama_index" + "pip install -qU llama_index llama-index-embeddings-nvidia llama-index-llms-nvidia" ] }, { @@ -50,9 +50,11 @@ "outputs": [], "source": [ "from nv_ingest_client.client import NvIngestClient\n", + "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", - "from nv_ingest_client.primitives.tasks import SplitTask\n", + "\n", + "\n", "from nv_ingest_client.util.file_processing.extract import extract_file_content\n", "import logging, time\n", "\n", @@ -66,7 +68,12 @@ " payload=file_content,\n", " source_id=file_name,\n", " source_name=file_name,\n", - " extended_options={\"tracing_options\": {\"trace\": True, \"ts_send\": time.time_ns()}},\n", + " extended_options={\n", + " \"tracing_options\": {\n", + " \"trace\": True,\n", + " \"ts_send\": time.time_ns()\n", + " }\n", + " },\n", ")" ] }, @@ -94,7 +101,12 @@ "\n", "\n", "job_spec.add_task(extract_task)\n", - "client = NvIngestClient()\n", + "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", "job_id = client.add_job(job_spec)\n", "\n", "client.submit_job(job_id, \"morpheus_task_queue\")\n", @@ -102,62 +114,6 @@ "result = client.fetch_job_result(job_id, timeout=60)" ] }, - { - "cell_type": "code", - "execution_count": 3, - "id": "07d41434-a5cb-4cc3-aaad-a57603abda44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'document_type': 'text',\n", - " 'metadata': {'content': 'TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.',\n", - " 'content_metadata': {'description': 'Unstructured text from PDF document.',\n", - " 'hierarchy': {'block': -1,\n", - " 'line': -1,\n", - " 'nearby_objects': {'images': {'bbox': [], 'content': []},\n", - " 'structured': {'bbox': [], 'content': []},\n", - " 'text': {'bbox': [], 'content': []}},\n", - " 'page': -1,\n", - " 'page_count': 3,\n", - " 'span': -1},\n", - " 'page_number': -1,\n", - " 'subtype': '',\n", - " 'type': 'text'},\n", - " 'debug_metadata': None,\n", - " 'embedding': None,\n", - " 'error_metadata': None,\n", - " 'image_metadata': None,\n", - " 'info_message_metadata': None,\n", - " 'raise_on_failure': False,\n", - " 'source_metadata': {'access_level': 1,\n", - " 'collection_id': '',\n", - " 'date_created': '2024-09-04T17:17:41.657851',\n", - " 'last_modified': '2024-09-04T17:17:41.657538',\n", - " 'partition_id': -1,\n", - " 'source_id': '../data/multimodal_test.pdf',\n", - " 'source_location': '',\n", - " 'source_name': '../data/multimodal_test.pdf',\n", - " 'source_type': 'PDF',\n", - " 'summary': ''},\n", - " 'table_metadata': None,\n", - " 'text_metadata': {'keywords': '',\n", - " 'language': 'en',\n", - " 'summary': '',\n", - " 'text_location': [-1, -1, -1, -1],\n", - " 'text_type': 'document'}}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result[0][0][0]" - ] - }, { "cell_type": "markdown", "id": "5a2a0a9c-ede6-4cef-b182-9f0b78223647", @@ -168,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "c2b0d5eb-f0db-4edb-a3fe-c3bfccc59227", "metadata": {}, "outputs": [], @@ -177,7 +133,7 @@ "\n", "texts = []\n", "tables = []\n", - "for element in result[0][0]:\n", + "for element in result[0]:\n", " if element['document_type'] == 'text':\n", " texts.append(Document(text=element['metadata']['content']))\n", " elif element['document_type'] == 'structured':\n", @@ -186,17 +142,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "0f220a41-fc55-4b6c-95e1-28b41bfdba0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Document(id_='9bad2140-a997-4af0-a4ea-c8236416def0', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + "[Document(id_='f8eb55dc-6c04-4898-a892-a363c9e25a11', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -206,25 +162,9 @@ ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "62478b8f-aba2-4233-918a-d3025e87c13e", + "cell_type": "markdown", + "id": "664c0f34-0af7-4092-9801-f18102973c3b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(id_='542181ac-d3f2-490a-979b-cf0c6e24c1d4', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='563e88da-dd0f-4121-98f9-c3d85f7a7bea', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='ab3c36fe-6812-41b3-9cf3-966f363e8e5f', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='440297f1-6e6f-4e5a-8af8-d56d7afa7b1c', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "tables" ] @@ -239,18 +179,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "a8d693a4-e647-4c20-bea4-56fe52c74541", "metadata": {}, "outputs": [], "source": [ "import os\n", "from llama_index.core import VectorStoreIndex\n", + "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n", "\n", - "# TODO: Add your OpenAI API key here\n", - "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "# TODO: Add your NVIDIA API key here\n", + "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", "\n", - "index = VectorStoreIndex.from_documents(texts+tables)" + "embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\")\n", + "index = VectorStoreIndex.from_documents(texts+tables, embed_model=embed_model)" ] }, { @@ -263,12 +205,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "c3eb210e-1106-4956-80a3-f950d30ac6c2", "metadata": {}, "outputs": [], "source": [ - "query_engine = index.as_query_engine()" + "from llama_index.llms.nvidia import NVIDIA\n", + "\n", + "llm = NVIDIA(model=\"meta/llama-3.1-405b-instruct\")\n", + "query_engine = index.as_query_engine(llm=llm)" ] }, { @@ -281,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "f397a34b-4639-4f8a-81a8-5a5a416404d5", "metadata": {}, "outputs": [ @@ -291,7 +236,76 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 9, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query_engine.query(\"What is the dog doing and where?\").response" + ] + }, + { + "cell_type": "markdown", + "id": "fe5a521b-aca8-4227-b73d-1c3fe8aef95b", + "metadata": {}, + "source": [ + "Alternatively, we can use the milvus vector database packaged with NV-Ingest. This requires pymilvus and the milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "572b2b1a-367b-41bb-a153-ad86227fa815", + "metadata": {}, + "outputs": [], + "source": [ + "pip install -qU pymilvus llama-index-vector-stores-milvus" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "88a9658c-6d5b-4cb0-ace2-2ef59cb8fe6a", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex, StorageContext\n", + "from llama_index.vector_stores.milvus import MilvusVectorStore\n", + "\n", + "\n", + "vector_store = MilvusVectorStore(\n", + " uri=\"http://localhost:19530\",\n", + " dim=1024,\n", + " overwrite=True\n", + ")\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "index = VectorStoreIndex.from_documents(texts+tables, storage_context, embed_model=embed_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ae32590b-2ff3-4613-b85f-75942b1b5eec", + "metadata": {}, + "outputs": [], + "source": [ + "query_engine = index.as_query_engine(llm=llm)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d30cd73a-88e1-45cb-94e6-3d07deffe0df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The dog is chasing a squirrel in the front yard.'" + ] + }, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -303,7 +317,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ee5de708-c5e3-43fd-8447-59fb97153eee", + "id": "f2979c08-56a0-441f-87bf-2c6d0c8391a0", "metadata": {}, "outputs": [], "source": [] From 2c44401d6f83c49908a0a2266437e7df2ca85b78 Mon Sep 17 00:00:00 2001 From: Chris Jarrett Date: Tue, 8 Oct 2024 21:25:24 -0700 Subject: [PATCH 2/6] Add notebook for storing and displaying images --- examples/langchain_multimodal_rag.ipynb | 212 +++++++----- examples/llama_index_multimodal_rag.ipynb | 226 ++++++++++-- examples/store_and_display_images.ipynb | 403 ++++++++++++++++++++++ 3 files changed, 732 insertions(+), 109 deletions(-) create mode 100644 examples/store_and_display_images.ipynb diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index 436c01a3..fd6eaa0c 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "bf097db1-9699-442e-88b7-7b67a8a2fecf", "metadata": {}, "outputs": [ @@ -143,8 +143,8 @@ " 'raise_on_failure': False,\n", " 'source_metadata': {'access_level': 1,\n", " 'collection_id': '',\n", - " 'date_created': '2024-10-03T22:15:35.818603',\n", - " 'last_modified': '2024-10-03T22:15:35.818339',\n", + " 'date_created': '2024-10-08T16:31:46.121257',\n", + " 'last_modified': '2024-10-08T16:31:46.121101',\n", " 'partition_id': -1,\n", " 'source_id': '../data/multimodal_test.pdf',\n", " 'source_location': '',\n", @@ -159,13 +159,13 @@ " 'text_type': 'document'}}}" ] }, - "execution_count": 3, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result[0][0]" + "result[0][0][0]" ] }, { @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "b0c6d966-9a38-4b1b-b5f2-cbe214d1718d", "metadata": {}, "outputs": [], @@ -187,7 +187,7 @@ "\n", "texts = []\n", "tables = []\n", - "for element in result[0]:\n", + "for element in result[0][0]:\n", " if element['document_type'] == 'text':\n", " texts.append(Document(element['metadata']['content']))\n", " elif element['document_type'] == 'structured':\n", @@ -196,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "id": "7bb4b918-9029-4ffc-badd-dddc4022a750", "metadata": {}, "outputs": [ @@ -206,7 +206,7 @@ "[Document(metadata={}, page_content='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.')]" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -217,20 +217,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "307fdf5d-587b-448b-8b79-e4fa88fb5b0c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Document(metadata={}, page_content='locations. Animal Activity Place Giraffe Driving a car. At the beach Lion Putting on sunscreen At the park. Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard'),\n", + "[Document(metadata={}, page_content='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard'),\n", " Document(metadata={}, page_content='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost Chart 1 - Gadgets and their cost'),\n", " Document(metadata={}, page_content='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray'),\n", " Document(metadata={}, page_content='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000 Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 10 - 100 - 1000 - 10000 - 100000 Frequency Range Start (Hz) - Frequency Range End (Hz) This chart shows some average frequency ranges for speaker drivers - Frequency Ranges of Speaker Drivers')]" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -244,12 +244,12 @@ "id": "15410af0-fa5c-4aed-b554-f2165c6f482e", "metadata": {}, "source": [ - "Next, we'll set our OpenAI API key and create a vector store to embed and store our text and table documents using OpenAI's embedding model" + "Next, we'll set our NVIDIA API key and create a vector store to embed and store our text and table documents using an embedding model from NVIDIA's API catalog" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "53957974-c688-4521-8c61-09f2649d5d53", "metadata": {}, "outputs": [], @@ -270,12 +270,12 @@ "id": "12d14f63-31b8-4d72-a029-a54328ff1c5b", "metadata": {}, "source": [ - "Then, we'll create a retriever from our vector score that will allow us to retrieve our documents by semantic similarity and an llm to synthesize the final answer from the retrieved documents" + "Then, we'll create a retriever from our vector score that will allow us to retrieve our documents by semantic similarity and we'll use an llm from NVIDIA's API catalog to generate the final answer from the retrieved documents" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "a12f53f7-2407-4890-9586-82956cfccf13", "metadata": {}, "outputs": [], @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "d16331bb-95dd-46d7-8b71-9d966a8ef3ba", "metadata": {}, "outputs": [], @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "b547a19a-9ada-4a40-a246-6d7bc4d24482", "metadata": {}, "outputs": [ @@ -338,7 +338,7 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -347,12 +347,20 @@ "rag_chain.invoke(\"What is the dog doing and where?\")" ] }, + { + "cell_type": "markdown", + "id": "3d488c44-30e2-4b36-b8f0-8fb1cb5cac68", + "metadata": {}, + "source": [ + "## Milvus" + ] + }, { "cell_type": "markdown", "id": "1860f415-d281-4d31-95fb-fcd37ce68ee3", "metadata": {}, "source": [ - "Alternatively, we can use the milvus vector database packaged with NV-Ingest. This requires pymilvus and the milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services)" + "Alternatively, we can use the embedding NIM and the milvus vector database packaged with NV-Ingest. This requires pymilvus and the embedding, milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). This has the benefit of directly sending the extraction results to the embedding microservice and then to a vector database without roundtripping between the client and the NV-Ingest microservice between each step" ] }, { @@ -365,102 +373,126 @@ "pip install -qU pymilvus langchain_milvus" ] }, + { + "cell_type": "markdown", + "id": "b01cde1d-1c2b-4419-8dc5-ff343c2f1ffb", + "metadata": {}, + "source": [ + "First, we'll creaete a new job spec" + ] + }, { "cell_type": "code", - "execution_count": 11, - "id": "87867fb8-e56d-4d49-97af-9c7edf5117ad", + "execution_count": 17, + "id": "50a0d559-0445-437a-82cb-468310d84029", "metadata": {}, "outputs": [], "source": [ - "from langchain_milvus import Milvus\n", + "file_name = \"../data/multimodal_test.pdf\"\n", + "file_content, file_type = extract_file_content(file_name)\n", "\n", - "vector_store = Milvus(\n", - " embedding_function=embedding,\n", - " connection_args={\"uri\": \"http://localhost:19530\"},\n", + "job_spec = JobSpec(\n", + " document_type=file_type,\n", + " payload=file_content,\n", + " source_id=file_name,\n", + " source_name=file_name,\n", + " extended_options={\n", + " \"tracing_options\": {\n", + " \"trace\": True,\n", + " \"ts_send\": time.time_ns()\n", + " }\n", + " },\n", ")" ] }, { "cell_type": "markdown", - "id": "cdca2679-94b4-4636-af44-3716bdf92556", + "id": "c49e355f-ab3f-436e-8a60-ce1f276234cb", "metadata": {}, "source": [ - "And then we'll load our documents into our new store" + "Then, we'll add the extraction task again, but this time we'll also add an embed task and a vector database upload task" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "ae6380ba-a369-48d3-891d-a58c891ae906", + "execution_count": 18, + "id": "05631715-9ece-44ab-8a41-d11c569e6906", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['2cfd802a-c655-495b-acc8-a51ec098bc95',\n", - " '4c4fc62d-ae33-4f89-b0e0-667f702d5dbe',\n", - " '8b9e6af9-7c48-4165-b9ab-116ed8a834e6',\n", - " '45e6cd4e-0bee-47e9-bec1-3bacc7cb37f2',\n", - " '71fe6278-99bf-48bb-8aae-85bad4370466']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from uuid import uuid4\n", + "from nv_ingest_client.primitives.tasks import EmbedTask\n", + "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", + "\n", + "extract_task = ExtractTask(\n", + " document_type=file_type,\n", + " extract_text=True,\n", + " extract_images=False,\n", + " extract_tables=True,\n", + ")\n", "\n", - "uuids = [str(uuid4()) for _ in range(len(texts+tables))]\n", - "vector_store.add_documents(documents=texts+tables, ids=uuids)" + "embed_task = EmbedTask(\n", + " text=True,\n", + " tables=True,\n", + ")\n", + "\n", + "vdb_upload_task = VdbUploadTask()\n", + "\n", + "job_spec.add_task(extract_task)\n", + "job_spec.add_task(embed_task)\n", + "job_spec.add_task(vdb_upload_task)\n", + "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", + "job_id = client.add_job(job_spec)\n", + "\n", + "client.submit_job(job_id, \"morpheus_task_queue\")\n", + "\n", + "result = client.fetch_job_result(job_id, timeout=60)" ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "1a35efa2-20f0-49f7-9d23-bc0dfdcc97e7", + "cell_type": "markdown", + "id": "bccb6025-6587-4555-80c7-40fcf77f208f", "metadata": {}, - "outputs": [], "source": [ - "retriever = vectorstore.as_retriever()" + "Next, we'll connect langchain to our collection in Milvus and create a new retiever from it" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "f3f78d40-f7dd-4bf7-9068-f8a743c71c12", + "execution_count": 19, + "id": "87867fb8-e56d-4d49-97af-9c7edf5117ad", "metadata": {}, "outputs": [], "source": [ - "from langchain_core.prompts import PromptTemplate\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_milvus import Milvus\n", "\n", - "template = (\n", - " \"You are an assistant for question-answering tasks. \"\n", - " \"Use the following pieces of retrieved context to answer \"\n", - " \"the question. If you don't know the answer, say that you \"\n", - " \"don't know. Keep the answer concise.\"\n", - " \"\\n\\n\"\n", - " \"{context}\"\n", - " \"Question: {question}\"\n", + "vectorstore = Milvus(\n", + " embedding_function=embedding,\n", + " collection_name=\"nv_ingest_collection\",\n", + " primary_field = \"pk\",\n", + " vector_field = \"vector\",\n", + " text_field=\"text\",\n", + " connection_args={\"uri\": \"http://localhost:19530\"},\n", ")\n", - "\n", - "prompt = PromptTemplate.from_template(template)\n", - "\n", - "rag_chain = (\n", - " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", - " | prompt\n", - " | llm\n", - " | StrOutputParser()\n", - ")" + "retriever = vectorstore.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "cdca2679-94b4-4636-af44-3716bdf92556", + "metadata": {}, + "source": [ + "And finally, we can perform RAG with our Milvus based retriever just as we did before" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "f1200811-b4ac-468e-bb32-77c102aa9e30", + "execution_count": 20, + "id": "1a35efa2-20f0-49f7-9d23-bc0dfdcc97e7", "metadata": {}, "outputs": [ { @@ -469,19 +501,37 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "template = (\n", + " \"You are an assistant for question-answering tasks. \"\n", + " \"Use the following pieces of retrieved context to answer \"\n", + " \"the question. If you don't know the answer, say that you \"\n", + " \"don't know. Keep the answer concise.\"\n", + " \"\\n\\n\"\n", + " \"{context}\"\n", + " \"Question: {question}\"\n", + ")\n", + "\n", + "prompt = PromptTemplate.from_template(template)\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")\n", "rag_chain.invoke(\"What is the dog doing and where?\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "9d5ea762-70cc-4eef-8bb8-b1347cd43392", + "id": "f1200811-b4ac-468e-bb32-77c102aa9e30", "metadata": {}, "outputs": [], "source": [] @@ -503,7 +553,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index 01a53c89..b2324060 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -82,7 +82,7 @@ "id": "2aa9a74c-f7e4-475d-970d-cf820cd8ea19", "metadata": {}, "source": [ - "And then we can and submit a task to extract the text and tables from the example pdf" + "And then, we can and submit a task to extract the text and tables from the example pdf" ] }, { @@ -114,6 +114,62 @@ "result = client.fetch_job_result(job_id, timeout=60)" ] }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8609f938-128a-4d61-a0dd-a36fc8d2b077", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'document_type': 'text',\n", + " 'metadata': {'content': 'TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.',\n", + " 'content_metadata': {'description': 'Unstructured text from PDF document.',\n", + " 'hierarchy': {'block': -1,\n", + " 'line': -1,\n", + " 'nearby_objects': {'images': {'bbox': [], 'content': []},\n", + " 'structured': {'bbox': [], 'content': []},\n", + " 'text': {'bbox': [], 'content': []}},\n", + " 'page': -1,\n", + " 'page_count': 3,\n", + " 'span': -1},\n", + " 'page_number': -1,\n", + " 'subtype': '',\n", + " 'type': 'text'},\n", + " 'debug_metadata': None,\n", + " 'embedding': None,\n", + " 'error_metadata': None,\n", + " 'image_metadata': None,\n", + " 'info_message_metadata': None,\n", + " 'raise_on_failure': False,\n", + " 'source_metadata': {'access_level': 1,\n", + " 'collection_id': '',\n", + " 'date_created': '2024-10-08T19:16:03.465614',\n", + " 'last_modified': '2024-10-08T19:16:03.465459',\n", + " 'partition_id': -1,\n", + " 'source_id': '../data/multimodal_test.pdf',\n", + " 'source_location': '',\n", + " 'source_name': '../data/multimodal_test.pdf',\n", + " 'source_type': 'PDF',\n", + " 'summary': ''},\n", + " 'table_metadata': None,\n", + " 'text_metadata': {'keywords': '',\n", + " 'language': 'en',\n", + " 'summary': '',\n", + " 'text_location': [-1, -1, -1, -1],\n", + " 'text_type': 'document'}}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[0][0][0]" + ] + }, { "cell_type": "markdown", "id": "5a2a0a9c-ede6-4cef-b182-9f0b78223647", @@ -124,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "c2b0d5eb-f0db-4edb-a3fe-c3bfccc59227", "metadata": {}, "outputs": [], @@ -133,7 +189,7 @@ "\n", "texts = []\n", "tables = []\n", - "for element in result[0]:\n", + "for element in result[0][0]:\n", " if element['document_type'] == 'text':\n", " texts.append(Document(text=element['metadata']['content']))\n", " elif element['document_type'] == 'structured':\n", @@ -142,17 +198,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "0f220a41-fc55-4b6c-95e1-28b41bfdba0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Document(id_='f8eb55dc-6c04-4898-a892-a363c9e25a11', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + "[Document(id_='2b6bac68-5e1e-4a5a-a1ff-ef78b00ca711', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -162,9 +218,25 @@ ] }, { - "cell_type": "markdown", - "id": "664c0f34-0af7-4092-9801-f18102973c3b", + "cell_type": "code", + "execution_count": 6, + "id": "9014cbaa-1c4a-4d5d-bf34-f41b9b0c335a", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(id_='0d2024b3-c41a-4604-9779-e83106d07d78', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", + " Document(id_='ccfba084-b81d-4319-be0d-573bc5742d45', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost Chart 1 - Gadgets and their cost', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", + " Document(id_='49e27e70-246f-4963-8da2-22c81c7a7722', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", + " Document(id_='e456b1f7-814c-47a1-918a-e6e99766deda', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000 Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 10 - 100 - 1000 - 10000 - 100000 Frequency Range Start (Hz) - Frequency Range End (Hz) This chart shows some average frequency ranges for speaker drivers - Frequency Ranges of Speaker Drivers', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tables" ] @@ -174,12 +246,12 @@ "id": "eaf6a9b8-83cf-4061-bf57-d50edc3978d0", "metadata": {}, "source": [ - "Now, the text and table content is ready to be embedded and stored. We'll set our OpenAI api key in order to use OpenAI's embedding model, but any desired embedding model can be used here" + "Now, the text and table content is ready to be embedded and stored. We'll set our NVIDIA api key in order to use an embedding model from NVIDIA's API catalog" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "a8d693a4-e647-4c20-bea4-56fe52c74541", "metadata": {}, "outputs": [], @@ -200,12 +272,12 @@ "id": "d7e23e9d-a3ad-4b80-a356-1b233633b82d", "metadata": {}, "source": [ - "Next, we'll use our vectorstore to create a query engine that handles the RAG pipeline" + "Next, we'll use our vectorstore to create a query engine that handles the RAG pipeline and we'll use an llm from the NVIDIA API catalog to generate the final response" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "id": "c3eb210e-1106-4956-80a3-f950d30ac6c2", "metadata": {}, "outputs": [], @@ -226,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "f397a34b-4639-4f8a-81a8-5a5a416404d5", "metadata": {}, "outputs": [ @@ -236,7 +308,7 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -245,17 +317,25 @@ "query_engine.query(\"What is the dog doing and where?