diff --git a/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb b/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb index 608726f7..7ac2da7a 100644 --- a/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb +++ b/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb @@ -1,6213 +1,6241 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "XzoiJTAoZobv" - }, - "source": [ - "\n", - " \n", - " \n", - " \"#Vespa\"\n", - "\n", - "\n", - "\n", - "# ColPali Ranking Experiments on DocVQA\n", - "\n", - "This notebook demonstrates how to reproduce the ColPali results on [DocVQA](https://huggingface.co/datasets/vidore/docvqa_test_subsampled) with Vespa. The dataset consists of PDF documents with questions and answers. \n", - "\n", - "We demonstrate how we can binarize the patch embeddings and replace the float float MaxSim scoring with a `hamming` based MaxSim without much loss in ranking accuracy but with a significant speedup (close to 4x) and reduce the memory (and storage) requirements by 32x.\n", - "\n", - "In this notebook we represent one PDF page as one vespa document. See other notebooks for more information about using ColPali with Vespa:\n", - "\n", - "- [Scaling ColPALI (VLM) Retrieval](simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb)\n", - "- [Vespa 🀝 ColPali: Efficient Document Retrieval with Vision Language Models](colpali-document-retrieval-vision-language-models-cloud.ipynb)\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb)\n", - "\n", - "Install dependencies: " - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XzoiJTAoZobv" + }, + "source": [ + "\n", + " \n", + " \n", + " \"#Vespa\"\n", + "\n", + "\n", + "\n", + "# ColPali Ranking Experiments on DocVQA\n", + "\n", + "This notebook demonstrates how to reproduce the ColPali results on [DocVQA](https://huggingface.co/datasets/vidore/docvqa_test_subsampled) with Vespa. The dataset consists of PDF documents with questions and answers. \n", + "\n", + "We demonstrate how we can binarize the patch embeddings and replace the float float MaxSim scoring with a `hamming` based MaxSim without much loss in ranking accuracy but with a significant speedup (close to 4x) and reduce the memory (and storage) requirements by 32x.\n", + "\n", + "In this notebook we represent one PDF page as one vespa document. See other notebooks for more information about using ColPali with Vespa:\n", + "\n", + "- [Scaling ColPALI (VLM) Retrieval](simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb)\n", + "- [Vespa 🀝 ColPali: Efficient Document Retrieval with Vision Language Models](colpali-document-retrieval-vision-language-models-cloud.ipynb)\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb)\n", + "\n", + "Install dependencies: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VIly_Pymmbyl" + }, + "outputs": [], + "source": [ + "!pip3 install colpali-engine==0.2.2 pyvespa vespacli requests numpy scipy ir_measures pillow datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "qKFOvdo5nCVl" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import tqdm\n", + "from transformers import AutoProcessor\n", + "from PIL import Image\n", + "\n", + "\n", + "from colpali_engine.models.paligemma_colbert_architecture import ColPali\n", + "from colpali_engine.utils.colpali_processing_utils import (\n", + " process_images,\n", + " process_queries,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yGfNhRP4RKBJ" + }, + "source": [ + "### Load the model\n", + "\n", + "Load the model, also choose the correct device and model weights." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose the right device to run the model on." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + " type = torch.bfloat16\n", + "elif torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\")\n", + " type = torch.float32\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + " type = torch.float32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the base model and the adapter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 624, + "referenced_widgets": [ + "63b7d9faffda49adbe8cb927978897ed", + "5b0ab9446d424066bcfb850ec3367c51", + "292d54e5961e4b03bfbe30394eb4f4a5", + "b0c067a5970a490a9fbd2e4130db7717", + "34e6c7d235a7401a92a28fa3a1b30d7d", + "0b2df6b5ff4142f4a73f5c64f68b6f33", + "984fb47b2e6349df9801e8fce333167d", + "96fe2fb513ba405cb018acff742138e9", + "839213a9b01041f5bd444cec7a236aa4", + "a023a3b3ecd94b9e87f62c97166cae4b", + "7ae80928c7ca40e4ada9c4202ff4dcf1", + "07fa4fd379fb4abaa2acbe3b712e6aaa", + "87aa782d0ee640b29475bc97c152ad1b", + "c8ecca34fb8240219183e3c379207d99", + "e197d08fdbe6451dbaf0cea1ad3628d9", + "42a92bd9a6e6445c90346671ac9b01b8", + "63dbac889ca747beae51e1f0608ba1b8", + "3980f62297284bc991552e57057d9e1f", + "66e424d41c304e658b10357591d0c0d5", + "ed729a0d26594df0b39551fb58cab644", + "8e3690b1a39b429e9311fc65a821c450", + 