\").response" ] }, + { + "cell_type": "markdown", + "id": "c22058f7-470a-4659-8fdc-5b33988c4cf5", + "metadata": {}, + "source": [ + "## Milvus" + ] + }, { "cell_type": "markdown", "id": "fe5a521b-aca8-4227-b73d-1c3fe8aef95b", "metadata": {}, "source": [ - "Alternatively, we can use the milvus vector database packaged with NV-Ingest. This requires pymilvus and the milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services)" + "Alternatively, we can use the embedding NIM and the milvus vector database packaged with NV-Ingest. This requires pymilvus and the embedding, milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). This has the benefit of directly sending the extraction results to the embedding microservice and then to a vector database without roundtripping between the client and the NV-Ingest microservice between each step" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "572b2b1a-367b-41bb-a153-ad86227fa815", "metadata": {}, "outputs": [], @@ -263,9 +343,97 @@ "pip install -qU pymilvus llama-index-vector-stores-milvus" ] }, + { + "cell_type": "markdown", + "id": "a7a3c662-9ea2-45f4-a600-af35802f77b9", + "metadata": {}, + "source": [ + "First, we'll create a new job spec" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b3891199-34ba-4117-8300-3a0d8294a07d", + "metadata": {}, + "outputs": [], + "source": [ + "file_name = \"../data/multimodal_test.pdf\"\n", + "file_content, file_type = extract_file_content(file_name)\n", + "\n", + "job_spec = JobSpec(\n", + " document_type=file_type,\n", + " payload=file_content,\n", + " source_id=file_name,\n", + " source_name=file_name,\n", + " extended_options={\n", + " \"tracing_options\": {\n", + " \"trace\": True,\n", + " \"ts_send\": time.time_ns()\n", + " }\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "915d8ce7-d800-46f9-9350-139c883e8f5a", + "metadata": {}, + "source": [ + "Then, we'll add the extraction task again, but this time we'll also add an embed task and a vector database upload task" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, + "id": "6ce6d9e1-f52f-4461-bdf1-b838c0c4c1c7", + "metadata": {}, + "outputs": [], + "source": [ + "from nv_ingest_client.primitives.tasks import EmbedTask\n", + "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", + "\n", + "extract_task = ExtractTask(\n", + " document_type=file_type,\n", + " extract_text=True,\n", + " extract_images=False,\n", + " extract_tables=True,\n", + ")\n", + "\n", + "embed_task = EmbedTask(\n", + " text=True,\n", + " tables=True,\n", + ")\n", + "\n", + "vdb_upload_task = VdbUploadTask()\n", + "\n", + "job_spec.add_task(extract_task)\n", + "job_spec.add_task(embed_task)\n", + "job_spec.add_task(vdb_upload_task)\n", + "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", + "job_id = client.add_job(job_spec)\n", + "\n", + "client.submit_job(job_id, \"morpheus_task_queue\")\n", + "\n", + "result = client.fetch_job_result(job_id, timeout=60)" + ] + }, + { + "cell_type": "markdown", + "id": "98cd083f-7e4d-4a96-ba36-c4c9c487f3c9", + "metadata": {}, + "source": [ + "Next, we'll connect LlamaIndex to our collection in Milvus and create a new query engine from it" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "id": "88a9658c-6d5b-4cb0-ace2-2ef59cb8fe6a", "metadata": {}, "outputs": [], @@ -276,26 +444,28 @@ "\n", "vector_store = MilvusVectorStore(\n", " uri=\"http://localhost:19530\",\n", + " collection_name=\"nv_ingest_collection\",\n", + " doc_id_field=\"pk\",\n", + " embedding_field=\"vector\",\n", + " text_key=\"text\",\n", " dim=1024,\n", - " overwrite=True\n", + " overwrite=False\n", ")\n", - "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", - "index = VectorStoreIndex.from_documents(texts+tables, storage_context, embed_model=embed_model)" + "index = VectorStoreIndex.from_vector_store(vector_store=vector_store, embed_model=embed_model)\n", + "query_engine = index.as_query_engine(llm=llm)" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "ae32590b-2ff3-4613-b85f-75942b1b5eec", + "cell_type": "markdown", + "id": "9eef79dc-6976-450b-b04d-75f2a517569c", "metadata": {}, - "outputs": [], "source": [ - "query_engine = index.as_query_engine(llm=llm)" + "And then finally, we can query our Milvus vector database just as we did before with Chroma" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "id": "d30cd73a-88e1-45cb-94e6-3d07deffe0df", "metadata": {}, "outputs": [ @@ -305,7 +475,7 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 10, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +509,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/examples/store_and_display_images.ipynb b/examples/store_and_display_images.ipynb new file mode 100644 index 00000000..cff6945f --- /dev/null +++ b/examples/store_and_display_images.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7c21cb28-8184-40d0-ba5f-030b31fe1c4e", + "metadata": {}, + "source": [ + "# Storing and displaying images" + ] + }, + { + "cell_type": "markdown", + "id": "7d76f30a-be57-43c0-bf65-0d4b30552745", + "metadata": {}, + "source": [ + "This notebook shows how to use NV-Ingest's extract and store tasks to extract images, charts, and tables from a multimodal pdf, store them in a Minio object store, and display them" + ] + }, + { + "cell_type": "markdown", + "id": "2a598d15-adf0-406a-95c6-6d49c0939508", + "metadata": {}, + "source": [ + "To start we'll need to make sure we have the minio python client installed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee625cb-3f51-4a1b-9904-34a67620e3c2", + "metadata": {}, + "outputs": [], + "source": [ + "pip install -qU minio" + ] + }, + { + "cell_type": "markdown", + "id": "0b2162bf-5934-4d51-b1bb-63c94943a3f8", + "metadata": {}, + "source": [ + "Then, we'll use NV-Ingest to parse an example pdf that contains tables, charts, and images. We'll need to make sure to have the nv-ingest microservice up and running at localhost:7670 along with the supporting NIMs. To do this, follow the nv-ingest [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart). We'll also need to have the minio service up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). Once the microservices are ready we can create a job with the nv-ingest python client" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "33117d09-3cfd-4483-b6dd-075ecc8dfc17", + "metadata": {}, + "outputs": [], + "source": [ + "from nv_ingest_client.client import NvIngestClient\n", + "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", + "from nv_ingest_client.primitives import JobSpec\n", + "from nv_ingest_client.primitives.tasks import ExtractTask\n", + "from nv_ingest_client.primitives.tasks import StoreTask\n", + "\n", + "\n", + "from nv_ingest_client.util.file_processing.extract import extract_file_content\n", + "import logging, time\n", + "\n", + "logger = logging.getLogger(\"nv_ingest_client\")\n", + "\n", + "file_name = \"../data/multimodal_test.pdf\"\n", + "file_content, file_type = extract_file_content(file_name)\n", + "\n", + "job_spec = JobSpec(\n", + " document_type=file_type,\n", + " payload=file_content,\n", + " source_id=file_name,\n", + " source_name=file_name,\n", + " extended_options={\n", + " \"tracing_options\": {\n", + " \"trace\": True,\n", + " \"ts_send\": time.time_ns()\n", + " }\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "106c13ba-0443-44d7-9acb-1045bac8ec3b", + "metadata": {}, + "source": [ + "Then, we'll add an extract task and a store task with the relevant minio access key and secret. These both default to minioadmin" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "754b857f-36ff-44e0-8790-d16079b58f78", + "metadata": {}, + "outputs": [], + "source": [ + "extract_task = ExtractTask(\n", + " document_type=file_type,\n", + " extract_text=True,\n", + " extract_images=True,\n", + " extract_tables=True,\n", + ")\n", + "\n", + "store_task = StoreTask(\n", + " structured=True,\n", + " images=True,\n", + " store_method=\"minio\",\n", + " access_key=\"minioadmin\", \n", + " secret_key=\"minioadmin\",\n", + ")\n", + "\n", + "job_spec.add_task(extract_task)\n", + "job_spec.add_task(store_task)\n", + "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", + "job_id = client.add_job(job_spec)\n", + "\n", + "client.submit_job(job_id, \"morpheus_task_queue\")\n", + "\n", + "result = client.fetch_job_result(job_id, timeout=60)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1d21baba-dc03-4682-a6a4-5c7b97423e3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'document_type': 'image',\n", + " 'metadata': {'content': 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',\n", + " 'content_metadata': {'description': 'Image extracted from PDF document.',\n", + " 'hierarchy': {'block': -1,\n", + " 'line': -1,\n", + " 'nearby_objects': {'images': {'bbox': [], 'content': []},\n", + " 'structured': {'bbox': [], 'content': []},\n", + " 'text': {'bbox': [], 'content': []}},\n", + " 'page': 0,\n", + " 'page_count': 3,\n", + " 'span': -1},\n", + " 'page_number': 0,\n", + " 'subtype': '',\n", + " 'type': 'image'},\n", + " 'debug_metadata': None,\n", + " 'embedding': None,\n", + " 'error_metadata': None,\n", + " 'image_metadata': {'caption': '',\n", + " 'height': 429,\n", + " 'image_location': [72.0, 159.42269897460938, 540.0, 376.47271728515625],\n", + " 'image_type': 'PNG',\n", + " 'structured_image_type': 'image_type_1',\n", + " 'text': '',\n", + " 'uploaded_image_url': 'http://minio:9000/nv-ingest/..%2Fdata%2Fmultimodal_test.pdf/0.PNG',\n", + " 'width': 925},\n", + " 'info_message_metadata': None,\n", + " 'raise_on_failure': False,\n", + " 'source_metadata': {'access_level': 1,\n", + " 'collection_id': '',\n", + " 'date_created': '2024-10-09T03:25:53.126013',\n", + " 'last_modified': '2024-10-09T03:25:53.125778',\n", + " 'partition_id': -1,\n", + " 'source_id': '../data/multimodal_test.pdf',\n", + " 'source_location': 'http://minio:9000/nv-ingest/..%2Fdata%2Fmultimodal_test.pdf/0.PNG',\n", + " 'source_name': '../data/multimodal_test.pdf',\n", + " 'source_type': 'PDF',\n", + " 'summary': ''},\n", + " 'table_metadata': None,\n", + " 'text_metadata': None}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[0][0][0]" + ] + }, + { + "cell_type": "markdown", + "id": "4eadb8db-d078-475c-88f1-4de087251284", + "metadata": {}, + "source": [ + "Now, our images are stored in the `nv-ingest` bucket in minio. To browse the uploaded images we can go to the objects tab in the Minio console running at [http://localhost:9001](http://localhost:9001) or wherever the Minio console is running. We can also download and display the images using the Minio python client. First, we'll create a client and connect it to our Minio microservice" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "faf12a54-1575-4e9c-aa55-23a4a704a97d", + "metadata": {}, + "outputs": [], + "source": [ + "from minio import Minio\n", + "\n", + "minio_client = Minio(\n", + " \"localhost:9000\",\n", + " access_key=\"minioadmin\",\n", + " secret_key=\"minioadmin\",\n", + " secure=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c4e4a15a-9ec5-4863-9e69-d63d19495ca9", + "metadata": {}, + "source": [ + "Then, we'll get the name of the result folder which is just the url safe version of the name of the file that we performed the extraction on" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "071b9c40-4201-4e8b-b98b-502d0ed3ac69", + "metadata": {}, + "outputs": [], + "source": [ + "from urllib.parse import quote\n", + "\n", + "bucket = \"nv-ingest\"\n", + "results_folder = quote(file_name, safe=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "40a93852-3b42-4f1f-86b6-6eadef53190c", + "metadata": {}, + "source": [ + "Next, we can use our bucket name and folder name to get all the objects that were extracted from our pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a4799265-14ab-4ed0-a0ee-200a79b48d2f", + "metadata": {}, + "outputs": [], + "source": [ + "objects = minio_client.list_objects(bucket, prefix=results_folder+\"/\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3d7c826-f2dd-4dd7-888d-e2d1f09a2541", + "metadata": {}, + "source": [ + "Finally, we can use the minio client to get the url for each image and then download them and display them in our notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "72391652-ddea-49d6-80db-c1f722711335", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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mLWdN1qmo1FgaHoH96n0FC4FEX848TX7ShY/bWoY8Lb+ppSb2KJm1PsnQZ1I3R5GBcsrvMA//mnmtrlqwvorqGgslMlVYFfNi73a3u3XkzNjxi2zI8AnnTlnCR84UFGW85JJLRBCRdSiLMBKL1BWagQ2pM0vbyVDXNVDOumAtiXRRfmFYCAay8jRN9rvtttsW4mT2i1AIt15w+j3veQ8a7qkah2g7PSW68dOf/tTJqLpppa4kNNxV5C9PZUaY5IxH26klBesaRyoYraLOLBUVMSQ6BAMVHqlMai95yUtqJvJTXKkOH7XXbalLtXTTQNPAEtXAf13YVGM0siYZk35tLcm3iGfUYmVklriF+YZtOuWUU4Ra//KXv4jUPulJTxJqQqOUUS3fhGQ3f8UVV4y9QP+zn/3MLr8dLntVG2ywgbOq+KTGIoOy5557rrJXXXWVIscdd9zll1+u3i222CKhX5l8a1X//Oc/Z26ctV2wYEEOe4VJfmN38LEx9Otf/3r11Vd/3OMed/e73z0S1pQlHUlIe+KJJ37ve9/705/+5Pjv5ptvfp/73KcWstBLxH794Ac/OPXUU/nc1u5iHhtuuKF8fPIUmeK3u93tzBNf/epXf/zjHzP9yDS/ZovY5agxFTl1cNe73hWNoFSKi+OKeWBCD8xoCuqa448/npzOWd7jHveQGZEwOf/8820Kk5y6CKZF6lVwnXXWoa473OEOddVK5ZKJ/re//e1JJ530wx/+UL87K4w+zImHLL0JGAsXLtRTiqB59KMfrQvyKJwpUJzGEUaoEE13flpxnQ4S4fPd735XJzorsuaaawKPrqxFIoY5UhWXXnopzo961KOylKppbpR6RCQ4wUpwyGb0V77yFc1XiuTaW8iSwBkCcbZAok9sdQQXp3CGfKpGRqo//vGPuMEtGENCdiTw0QRHrr/1rW/JJ1s4l4bjr2pg61Rdk0nbu//Od76jr29/+9s/7GEPU+QWt7gFJh7hf/bZZ6uXAmUeddRRhsNLX/pSmwZFznCLMnUWZaIBG/1eYJOniuj6K664wp6ysYOJnQ2YwZnqwmfgr+KuH/3oR/CgOHhjrhNlFjGCTNE4hyXgwXBDI43GXjMwayaaVVZZRY1++xUh0x26m85tYTvhA9hA5Yo2/GYrw0nNn/zkJ+TH3+goMkgoq7NUR28GGuQjeMhDHpLq2AqDQtuR0bnu1nAGgSHqyIMVXWXIqAssQUgXg0qpThF8uHGqw5NdgmGt43+j8VTbqctTera7Re3s4TOf+cwHPOABChpfxqMuI4OCgUrNvBYJE8JwRvE85phjlFKdphEJK5RYGUoOYIDQE57whGIcMAdjxnnrrbcObCI2DVMRqAjTynnGM56BP5NVBJCgbQOctiGQitSl00NAe4akLiMGWBpld7rTnTbbbDOsOheBXdTONgY8whod8OBJYEMAANQFkFpBkwQmfxqIhpXTEKPbGKcEpoxU9OY1BlUwUwhYGPSG82Mf+1iSRNr8umWOiOFk8EorrURaA7kjbblVkSp0N2XKxB8yaYxUQI6PgcbkmlAolhilisKhJZoGmgamQwPGXv9iOGT6NQ8RwiTt1oxSfvfaay/5Bx10kFOktZTME9OsYIjZa0+PPvpoBfkE8vfZZx/mQCarF1eY65DzRnGdSxW77LJLzVmaG8RjCKs999wzfEyoOS3AKjFPnuYgXQ71snfiCjUffliaEzKuiacW3Kk3DecuxxyrItMbq/q+970PTRFS2oXexZS/+MUvTi3kSULV5mxPkSEwbXMZbeqZVkOQ3xwUxnaU079Z57L6j7OOTBSc2cWH0eQtyTFlus3ZNfNZzSp69pRDKZ/rIP35z3+eh+TWtBR13e9+94u6Oi3KrSmTPtHTuSISd7zjHXkVWCEIDZe0tKX0hSMfNQ3JlbUpbKaRKNdrX/taK4pMDyXTxi7Xkx5SBQjp5TyNDNIQRQmhUVGuKFlcza4fpZl0eQmFrQS4pkhhvu+++wZ+ZT3GITBLYRjoitXpdNo2k9VuKHo70YWMDvE3scnBPPyl9b58ZaVdOWPKW5IOmV/+QfSDUhVmcQkjiHPgafD5whe+UKbXIs36EkT6zW9+EyYjfG/kZsYtmyfpC8SYawVW0SdiYyG9XwKZPDOzvkfBkmWYguUEuYKK65QyGAsmbffrwVKQn5eCNuglXHTuqdVjJFeQlyNfM/fff3+PgiIJVfjlQoV5kR+xc0QeURQ0cg3BOGfKRyoYvQ488MCwCjfdEf+YovjxoXnNa16T1r3zne/8T7HqH5KHg99cYWUfuaAu5NxQ4wINAhexWbNokjWLoWBevB6KBhr9HnHEEcp6c6AoMJA44YQTvNvnURlflPPlL385zP2WK8rhrCP+3Oc+99CHPjTC5NdLWvxCzmWd6eAW8JAQE2JkkAKV22D7ox/9KIEV4aKlILYCsZx+NLxbRkwp2i7WLGTvf//7IxjAJ6f88sLzqPyS3FUbtNKzloWB8agiR+Q85JBDosCMAmwtXTjKHkVmv14d9pQJvf/971/q5bB+4hOfsJBYe+21S6aETld7+EswxZmMPCrdyrxgm64sYkdvGKJUUQg8ffOb3ywH3oqpd+sCb7PSSFNHYVz4tETTQNPANGhgZJ3avzIa/Y7lwr7xjW80egUnmHWrcOENC9PMVe9+97sxjAV/wQtewOjwdVKFlatSVtiWs6YlMQDznJy8IhbbgTK1s7nf//73Bb0QmE5Mrq56YjD7qpRDw4K8973vRcbqsZjhk7pYKxf7iBtHmeOCzDRjxZ9a+i6sKmKg3/72t5se+I4saQI5iIkXk5oWJZ1ze5bj9KBd6nr+85+vosziKiKVM8RmOwb3Wc96FodG8I/l5U+IxTKXhe3OO++sIGeOY2pSF5OIxRR25Ukgcy7ZTMCnkY424jTgTGxtT/OVNSVQNTJ65r+6TK58Gur61Kc+pV/4eXEHowqUEi7SPuhBDyLtZz/7WcSmNEG1aDITIRri4U94raAlFwfXNEwM0zNW8cCscFSElVl24WjIE1q48pwVvSCGQaWUxjPIGWuTdKddApCCqcQQzY0fwKtDk2ZKuMjj1zpBMJ7v4tp+++2V0uPFATXzoYlUWdXAD5BQFM577703tQOz7g43KzFuU9qoU3QEbsSjRpRpI4biahrIF48Yykaw5z73ufLpTb6r48KGJu9EAj8N8EFJkgbm5cKI+rKXvQwfjg79GAUAn/yw9RtpDTRScZHPOOMMchopvAQ5L3rRi9DEgfv0pz8tR6eDAfzrSktQOfx+7kv4KCindmEVf/nLXy4TAg1bQlo+iQTLscyj87SFIwjMlMMmaIVVBDGUjd/w1re+VUFDgx74OsqeeOKJntadCMxidZZAnvKoUGqsxSQyKCW2xQN08dSZBcPnIx/5iI6mmVREfvxtmBgdVpv6kdfCO091WUVDqSbkaP5b3vIWaerKsFJLrozomAUOHLhSFIS87W1vIxi/k+MeXYGWHJE8fjMJAcbRf7XztESC0zTFdR/AWwFarFo8G008RUgzBPiIgG1IZmsFWdob/pEnaQAziLBiGURbNd8bCxTu4owKQ9p/YEaEIbPylFBcWVplFYUzoyWZep/YAsOaT0t6XPCVL0vhqVFDNJO2Vbfjjjuec845qvvABz4gRysYE2QwwCpaLcikK3oWpE/x8hsNALDqmCZik4HdyMGGZz/72cQLMvNOLafZTAGZyPSgvrZoMTSQhVIzKY1UepDYpAJgaqE38gMtMyITNzmKk5wwqSILvFe84hWgTuc2CuCZYEwBmtqkR2xbKNDFLfYoOTCsahZP/1qZWJtp9cajJ4yzJKuZFCW0RNNA08AS1cAkXdg3velNxr/NGvNEkY9NZzie8pSnyKkNBwMdGnaQFRA5KEVYWOFAVpjdlMlUlUdJZAo00+Q2BJxFtcdMpyJPha9kMpGhjAvLdtu/S05+eZDIuH25zVyVKGycA7MvAtNwXYoHY24WMGbOaiFjtnbbbTeW1PGAUsS0x9ixuTF/nEITCbYJ4BUy+3cyzWTJMYXTj+mNQ1NoJGJt44Sx8oooKD+18yTokPvC5rK8KSiig8xKw20ihXyaPMrvk5/8ZAS8BLfF+EZaVt7MxB2s6cXD0EcG+WYgtx/84AdrGh0t07JHp4RVgvQcrJosbjqxa+/B9A88HqGkYTO9KJooSE3jkXAUFekO6VQRer8os9JI6E5OLuollUlUM3H2VjvOPHKz440kI/9mruWpJBMgE74qOckXiMJNkDW3Jma3W265pVvMXZGKDy2/LN5qFzYEpOXEa7I5W9nA2OTKC+RAlGEFWvjou44eUntYcel4TmDDkUq+Xwo0rPQj39Ethtw7GEZcaCTiu1sXJbN2YYMKvikmpu2aOWK+FMHM5SmIDPx4VGWwy9coayFjsDRHJidVq32rSLoAT5rq/Cb6ZQNBulwRXnUMSMmUiB2Ie+qWg4iGYKEJc4475ey6664yU8XrX/96ZAXJIc5vCBglaz+lSvflafrUmi23GQJf/OIX3ab7JLJ/DRWh4cKqy/DMtkkyuVkwbAWV2/zyrlB+6UtfcttXS/YrOsC2+FFErL3mA64kZ59lao62WPzo9+LCokcQ9y7t5YziY6jmNsgnoWBtzZmPiCwx5uTz8PiUiafWlNJBpmUkw+isQsx7aHjwCaWzM3IA2zIDmX2zmgm/UHUWSzIJRsPMqRxvGNdkbHIhK/lZClryJYddRZNBWmgsM2x98PgDzrTd00jOkII9wfRFcrKGYUaslAoTcxAwlxVsyW+JpoGmgenRwLLG9qQv7qAoCCuZcS6cwxAIKmDISvbZmkSZA34Py25GRMACWjovXLgwu0h1KZQulgsZq1fSbsUGhCEToGX7UpGJX3H2KLcsi4T9a9Mh8WIE/caCx2qHsvzSuLSJEB8ToXapV5zJxZvhIli7czI8xaeUkhCJUQVvgEVTRA7LyJn2MlzICKMhnFrBuUjiViJBo2gMJT1opi1argAC8uTXZKnSOK9C3VwfpxKVMs2YP7Tl8Y9/PC+cnPFWsUqsRb40zVCvwKEAuSBKRKJAEvIw3EZXEmrxK5hq1mH3+RniT1GLPVPTXg4ncI8sDEQ+hEgJXC49okXWGxyR8EzZxL2sEDQHcVptRuH0lEw1Ugj1Rgyy8TUx1L8WAOkLxBxfQpIEWZhL5IqSTTyZ6VOX6hQRThNz0nZleZM4iwoL7Ok1TFCqOkrmgSmCIUqVKsi19VRmQG66IrYAjJkMWUeGSDKeX9KapEWMcjIkMKZSgS4LmKCo8LEGUCl5SFIyJVJ7FmmWjno5bUHGFea7C7dDHUrOhLiR2BvnTFsQpDnWQjQmhseTKAxTRZjDJJ6cxTCPuvxGXVnvodcc8AMnkCu60iiBOt3HpTB2dB9KRkP3ZRkTkJTqSKXtbiNb5HSrL5QlACcykqeZ9fAhkg61wLBCDkOjQ4J6PeKvSKdUxNBerOSEOL8oJSgKWmwRQA4ZciHmF9Jn9pqQ8fk+9KEPxZVP98lkA0kb4+Y2DURDVPUGb0DFnlAChnHjUAryyeEhlVIS5VK7NOtBQjK75NCtuhJux1wmUS020GQcleIl4ZFAL0hQyyibkR/2BB9LKb8o/UIaI7DddtupBU80ErW2NaSgUZehkVNqkYgmxdrlM2hQHSb4WNJYxtjQ0KEond4BTgtmweCoWhEJq0SYZ1Hxj2Dy6TMYwM0tbtrraeImRQk0g3Oxq6ylW8YKgZFFcr1PDF2p1bxVTyOwxFhXZDBerH5xSO2Z8jjoSiFYJJOxmLf8poGmgclp4D/+3+QKm7QMWlMFy+LKnMGsDOTGKjHQr3zlKzlS4gcMgVuxQ3FWPla/CIuAZwxHYR4zEc/MLi3bxMfixjFJCaqppWZlaomEhY+65NhSJCe2tdFxq2wCmQxl6go36cwKrFU5Y+dROJiVuRHcEeFSK/sFCxaQMM5BzR89A2pukJCfRkm4UktCUKYKOZ6qNL+CkXK0FJk9MkpzdEE4TVyE/6oXzLV8MvrnUpjn5HBKlEqkx3xpx008T7DNIVSBTAVt55XTY1EO5hLUYlkiWmy/XuDBxdDjb0Ypr8VQOC9QdEdbVEo2Bf1Km/h5AILQEmmUX05MnkqjidLMTHWmfE/lpJRIFZ7mOVGu5PiVY4li8hBSKpl1QnHMTW8oMfTr4hdaUYgvktmUo39lEg/x6PMRSqU8MquRXBX8P09d8INbBI4AmqyLacAEGbJagPGkVYrM4o1nr6ec1hAL1yIAoyjI1MWdQZSBRhiS9KsIMCyxCIwgNNJZb0gogr96Q+MWTdpur5ZHldozNDr80xGFuVJRbDBJmfU4UpdbBBIurPbZZx8D01rOBTCAxBv2m1qiilLjqOwjDUwtEmFSiA0fvelpKkU/Ws9IRQa+VRBf2SCCdu/68E50ZdyL8FEwDUefumr+pToF5WuyPkpFEYBjFN8olIlKWvEa+/QAErqJupRVKkXyG+8t3ecR1834csbDuSYX181SrR5fHbUUVpofzaiiyN8ZR/IjXilVJxRnDaziOLLFjuX0gvyaUlp1nMioqFQXmtRS0nq80+Q8ylgLeKJ8+RgyVkn4zVhGg2e0jQAx8fjZhoNhyyJFDPSkKm0sUiVyQQwEfguBW5dO8WtBbs1Q+KgiJt3KNjtOo7SL+MlITBWpXYGBbV8Eo/a4aaBpYCo0sFguLCvgGqcYbIeRz3Zznuzi8bHsALqYFSefOLKxX8O5hUZAUdCRfeR2mBcFQe2ZinwIjHXkQS+nmC3MTUt+TSrmvFi6To0eyRHbYEB5S4WhGUj0Ina/ZIa5aVVcWeBT3Mt8xvFyOf5vJy7GulQxsMbyNJEw00bN31P7gH5ZzzRfUIcLSwl2LYVmPfXuuTiHGJtMEQJOJLeGz4qVZuLmQK39fU42H5ffLzqy1157sel5R6GWSlotqhBM5T7qIBFHPpYokeC3o2N4JgAjQSdF+CiZilSHoORL4FlaVPIHZpanNI8hF9xGoR4pxUFIOlotmaXUwAQhBRo9wtNvJqHIWdOjSdNClkdUUdNIEwCEiFSTdWjc1pDrP00/2uj0F4AECC1awNgBUAKYbtNldSktHdjYZGoRYmVrmpImiXRajaZmKx1Um8sFqDqPcpuCMFYYJr9gsjOOCpYkVG0r1s6A4zqw5Nywke6yBnOU00oAqw7bgTKUzMK85JSERzRp39nQIxunhFbhh39mOEyoFuhFj8lYnZju0y5uKOaimI4JccIYB+riNHeqc0u8YCkJfjwJy/iiGb7sTjvtZHwFqx0OaebA5sscSFw000nw8oU27S0wxZx+CwzGAZO8OFgTD6yuJlhkmibpMMgsQpZEio+l7SATLDvIHCjVcCXEpFvRmSk6xsRwhhOSdKQaq2nIxkk5FoeW3zTQNDCFGvivCzKFTAeyKoNf1MElFitSYkfJdip/VI4wQOaGgcVlsoYsjlcQbOE54eeUW46jhd7c78BZx76gL/OQhCtbipzOuF81vadY8QVlmk7yTaKBwtSlSppD6WIibcF7gYz/akLlCAq6hPNAVnVmqiYhPaSIX5cctYj5xXwLo5LfDi+Xl8dMzuyW2ilz+FXtOdGYLU78U4qTZLXg4vpzfCVEyKhd5JgXQlFFktCb3uzIuwSl1MXZtW3KJ6AZ0UoTrQWDCVtaQUKS0CU2KU28wm1yicSqfRzH1D4Wh8jZfyqfDMmXMEcSVabjGTKpschZyiIT3RHLEYnk3ZZ8akFcc6Nz5yiEhcItlB0a3Vfrs3CTwIq2Yc+qADyswRxdyEuTIXMasu8D1RzqdARLi7SxwCYVCV9pEQeLqDY9ZJKcqK4wUbzTnPpRaISEZUJgrQRpnOWrOuMoxJ3fcMPB240u2MhaiAvl3SYHWwkcmk5Bt6oY61GfWI7+dUqS/2qF7OXO+MfybVOIcw8sMlZmBmx0VWjIAyHGgtUycNK2N4QMJW+VaVqBope9DMDhkofYAVlvzbsEGo1i48uSxnFPh0Y647HIsPgJCrcmZECgzssDRjF7JRLPljIFnpaGLH5dOAQ8dEV7rvCUYMMhCjKNI7ZCDppaaXI6yFwcefQX5uLcebNiIKupajjJ1eVXLXWLBlbaMpsGmgYWUwMD9iUXk2O/eMYzG8Ed4VfZq7WwZjqdnLNPzawIoHJnFQxlzaFYFuY1cS8vjvBfna/iv8rhRSWfEawLJp2JFlvVmRjYlLz7Yjle7EtJIFBKMAx9Dp7a2MU/sQRHC0ww5jA0hPEbaZXiizg3phXahZvjhk6aInbMSxCo0EsMv+zs48nr1WoJ7YrMXFK32ceXKdjjDIZXgEVxnKWjUpW6srPJB3LEgjfsAIBSfCnv3PCtTz75ZErQFlvAYjCmTI9ooxYp7eKwohcpj3q9e2HL2wtw6Lkg6OU4d2gHMJuA6U35Zn1nWM1JCWzUnCeahg3V2aWlAfz1gr4gnvCzYHPekom0NWdKkElsCb/pDngTk3aWgL+IWFyQWoSW0bilE5c0BKIXTotvKsdlHkUsgSbcRK24mE7gcWUUjwNHNjSqJi2xdZ/t3Y5guQ2ZtDd+pL1mxH/VOpeKOElYFczXHIaksw2KYQoSNRqw0oOocsyAYE5beoqVtqQ5gogGDm07TiCfSKmoyBlM6vfk4BzmeS0MJlOkFExxbZGwp+/Esy97uAU8Do0DNnqQ0oBcZnCeIqVeCY8wpMx+F9fEdRol3Gqj7QL+K33qOwQ5SFBTqjTSpr/qR8mHbRhweBeePaUownvEFWYcxI9leqGQ/+oAQL6PoboodqAVShUalebYKjFUDV6aVBAs9ZSYKOEzviJGLdhUpbXdKWQHCVwMqT0ZA5YPzXzR9oRqISRuaZHfJDocMtasItKigkwxC6ClQ/TOZgh4EyNjbRSYI1aUep3NcOBnEtH6jhg5T8Vrl0/hetOvtI9q6VDnnqUHyt/hM57btNRvEuMp0miaBpoGJq2BRbiwYw3F5PdHaYe+JpPm8bAjNtxtxWa/jNymbb/COQPbwFFgKEVY/WYblL1DmWNM5kKZfoVysS0mFQGT5FZ4wxseEqpTKYcyr6nmXWZk6jW15LBU+HOOxeEEV0zAQowyY2FNYFzn0KfhfjMNex2Bm2hSV0V8GrWzyG6FGdRS68Ftuep8rDiOIhPmNhMMgbHCgdL84Ry3CSHE1Aq4MsSWBGTLyULFTRhmX29MazW/VjDJHKkuXhFfUMAbk2hPJufepNVROyaRDb1PBES9yQk9z9gttTj+a7Ix77rFk5zaws0VX3HQOWcwPEoDw6H81q0umTUxwUxdtGFi0xzM9YJKtV0kmEOfGG2RNkzcaqBp0svdmqxUGutkJF3RmEgkziYtzj1fwakPTkN0wuP0xUd8OBOFm0o5MeLQSoUb5siIAT9hrqW62HKCL4gbjSH2wSZfhNBM6XDj1gMh+FEaVjIxJ3BWRNIuQloLOZmnpaVpAxUYnn499SuOzgF1SsTWNgG0yC+/wYkRqx3RdNxscyPjNFhckSTNIYzIpeZoNeGxoiJlQY4waBTkpfGxnATgIoezhhPbPgNKrk8tTOQpORrlvJCYKPAoladUrUbgdIt/pwgBjD4CGD5EilQYIutQlszk+1UFhkWl+FiWOPw9UvJG11wpJgWZ/ooeirQScsgmEs8I8J/AW8Hoiticb7ji06NMJ2bPgZBaSjnOSFCUIpgUtiN131i7euX75cUOHF8ZjyErHCRqJiU/mYV5J3/gLc7MlH7nILpAF0SttDVEKzQ/pYZUFwINRM8aYAjVCgJDqVEirIDHcteXarQXQfBDAObFeoaxUtyK2ti0FPear1LRNqmYdL+QqRSycTa2lj+aIeqCBQts8RkgBoWeghO/Blpeac1atBa+ZlLyFymAitBYG/OYNbwOdhQmLdE00DQwxRpgHfqX0SjTbzbTbfy55Q+VX8coycG1kmNeZ2skOBwybUgVMpFLOfnaFG7W/bEXooA8Km94xC3bdNNNzQ0IXMrmSnX56JUJD6W3kcyF5hI2Aluuks16p8dsN5tlE6R0yjPFzd9ozN9mMgaF52HNnY+55LMAkZntznsMZCgfYcmHk4hqt50va3c+U0s+IZSCqSVp+mFnzQfUgoZljFuZr3KiNB+z1Iy+udmtUmmdA6mENPPJNK/4zRegGFl+mHwyJzzGoKdgauRz04my3FYRBY/CkA8k0yVOKVPXUKnIUL5HQ2OmYZut+YIPd1mEMmz9ukZ7YOSj+nHxxYo0n0jkpEauFQONjAx42pFUkRNmPFe9kHMLophsNz6Rh6eLxrpFKTnJdIZEpjBwnWmSk+lTXzKJ7dfCwBanTDu2PFfa4Hq6NatFTr/IXEnQg2giOWlMv+s1/pluVcSmLc0jC2f+azrUFjAHy0qAh4fMYV800TBgcE95VOZp/jSvVDQR0pBxYnjqhTJtFOVVXExO1TzsoNHkTTwMyWZcKGtACRPKdOglPegFR4E9MhCS9sSc5JtfRxp2ww15c1+XSUd7o9n//Ym0+eYrD0B8i+OlFECqzroLaVrNn9PjMgms1d7czziy885h1RyXxMajn7oEGHKmRmsqnDF0ulovAHn6JX+VI8y5QThrdSTDKoLpBflQqkZxfcuJDLcI1mmRUnKclFWEuvROYGx/w8iN24F/yCTswqME0VSaYYs/gMm0Iy+YGpTmI3QRNQF4g9HHSZyKEYMMzzCJ2ELXAu2YiyLTpyM3MODWdgQJXUZ0FMXZNd6pxS3A2IFBJroZbtkucNjULc4kd1FyBouWZnzRkiFPWhHBUKa4X/R+jURsHZnNUwJI5OtgWi09KtRIZgxIakxdwpxcq4x0BGVYYVgutksRqEYgOgDzBkgcdExSXWxLOk4TUGbVB/nUAgwRVX6uaJIG4s4yhjRp28EIVS/MI0uP2NkIKnQTnGAbe5VvyYUPGYwOBUUWFJQZqbLmLB8jS2YsjF5DGVGd6dfjMGxFavFgLBunuKkr3PzmSnUJauh0DJOjgegBGBmxk2lGk5kob6o2tclxpYHJvJF3+7dpoGlgijUwEhLoXzFGfq2Pzan2oNFkNOaXcyDfJ2bkl/FsjpTJphdiH0WXwzsJmV/zh88SsSYZ50yniT8TdscCIk4Oo2bXieEzqQiNyBdMyjyHicU9f8s8xCVSVzYo0QhkujU3nHLKKXFQEAtKmYCLuxwzZGIQoCIJQ6xgGmgmiBcSOW1eD/zbOUVIe/fmsKz7U5GqE3BCw4zaL2Y0xXHdqje1+HNBhORWpt7II5zssFrq9StqGNe56EfCJQDGI+Fk1ww5izKpK385KZQIREoyo4ct/etZW6UeofFbrtzyUPkHCXYqYhLiC1oVICNkaMxw5oP4gmgokEPAf0WDIG0xY2ngwoULZWpyWm3almk+qzN9FVJmPdl7KroJTgUtPKe9996b95Aq/OaKPJRsyhHvhDFuWVoKHjgADMqQRTChXBNkJleUoAVmNYH4K1eYW4lSPDjc+Bn8v3gDiHNpsioyMSOzXjLLckk1RzhcvZmnOXkcdOJZ3igoXywqHo9S3Nas0PhMhIkzgQaK8DEBS0d7Ep0rLXL0pYYNdz+l8jQ1koFmSsDMws9cnjkeQSjpyieKyCl2paJkakheoYseDA1LUE9LKU0mp3EU2eTnUpw/QY0p6Bc4OdwpG+Lym7pEB61XceOz7jX6J764sHwarpjTvSkYVfAqkBX/XiavpQTV0FsAi7ai8Q6TgviTSkIRqxFLMrFAXkge+c0VGmJY65ZupSt9GuwVOXOGR6NAVMdxfC01VXfiiSeGlfHodo899nCbUmFu7OhunmLUAoe0LYheyFLcb+ihDp8cKMInzU9d+U6tnGSKF5QalbUc5cTzMjMwMWQn+coWsayKwKR+5MbpFJJY/yAgm1v+IiMQAcKZ5cSZSmXKwZwhlSmYqqfYh4iaXzSuNBnOs5JMY3VlViY1PS9WGxPbRsaI6Ur8CxM1GlkWCfkMWVECNZIqf0MEjUuRWBhLa+kyPcFwPUBMChbGCIgRSaRdkdkYF4zXKRgmh31QUYoUnsSWqesVDBPzjgWbwdJZGo3ybj9NA00DU6yBZfCLZRn4awAbmea8MtmHzKh2dfKxMrZRljmyUxxBnDxn+TlzyJgJ5g/P8migGDKJ4RfzQskoM7I4mG49iqNgnZ0qOsIwSSJh5iHmu66icNMczEvZJFTBleHJMalKFeKaQ50vgMHJNjWSqighxMSTkBnOyRzV4v+osVSBj2N8ptixqk4Di9EPQ78mLROqq+QUnjTAl9UpYlpRWnlUiCVKJpnRm7bJkOm2PCoJPBlrvUO3Gl4Xl+4AILUglt8XsgMeZEGdLrDIgRNaTXAxfMpvhNFw4RCeh4Ar75MX6OLQWLegLAJL15z5+iCBc+mXUIKWSY6Q/BKea7qDD13OMBT6iAHP5FRXouZBo+5OE4qoBWapxS/+iDl56UqtwLk0s4+QwqpOlNZFTp1lZkVQ8qVLq42+os/gpCYLW/USI8KXpzxICLdo4Ul0mKPpdF/4lF8goVLd0Res0NQJ0rolg0sCc7+LHD5obNQArSWBPnWLD7BpSGc85pHfTgfJcRVd4aZndQcvMByijaKT9LsBlbVcp99Tex/qpVFENYLopDO+Ikb5zTgqxi35A7HRrzEiRQY76VbaHPpErAt/sHF+3TAXo1VLB4RDqssjlUoM1GRRVMAz0KAVbVur0Kfhpu9qbaeWNGRySihiaCkMs36BR8lPFeVXvup0k+qSOVDbIeugayyehXlLNA00DUyVBhbhwk5VNYUPazUyKY1OS8ns5xTiJNiOTLQlv1Okc1vIBhbHjcVZpAD9Svs5dUV9tv2cmn5Iul9weNVDWJVHrKqrnmP6OYVYwlNardU+npzhHVHzH2caQ5S12H3lRFodat4VsRakEWvkUpRSA1vaF7VwRo8bf0tM1wTmBXPeSQFMXyT0ZCgEA6tD0C8opwiJoAggPYmrD5J+Tl+GfqV9GsL0yfrMB8pMGxjWQAq3Tk5ddqAANcGQtLIdlQ6saDzC98XoKAGBTi/93qcfImdfLf2cIcUn8ShNFkO1FSByaUO8ZmJDxkaTnaKFCxfWjapp+ulxytzRGz59/fe11y/VF2BCOX2GfTEmxHAgsYYEgZRTsDGQsmU2DTQNLL4G/ucM/kB2Yw3Fgfn9zE5OGd7yVWeQ11POQAEyCRV6NCkSq1dz6NSVW8UlXKmuP6UVkWqLE7JShUf9grW0eTqkoiJAXUpafl2vnA4r4o1Vdb9sGPrt8HTrqsVLDsqBl6cq7dB3xOjTDOzKsYRUvFN1n7J0jUeIU2OnVH3LixXTQqag+UmiXDWZ9Hg422Ev015BQr+NqsAwuqqr6zSn1Bj6yJBS0jKLejsFO7eIB14pHoYRozAs9EUGZDLrSvs0RU6PauZu8ekzHyhnqohUqXFgpaX2MPdbJMyjzm2h71RKsFJXEbJDo2xpjnTdzMJWYpG6CkEfGJ3qOrepYhJqGchnPJlo0hwJO+k+rucMj0ikgzTWZjbrHb6yS645Tq34jWM3Hs6lFfiPpUaPiraLJH3wRJkIQhPO0VX962m/onFmdsQYq4pSXZGkzllk7RoSefqUhU9LNA00DUyVBhbtwo41FAfm9zP7OUSXOTB/SKv69LF6dZEOTbmVKOmavk4PJOhXURfpp0fr6XpmIRvI36Mh+WM9KvUOJBiYWWQY8rSwLQnEi6RfJM1ADuPPJMwiqyits9uLOFuQEovsviGclc0beGEyhLLUjqaoLmLXt4WszhzIdjx8aiZ1eiDDmkB6nDSdUoss2BG7Lj6eGmv61FXnjMW8n9+vq08TzmPld+odTtbHWIe+czsh5h3i+jbpgcw7meWWa+UYjDPT++yzj3PS3qYNE4cunN30VzZEYdHE1Sul6krHn1mXKqIOLF5TIhhOM/Dp+DPVNVLB/47TWoA63Sfr54RhXWpgToeg3TYNNA1MlQYGHCTI6nOqKmh8mgamRwNw6+SiqJiDbn3HYqIy4OZknlK4DZy6Jsqw0TcNLHUNQHWGBmw7KuO4p/UehDuiSrbsOSx1IZsATQNNA00DY2mgMx0PcGHHKtnymwaaBpoGmgaaBpoGmgaaBpoGZoIG/nuQwAKde+s7UF7DX/wg1kxoW5NhDmsAViG200A5Lo9cnUeTuM0xxzYWJqG6VqRoAH4CpJKz1BMZJrUYUzVkap4tPbs0AAOAMbtkbtLONw2wpd7V9gXPTPSa/18XNrqwFev7U6GYb9pp7Z1FGvCulT3Q/nshs6gJTdT5oAHvF+a7gfOhsa2Ns1QDzpD4cJvPmc1S+ZvY80EDVllirA4+cVBLe7suLCfXOzH5UzSFqCWaBmaaBvzVA1/TLN+xn2niNXmaBmiAOfX3CPwBixbLb3iYyRrwhQpfyM7fP5vJcjbZ5rkG/OWdzqbWf/4odq2XULQ9hVonLT1zNBBkQmkD6szplCbJQA2AKLgGqAMJWmbTwNLVQDOnS1f/rfZxaqAAtUM/wIXtULTbpoGmgaaBpoGmgaaBpoGmgaaBGaWB7kGCyQnHQY6PrHh7M2ByOmyllrQGphClYdWgvqS7bB7yr1Gq+YuJsQbUeQih6Wny1AI1OxWLifbpaXirZUZpYHFd2PLni4CvNEym23b8qyikJZauBgoga5Rmk3dyL4RNg6kls0mC3oyjNpSWLn6mrfaY0053F/ROQowlB1TgLIKVoVSPr0lI24rMCg2k671N2+luL4Qxp53Mcbaog/lxlhoPGXC6wl+CeNKTE3I81TWaadbAYrmwABEP4Dejl5fFfCLbSzb5UHbBzTQ3qVXXNFBroKD06quv9sqCb274u5p3u9vd/KUiZJND6e9+9ztvmgM/qN/sZjerq5uSdJF5Srg1JjNfA1muxJxCqetvf/vbHe94R+Z0hRVWIP8kgKrIb3/72zgW0B7mU6KKCJM/g4dh4TwJIadEnsZk2jRQut7fxfD9zf/7v/9TNXN6j3vcI5ZwEhjweZnf//73Ct7ylreM8zBVzTGsOKyuMCyJSQg5VSI1PlOrgUm6sJCR1cxXv/rVQw899MwzzwwE2TJQ3mSTTXbZZZeNNtooZFMrcePWNDB+DcSEnXDCCe9///tPP/10L97GeLG5/pzmS1/60kc/+tHjR2kocXjGM56B2/LLL+/PdW644YbCUWUWH79sfUr8Ix5T+7GPfeyss86S88xnPtPf/2w2t6+uOZNTEPj5z3/+Qx/60Nlnn/3HP/5RJh/xXve614IFC3bdddd11lln/BgIQ39/a9NNN73sssv4r6eccopPzYyfwxDdhokF4ac+9anvfe971oT+RO3666//nOc85653vWtpyxAO7dEs1UC6/q9//ev73ve+I4888qc//amVvLZwPVdbbbUnP/nJL3vZy6y7xg+zWM4TTzyRRZV+7GMf+6UvfWkKIcRL+e53v4vnxRdf/I9//OMud7kL5+RpT3uaDzNNYS2ztDfniNg6MhfYSVxxxRU//vGPJXL7n2f/+49H0GZxv9tuu42lBbP7gQceqBzKunQpq3i/CjnlUkp6IFnNsKXnmwagQpPPPfdcoX+J3PaVIB/2/L7hDW8YC6Xy99prL2U7KA1b2OvAL3UhXm+99cLz5JNPRoysFiBVy+yzDWcEuUpFbmsO73jHO4rMBxxwgEdiHjVBS88KDei1M844Y3jfBQlcgR122KF0eidxq1vdimuryX1EyQnSagglbcG24oorYmXC/uUvf9kvjkxZV122KFZmruSoKJdb67f73ve+HSHvfe97n3rqqf1aCsOWmIEa0MWkYktZVIncDpRT78vntj7kIQ/pdH25XXXVVX1FDlmICx9s5UCa37oKOWiOOeaYcNh8883d1gThMLBszVyRUgpxLsxf/epX988MPPCBD/zhD3+oOLLCpCVmuAbSv7xTPipRS3dPJgqrvCgRz+C9732vOAGgrLHGGo95zGMswa34jz322F/96lfAsfvuuzOgz3rWs1SGPrUKVtWQitZKeL9+lFJTEtwqA6wl5o8GglLrqLe+9a1B6corr7zFFlvYJbD/JXrqT3hA17777isia9MAYgM2wINDV8FeB6V0iGFo/NYqHQjywjmUdZEOyPkcp512mm2Nb37zmwaFdaCyIhx1FS09xzQQoL7iFa/4xCc+EaCuvfbam222mR3VX/ziF7a5oOLaa6/dcccd/Vmaxz/+8QVOSRTjSS1y3NYAC1D9dpQ2HpDXfAJUTEgruPv85z//kksucXvrW9+aVIYSN/3yyy9/3vOeZ0eO5Mjq4p3a2+2s00AAA4pPfepTf/KTn0AUsDGnvnmso+0bfOMb34C9Cy+8cNttt2XEHIAJBvwqy5bWeCgYjh48ytNicot++iAv0C00Kes2j8qIOPzwww866CA8yUBO58fI6QwY+XfaaaeTTjrJyYcG1KLG2ZrQhbl0v8Qio7AghcwWp/k1lnG77bZzCvY/XG64wXI/n/IGLJ9KZnw9CnMJlFZ73/nOd1g6nm5K1U+5F64//elPHkGbjaqkQ9l+mwaCluFR2KDUZ5D9GY+YxSc+8YnOBRbtOWtodx5E2bv73Oc+kOYRzikobSAINYEfNKaUR6laYoMNNjDgFbc/62liCQXGdoGFIthxAqRseeTWNtwIxG8EuRM4auEWePTsZz+7tiOR/D3veY9HwyN5qaX9zjQNLDIKG+R861vf0u8xpyZXRwNLQ0y3tmgzK2+88cYYwlKu0MAYcwpvOXsgszzlcIiM4synrKOwBeRACOSuBDaULY+kDYpRnF4ZIXFgtOUfddRR5DH9i1zkzMPChQtVESE9RZMiEu2a4RqAFhIuMgobYLz2ta8FJ1O/PzjnGEndtM9+9rMyPUIgLuARDIS5tHn/nHPOYRLhOdjIo6St05RyOUiAuJQqiQsuuEDZ73//+7GTNQ3BCA+oTLp8l6nhoosuknD2JmxtZKUij5zMAVR/h+y8885DUwN+pHC7ZqoGAoZ+FHZkVZ0rFIt0YQOFnXfeGThM4SJbcTEdNPEoxvdrX/taoMMol4g9MnFZ57FiqRE4ofWkJz3JxgQBFPf7kY98hCl0vfOd7/zc5z6HGNTsxHnUoJZuar8B6nAXNijNEQIoFRLIeqlGqYk/ky4oxhMNCM3TW221lb/7paBLAkpzLDVsQbHvwkYqBwSF0/gNebPBGUHLuY9//ON6DUHcUMG2UYzfwWkB58lsyCJmoNE49ZiBI8rlzFbSzYWdvZhfpAsbs+a4s74GNvHXmFAFiznlKwQJ4BRPNDj85Cc/ue6668r0lOsAdU52eb2GusJ2oAubR7yBrbfe2tHAUYyPgNwaL5YW84DZwQZA9YiHvcceezjjaAcW85e//OWRZ//993cbYsG5NCHnxwL12dtx80fydN9wFzY0jFv8Px39yle+kor0cq4AUjArwLCH4GmQZmX1ute9jpXLX68VU1hrrbVsNCHAVnGJvgvrkcujT3/6005tOQmDM5ATwHHbeKvhLw2WgOrdBgs5TrCdAUZY2Re/+MVbbrmlwUUGxDHvj3vc42CeyW1+BRXNoit4WFwXNlwYWadh4ACqLMtoIQiOOmBF5NUZwYULF5544omCTPIVLCe9bIxaHjl1ELiLMQiPBY4OiSeTKU/Cr+PYOIRAol3zXAMB4RAXFkGuxFlB6EUvehGlxVxGewjAmOMIpS6TvRyPjj/++PrvLxcQyvQIARy6Oi4szorzHsoZWQUTkAgHHkAR4LDDDksmU55B5DY+tInhuc99ri05xFyKkDUXljZm6QUYQ87CBnICS/e///3T13EKC1ARAJv3pRhSKGVUpaOKN77xjSnit0Ya03rVVVeFc9+FDedvf/vb8QkKhyScuBV9wD9kfNwwL9EsMQV+gOJvf/vbRbbExlj+eAYhRv+BD3ygcJilvTavxA5Uhruwmd+dbgpO+H+i75BZ5v0A1TFC2HBZIMlxmf0Z4ZQKlkpaFKzgpO/CYu7p3nvvXehL5EsOyynsij8asQkrMZnOg93vfvcLPed1YCcKW+RDNE6/GB1owmQgccucURpITy2uCxtgQQ+4BCu2D7AuUB6rzYL2oRcP43wwkcK95vtslQq4puAhhxxiUo9FdjD8LW95i0hDg9pYWp2f+YHycBeWZqy8i2cAV4tEKQI7pykiOmvFlT1WAdEEa9nHQBHa+y6sGn3fAMhB2gLPWVsbZ7xPvkKKy0l/ffCDHywgf8ADHsBMi8uKcHiapiUhWpAh01zY6G02/jJ0Q1zYmFMnARJJ1d3HHXfccKAGIbwExHAl4PSud70L0ix7RGRjTl/wghdEV30XVnEeM9Oa4ttvv70AAZxb4wWlnFQrsdQisAqocR0Et9797ndzNeLdlr6I5bdvK0aLA9M98G2eQt8SM00D6evxuLBl7S2kWk5ejdWcYNvSHdJAyJmTr3zlK7ZkMQlU5Pv+Rop3XNhgzFlV8AMqKyuhfSA3Oh7+8IcH5D5/kbJcWK83oMTQoxe+8IWq4EN7SgaXBhoIPGarrHyfjhMSl8PTseRv+TNNAwFq34XtHvOHg0VeXp51hcwR6aCnLqXxUJgcEIRgOdtssw2E+e6GJZRHlkG2a+3PKs4nDnG0xiz6RAujbHegsO3XUh61RNNAXwM+q+lK/iJRCl1mX5DzAo30mmuu+dGPfhR0Feetnn/++SZme7heVeTaGkud6iBc6OsLX/iCIkDOY/alLTR8WQ6B98nw9G6BXa0UhHMg53OI7DLohRsyzF0YSpf8lpjDGrBnJZCpgcBjwdPv99qcQpfL0RT0cCLaJHIf5ZiY7aWyzF/84hfFaK24EORR+cVcLM07NxK2X324DdI8BfIf/ehHvG2BtKOPPppHK7NYY9uy/NfCBFtzf+RU3Ch7yUtewqdBwCd+6EMfiiBjpxRpiTmgATsAaQWUDnzHFCqYtQBDpNZiiacICYB08MEHWwUpbn9VcIFri8y5Qe9+hb7WT3Jy/kpZS7LXvOY1IeA2MK2GDIfYwkkIICBH5iSu6rgZhZWqieSX5Gxy8i0XHWxwukCRftWlbEvMFg2MTNITvUz2rpQqXkLNBDIgOFdMJLf1y1/+slncNwqg2acu4NJBQJSQ1DG1csz9/FeWPav8mnlLNw2MRwMdlAJVp1SN0uDZ7lgA6Uwqw2cZFt8iR7s84sh2mJRbsTRrfUh2Ohx6mU5l/QqmxohbPpY5QCnyGBT8V7XUIEfMRylsW2LOa4CdTI+DxCLNKUq4sqaKWrwSXpBm3bX66qtDIBfBSzMI+piXCcMBucPWjHMBeb5nNBDkj3zkIxV08EZdEiBqvKBUXF2CtYkKW5J5AVylHiFr1xzTAB8xLYLSEqKq2wicDrxm3pf/85//PGdaHGAVPS1A9fEiNHBiKcVmouwAFR8m0ZoqzJnQUta6ywXkbGkISlm+qU/Re2SA+JWPiYTLMTBfRtp0001FYRXkENsx87QWvqVnqQZGluDjv2KbzLsOlAR8jkPVxYMniyRGLRBh/rwQIDbgVIBVl2OvNX1m9zon6bzTwEQi6D9tOU0Di9SAUEG+8oaSf1lPq0EpjDlfGKdBFMr6Pp8sQA+xaII9iZy1khBX8LRm5TaXuTxsjQ62PjRsMaPp1ojwIQIXOxsypZhaaTQN5DdqcX79G5BAqV0CCAEGgaXySh9dBCqg5SysNHp/iYNVzFpIQkH4QZlfuJVGFqAO1GYBKlTjWUCesnL6ZcmGlepSizTb7tZhsKc//enOIcixbyb6m4GTdg2svWXOXg3wRCO8vXsvUTnE4jZ9DTYufz4gJwYZPSsiL3DH04VSUVvgQQxvLLOyrF+sYnDYUQvj7CMGMtELZgV4wWoBuYO2dSkCYIgGMkMpEQJFPvOZz0hb+4n7clocNjCUpDnHBdU1t5aeLRqYmIMIgtY08Gc7INgtHyXOigdk5duQcu7E6tzl84FyfGHAmwpcBMewxGIhyacEfT4GN5oKq1plcvqZNUFLNw2MpQHIgSv2S1goNM6hspjy4dOjoNSnMJ7whCf4Sy0uaZTF9Yw/MWqWR364np4qnjiEnFK1dDCcR/KVNRZGio0+sniLHRd4yDu5pSyGrnLbEvNNA3ofSEzhPj4YJNiqSmbMaUArHPCUpzyFLfVr2uYNwBJdmX2hC/xGsTaCQ9iT77agsajUU5fbGuQhTvEOyEtBiTgEJSf+q0CvreH4r+JkjjNaBII6boWyJeaGBgJOm0t8U2lQceYKKnQ3ELok3PqWVsxpPgXD3MU7TNQ2CPTLPXXRjFh+kBz+0RUCECqP3CqezEArQJXuH2YIUEMmbPG2t73NIS6HtRQ3lFRqp8JRRhWh9GrX3Oided6KibmwlBV8+IClBC/BJy29mgqpWfpAJEA7KuBWmqcLNADkECHQuPydT0cImGzvDViNzXPtt+YvIQ0EpT7ywjgCp018mwDgxzL6jd30HoxH0l7hyjvXtqhS0NsAyGDYJeHEdpBvo1YCT5nJCUNpTCztPHKa1tluCReyvLwo4euzJoAU99uupgEaYBv9AmrMqen2wx/+cAEeJ4BX6jstYOay9e/LWbDknCt0KeLoi1uXW28ECi9JKOWMILbSrtTCc0UmDcMF5J6WujogRznwipxeAuO/Wvipy2Fc79lkp4KQqWVg2ZY5SzWgTwHVW1wOrgAAsykgZR9f70u7WFEHVEQK5CD2louWwmpeQvA3L0SsAgy/AlhQLWHNU0Kq0Qzm8sESw4Bcvhe5ZOYSBQvIIW2VVVYZqM+MKRsaPGmHwvfaay9lMSQb+hK7zSgYyKFlziINTNiFhVd974/E+Fim5ThkOJrtFP8RRxxxwgkneD/ASRcrcii07uGtMp2cWqiSo2ABEKvne4e4UVYD0yxCzKwQFa5AzpTvG1XgB6VW5CIEtpOgFPAA2BcHkUEpmnxpRaaNfkD19SLfDbCT5eJSKCKTh5oPXZn1Ha5SlmV0ntv2mcAAp2HjjTeGZJu8TKc3fC36ucJsPXrCqB39rNBeE3LaNAASMOPlElhNdNMpPS9p+QMBUAd7zmFDIxqBLvkxmLAkR1qwwJaXWKy3Br2k4nCXfCcCLcY0AWK9zY0MjL3pwo1QhYhpYmmiUD6A5RH0stu8ZyAX2WLYx2p+nAPm3faFdRrhBSl8NYm03i73OqNfYpNhLA4tf1ZrgGUTeGJRHXK1hhF25baaygU72UaTO6OHwMFTGBClylsEMl//+tcDDGPrpMF+++0Hk7BkY4FlppB4uuDEu4UfzGUWkPsjoIL9QG6Rtueee4I65g6uiI4hy4iotRozawvOd5Pw9xkEqywHcx1+YPNhNQY5r5XXBVt6VmoAGnKBlMQi/7QBmlBCkhdgh7QZjmE3W0u+uY0S2izWRXAX3PgJ9yzjHDOIDF4bROPyaQw5hkry22/TQNFA4Dfko1qhROZysE/kYAhKWV5TOKSZ3RUUmi3EVvn1Qj+vqrDCyDjEhUw8INO2OFb5hobvvLCwZT+X31AOGPhj9wG5jxtg1Qc5seXzp0PGgrvNOJJo1yzSgF4b8lGtNCQA4F/aqC2g6id8FQhlLtM5JzI0TCikwVtuuadqxDmAEUSQzzf165HImUc+0xZiv5Ze+cZWcuoP0/IhgkBfTiwMHaUVjUMc0x0XJGXza4ctVQfGaWP7nbEaSDcN/6hWhIc9CWddBn45O73PO8ynsmAAZ3F6H8rII2e+AdWyKrecB4dlU7vPWdikkh+gWhSpi/NQVlPABlfeBU9ZgYZ8LZ48DuY6pAuoHFaDSE7GiISdt9D7xaFU7da2m6lB7REAcbtmuAbSU/2Pao1EQHOFYjwuLPoQCzhZXdkOKEBJwhYAU5jJHp4QWz9ZtNVk/uR3+VKGNVxkKJ9uyTcvMmb+I1/7p2lgVAPB3iJdWLShdJRKtCAmskagfa69997b00IZvDnrUl5cCL3bfLA9YMaW8RW7LSdhRLDSOQyrGFhdC3MveMZcIsjUXv5+R8IMfZBHbGG58Gl/7ii6nY2/enyRLqx2BQNiqP7wVZnyC4q4mDCJDDBySXMlfcrKXF7IJHxvSGy1UEoIfVkLlVODYmA4yPfnkeC/LsuMZ7FEmMhT1n75vFHQayc3WxZ12TrtT9WEMhWpq10zWQPppvG4sFoRYDi+4l2X2iMEAD6iF6ROPPHEQhZiQVARq/imwYmdAV91LV/CDhkTauuAJ4omX3TBh5m1KVHMbIqLv55S/VlvCI9LTYDyV5Q1CltRCRHf8g37FLfo8uGtOLsNopQ8W650Vt+FHdncT9dKwBlAMI45FFjDLjT1b+jlsLxg6uiJOJMdAR8VssQJ7EKTXxKcdNJJJAB9BC4gzncxFLHpgJXafWhDQlAhZ7aGy1DL09LzRAOBk8nYZG9lX3A4sPnlKSfSmSrbSXZOTfyCT/ZAfT1AqUJT0uZpnodTrXIccmU0Tds1WepCIAZgwmZzxV8ZTSYYzpWFcxWBMZwnylWKK5LveLCtQwYaHz3f2hQJ5kOX4qm6/c4KDZhEzfc+lSo6NVzg0r+2O5lT0BJqhU+9D0Jm/UKAT0nDM/42Xs3isASoQWD2UkuNXm2xaebWnztmfkEUAXR5kyEgF3RQtvM9AcaZiVYKZ1hNpVpkt4FshXknQRJ7uM1ud9QyY2/TreABePbWC7TGEjjg8RSoIMSULc3Q+eMF2dkvBPJL2l/tZhK5FoDEWiL2tNSVhGiCqC0a2wWGTHnKtQjIQVdBX4+pQS5SBpB+7YYBno2vUlAVLu3KV70ZalOGaG7k7JCFuP3OWA2kv7xFCh7W26X7Ju/CaiouMJqVU6flpnNWshiyUl8hK+AuOS3RNDAeDQRL43RhMRw/SlN7PNGOJJ1MPF0dR0GRgaiWaSCUsdDh3G7nsAbG78JSAkQNMacdMzsKwEUjEBnOfex18JwuGJg5h3unNY0GIAQ8xu/CKjKWQRuIyXGaxIFkAxlGgL7tHdibAyE9FtuBHFrmDNFAgNp3YRcRGxguPegzrFjDX00JXh2Di7Imc4um5EjLwaHkhKDm2dJNA5PTACyNE6Xh3yceiGdswR5ilSoArlEdbn0kjxPkfeaTa34rNVs0MCGgInYVkKSN4Oeq24vGbSHzNDnjAXm/VDhzC+oq+umO8e8TtJxZrYFgrMAjbYGrgq66dSGuMRPKmkYaGcOYqxBIuDoVoXTVxQvzPvD6OMdQcb81h5aevRpYLBc2zYaGPnT6GumTjSenz6flNA1MQgN9sA1hMk7ijiUNw0WWXSRB+AxkPkTm9mhuaGCc8JgQSAZiaZEVDSyl3vFY+7nRF60VQzQwFjwGFhkPZgDS1S++yIqGM8dzOEG/xpYzizQw2IXNYmgWNaOJOn80AJylsQ2oRRUtMQM1EKw2lM7ArmkiRQPNnDYkzAoN1ECtBR7gwo6uhQavh+qSLd00sFQ0kJW6X6vzYHWpiNEqbRpYpAa8xQWi+V0kcSNoGph+DcScFlua2+kXo9XYNDBcA0FmH59dFxaF77H5mMVYPu/watrTpoHp0YDXS73y7y3U6amu1dI0MAkNOKXn8sn3tpU5Ce21ItOmAbaURfU1gGmrsVXUNDBRDcQ77Xix3S8SeDPRt4eawZ2ochv9dGrA+kpkyzF/VwfQ0ylGq6tpYJEaYEt5sYskawRNA0tLA8ypKKzLBzSaOV1avdDqHY8G2FIfGaw/ptmNwqLwdUDfwrQma2gej04bzTRrIP6r71n6CLEPZzazO836b9WNXwPMqc+t++pqCwqMX2mNcjo1EHPqI8G+nG3eb+Z0OpXf6hq/BgDVpqt5vxMR6LqwofMNYd8Kbi7s+PXbKKdNA4EonyB/M7CttaZN862iCWmA/YxD4A8T2DSA2wkVb8RNA9OggZhTf2SbRTXvN3M6DTpvVUxCA4DqL6vxYjuGtOvCYo2Cn9u2aCeh5VZkGjQQfPpNogF1GnTeqpiEBriwwKmgXxe4ToJJK9I0sEQ1ECs6YkzbvL9EFd2YL54Ggk+/HTb/84ng8mxy8dc+98KwJZoGplADBZ8lMYXMJ8oK7DkoEy3V6JsGBmoAnFwDH8lsSBtLMy1/0hooVrQkJs1q+gsOGSzTL0yrcYlqYCA+B0RhJyQEALGqWOc8uLQLh3b2a0JqbMRTroHgEDJdHeZ5JBNoBz5KqRFXYvSvL/Y5lFIIQN3uhu94lMyWaBqYqAYACSwhLZbTPliw14HoLW95S0e8PAr/ILlDM9GqG33TwDg1AHgFe/0iweE0YJIMqcVgUal0bqWH2Oq+wC1nDmhgsVxYdvYWoxdIXXvttY7ROEyTwwp//vOfaafhaQ5AZJY24Ta3uQ2L9re//a3zggJM3va2t9Uo+dddd10NUTDmIji2CMlcUmdtgZnH4KrJikLQI1bFJZdc4pWdcsycPfWoreKKolpiuAYAxjEv2JNgSAEJeuEHDt3GMwgCL7jggpVWWgnq0MhHJh8NvA2voj1tGlhMDcCY2R72xjKGweGkMQnSOAftQ0RFZrA4X46G7WWopeW49WmwjIshxdujOaaBbhRq/M1jbW93u9tdddVVBxxwwGMe85i11157zTXXfOhDH/qiF73oa1/7Wg7eohk/w0bZNLD4Gojv6O3a7bbb7vGPf/zPf/5zZjc4zCNm7vnPf/6CBQs+/OEPs30FohJuP/nJTz7qUY/6yEc+YjH26U9/WhqYpQtZLWFM+c4777zhhhsqiIwBRcCI+1rCIm1xzaql560GQIvz6tux+++/P1iyog9+8IM32GCDPfbY4/zzz7fcgtuA813vetdGG230xje+0coKuryCs/322z/zmc8UL+DvIpu3OmwNX9IaCAIPOeSQzTfffMsttzTjl+uxj33sZpttxt7m47LPec5znv70p/vE7IQwCc9sJss5vCEMrBHhu/UkeeITn8jrMFge8pCHqJ259jTrwOFM2tO5pIFJRmEBGpKOOuqo17zmNVdccQWNPOABD7jDHe7wq1/96vDR68lPfvL73/9+3zzqR7Biageu5Can2fEwHA/N5GpvpWaUBuCKIQO8yy+//Hvf+x5ry8wJDxASBgRWf/SjH3E33TK4z33uc5lO+Uq5BL08Ou200573vOexvz/5yU+kn/SkJ8UWdyCUW3wgnA8R5zWqOOmkk1hw3oYxIh/n5Oe3FOzk1zQtPU80wJBaYl188cVmfcjU6jXWWIOHCnvf//73mdD3vve90AhO0GLfIF4CMreAd+yxx8o88MADJ4GlDp7HUjiySTAfi1vLn6UaAAPx13PPPffEE0/UBOnajrFy97znPdlP+V//+tfZW4GtgWv4gahDyYQef/zxK6ywwsMf/vCxVBSvY+HChS9/+ct/+tOfIrvvfe/rG6DXXHPNEaPXxhtvzLVdffXVCdCvvV913YSxKm35M1wDk4nCwqu5+ctf/rL1Fv/1ZS97me0tBvc73/kOYIlaCSR4KtYF0zWSFHTJiU8gXTAUNbkF077KZNaUhUx+GOIp0S+LMrWoMTRu+/xbzlzSgC4WEN1mm210OkyWgwTAIOeUU07RWH4D0F544YUSgRPv9tJLL2Wj2UTBVzSCsujtJxSYuQ2iPC3zumgu52OHHXbgXsAYgle/+tVPfepTL7vsMr5IjcmUVRCfsRA7lzqitWU8GgCG/fbbD4Se8IQnwOQZZ5xh4XTRRRftu+++7OcrX/lKf2tGdMocL16A7B3veEfiAoBkyncVKAbJnUqT6bfkx1Sq1xVsl0dJyETvQgmohX+HrN3ONw3EJL72ta/9zW9+Y4PLRzpdFmA+fvzd735X4ABg7M3WmIyKYIkd9ttHHbCxkyIObOarXvUqty6UHd3KZNXZ82233ZabYf/hvPPOMzQMFpsVp556KqMtLRzrzzNhGA5+w41gMby1PMmBcDQISo0pVW5LYqBg5WlLLBUNTNiF1bsme9HW3XffnYV9/etf/773ve8+97kPaAIEiNu95b+uvPLK3/zmN7/4xS8yvgGH7gdumwVumWABf+nY0NJynHEAqZIj4VamRyWzHIWRH4aGh2HjVi2FjKjKIuCmqNEjac53TVOIW2LOaAAO4UEQFAAYVtYWYKSBzdmpb33rW3e6053YQfuwwrQxdp6i+eEPf2iLav31119xxRXluGDVBUWss1tnZ2EJjKWpKxW5fdCDHhTQegrwsO0Rg1uAihjqhCggUI1590u6g9g50wWtIePRABQBw9VXX3366afDhhMCq666aqwTbLi1h8Bz/cIXvgBIcIgY0mBMGsBUMYLOG6deCPTIFXAWAcA+UEy+35hB1tvFHrqV6UoRnMFSXYEr/mURWHi2xPzUAJDAA3iwh/5CUrnudre7MaqQgwCACyajpZg+m7QmcRMxODGSUOcpeqXAW06AB5DS9XQfMjBmsXkddiRe+tKXfupTnxJtBVEcYFX89Utf+pLjNz/+8Y8PPfTQYlcNB2mcVYc4hpc8ipDHL3lIG3mwisAMuCGjVG7LL1ax7SWnJZa6Bv7HWRyPNLpfL3JSLbzWWmstgQHA4hnId0HDb3/7W/7rs5/9bCb15JNPlhm2MHHcccdZaTkva2/30Y9+tKNdfAJgDQ2MvuENb9h6662t6pIpHwQFHmzmvvWtb40dl/OBD3xAjvWfoAWGjsKsu+66NoXPPvtstYRbkE2egw8+OCfM/F0yjotNXngtYB1PkxvN7NIA08NUPfCBD7Qna5cAfsAJEuAHaH/wgx+st956L3zhCzXKplg9PVvKy4TM+AExYUyqCIEtBRh72MMettVWW331q181BMDMJWEJ97jHPY4XAnsiZ4qDJYA5FC69yy67IHOLJ+O79957s7a2KXAjg6AvNCKYXRpu0k6hBiAQOCEkeJOWw6LaDH3d617nIMEWW2zBxsIJ++nc4Wc+85li5SIG/EA4m8wqfuxjHyvzN54wbzfAjoS5H7EqUH7uc5/LARsGHHMMZTLX+KC/8sorGeGDDjqIcWbeuQX1Oe8pbHhjNUs1EMSCR33xBQc2B6iYPn/96y1vecumm27q6KrZH+psXoEcJ9Ii7RnPeIbTMsDp/Iwjtk7W9l9UgPkTTjjhzDPPFC/jJxggdr0wB3K/vA5e9a677sqTFpQ1XrgT+DtQnmgFDHMDdtxxR0Iy2uIaloibbLJJ5LEB8olPfIIw8O/1Hk4Fg48JDvinXRi+6U1vYv9/9rOfGS8qHdjeljnNGpiwCwtn1u4LFy6UcJ7aUgZ29a7bXHCg783f3/72t+2CMcTygcbLCoBiqQSCnE4OqP1WR2YdZFEEIJAp8o1vfMObDRZMFAE9EqIUbDf3Iksuv4JncjiyvAFpngpIOc3NLou6wbppAPgMMJ40GX79618DKzJRYSOElW9+wzTjbDqrAyQQZdFYTGnOJTAAGJCwgB498pGP5MXe4x73cKiAwQI/APYGmFuUQAXSCsZIwSTPFW6FHEDL3gKbiGccBWh0hEamiR8TlpGXDHjay/G1GJOPD8to44KHwY4TgDF1dOyjH/0oH1dUGBohdjpV1OqaCRqAMd7AXe5yl9VWWw0ATJCWNHaTGFW/Hll1m5XBFSChFJygxZIslrBuAtze/e53d5pQXIBPAIqZ2mHeisvhLnWBmXwz97Oe9SwDwRCw4QC99mT5yiCqCD4GAiPMVJqtObJuw62urqXnswbADyTM6fUls6+TmD47+xZRVu8cWZu0Xn5lP3mTzs/AJ5w7wcVaKi70wDFgQrmkoFjcR+j1VPhJghfBdKNUY/LVQh5AFUpwlsAQwDPC8HqPPvponoaDXgK06OHcoo7rIijGUcHNKMCZD62gUSaizFZzJ4qRTyuUcsDX0QV7dPin6n6TW840a2BiLmx8SsEkR7WkLeL9dvrSLXNsMcRrFOrX2Uwnl5fp9CeYBUqljznmGCdpXvKSl3BMrajgJs0W8YLF2kCnRpmYSCPzy5OQ47Vx7imz7vy4YzF77rkn31ctcSDQeBmC7d5pp51g176DVx/OOuss6FdKnMzSEDSnWd2tuunUABcWWmDMkh1gwJI/SgCBJQ4o99HqiNPAqLkA0hEr8VH+BPsIxsHb5z//eVM+w4qP9b3vD/BBBa6CUjRYYY6DNCPofC0maoE9ptkrj2wxhHNeuQtve9vbGMGPf/zjJEEAq8K07DWCVDed+ml1LXUN6HTgYbWcwLYQ8i4Lx1HkFVTAho1yssUlTVTzPWJ+Q0dsWIVwSy/HAdlDZdFkXhepEp0FzmyL8XHf+c53IgN1yDzyyCPZRobaVhUrysYqhRs77FCN6INtCpi3wWWyV3Wn3nY7PzUAkKJCnM5cdrq4dxAIOR2FgDcoCqna+xLXtPqStgngSCtPEc5ZS6EBgSfIhD0BWjFOs7NNA9AtkMOZqczuFpoMB3Xhzw47ygWxLms/LodNYJl5yg/BxCrO6QKc1W4QCX6BvXd4uA0f/OAHWXjbxRaNFmy8ZwlGXo1WcRkOalGK3+L2xS9+MRc2MY5OY9vtUtHAxFxYIkIPsPJipeEjfdwRXffzXAG9wNqcjcYxcHEFS7HEWS3L7n3vewMQPMXm4sbPgJiaoVuZHpVM/OU4+v3mN7+ZP4EhSLn1fqKxAZfQLLhrawDa4NJMoCyRhNPEG4wNYQag7FRU+LfEbNcAlHJDdTeIWrdwJU3PHFZTss9qOmOg6+3JaibjhZgHKS4FVPzaBMA8igHdbbfdTPDwydEEPCFYv4wpbMeS1qCFMYAMriRckAlpPGAeAwfFQkspl7oEJIQiPGLBy+bvbNd8k39CGoAEnqJz25Y04kAW3kyTF66tsoTqfWrAU5nQgm3fEqau2EMQfdrTngaN1urYSsjhNAA24NkwBU4reY/gWcgWT5cl/dvf/nYc4LNwU6m9MhO/RWCM54Qa1YjnqgYyCzs6ZeKGKBub5WJI4Q2i6rbDFRvos9mMKheWmeU5QBc3FOYlPLK+YiSRKYh/0jUfsAda+dwGNBZ7MbB+mWizv0lfxNTFkLLwLrUogngE4tdfz3MVyXIcFj1X2HQgbbgRj5ciRyDWpgT+3HFszQIcayEG4rHeyMwmloKKC98aR3LqZrb0UtTAgOD/eKRJFwZJQ+gRQC2QCQxYGLGkgAgH4AW+3At7CiIBImGOFng0hFX9KPU+4hGPkIAnVUCYQQKIMGdhZ3hYTnEOOCuAGMwZHhwFG8dYOXOzSOHrGlt6dmkgRude97qX0JSNJLF/q3NrG9v6LKmtW4Bhs9hcO7P2Aax5mGBt5DfETJf2WtODCnACMDsLQm75rwHVIlGEgBGEN4AXLfjKV74iAf9Bo7SKhAdstJUaW2JeaQAYbP2L/TtYIoZqGrat6WIzXTY0XQHhELVgApM2UmHMHpe3bAEVYjnEkOasIT/YKReWFhqFxBhJ+cCJRr4EEAq1KmXsmPX5uOyzzVmc24Q9RPPz6lGQsMoqq4ArkwgbaT788PnApgMVBBxEW/a24J3sYu7Ej5wrQAlsiMc/6SN2qa7YZwloFyt1SKDuBZWCvQO1qgsxgRn8YsMt4SzMhGwNGUOPn8CwkxN/CYadg+44rO9zmR3sFWudPTq7cBwMO88leFFX2tJLSwMTdmFhgpnT/fYOYBFcdHBHejmWVlACqXADtSJY1j28zBrlXE9RUmXZ0A6H8dwC5Siq/7MeYou5Jgqqq/yK7/rgYs0tRUilIRkS9dOWnjMaAEIAY8i4sKyPwwD5nJYciyiLb39Pi0mSaVHO2UUDP5ZSwVXRA6gUmJVEeTqehFJGil9bUc5m1UUMHxfrWWe29LzSADRyK82dpkY4tGvvsmMgLmufKkekbFUN1wl0wa2TWs722aj1hoBwEUvoawb2H2w4MLzssEldLV5U6HADQuOlXpVJu8jWoWy381kDYKb5Plzl6At4mHOjDeCBKwgEpFo/8uWY5b27YifWQVWGFxMXYk/jYtZF+mnEyIwRaypPY0sl5DPOYhN2LbAKmYgYd6KDW2RyAvLIY6fCJq3lIk8aK5nkx8TlVnV225w9wM1Jccs/dpvAGs75yelwZO2aCRqYmAsLJYDLE7UIY1sFtxyx6jRD9wtusaEgC16veMUrECgYcHSIZXrk6uTXt+NBeehDGfgaXZiL1DpwY3ooVQCrR1xwfoN0XVFLzyUN6HH9Lh4PD/aYzN8iW5ZVgq+xntL+0oxMMGa/GD77sKwtyhoYBTmLo5zYeqspEd/CHz7lu2yNtbOGi6Pe2VsWukzqJkXTpEW+uTbrGeD0Z7dEtpw2YUsdOmRLh1tCcIJbUy8X1qanrwoAtvC/ECzXFltIY72BjUPMhksH235FHBhtBDHI9CnT5Xb26rZJvoQ0wH5aEcFtsZOgoi4hg06N8ANae+21l8MqIPeCF7zASUIBfsB2qtDh78KhU7BzC/lcWHuqXGEHw9zizLBDtTMJdndDb/g40m3Xl7NbD5aIF2wT0ttdvAIDgTyiquQRIXZgjIdNHhcT7YsxZgo5RpAEX9ZS0IcUtD3cOhK226WlgYm5sKSEA5bOsl6n2rECRMF8SIrj6CnLCLVW/178Zz2lWWfxLaF4Z028iA1bIUaZoyfOY9XtxyQY9etiW+unJQ3TniJODm62iaUTi/WranMD3Kc6j4CPr2DjANYLn5aYkxrQ18yNLSRGSjBeTMsbKoyds7DyQYIV9iKLtvNisw/Ao5VfELWYasEnrKCUZywtBuYAWWp3y1aytoYA6C5mXa34bNQABHJbvRrolT7RU6dRy0rGI+f5fCbWEReZ8WsHthHOR3A2CicAc3LG9oLTUzwMr8gowqmFahhjqLkO3sQ1BPwxRTYwk7FHzna7xWRgFS2zaaDWANhAFPPlt+TX4AkmYdhKDLQc6ba9IJAp8oWeASwh1SCwMHHLWiazfpR8G2jel3VIwHkb7704A8AVMTR4nDDMDWXJHSD07WQIL/AuzEnIl7Cu4786mUMeTIo8RmKhJDmH219uco4cGWdXwteTjKwSgCjELbF0NTDhMCTgMqk+D+S1a9bQC1U8Rd1vcQNPgSaU+AgRSjtiwZa3E8DCpi0CDY49dSxVJJ+D60uxgKg4ew2sbDduhod1j0zoUQS3oqmA25vdMlHiBnACFYJtMMpLEH4TeMjrjc56o2HQscXN0RnV+cKXeolUeLbEHNMAkARmzuYzZ+95z3vglhFkVWMlQc7yBk4EAz772c/Kh9K+4ZuoWtRrRPhNAvZUxKp6Axf+nTiUA4oACeHvfve7oVHYrKFxonqeA/QQAormRYaIlbO0tjvERgEGCLFpdrrgxAxqetZe9KXV8VkRcwj8squesnuW7nxWL7MydCZ70QHf5MomKSvNwUXvPS18eABwqC4LPLExG6bShX9LNA1MQgOZkVnRYJit84Eqdlgcgf8Klg63eCSYxRLiH8fXr0wXUwn5rnq6R+aW9fYlDVEG4QZfewVjgwLsETOerCuQ+9qGAWWzy6N66zUNUYuRQh63Aqv8V8d1yENIPLnFIfNrKJHENzqt+ry94CVII1Q8jhgRuFC2xFLXwH/9wnGKonchEkQsZdhE36Rw8Nnqx1c2hDy9TeVbVxxcAVGLGB6DVYte9xV3QLGbwF3gp7LO/Fcft/IrmG+PTPyADwHoiB2j9rYByArcik/Ye4W82nyDKWl9Rh5DSIUzoSyf4XBKxvYZv4R1VotPY0AeuLPRBDYTYIu5sg6Khck4W93IZqkG9PKmm24KP1k+mc5BApZcYMxjiE304rbPF1ias4wepbEMloIdY+qpTNa2KEQ6+PRIdWAsBgDGImEACYoMus0Hr8QaIPawYB4aXVZcjKNxQYa+wS38W2KuaiATs7MuXsNiPxlM26BmUzbTr+gsCwY8TuDxREErSAsgwZjdk8/HzeZpgA3VbCAQ+nybABh3VuQVkwDeu4xmfa/XCNBCYF7YMvHTsCBT1lF9hM9V/bd2TVQDA01iYQKBok45ixVMAp5teibODpiQkymYyfWZNsdj5DCb7CS8KWjpBaj8yHxPgD8gv3CWgH98fF9IQNc5GRysxMStbGT5dbbVnO7tK/tsXsDiPGSYFOOMQyoij7Q/4+y7cirlwziZwFs1WPAnTyhNBNaWhpKnQr+mBnMHqcIWTbtmiAYm7MKSWy8ysnoUkizfvS4j0MVxFGryiQ0fTuO/Mr7e+2NPEet4Xx7wlValGGu7Y+KjPtoiTsD9Fce14kEGNKK2BoDgAacWQ/FUASo+KD6QWjANzcSw2FIWH5VCmLgvL8TXko0HwOU6MP38Wq/f4iPW5bIQdOvzWw61EAbZDOmGJsaS0ABQgRaImqqhArRsHcgJkGI9rewlXAJU7C+yAjN2EPCgt8jmkRxXPu8iXw4fQg70ug0rb8jyBryLo0bLOTxFCCzYvFLGslv9s7P5rIyR4l1d75A141iUPK8SAGMiP+yww1gk3x9wnIA1Y0hZKmFRLxpa/PsQW+ABRZAGllQEVN4ssf6X9mk2wLZe4p56CvAsoS0yNE95ylMC6dhhJ/ys4XFTHQNr28Fyy9FY9bLYyjKJI/getbfzqiNaY8ejAbMqbASBHXqWENIgECalwcm0y75BJp9S7JPRs1pzayKGOt8/HgXaiIfA0xVG9ZYhZIKip1ZZscallgAYaB1fZLS9fcu0ZrDIxE0OL/aII46wtCsRAYY6tRDJhb/RYUw5b0AM8hDSKCDVggULlMpgQRlLzoUQ5WXh+bIkLGyLVC2x1DUw4bOwkRie4sU6KSKw6lVr6yfOAV8BRKxp4BVcCiAQww3QCC04ZmC+9+qMdT/kYQhkTKfivFvLKV9xs0JSBXrfH2bi+a9gWgAU15OjwLg7Z8NYO9oFgoy+0AI+yoKggSEUIQjn2K41lhwusnq50R4tddU3AZa0Blgi0IJJf/fInqz5m1nU9fJVDSSg4oU/L7RKAEZ5hEDaI7hVKvnhJpDAIls44SAHJqEd6kAXGayCvRWdQcFbFXAVfwVXA8Gjww8/HOCttWxWsLP+0rdXdtRiSiDMktZG4z8DNQBCjKHIkJcH2FJhJDtFUCc4ZHIVasrWP5i5LHVgT+wgSAMbUdWcMrSq59H6jU/s69c2AaygrJQ4vkGXX5x9DtOEzQ6LinnEGMKkUAJ8YguoQTg3Ygaqq4m0tDQQk+hvWzqqx2TFJHaEYehg0ixscwCYIRYxeNssBTmup1WWlxN88c1i3slvZCKdWCkIikBr8WZvistoDaa4Susqwt+44CR4t0EVBguv2o6rJZ/3c+z6KsKYo1TQWLCz4XyOaCtuGQUQ7vgWL8UAcZCAX2E7l/H3gQJehNFU6mXJGX8f2GKu+Sqmko48tWwtvbQ08N93TvW3HrJa0q8AMRCjHSlZTFjhFsjX8cCRUyzSuh83VykSIytWDwooLdc8gr/UGzI0QI9JUIhYAn2qgGzE8GqdB3DWW46nKGiQsL8kUWlBqnzEoYddzZGDsxyVSteyuW3XbNGAHgQMixxzbWzT8K5Ez1/UunR9TSwHbERMPYW0GvMe8QngCqKY15SSKUc++qBIDkB6CoSuQgbGhIxKC6XbSIISJhEYCLyK2NYQt985owFgYLuc+bOnBDagMlbT2D0YYOVSRCnYgMzAQ6ZL8YBKJkdBDm4ylQp+ZOKTzEBXOtir6x1uhzHsILwu29JzTwN6HNhM+g6qCmoyd4FWv6UFbPCZ+b1PIwcZiwq90jAJrhgykoCHeeZ9h7DhFk3H6mYIhAnoDpSkM1jwJH/GlyJqr82penEjLZnLkIk8BJNZ5EGpIFOPoQQarbCktIwUpnXwBj3mA0VSRbuWtAYon/8mFmnStzgvfTHJKGzE1dPw5IQfduCod83NQVgNo0IMSZ4G3LAiP6VK44MhZCludg+x7QDMpVWE2FNpF24eQbCREM51vYq44vgiVjCmP+lSaUvMbQ3AQCAq0el6OQxTTgV45LaoQjo+AUQVUMlk9QoaEcthjgv+U1xmoJ5b5jWUfkniNwzjvBbm8ts1PzUQDARI0q6+IS2gCkEUJTP2zW1gJiGzuLMlsyhW8b4dlqlUygbhNcNStiXmswYK2GoE9hWCjGUzHXsEfsEVuyfhKvN+aDpWN0NAQZQdW10qUjt7Ww8WoQfVpYjfQimh3r5xltOXp/gJOBgCxLO76z0caSEza0JFxhKprrGlp1kDi+XCkrWGGmQMB3f9dCw0oCkqKPa3JFJj1nZZLWUMEKMuWDhI1BXVfGqalp7bGqgx0Gkp5IyFihqupVSffiDzgWUxqYnHqrfU1RLzSgMFG2MZ0oGgKqVqXQ2kLAT1037xPsJLwZaY5xroo2WgQmqAhaAULIk+DcrydCDbkgmihXKswdKpt5SVqIsXPkngJtTn7MGCBQu8UonY2VzHbMR3C2XNqqWXugYW14WtGwAZ9e2SSKvCksv5LXtqPodU7/wuieoaz6aBpoGmgWnWwDQY0mluUauuaWAJaWDKB4t9OUcIfMpDjMzpc8dkbfNKT3lFS0gh843tVLqw06C7uLDOaIvt25JwfsBibhrqbVU0DTQNNA00DTQNNA3MYQ1wMLiw3gB+3/vel2ba8m3+60zu8cEurI7MNTNF57zmvGzzX2dmBy1pqYBTFTeCdOTfJV1j4980MFENBJ9KlcREOTT6poFp0EDBZxJ+p6HSmVyFk7XO2kbCgQceZrLwc1i2gcjsfpHAZy/OPfdcr+k5FDKHddGaNts1EHwOxPRsb1qTfy5pAFAbSudSh87JtjRzOie7dY41iiH1nqsPBforGMWudqOw1h++LuE7hYVijmmhNWe2ayDIdNbeF1gcWmpAne0dOoflZ059G9j3L9t+0Rzu5VndtNhP2+Xewff1/mZOZ3VvzmHhg0x/xZBRrZvZdWEdBPFVbX9AqCZq6aaBmaYBBpdn4G8KzDTBmjxNA7UG/BF2L57WOS3dNDDTNOCPB/kkfJv3Z1q/NHk6GvD6Ex+1zuy6sJ5xcjm88Xlr0pZuGpgJGggyodQp+1Gcto3amdAtTYYBGvBRSRAF1PYBtQHaaVkzQAMxpyDa5v0Z0BtNhDE1UOb9DsUAFxZFDm+1I1wdZbXbGaKBgs+SmCGCNTGaBmoNFHw2W1qrpaVnlAaAM/isf2eUhE2YpgEaCD47qhjswnaIhtwmDFa4D6xjSPHhjzAvnIdTtqdNAx0NDAHPkEeY1E+lFwnpRC8cdlwkZUfCdts0UDRQo65kdhLjoekUWczbfo39EWFfD/Kn6rBvaiz1tjG1mD045cVL1wzhPB6aIcWn4REJayEbzKZB50uoism7sGZuMnVmbuZsCv+IRQPWEur1+cB2CHiGPKKZ+mmdHktpUzV5j8W/5c8HDQRpptWxIDfk0ZLTT1+Yfs5UGXwNNH3kxEWpRaaJZqqqWHKKmj+c0zXD0Vi6bwaqpay4aiEd+IGxOmcGSt5EGqiBSf5dAGbFzO1yuvaKK67wB9n8BWEVTKGtATWv7Pj7WwPlbplNA0M0ADy+HJy/x90nAyrQyhqs81SmvyUI1fKdD5Nm3To05ZYdl7788su/8Y1v+NiHdHLUnkOQhbIlmgaGaCBwhTqTaCDUIY7HkO9hD8Rth76+RQ+NEy2FgyIGUYAdhvgYEcaF28gp5/jjj7/44otLTign+psGxn/11+p/+ctfXnbZZV6TpxBziqeuifJs9FOugZhEcB3L20s3sa41bMYpBiy5OsQYypwEejt8cosPOMVvufLKK/kt3mPzCPC0aKpqGVh1y1xCGpiMCwtVQPCzn/3shS984b1Hr5VXXvkud7nLggULPvOZz0TQxbE4QdJ3v/vdBzzgAa95zWswbNhaQt0/99gGeCbaRz/60WusscYPfvCDgp888ob4JptsAlpvfvObyyMJdtnv/vvv7/3x97///dJ+pT//+c+XpxLlCjfIfPazn/34xz/+ve99r0cBKisZm1iIW6JpYKAGgrqTTz551VVXfdCDHnTCCSf0p1KgkvmlL31ptdVW8xcvL7jgAqzGbxLZamj0O1CAgZlhfuGFF5LKn0JEEzmPOuooIyJ/uCiO7Cc+8Ykttthi66235rIQcnJmPw30+7GPfWzTTTc1lajlPve5j287PvGJT/zCF76A86SZD2xgy5yoBgKA/fbbT9foI56fvq5BmFvd9IpXvALNM5/5zDwdPySg1NURDEOZE0Jvh0O5JQk+xhqLfa/Ri99y5zvf+aEPfeg73vGO3//+956OX9rCtiWWrga6iFmkNHCpp3/0ox895jGP8Y2uVVZZhZW55S1vyaM98cQTTzrppO985zvm/lgcv4VhAC2nzixPkwhNqhAA8O1PV4fGLZwFagO5eVSqGCUcuS05fW4tZy5pQEfrdB+Gu+c973nGGWcA5MMe9rCgBa44l9///vfla/KXv/zlN77xjfkrHkp5ZLkv85prrllzzTURMNPSYkLRTwfAqchY4BBfffXVKYJSXR//+MdtTbz0pS/1hwrldLAXTMpXNpzb7zzXgHCjmBAl7LHHHqeeeuotbnELIAlsAl1/K+gNb3iDeD+U9uNbBVGKpFT0CbFuGWS7BE9+8pMzEDqoG6ssDjYrSBXBihhGRAJXqWj11VfneQteEAxNXfsQznV3x9r7eu5zn/tcHrxHj3rUozjr8s0yXxu9tt9++w996EN9taS6NLNUPaTejoRFDPnShUPJb4mOBvzlIwA47bTTLPVdNF8IpGGAvf3ABz7AkMJqtFoIJAZ2Tcig+tBDD0Wz00476egQ65Ef//jHRxxxBEhsvvnmKMeP3rpeaeIpK2yxzz77uH3gAx8o7uDL4pdeeim/Zc8997QY+9znPmeJqJYgIYIl3cFYxCsENXJKZkeAsfI7ZO12whqg2Vw6ScLUCzQSuf3Ps+ofqzF3bIqaXvziF9v8Kg8h29JZ/le+8hWZcJxHJZFbHMKkFJSQU9eoyDe/+U3IsGDK07HKoqwLFp6jlYyImqvD/8bs9u/s00C629+Qs7whfb/3g7ePfvSj8LPVVlsVmuSL68u3+F5++eW5s54WqPz85z9nPWHYilw+Y4fygx/8IIKUlelym0Tnt0gSd/anP/1ph6BUVPJrtiWzJeaMBoQqrZf8jtWiAODYY4+FtEzPBx54IOICjCTe9KY3xbLD5w9/+EMEBYSFMlXUGAtNdhve/e53IygQDYexyqbgeeedRyohN8Rpwoc//GE5GMrplJVTLrV0nrqtq64p5YtWqEUD/f7kJz8pTyW4FyLBHu2+++5uS6tD47Zm269XTqdIzbylaSAKZEtZ1HLb10w6dNdddwUA3SFodc455yCLeqNnbuhGG23kKZoNNtggRcIfWQcSclylRpECDuWtbnWr2F75of/kJz+J4S677CKnvrDtMHSbumqypFPRQQcdhNUd7nAHG2s1DZ9nu+2284hfa48Ok5qPdIqXIm5rAvkdSeR0CErZlpi0BqJS3qn+qjU8sTiQkuwscxbrJs50s5vdzGJdp+rFRzziEVbSoOB0lN9cKrY4k7aatwVmDYeDC6sbSf6zQoJ7NNwIJ2kUcZvWFrLUrqxztza5HMBi+0JZaEiSs4yjlSxr1VgqzdgrlC0xVzWQjoZG4Dz99NNZZznBIXgcd9xxDr+ALhifcsoplFBg9r3vfc+SzDwqiBvleKQILIGZkQN1TK3bWnWKlAOCeLpli9FLGxpuQ0yAYJKVhEnc5COTX3Nr6Xmogdg6G5qWT29/+9tFhgKMgJZJfOc732lddL/73Q+iajsGnyhpzMlR4GQYg7GAik12jW7P3hSZtNO0iqD3i1KmAPBFF12kigBbZgjSC9L92zoHT+CvD517SsLC2alWA8Ft2tjpXHLKd35A9E4DnRlw+MeIk+/XZTAKj6Hh2dvoI55HLnOESt16RF38HpylVeTpJZdcYohl4KeIp+iJ6mktf+TJo35+R9p2S0Wuxz72scyaRQVlRicS9Cxq4PifE1wyAaOoSxFPCyT6YIMQXcDquvjBmMO5UpikT1lyaVhNjR4lX6bzrBgWT8CjUm8tG29BSAIfcHr605+OTy4Ys1/HUX74wx9u+ZTAh0fK4pnT2IS3S2DPWaZHbtUOXTAGe2TQtNSlIcZRZ5DmkYrAr7+Fkqftd/Ia0OW59I2EmXVIFDY0+smelI6065qyuke3yTc9n3322eypfMTyJZziYp3TzVZvDk7xgOXnaX4XLlzIWuUozAorrPCud71LjlY961nPCmXIzj///Kc97Wn+BK5HYOTUlPVZzuWoHSXv2TTwnve8B6wxFGlDKeT2yle+0iAhkgtZu2avBtKDQ6Kwo508gr2NN95Y73/961/X2NhEkVGwASrTtsSWW27pEXpmSOL5z38++k9/+tNRTracWDd7uw9+8IPRe+oM1QEHHFCXsgkFcjzjcLjd7W7HUKIEY9eCBQswj8zQu8022xgCnho+66yzjjk7rFJj+51jGmCUxhOFtdEPEtb/iTk5fkoPYBOj99SnPtVTkVpWVyLGszw9/PDDbbsHnAyjbSteY9Qo2sQnENlSyq/4k79mlyAuAlu9XmYof98Opb21RDhiS1WkoD1cxMmxmy9HSFiOydjvmWee6U862Y6TBvII7O88lVM06FdcccXXv/71zkKExm+uDAoDk/eATNvlZ5wWgjBk5DfccEOnGJPPtqtURJBfYnwp6yyjR3yOt771rQZjtOGM0CMf+UjzSEp98YtftHY1EbgN2/xyRByHMyuljSGeP7/pBQ7ZeKKwu+22G21bcjzvec+TsMVPUfSGCQ7+Pq1AZsC83nrrxahGyVwC1rUGG5AHbAj4xMxmzrxCqfRLXvISjoQ03OpNXamsfOdK0n1wotNBixguT1/2spcJkHmaFpUejBiAgYz5LQIXgvQ7Uyzqse+++yYfltZff/3NNtsMmI0LjkRcEU9NKHBFJAzJBm/OVIQJN3fttdd2GkHsDGUk8etyOBhoa8+nCNAS49EAHSLrR2H/E4LyLBTDXVhkQeSrXvUq/adLrGks/QdKEErLGpQWOm95y1vsye68885u+ZSxpOl4VibHXxhrGHrRi15061vfet1110Wp4zHPIOF2BLIg5bjhIYcckm0LiyowDasjjzxSKc4B9K+11lqvfvWrnS7PkURuMVYB9ECBW+as0ECAOsSFLb0MckwMF1NOZlwIBA8zsRzH+IRLLa/TanEdbyHAoRBOcvbee2/EieayWa997WtN1VkU8SfQZLqNNY8zau1kT+qud72rgs4wAKojjEEmga245BsCxCBbwMwFwSqDJfW23zmjAV0/fheWFwUGsWlOhUYJcQhgDOy9fQI/mQUDqkzMkPy2t73NUcIddtgBARgLTSnO5+MWm1BlcimkObh5ZNmffG8yOB4AhOCKzLRtIASN43FhRU+VcqxQdUwrIQ0o6z2Zm2yyCZA7bs6tcat1omsZvGlaarGqtORjrrnUnpaBEJOumWmpW1eeWgpiKF7rT0xza0gez4anm3xbxl4sjr9lKuEMqRF/Lr4rQ15dGMrP/nJ8l+REvHnymx4Zpwv78pe/nIZz1p9ibQsAUjqIB+mRjmDoJAApysQfjblYpp76yOgFdW4tSxJYZWlFpnirzO+2227LWTS5m+4hlvllwyFcmjNQjn5ZfeFgsOg+7xdCl1vEVkpqdJXuS5qXjA9vxG3pZXAaxdfIj3y/HoVexJRHznNw1BtnJ3Gd8cXTeBSMg1iuxWc/+1mbA7wgBFlEKcsbcfupT30KMW5BrGWS2o2LTEOpokjYEuPRQJQ2BS4sRi6LDJjQVS7vjTKdVmZl7i84sIKxuF9ppZWcEChSOimr1BOe8AR8UOpUUJZj+i80rCcAycxZ2PgK+ToBs1jILJUgTECrnKDiSbgFMgujQmbZJNNaP0gt+S0xGzUANsQejwubcwJOZen3mBIvtUCC10RwEI8BsJzbdiue5FZQJ7CUA2lyzJHWRW5zWQgxRgDvNrDccccd8Tz66KP/Q3HDDRuPRn+BPzkYury4jRsLWMhEJszBBoi1u0w05VFLzA0NAN74XVgmUau9UcB8WYRDF9to2hPLBxWsfEYDhOLComTNLags9esgwmGHHYZGEKsoEGLlsM8lRyLx1MC45Aei2U+QOR4X1hAD/hLcUipBCiHSwpaV5luQwXacTDY/j5LIqTMB5oym4aMgo9j4tfjEkGdv3y/ceAlyOFW1NmyYyORthEZs2G1UkbmADGYfnhOHCU34h3ie/EbhE3Jhva5NORYnlJkzyoynGKojBPJjdePCpovznpYobK1Sr4Mr7o2XkinOxR6W2ySOOeYYZKL4dX7WdQJedX8JMaDk0aIsGEvrOMp2Kjx1zsHTulTNtk47D8BvVsRKLNjwFDe3MklViPkevHnrRkVkWnyaILKoQx9JDj74YKXi4RTZCoeWGI8G0pV9F3ZZmp3QpXvQiyfxC239iLRbY4nFAqgut4RyoJAJhhJk3/rWtxgUYbAS8JdpwWTxJPLKr0UJAay8sl5FJCXLwpY5F2V9UwRjJaWt0sQSLMVKvj1ZQ0UpK+xkonSLBqAlsIIYNKYBNJZW5KevwqEl5qQGAhhxJktkrx3YWpVjthOX4hPkBZGswUA0GnBaADayWIcZmWEijArVcvgTUM3HhR+rNQTQ69ctpAVU0IvSr3zjIqWwFfpSER8lXoKnLtua4kYGCMPqFpPR7PYzTzUQOPH2bEMBLU/LxV173eteByocwdjeoh2rL0iz0GLcSibn1WzKOAumQhQEmr89ZfoKhkPscFc2EEpZLogqHO8rOeNJFPBzYnyaw3qPPD6toGy8cFaaNTZTZFLotIKcKC3ksr9RahTP42cLm4kQu8TAOLsp61ejDGT6sYNMCWTAR4uortbG4x73OMQZrTgL7/nNlx/lu8SAuV8aLsJtCGfIFxlaYqAGaImu7MQ6gCEC6sOFQKgLEjaSKKWKPvtgY0jpP2ADDNZSQZfDAIAqx6+LX4gb8EuzwH7d+nQAznYeCn+ZXGoeglCCdCSUyOUMYWAmBp+cmGv+kA2xgjHLP4hKjchUB7pCrbChXrKRSsBORDb+d1jZDTDihPZzINv4te3w7W9/29n0EYSNvoZhUFgmPelJT1JETgq23ynRwE0nx4XVYHAdZ3E5mcRtZTRdgqB+ISy95bSADrMucWgpBiK/AlRQxbI4Wag4cIC44LwEQwYoEh6RLVBLrxsnGSoK4sAlhUs2CJkidUNEznBw4aYs22rVbhh0yOoiLT2XNKDT9T6DZeI0/3mpSwTLXMVQlsMAovLmP/Oi/U32JVu3eRcheItCMMFKGpZYxpxzNTcPVBcC4yLFJQAvALZOgz3RVnO5nIiH2BYnPt5TGcitZc5DDYDHXnvtZSa28ocZ02EimtASXAVRNBPr6jyi3fyASiYaiyI4F1cz9Srlku8XGj2No2wUuOQ7OWb25UnwcX1+C594CR5N4rJU44KIUIhZYJXBImE6yIyAZ+QpzEklbUCVJuSR8F5se6HkN3BJc4uYkZeWMDAlzCD5CDQ32kAzR3Apst+SFhnFNmSEe61jjcccb3AiiJId+MEBQZSTKtrvWBqAIpezH5xIy/unPOUpdgmsxnP2r9YhMh0kOOXCzZQdsJm42WSP0jWKlFISMZvpjj569ZfuU1ZQU/izCMnjNMXzNDA33SMoj8LTbYILEmFuW+BNN37rI8Sq3njjjQMtNOaFuCU4jDZ6mXwmnPciNJvmOLpguJEzNSrizIMlnBUmay8/QTrxC/EUNB38FyFbYnIamIwLqy9jNeBP11qKufiyrAazy6T6tcYy92eXh4VlHAGiiOgotIVLmGR5ZDzoXVdodHNBm5w84mpArTUTrxfULNwhWBUIauZuCVYgFYaFc27b75zXQHrctMeFhUBmhZOq1dbQfqGL/wqlXvIAJ2gEUVN+vodVWxkWs4OlSaguC3TrcjsPGBpBYWIgsGt13GgSzFuRuaQB8LCjyovNyU47XZmnC2ZKY/OClH1/RrgGlfnbhqzMQlknMi4YzIQ2nRYAb7aUuebFouzY0rrsItPZ5sq7B4WY5HiqV0V1K5LOwXGetLJxHVLQCd28ncOHsDftmwwGbB6loCbjmXQShrlAoF8zBVauui3ozTgCz5YEfBcuLCPAI8E2njHxiswtsUgN0B7n1crErjq8CYLSdl+Huka+E7Di6A4xd8Dm6SIrKgQh5qoGZuJler9wwNmbVU6SlJxS0IgAM+s0kS9bcwgip2OKOaTr1mjK4Wk+aCmIMo2ScKnOEUqOu6gqXxwl082GyxQALmBzrteH53Ia29OvfvWrsMdBwhbNWAOzVNoSE9LAxFzY9KLOs951VtWqS6fqFZdaTcb+RpETAnwC7ixvIGhwNNsZRIhP52FSo0QaE7is5UZT30qrwh6ZU9KOLjlj4Ags08NIMUmw0oEFhp3i7Xa+aSAYsK3DuDhfaKV04oknwkxe685TXyTgwtpD8NT+EYtm54i5qXU1JVgiAz6CPdZgZSCoxZwaYyrdwXAtQ0vPHw3AA+snROpwpwmSixCr29eA2VGmiTzf4OxbV0jugwo3Vewzegk9cJGdzuJ0CiJ4pUl2v0i/6rFy4Bl/IVVolyhkBeQlRyIjiwxcdmE8u7oaws5HgByfDb2Fn3ynKTrFwyHNtEA1nFXk3SAnEbksGuWIjmVqyCKDmcjBSl8dcUBIeMyRemcPmAX8BwpZ19jSHQ3QmLnY4sexVDvsiRzVNAEb79Z7h06jUjufgbaBTTfJnATYLGmUEsMSdPCajd4vHZcREQHS6X71LGKHy70K5rRYTpznqciFK/R2LQRxMSSenBCUROAh2mq1Y/fDSUVxZe4yYpROKgrKpi3kwVMtZhZuEhjblHbwMmcPiqiptP0uvgYm5sLqSP3k6CpDYBXL6OiwwNQjczOfEqBt5WSDIIcOubNMm4Kl/6yqbTk5d2i/CfRxcBvQSAeUtZurrM1W/iu3mIucRygVqVG7+OpoHOaMBmADQhxHsebhvNrWAVeWhfWE1YDNdwZg0lkCM6iGlzcU83QxVREkqwufxAY4JQYCe1r42/biXgsM+BRL8LyYlbbis10DsOGCE6udgW3xNPnOxkjbzfSKCVAV6+plFzvpvArBp5oDu8osM5j2BBz74+GxpTmvBaWKW7/V9EmrwlNX/Shmv85Jmj0nuelcHFQiZf2eddZZfB0iOY2KVUTNU2LwOMVciWSo4hNPSBUulJomSGF05E2afqX4yHRwVkHhMVvbhcZkVNKpjrvs5TNTifCz4+nYWriikSiULTEeDaQTTfEc0ygwHVHKxqDZJtWzVhTABh6eBgBDwKbfCxOJsJWJobLwYLvMIRNBekOgwElC76PnnMBe4ZCe1ekQ4mCrLxRxOayyOBVhCDZCDBxNcTRnYOrDCYUJ5ug1If4r4vJIgipSi3QoRda4sOLT8GaZ5B1Hy9Eial22pRdTA8tOqHzA5NQRP4Bjmj2sWE+/cOMFQ+F6SM2K2UlEvcsAQUwQn+osfL3+lTMAXGH4ZuM4qVCiCsssv77cgTg1Sjjg5deGRXFtPbL9lPcfgcPTdjUNFA2AR1DBMZXwbjIQZseQuYnRYQqtzs1k3kcEaYtpxQvkCquJJhhB/FlGeM4SC8id+hcTyvnFwtDb4owdL1ZOw3BRS0uA6IgH979zeUct+aox6Mov1tV7Ts94xjNe8IIXOK6XTL6ChH2GgkY0tmIBPv5risvxGoN0mYzZcwXtTvhlkz0CZqOjZiWzXABsqSama2FmWag6Bc0LEqZ8e/f2WxEX/ino1qeaeNviwV7cRpwJRe1MvXplOr1qjeec+sAxkgHLL1c2QZNCdtRRR6mljOhU7UyRTGydeeMDxW8mauRpvxPSAJUydBRelNwpDi0uJ1Xiv3pK1UCVrwoUMOhoQIVMfPSjK3xkokEvJ4CUn/f94yGUjoM6yPfZovAsnBEQz/kxxt96z/6GYKrqMIQ0Cf6rAK2TKih5Jql3YHN4LErZN0BTMObgiteFYTU1Rh5YdarBh/Ot3LDKe4RFpFTRfqdEAxMbt0GDYLsPEom6O/NhO0CIyxkXrqTzHwLsAO1dRV9+lRBe8v02611bqA4CspIOIQCZkwZW5A4DsNEOJKDxyFzuiLellSivaBmAQlhpJOOIp40AsFA1egZIlF78vyYDF7dlAJTiMl3ltiXmgwZihlgT9sWLHUIywq4aLt/FBsGJTwTAkrUQLOWTLrFByCRgptxGYwrKrAEmLUc+gvgcgI25OZLPql4DgSF2ulFoygDhsKqRUTZnO9htmW4spLpU0X7nmwb6oJIDV65aFWBWI23TTTdlJy3AxHhsIwCVqdQJWglOIbtqW0zxoFGoDKWpmj9hW9Ourlip44lu0fMnxJ+Ei/BPjaZbPgdwst7IwFi+UvKFl7xtJlQRSkUiJ8xL26CX70tz3EdupXiwv23rM0y45ZOZ9YCSxpCEXgxX1rQiWiZUZgpwvIfj67SlT2KJYJHByjB+Q0ddGXTmGgnfVMqOMLGVdbCN35Ox6TdVe1/H1wyMPvu83FlOjIKhSYva71gaoEDdVPdg+qKfAxJRqZAn2Jj9fVoraycrJWDTQVjFq4uF9FUiULSq1/s2rMJTWXy8dQdy0Juwl29v24+Fmf3228+Wr1J22PI1N28Bdjo0YoCBKCykYWXRAhjWRRhCvi9+CPA7rOLAA2wETjRAPFdUESYq9TTIYcwtyXwOFmI9xT9tkUbDLc6b7oQUJbGu87TW0lgabvkT1gDN5mIdJMzl0CCR2/88q/6Rn8uh5pwaqavkZebDbGh0pF9TdVa9yEqIXnQWYnCF3dBkFi+s2CMnaN2y0cjYYr8+eAEo4WOEAIQljgOycuA7MvJr3dobKswlRHATGIM5t2oMcfudjRpI95luzVXDezOUbA07CBV2DwKk5MOe4nnHy1Om020y8+vMnHyr8+TDswTLK1NUVTo5WWHbw5Ij0OuXvTZHInM5IE6A1IhVbCKXOrO+hZnNqcJKol1zSQPwZj4O6ga2K0iz4QgqC0a/1xao1MTJMU/brUKWv00fngxavhIvv1hXkQVxJqVigYVj88Y9GpdYA+ZmbrbarcVVNgq4j/62rZwYcJuqyOzvm4xlijwRVaWxt3I4qQhsu0lbJUqrzpVSnE755RLsFKwKjd/OlVJGYgKipVQS3AvfElFklP0I//KnDcInxU0oHAVF+BA5QeHviol3yJFAGTVG4UaifGT5ZEE4hNt8+41a2FIWtWipr4ToLUFKaxIEyakpkyOkRbf8UbdhbrfWcRGZBWzCWLxPOdb5OARs4lNlW8DyTL5+wcHb4ShzWYmlRjbWyelkFuR7UQ+rlKoFCyu/QCJSFtt7I8uRfwUvrJEQqC5gMNyCYUG3FA/bfJxekZxR0S4LrXyKwQKypuRH2ehAmW+N9dWFuF3j10CwRKt8VKVyK/HfQ/cjN8sso4+9mpcZ2u1I9w66QmyFba3sUDwf0U6BUlZX7GyeKlcSyASfMGc3IY8LW54WGtZQFBYErVp4tLwBBkg0Hk806MnDgUDmvUKegX00hlWOVkE8FKLRPCOBxcy6J01gea38QJBDHHuNsl2zVAMBjH63vq/BNrA5IYYr5146qMgjE7zpHOpgsnDLI7hSC/MEZnLwByejA5bA2PH8kJlf7WGZaIWsCpmVm0pFoYwLJxkst0Js/8EnA/3yYu2NYsLM5dFA+VvmrNaAect306ydSjin05x0vWmShwc/UNQhKLdY2Z2HK3YPqOQX2IgnifdzZ9lAmwmu+mk4QKNdVHYf5OJPeP3At1f9qtc2q6iBwCo+LKeRgrmCAC8exrlRo4Ixpw59KWXLnqFWKTzzPOxvRB5VQDs7zCV1YFFIDGeDC4c8ijyd3zzCgZwO2xAVgdNoqohfWwjIQAkkzxgMn1QtyCeWoTi326xh3FGaYHBRLDJzgb7gxxPJzjI+OKRdHZHmyW1Upx/N5l7Pz22/7ckHZngoCOnoLTTCoroexgKYZLKQVM3J0xfymT4GWV8XVukFMoiMch9rRHlk2YaDvhMlhQpTOTeUh6Ai5hce7AxbqEBLX/KSU+DH/Crl13YuOYVmoUU8rm475LDzauQzsNWYlKfAY7SqVJzC3xvDQRiCAr15CXjIiEczOIjaQrIVlwFVai/ytMSENBD98zYtLaCodMckXVh1j9UlnXw1Ie5gXU6RoJN2O9bV4YysnzNW2ZY/ZzQQ5IzThV0qra6xXQswEK4DM+tSLT17NWAaG+7CLmbTBiJNJrZ9k1vX1UddPwd9P3NgjTXngaXGyqwL9usqT4c8KjR9wfo5hdgmr3ie+JmDPfGHyqP5loiWFunCLo5a+t3Xz8F/rP4amD+Qg0ywH4J8rFwDN/THCQPFO/wHSqI5AnCOsni3zCnwsWgWR6vzrWw033dh/3PUYxLqSGApC45SXKar3Eqkv5HVmWhqHPRpwiTMS9hfJihoCVYBYnJkogkT+UrJd9U1yvRorFhITdnSc08D6f0+KrQ0gJEoECrNhytXp1Toga3AMmR1cU+RyfdbU2IV4mBVRR3mpeqWmFca6INqYPMD4w7SUMqv6Qsy68yUlZPigaJ6QwOQBZw1JktmgbFEPZoGSl6XCv+aZy1VnUaDWxk1HrlNvR7VlAMrRZnihVJLCyUOCAT5TIH2A/mvosg5pdZhXoq3RF8DMV80NkRpRecFh4jTrWGoI5Ijc4TRjZ1behAH6VJcOvUqHvRKKNXp7rDty1znoHGFm0R5hFuprmQG5x2fQal6uI1Iv+yyoYxsNvSsWsWYHbHFKl93Jmph2xJTq4HJu7Dk0J2dDh5LuD4++pR9mj5zcOkUHIXQfzPHEqnPvMOn3c5hDQzp/bEAQxsdaEU/ffqxyAZWOpB4Dmu+NW08GuiDamCpgYhCOVZ+zaRPA4o1gfRAcPYza1ZjSd4v1alr4C1uNfOBNDLHqrRfvFByMnD2kXlHeHHwrpt3LRxnjBc1VkUtv6OB8XRr0XldVsH6Vnogq34PptSEiDsV9W8HcuuTjQXFfn5y4qc6R7FgwQKOLIb+RpfzKvL7RfrVtZzJaWCwC0vprslxbKWaBpaoBgoyR0HagLpEld2YL5YGgtUAdbEYtcKLpwG+kV7gW/gzUU49+uPSjtPxX5O/eLxnd+kCUc1oQF2cvowmvdvgY0pOEHk1yCH4hrHFUWldNuqtc5Ie4MJaoxjYrj51y2kaWOoaCDKhNJ/LaUBd6j3SBBhLA4FovqMyFk3LnwYNxEp4q8ZVqmNDSnreJqIZEG3z/mJiIJr0eQQHrAurhrGiisVMRL19fXZdWBFvn17zJuli1teKNw0sUQ14IdTror4Fs0RracybBhZHAyIHthSdv4z9XRxWrezia0B3OFGgL1z9uXDx+c9eDj7859ssbd6fkh7M0VgAaxibEn3WTHwuMN9jKZldF5bS2VxfDhIAL0Qt0TQwozRgBrJT4xs6LO9Y+wszSuAmzLzVgPksf1lw3mqgNXyGa4A5hVIzPqA2czrDO2s+ixfvtLMw6LqwPAPf2/PHMH10DbLns75a22emBhhZ214+2uqPDPmSq1hsA+rM7Kl5LlUWWoDqw5PeTG3OwTzHw8xsfsypj2p5DwlQmzmdmd3UpAJUH5m2UcBHrbXRdWE9Q2pN5mqeQa2plp4hGoBP6zC/wgYNqDOkU5oYfQ2wn/ApP7YUYvs0LadpYOlqIOaULZVo5nTp9kWrfYgGgs++FW3n2YcorT1qGmgaaBpoGmgaaBpoGmgamIkamG4XlhPd96PHo5jJlRoP50bTNNA00DQwlzTQrOVc6s3WlqaBpoGxNDDgIMFYpHU+E5n3vWzpjnXeoE8jJ2fCsrNWM1xk2vHHdkxnkVpqBAM1ECj6DVwDXbjtHAwfWLZlNg0saQ1A5kCTODmI4sZadk6MLekmNP7zQQMsp6s/4/fdAHimkCXxSf8MFjW65oPOWxuHa2AyLmxM5C1veUusvRI+8MWvmsbXOnifiG9+85v//ve/Z159O21gqb6s+BgGfh02z7s7fZqW0zQwRANsroXT7W53OzQMKyje4ha3kJb/l7/8RaJvkYdwa4+aBqZWA4yb1xSCyQ7nfHYDPicEUdx8bM5bubgBeYdnu20amJwGANXE3f+TmbhxA1zFYeVc+stn8v/85z9Prq6xSpGB/8CYx3pPaFyMxbPlz2oNTNiFhSHW1nth73znO7V8t912e8hDHnLttdfWSyLw4uD+/Oc/P/DAA1nhV7/61T5xAG3nn3++r/7e/e53P+KII+50pzuNM6pqYLzmNa855phj9t577+c973lGRV3XrNZ+E35JawAUmV2YOfroo7/5zW/CpAWVD8uts846j3/84zfZZBP4tJpqiFrSHdH4D9QAfN7qVrf6/ve//773vY+hcxsyoF111VUf9ahHASrnQP54ZmtkXIdDDz30He94B0v71re+VdkhBRnzIU8HCtwy56cGAlR/d+pb3/qWUFSCrMymP0a19tprP/rRj77Pfe6TiAAX0/cNdt5553vc4x577rlnUD0lMCMDv8JXlt/+9revu+66u+666zgDYfOzy+ZJqyfjwsLoVVdd9elPf5qOfv3rX3Mu+wAF3H333ffzn/88GsZ0zTXXhD+fo7/44ot9fC6GdfwG9Be/+MXll1+uUmNGqXnSN62Zi6kBkLvNbW7z3e9+95WvfOUPfvAD3FZYYQXOASN44oknvutd73r605/u12rKt5CbF7uY2m7FJ6EB1ow5vfTSSz/3uc/1i4t4cQX2228/BnY81hINGP/qV7+64oorfvnLX/YZlhyULlZajkTJb4mmgYEaABJAPfXUU4888sg+gbDUAQccsN122wkW8DJN9J/85Cfve9/77rHHHsFYv8gkciIDeJOBl/yKV7xCTt/3mATnVmT2amAyp0nghm0FTebyhBNO+OxnPyuwX45eWZ+JBHzta1/jv4IXMsQSIrUbbLDBmWeeecopp/j71Al9YYXeb0lQJc/DVXQq/aEPfejkk09mza3zsEqRQpAEsjo/DAsfj1x1kZrAo1AmsyZr6VmqAR0qvvW9733vyU9+Mv91m222Oe200zivP/zhD8VigVOUiyncYYcd+K9Qquu1NCiSKJBI8z2F8BokNZxSKhxqdRVudWZLNw10NCCsBYHrrbeej8j+aPQ6++yzxVDtd733ve/lDViJBXspWMAZe1UeYeLvfdj1Yi0VLDZZqQBYQZftL+6I5ZyC9VaYR2HVYduRtt3OWw3AIYzxHS2Qzj33XFDl1G6//fYiWTvttJNb/isIcQxAGsA6igoIa4yBXIfGLQL5iDtptzJBlwwkqQuiL0Xq/Jae8xqYcBS2aARirL34lKKtj3vc48rBAA6rRZhNf+Eul9VSinA9QZOZdmut5hYcAd3JLXu7Mjkc7KlLKZRssZGAjCFeccUV73Wve4ndcnwzijjE8j0Nc6wMHuDmjqCRCeU5MZO9BhWhSb0pQk5FVIetnWU1SuDgIn/hHOL2O+s0kOlc/NUfTH7xi1982GGHaYIuBgMQEn996EMf+oQnPOHb3/72pz71qZe97GU2BxSBPb9QbRkGA+jhHDagApykAUY+OMkEJxDFFrEExMosilJRuIF3jdVC0BJNA9EAqIAWS2hZJe2S7wgBK2fP1NbtS17yklgkjyTYK4lsHSjFxMFYoIgP1DkhIwGQKQWWEFsft73sssve+MY3fvWrX+UoCw14RYE97LAF9c4JsdZf81wDUAdXDg+IsLKNIAd+9vRtk9rsEiNwqjCzOTJXra6OFU1ZNIxtoBtiZDwB9ha8g1tP0UiHIDKUW5nSjDMyImW6r+tt6bmtgclEYWkkmFtrrbWcR7ELdtBBB2X5BZH8g4985CPnnHOOR/7aR4jBjmfAmXjBC15gcwGZHEW+/vWvP/e5zxUkg1cmdbPNNmN85XznO9+BY9B0sbwf+9jHnv3sZ4vgQrbjNc9//vNVavBggj8axM4zKHj88cejZ46h+ZBDDhGBe/jDH77RRht55DRkjDh6ZR1pEIE7/PDDWXxR3ic+8Ykbb7zxU57yFMd9Cue53fdzuHUApluB4ayzzrr//e//lre8hWnjpHIlPTI3X3PNNQ94wANe+tKXmtpPOumkGD7ociTReWt/YBkCn/GMZ0gDEnQpC+RbbbWVNdgjH/lIMOb7ArCBYMHDAxaEiB9QMAlsBoIQBXhndMxhhbemLb4GINOiyMX1hDe3q622GrZAC1ScUb8sG8h94hOfYKnWX399h2Ut0ryZwOqO2MrRw4ICY6zlZz7zmaBOprFw5ZVXensBGp/1rGcdfPDBEl/+8pet4lZaaSXDAVv22VoubBlhkTZBtbBd/KY1DnNJA6wlzASoQlRM6P3udz/4hLSxmgm67KE/cO/1GIEDVtRs+6IXvYjtZV1jM5WVEA7gPLz85S9fsGCBiXvbbbc1RxsLY03KKlXkox/9KHPtlw0v3MYSpuXPJQ1M0oWNCqzRvc7FFfjgBz94+umnwyKcXXTRRSIHK6+8MheWLS7KYiKBnjPKtkIknEGb9xicqYU8YBUPsB/hFBcCvoKtNAyDXWcPHFf42c9+xiiLH/AyxQ8MCfDFx+DhAasUK0WwNQE87WlP22WXXSwNHTO3RGOdubM218ITjZO1MhV56lOfKsjhVTOljj32WP4xedhuVRfhW2J2aSDzvVMuEk960pO8WCA2oNPdakiwx/V0StuWqx0D+JGPAK7s27KznIMvfOELl1xyiUzTvxWOdwqBhEMM5GC8xRZbACrzLXblfIIh4HxtnAb8+QTghDPcCloMMe6zS7FN2iWnAZgBFZcdWF9fYTAtyyGHQwmEzBHnletg+WRlxU/1zQHOBH900003FQtgsji7KHm0UGcNJi2H/wqZ1l277767RxdccAGvV3ErfMswZS3SIJY/YZ1vOGCrlve85z0eGQ7Ni11yPT5LOZt5QQtQTayAykgCGAhZUzGksbF10zxiM03cnNfXvva1QMg9gHa+KSvqNBeIgjeoS5jcAc9M7asaWEEgZHJPmWtFsKo5K2WwiEDZZ3NmjFvcr72mb+m5p4HFcmHh1VkCIS7+wZve9CaGD7jf9ra3WWzJvNvd7sbC1ipjgnmQpvySiR4uofbBD36wE4pcBFsSbHScXY9QQi1kSxsGboFVWkiVf8zKwz2e541eEMwVBmI+qOWdzWL+NEoDzC2sk034ltuR+cA45OOqkYt84YUXIvYyLyFtOovDqaUzYIrYLTGTNaDXdCJ4WPNICzX5LaZNtwKMC/bM1swu7OUpLLHLCMzupnlo/MpXvgI2XvkSGBCyBZIvfelL9stEsFThPRvfegNOiyUcuLyJ5gaTTCorLNz1wAc+kJeAfiYrrcm2FDUQO8Ns8lnZK7/WUQKiYGaVxR7aSAUw3uQHPvABUz4XlrX8xje+YWOK88pPtQngNReWDSu/MAyW0mymva8dd9wRB+6pDQEXYwjn+++/P/SyhNiye94ng1U2ENszzjjjuOOOoxCYF1PAJBIuRRW1qmeOBqyCHG6BUv6lbSuxIehiJHP0pW/ogkm7WGZp4VXFv/jFL5p5jzrqKKaSFbXaV8r6HzjZUtM6Ap4uHJqdubn201QE0uiLHkD3jne8oyAUlK6++upQ7a1xlraY+kLZEnNYA4s1rYKatZEo5pZbbiniJYQJgvzRzTffXBDUIwQd3bG2kFdnynnOc54Dzba0hHX5Fvb3odA2Fi8Esg0AwEWWUjwSSAVuHkaCXuw1s4tApYpzrL2yIxOy+R+GBz6CarxbW72GRFxhVfC5Qd+3loQoOB8K2rZw6Nb0YD5ohrvuptmVBhsdavImNj+1TMDywZJryxnVyxJ8U2gphg8lIO2zzz4s8iqrrAI/lmdeXOAWCNVb81hcyRHZdYrGOw0CtPBs7STYbwiwzjCZ6th3tbPvkFYEmF1qbNJOjwYCD7H8rbfeGmBcbCD/1e6Bt7zN0wgYNHh2NMW7sGJUfFC4YhjZXsst9oodY/3kIIbhJKzT7HSJk3GFRXMZQPaQMXzMYx6TV8dswjpU8+EPf1hd73//+9UVuHq94VWvepVtMd5DzOz0qKLVMpM1AFTE4zU+9rGPNe8Dqm1Y5nHDDTeEQ9M98PTll8/HRQZIIlBxG5hcaHfUkB3OMgnCnZCR7xCL9Rszq5TpeK+99oJ2hloOAx7+rC5sixoYKQ7mxn8QeOq7HH15Ws5c0sDkX+eihRg7qHrzm99sDeTXzoJp3v4pOI5n2g7ifVgON3iFP54Hq6osv5MhroeENODyKmwrALQ4gU8cKMIoW7RZojG78US9YcYL4YLE9DuvE2FwyE6HoRjODvFwcVh2fFTn9y53uYstj+LTzKXOnldt0eOl09Nw4AEt9lfIqqiCHWRYfUDjYQ97GPrYaOengQECgVmm0Jf1jM2vYMa8DmZBuARKexFcBB/jhEnrK5mOqXBhrcp4t+2dmKLtlhiogeAqh68CWlhlhXixpnlrJ8dYLaI4nVZNwGnRxY4xvMjEUFktaU+VDavUklt2VWb8YPQeyecTSCRGgKcXA+CfkwG6Ycuchq1QwkCZW+Y81EDQ5VSVIwExa3AINuZfIViHqay4YkJr5QTStvsZWwexwoSFNMmyouhjTgUXRKY8tb4SXVLKU5l2sWx8scAy2epwZsntnTr6YrPXvgRXmBuAvq63peeDBha3y5k5dtBurJCnwBWV2QgQ74Q8pnCcGjQM4DX0EJxrYFmPEBs/vGRbupaAPFreswCGdZtTuYRB4y8p2GITnHCEQIzBCDFODDm1xIgX5kZFPFc5Q+ot9C0xKzSgu035pm3Sil3pWQm/utuixQmTtAIYBJnYvtyW3yz3A0i/ThrYhHVA1glCq53AqTi4SmErIGGnzN/sEGawkOPLOkXg+KyzYvgbJoV5SzQNdDQQfIqD2matH/k8lp0lnoGtJ4cBhFo9ZdnEU2uypMGMfQur5EjDqo0mtwsXLmQ5LdelGWcnZQ0QTrPbfDTGBprobArWv/3RUT9t6XmlgaBL5EgUgI0tU7zTLDYwvf3CkeVNetRRC0pre9OxV1YcIWBF2UzmlxXlm4aVCdrJFrD0dSM5qcsv48lowzYwYyvh14LNjC/Hrq+XwxzCabumHZ3Pk9vFdWGpCTqBz66TaR6kTNsJYQZq49EjmLrGQ6kuzL2oa6EmyiUW6yMGDiwqyy+BdaPC+sw4ef3rX+82307yTS5rNcdzuSBl1KW68Vc9HvEazUzQgD5lAUWn4MRbhkJWXtCGRnjgm3pXwC5Y5BSgQuOR89mQgyD5BSQeCcSCNPdUXIqZZojtbVk4yWRGUboYYucKcHZEzGuI/laN9RUxfIzWAmmc2J4JqmsyLEUNgIqd0GI23YriOwjrJIBdAi5sgkxOPdnv4o8GV+ghEHQdMABv1q80Qb4cLgV6a35bvXZdsfUajdjqG97wBi6s28DeXrCdKwa8w5ZjnXhbYdsS81wDQMValpU5BPpLh7ZAbRqIjLKQBcNRlFsYszxzAAY+WVFBAXCV5jY4fAWoKJG5+tZSjvCtR3FhwxMsbd4KGXgnW8iM/0qk8JnnvTPfmj8FLiyEWVHZmXIYC86E+kENZPtLsSlRLrZMuX1bLqwawdfI4aQKIfAk+BmOOfJWRb8sDe2OqZSZVsR5L+n+CJkSqRqTGaUBOGTOFixY4NMBXjvwLjaP1qwPluwvzxUkxGgtaexniRzADwx39qEwcbjQW1n8V5O989aOpmhmnF3LJARuIQomGVmOMhcW6sTMgj2cVdcM64zCxowVBpBcRTzp4EqOdbtfNk0m6Fq3A2GI/XpqrynRrFI8iTBhnC26xFkBHmid2wZpPjFwMtdhiyG2agxbxY0XbC3h6swO/3Y7DzUAIeXSfMhxMYAMndVOrRBg84gVFfXnv4oXMIzOEqAJqBz5Q4ObW2R2CZyL5RwXm+kRlDrq6ql3bFKv4na6rMTsdDkYBsNewEUWVrUALT3nNTDevf7higAshtUWABxLuB1OvzhPgRtYeSfgbrvWO2ReqbGn4Dwim8vgOkMmIajAf7Ux4SIS79aRr8Wpt5WdRRqIMRV2Ehy1pBFeYuZYPU4qLxZKnXi20PLlIFbPewkm+D5uPULv/T94Zj35ryymSV2+vVe7ugXnwSQamBQwczZLOM0HOJldFnwW6a2JuhQ1AE5QWi5+J1xZFMFbZn3bqUBo64kzitgJGWiU8Aktj6yprNO4AqUJ0nJwEADzxRjnBxThH4C06BdzjbNRoKzDBk40+ohyYatqf8HOIy+MY9JgXLTaEqwieBSg2vdnDJ1vgTdvVtEPFCEQFDAds7d+HbaWY/sLkr1LECsqfCCNAA4BjH3O12Ns52bhBJyw52VEexG+ualelC5VZPUlBOsPKwhO+ag8yvK09dH80cAkXVgYjTdQNCUHCl0SJRM6kXVyZBYCsM54KDkSfeYhCx+/kG0fwSkxR2At48DdSURVI/NrRKGxU2bY8FRchgpiY0xdxcT3a4kMfZlr2Vp6VmhA53JJOQGiTUyb97GESG3x+54Aw8q/9H6AZY+DsNY5vjtohQM8mlYjzS20iFGxjGZ3bqvjKJxUO19OCHiRlhEPnFRn1WRrzIuGvnJg01bVvgUjM2xnhdKakEtLA7FFEGu2duX9KkcGIZN7ymrBFSzBnhwxVxM2NLJswClq5TMF7J4Dspndwy3Ak8NVtWNrsvcpLq8HcEkdIXC+1hdd7FkpCKsOcOOPrUVa2HJKwtbuVrGZS0s/rd4ZooGYR2shYaMAVYDAZ+C8xWW2FS+AFvi0wQWrYknxa2EMqKDI21oWUYJNnjqFxYqCunV+cIvMQRezuZcXBafA0lQO5wcccIDmM9csOZrIYM8BYrE1QAR3fSHOqzgiETNEUU2MadPAZA4SMJH8AAaXK1ALKr++lbbiR4Y4j4DY4slR7kLmFoHfkoMSTBVRNplyeBjIQLbkoHGkxgdcGWJ/1M5SDBPep/HjLI4AmM/OO3Xu3JjqTAaGlm0yn/JWMMIMbAL+kVl1IUuN7XfWaYClc2DAsVQxUTO3z166TPmm7YSjtEgsH4QchAUe3e3i4xbE4mBfTDhf8MCRFRjzMrg53tECf3qD7ysYxvgqhZVfBZ29dnTBkgk4BcyKZzzrtNcEnk4NsFrAY773B2Y79ZrsfenCdy1gicnyGo1wKeNm2n7EIx4BfhZjcC7feUToNfGzk7gFex7ZHPDUy4gOhUNp3AW1SPt6kQ0KNDxjT32MFlt1obHzixvX1uvnCOq4Q0fCdjt/NBDzyFl0dVrNHloXgR+05AstaGDPST8bU9ZXjK3XuZAxpCwktPvahmgUPMNbzDVi/itzbQPNtM4ltefAXJu7/Rlk1pi/m4lbpqWdX2FdXqyvHHo5gYPhlTI8Y5M7ErbbOamBCbuwwAFDXAFLqL7BrXWEkvmzaSV2xaSCqVWUaKhbePVUpmPd+HjhQFpOMm0oiKo6sJUc1fniAbDa1SpkfA4HDW2QWeohhuyYbLW4HLuxNWZ1aAAYTuYAfwLUN+dEI5x6jDBpgoUj3Bex1RiZuTWpqzxqiVmnATBjc71nwHQCAzdUqIAd9JaM1wF1NMNnNQ9LKMFGj7OzUAozUAcMsIEAciBKYEAc17tcvgNvY4upvfe97y3eUHACgeWjxXBu1sdz1imtCTydGojFAyRmUL0W2KkdIDmvTJzlk3PY3AI50AhUPk5kYWY9ZjdAcR6nHQaHYbI4h0YfZsGNo2AuZ/FM8HwLIdhyeAC2fWT+da973bvf/W5OhgFiUFjLbbrppoUtZ0IgwEqsNo/TqZlW14zSQICaiZhgBajMo90n0SJANXEDEs/SkszBFbj1qWwWkhk0QTu06uCWjS8rf5O73X8xV39syLTO8cUQwplQXzQyKfvOgI0vk7shYH738SxbXthCI/8BvFlaUI8Xi8bqy98+8OKjP12EefNiZxR4lqgw/12UwxmY2j9yeNRafAgIUIr2gxTJuAhD5GNAPeUixCGAUTGwUgofTLDyNGEwj2Qy0x0ytzJFF1yELDWGPygbOSW/cEAM9NlcIKe6IL4IIy0HsemhMJQITwyLa1I/bemlrgFdBjP+dos53kZSoDVEKsaRnWVD0TCR6Mutro/pTHGc9T4gFZDIlwkqiieTmZZpQcXThecaJ3JIxaWw/hGpddsgFMXOz19AAgAHSyynQQiQBupBPkA6nz3wKSMGeKxfniLGNjYqtjdpgETgEQInB1wxquq1EwWWvAHjBeDRoGRRHcFy4sXEL75lqwplYYsGepGpIjWm9vY79zSgf5lTk77zqT5GOcScosxEPFAJTCu0M4meopQIhNxCHdczVjQGc4gVRakgwIcyBjkcAu/4HqoAzoBZEVO5QZS6PBooYcuc1RrQrbrYut2kLxDpNr0/4SgsLSgJ6LxDiWJbB2rHprya0BSosZWllASYwh+4u8JBJq+iQxYEFz6lrvCvi0c8HDySr0i+UyNhjKmrMBmrCR2ZS10tMUs1oMfZVt1KfmmWNPBw20dOQUsQi0YixZVFXwg4DQXbEng6Y8AnAF2fKxbjV6MiOLSraWCIBgBMvICf2qfxKEasPAosA2ZIll+AHRoEvF52NWUV5y6w1V6REaktXsXll19ue5c9tA9WPI+BbFNjEaAl5q0GICETcV8DBWx55Ba0wCl+BjMop7aiBbS1FU1ZiFVLTKt0CCRwQOC3P3F7aviY3z1tJrffO3M7ZzIubJAUAzpcO308dUoBn6vDBBDHQ6ZUn39YFSjHP05mp65+LSEbi2eett/ZqIGCB5bRpQlj9fLA/BRP2QLXUMqUYJGdSXBqJT6Bk4V8iEI5GzXWZJ5ODYxli8aSIdgDNgR9xBZuCBhSX5RzAMbJlgULFtjb9dQrMv7SgSCW3V5HvQtWh7MdS5iWP3800JlDhzQ8NrMmSE6saAFtSdSUsZwde1sICrxLjsT4BatLtfQc0MBgFzbomQPN0wRtmRsNaa2IBmpw1ukJ6WfSqOgUdMtRYIi9ROgrGX4dOrTTISrAqnaIJyRhI54DGggA/OZaEi3CeSy2JnuhKce+OawOCwrEOlGA3ove3jv0lYNNN91UgFbxPpN+zli1tPxZrQEdXfq6Ti/RRpUaF1nL+CkXyaoRzGoNjAXOAS5sVjkmZolZ3eYm/JzUACiLLQEnH1HC7VIHqr1aHyKItu1z2cl1GLEZ3zkJv/E3qsASSgPU8ZedQkpo9L6LN8HDsx4vdmwHhsGmsPbGaoZrIOaULQXXoLTgdoZL3sSbVxoIUPvg7LqwKJg8H29zcmVeKag1dhZpwLzLU8wE7BDq0pXckDG6XMSw8MuWVm6XrmCt9qWrAcAATpBwPBpilxYkiEEAtUu46ETaJQGrS1dFrfaZoAHgZEtZVPP+UjenM0EhTYaZqQFLLN5pjFiRsPtFAsekfCCwLc2LglpiBmrABByfYObsFcQn6IyuGai6JtI0a4CbCKXTXOnA6gLRPGpAHaii+ZkJGInC8l8bMOYnBmZLq0HUB4O9LQ20wWo3Civ46kifr7IXitnStibnPNFAkOlbRY6cOtXXgDpP+n02NpPB9WcCvfXfggKzsfvmg8yxn+Kvvqvl62/NnM6HTp+NbQwy/VHMzgGBrguLTrR2rI8UzsaWN5nnpAb4BEO+pjknm9waNes0kF0CH61sW/azru/mlcBsKYva5v151emzsbH9lwoGnIXixWpbfmdjI5vMc1sDBZ8lMbfb21o3ezWQIwQz5CDB7FVjk3zJaaBY0ZJYcnU1zk0Dk9bAWPgc4MJOuo5WsGmgaaBpoGmgaaBpoGmgaaBpYBo00D1IMJ4qHfCKRzyQ2BnbaTj4RQBi2J5rO3QDe6Fl0gCQCIDVWM0BcJiZ8rcWAsjJgb/Iqbi03yUhYYPEzNTAWOY0YBgxcNULYdLT0wo4dKlr2moc0i6SZCC3ATJES9P8SI+40iPBSR8qwTZ/ANk0i7eEqtMinKfBw1lC8s89tpNxYWdC/xkSTkXMvf5oLZpCDQDJWFhlfFXUt7mTrn3SgGQTCdmXUz7x5ozpn7Ri53zBftf3mzwQqBy7JQcPnJcc834Dh+QMGSDjUd0Qzu3R5DQQzxUmO7DsG9W510Fzr0WTw8DMKTVhL9B3uU455ZTyx4egliUF5Zg8L4vd/e5332CDDZZcC2O4L7300u9+97trr732Ax/4wCVqypdcQxrnJaeBQMLf1z7ppJNMgWW1I7HCCiv44IavGagdejtWeBIilbpOOOEEbB/5yEeOnwkB2ERj6swzz7zgggu8FHzb2972/ve//7rrruu7Ifg0bI9fmbOLMj3rr2edeuqp//jHP+BQTt+c3vOe91xvvfXQHH744dttt93jHve4Aokl5GJmUFx44YX7778/MO+www4Uu4TqGt5lWorAAPHV0jPOOMPLyD5we/vb3/4BD3jAwx/+cAPZ06KN4aza06nSQLGZ55577vnnn3/llVcyqve5z3387Yz73e9+aikE0qeddtpvfvObzTffXGfNgZ4ylXz729/2AV0t8sdrpkqljc9iaQCwcsV6XnHFFT/+8Y/l5PbGhyP/JudXv/rV8PrWWmutlOpzqLlNOs1LVvZDH/oQMV71qldJJ2fSDFvB2aWB4IoBZRxJPhBmbI1HaAZilZvor2t+73vfQxPKxdFAXRfXM6wGSlXXgiA0n/rUpyzDOnKap/fcc09/xV6RkNVlW3q2aMBsx/fy2xc4sPFtuE7Xd2432WQTZbfffnv5T3ziE6VTECo4vksCGzGn/Fc1Wk1ZX6l0SVTU10mdU2p873vfy0PqqGXFFVckYRRbKOviLT1ODUR7bClrqchwZQYbp59+Oh+u0yNWGs9+9rMvueQSTEA0fBLMOuecc5Lpd5ZeaY4Fp8jC8ssvf9VVV2nIcF3N0pbOWLGjbd4pH7VW/gSisFmIW0599KMfNbm6FTn43Oc+xxXYdtttN954Y3xB3FIsFSBwqdgF30l7lJBtGQCFuBMPS75fBRF3SvkIiMVfZyWkoj7/UlFLzDcNBCG3uc1t9thjDx820ny7B4Kdxx9//Oc///kvf/nLBx988Etf+lKwqbFXgId+BMGjOK9Vx0C7BelCqbhLzq1vfeuaMgR9DvJdirzhDW9429vepsiLXvSiLbbY4m53uxsraYZ4//vf//a3v/0HP/jBUUcdRX4EmBR4u5X2q0a/LtzyVHpE4krmUkrmKO1/flKEDJ38mqall5wGonZ7Vh/84Ae5ibkVahXZEvh86EMfquMgbdVVVyXD61//+pVXXnmbbbaRjgv76U9/GkKsc3i3KEG99OMQMKQ5OCDW9cEGerfJQSDf73Of+1xSbbjhhixtCAYCKXWVsgrWZBkpKV6wWlc6FvwUcSn+kpe8xHRDhle+8pWPetSj7nznOwvEnnzyyQaIQU1XnoYzGcLZrbLShOlXKjPS+pV21QInJ79hMpaENeU8SVMUfTKbIHfttdfaELAtYLq3kPjJT37ykY985DOf+Qxn4Bvf+IZtLsQ0zB4q4kpvplPky+krLR2BMl3T0XzpDgXDpyaoyyru0UT5hz61SEeMPivWmJMTCftVtJyloAFdlSsgGBKFvZHwf/7deeedCf3xj3/8f3JvvIm1vfFuAv8OLJjMLATzJ+lf97rXYZqcgUUmUGUjnSUaCFDHE4W1YmNr7nvf+3Za5oDBW9/6Vrhl6b72ta95GghJDETReDLNphjaeE1dhIycnapzG4aWf4r46wzf/OY3O2S/+MUvHJLxdJ999vGoiNchq7l1Hg2UuUPTbpe0BoZEYQdW/fSnP12nf/WrXx34tM60+kL57ne/u86UHtjvAzP7oBpI1uE//ts+/kfHxEiUob76ZJ5GtoMOOkgbOUlnnXVWXUT67LPPzrY1F7bQd2jK7cB2DcwsReZPIvpfZBSWulBa/9/hDnfQKRbeHRVxakVhPXrCE54QYgQLFiyQkyhsh76+HQgMBAOxURcMTb8r+wz7aFe2U7Bzm4qSGUnEF8wmtsh+/etfp+rQtN9p0EC6YLGisLCYCxokcOQBCGtJC8rK1NlyXP7Ux3e+85011ljDX/uwjya91VZbWZmdcsopv/vd7x796Eff7na302YFeRjXXHPNqaeeeq973cuRL5ly/GKCEr3lnbi96fwRj3iEJV1KRYz6N8Jwa2zMqUgMI6xqmpaenxoASxhjdwIJAHOQQFjLPuxb3vKWN73pTQApnB9oAd4f//hHiIUlBEJfG220ke3UojpIW7hwIZ6CEI6uHnPMMWz6U57ylA4ykYk0WBA65Kq6zTbbDOcwQSnN4if+ut9++z3mMY/h6xQCzE3P9knFZUWbXv7ylxsvyhoLppAMH17OZZddJlwnKqAgScjsD0Fhvtpqq2288cZ2WqWJ4YSucBoBSoRYPiX88pe/9KekjVDjNDmljS0xbRqged2tunSBnpKGwJjTBKtEWC1puHFrrrmmMydApaOZcv3u97jjjlPKeQM4jBlkfp1BROOWMQQG1hV/nPU7W+08X95YAJtvfetbNn+d4QbCLPZC5i+NC3bawXfqNLLhaRwNtN4O7BYyoAIt3CDTSfTvf//7GLLtYqhql0bA4Dve+qAHPWjLLbdk3sPfo1wZO5yqAw88kAbe8573OJ9jMJbQHY2ts846djDsXXj6nOc8J4/sWlx++eWPf/zjMTz66KOtVA0Q/CnK+Tfymx0okw7pREAXmYs2DEbtMk7dEtIvSbAivCi4P66W/BsFnKf/0owlkyj4U5/6VOF/3eRKn+oR21z66ytf+YqggI7Q40VNsTz2vhhVfaE3N910U0+LtsOEpyuIa4/+jne8o5O1ZnzE6QsEF1100XnnnafjbOUL9LKENgpQeuT66U9/ygAqayPL6QWnGQv/JCAEokDaIW/yhL/M0rMSqoMEfEgiAfxYMY84tGvmakDP5QJHifFHYaEW/Qtf+EJt+8AHPiDN7LokzK8yTb177bVXWn7kkUfK98dt3ZaTMSGGbJnlmFfYClAxsimbX0ji0WLClvmto7DXXXedHHzsNzFMbL22hI/8ds0xDQSo44/CrrTSSswuJaSgBGy4ZJraoevEE0+UmXN1HAL0Mjm1cRz9xRqn8VLKrzUbK+n6whe+kBdK8oYNeZRKFDbANpFzEGU67VqKS+SpSj3iNJhoCVNkQxD0WvHbYj7kkEM4NHm66667KgL5apRweakRvf27vJ12q1vdKrMFf/2zn/1sKjWyUBpQpWrVSTsNLN+UU/Il2jW1Ghh/FDZdnKMCdWcFLYm58huI94lPfGKk7//3im30lKUFTg+5FDmCAsaf/OQnPQrC+QGeOsvoKEJO14QTb4DHiSwG1kCQ/+QnP1lOABOnRPgzORGMB4yMJ1r421yWg7mwRTjn16KRbx3UlXztZb21Pc3HxBXOUI2MD6F2V00gLUeM433ve58NQB58nj7pSU9ShAY4uBJ8HY4yhtzcqIKTmib7I+rZ+lDwaU97GmLDqlSNuTT3V37GUUSSOfeuqG54FDY0FjAMJn/RqlhORyeh4Qm87GUvg7EoyrqFX/jFL35RIIAyy+VYCPp0qwRTnP0HBHAYMlbOOsrT4NZ+lHw9XiBkkaYWvuZOO+1UOCfhqAO7mirSm4ceeiirWJMRKXIiCI1wm2UVGq5tiDWWDzP6fAQSLQq7FPGvN9Xej8KOrIRyhWJKXNhjjz0WcHmTDMfuu+8uUgWLajHByzfZS4NFxoBFsEx2RGbAmhxlnQyzZrLACnyNH+8/Rs7iwgZ8lvUmbzUGlKFJu9rvHNNAOndxXFgKCWxiDQUP5GALXeY2C6EPf/jDFvRmXMv9OATiClEjiymKA2zIGFn+hKiSR/EMIDziKZ4Fm5m4VBcOgX02SW29dZ4qnivEdZFXv/rVRopgm/AAmU0kvA2hWZ4rN8XMbR4SV3N8lnhma3ELxQ1GRtnsnorScBthbLQ3u3nk8tWYitrv1GpgqlxYKxldn1MlTLSPseyyyy661UluQSMGM4s0oSlI0PWcYGDgc3zpS19yCyFe50/TANWqLM4c4yygwBUQbsDNpE7g4BO61MgfVSqYiTsStMsJmZUYMmE5ZLHez3/+8+VYAXJ5Yc+Uw5212JMDty4jwqkbEQfbBSrt+4jhzMvhQLz2ta/FOTmR/8bx8T+IjYTPfOYzOR8mDo6I7QtuPWJ+uVrufe97G8sUAvlWhjhbqbrFk/bcChuHc1gZ+5aFpI0TPIcHSJo23IWNTiCNJllIRobe+jqpc5J2kAAYRL6Bx6IIGI444ogcReAvYpL1kr7G2Rs15npIvvjii/P+4m677YbGEsWvk99YOXZFAPtXViB529VpaWVVJMIK5GLtulKOAaJUMKlSOXqTKyKyYJjEo7C9Fo+CtFzevFZ7wAEHGGIWSJyKQBRysHI1FzZ6WCq/QdQ0ubC2EiBGGLVM/GnzxhtvLL/jwrKVMmMEYxmzy5BQRFEWy4iM9UlOXNjXvOY1bg0GzqspOaOitneleEvMGQ0EyovpwgIJPt4IAapXvOIVUU7iWy9+8Ytzm4pyapZdkymHFbOxqNS+++5bqzRnYTkBMpm/9ddfH43vZriNGS3EwSd/FMEb3/hGPBeJ2BBwOBSx4xl/JQxzGsGJCLcRWCIn1BP9ZejtJgs+xYnJnMGrSO2IF1l7Kmq/k9CArmeUOgAYyCd9N1YUVnBLf8WFTX9x0eQIStXc3vnOd8q0wy6zgAG8ZebMqHwurL11LiyXopQVCjXB8zJ5D8lMeJVT6DYejOUZPh0XduHChTITrE0zsy+nYBEAhzjctoZtKZRKvQDEKRFCk1ODMNVxaHAWPOs8LcXrRIo861nPUsS6tFb4jjvuKDNva5RaEiEmPCYWpUY0nZgd3WaACBwqZZDKCfO6urmUTjcNd2Gjt+jEiZGot+7fohCULo/yNB8uiCdaaGjVmiHHuJExUOZuEYHEuRRH6ZSUdZdTHAnSy3nHO96hR6yLnAkprDgYIGQpXptEKxNLJgxzYtUihCfqNp+gKWW9SINhdjZk1vsJbtPp3AyiWuClVHNho4el8htE9V3Ym+rFKb+gCk8n+ZxJBXcgsDj2u8iKFGRDxRjYU1YGjBShL/mg5vVY+0puCyvrOSeW2H3Y+vrXv250GQDWfIusqBE0DUCROZsecgARrhy55qqaj6OcwMyxVIk//OEPJdMkxyPMKh/ePC2Qkw+KZl+OCxvNG4bh8jQc8ptKCZBagmrWmQfApgO8HGZXcecHLPBKWTxZfMWNqYQ3RGQTtAgrlKIL0twFaUEphwUd+RWB8BG60DgFoYp4CYVzS8wKDbCo5MxLCH4hxAUJOlSMABiyld8BQw1gHCyx7CEoCGYKQhrMOJdSyCatCgyVdUqVAEaKX/wdA5OwFSBIXDLVaFoS0kNfpC31IpMWME4OSnxMYALPGSx++Z3yefaOAiNL1TayacMA8WuMOKfuPI9fBG7DLYM6A8SYdWbA+tC79onDoTFA/GaXJmxTcD7/pkdApaixr41OPwIYGl3mN7jViRxKKuVoprgc63Dh1ZyGCnOHQFg5kVEIj5UOZ+dfRWF1rluSiH8BgC1cxPgrizNWQvtwhUAVggvCrpZPME8eBf2itANmyWej4M1vfrMqnOli/MvHwkilrEwMF39QpKXtd0loYAl6e4ACXrACNK5xSi+Gb6hYftkMhZ5SUCTJhUnwh7O0RRiwQpjpWey2+a/jVHIjiwaYSAkRKb9wxYV1sXcOz/kFRSt4O01wGLwVvYGl4I0VVAyu/GDVpGjaszXpZSzRr2C1lKoTPEu3EaAUh3xBMpkjA2aZZdSuXm/41gWZb3VlepYQ93WJjYm5irOSlmDl64wp6JyZl8Zs4JrdmXU0tuGsBvOeSmlCXUtLz1gNAIYuyxTrN92XW5O0y5xtCytgsKDKgekOgN26lA1uNTZ83E5Jw4EQq/D0Szy38Fxn5ikxBtaYAWJAKeLSamTY2n5RJLcWezId2qk55NWIMkB46i4fqhNzFR9BzCtyPg1PwzMFLRH5MaYSAUJOs3CgjUQHavNacHRbVzE/02ZkDdeJzFTcyr4eiq6otzwFQre6O7+hSb/7FW3l45q7LRu8P8d2OcWhZ/mgvNUOPFQdJgGADlVLXkjFNpwReKVPvoRfJwcQW8bkFo1blxdebd4aHTbNVCSUaxkjIibw73SWBDsMCYWPRLtmoAZGlhpL7gqkhvMPsApNFlhWxp38QpBEnjJwQGZWTkVw2SFrt00DAzUQwDBVnooG+ZVjN81kxtg5gWfOY6a9A56nfSYDsW365PgizvQ8BMNhGwGwCjebD7HgUG1r7HnPex4+OYxbBBg1vyM4z0TOiIscmG6dJeDImvidwc1rahkO5gZhD+fDvIiTjxM52sUVEM9QaWeGKLW0xOzSQPoReLhrXsfmkFne8zmAIa8n9psTyPXzJ5QzFsIx71vjgZn96sLTx0A8MkDwUdDl1svsone5DBAbffJF3WomnQHisK9SlnlCbr5I4G0hg0WQT5EyQARlLRTV5bin2u0pc18MEKMvo6zmP2/TgGRhwCNkJ2nJ1VdFPp3B+6x7Xx/Vt3WpMOE1snI2hXyu25wu9mnxkBB7p6Db5OSXvcXNqiO34VzSYZ4lDUe5rlcaf5n8jTDR4y94wQu00Trf+WkyO1Gds7Cdgu12Rmlgybqwi2wqAxHbVCjNwZDXCd3LsXRmoOUHoHERTN5eqbYu3HrrrZ10kWnCLqxaommgrwFYsuUERcJUMGP9Y3pDBodOpnr9hSFzEsvM570o4Vh+pKfFLPYZJgcBzlb2PsdhD8Ep7cMOO4zF7wMyrARB2VBnZoQBcEhAyCOZRGKUyeM9G/nZfOjUqy7Eho9zWoK+fG6usw94mQMcYMhZiIyscM5RcnEOpdBwbnIcMMJ0mLfb2aUBYCCwXwdFxNe9HAMMAloBQ96475jZSTcwdaV433pPmm2/YF6c4E0m1KouNBkg1pbGCOfDuQIJDmi/eAaIpZrhzJt3vsIpHcPNXoQBYstOkegEZ8T5/oB3IlXhF9sMojZAiqKYNfFOLqb3nKilxLARUGBu7c47zodA5nguFtILWM7us0jex+IfW2B7m9Yhb2cDGM+x9B8cokHA+0wnpkaPxHGFUSFEjrMrfuM5FG5oeBQy+RviZQiscJx8ddaFDPYxTARkyHtmpVT4t98ZpYFpdWHhVePhEuDyy4jkiExRCnvkLLYFsaW2TBsHictyVUUULNdid4JgWwD8D0f1ebHWcCyaKuqhVdi2xHzWALSUiz3iJoKf09VsH08ur2eZ7Ww1OlTgRJSQQFmdM3PjV53p1rvPPEU7U+yyubDvxQIwYSzxH/vYx4okeU2BSIn3gK4rI8Ir26Zeh/Ocbcj8XYuBg1KCtV67FrTwzhlrPlDmjBdRWO618zbGiAnGV5DcYpunNeeWnhUa0HGZWWNLpU3bokc+0epkoSMuLGfCSxMC8MC2A5v8IdY7kgwsO4nMDBAurFAcf8IHxeS4Mjryiy0HnZuCzJQx1gARFLQ14SUKS0rLQgOEWpTlhxXBcCb/ggULjCPrT1MPO2C82BLRcE8L5bxN0A+1c/WcqqcEX1Nh6FjR0iP0z4J5AYC2xbmdZhm/rniu+OsgB5p1kIva8YfeRSqfFUUsEoFSguUkEm4WJPYi9D4xeN6e2oPyKDQwgEYIw9Cw5yYkL99iSRMsdQRiycDBVZZ9rhuilAuxTNzqRy29tDSwuOMzndqXvs6XjokBDmlBI5CyzDUGxLqEkQoxWJj7LX/5ryyXfGTW3KBphcTulOP2KZKJ37ssFkwCD7xYRi2ZfZFazjzUAJwAG+ObhF/2y7rfatsLtuZ732qRSTNgE3eTK+AW6sx54pqFoGgPfYqUnCRkgjSoO37AebW+Z0m9cIBtZ4plBEnlmwY8ZsFaMVTGVA4ZXJgYIz4WYzh4bdZuF4dbFaPVjohaLpwRJ5wgMzLzfa30EKdSCXyEIryvzaaLKzDQhgwZXIVVS8wEDfS7uEjVecRR0H2+mwY22UgNcnRufFaWUz4gsY0FDOHWYTVWZiELTrh0crh3aoz15q942byQjcVnSH6nbKFUozHLF9cobyJyLIB8ZGzceBm8zq2SxEdF/BaoYxgm+TVAPOUPGUEeJXzrcxzcX7fxQpIwypwccFLWABEQia/WGbY15/mWpkadwij5JoBZ24l/m6I39sbIv9bbPv7A73SwlV0dq0eiNzp3JQ1LOOfUqQ5yMdG8AvEpAC5dkCKlVBKW4lZrtssckWIJXSRhtFldFo+/gTOfwVdcLN29LeApGlXoeo445jo6TeuIAX58XB8zTr1ExQqNSIFgh5MSWM03DMzM9i7u61xMg64tOEsj5bgy75YcCS6mj2yLfjmSD15+7Ro4qISY5UUwWu6Gvffem5PBftlIdUSJ8bL7A6MCZiCLbXwCxOqFP4h3fgXinQDjmvhYjCiapwXukaH9zjcNBJZWNWIqMKP5rI/bvP8hxuOzPrbpQ8auOY5isuTegRMLZbZmHMVo7fXXewXS9W20GujKB0j2UXDI2yFO1rp8K8P7sMFqiNG4dSbPVGqLX6XGhU8nMv3Qblw4iYuSAWVhS8F6rMG2fK8jeHHBlOwbRjZM1es1FPi332cRmBk6NfrlwvJmBBuEJfyxLhyIUZ62xEzQQN3FtTz6GsDSoTFrDqKY4K1/LOzBAGJ1K2tpqQ8M+TAc6+qFa1M4/6CAoQC15i+dqj1Nfshkuk2mAwn2u4r1ttkK2P7Kkaex3inY4ZPMWv7khK2yqaJkJpEB4lyvEapp1ni+jGsQMex2TnhLvCiUzvk4S9YZIJGWliScODesfLzWVxqMRHJaW3JxDJD8jYZUF5Vyy7g1TmIIx9oh8agNkOjHb/TJL/SNVcoELedEKd8v/QMDa4mMkcmHAqPS8YABE26lzSi/+oWx9elMziJUW3uED84ArEMLjIMQW16Mp89fiODa9TIELOpARUd7gVWAgAAsv0MCLB5L6ySJgWMWYJzZWAbfOTE8WXuBMztaPggjHiz6gNI3FhPlDbyxIqFoPezxZLgi/A07xqQqQhZ1tcT0aUAH5IqVEcu0XpGT2xsfDvg3kLIKtwxiaFDoY5eENZDMfPAvZDLDUEwr387QQgF8BfmpiE3VaBCHHhANhrgdKBEAFmONhpfgFwRleoUlOSnlHXCLaZYI9FWXGhG0ay5pIN3Kz8tbBQN7OXiwJyiob93MPWV9XNbWrJ4v7DBPAgbUEsoRrIx+oRBokWX4sc4sNbtWkIaGiykOwdfMRwcVDwd1Icsft8Q2KDXli5Y5CeClq1JX6YsUBHUhh7yAlXoJYPK2p4lSjX4zrISjVFH+dNOIxP/+NyXkI0op67CNV1LEqFDWX6VFqTqLQNZWZLcvTJGqJaZQA2Awoe/CWj7pON1HhnR6ftlJ+WZr+XgGOT4C5dwhbOtTu7EecVUZ0jKh2kuFXqdHlM0fUEQToAKYtAswkjCFIzM959aZBLdmdLeqCxk/o7beABbrnaB+MO8bcAraClCQ8JHfH6mRqXidCagyi+VPvfVvmkkkYnBognC/jDxpSYg4NPnlymAYVag3Mht6vI1Slh9MZr4vSuu9yOM3xFwTvlE+FB2etTxzMp2GMyMsatHDWC2NTnxhgK+fP+IaxVKazwbnQ9RhErYiSvQczsoGDMhk7rPPPiiDGYDnfYYVE80e2sHXU74SYEshwqTLuKellETkUdxSqnQxG2ihUp6GBorqT7uAMQeULxsy0gqEMYyc1/AxTbC0GSzl08XIhAYEm6GRYc8XasMfn3YtUQ0EUbxTPqqKciuxjP/TZyM3yyzjgzvZr89tgcVYCQsUuLRmKhM/Sp3KP+CAMq91wfAULvW6jBCCJbKCRMkmkZwQy8nyF47FzPCxLGa2PC1SqVTVKsUh+X7Jj5XLYrG4v+HZfueMBoIBH5/ytzNyjKnM2Z02BloyAycJlIBRsFGQVhdkzQ0SrzlDnfwO0uTE8AFkXW/qUhETHG5hDo2AaiCU/DzNr5GSDSkG1NADeHh2SpV9RFDQHmI0LD5WRf7CSlkutRhVXOFQllEZI6sib7HY3/CijwBwqbowaYkp1wDwiPaJ9/e7bGBdzKZ+6XexftenxdzVZRWBQ48KyM2s9hkMDdMwypQtYOgDNdxSNZSGDzE6Nnyc1jszAmMeYIf5QPlTBbJi+et2JV1GKGGMSiFYxEDOwiMoT0Pcr7qMILE9zoeVZ/5ueSiLnkmCFR36xAHnW3xOrE5m3YS+bHMjJyqy+GFAeKVFY2O1rugcJZgpCDAiBWVtU3PogCo8OxY19OBt457Oua2Z65lZ9AAZM9uBcRGvVMep0MWcSxu8npZ86SKzAwBMJZ5su97vkLl1igxUoIsYKciAG7whlpOLtPgbU/UUcOPD9u8S0UA61KJC5NtALv27uC7sRIUtYErBscwEMgTFKEujdDscMaVVE5Wq0c8iDaSXx+PCjtUo6HKZnzpwwtnVQd3iTGNqqbkNlAeNSju1yImEA4vUmSjd1g0Zq1IvmQkyORbmLAH6ukjNsKWnUAMTdWEnVHXfeI4fDBOqqBB3oNUXoFBOYUKluHXGkcz+qBlYKcoa7Uq5OtxS0KEL2xTeDxbH7RuHgcznQCZt0M/4XVhNVoRWOyZL/njm6IEa6wBponw6xSNJR7wODAZKi6YGRp+tUiPoGQM/A5vWMqdKA9QOqH0XdnHPwuJLxP50OFY+iHhUngZn5ba0NkgaJfwP/w4iUXraqddtn1Xh2RLzUAPBQ6fh0BWAdfLhJxAqKCr4lF+Iy9OSk0Q/Xy39zE6pSIIslJ6OSjHyjYIOpVs0tSQh9sv4hnikYaMnXMMtxDZwnf+zoYzGZhmCgdY5HNrvUtRA3WsdMfpdDyGhR5mOzq/M5LstYMij8CxP6yr6mf0c3GSW/DI6igAS5WnNPPm1DHk6FnFdtjQhxB6VdtVk0gO5pXgGiIK5QiytiF9nJUVeubDyDRBRtzZAOrqtb2msYK/oPJk1mXR52s9HXzLDrRAPxFW41aUGFpc5UJIOisaiIcNwMRR0FZoiQ0ssLQ0srgurOweKPlY+4hEI/G+pzm1hOEo4mH/4FMqSGItVIWiJeaWBSeChj7oOk85t0efA/IGZpUhJIBsP5Vg0MdCFmwTK2Flpr6U7E+nVBCfXvdlmOs8kUdO39EzQwFj9S7aBj8bK7OQPv03DOzRDauxQDr8tWu2QjVVpoe8kFB/IoSYbQjBwgJSyCxcuNEYcx/RKkEBsGyBFM0MS0fYQnSs71tN+vpxOZud2CLc86tP3hR+p4399jw5Nn2Ag/cDMDqt2Oz0aGNOFbZ00PR3QapmoBjrI7NxOlNscpi+a8Zluh/+cr81E3p/O57ASlm7T0gWlI5auMK32vgbSNT4MYoB4xyhLu3k1QDrg7Nz2NdZymgaWigbGQuYAF3Ykkj56jVVmqTSgVdo0EA3AZpD5H5jeeNv009FAUZSPy7g8dTSzTM9tdHfUtSRus3/tt6h9SdTSeE5aAxkj83mARAN+6dCvq1mGScOpFVxyGggy/Xaq+O9uYygc6/YJDK/adejabdPAzNFAjeNmcBfZL9Tloqimq0XqasoJovkpZ9sYTqEGRsfH/B0gml+U2UxEUUVLzEAN+BaEj1TWXyLqRmEFDHwazZdHnGefgQ1oIjUN0ICYlrWWP6vtSqCrqaVpYKZpgDfAigIqg5sXVmaahE2epgEaYE596tUFqM2cNkjMWA2woj791oHoYBfWl9WcDWoLshnbl/NZMDEDuwS+Xuxwpy+hWpY1oM5nPMzYtoOlkxu+9etDv15yr2NdM1bmJth800DMqY9hw6p5v5nT+QaA2dJeQPV1Xn/8eREurPaggGNfEm6ewWzp3XklZ1wBv2xuA+q86vrZ1di4sIDKlvp1zS75m7TzQQOBJVsq0czpfOjxWdpG+GRRO/6rtnSjsGke0lyztLVN7LmtAeDUwBtBOvLv3G5va91s1EDwWYA6G5vQZJ4PGugAtZnT+dDps7GNA5E52IWdaPM4yK6UKuNhokxmMn1OBtdf00x7B+q0bsg4yeoiLT2FGlik/q3q0DgNtsiuLFItkmehnFAibGvm4xdpQhU14hmuARhY6jCoBRiirkkMnyHc2qNZoYFgoyCkb6bKo5nQHMJEHsIQtS/tTBCyyTBpDSyuC8u3gwlnEx32kmDR7Jq5JuQTTFr6aSvojyZrnXMYqVHaH9TW2OEvvVECrxcNymkTtVVUNKCbwNKtbbKSWSeYNn8vG3q9yjD+PsJTz2brreY26TQx1A4qQBWBYcYgUsUcG0eTVtE8Kajf9ThAAgP0LkUYEMA13HbBrT9nD7QTGj7zpCvnajNBgo3S6eABAGyU4wcSbkuTEQS98kvm9CfUzq4S1WVYuSU8aWNsp1+eVuOS0MDkXViAcK2wwgpA7DD4Nddc87e//c0bNne5y138Xjd61bBeEtIvCZ5Qjm0tuQF53HHHgf7mm2+uyQan9NVXX818eyNezkAxkFHIn/70Jx8d5CeNRTawbMtcfA3QP2T+7ne/Y7yyAunw1CN8hfPPP//HP/7xFltsoSt1vVIdsv6tN8kAAPIBY0i3MpQutbv6TEpOaG5/+9vzA6666irjyKM73elOxlEy48gW+paYkxoIkPS492h9xIB5sYaJOZXJkoDccCBNoVqMAmh0GTheoRgI8gyfc84554ILLjB8GMMMn779nELBGqulrgFo/POf/3zZZZf99re/ZT99xMA7tZApvlPw+Yc//AEMgKfkjFPsKQQPVrcYvbxPeeWVV5oI3N35zncmMLHNy0Qaj7Ufp+SNbGlpYNjkOkQm9gs6zfpHH3301ltvvcoqq6yzzjobbbTR6quvvv766++xxx6/+tWvuG6m5yFMZuCj/2/vzn5tK6o1gD+Y+HwfMUJANCGAEGJzg0FgH4wSwBBQsEEE6XtsEEMrcFQIErBXEJC+CX0UJF4EPPSIGEPI0aiARP6Fy+u9v7M/HFTmWmuftfdZuzmh5sPcNWuOGjWqxlejRo2quTZMG6KugJv8cP+3v/3toIMOOvTQQ//0pz8x6PzRRx55ZPfdd/ev5+P3jDbE+PHbZP7pC7Lrr79+EtlowZ4zkx6gOHbq1Vdf/dCHPnTggQeyuQN3MwDmFhxzzDFHH310dLQAXL2iU6Us6E8//fQ99thj48aNsDGpCEo48f9+Fl69KA5Rmvyzn/3MAmnHHXf87/nr/e9//7p16/zrdg7NArXMpK86k1XvAWixZqboO+64g6mh/Q9/+MN77rmnf3n6sY997Pzzz+fRsiGTwDZb+eFcXVddddVuu+32u9/9bqztyvB58803v/zlLx911FG33npriRf7uVjfZbZN6NyWowfMiazoz3/+87m5uSATSiUOO+ywP/zhD+Y7+EQDP1/4whe8euONNxjh6UELMwFPJt8taQIZuB8iAueccw6HZKeddjKOuCgcFf9A+K677mKWVQfGW1JLL7sWemCJUVggY3O/9a1v/eAHP9AMjsJHPvIRC3GLM/9v+vvf/z6U3HLLLaywpXxrzoAmuMHBNeiCeiV/nnATwlrKECRzgbJ5NZZDlaq6QqYWLeLrXHrppXFTzCiIxUKsMg8++GD2Or85wlgLMVt0WsmlCRmlrZypRZH5WMb/hqyqllhYtpayp5fWA/pc/FKogDaj65aPtzK94jG4Qy9FD9SUUjLpNxumOUliWS/MEHo0o9qXAzzWOb/+9a8/+9nP7rvvvqJobVw/kiBj4oUHjjvuOGF+mZ/4xCd23nlnlH//+9/lvPjiiw888IBx9J73vIe3HfFKKvQ4yEy+R6/yVnqQLyeZSdQ99MWh8ntixXqACmicKk8++eQbbrhBvaZbP99tlmVOOZEs0t13381NNAczKTGnreKmhEFa1BaUrscWA9IsIdvF0EmjUYXi0q4kZAI5h2DDhg0ECz6NuO9973tGx5lnnimEPLqBED44tJNCBOv3tdwDFGfiu+iii6CRnJ/+9Kc5r8zaM888w8pBKegefvjh8OmtrSSLLtoPWtp2bQLcvOM4D6W3HAA5hgDYXHLJJVzP0047LUZ7UHxK8CDD5Omnn/7KV77y2muvcYs52dtuuy0w//nPf/6f+euUU0654oorWv5kyOMm+eYjdCV2cvLY4la+zJZJW2TSq6LpiZn0wFJcWEscu6jf+c53+K/slGXZZz7zGZiLQJY+55133o033ii49fjjj9sPbSd7ZMJOtC5ThEmRFhNw7BF/6LfgQ+kxlCGTqSyCoKftggigoMxgXfGMBBVhgkOhTQIrZPIjEhr+hEehL2P11FNPVdwjhmJpgs3YGrGx6YoXB/n8G8wZ8dhxryJYatSoPOZOcvWqy4VSFaP90NL39JJ7YJOS5k8tT+IASGZclwTlBmNREK0BRpAGD2Ku//znP0VIrWe8CmdvochE7rEwlrqA59lnn/3lL3+5/fbbf+pTn6Jl+S1opVUHCSeeeCJvlQ/9i1/8QugCq3B46aWXgJAh5g1YECbTWzUqi6HiBJNwyScMgYGKeNLkAd1gb8pRkyr6fYV7gDah5Rvf+AYnAFquvvrqT37ykxQnn1pff/31s846695777XO+f3vf29uplb5sWxRPfsDSIwPyQsGQVcLg7QLZ6+Q4RPTJz/oDVqKTC0ulEGa9LyFe/s8g0r5Aeix4rtoBQIwtm97/PHHv/e975WpFWGIGAd+OTTiyZC6tzWGrN/XYA/QHSvnxMiPfvQjYLv99tttvQafVA8DZnxbrx//+MdN9wwpXNEsdbdtwcQjkHgrAbpQJwFdyQcJrBhYLiw7hqDAE6ioGls1ovQqBZVtL/lEFQI44ogj4JATctlllwkBhEZZDje7CqXve9/7zj77bH4znli51IJg08T87neTjcC4uZKDg7fy5SD2mIbIbAWQ1vBYaQwHr/rjzHtg0S4svUKSf0LL1aPCn/zkJ5///OedDqRXwrk7xsepBXdT+M0333zBBRfQOpUDisMxFme8AeiEKkFN3CquQPGJWTLTzqwIA7z88sveotxhhx28QgBwuHGgoSo1Vo/gjBt3Uw4Qk00MA4iByf6soSWEhgATF1NLZvbUgBFUcxpSLbZCpDGX722GTbwEp9NUh7myqdEjbtqlIi3CnMk2AxG4BS4yVwkprYh+wPBf//qXflAqwd30UlH2xEx6YL773+7/UZ7+2weDSOlsa4gBBsIZYvFRSLCIh2F2UFkhB6t5ekcJCUCIBnjkMIhOWYEB/QZd6GHeK1hyUbrHEkA+zjfddNNDDz0ENmJsdrvALwDD3xGU6667bq+99nrwwQcFdIU9jAj5IhzqhVsSOolopEiDHG6KG3dk0Bw83RUhzzSjhmyYl3g9sTI9AAYsjIWKOZX9+dWvfrXffvvRV2qnEfbh2muvdZaJMbztttt4ut4CCXPnTvVoIJDF22677QKqwIDBBLmCQXDlDhKQ4xVDjVJZHGIha1tJ7TJzAQZ0MfiGSYwVziWeA2MxyIaP4l6x3hoiHYMsjZhgEsyddaCNEbU7LBF8RrAw7Pe12QOQwOa88sor9Ou44AEHHAA5RJUPeDxCEz2Imgct182AQU7bFgBgVOnapCzIxSiZcCEWJFhLlFDNSIIE/EiDKDtsaISVfFXjD1dwC66m3RjbtpaI5BVX2NRvi0AQAQcMi0y4TUGu7eWXX+7AA/vJByWzzDgexCOkKrLc0sDkIGPhTdZGBPEwjFcgitdiWBol8BODTVZ7Vd0Ty9EDiz4LSyXQfN999zGFdkjtk0IetdGrC+xAjTkTM7DRYIKHUXLLh1SHunbZZRehLBOzCdvp0r/85S9gykS6JMzZTq44WHP//ffvuuuuzidY2AlQXXzxxaowBkRDP/rRjwoAg5dRgbO7tLkcW/ZdRTBn+4BjrQrFHWZwQvfCCy9UBQnBCx++iIpMGHZA7IJJCxjbblAjmR3i4UCw/l/96ldJpUVEtclr3OJfoPSKm04eJyNVZO/vjDPOYOjRRLaBwhQkADGsWXNoWClCfvGLXzRmqkWDUv1xmXqAjkDF1hWEPPfcc7QJG7GMTlBBL6w6OwVXP/7xj7/5zW/azLWUAmNQJJK7PbUPfOADijPrNn+5oZAPXb/97W9ZRiaSrvGXxkdUtVUxIw4qzj5idcIJJ4CBqR1yNo2i+XEEbJh//etfX7duHYuJFWl5qOQRNv7jH/+oUmnnrdVIcoPigx/8oDiuUQmQYMl8a07k8WgI4NCOGp+y4QDkLaqXqbc727E9EJsgyk6D7A//lf0JBgIDZpbDJxDrFQ7MKZ2akufm5kQ64cGnVCwYsFE05X73u991hhUM9tlnHzBgPK+55posz0AIjVNezJ0dVW4HkEsDMHgAechaOQGG32wg4BNTDPPsP7HVhd7qTnHcDB+HYjHkpPIGEFvsiYRpBUpv7d9CMqsewQDPCRkjosxpW29Pr7UeoCbAIxW/zSpaNIfRYMQsYGQKbZqC4Y2lkjkQPgAAEufxHJSCDbZL4nOf+1wmPpbQ9AqK0M5RhjeWlhGOZQMSphVg8AceEAJ+cLIowrmti5AQ+49//IOfAKWMNviZvmtAkQ04ueDcA6Mmjjg+d955p9rxtBOidpA2soDWCo2zC9XM+9577+3V/vvvb9JXC/Cz7UoJMeBgcJHEXSlb0KQVq5ZOfitkT8+2BxYdhQUCMOUCmsItuShygFrIBhrHYuAVdLyNFePUckzhxglaoOQ0MI7QAP3sI+xiyDRbvrDCYgN200466SS/dcCvNT1DPFcPtlACHHRa/TDoUKtGkEVpbAANG2qbw1aCozBgRwBgwvPf//63TTrdpyISqsjMsX79emvBI488krtgMvCvIO3ZEZj82PJKTS2KYG5qMWCU9ZiR4xAYJxiIuSBq8UmQj3IsE0VNJikJZ43ScEPRKDISuM7GJ2fCbnIduphUvOfPtgdok1ohgQ2Vhm0J8+6jjz7KvPq+SozWloJ8axhm12otqkdMj6wtJVqNcE9h0vkqKx+YsVJ3PMBJ1ueffz6Wl+Ob5X7hx8ARJ2DWwYx3AmYSaZ0qXChBnQv7ta99zSPEChLgQ1riwQyoiyKo0VtfFnJNYPiHP/wh10G4wuc4zlYKA5CQ/dUuo4AnBOGYZNQwvjVqDD3gnG33dm6b7QFKZzEYQ5RmR3oBtpSiVhcYQB3vlkXyVrgIcsyLYMDmmIy9BU6Turdg4AIDR7zA4K9//asEWHI4FKdi3AJ4s6+PUwUg4NN2EMNoJcNHgZnM66omBlCJ/toBQMZ+MnEWQiAnR724WXeRhB8jbbwI0zq9rUUENq/bmgBsQLWNcMghh5Dc6p1DAPmGFecALDnicF6t3myPdYKV74EYRpaNQk2vpkuTuGWPgChh4JDeIdl0HNi0EoKlaU4pKBXOtM4xzwKw1btAGAP78MMPAx54WIHfc889fAOwVCPIwZX1m0mWOTVNW7qriHNp7xc9d8KQAZ4yXGrnubLGQA6l1nJkK7sab9JddXgSTFoVaoRYMGY8X3jhBS6KSJb9DZxJy9Xh55gUkIExb8TUIMrG7FuScXnN5tBOYAxJYoSytLx8vgf+xkXbGz098x5YnAtLSQDBeDFn0ozRKGQjIs1ZQkmDtXmU4gGOjXNn2uT7rJvHybw6j8J7iwmDLQUZOBEvGA0r2LUg4+mywlZgdgd4mU899RQksYAgC3zsJqz7NEcR53X4r845mLBhCKr4srjZtDWdW+3JVJ2KOI5cWF4CUWGdj8LKG1FsLmcUZFGqAqVaXJHHXdvdhZBNGM4AJR+ONZBvoTph5tBUEaxIqBUQz8MWPMtUdOyxx5pyhJnZdNyMqBqQVbYnlq8HAjl4oCA+IpBwEEXTBaVSKZ0yRueee67YqhxqpSB3qGDvWMmQGQhWRBYkQgViYKybzSwurHUXqKMBML6jUqE3jphvlo5PyS4bJnmFswEywIBMRjYF4ZARJyHoQpRMLghPhfAkFO4FIXOAyYb9ZUwhn7U1dsQGNC3LLa3mqRg1SkGjIiVYaun3FegBaqUI5ovFo/E4fAUDRsnbgRhiSHIUtLWqFOWyVKZbxW09WbdQqJWJoD6wsUiBAaNk/xRnBeFHgmNK+5yJ8Gf6AJXDCgw4AHPEEHIDKlN4yKyxxQIcfWGEk1PDRxH2ViZ3nM1kfu0UyzSjuxhwQ4xglu6grhTJud0cYl641V3hP2z7fU31ADBQkG10x10YEFbOZdqiQZd53KKdltGUv1jywwAXVsif/2qb9Morr8xynY0yF3MQbSixltZUBsJvfvMbYLZvgA8yGLYjyvbCEvcRqgMea36w+fa3v80ylzVOjUR1fNwdMUNKqrgWcvghJVUSuGVAYYLAWQhG0kBQhADmaOIJpRkCCMjD5TCCLOR4LIyqqcF5A11hrSjEliiDlaGAtG7hClftg3r74wx7YHEurIppGrDoRhpEaFTOWIH4BPKhwQTJJZV2TFsR07ZMVuxLX/oSv805MPEqgaLYTQyZQgYuUU+TdF5xmlXqkV3mwvIFJRBzNGHIzyeBl/lbVElE1ogSVFAj5BGDS+oRmSncuk1FBFBWnNjZBiBOfEK+oIJ8CcMJ4pXlaOIj0yWRC1sJ5p7/yqFHj5JDTwZD9LHHHrOZ+x/at/7qJZdpw10oAlulwoc7Yu5xpEG+HBWhGRTvj8vUA/OKfUuz0rxS2DC1M1hQAaVMFTvFSHnMSesggY/LfwUYejQHM6ZADqXkNEAUj3F0D6u4DtUKKubUYmVEBAxeeVQjkKhOwqOLneXmQjsCsmFu14KLoF4icXR4DMYIv9mk4m1AZRGFnpfjLackLiwn268gqdqoMXOoxUSidYwvziVbT6xYD9AF88iymeaZKTpVNaWDxBNPPGGTKjCQCQagYn7N2Tvg4VCynxwLJohC8QEDKyhmEAyiUO6FsvZDVVEqxp/vCOTQSwCAEVUFKisusy+HFUGIkXmMVWQPszK3+Yu/ghEVsYQLGgkJ7XIUYathTykWXmCMtee/eqVFmmlPVmRLuCH7y4QPbsOq39daD8ADK8FpE4DkjwpF0anYquAUp9PExz3laNLjQHI4AYmAB5m3MCAHYsVZLXi4tmiEPAMzr0y+zJqEKO+TTz4p33KdS2qSBR4ciMGztO8vViXaCttBY6pmclUBfsGwNGgZL8ITRkplEtUOamZqmchsQdgZQBOBlTKgBKQkakChF8FlV2HY0NMigSdhLyFqTeD4mscVzxYr/HdUD/Aw88dFu7ABRBZbDBboyBkrVmBBhZxRkyUbnRARFHoFASwyuykUavUTUxs+KL1Foyz+QW3qAlYYErWyDGJJDR5FTPnu5nhF+BCCW1wNWw8RzB0HmDMAhK/Aq1BFABUlJ+2qVxromtS0yMmxkMABZfohMTkDG0+TUFucbEyAfpDJ/zDAyKO4gpoG/daO5oCcJQj/fl/hHqALppNCwYxO1Q45zBDzR5vBQxTtFahQXGXyUGmW3r1CI5/Gpd2lXTI9tpdMj4wjJOQtDmoU6rDWaimZb1G0yuHrqFopHCRsUzjT4i2DDucBktCsnCCQ/MJsbD0yR3RsZahOLA0BU6shIfPYrxXuAT2v/yndvEuVgQEEwhsvQYS1lYfe+ZpMhEw0HiUoFwwAj1XkXshxFlDsinlkeN3lqMXVIpDtwkHVLkBSHXhY1QjlykGslAuqQ6asi5wy1TXgNk+7aURUvnQwL8G8K+twC5sc3xcZzpaLCjqN42049Psa7wEzFD/VniGHlSWx5hE3FbKxs+8ueASEANm2ggFkTp2GcvbAzo8gJRsFljxIeIOE2Ew4cSkIDJDjAjyPtgvksIdm9hY8ZlK4xcemPyZtjUEp/pWfyVdobOBhA2RCWikef0C9wGmy9oPinAr2n7Meu6oh1mPYxmIbsE5HmM3Fa+0n81vslTkdYXLnYSuYKaCVradn3gOLdmEpmKb5juZIqFrA+lAzZcMiTDPQ9JowQ4p4BbIJaxkYLR8Fx+oeDRBvs802QkqOrbDXVkhAbBQ5+MUJJhuQ4cwjFHP1WHWBtXCaQ+KYEyn9GCC2VS+qfyEYfVtcuEuOxuI8YKVFxo+Bgd7S0yM5QyMtgss0tJmD4v1xuXuAXhhTMScRcUcD161bJ7aqUrtaHALnPTzCFTsVSaCLvpYslbowNCLsGwgzSIMThkDi1w/yratAhRiVfTTRiAKtGgNsAoOQC5ldNsEAEQtMBDxceeuOXl1yBL3s5xos4rX485D4MQJjhueWNGTJPdAL6gGGwpEA2uduCkeJ5VAodVCKmJDdUjRslxnUd9+sJV8TQbpOwoUYHiQCA991gQE7OY+C//LWqxYMKRuwJe2OjH1GyUB5lMirLQR5mAhrYchQ82AwlBl5DCU2WaOC5xD3+5rtAQihOMikR5gUQxWkdErKVOvOo2U2nZ4KhKoVVI+Yb2fKtk0PyRxEOwnmSnYPWYGtilTCq4DHKsjKvAWPgWDvPh5F0SeR0FK85ODfKCO8RTu3EhMOjDOsDlkRoy2LDH1sZu52OXjnWYMZpJhoviIEQ8DZsPCbm5vjYXPHxYYNPSE5nyjwNLgBuLX8e3o5emBxLiy1BQ1MrRWYA6kO4w8giAA6nct2VIVbyQPgqgKxuZndDDLSEgVlygHHtm0tTZsvHQEsfexAqcK/VnJaxXzsPCuvmuHOBoRJ2i4D+kDNnQcQuBtL5YUsUNGg3kmPg7YTACX+o9hFqRNcKrVatY/Wug56TL7Br/e2XKpJ0vb8BXqAynioXFhY4sU6ROjbQYs09oux9uU15baqadMLsB37SlljwbrLFO4UDT9V/J72ycDCOkQFrvxOd1ZeDkqgigVvGcqBakPAmRYMCelsDH/XDpcohfgEgshpoDm/6MCZz86cV3GMW7zNL84aNRaQiX+0nHt6BXqAamjZPGoiFDp1bEDgh8qogzYta33gwiB4FLsCP2o1icop2aLcwEBx568oGgwksEVv3wmcCgZVUKK1XdJmd9ymN8Utq4XTIIqzRSD3usCmRnVBtXb1yX7hDlwLb5kmU6rFvM13R4/gltYo0SXiyGY6JShaCX6jc58cUVs/QMHl5eYyeomCiVzGPZjUQLAJePxLBZ/PcmdjqVRqojeZAi2zWeYLPcHMrV4xcbxYZpxI6OU4QYsgA4r/KuHAYVu1t3k0XkjoTKCDgmh43lptjuYck99iMm1EBsA+bvODzSIC+oGXrC5bWwgkWuY9vUw9sOhVAsW4TIf0TWFQa7nPDAEEjQKQtyBFo7Y1ad00zOza6+Qf2OL3Cg1KZMw0GglLFjnTtBAyeH48VNOzxZ8RlY9sOLUEcJmSGW6bU1xVqA1PCbEuBtQhWvKocbN1kQe3haXCKmMGQ8TSjhC4a6wa21q81WqjzmlF+Rwj5lsCMUnUYu+YU25Vl7GxWfE6waJ6QOePXq2CogUK4g5aUjsh6lso+uVbWIhbcmSBFH1NWXUR076rrc4r8hg49oVxc56MIWYNjQiURorz4u7SPsRR0FIQosaiUb5tLIMCvBl6kQlsjTLcSs6MGi6R7x35NDxmNtdbhxNUUWQ9sfI9QLmUZeEh4UNPoSbWkjmla5CwgBGdJZXPt+U4iipkCzkFrQisbGDAgICBfd6xMGhbJxKGCe1jq7gaY4otljwO+LcFp0nj4MLcHX/mXUKUThpnl0cGkF9OWku4GMNpOHeaVekBimNPfvrTn4pYmUPZSX4hSagyWrZsLo2XhHJglXJNx1bOHF9eAYvHEZSpLANbxJWQr7ogU2ZmTHGEOIUFHkdOgQdnSFZRisfQCQcYKRwAoVZiRwySCB6LdrGxVu9cFwX9qALk41m1S6A3JBXnuZLT9wMOyPqFzTjTShWx6ow4wVezg49zBJh9sGj2V3ufyquXljuxaBeWdi2/qE3gh4W1g8DsQjDLa+FiV8jl3LRZn8p9O2KdxOxaoMAl9DO1yOAAmU8HnCxxegaSzMEDJE1qOSzi7LMGRSyJOBy+YBBpUBF0Gh6+c+QBOCBrZ011BDbkQNYuKqmkSTKWOeyiR8B7IAz/G4dJlPL5AQaD/QWl9IAAgwZiAsHuCBTHx2Aggya753dzHIf3ViuMAf1msWgJ6xdD0gMpO7benrmEHtDJdESb4lLz8HzrRh0tN93O3llL+BV3P9nGXjvVlENOzq5ABfpFqUYRVUf7tDyw13QNwLYRTPB2M5zTAksSoszOMinJYN+fTTTWJtlEIqUKzofmsMiBvTnDo0ojszv5nSWQaWvMQMiPyGI76Ie2T3p6uXtA5zOn9GuVwpiwA2wXYwIDLgknX+HQ7/RRK3PKxMVKDAQDHnxwgxaveBiBAd+X6gsGVcrEzJDCW0yxkJXTTSBney3h2KKcMkGqVAT2OLskmDjchBt4AM5h81atrwwKnoGxJsrFJvNOPE6yyVPW3smWtQdiPUz6amE6RI7A0jzuYlphEpzQCH9SKyTAHkpuKAzAHiODBp4BjNFDKWHJ5PgTMqoPeEBRQdYMQphBZcHYuJBpCHAzwNUr+XDueBXwcEmVwrCar1IyCPqiYegMHJBTHYYSwlsksT/s0I6dCmgn2+iAUgtDag1pZmf88YRnxTkG9n6rLgWRcWCcbOS/+kEPe1+Oz6ZPRtlWwZ6YYQ+MWQZNyd3RFmoz+/pSxA9tQANs+ZZQWDTTpx/64VkK/mOIQIxBoBGSzKNgJ3wLZwDkNDS/U7CBykEH8ga69yjTFcHQQLZTYnw+GIV4R8WNpRyoNR48clj9VpciyNBbG/ktGCPB8TIINqjC06tqrByvzBk+neEWg6OyRp0mZACXAIokx6CyN+HkIhut4Q4jmgkMOZvR2sV8Y4XSh+rWhT5cEwYTLfaTHDxdmxF+iIDY+sE5dzy5TaBfG20lWE9sSQ+YHU3tdET7mSYpWoJt8om0/i/IyTfd2hUy14K0SmkQAVT7/skOr1VTXIFNWJyA0iAqVTDoKrJjpTjzJ6Drzhp6izlK5o/fycjibEPNaSoiiTqoxSfknFcXc+ynD5ABRqaH1F59gptNiQwuADO1MKOgaNdP25GlOqVg0s8mwGTcGjQZNV4Vt55Y+R4AEuC0GhcUZ7hsttpzt8BgJMUI2KKseJlTimZOERNyAANwYqlQsrGBAdeQin32GhhUuyBQWjzMctqRA9ASH+UQmJ75BP5pFqTBfI2LKigBS+oNyJPfkuEM4SQXm2DVodoIAnsxKpztGoM3MBsOLK1xIcpgjNgZgMyWZ1tjT6+FHqAd/pwjnuyMj/D4sj6Ech4ARMVfoI7N9C1HfrBPsMAmAGtmDrWXZXK0F29DHyp8nmiTnZmFansF3NkyPiDBdvng2z6Y8KoqOKz4uEyUWAGPfKxIYm3vMy8Atr/vsQUPhpDMXPsteSemyGlMCf0STxW2SZ1nMOdiyyXIWNDDLbCl47CCLs/B6d5M0L5X8/tFfB5VlNMcYm4rA651OsQoVlz+WlDcO0GGpbiw1MOJtDqxIIMDCF6/fn3bWTw2NsvvTfAezMeIxQZ4crAIWK4QM3Dw4V8ewFxQyJZBFfriFojIjCssX44xwEyvW7fO4oxBZwQVkY8JDgy97Ve/osVNLD58UwiDeHUZG9CMJ/GUKpo4pqy57Qmm1sVDFWo1x/CwWfYAt0SK5Cx1cRB/9S+RzBkkJInZyAkeOShZcFvSKjWEpEX7XDoH3BXXMzITRyluPbElPUBNtAwYLv9SaJSVnQHBIUsgSkGJnn55kJbsQGJngMvIyxQVizcphmTJhCZFBnZKLfiwpyqKxfellH03hwQS+/TDK9Y2OBfkkHmEMX6GEWQripVv5YQfp1cRIGM3C3g1FlKR4JwpxGDkxLChZHZ4V1zEaR8rq6pOG40CM4EghCGQUYNDW2NPr3wPUAFbwaBBCBhY/1NQK4bp1mTMgSj7AIQsEoXGIgUGVl9+h4tvWjCAYUGjAQyCB2dOUIJWVcRvdvKPV4HABcnwDNVFIBPmZUJjMuUQqYaPTCKJIDipwsq5eKg+mUBvt4H95LXwLUjLoiImJxtouGl+x2H18xpMRPXWJ6yKmd1Unt8ALlEBSZgAUGOpTGdOidjCcjnvNzc3x/sUPxK0EqmhfROfCBTrZKkT2yuT7fIjbpxO4XkXA8WmAZiFFhxamwkPMYPwpl57rayxR8UH4Al6+Z2OLqgXE1eJ6q2hxG/hk0B7ypqvsfUYMsMKGbhyebkNrtRLNqEo96JUXFomb0TUQIwAqottVdoTy9cDm6btVm3CP4AlVGMipMUFKoY5oSweJDWberl9NGchxWzRIsOUHYFwQGxlzyyK2loDQYxlGUTyGIBeRcTAzUE93IwE4ANNZUEHH+cNjB8OaImK2L6bgJN8E3Yrp7qElxx+9XkgAtC0toMt/orxgFJZ+wjsrOgvUTOEirMWeSsywbCKH1hEGj+Whsgw4Z4SyU6K7WYt1VGAy7lRnOPuB78U1zrI1iKULpSWbtpr2YoVAqx8e0EAZt1A0q7s35F84T5vm/mOTQcqNCtoDW+t+qpP0vmQo5/1Ki3Uq4BNjm63J0AL1E3LwEDjrK0T29YtluCQg9gluG5tbWjAOcTCMAfCx14cjQNXhAAAB+RJREFUwaxAIEo0wreoTgUkyKS6Tbp/17uUxZ8MUMqDHDi+yNRiLUdgcQVDwzlpNGIGGUdGDdAGTuw+wICienFTRPH0BiaCBMQjrSUc+y6fZ4wzPxgZAXQUAaz6bHvZDeAqdddBzyzfRRdUKRpq4NNd9DWpOgpiW8zi6GGGcWMrIFxZ4JSwbsl0i61XQAhd1sywh7MrVYABSLcwcGqQzQEDVdhWsqgW+LcRxEXgi9jPlQlO8MzmAxIJ8WQA/eYMmybuBTky8RcV5pISxpyNGwFAznjhK2eLFpmFn9EByQy7WcArmcQTnDNmWXJsoRpnwhuAHYT6ZxUvqqFuk761upl3rDmNeIEoGMCGSY3FQ2wbXdzdWT5MqBIkQAjeQAUNl4CZBTDY9sEicJoKBf45fOpiMM345j45wRhgMIBwoiCD7JV8PDE0KGDPW6Aya9soI0mm2rG9R1roMmpAFEP+AHo+AJ5MtIFpEMWukpz3YtzxE6wk0wOKE8ZBBfM7yVlO7oq2EIAYZnP2GaXeczciHAHKzxc4WslKk3msVD1zyT2gq5lHgDHpw4ZHVg63pbuwCuNC0+AFoCWZTK4qiAy0iFIO57IoYQhSi0xB5o+U8l2RDzECYPI2DmiKe1SpC9uEvoqthFGEj1KVCeuu1KUs1BIbmbJVUYgxVNbb8EEA6BGbAMq6EBA1cjLNxQEBnplpFEfpTgyDB9A1FiX+YVil9FXEqJxI0u9je0CvUt/CLmwKBjljmcjU55TFyUMmTadMKv/P1G7/KKqkkWDSBG8HjVFm46iPQtlriovKiBQ0lpbxb7XvsaUfiAQS+AQn9SrADlqSiaF6keVVUcpXHA4rh/cgkwV3D2iDWI/CYFxb0RSbX+x7RkQV7IkZ9gClQMiULqx6acfF8lBWKwZwwlWrKZxbixRiZReGAbSXCytEKmpb6PIKThR3RRJiGGVsZsUy8B+FHzQSjGUjYcriqaD88ME24qlCZts07dI/ZS1D1u8r3APUSl/TuLAEQ+yiR0VKTpqlSvmlyhYD7B4COQwpI5lSQCU/eGgxhgxC2skXqObr/D+GGvxSXA5cIa4aS5g2oV5FFAwy8wpQVS1dZXGLkScVYYq4FQY9buwq8Yhddl5BXcED5lIj4N3y6TkGxbyVp6e3pAeoSeePurBLOUhQclA2+0XrTFhlStBfa3DzSiZMWKAUZYq3j/iAF8qCkbfMXEq1PBEASupt88NNTpVKTiuSsiAYL1l+CVCUeesRJQJ91wogM1iPnLWri16OqxiilM7wDqvQuLel6lUV7ImZ9MAAAwOeQKLn4+FFUyLiFuLOTtm0Eqo03+MgaOScE9vks3EhWGiHBxdFpxS2EkGjRAEgb6N9NC39QJIUKcp6S8LillrSoraW5Lu3IytNSw4OMbKcftFl/quQiUMUqmuZV6U9sVo9QK2u2MBWBmqi0DYH/FqLlFfKSkyCQXFAFoaY+EY7+cmsKjyyw6MgH4Vfhg+GqR0H6Rp3LVsVDZqG0lWV9sTa7wEKdQUbJa0cenSvHI+Fgbxyb/0ExIWHtqz0YPLFE7FrYB4VR1w1jk2g4XK0U224DQpiTjaNkmhftcKkIIbtEEDgI0hxWWcFxWsdobF73OMCY3WxfJlb5MJGrFbrCwsKIkCwAM1YVpNKTcoP/9m+HYjdMh+8Gm3d2EZtttQon56z2B5o1TSpbGmHbXIohbdqJ4gxctjLCpsFzDLJz2z5ngaTzOuj3CbVVfxHiwxypqGcVAtWo4hKjtiAqIa4skNsyGyBcdBFJrJWHMjQH1e9B6aBASFH1R3JR/MHOZbfUA3twdLgbTV/rBij8JuSLGzHEleNPbG19MA0epwGKmP5jBbcEvBM4jbo6klko/mVYxCxqz7sye8qOLvlC3KTxdhGDarrjzPsgfEurHnaNcNqOqveA7PqgQJnErMCKttkoe/A04YNG5ygdajAUVr7UD5bdATKCSq+YCL3s6pxVh2yMB/tYlidH3B+iI/uGJljiwnBbl0NWbiZa/Ntetg91+oKGST4tBTIHX5N2IlgqytVr311e+A/2NwEg7WA0tXtjelrN5pMBz7+9jWtwwNOoNnsEkKW38fU9N04PeUkcL7d3Sj0vm9WHMR2VDmz9fQVdMreAyvTA4DqTIyPnxzrXsJ/HFhASPjnp3Jb68xWEZvvpa2wtzrzFJOqxxJvY2RdW2NDShdbS0In62rfvvh8xPQmbLO6kgcJ9hYkRGHJlpzVlarXvro9EHPqayfHYX2YJUgPFasr0lZRe8ZOju0S2FEEA7zb1eXTHaCalzdu3OjTuoU+5/L1tO8PfPRnLu9QXj59dM5L7gFQZimESPMFFcMxW6Dij6e7i5CYq87dtWSZV72gFrk0IW1ZdXneIQKAEKD6rm7tgCdIsJ5ZOyK9Q8CwNpsJomyCiJULUGMl1qaoa1CqjKZuV1dANYDKajm+70da/GKPx1iw4UECqwrfulIMmRCtgGS9it4Di+0B+PStVcyHsjMHKps+KtLMaxmtYvlyjPY69bhVN2T5umiZOCccsHb6vJCwdkR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UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUV1/wAek3+438qlqK6/49Jv9xv5UAOh/wBRH/uj+VFEP+oj/wB0fyooAp6hGkslujgldzHgkfwn0qD7Db/3X/7+N/jVq8/19t9W/wDQTSUAVjZWyqWKsAOpMrAD9aRbK1dQygsp6ESsR/OvPbHTIPiL4u1+41wyXOj6Rd/YLPT/ADGWIuoy8jgEbjnGM+tXrXwvpWg+KbC98K6pZadblzHqGm/a90dwp4G1MnEgP+euQDsxaWjOyKcuuNyiZiRnpkZ4p32G3/uv/wB/G/xrmNK1DT18beMUt9JSG9s4reS5ulkJN1mMsoI6LgDHHWqmifEG91XQX8Q3Hhya00WOzedpzOGkkdf4I48ZK9txxz7UAdl9ht/7r/8Afxv8aDY2wGSrAepkb/GuOfxvrunQ6fqGt+F0stJvpo4lljvhJLAZPuGRNoGORnB4rnvGiatrHxR0zSbnw9bahYwWs01vay6h5aTYYAysAvBHQKc/WgD1I2NsMZVhnpmRv8aPsNv/AHX/AO/jf415XEmr638Zbj+0vDltcf2fDbmFH1H5bRC5ImQbcMx67e3TNdX/AMJfrep3N+/hvw2upWFjM8ElxNeCAzyJ98RLtOcHjJIBNAHU/Ybf+6//AH8b/Gj7Db/3X/7+N/jVXw9rlr4k0K11ayDrDcKTskGGRgSGVvcEEVp0AVvsNv8A3X/7+N/jR9ht/wC6/wD38b/GrNFAFb7Db/3X/wC/jf40fYbf+6//AH8b/GrNFAFb7Db/AN1/+/jf40fYbf8Auv8A9/G/xqzRQBW+w2/91/8Av43+NH2G3/uv/wB/G/xqzRQBW+w2/wDdf/v43+NH2G3/ALr/APfxv8as0UAVvsNv/df/AL+N/jR9ht/7r/8Afxv8as0UAVvsNv8A3X/7+N/jR9ht/wC6/wD38b/GrNFAFb7Db/3X/wC/jf40fYbf+6//AH8b/GrNFAFb7Db/AN1/+/jf40fYbf8Auv8A9/G/xqzRQBW+w2/91/8Av43+NH2G3/uv/wB/G/xqzRQBW+w2/wDdf/v43+NH2G3/ALr/APfxv8as0UAVvsNv/df/AL+N/jR9ht/7r/8Afxv8as0UAVvsNv8A3X/7+N/jR9ht/wC6/wD38b/GrNFAFFLKA3Mq7WwFXH7xvf3qX7Db/wB1/wDv43+NPT/j7m/3U/rTp/8Aj3l/3G/kaAIRZ2hOBkn2mb/GnfYLf+4//fxv8a8l+Gfw88K6/wDDvTdR1HSVlvpvN33CzSI/EjAEENxwBXQeFb++8P674k8LXd3NqFvpdst9YS3D7pfJZSfLZu+DgA0AdybG2AyVYAdzI3+NIlnayIHTLKwyGWViCPY5rkdC8X3/AIq0GbUZ/DJt9Fk0+SRpJ7kZmYKcoqAZ2EZG44+lR+GfFdrBbeENOt9GWw03WLJ2tSkxcQyLlvK5HORyDnrQB2ZsbYAkqwA7mRv8aRLO1kQOmWVhkMsrEEexzWDqniSNte1Dw+NPF1Bb6TJeXsjSYVQQQsWMdWAP0FY0fjK18O/D3wtqNhoT/ZdQkhtorG3lLNEH3HCk/fOR0OM560Adx9ht/wC6/wD38b/Gj7Db/wB1/wDv43+NctbeMNXt/Eum6Vr/AIdXTo9ULraTRXqzkOo3bJAAADj0JFS3Pi3U7jVdSstA8P8A9pppjCO6mlvFgBk27vLjBU7iB1JwKAOk+wW/9xv+/jf40fYbf+6//fxv8axPA2sX3iHwzDrF9Pav9sJeKO2iZBCo4MbbiSzBgcnjNQXnibWZ/EF9pHh7QEvm09U+1XF1dfZ4wzDIRPlJY46noKAOi+w2/wDdf/v43+NH2G3/ALr/APfxv8azfCviNPE+jG9FrJaTxTyW1xbSMGMUqHDLkcEe9Utb8V3dt4gj8P6HpI1PVDB9pmEk4hht4s4BdsHknoAKAN/7Db/3X/7+N/jR9ht/7r/9/G/xrA0nxg1xa6yNY0ufTb3R133UIbzlZNpYNG4A3ggHjrmsiXx9rdjpFt4g1PwqLXQJ2jzKL0NcRI5AV3j2gdxwDkZoA7b7Db/3X/7+N/jR9ht/7r/9/G/xrnPEfjC70bxJY6HYaJLqd3e2sk8IjmEYDK2MMSMKuMkt+hzXS2T3MtjBJeQJBdNGDLEj71R8cgN3A9aAG/Ybf+6//fxv8aPsNv8A3X/7+N/jVmigCt9ht/7r/wDfxv8AGj7Db/3X/wC/jf41ZooArfYbf+6//fxv8aPsNv8A3X/7+N/jVmigCt9ht/7r/wDfxv8AGj7Db/3X/wC/jf41ZooArfYbf+6//fxv8aPsNv8A3X/7+N/jVmigCt9ht/7r/wDfxv8AGj7Db/3X/wC/jf41ZooArfYbf+6//fxv8aPsNv8A3X/7+N/jVmigCt9ht/7r/wDfxv8AGj7Db/3X/wC/jf41ZooArfYbf+6//fxv8aPsNv8A3X/7+N/jVmigCt9ht/7r/wDfxv8AGj7Db/3X/wC/jf41ZooAowWUDK+VbiRh/rG9frUv2G3/ALr/APfxv8afb/dk/wCur/zpLy7gsLG4vLlwkFvG0sjHsqjJ/QUAN+xWxJADZHUea3+NH2G3/ut/38b/ABrybwg19o/iXRvE+oSSBPGRmW4Rm+WKQtvtx+KDH41t+KfEHiux+JWh2GmafHLaSxXBjgN+I1vMICS/ynZsJ4659qAO++w2/wDdf/v43+NH2G2xna2P+ujf41HeT6hFpDz2ljHPfiMFbZ5wiluMjfg4xzzjnFea+E9c8Ra18KdVm1i3EkJsL3ZqD3YeSVgXG0pgbQBkZz2HrQB6cLG2IyFYj/ro3+NH2G3/ALr/APfxv8a8x0Lxlrmh/DfSNS/4RZ5dFsrGFZriS8VJnUAK0iR4OVz0yQSOeBXaa54pawl0uy0qwbU9T1QF7W38wRKIwAWkdyDtUAjtkmgDa+w2/wDdf/v43+NBsrYdQw+srf41h6L4mvLnXptA1vShpuqpALqNY5/OiniztLI2Acg8EEVh/Fa5Nna+F7kRSzeVr9u/lQjLvgN8qjuT0oA7j7Db/wB1/wDv43+NH2G3/uv/AN/G/wAa5aPxjq1jrmnWPiLw+mm2+pymG1uIr1ZwsuMiOQADBPqMjNWL7xRqk/iC70bw5oialPYKhvJri68iKJmGVQHaSzY59BQB0P2G3/uv/wB/G/xo+w2/91/+/jf41z1n44tpvC+qaxd2NzbT6U7w3tjw8iSrj5VI4YHIwfeqR8aa7praZc6/4ZjsdN1GeO3SaK9EskDyfc8xNo698HigDrvsNv8A3X/7+N/jQbG2AyVYD/ro3+Nc/rHiu9i8RHw/oOkLqepRQie5MtwIIbdCcLubBJY9gBWVqniKfXPh94wt73TJdN1Kws5ori3d965MZKsjgAMpFAHa/YbY9Fb/AL+N/jR9ht/7r/8Afxv8a8307xtrWheBdJ1Sbws50K2tLdJrl7tVnK7VUyLFg/Lnpk5I54r1BWV0V1OVYAg+oPSgCv8AYbf+6/8A38b/ABo+w2/91/8Av43+NWaKAK32G3/uv/38b/Gj7Db/AN1/+/jf41ZooArfYbf+6/8A38b/ABo+w2/91/8Av43+NWaKAK32G3/uv/38b/Gj7Db/AN1/+/jf41ZooArfYbf+6/8A38b/ABo+w2/91/8Av43+NWaKAK32G3/uv/38b/Gj7Db/AN1/+/jf41ZooArfYbf+6/8A38b/ABo+w2/91/8Av43+NWaKAK32G3/uv/38b/Gj7Db/AN1/+/jf41ZooArfYbf+6/8A38b/ABo+w2/91/8Av43+NWaKAK32G3/uv/38b/GmTWNuIJCFbIQn/WN6fWrlRz/8e8v+438qAL0P+oj/AN0fyooh/wBRH/uj+VFAFa8/19t9W/8AQTSU3UJUhkt3c4Xcwzgn+E+lQfb7b/nof++G/wAKAOCilu/h74r1ua4029utA1i4F7Hc2UJma2mIw6uo5wexHt71kf2NpHi/xZo83h7ws+n2VleC+vtUmsvsxlK8rGgbliW5NeqDULYHIlIPsjf4Up1C3PWUn6q3+FAHFaVa3K+PviBM9vMsU9tbCJzGQshELA7T0PPpVLS08Qad8BLVNHgnj1qKxwkflkSr+8O7Cn+LbkgV6F/aFt/z1P8A3w3+FJ/aFsesp/74b/CgDxbX7XS9S0WxbQtO8Tajex3ls9zeX32k+XhxuBEhwzE9lUgcnIr0HUbadvjHpF0sEht00m5RpQh2Bi/AJ6Z9q6n+0bfOfOb/AL5b/Ck/tC2xjzTj/cb/AAoA5fSbeeP4ueIrpoJRbvp1oqSlCFYgnIB6EiuGsNC8O+HJ9S0/xVomrS3YvJZbW4tFuZEu4nO5dvlnaGHQg4r2H+0LbGPNOP8Acb/ClGoW46SsPorf4UAYvgayaw8IWcT6Omjli8n2FZGfygzEjJYk7iMEjsTXRVW+323/AD0P/fDf4Ufb7b/nof8Avhv8KALNFVvt9t/z0P8A3w3+FH2+2/56H/vhv8KALNFVvt9t/wA9D/3w3+FH2+2/56H/AL4b/CgCzRVb7fbf89D/AN8N/hR9vtv+eh/74b/CgCzRVb7fbf8APQ/98N/hR9vtv+eh/wC+G/woAs0VW+323/PQ/wDfDf4Ufb7b/nof++G/woAs0VW+323/AD0P/fDf4Ufb7b/nof8Avhv8KALNFVvt9t/z0P8A3w3+FH2+2/56H/vhv8KALNFVvt9t/wA9D/3w3+FH2+2/56H/AL4b/CgCzRVb7fbf89D/AN8N/hR9vtv+eh/74b/CgCzRVb7fbf8APQ/98N/hR9vtv+eh/wC+G/woAs0VW+323/PQ/wDfDf4Ufb7b/nof++G/woAs0VW+323/AD0P/fDf4Ufb7b/nof8Avhv8KALNFVvt9t/z0P8A3w3+FH2+2/56H/vhv8KAHp/x9zf7qf1p83MEoHXY38jVRL23FzK3mHBVcfI3v7VL9vtv+eh/74b/AAoA8r+Hni+Hw14C0/Sb3RPEL30HmboodLkbJaRmABOB0Irc0DSdXurnxP4q1iyeyutTtTb2lix3SRQIhxux/ExwcV3P9owf89m/75b/AApPt9t/z0P/AHw3+FAHJ+EbW4g+D1jaywSx3C6VIhidCHDbX4x1zWGdEvpvgnoMtvbSrrGjRQ31vEUIfzI2JZMdcldwx9K9I/tC2znzTn/cb/Cj+0LbOfNOf9xv8KAOI8LWd3deGfE3iG9tJoL/AF0zyrBIhEkcKoyRRkdc4BOPesWTT73/AIV18N4Psdx51vqdm00flNuiA35LDGVA9TXqP9oW2c+ac/7jf4Uv9oW//PVuf9lv8KAOV8YW08/jHwRLFBLJHDqMrSsiEhAYsAsR0H1rjJ73VY/Gfir+yBr0dvLdhJzoljFdxMwjALFpGGybsQo447165/aFsOkp/wC+G/wrkj4ZWzvbybQ/FOpaTDeztcTW0dvHNH5jfeZfMQlc+1AGj4EudEl8KwWugC4S1sWa2eK5QpNHIOWEgP8AFk5P1rkL2dJfGGtQ+LY/Ec6rOBpNnYLP5EsGBjb5WAXJ67jxXa+H9O0vw5YSW1rcTzSTzNcXFxcZaSeVvvOxwBngdBWv/aFuBgStj02t/hQBxXwmsriw8OanFcWE9ix1e5ZYJ8llUlcDJ+96bsnOOtN1GeTwn8SL3Xryzu5tI1Sxiga5tYGmNvLGejqoJCkd/Wu2/tC2PWU/98N/hQNQth0lI+iN/hQByD+IfE2t+H/Et5o+mS2scUO3R2niZJ7h8fO+xugz93I5rz3xJbafqvgVxpun+KdU1oCFrq4vRcnyW3rvyrEKTnICoD68YzXuP2+2PWUn/gDf4Uv9o2//AD2b/vlv8KAOUvra4b4uaFcrBKbdNIuEeUIdisWGAT0B9q7Oq39oW2Meacf7jf4Ufb7b/nof++G/woAs0VW+323/AD0P/fDf4Ufb7b/nof8Avhv8KALNFVvt9t/z0P8A3w3+FH2+2/56H/vhv8KALNFVvt9t/wA9D/3w3+FH2+2/56H/AL4b/CgCzRVb7fbf89D/AN8N/hR9vtv+eh/74b/CgCzRVb7fbf8APQ/98N/hR9vtv+eh/wC+G/woAs0VW+323/PQ/wDfDf4Ufb7b/nof++G/woAs0VW+323/AD0P/fDf4Ufb7b/nof8Avhv8KALNFVvt9t/z0P8A3w3+FH2+2/56H/vhv8KALNFVvt9t/wA9D/3w3+FH2+2/56H/AL4b/CgCzRVb7fbf89D/AN8N/hR9vtv+eh/74b/CgB9v92T/AK6v/OuR+JSXupaJaeHbCKcvrN2ltPLGhIhgBDSMxxgcADnrk100F7bqr5kPMjH7jev0qX+0Lb/nqf8Avhv8KAPOPE/wzmj8M3Eun+I/EV5dWCi5sra5uhJH5kfKgLtHOAQMVPreo3D674H8YS6Zfmyjt7hbtIbZ5JLd5YxgMgG7G4EdK9A+323/AD0P/fDf4Uv9oW+c+a2fXa3+FAEsMizwRyruCyKGG5SpAIzyD0PtXl3hNp7P4aax4VudP1CHVLO1viyvauEkDFyux8YbO4YA5r0v7fbf89D/AN8N/hS/2hb9PNb/AL5b/CgDgrqyuj+z6LIW0xu/7Djj8gRnfu2rxt659qo+K9Bje78J61qWl3d9pVrYfZL6K28zzYNyqVk2oQxAOQQK9L/tC2znzTn12N/hSf2hbZz5pz/uN/hQBwXg6z0STxe954e8O3MFhDaFG1S8NwjPIx/1caSnkYGScVb+KEd4bbw1PZWFzfSW2uQTmG3XLFVDE+w+p4rszqFsespP1Rv8KT+0Lb/nqf8Avhv8KAPPdb1MePNc8OadpNhqSxWOpJf31xdWbwLAsYOE+cDLEnGBms7VdD0bSvHeu3finStQnsNTeO5s721E7qrbdrxuIjkHOCMjpXqh1C3PWVj9Vb/CgahbjpKw+it/hQB5pDDqdh8O/EN54U8P3OkTT3Ie1XLvdTQggNKVckq5Xdgf/WrD1y00rUbfSH8P6b4kv54tTtXub6/NyQgD8giQ4Leu1cDHUV7N9vtv+ep/74b/AApf7Rt/+ezf98t/hQBw11cN4O+JOtazf2l5LpOs28AW6trdphDLECpR1UEgEcg4qS/1bVvE3gbxfK2kz21i1pLFpiSRMtxcL5ZyxQ8gFvujAJrtRqFsOkpH0Rv8KT+0LbOfNOfXY3+FAHB+IrK6l+Ahs47aZ7r+ybdPIWMl9w2ZG3Gc+1d/ZgrY2wIIIhQEHt8opv8AaFtnPmnPrsb/AApPt9t/z0P/AHw3+FAFmiq32+2/56H/AL4b/Cj7fbf89D/3w3+FAFmiq32+2/56H/vhv8KPt9t/z0P/AHw3+FAFmiq32+2/56H/AL4b/Cj7fbf89D/3w3+FAFmiq32+2/56H/vhv8KPt9t/z0P/AHw3+FAFmiq32+2/56H/AL4b/Cj7fbf89D/3w3+FAFmiq32+2/56H/vhv8KPt9t/z0P/AHw3+FAFmiq32+2/56H/AL4b/Cj7fbf89D/3w3+FAFmiq32+2/56H/vhv8KPt9t/z0P/AHw3+FAFmiq32+2/56H/AL4b/Cj7fbf89D/3w3+FAFmo5/8Aj3l/3G/lUX2+2/56H/vhv8KZNfWxgkAkOShH3G9PpQBrQ/6iP/dH8qKIf9RH/uj+VFAEM/8Ax92v1b/0GrNVp/8Aj7tfq3/oNWT0oAzLDXtP1PVtT0y1lZrrTWRblShAUuu5cE8Hj0pdX1q20VbRrmO5cXVylsnkQtJhm6FsdF461x/gn/kp3xC/6+LP/wBFGtH4g6vf6RB4eawuWgNzrlrbTFQDviYncvI74oA7GiuBvr3WPE/j3UfDthq8+k6fpNvFJcy2qIZppZQSoDMCFUAdhnNN0XW9ZstT8S+GtTvvttxptot3Z3xjVHkjdTgOAMblIxkDmgD0CivHLSXxle/CxfGEvi6eK7gsGu4reG2jEThAT+8yCWZsc4wB2Hr6noWoNqvh/TdRdQr3drFOyjoCygkfrQBoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBWi/4/rj/AHU/rU7sEQs3QDJqCL/j+uP91P61Jc/8esv+438qAOIi+MHhCWAXAub4Wx6znT5/LA6EltuMV2ljfWupWUN7ZXEdxbTKHjljbKsD3Brxz4Z+M5NN+Gum6dF4U8Q6lIglVXt7MNbyZkY43lsY5wTj1q54et9e8Fw+BNEuJhbHUL+7e7tIyGVVKl1jz7Z7d80Aev1DFd20800MNxFJLAQsqI4LRkjIDAdOPWuS1/V9QtviV4U0qC6eOzvoLxriMAHeUQFTyM8E5rlvBWgakfHXjBf+En1HNvdRJK3lw5uC0PDN8vBXPGMDjmgD1aC7troyi3uIpjC5jkEbhtjjqpx0PtVPRNatte0/7bax3EcXmPHi4haJsqcHg9q87+FukX0OueJ53169mittcuIpYHSMJcNsX945C5DcjoQOBVWXxdr6/BHUNcTUX/tSK/aKO4KrkKLkIBjGMbeOlAHsFFeY6+3ijwlLo2tXHieW/NzqUFrd2Rt40t9khwfLAG5cdiSSe9aF7favrnxA1PQYdcl0W0060hmT7PHGZblnyS+ZFI2LjGAOvU0Ad9RXA/CrddaNqOo3NxNeXst/NBLeyTMy3SxuQroudqLg4wvHFF9e6x4o8e6l4dsNXn0jT9Jt4XuJbVEM00soJUBmBCqAOwzmgDvqK8/0XXNZstS8TeGtTvvttzplqLu0vjGqPJG6nAcDjcpGMgc1zlrN4yvPhYvjCXxdPHdwWBu4reG2jEThAT+8yCWZgOcYA7D1APY6KoaHftqugadqLqFe6tYp2UdAWUEj9a8nvPEuptrWsW+seMLrw3q8VzIumWk1uiWUkY/1ZZ2Qhw3c7hjPSgD2eiuD1PXNat/FHgG0luIov7RFx9vjtyGilZYQw2kjO0NkjGKt6tq9/b/FDw5pUVyy2N1Z3Uk0IAw7KBtOcZ4zQB2NFeYaOPE/ivWvFFsfFNxp9hYarJbwLawRmXoCAWYEBB6YySTk0yy8UeKrj4Sane2v+m69YXMtn50cAZnCSBWkEfQsEJOO5FAHqVFeX+EdbkvvEVkmj+NW1q1dXGoWOqKkVzEccNGoRTweCORjvU+lv4g8eX+tXsHiK60bTrK+ksbOCzijJcx4DSSFwc5J+6MdKAPSaK818Uav4v0L4bW893c20WvLfQwNPCAY5VMu0EjHG4YyB74qPxTrGs+C9GsbS78SSTX2sX/lvqD2gIs4wuX8qJQc4xwDnrzQB6dRXlGgeLXt/Gml6ZYeI9Q8QadqAkSf7bZNG9q4Xcrh/LQFTgjB6VQk8brrmr6w934xv9CitLyS1srWxsDKCE48yRvLbdk5+XIwBQB7NRXkl7438QXXwmtNXgmW21calFZvN5JVJf3u3dtYZCsMEj3OK0NVfxJ4R13w5d3HiW41OLU9SSwu7WW3jSIbwcNGFGVwR3JJ7mgD0uiiigAooooAKKKKACiiigAooooArWf3Jf8Arq/86s1Xs/uS/wDXZ/51R8Ta3D4c8M6jrE2ClpA0gB/ibHyr+JwPxoAi0zxbousa9qWi2N35t/pxAuY9pG3nHBPBweDjpWrJd20VzFbSXESXEwJiiZwGcDrgdTjvivCtG1LQ/DMvgvV4Nd0261KeR7fWUhuUeR/tJ3lmAOcJJiuk8Y6Nf3fxg8MCHxBfWpuYbswmOOI/Ztsa7gmVOd3fdn2xQB6vVJ9WsF0mfVEuo5rKGN5XlhYSDagO7GM5xg029sbi50ZrOLUri2uDGqi8jVDICMZbBBXJx6d68n+H+m3UPwX1K9fVrqa2ksL9EsnRBHEQ0mWBA3ZOD1OOTQB65pmo22r6Xa6jZuXtrqJZomIKkqwyDg8jirdeK28HijSfg/YeJbbxTLC9jpsM0FjFbJ9n8oKvyvkFmYr1ORyeBW/4z8bSW934e0xdTfR4dStmu7q9htzNLGgAwiLtbkk/eI4AoA9LorzTwV4qmm8Yy6FDrN3rmlyWZuIry7tGilgkVgDGx2KGBByDjPatT4latrOlWvh9dEu0trq81iC1LSJuRlYNww64yATjB460AdvRXm+py+IPBOu6Bc3PiO51ew1O+Swu4bqGNdjyA7Xj2AbQCOnNN8T6tMniu7ttW8ZroGnxxx/YrawaN7mdiMs8gKswAPAGOaAPSqz9c1qx8O6Nc6tqUrR2lsAZHVSxAJAHA5PJFeaW3jTWLv4OeJNUTUfMv9Lnmtob9YgjSqjLtkKEYBIbpil8bWOsxfB3xBfazrTX895bW7iFYVjht/3inCAcnqMknnHagD1hHV0V1PDAEU6uC0LWtR8Z6yk2k3bWnhvTH8qSZAPMv51AyoyMrGvc8E9q72gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKiuv+PSb/cb+VS1Fdf8ek3+438qAHQ/6iP/AHR/KiiH/UR/7o/lRQBDP/x92v1b/wBBqz2qlfNIk1sYghfc3Dkgfd9qj+03/wDzytv++2/woA4j/hHvHGj+M/EWr6EugTWurSQvi+mmV02Jt6IuPXv6Vb1Xw/4r8SaXpC6uNHhvLHW4L1haSSGMwR9eWXO/JPHT3rrPtN//AM8rb/vtv8KPtN//AM8rb/vtv8KAOb1Tw9runeL7nxL4aNjO99AkN7Z3rtGHKfcdHUHBAOCCMYo0Twpqkc+vazrVxavrOrwiAR227ybeNVIVASMnk5Jx+FdJ9pv/APnlbf8Afbf4Ufab/wD55W3/AH23+FAHOWXhK/tvhGfCjy25vzpklpvDHy97KwBzjOOfSui8PWEuleG9L06dkaa1tIoHKHKllQA4z24pftN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA0aKzvtN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoAsRf8f1x/up/WppkMkLoOrKQM/SstJ70XUxEdvuKrn52x39qm+03/APzytv8Avtv8KAMj4eeHbzwp4H0/Rb+SGS5t/M3tCxKHc7MMEgHoR2qHxv4d1TVpdG1TQ5bVdS0i6M8Ud3uEcqsu1lJXkHHQ1u/ab/8A55W3/fbf4Ufab/8A55W3/fbf4UAchb+G/Fuo+OdC8R63PpUcNhHcRm0tC58sSJgEOw+ck9egAAxnmrth4e13RvHmq6nZPp82kavLFLcrMzrPCyJt+TAKsDweSK6L7Tf/APPK2/77b/Cj7Tf/APPK2/77b/CgDnfD/h3XPD3ivV3ifT5tD1S9e+dmZ1uInZQCoGNpGQO44rFk+Hmrv8J73wsJ7P7dPeNOr728sKbgScnbnOB6da7z7Tf/APPK2/77b/Cj7Tf/APPK2/77b/CgDI8b+HbzxHpenW1nJCj22o2925lYgFI2yQMA8+lcHrWkyeLfiXr0cT6LILCO3hMOuQmUqdpYmFVZSqHdyT1Oe1ep/ab/AP55W3/fbf4Vk6t4f0/XZkm1XQdHvZUGFknj3sB6ZK5xQBQ+Huu3N+NV0a4ttMQaNMluk2lAi1kBXdhAehXoRngml1Tw9rum+L7nxJ4a+wzvfwJDfWd67Rhyn3HR1BwQDggjFbdhbtpdolpp+n6fa26fdigBRR+AXFWftN//AM8rb/vtv8KAOb0Twnqkc2v6xrVxayazq8Ig2W27ybeNVIRASMnk5Jx+FLZ+EtQt/hE3hR5bc350yS03hj5e9lIBzjOOfSuj+03/APzytv8Avtv8KPtN/wD88rb/AL7b/CgA0Cwl0vw5pmnzsjTWtpFC5Q5UsqAHHtxXGX+heNjYaloh/sbWbC7eTyLrUpXEkKPn5XQIQ+3PBBH4V2f2m/8A+eVt/wB9t/hR9pv/APnlbf8Afbf4UAcZqHgTVrLR/CLaFd20+p+GwVQXm5Y51ZNjjIyV9qls/DXiu98d6T4k1u40tY7S3nhNpabz5e8DGGYfOT36AYGAa677Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KAMnwn4dvND1DxHPdSQsmpao95CI2JIQqoAbIGDx2zWRpvg3XtO8H6np1pqsVlqc2pzX1vPEWZMNIGCPwDgjg49e9db9pv8A/nlbf99t/hR9pv8A/nlbf99t/hQBx/8AwjXiPXvFmi6vrlpo1gukyNL5ljK8s1wxXbt3Mq7U5zg5p0Ph7xX4W1PVf+EZGk3em6lcteLDfyyRtbTPjdgqDuQkZxwa677Tf/8APK2/77b/AAo+03//ADytv++2/wAKAOQ1LwRrV74Ct9Gm1Vb7U/t0V5Pc3LsFOJd7KvBIAHAHt2ra8ZeGrrXrWwuNMuo7XVtMulu7SSVSYywBBRwOdrA4OOa1ftN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoAwLCy8Zajr9le6zPZabYWYcm00+d5TdORgeYWUYUdQPWqMGgeKvC2paoPDa6VeabqNy94sN9NJE9tK/38FVO5CRnHBFdb9pv/APnlbf8Afbf4Ufab/wD55W3/AH23+FAHJap4L13UfAtrpNzqyX2qC/iu57idmVOJN5VOCQAOAP5VreMPDt5r83h57SSFBp2rw303msRmNA2QuAeefatf7Tf/APPK2/77b/Cj7Tf/APPK2/77b/CgDRorO+03/wDzytv++2/wo+03/wDzytv++2/woA0aKzvtN/8A88rb/vtv8KPtN/8A88rb/vtv8KANGis77Tf/APPK2/77b/Cj7Tf/APPK2/77b/CgDRorO+03/wDzytv++2/wo+03/wDzytv++2/woA0aKzvtN/8A88rb/vtv8KPtN/8A88rb/vtv8KALNn9yX/rs/wDOud8beG7zxVFpWnpJAumLfR3GoLITuljTkIBgg5OM5x0rTt571Vk2x25/eNnLt1z9Km+03/8Azytv++2/woAxfEngLRNa8N6hp1tpen21xPAywzpbIpjfqrZAyMEDpWZqXhjxPO/hXWrefTX17R4JIbiO4dxBP5iBWIYLuB4z0711v2m//wCeVt/323+FH2m//wCeVt/323+FAF2HzTbx+eEE20bwhJUNjnBPbNcD4f8ACHiHRvC2r+FpJNNl017e6SxuFdxKWlLECRduABuPIJrsftN//wA8rb/vtv8ACj7Tf/8APK2/77b/AAoA5y48JX8vwhHhRZbf7eNMSz3lj5e8KBnOM449KbrHhPVt/h/V9FuLVNZ0iD7O0dxu8m5iZQGQkDI5GQcfhXS/ab//AJ5W3/fbf4Ufab//AJ5W3/fbf4UAY2h2Pim41+TVtfura2t1t/Ig0yxmeSPJOTI7MBluwwOBWJ8W47iW28Kx2k6wXLeILYQyum8I+HwSvcZ7V2n2m/8A+eVt/wB9t/hVe6hkvjAbuxsJzBKJovMy3luOjDI4I9aAOZk8O+KvEuvaRN4l/sm207SbgXaxWMkkjXMyghSdwG1RnOOTSReHPFOheK9cv9Fj0a6g1iZZjPeySJLbkKBtIVTvUYyBkfhXX/ab/wD55W3/AH23+FH2m/8A+eVt/wB9t/hQBwsHw/1pPh54p0Ge9tJ9Q1a9nuI7jlEbeVOWGPlPyngZx6mug8YeGb3X/hxd+HrSSBLua3iiV5WITKspOSAT/Ce1bX2m/wD+eVt/323+FH2m/wD+eVt/323+FAHOy+FNQ0rxPDrvh2W3j+1bU1aymYrFcADHmrgHbIPpg967Os77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGis77Tf8A/PK2/wC+2/wo+03/APzytv8Avtv8KANGorr/AI9Jv9xv5VT+03//ADytv++2/wAKjnuL428oMVvjYc4dvT6UAaUP+oj/AN0fyooh/wBRH/uj+VFAFa8/19t9W/8AQTSUt5/r7b6t/wCgmkoA4vUfFOt6j4mu/D3hOzsnlsFU31/flvJhZuRGqryzY/AUlp4k8RaP4isNH8VWlg8epM0dpqGm7wnmAZ2SI/Kk9jnH9Kfw+YWXi3xvpNy2L86obxVPV4XHysPUD+tdVqviGx0nVtK0u4Er3mpymO3jjUMRgZLNk8KPWgCXUvEOi6NJHHqmr2NlJIMolxcKhYeoBNUfFHi/TPC3h19ZuJopo9oaCJJlDXHI+4Tw3BzxniuVsFOq+KfEl3oHhyy1Atd/Zbu/1m5AXeigGOJBGzBAPXrXKrBHN+zrrPnQwO1pdXCwbRvEI89eIyeQOT+FAHtGmarYaxZi6068t7qHOC8EqyBWwCVJHcZq5VPTLO1stPhjtLaG3jZFYrDGEBJUc4A61coAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAhT/j7m/3U/rU1Qp/x9zf7qf1qbr0oA5fRvE0994o8V6ddi3htNGkhEcoyp2tGWYuSccY9q6D7fZ/2f8A2h9rg+xeX5v2jzB5ez+9u6Y9680tvn1n4tsvIMSrkeotnyKtOyr+zluLAL/wj4XJOBnYBj65oA799SsYhbmS9t0FyCYC0qjzQBuJXnnjnjtVa08SaFfw3E1nrOnXEVsMzvFcoyxD1Y54HvXnuuafaaofhdZX0Cz20pAkibowFspwfUcdO9WdZ8N6LJ8X/DsLaXaCCXTZ3khWILHIYzlNyjhsH1HYelAHf6brGmazA02l6ha3sSttZ7aZZAD6HB4qtN4p8PW0cEk+u6ZEk5Kws90gEhBwcc84PH1rl9ItLfTvjVq9vZQRW8M+iwTSRwoEVnEhG7A4zisz4V+HNBvfh9LPeadaTvdT3KXUk0Yc7RIwAyfugDnAx1z3oA7rXru8t7WzksL7TbUyXcSO98TskjPVUwfvntVu71fTLGSZLvULS3aGMSyLNMqFEJwGOTwM8Z9a8atppZ/g94P812kWPxDBFC79TEszhP04/CusutJ0/VfjvP8A2haQ3S2+hxyxpMu5Q/nEbtp4JAJxnpQB3WnanYavai5029t7yAnb5lvKHXPpkd6z9Q8X+HdLE4utZshLBw8EcwklBJwAI1yxOewFcfpdvFp/jf4i2llLFp1v9ht5gyjZHC7RPmTA6epxWBpuny634C8P6RpHhS8tNUR7eYas1sqRJtYM06y53NuGTjGTuoA9ktbmO8tIbqIOI5kWRQ6FGwRkZU8g+xqlJ4k0KLUxpkms6el+Tt+zNcoJM+m3Oc+1P8QXFzbeHtVubPJuY7SaSHHXeEJH61weh+HPD1z8FIpLmytXWfS2uri6dAX87aWZy/XcG757UAei3F7a2skUdxcwwvMSsSyOFLkDJAz1wOaq6fr+jas0y6bqtjeNBzKLe4WTZ9cHivLp4f8AhItG+FMetxm5+1Snz1lJ/ejyuN3rkAZ9fxroNS0+y0z4veFTYWdva/abC8imEESoHVVBUEAYOKANrRfH2ha5rl/pVtfWvnW0yxQn7Ujfa8ruzGAckDoetat94j0PS7tLS/1jT7W5kxtinuURjnpwTXI+CdLsI/HvjVo7C1Vra/gEO2BQYgYv4ePlz7VD8M9H03WvCd5fatYW15f39/cjUHuIg7EhyNpJ5AC4wO2aAPRgQQCCCDyCO9UNT13SNFVG1XVLOxEn3PtM6x7vpk81y/wmmkk8CRxmRpbe3u7iC1djnMKuQnPcDkfhVKUf2h8RNbl0XQLfU9QtYobW6utUuQkEHy5CRKEZjkct2zQB6BbXVve20dzazxTwSDKSxOHVh6gjg1gy+ILpPiPbeHBHD9kl0t70yYO/eJNoGc4xj2rn/hOJIrbxRbOlvEsGuTKsNqxaGLKqSqZA+XPsKtXA/wCL52Pv4fl/9HCgDp9S8Q6Lo0qRapq9hZSSDKJcXCoWHqAT0qzNqFlb2IvZry3jtCARO0oEZBOB82cc5GK8909TqvifxJd6B4cstQ3Xhtru/wBZuQFLooDRxII2YIPeuUclv2cNSjcoEi1Bo1EbZVFFyvCn0GTigD2WPX9Gm1RtLi1axfUEzutVuFMgx1+XOayL7x9oOneLI9Aur61ikMDyyTyXSKkLKceW2Twx6gVzXxK0PSNF+HUd1pVjbWt3ZXFs9hNDGBJ5hkXow5YkEk5Jz1q1qemWNx8aNKFxp9rJ52jTySK8CsHkDjk5HJ96AO01LW9K0aJJdU1Ozso5OEa4mWMN9Mnmp476zmsft0V1A9psMnnpIDHtHU7hxj3rhtCs7XVfil4wl1O3iubiy+zQWiTxhxFAUJyoI4yeprKs40sLr4o6VYIselQWwlihQYjimeBjIqjoOgOKAPQW8TaCl1b2ra3pwuLlVeGI3SbpFboVGeQe3rWrXlVj4b8Pf8KFE0lhaDzNH+0yXBQF/OEeQ2885DAADPGMV3Pg24ubvwRoVxeFmuZLCFpGbqTtHJ9zQBuUUUUAFFFFABRRRQAUUUUAQ2/3ZP8Arq/86wfH3iG68K+C7/WbKKGW4t/L2pMCVO51U5wQehret/uyf9dX/nXFfGT/AJJXrH1h/wDRq0AdhJqVnbGFLq7t4JZYzIqSShSyqMsRk9B3Pao9M1vStaSR9L1OzvljOHNtMsm0++DxXDeLdNs9W+JPgS0v7aO5tmtrtmikGVYqiEZHcZA4PFWI7K1034326WNvFbJc6DI0yQoEVysoAJA4yPWgDvZZY4InlmkSOJBuZ3YKqj1JPAFUNN8Q6LrMkkel6vY3rxjLrb3CyFR6kA9KwPiRLYNoNnYX1pc3zX1/DDb2cEoj+0SglgjsQQE45/Cuav4dStfiT4IlvdI0bS3knniVdOmLu0flcq/yKCo7deaAO78YazceHvB+q6vapG89pAZEWUEqTkDnBB70+08TaRPPa2Mmq2C6nNEjm0+0KJNzKDjbnPfp1rL+Jv8AyTHxD/15n/0Ja5TxzoGk6R8JrW4sNOtre4t5LKaKdIx5gdnTLb/vEnJ5JoA9N1DUrDSrU3Wo3tvZ24ODLcShFz6ZPeiw1Kx1W1F1p97b3duTgS28odc+mRXFXsEOq/GyG11OJJ7e00U3FnDMgZPNaTDuAeCwHHtTbK3h0v403dnpkSQWt3oouLyCFQqCUSYRyBwGI/OgDqpfFXh63W3abXdMjW4z5Ja6QeZg4O3nnkY+tTXPiDRrPUItPudWsYb2XHl28lwiyNnphSc89vWvMPBHhzRrj4JXl3NplrJc3FveNJM8QZyVZ9uCeRjAxjHrTbnQNJX9nhr0afb/AGt9LjumuDGDKZcqd28/Nnt16cdKAPWr6/s9MtXur+6gtbdPvSzyBFH4mmadqmn6vbfadNvra8gzjzLeVZFz6ZHevNvEv9pan428G2sVvp95/wASt7qKHUnYQyT4Xc2FB3OF5HHcmtHw7peo6f8AEue4vW0Gwlu9N/e6dpsj7ptr/LMVZQOOVzQB6BNNFbwvNPKkUSDc7yMFVR6kngVS0zXdI1rzP7K1SyvjH98W06yFfqAeK5P4jol5qHhHTLzB0u91dUu0Y/LLtXKI3qCe3tUPjCwtdI8W+Cr3SrSG11CXU/sjiCMRmS3KHerAYyBweelAEj/EFbvx4+i6fqWgxafaLG1xPc3GXnLZ3JDggblA5znBpdC8dy+I/F2o2VjfaEmm2kjwxK0xe5uiEz5iAHHlg+gPAPNQaFpWmt8XPFqnT7QrHa2ToDAmFYq2SOOCe9N8BadYxal43ljsrZJINYnjidYVBjXyx8qnHA9hQB2GgXtxP4ct7zU7/TbmYozS3Vi2LcgE8qSegHU+oNSab4g0XWZJI9M1axvZI/vpb3CyFfqAa8ftj5nwZ8B2MzFdOvdVigvucBojLIdrH0JAr0rUtD8L2WvaDdyxw6bfx3BisfsyCIzErgxkKvzLjsfzoA19S8QaNo0kcep6tY2Ukn3FubhYy30BNX0dJI1kjZXRgCrKcgj1B715J4etde1TxB4uuYdJ8PX039qy20raqzmVY1ACIAFICbenrzXW/DWzl07wxNYyX1jdLb306ItlIzx24yD5QLAH5ST+YoA7CiiigAooooAKKKKACiiigAooooAKKKKACiiigAqOf/j3l/3G/lUlRz/8e8v+438qAL0P+oj/AN0fyooh/wBRH/uj+VFAFa8/4+Lb6t/6CaSi9hWee2jcsAWY/KxU/d9RR/ZcH9+f/v8AN/jQBg6/4N0XxJPDc31vKl5AMRXdtM0MyD03ryR7HNM0PwTonh++e/tYZ59QdNjXl5O08u30DN0H0roP7Lt/70//AH+b/Gl/su3/AL8//f5v8aAOYufh/oV1qd1fEX0X2x/Mu7a3vZIoLhvV0UgHPf1q5Z+EdEsfD1zoMNkP7MuWkaS3ZiQd5ywHoPTHStv+y4P78/8A3+b/ABo/suD+/P8A9/m/xoAzdD0K08P2BsrJ7pod24C5uGmK8AYBY5AwBxWlR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQBCn/H3N/up/WotT06PVdNnsZpbiKOZdrPbymOQcg/Kw5HSpI9OhN3Mu6bAVf+Wze/vU/9lwf35/8Av83+NAGPoXhfSfDmnTWOnWxEU7s87SuZHmYjBLs3J44rCX4U+FRby2z297LZsGCWst7I0MO7OSiE4B5ODziu0/su3/vz/wDf5v8AGl/su3/vz/8Af5v8aAMl/DemySaM7RSbtG/48/3h+T5AnP8Ae4A61NNollca7aazJG5vbSF4InDkAI/3gR0NaH9l2/8Afn/7/N/jR/ZcH9+f/v8AN/jQBnpoljH4ik11Y3+3y2y2rvvO3yw24Db0znvXn3g74Y2knhOOPW7fUrG8llm+1QQXrwrOhkbb5iqcN8pHPXFeo/2XB/fn/wC/zf40f2Xb/wB+f/v83+NAGPd+F9IvNLsNNe18uzsJopraKFigRo/u9Oo9u9WBoliviOTXhG/9oSWwtWfedvlhtwG3pnPer/8AZdv/AH5/+/zf40v9lwf35/8Av83+NAHPap4d2ReINR0YCPW9TsxD5kzboyyKQnyngdf8a8yXwfaJ4eFhp3w/1+z8RCEIl99qEccc2P8AWGUS4K55wF6cYr27+y4P78//AH+b/Gk/sq3/AL0//f5v8aAK+mw3MGl2kF7MJ7qOBEmlx/rHCgM34nNczJ8MvDckjr5V8llJJ5j6dHeyLas2c8xA4xnt0rr/AOy4P78//f5v8aP7Lg/vz/8Af5v8aAM280HT7670u5mhIk0uQyWgRtqoSu3oOCMdqdc6HY3et2GsTRubyxSSOBg5AAcYbI71of2XB/fn/wC/zf40f2XB/fn/AO/zf40AYjeEtJPiX/hII47iHUG2+a0Nw6JNtGF8xAdrYHrVHUPh7oWoX1zdf8TC1N2d13FZXskEdwe5dFOCT3PGa6n+y4P78/8A3+b/ABo/suD+/P8A9/m/xoAr2Nha6ZYw2VlbpBawIEjiQYCgdqw9R8C6NqWrzamzX9tc3Kqtz9ivZIFuAowPMCkZ447V0n9lwf35/wDv83+NJ/Zdv/fn/wC/zf40AZWg+GtK8NQ3UOk2v2aG5mM7xhiVDYA+UdhgDim6r4Y0vWdSsdRuoplvbI/uJ4JmiYDOSp2n5lJHQ1sf2XB/fn/7/N/jR/ZcH9+f/v8AN/jQBy9z8P8AQrnU7q9K30QvH8y7toLySOC4buXjU4Oe/rWR4x8GJb/C/UNA8N6fI/m3CTJbK2ckzKz4z2wDx6V3/wDZcH9+f/v83+NH9l2/96f/AL/N/jQBylt8OfD1vf2135V5Ktq4ktrWe8kkt7d+xSMnAx27CtDXfCeleIpra4vo7hbm13CG4trh4ZEDdRuUg4PpW3/ZcH9+f/v83+NH9lwf35/+/wA3+NAHP6z4M0nWr6O/lN5a38cfki7sbp4JWT+6zKfmH1qKfwvZ6R4J1fStEs2DXFtOcby8k8roRlmY5Zicck10v9lwf35/+/zf40f2Xb/35/8Av83+NAHnXh74X6Q/hfSItVtr+OQW0TXen/bJFt3mAG4vEDtJyOcda9EVAihVUKqjAAGAB6Uv9l2/9+f/AL/N/jR/ZcH9+f8A7/N/jQAUUf2XB/fn/wC/zf40f2XB/fn/AO/zf40AFFH9lwf35/8Av83+NH9lwf35/wDv83+NABRR/ZcH9+f/AL/N/jR/ZcH9+f8A7/N/jQAUUf2XB/fn/wC/zf40f2XB/fn/AO/zf40AQ2/3ZP8Arq/86qa7oVj4k0afStSjeS0n271RyhO1gw5HuBVq206F1ky03EjDiZh3+tT/ANl2/wDfn/7/ADf40AZtxoNhdazp2qyxubvTkkS2YOQFDgBsjvwKc+iWL+IYtdaN/t8Vs1qr7ztEZbcRt6Zz3rQ/su3/AL8//f5v8aP7Lg/vz/8Af5v8aAMzXvD2neJNPFlqULvGkiyxvG5R4pF6MrDkEVmWfgHQ7TUrPUyt7c6jaSGSO7u7t5pScEYLMeVwTxwO9dN/Zdv/AH5/+/zf40f2Xb/35/8Av83+NAFLVtKtdb0m50u+RntblPLlVWKkjOeo6dKg1bw9p2taH/Y19FI9l+7+VZCrfIQV+Yc9hWp/ZcH9+f8A7/N/jR/ZcH9+f/v83+NAGNr3hbS/ERt5L2OdLm2JNvdWszQzRZ67XXnB9OlGheFtL8Oi4axjme4uiDcXVzM0002OBuduSB6dK2f7Lg/vz/8Af5v8aP7Lt/78/wD3+b/GgDJ03w1pmk+HDoFpFIunlJEKNIWbEhJb5jz/ABGkfwzpknhUeGmik/swW4tvL8w7tg6Dd17da1/7Lg/vz/8Af5v8aP7Lg/vz/wDf5v8AGgDG1fwtpWt2FraXkMmLQqbaaKVo5YSBgFXHIOAPrTdD8J6ZoFxPdW32qe9uFCS3d5cNPMyjou5ug9hW3/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQBQ1nRdP1/TZNP1O2E9s5DbSSpVh0ZSOQR6iszSPBOkaRqS6kpvry+RDHFcahdvcPEp6hNx+X8Oa6L+y7f+/P/AN/m/wAaP7Lg/vz/APf5v8aAM+20SytNbvtYhjcXt8kcc7FyQVQYXA7UmnaFY6XJqT2sbq2o3DXNzuctudhgkegx2rR/suD+/P8A9/m/xo/suD+/P/3+b/GgDDi8H6JH4UXwy1l5ukqpUQyuWP3i2d3XIJyD2qtpPgXRtJ1OLUVN/eXcClLeS/vHuPIU8EIGOF449a6X+y4P78//AH+b/Gj+y4P78/8A3+b/ABoA5zVfA+karqcmpbr+yvZlCTzafePbmdR0D7Thvr1rW0jR7DQdMh07TLZbe1hB2oCTyTkkk8kk8kmrv9l2/wDfn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABRR/ZcH9+f/v8AN/jR/ZcH9+f/AL/N/jQAUUf2XB/fn/7/ADf40f2XB/fn/wC/zf40AFFH9lwf35/+/wA3+NH9lwf35/8Av83+NABUc/8Ax7y/7jfyqT+y4P78/wD3+b/Go7jTYFtpWDT5CE/65vT60AXof9RH/uj+VFEP+oj/AN0fyooAhn/4+7X6t/6DVk9KrT/8fdr9W/8AQasnpQB5HpvhbTPFnxM8brq4u5VtJ7YQCO8liCBosnhWHoK0IIrz4feO9G0uHUry88P62ZIUgvJTK1pMq7hsY87T0waztO8WaT4S+Jvjg6xJcwi7ntjAUtJZQ4WLB5RT6itCKa6+IXjvRdTttPu7Xw/ohknW5u4TE11My4UIp52jrn60Abd344vZtYvtO8O+HLnWTp7CO7mW5jgjSTGdil/vMB1Hb1qeH4g6NJ4NufEsguIoLVzDcWzx/vo5gwXyiv8Ae3EAfUVy/h/XrD4e6l4j0vxI01obnVJr+0uDbyOlzHJggKVBywxgjrWLe2U6fC7xhq9/YSwJrOqm9tYJW8qWJC6BJNp/j43BOp4HegDrbrxr4k/tjSNK/wCEdj0mbUpD5U+o3AlQqo3MmIiSJCOgJA9z0r0CvHtK1u3vPE2i3fi3VtVLWshSwW60J7CFp3G3c7ZYFiOnIHNew0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVov8Aj+uP91P60mpXqabpd3fygmO2heZgO4VST/Kli/4/rj/dT+tJqVkmpaXd2EpIjuYXhYjsGUg/zoA828K+D4PHegweJfF893fXWogzQ263UkUNrESdioqEducmtzRtA1jwzNrNn9vku/DzWxksRczmSe3fB3Jk8lO4yeKwvCfjK28DaDB4Z8YR3On3mnAwRT/ZpJIbqME7WRlB7YGDW3ouv6x4nu9av1tJbbw2tr5dj9ogMctxJg7pADyE7DI5z9aAOV8GeOdU0n4YabeReGNR1GwsrZmu74zpHwGJYorHdIAOp4HB9K73WfGtppujaXe2lrPqE2rsiafbQ4Vpiy7hkscKAOST0rl/DcMq/s7mJo3En9jXI2FTuzh+Mdazb7Ur7S/BXgCGW7vdL0SW0RNSvrWImWHEK7FztJQMcgsBmgDtdG8YXN34iOgazok2k6i9ubmBTOk8c0YOGw69wT0IrNm+It3JHqV9pnhe8v8AR9OmkiuLxLiNGJj++UiPLAfhmub0AWU/xb0i60p9aurA6bcKL7UJJnWVsrnYZOcD1wAT0ziqGqXXh+CfVrtJtY8JeKxNKTaWTSut3ICdj7NuyQPx0x159aAO71jVtFvdc8E3UlnNcvfSySWE6ytGIsxbssv8WR2PSmzePruXxBq2haP4avdSvtNkRXKzJFEVZQ2S7cKecAck4NY+oNq15q3wwudWt2TUDJI92qpgI5g5yB93ntWp4MjkT4gePGZGVXvLYqSpAP7nt60AXdN+IGm3XhHUPEF9BPYLpsskF7bSYZ4pUIBQY4JJIx65rLv/ABx4kW60qyTw2mlSapP5dvc6nOHjHyltrLESwcgcA4HvxXLzxrH4A+KH2q18xH1q4ZVkfys/6vDAnqQeQB1Ix3p2n63Bdavodx4u1jVzb2MytafatAeyiecjarySZYE8nH3Rk0Aet6vqtromj3eqXzlba1iaWQqMnAGeB3NcgnxCv7cafd6x4VvNO0m/mSGK7a5jkZDJ9wyRryoPHrjvWx49vNW0/wAD6rdaHCZdRjiBiUR+YQNw3EL3IXJx7V5N4jn0jU9F09tH1bxJrt0t9avcTXEkxhhHmDO9CAgJPRQCR+FAHu91cxWdpNdTttihRpHbGcKBkn8hXDQfEm6NjaazdeGLy38P3bosd/8AaI3ZVc4V3iHIUkjucV2uoPJHpl08NqLuVYnKW5bAlODhMnpnp+NeHNfaZp1hbz+BrvWtM8RvLHnw3iWSEOWHmI6OuFA+b5gQKAPUde8Yy6drsOg6Ro8+sas8P2h4I5ViSGLONzu3Ayegpnh/xwNb13UdHm0e80+80+3Sa4juCuQzZ+UYyCOMhgcEGsHUNTh8G/FO81jWllh0vVdOiijvFiaRI5Yyco20EjIORUfhTVY9d+K3iS8t4LiKCTS7ZYWuIjGZFywDhTyFPbIBxzQB1vh/xZFr/giPxNHaSQxPDLL5DOCw2FhjPTnb+tc/qniLSNb0PwVq99pU8g1HU7f7Ii3BQwSsG2s2MbgMHjvXM+EvFdnonw2bwlc2uoP4htYrm3bT4rSRnJLOQwbG0Lg5yTSpBN/wr74WJ5Mm6PVrQuNhyow/J9KAO01bx5NZ+Kbrw5p3h691PUYYI51WF1RCrZyWZuEAwOvUngUy+8fXEPim98Oaf4cvdQ1C2SGQiKRFj2uMks7cLjgdyT0qLSo5B8Z/EMhRhG2l2oDFTgkM3ek0GORfjB4wkKMEa0sQrFTg4VuhoAuX3jS+fWb3TPD/AIduNYewIW8mFxHBHG5GdilvvNjGQOBnrXO+PPE0PiD4RXWp2H2i2dbyCCaKT5JYJFnRXRsHgisV7DQ/D/i7xJH4tutYsFvb5r2yuba5uI4Z43Ayv7rjepBBzz0qTUdJM/wY1p9I0G/tY5tRW8jhnlkmnuI1lQmYhhuBZVJ289M96APaKKpaTqtprelwajYuz204LIzRsh4ODlWAI5Bq7QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXs/uS/wDXZ/51xfxllkg+FesSRSPG48nDIxUj96ncV2ln9yX/AK7P/OuK+M0ckvwr1hI0d3JhwqKST+9TsKAL+v8AjVPDepaLpf8AZd5f3GpxSGFbXBbciqcYPrnqSAACTTtA8ZXGo+I5/D+saHPo+ppbC7ijedJlli3bSQy8ZB6isvW4pG+K3gRxG5RbW9DMFOBmJep7VJdRyH45afKEbyxoEql9pxnzhxmgBPiv4i1zw54YW40W2k3NNGsl2kkY8nMigKVbk7skZA4rK8d65qMVj4R1HUdKu9OmXX4vMs4pVnkdQr4A8s4bd6Vt/Fmyur34eXy2lvJcSRSwTGOJdzFUlVmwO+ACfwrJ8WavZ+JB4I1DSmlntv8AhI4csYXQrhWzkMARj1oA29P8cXjeJrLRdb8N3WkPqKSNYyyXEcolKDLK2w/K2Occ0++8aXr61e6Z4e8O3GsvYELdyi4jgjjcjOxS33mxjIHAz1qn4yid/iJ4CdUZlS6uixCkhf3Pf0rjnsND8P8Ai/xJH4sutYsEvb5r2yuba5uI4Z43Ayv7rjepGDnnpQB6r4b8Q2vibSBf2scsJWRoZoJl2yQyocMjD1BrzXxHdatrXxft9LuvD99dWVnZGaG1i1NYVf8AfAC5OGGRjjYefauv+G1tbQ+H7qaz0m7062ur2WaIXk7yy3CnAEzb+VLYzg/XPNVTG/8AwvfzNj+X/wAI5t37Ttz9o6Z6ZoAnPj6efxTqHh/TfDt7fXVhPHHNIkipEkbqGLlm6YzwvJODTrnxze3GqX9n4c8N3Osx6fIYbu4W5jgRZByUTd99h36VW8GxSJ8QvHrMjKr3VqVJUgN+57etY/hrxDp/w+l1zRvEhntJX1Oe7tZfs8jrdRSEEFSqnLdiOtAHQ3fxH02HwBN4tgtbiWGCQRTWrYSWKTeEZGHYqT+P41C/xCu7TUNPOp+F7+w0nUbhba3vppY929/ub4gdyA+/PqK4bVNLv4/g14ovriynt31jWP7QhtXQ+ZHG88e3K9QSFzj3rtvilHJLoWiiNGcjW7MkKpOBv5NAGfqfi7xVa/FZNJtdCnuLIae7pardQr5wEgHn7jyAOm0nPtXY+K/EI8LeH5tYks5LmG3ZPOWNgGVCwBb3xnOK5PxJfQ+Hvi1peuaks8emy6TLZi4SB5FWXzQwU7QcZFd9qFjb6pptzY3SB7e5iaKRT3Vhg/oaAMrxF4og0Kx0+eOBryXULqK1tYonAMjP0OfQDJrLu/G1/LrOoad4f8N3Gr/2cwju5vtMcCLIRnYu77zY+grjvAMOo6v4psdL1SNynguGW2MjdJp3YpGw9cQrn6ml/tmK48Sa3beKtW1+1vYr547DSdN82JZoOPLZfKGZGb1Lce1AHXXPxH0+L4enxhFaXD26usclu2FkRvMEbKevIOfrTrjx82n6DPq2p6Df2cbzpBp9u5Uz3jP90BAfkJ9G5rzcWtwv7O99AbW4SX+1CfKkBaQf6Wp57k+9d98TrW5Wy0LWYLaW6j0fVob25hiUs5iAIZgB1IzmgDA8UeI9YvNb8G2mp6De6NM+uQOv+kJNHKmGBUuh4YEj5T+GcV63XknijxZpvirWvByaIt3d28GuwSTXS2siRRnDAKSwHzck8dMc44r1ugAooooAKKKKACiiigAooooAKKKKACiiigAooooAKiuv+PSb/cb+VS1Fdf8AHpN/uN/KgB0P+oj/AN0fyooh/wBRH/uj+VFAFa8lSCe2kkJCBmBIBPb2o/tSz/56t/37b/CrlFAFP+1LP/nq3/fDf4Uf2pZ/89G/79t/hVyigCn/AGpZ/wDPVv8Av23+FYviiw0zxPpK2Ut7cW0kU0dzb3EMZ3RSocqwBGDz2NdNRQB59L4eu9Wlto/Efi2XUbC3mScWsOmi3ErocrvYZJAPOBiu1/tSz/56t/37b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/ALUs/wDnq3/ftv8ACj+1LP8A56t/37b/AAq5RQBT/tSz/wCerf8Aftv8KP7Us/8Anq3/AH7b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/tSz/56t/37b/Cj+1LP/nq3/ftv8KuUUAU/wC1LP8A56t/37b/AAo/tSz/AOerf9+2/wAKuUUAU/7Us/8Anq3/AH7b/Cj+1LP/AJ6t/wB+2/wq5RQBT/tSz/56t/37b/Cj+1LP/nq3/ftv8KuUUAU/7Us/+erf9+2/wo/tSz/56t/37b/CrlFAFP8AtSz/AOerf9+2/wAKP7Us/wDnq3/ftv8ACrlFAFP+1LP/AJ6t/wB+2/wo/tSz/wCerf8Aftv8KuUUAU/7Us/+erf9+2/wo/tSz/56t/37b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/ALUs/wDnq3/ftv8ACj+1LP8A56t/37b/AAq5RQBT/tSz/wCerf8Aftv8KP7Us/8Anq3/AH7b/CrlFAGZHqNqt3M5dtrKoB8tu2fap/7Us/8Anq3/AH7b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/tSz/56t/37b/Cj+1LP/nq3/ftv8KuUUAU/wC1LP8A56t/37b/AAo/tSz/AOerf9+2/wAKuUUAU/7Us/8Anq3/AH7b/Cj+1LP/AJ6t/wB+2/wq5RQBzfiay0vxPokum3N1PCGdJI5oUbdHIjBlYZGOCBwawLnw5e61HHZ+I/F8uoaYsiSPaw6atv5xUhlDsMnGQCQMV6HRQBT/ALUs/wDnq3/ftv8ACj+1LP8A56t/37b/AAq5RQBT/tSz/wCejf8Aftv8KP7Us/8Anq3/AH7b/CrlFAFP+1LP/nq3/ftv8Kw7XT7C18Y6h4iF9Iz3ltFbmEwthQhJznvnNdRRQBT/ALUs/wDnq3/ftv8ACj+1LP8A56t/37b/AAq5RQBT/tSz/wCerf8Aftv8KP7Us/8Anq3/AH7b/CrlFAFP+1LP/no3/ftv8KP7Us/+erf9+2/wq5RQBT/tSz/56t/37b/Cj+1LP/nq3/ftv8KuUUAU/wC1LP8A56t/37b/AAo/tSz/AOerf9+2/wAKuUUAU/7Us/8Anq3/AH7b/Cj+1LP/AJ6t/wB+2/wq5RQBT/tSz/56t/37b/Cj+1LP/nq3/ftv8KuUUAU/7Us/+erf9+2/wo/tSz/56t/37b/CrlFAFP8AtSz/AOerf9+2/wAKP7Us/wDnq3/ftv8ACrlFAFP+1LP/AJ6t/wB+2/wo/tSz/wCerf8Aftv8KuUUAZttqNrGsgZ2GZGYfu26E/Spv7Us/wDnq3/ftv8ACrlFAFP+1LP/AJ6t/wB+2/wo/tSz/wCerf8Aftv8KuUUAU/7Us/+erf9+2/wo/tSz/56t/37b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/ALUs/wDnq3/ftv8ACj+1LP8A56t/37b/AAq5RQBT/tSz/wCerf8Aftv8KP7Us/8Anq3/AH7b/CrlFAFP+1LP/nq3/ftv8KP7Us/+erf9+2/wq5RQBT/tSz/56t/37b/CqupXi3WnTwWWpPZXLriO5W3MhjPrtYYP41rUUAct4V0zTfC9hPCL+4vbu6na5u7ueM75pG6kgDAGAAAOlbv9qWf/AD1b/v23+FXKKAKf9qWf/PVv+/bf4Uf2pZ/89W/79t/hVyigCn/aln/z1b/v23+FH9qWf/PVv+/bf4VcooAp/wBqWf8Az1b/AL9t/hR/aln/AM9W/wC/bf4VcooAp/2pZ/8APVv+/bf4Uf2pZ/8APVv+/bf4VcooAp/2pZ/89W/79t/hR/aln/z1b/v23+FXKKAKf9qWf/PVv+/bf4Uf2pZ/89W/79t/hVyigCn/AGpZ/wDPVv8Av23+FH9qWf8Az1b/AL9t/hVyigCn/aln/wA9W/79t/hR/aln/wA9W/79t/hVyigCn/aln/z1b/v23+FH9qWf/PVv+/bf4VcooAp/2pZ/89W/79t/hUdxqVo9tKqyMSUIA8tvT6VoUUAMh4hQf7I/lRT6KAP/2Q==", 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import requests\n", + "from io import BytesIO\n", + "\n", + "for obj in objects:\n", + " url = minio_client.presigned_get_object(bucket, obj.object_name)\n", + " response = requests.get(url)\n", + " image = Image.open(BytesIO(response.content))\n", + " image.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed68d1a2-8644-4fc4-952e-2df18c9ae26c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bebde2a4c5a2b37baa10485920cd3df23caa282c Mon Sep 17 00:00:00 2001 From: Chris Jarrett Date: Mon, 14 Oct 2024 10:29:27 -0700 Subject: [PATCH 3/6] Small changes --- examples/langchain_multimodal_rag.ipynb | 8 +++++--- examples/llama_index_multimodal_rag.ipynb | 2 +- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index fd6eaa0c..729e7b34 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -25,11 +25,13 @@ ] }, { - "cell_type": "raw", - "id": "2dcc96cf-f8e1-4baa-b1ca-b6521d23dec8", + "cell_type": "code", + "execution_count": null, + "id": "bacbe052-4429-4c0a-8b1e-309ac55ad8fb", "metadata": {}, + "outputs": [], "source": [ - "pip install -qU langchain langchain_community langchain_chroma langchain-nvidia-ai-endpoints" + "pip install -qU langchain langchain_community langchain-nvidia-ai-endpoints" ] }, { diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index b2324060..32d12336 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -438,7 +438,7 @@ "metadata": {}, "outputs": [], "source": [ - "from llama_index.core import VectorStoreIndex, StorageContext\n", + "from llama_index.core import VectorStoreIndex\n", "from llama_index.vector_stores.milvus import MilvusVectorStore\n", "\n", "\n", From cc8f8c02d7376154bf1e1dcf227bd69393553754 Mon Sep 17 00:00:00 2001 From: Chris Jarrett Date: Tue, 15 Oct 2024 14:51:01 -0700 Subject: [PATCH 4/6] Address reviews --- examples/langchain_multimodal_rag.ipynb | 387 ++-------------------- examples/llama_index_multimodal_rag.ipynb | 344 ++----------------- 2 files changed, 70 insertions(+), 661 deletions(-) diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index 729e7b34..8a00be96 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -10,18 +10,18 @@ }, { "cell_type": "markdown", - "id": "12e78be0-9abe-4d1a-8aaa-61230b059792", + "id": "91ece9e3-155a-44f4-81e5-2f9492c62a2f", "metadata": {}, "source": [ - "This cookbook shows how to use LangChain to query the table and text extraction results of nv-ingest's pdf extraction tools" + "This notebook shows how to perform RAG on the table, chart, and text extraction results of nv-ingest's pdf extraction tools using LangChain" ] }, { "cell_type": "markdown", - "id": "cdfad056-baef-448e-b5b3-4544e6b06472", + "id": "81014734-f765-48fc-8fc2-4c19f5f28eae", "metadata": {}, "source": [ - "To start we'll need to make sure we have some dependencies installed" + "To start we'll need to make sure we have Langchain installed as well as pymilvus so that we can connect to the Milvus vector database (VDB) that NV-Ingest uses to store embeddings" ] }, { @@ -31,15 +31,15 @@ "metadata": {}, "outputs": [], "source": [ - "pip install -qU langchain langchain_community langchain-nvidia-ai-endpoints" + "pip install -qU langchain langchain_community langchain-nvidia-ai-endpoints langchain_milvus pymilvus" ] }, { "cell_type": "markdown", - "id": "2487f1eb-eea8-46f1-9ab0-0fd3bea77bd5", + "id": "d888ba26-04cf-4577-81a3-5bcd537fc2f6", "metadata": {}, "source": [ - "Then, we'll use nv-ingest to parse an example pdf that contains text, tables, charts, and images. We'll need to make sure to have the nv-ingest microservice up and running at localhost:7670 along with the supporting NIMs. To do this, follow the nv-ingest [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart). Once the microservice is ready we can create a job with the nv-ingest python client" + "Then, we'll use NV-Ingest to parse an example pdf that contains text, tables, charts, and images, embed it with the included embedding microservice and store the results in the Milvus vector database. We'll need to make sure to have the NV-Ingest microservice up and running at localhost:7670 along with the supporting NIMs and microservices. To do this, follow the nv-ingest [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart). This notebook requires all of the services to be [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). Once everything is ready, we can create a job with the NV-Ingest python client" ] }, { @@ -53,6 +53,8 @@ "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", + "from nv_ingest_client.primitives.tasks import EmbedTask\n", + "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", "\n", "\n", "from nv_ingest_client.util.file_processing.extract import extract_file_content\n", @@ -63,336 +65,11 @@ "file_name = \"../data/multimodal_test.pdf\"\n", "file_content, file_type = extract_file_content(file_name)\n", "\n", - "job_spec = JobSpec(\n", - " document_type=file_type,\n", - " payload=file_content,\n", - " source_id=file_name,\n", - " source_name=file_name,\n", - " extended_options={\n", - " \"tracing_options\": {\n", - " \"trace\": True,\n", - " \"ts_send\": time.time_ns()\n", - " }\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5cedc329-e9a1-428e-9fa1-2aee965c2533", - "metadata": {}, - "source": [ - "And then we can and submit a task to extract the text and tables from the example pdf" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8bcd6b2a-c832-4685-bd6e-53b60a107e28", - "metadata": {}, - "outputs": [], - "source": [ - "extract_task = ExtractTask(\n", - " document_type=file_type,\n", - " extract_text=True,\n", - " extract_images=False,\n", - " extract_tables=True,\n", - ")\n", - "\n", - "\n", - "job_spec.add_task(extract_task)\n", - "\n", "client = NvIngestClient(\n", " message_client_hostname=\"localhost\",\n", " message_client_port=7670\n", ")\n", "\n", - "job_id = client.add_job(job_spec)\n", - "\n", - "client.submit_job(job_id, \"morpheus_task_queue\")\n", - "\n", - "result = client.fetch_job_result(job_id, timeout=60)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bf097db1-9699-442e-88b7-7b67a8a2fecf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'document_type': 'text',\n", - " 'metadata': {'content': 'TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.',\n", - " 'content_metadata': {'description': 'Unstructured text from PDF document.',\n", - " 'hierarchy': {'block': -1,\n", - " 'line': -1,\n", - " 'nearby_objects': {'images': {'bbox': [], 'content': []},\n", - " 'structured': {'bbox': [], 'content': []},\n", - " 'text': {'bbox': [], 'content': []}},\n", - " 'page': -1,\n", - " 'page_count': 3,\n", - " 'span': -1},\n", - " 'page_number': -1,\n", - " 'subtype': '',\n", - " 'type': 'text'},\n", - " 'debug_metadata': None,\n", - " 'embedding': None,\n", - " 'error_metadata': None,\n", - " 'image_metadata': None,\n", - " 'info_message_metadata': None,\n", - " 'raise_on_failure': False,\n", - " 'source_metadata': {'access_level': 1,\n", - " 'collection_id': '',\n", - " 'date_created': '2024-10-08T16:31:46.121257',\n", - " 'last_modified': '2024-10-08T16:31:46.121101',\n", - " 'partition_id': -1,\n", - " 'source_id': '../data/multimodal_test.pdf',\n", - " 'source_location': '',\n", - " 'source_name': '../data/multimodal_test.pdf',\n", - " 'source_type': 'PDF',\n", - " 'summary': ''},\n", - " 'table_metadata': None,\n", - " 'text_metadata': {'keywords': '',\n", - " 'language': 'en',\n", - " 'summary': '',\n", - " 'text_location': [-1, -1, -1, -1],\n", - " 'text_type': 'document'}}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result[0][0][0]" - ] - }, - { - "cell_type": "markdown", - "id": "72d85eb5-2d66-4d6d-95fe-f69b2c6c5489", - "metadata": {}, - "source": [ - "Now, we have the extraction results in the nv-ingest metadata format which we'll grab the extracted content from and load into Langchain documents" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b0c6d966-9a38-4b1b-b5f2-cbe214d1718d", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.documents import Document\n", - "\n", - "texts = []\n", - "tables = []\n", - "for element in result[0][0]:\n", - " if element['document_type'] == 'text':\n", - " texts.append(Document(element['metadata']['content']))\n", - " elif element['document_type'] == 'structured':\n", - " tables.append(Document(element['metadata']['table_metadata']['table_content']))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7bb4b918-9029-4ffc-badd-dddc4022a750", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(metadata={}, page_content='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.')]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "texts" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "307fdf5d-587b-448b-8b79-e4fa88fb5b0c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(metadata={}, page_content='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard'),\n", - " Document(metadata={}, page_content='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost Chart 1 - Gadgets and their cost'),\n", - " Document(metadata={}, page_content='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray'),\n", - " Document(metadata={}, page_content='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000 Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 10 - 100 - 1000 - 10000 - 100000 Frequency Range Start (Hz) - Frequency Range End (Hz) This chart shows some average frequency ranges for speaker drivers - Frequency Ranges of Speaker Drivers')]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tables" - ] - }, - { - "cell_type": "markdown", - "id": "15410af0-fa5c-4aed-b554-f2165c6f482e", - "metadata": {}, - "source": [ - "Next, we'll set our NVIDIA API key and create a vector store to embed and store our text and table documents using an embedding model from NVIDIA's API catalog" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "53957974-c688-4521-8c61-09f2649d5d53", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from langchain_chroma import Chroma\n", - "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n", - "\n", - "# TODO: Add your NVIDIA API key here\n", - "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", - "\n", - "embedding = NVIDIAEmbeddings()\n", - "vectorstore = Chroma.from_documents(documents=(texts+tables), embedding=embedding)" - ] - }, - { - "cell_type": "markdown", - "id": "12d14f63-31b8-4d72-a029-a54328ff1c5b", - "metadata": {}, - "source": [ - "Then, we'll create a retriever from our vector score that will allow us to retrieve our documents by semantic similarity and we'll use an llm from NVIDIA's API catalog to generate the final answer from the retrieved documents" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a12f53f7-2407-4890-9586-82956cfccf13", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n", - "\n", - "retriever = vectorstore.as_retriever()\n", - "\n", - "llm = ChatNVIDIA(model=\"meta/llama-3.1-405b-instruct\")" - ] - }, - { - "cell_type": "markdown", - "id": "b87111b5-e5a8-45a0-9663-2ae6d9ea2ab6", - "metadata": {}, - "source": [ - "Finally, we'll create an RAG chain that we can use to query our pdf in natural language" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d16331bb-95dd-46d7-8b71-9d966a8ef3ba", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.prompts import PromptTemplate\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "\n", - "template = (\n", - " \"You are an assistant for question-answering tasks. \"\n", - " \"Use the following pieces of retrieved context to answer \"\n", - " \"the question. If you don't know the answer, say that you \"\n", - " \"don't know. Keep the answer concise.\"\n", - " \"\\n\\n\"\n", - " \"{context}\"\n", - " \"Question: {question}\"\n", - ")\n", - "\n", - "prompt = PromptTemplate.from_template(template)\n", - "\n", - "rag_chain = (\n", - " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", - " | prompt\n", - " | llm\n", - " | StrOutputParser()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b547a19a-9ada-4a40-a246-6d7bc4d24482", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'The dog is chasing a squirrel in the front yard.'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rag_chain.invoke(\"What is the dog doing and where?\")" - ] - }, - { - "cell_type": "markdown", - "id": "3d488c44-30e2-4b36-b8f0-8fb1cb5cac68", - "metadata": {}, - "source": [ - "## Milvus" - ] - }, - { - "cell_type": "markdown", - "id": "1860f415-d281-4d31-95fb-fcd37ce68ee3", - "metadata": {}, - "source": [ - "Alternatively, we can use the embedding NIM and the milvus vector database packaged with NV-Ingest. This requires pymilvus and the embedding, milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). This has the benefit of directly sending the extraction results to the embedding microservice and then to a vector database without roundtripping between the client and the NV-Ingest microservice between each step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ee4c94a-5f20-4608-aee1-fed664eb4e10", - "metadata": {}, - "outputs": [], - "source": [ - "pip install -qU pymilvus langchain_milvus" - ] - }, - { - "cell_type": "markdown", - "id": "b01cde1d-1c2b-4419-8dc5-ff343c2f1ffb", - "metadata": {}, - "source": [ - "First, we'll creaete a new job spec" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "50a0d559-0445-437a-82cb-468310d84029", - "metadata": {}, - "outputs": [], - "source": [ - "file_name = \"../data/multimodal_test.pdf\"\n", - "file_content, file_type = extract_file_content(file_name)\n", - "\n", "job_spec = JobSpec(\n", " document_type=file_type,\n", " payload=file_content,\n", @@ -409,27 +86,25 @@ }, { "cell_type": "markdown", - "id": "c49e355f-ab3f-436e-8a60-ce1f276234cb", + "id": "7fa987e6-8bb1-4ed1-97c2-540e136e2d19", "metadata": {}, "source": [ - "Then, we'll add the extraction task again, but this time we'll also add an embed task and a vector database upload task" + "And then, we can add and submit tasks to extract the text, tables, and charts from the example pdf, generate embeddings from the results, and store them in the Milvus VDB" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "05631715-9ece-44ab-8a41-d11c569e6906", + "execution_count": 2, + "id": "8bcd6b2a-c832-4685-bd6e-53b60a107e28", "metadata": {}, "outputs": [], "source": [ - "from nv_ingest_client.primitives.tasks import EmbedTask\n", - "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", - "\n", "extract_task = ExtractTask(\n", " document_type=file_type,\n", " extract_text=True,\n", " extract_images=False,\n", " extract_tables=True,\n", + " extract_charts=True,\n", ")\n", "\n", "embed_task = EmbedTask(\n", @@ -443,11 +118,6 @@ "job_spec.add_task(embed_task)\n", "job_spec.add_task(vdb_upload_task)\n", "\n", - "client = NvIngestClient(\n", - " message_client_hostname=\"localhost\",\n", - " message_client_port=7670\n", - ")\n", - "\n", "job_id = client.add_job(job_spec)\n", "\n", "client.submit_job(job_id, \"morpheus_task_queue\")\n", @@ -457,21 +127,28 @@ }, { "cell_type": "markdown", - "id": "bccb6025-6587-4555-80c7-40fcf77f208f", + "id": "02131711-31bf-4536-81b7-8c464c7473e3", "metadata": {}, "source": [ - "Next, we'll connect langchain to our collection in Milvus and create a new retiever from it" + "Now, the text, table, and chart content is extracted and stored in the Milvus VDB along with the embeddings. Next we'll connect LlamaIndex to Milvus and create a vector store so that we can query our extraction results. The vector store must use the same embedding model as the embedding service in NV-Ingest: `nv-embed-qa-e5-v5`" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "87867fb8-e56d-4d49-97af-9c7edf5117ad", + "execution_count": 13, + "id": "53957974-c688-4521-8c61-09f2649d5d53", "metadata": {}, "outputs": [], "source": [ + "import os\n", + "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n", "from langchain_milvus import Milvus\n", "\n", + "# TODO: Add your NVIDIA API key here\n", + "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", + "\n", + "embedding = NVIDIAEmbeddings(model=\"nvidia/nv-embedqa-e5-v5\")\n", + "\n", "vectorstore = Milvus(\n", " embedding_function=embedding,\n", " collection_name=\"nv_ingest_collection\",\n", @@ -485,16 +162,16 @@ }, { "cell_type": "markdown", - "id": "cdca2679-94b4-4636-af44-3716bdf92556", + "id": "b87111b5-e5a8-45a0-9663-2ae6d9ea2ab6", "metadata": {}, "source": [ - "And finally, we can perform RAG with our Milvus based retriever just as we did before" + "Finally, we'll create an RAG chain that we can use to query our pdf in natural language" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "1a35efa2-20f0-49f7-9d23-bc0dfdcc97e7", + "execution_count": 16, + "id": "b547a19a-9ada-4a40-a246-6d7bc4d24482", "metadata": {}, "outputs": [ { @@ -503,12 +180,16 @@ "'The dog is chasing a squirrel in the front yard.'" ] }, - "execution_count": 20, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "\n", "template = (\n", " \"You are an assistant for question-answering tasks. \"\n", " \"Use the following pieces of retrieved context to answer \"\n", diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index 32d12336..6dbe8a99 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -13,7 +13,7 @@ "id": "4c557723-257f-4746-9b84-ec77c50cf405", "metadata": {}, "source": [ - "This cookbook shows how to perform RAG on the table and text extraction output of nv-ingest's pdf extraction tools using LlamaIndex" + "This notebook shows how to perform RAG on the table, chart, and text extraction results of nv-ingest's pdf extraction tools using LlamaIndex" ] }, { @@ -21,7 +21,7 @@ "id": "baecfda5-137b-43da-a8d4-23dd47131be9", "metadata": {}, "source": [ - "To start we'll need to make sure we have llama_index installed" + "To start we'll need to make sure we have LlamaIndex installed as well as pymilvus so that we can connect to the Milvus vector database (VDB) that NV-Ingest uses to store embeddings" ] }, { @@ -31,7 +31,7 @@ "metadata": {}, "outputs": [], "source": [ - "pip install -qU llama_index llama-index-embeddings-nvidia llama-index-llms-nvidia" + "pip install -qU llama_index llama-index-embeddings-nvidia llama-index-llms-nvidia llama-index-vector-stores-milvus pymilvus" ] }, { @@ -39,7 +39,7 @@ "id": "45412661-9516-47f9-8bea-6f857e0e173f", "metadata": {}, "source": [ - "Then, we'll use nv-ingest to parse an example pdf that contains text, tables, charts, and images. We'll need to make sure to have the nv-ingest microservice up and running at localhost:7670 along with the supporting NIMs. To do this, follow the nv-ingest [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart). Once the microservice is ready we can create a job with the nv-ingest python client" + "Then, we'll use NV-Ingest to parse an example pdf that contains text, tables, charts, and images, embed it with the included embedding microservice and store the results in the Milvus vector database. We'll need to make sure to have the NV-Ingest microservice up and running at localhost:7670 along with the supporting NIMs and microservices. To do this, follow the nv-ingest [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart). This notebook requires all of the services to be [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). Once everything is ready, we can create a job with the NV-Ingest python client" ] }, { @@ -53,6 +53,8 @@ "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", + "from nv_ingest_client.primitives.tasks import EmbedTask\n", + "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", "\n", "\n", "from nv_ingest_client.util.file_processing.extract import extract_file_content\n", @@ -63,6 +65,11 @@ "file_name = \"../data/multimodal_test.pdf\"\n", "file_content, file_type = extract_file_content(file_name)\n", "\n", + "client = NvIngestClient(\n", + " message_client_hostname=\"localhost\",\n", + " message_client_port=7670\n", + ")\n", + "\n", "job_spec = JobSpec(\n", " document_type=file_type,\n", " payload=file_content,\n", @@ -82,7 +89,7 @@ "id": "2aa9a74c-f7e4-475d-970d-cf820cd8ea19", "metadata": {}, "source": [ - "And then, we can and submit a task to extract the text and tables from the example pdf" + "And then, we can add and submit tasks to extract the text, tables, and charts from the example pdf, generate embeddings from the results, and store them in the Milvus VDB" ] }, { @@ -97,15 +104,19 @@ " extract_text=True,\n", " extract_images=False,\n", " extract_tables=True,\n", + " extract_charts=True,\n", ")\n", "\n", + "embed_task = EmbedTask(\n", + " text=True,\n", + " tables=True,\n", + ")\n", "\n", - "job_spec.add_task(extract_task)\n", + "vdb_upload_task = VdbUploadTask()\n", "\n", - "client = NvIngestClient(\n", - " message_client_hostname=\"localhost\",\n", - " message_client_port=7670\n", - ")\n", + "job_spec.add_task(extract_task)\n", + "job_spec.add_task(embed_task)\n", + "job_spec.add_task(vdb_upload_task)\n", "\n", "job_id = client.add_job(job_spec)\n", "\n", @@ -114,139 +125,12 @@ "result = client.fetch_job_result(job_id, timeout=60)" ] }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8609f938-128a-4d61-a0dd-a36fc8d2b077", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'document_type': 'text',\n", - " 'metadata': {'content': 'TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.',\n", - " 'content_metadata': {'description': 'Unstructured text from PDF document.',\n", - " 'hierarchy': {'block': -1,\n", - " 'line': -1,\n", - " 'nearby_objects': {'images': {'bbox': [], 'content': []},\n", - " 'structured': {'bbox': [], 'content': []},\n", - " 'text': {'bbox': [], 'content': []}},\n", - " 'page': -1,\n", - " 'page_count': 3,\n", - " 'span': -1},\n", - " 'page_number': -1,\n", - " 'subtype': '',\n", - " 'type': 'text'},\n", - " 'debug_metadata': None,\n", - " 'embedding': None,\n", - " 'error_metadata': None,\n", - " 'image_metadata': None,\n", - " 'info_message_metadata': None,\n", - " 'raise_on_failure': False,\n", - " 'source_metadata': {'access_level': 1,\n", - " 'collection_id': '',\n", - " 'date_created': '2024-10-08T19:16:03.465614',\n", - " 'last_modified': '2024-10-08T19:16:03.465459',\n", - " 'partition_id': -1,\n", - " 'source_id': '../data/multimodal_test.pdf',\n", - " 'source_location': '',\n", - " 'source_name': '../data/multimodal_test.pdf',\n", - " 'source_type': 'PDF',\n", - " 'summary': ''},\n", - " 'table_metadata': None,\n", - " 'text_metadata': {'keywords': '',\n", - " 'language': 'en',\n", - " 'summary': '',\n", - " 'text_location': [-1, -1, -1, -1],\n", - " 'text_type': 'document'}}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result[0][0][0]" - ] - }, - { - "cell_type": "markdown", - "id": "5a2a0a9c-ede6-4cef-b182-9f0b78223647", - "metadata": {}, - "source": [ - "Now, we have the extraction results in the nv-ingest metadata format. We'll separate the content out of this and load it into LlamaIndex documents" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c2b0d5eb-f0db-4edb-a3fe-c3bfccc59227", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.core import Document\n", - "\n", - "texts = []\n", - "tables = []\n", - "for element in result[0][0]:\n", - " if element['document_type'] == 'text':\n", - " texts.append(Document(text=element['metadata']['content']))\n", - " elif element['document_type'] == 'structured':\n", - " tables.append(Document(text=element['metadata']['table_metadata']['table_content']))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0f220a41-fc55-4b6c-95e1-28b41bfdba0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(id_='2b6bac68-5e1e-4a5a-a1ff-ef78b00ca711', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "texts" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9014cbaa-1c4a-4d5d-bf34-f41b9b0c335a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(id_='0d2024b3-c41a-4604-9779-e83106d07d78', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='locations. Animal Activity Place Giraffe Driving a car At the beach Lion Putting on sunscreen At the park Cat Jumping onto a laptop In a home office Dog Chasing a squirrel In the front yard', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='ccfba084-b81d-4319-be0d-573bc5742d45', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some gadgets, and some very fictitious costs. >\\\\n7938.758 ext. Print & Maroon Bookshelf Fine Art Poems Collection dla Cemicon Diamtháhn | Gadgets and their cost\\nSollywood for Coasters | 19875.075 t158.281 \\n Hammer | 19871.55 \\n Powerdrill | 12044.625 \\n Bluetooth speaker | 7598.07 \\n Minifridge | 9916.305 \\n Premium desk Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost Chart 1 - Gadgets and their cost', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='49e27e70-246f-4963-8da2-22c81c7a7722', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This table shows some popular colors that cars might come in. Car Color1 Color2 Color3 Coupe White Silver Flat Gray Sedan White Metallic Gray Matte Gray Minivan Gray Beige Black Truck Dark Gray Titanium Gray Charcoal Convertible Light Gray Graphite Slate Gray', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='e456b1f7-814c-47a1-918a-e6e99766deda', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='This chart shows some average frequency ranges for speaker drivers TITLE | Chart 2 \\n Frequency Range Start (Hz) | Frequency Range Start (Hz) | Frequency Range End (Hz) \\n Twitter | 12800 | 12700 \\n Midrange | 13900 | 13000 \\n Midwoofer | 9600 | 13000 \\n Subwoofer | 0.00 | 13000 Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 10 - 100 - 1000 - 10000 - 100000 Frequency Range Start (Hz) - Frequency Range End (Hz) This chart shows some average frequency ranges for speaker drivers - Frequency Ranges of Speaker Drivers', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tables" - ] - }, { "cell_type": "markdown", "id": "eaf6a9b8-83cf-4061-bf57-d50edc3978d0", "metadata": {}, "source": [ - "Now, the text and table content is ready to be embedded and stored. We'll set our NVIDIA api key in order to use an embedding model from NVIDIA's API catalog" + "Now, the text, table, and chart content is extracted and stored in the Milvus VDB along with the embeddings. Next, we'll connect LlamaIndex to Milvus and create a vector store index so that we can query our extraction results. The vector store index must use the same embedding model as the embedding service in NV-Ingest: `nv-embed-qa-e5-v5`" ] }, { @@ -259,12 +143,23 @@ "import os\n", "from llama_index.core import VectorStoreIndex\n", "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n", + "from llama_index.vector_stores.milvus import MilvusVectorStore\n", "\n", "# TODO: Add your NVIDIA API key here\n", "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", "\n", - "embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\")\n", - "index = VectorStoreIndex.from_documents(texts+tables, embed_model=embed_model)" + "embed_model = NVIDIAEmbedding(model=\"nvidia/nv-embedqa-e5-v5\")\n", + "\n", + "vector_store = MilvusVectorStore(\n", + " uri=\"http://localhost:19530\",\n", + " collection_name=\"nv_ingest_collection\",\n", + " doc_id_field=\"pk\",\n", + " embedding_field=\"vector\",\n", + " text_key=\"text\",\n", + " dim=1024,\n", + " overwrite=False\n", + ")\n", + "index = VectorStoreIndex.from_vector_store(vector_store=vector_store, embed_model=embed_model)" ] }, { @@ -272,7 +167,7 @@ "id": "d7e23e9d-a3ad-4b80-a356-1b233633b82d", "metadata": {}, "source": [ - "Next, we'll use our vectorstore to create a query engine that handles the RAG pipeline and we'll use an llm from the NVIDIA API catalog to generate the final response" + "Next, we'll use our vector store index to create a query engine that handles the RAG pipeline and we'll use an LLM from the NVIDIA API catalog to generate the final response" ] }, { @@ -317,173 +212,6 @@ "query_engine.query(\"What is the dog doing and where?\").response" ] }, - { - "cell_type": "markdown", - "id": "c22058f7-470a-4659-8fdc-5b33988c4cf5", - "metadata": {}, - "source": [ - "## Milvus" - ] - }, - { - "cell_type": "markdown", - "id": "fe5a521b-aca8-4227-b73d-1c3fe8aef95b", - "metadata": {}, - "source": [ - "Alternatively, we can use the embedding NIM and the milvus vector database packaged with NV-Ingest. This requires pymilvus and the embedding, milvus, etcd, attu, and minio microservices to be up and [running](https://github.com/NVIDIA/nv-ingest/blob/main/docs/deployment.md#launch-nv-ingest-micro-services). This has the benefit of directly sending the extraction results to the embedding microservice and then to a vector database without roundtripping between the client and the NV-Ingest microservice between each step" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "572b2b1a-367b-41bb-a153-ad86227fa815", - "metadata": {}, - "outputs": [], - "source": [ - "pip install -qU pymilvus llama-index-vector-stores-milvus" - ] - }, - { - "cell_type": "markdown", - "id": "a7a3c662-9ea2-45f4-a600-af35802f77b9", - "metadata": {}, - "source": [ - "First, we'll create a new job spec" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b3891199-34ba-4117-8300-3a0d8294a07d", - "metadata": {}, - "outputs": [], - "source": [ - "file_name = \"../data/multimodal_test.pdf\"\n", - "file_content, file_type = extract_file_content(file_name)\n", - "\n", - "job_spec = JobSpec(\n", - " document_type=file_type,\n", - " payload=file_content,\n", - " source_id=file_name,\n", - " source_name=file_name,\n", - " extended_options={\n", - " \"tracing_options\": {\n", - " \"trace\": True,\n", - " \"ts_send\": time.time_ns()\n", - " }\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "915d8ce7-d800-46f9-9350-139c883e8f5a", - "metadata": {}, - "source": [ - "Then, we'll add the extraction task again, but this time we'll also add an embed task and a vector database upload task" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6ce6d9e1-f52f-4461-bdf1-b838c0c4c1c7", - "metadata": {}, - "outputs": [], - "source": [ - "from nv_ingest_client.primitives.tasks import EmbedTask\n", - "from nv_ingest_client.primitives.tasks import VdbUploadTask\n", - "\n", - "extract_task = ExtractTask(\n", - " document_type=file_type,\n", - " extract_text=True,\n", - " extract_images=False,\n", - " extract_tables=True,\n", - ")\n", - "\n", - "embed_task = EmbedTask(\n", - " text=True,\n", - " tables=True,\n", - ")\n", - "\n", - "vdb_upload_task = VdbUploadTask()\n", - "\n", - "job_spec.add_task(extract_task)\n", - "job_spec.add_task(embed_task)\n", - "job_spec.add_task(vdb_upload_task)\n", - "\n", - "client = NvIngestClient(\n", - " message_client_hostname=\"localhost\",\n", - " message_client_port=7670\n", - ")\n", - "\n", - "job_id = client.add_job(job_spec)\n", - "\n", - "client.submit_job(job_id, \"morpheus_task_queue\")\n", - "\n", - "result = client.fetch_job_result(job_id, timeout=60)" - ] - }, - { - "cell_type": "markdown", - "id": "98cd083f-7e4d-4a96-ba36-c4c9c487f3c9", - "metadata": {}, - "source": [ - "Next, we'll connect LlamaIndex to our collection in Milvus and create a new query engine from it" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "88a9658c-6d5b-4cb0-ace2-2ef59cb8fe6a", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.core import VectorStoreIndex\n", - "from llama_index.vector_stores.milvus import MilvusVectorStore\n", - "\n", - "\n", - "vector_store = MilvusVectorStore(\n", - " uri=\"http://localhost:19530\",\n", - " collection_name=\"nv_ingest_collection\",\n", - " doc_id_field=\"pk\",\n", - " embedding_field=\"vector\",\n", - " text_key=\"text\",\n", - " dim=1024,\n", - " overwrite=False\n", - ")\n", - "index = VectorStoreIndex.from_vector_store(vector_store=vector_store, embed_model=embed_model)\n", - "query_engine = index.as_query_engine(llm=llm)" - ] - }, - { - "cell_type": "markdown", - "id": "9eef79dc-6976-450b-b04d-75f2a517569c", - "metadata": {}, - "source": [ - "And then finally, we can query our Milvus vector database just as we did before with Chroma" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d30cd73a-88e1-45cb-94e6-3d07deffe0df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'The dog is chasing a squirrel in the front yard.'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_engine.query(\"What is the dog doing and where?\").response" - ] - }, { "cell_type": "code", "execution_count": null, From 07759769bbbc6ce4664e0d0494cdd379f30f3e06 Mon Sep 17 00:00:00 2001 From: ChrisJar Date: Mon, 28 Oct 2024 18:15:52 -0700 Subject: [PATCH 5/6] Clean up --- examples/langchain_multimodal_rag.ipynb | 7 +------ examples/llama_index_multimodal_rag.ipynb | 7 +------ 2 files changed, 2 insertions(+), 12 deletions(-) diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index 8a00be96..ce738a0c 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -50,7 +50,6 @@ "outputs": [], "source": [ "from nv_ingest_client.client import NvIngestClient\n", - "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", "from nv_ingest_client.primitives.tasks import EmbedTask\n", @@ -140,14 +139,10 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n", "from langchain_milvus import Milvus\n", "\n", - "# TODO: Add your NVIDIA API key here\n", - "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", - "\n", - "embedding = NVIDIAEmbeddings(model=\"nvidia/nv-embedqa-e5-v5\")\n", + "embedding = NVIDIAEmbeddings(base_url=\"http://localhost:8012/v1\", model=\"nvidia/nv-embedqa-e5-v5\")\n", "\n", "vectorstore = Milvus(\n", " embedding_function=embedding,\n", diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index 6dbe8a99..ffd8d119 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -50,7 +50,6 @@ "outputs": [], "source": [ "from nv_ingest_client.client import NvIngestClient\n", - "from nv_ingest_client.message_clients.rest.rest_client import RestClient\n", "from nv_ingest_client.primitives import JobSpec\n", "from nv_ingest_client.primitives.tasks import ExtractTask\n", "from nv_ingest_client.primitives.tasks import EmbedTask\n", @@ -140,15 +139,11 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "from llama_index.core import VectorStoreIndex\n", "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n", "from llama_index.vector_stores.milvus import MilvusVectorStore\n", "\n", - "# TODO: Add your NVIDIA API key here\n", - "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", - "\n", - "embed_model = NVIDIAEmbedding(model=\"nvidia/nv-embedqa-e5-v5\")\n", + "embed_model = NVIDIAEmbedding(base_url=\"http://localhost:8012/v1\", model=\"nvidia/nv-embedqa-e5-v5\")\n", "\n", "vector_store = MilvusVectorStore(\n", " uri=\"http://localhost:19530\",\n", From 2cd20be324841f9de326b41f4a05b356dd209001 Mon Sep 17 00:00:00 2001 From: ChrisJar Date: Thu, 31 Oct 2024 11:32:15 -0700 Subject: [PATCH 6/6] Fix missing import --- examples/langchain_multimodal_rag.ipynb | 20 ++++++++++++++++++-- examples/llama_index_multimodal_rag.ipynb | 6 +++++- 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb index ce738a0c..dc995fed 100644 --- a/examples/langchain_multimodal_rag.ipynb +++ b/examples/langchain_multimodal_rag.ipynb @@ -160,7 +160,23 @@ "id": "b87111b5-e5a8-45a0-9663-2ae6d9ea2ab6", "metadata": {}, "source": [ - "Finally, we'll create an RAG chain that we can use to query our pdf in natural language" + "Finally, we'll create an RAG chain using [llama-3.1-405b-instruct](https://build.nvidia.com/meta/llama-3_1-405b-instruct) that we can use to query our pdf in natural language" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b4c9e109-395c-40e2-a1a5-e0c0ef217e24", + "metadata": {}, + "outputs": [], + "source": [ + "import os \n", + "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n", + "\n", + "# TODO: Add your NVIDIA API key\n", + "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", + "\n", + "llm = ChatNVIDIA(model=\"meta/llama-3.1-405b-instruct\")" ] }, { @@ -209,7 +225,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f1200811-b4ac-468e-bb32-77c102aa9e30", + "id": "b5b3f079-65a6-4d32-a190-1df96925c5c7", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb index ffd8d119..496a55a6 100644 --- a/examples/llama_index_multimodal_rag.ipynb +++ b/examples/llama_index_multimodal_rag.ipynb @@ -162,7 +162,7 @@ "id": "d7e23e9d-a3ad-4b80-a356-1b233633b82d", "metadata": {}, "source": [ - "Next, we'll use our vector store index to create a query engine that handles the RAG pipeline and we'll use an LLM from the NVIDIA API catalog to generate the final response" + "Next, we'll use our vector store index to create a query engine that handles the RAG pipeline and we'll use [llama-3.1-405b-instruct](https://build.nvidia.com/meta/llama-3_1-405b-instruct) to generate the final response" ] }, { @@ -172,8 +172,12 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", "from llama_index.llms.nvidia import NVIDIA\n", "\n", + "# TODO: Add your NVIDIA API key\n", + "os.environ[\"NVIDIA_API_KEY\"] = \"\"\n", + "\n", "llm = NVIDIA(model=\"meta/llama-3.1-405b-instruct\")\n", "query_engine = index.as_query_engine(llm=llm)" ]