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"1fab9e005a8b43d384d7cf07ee9f068f", + "6a3e1019955041dc909f6d72edf84c9f", + "5d1b15bb1fda4704ad9de212b7a44d95", + "082255bf4243466e9c5f6f158fc2be9b", + "630e6b3b505441aca8ab027a4c3130f9", + "859a481ee7024e858510dafbde2f99e0", + "c551d36ebf3543cb87fd71922fb08bd4", + "1a6c2da3dc004653ba38a43274e8b1f8", + "243e816f39264b95bc8e0ee980ddfdfd", + "85dbe8aa26d04916b27a494d05574e39", + "c8b73c55e2844644a1ad410a4bc202bd", + "18f1f42017324be7bd17ab4612cff888", + "a66e9945421b4f52a0611ff4215ea51c", + "1738ecdd88a34840ae2873a5c65990b5" + ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "VIly_Pymmbyl" - }, - "outputs": [], - "source": [ - "!pip3 install colpali-engine==0.2.2 pyvespa vespacli requests numpy scipy ir_measures pillow datasets" - ] + "id": "bpvPYA1HnMDp", + "outputId": "4da48909-2eb2-4af2-d1ab-bf43870033f4" + }, + "outputs": [], + "source": [ + "model_name = \"vidore/colpali-v1.2\"\n", + "model = ColPali.from_pretrained(\n", + " \"vidore/colpaligemma-3b-pt-448-base\", torch_dtype=type\n", + ").eval()\n", + "model.load_adapter(model_name)\n", + "model = model.eval()\n", + "model.to(device)\n", + "processor = AutoProcessor.from_pretrained(model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PUqnrKWLak3O" + }, + "source": [ + "### The ViDoRe Benchmark \n", + "\n", + "We load the DocVQA test set, a subset of the ViDoRe dataset It has 500 pages and a question per page. The task is retrieve the page across the 500 indexed pages. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "_-1v-qZ32OgW" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"vidore/docvqa_test_subsampled\", split=\"test\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ColPali model to generate embeddings for the images in the dataset. We use a dataloader to process each image and store the embeddings in a list.\n", + "\n", + "Batch size 4 requires a GPU with 16GB of memory and fits into a T4 GPU. If you have a smaller GPU, you can reduce the batch size to 2. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "NRp3P9SlTK97", + "outputId": "b80587ba-4131-45fa-9803-0f42ada54019" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "qKFOvdo5nCVl" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import DataLoader\n", - "from tqdm import tqdm\n", - "from transformers import AutoProcessor\n", - "from PIL import Image\n", - "\n", - "\n", - "from colpali_engine.models.paligemma_colbert_architecture import ColPali\n", - "from colpali_engine.utils.colpali_processing_utils import process_images, process_queries\n" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 125/125 [29:29<00:00, 14.16s/it]\n" + ] + } + ], + "source": [ + "dataloader = DataLoader(\n", + " ds[\"image\"],\n", + " batch_size=4,\n", + " shuffle=False,\n", + " collate_fn=lambda x: process_images(processor, x),\n", + ")\n", + "embeddings = []\n", + "for batch_doc in tqdm(dataloader):\n", + " with torch.no_grad():\n", + " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", + " embeddings_doc = model(**batch_doc)\n", + " embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate embeddings for the queries in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "yGfNhRP4RKBJ" - }, - "source": [ - "### Load the model\n", - "\n", - "Load the model, also choose the correct device and model weights." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 500/500 [01:45<00:00, 4.72it/s]\n" + ] + } + ], + "source": [ + "dummy_image = Image.new(\"RGB\", (448, 448), (255, 255, 255))\n", + "dataloader = DataLoader(\n", + " ds[\"query\"],\n", + " batch_size=1,\n", + " shuffle=False,\n", + " collate_fn=lambda x: process_queries(processor, x, dummy_image),\n", + ")\n", + "query_embeddings = []\n", + "for batch_query in tqdm(dataloader):\n", + " with torch.no_grad():\n", + " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", + " embeddings_query = model(**batch_query)\n", + " query_embeddings.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have all the embeddings. We'll define two helper functions to perform binarization (BQ) and also packing float values\n", + "to shorter hex representation in JSON. Both saves bandwidth and improves feed performance. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import struct\n", + "import numpy as np\n", + "\n", + "\n", + "def binarize_tensor(tensor: torch.Tensor) -> str:\n", + " \"\"\"\n", + " Binarize a floating-point 1-d tensor by thresholding at zero\n", + " and packing the bits into bytes. Returns the hex str representation of the bytes.\n", + " \"\"\"\n", + " if not tensor.is_floating_point():\n", + " raise ValueError(\"Input tensor must be of floating-point type.\")\n", + " return (\n", + " np.packbits(np.where(tensor > 0, 1, 0), axis=0).astype(np.int8).tobytes().hex()\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def tensor_to_hex_bfloat16(tensor: torch.Tensor) -> str:\n", + " if not tensor.is_floating_point():\n", + " raise ValueError(\"Input tensor must be of float32 type.\")\n", + "\n", + " def float_to_bfloat16_hex(f: float) -> str:\n", + " packed_float = struct.pack(\"=f\", f)\n", + " bfloat16_bits = struct.unpack(\"=H\", packed_float[2:])[0]\n", + " return format(bfloat16_bits, \"04X\")\n", + "\n", + " hex_list = [float_to_bfloat16_hex(float(val)) for val in tensor.flatten()]\n", + " return \"\".join(hex_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Patch Vector pooling\n", + "\n", + "This reduces the number of patch embeddings by a factor of 3, meaning that we go from 1030 patch vectors to 343 patch vectors. This reduces\n", + "both the memory and the number of dotproducts that we need to calculate. This function is not in use in this notebook, but it is included for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.cluster.hierarchy import fcluster, linkage\n", + "from typing import Dict, List\n", + "\n", + "\n", + "def pool_embeddings(embeddings: torch.Tensor, pool_factor=3) -> torch.Tensor:\n", + " \"\"\"\n", + " pool embeddings using hierarchical clustering to reduce the number of patch embeddings.\n", + " Adapted from https://github.com/illuin-tech/vidore-benchmark/blob/main/src/vidore_benchmark/compression/token_pooling.py#L32\n", + " Inspired by https://www.answer.ai/posts/colbert-pooling.html\n", + " \"\"\"\n", + "\n", + " pooled_embeddings = []\n", + " token_length = embeddings.size(0)\n", + "\n", + " if token_length == 1:\n", + " raise ValueError(\"The input tensor must have more than one token.\")\n", + " embeddings.to(device)\n", + "\n", + " similarities = torch.mm(embeddings, embeddings.t())\n", + " if similarities.dtype == torch.bfloat16:\n", + " similarities = similarities.to(torch.float16)\n", + " similarities = 1 - similarities.cpu().numpy()\n", + "\n", + " Z = linkage(similarities, metric=\"euclidean\", method=\"ward\") # noqa: N806\n", + " max_clusters = max(token_length // pool_factor, 1)\n", + " cluster_labels = fcluster(Z, t=max_clusters, criterion=\"maxclust\")\n", + "\n", + " cluster_id_to_indices: Dict[int, torch.Tensor] = {}\n", + "\n", + " with torch.no_grad():\n", + " for cluster_id in range(1, max_clusters + 1):\n", + " cluster_indices = torch.where(torch.tensor(cluster_labels == cluster_id))[0]\n", + " cluster_id_to_indices[cluster_id] = cluster_indices\n", + "\n", + " if cluster_indices.numel() > 0:\n", + " pooled_embedding = embeddings[cluster_indices].mean(dim=0)\n", + " pooled_embedding = torch.nn.functional.normalize(\n", + " pooled_embedding, p=2, dim=-1\n", + " )\n", + " pooled_embeddings.append(pooled_embedding)\n", + "\n", + " pooled_embeddings = torch.stack(pooled_embeddings, dim=0)\n", + "\n", + " return pooled_embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the Vespa feed format, we use hex formats for mixed tensors [doc](https://docs.vespa.ai/en/reference/document-json-format.html#tensor).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "vespa_docs = []\n", + "\n", + "for row, embedding in zip(ds, embeddings):\n", + " embedding_full = dict()\n", + " embedding_binary = dict()\n", + " # You can experiment with pooling if you want to reduce the number of embeddings\n", + " # pooled_embedding = pool_embeddings(embedding, pool_factor=2) # reduce the number of embeddings by a factor of 2\n", + " for j, emb in enumerate(embedding):\n", + " embedding_full[j] = tensor_to_hex_bfloat16(emb)\n", + " embedding_binary[j] = binarize_tensor(emb)\n", + " vespa_doc = {\n", + " \"id\": row[\"docId\"],\n", + " \"embedding\": embedding_full,\n", + " \"binary_embedding\": embedding_binary,\n", + " }\n", + " vespa_docs.append(vespa_doc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure Vespa\n", + "[PyVespa](https://pyvespa.readthedocs.io/en/latest/) helps us build the [Vespa application package](https://docs.vespa.ai/en/application-packages.html).\n", + "A Vespa application package consists of configuration files, schemas, models, and code (plugins).\n", + "\n", + "First, we define a [Vespa schema](https://docs.vespa.ai/en/schemas.html) with the fields we want to store and their type. This is a simple\n", + "schema which is all we need to evaluate effectiveness of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import Schema, Document, Field\n", + "\n", + "colpali_schema = Schema(\n", + " name=\"pdf_page\",\n", + " document=Document(\n", + " fields=[\n", + " Field(name=\"id\", type=\"string\", indexing=[\"summary\", \"attribute\"]),\n", + " Field(\n", + " name=\"embedding\",\n", + " type=\"tensor(patch{}, v[128])\",\n", + " indexing=[\"attribute\"],\n", + " ),\n", + " Field(\n", + " name=\"binary_embedding\",\n", + " type=\"tensor(patch{}, v[16])\",\n", + " indexing=[\"attribute\"],\n", + " ),\n", + " ]\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import ApplicationPackage\n", + "\n", + "vespa_app_name = \"visionragtest\"\n", + "vespa_application_package = ApplicationPackage(\n", + " name=vespa_app_name, schema=[colpali_schema]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define how we want to rank the pages. We have 4 ranking models that we want to evaluate. These are all MaxSim variants but with various precision trade-offs.\n", + "\n", + "\n", + "1. **float-float** A regular MaxSim implementation that uses the float representation of both query and page embeddings.\n", + "2. **float-binary** Use the binarized representation of the page embeddings and where we unpack it into float representation. The query representation is still float.\n", + "3. **binary-binary** Use the binarized representation of the doc embeddings and the query embeddings and replaces the dot product with inverted hamming distance.\n", + "4. **phased** This uses the binary-binary in a first-phase, and then re-ranks using the float-binary representation. Only top 20 pages are re-ranked (This can be overriden in the query request as well). " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", + "\n", + "colpali_profile = RankProfile(\n", + " name=\"float-float\",\n", + " # We define both the float and binary query inputs here, the rest of the profiles inherits these inputs\n", + " inputs=[\n", + " (\"query(qtb)\", \"tensor(querytoken{}, v[16])\"),\n", + " (\"query(qt)\", \"tensor(querytoken{}, v[128])\"),\n", + " ],\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " sum(\n", + " query(qt) * cell_cast(attribute(embedding), float), v\n", + " ),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " )\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"max_sim\"),\n", + ")\n", + "\n", + "colpali_binary_profile = RankProfile(\n", + " name=\"float-binary\",\n", + " inherits=\"float-float\",\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " sum(\n", + " query(qt) * unpack_bits(attribute(binary_embedding)), v\n", + " ),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " )\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"max_sim\"),\n", + ")\n", + "\n", + "colpali_hamming_profile = RankProfile(\n", + " name=\"binary-binary\",\n", + " inherits=\"float-float\",\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " 1/(1+ sum(\n", + " hamming(query(qtb), attribute(binary_embedding)),v\n", + " )),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " )\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"max_sim\"),\n", + ")\n", + "\n", + "colpali__phased_hamming_profile = RankProfile(\n", + " name=\"phased\",\n", + " inherits=\"float-float\",\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim_hamming\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " 1/(1+ sum(\n", + " hamming(query(qtb), attribute(binary_embedding)),v\n", + " )),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " ),\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " sum(\n", + " query(qt) * unpack_bits(attribute(binary_embedding)), v\n", + " ),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " ),\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"max_sim_hamming\"),\n", + " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=20),\n", + ")\n", + "\n", + "\n", + "colpali_schema.add_rank_profile(colpali_profile)\n", + "colpali_schema.add_rank_profile(colpali_binary_profile)\n", + "colpali_schema.add_rank_profile(colpali_hamming_profile)\n", + "colpali_schema.add_rank_profile(colpali__phased_hamming_profile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy to Vespa Cloud\n", + "\n", + "With the configured application, we can deploy it to [Vespa Cloud](https://cloud.vespa.ai/en/).\n", + "\n", + "`PyVespa` supports deploying apps to the [development zone](https://cloud.vespa.ai/en/reference/environments#dev-and-perf).\n", + "\n", + "> Note: Deployments to dev and perf expire after 7 days of inactivity, i.e., 7 days after running deploy. This applies to all plans, not only the Free Trial. Use the Vespa Console to extend the expiry period, or redeploy the application to add 7 more days.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To deploy the application to Vespa Cloud we need to create a tenant in the Vespa Cloud:\n", + "\n", + "Create a tenant at [console.vespa-cloud.com](https://console.vespa-cloud.com/) (unless you already have one).\n", + "This step requires a Google or GitHub account, and will start your [free trial](https://cloud.vespa.ai/en/free-trial).\n", + "Make note of the tenant name, it is used in the next steps.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.deployment import VespaCloud\n", + "import os\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "\n", + "# Replace with your tenant name from the Vespa Cloud Console\n", + "tenant_name = \"vespa-team\"\n", + "\n", + "key = os.getenv(\"VESPA_TEAM_API_KEY\", None)\n", + "if key is not None:\n", + " key = key.replace(r\"\\n\", \"\\n\") # To parse key correctly\n", + "\n", + "vespa_cloud = VespaCloud(\n", + " tenant=tenant_name,\n", + " application=vespa_app_name,\n", + " key_content=key, # Key is only used for CI/CD testing of this notebook. Can be removed if logging in interactively\n", + " application_package=vespa_application_package,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now deploy the app to Vespa Cloud dev zone.\n", + "\n", + "The first deployment typically takes 2 minutes until the endpoint is up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.application import Vespa\n", + "\n", + "app: Vespa = vespa_cloud.deploy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example uses the asynchronous feed method and feeds one document at a time. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Choose the right device to run the model on." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 500/500 [01:13<00:00, 6.77it/s]\n" + ] + } + ], + "source": [ + "from vespa.io import VespaResponse\n", + "\n", + "async with app.asyncio(connections=1, timeout=180) as session:\n", + " for doc in tqdm(vespa_docs):\n", + " response: VespaResponse = await session.feed_data_point(\n", + " data_id=doc[\"id\"], fields=doc, schema=\"pdf_page\"\n", + " )\n", + " if not response.is_successful():\n", + " print(response.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j2pUyGjYf4Wv" + }, + "source": [ + "### Run queries and evaluate effectiveness" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use ir_measures to evaluate the effectiveness of the retrieval model." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from ir_measures import calc_aggregate, nDCG, ScoredDoc, Qrel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple routine for querying Vespa. Note that we send both vector representations in the query independently\n", + "of the ranking method used, this for simplicity. Not all the ranking models we evaluate needs both representations. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.io import VespaQueryResponse\n", + "from vespa.application import VespaAsync\n", + "\n", + "\n", + "async def get_vespa_response(\n", + " embedding: torch.Tensor,\n", + " qid: str,\n", + " session: VespaAsync,\n", + " depth=20,\n", + " profile=\"float-float\",\n", + ") -> List[ScoredDoc]:\n", + " # The query tensor api does not support hex formats yet\n", + " float_embedding = {index: vector.tolist() for index, vector in enumerate(embedding)}\n", + " binary_embedding = {\n", + " index: np.packbits(np.where(vector > 0, 1, 0), axis=0).astype(np.int8).tolist()\n", + " for index, vector in enumerate(embedding)\n", + " }\n", + " response: VespaQueryResponse = await session.query(\n", + " yql=\"select id from pdf_page where true\", # brute force search, rank all pages\n", + " ranking=profile,\n", + " hits=5,\n", + " timeout=10,\n", + " body={\n", + " \"input.query(qt)\": float_embedding,\n", + " \"input.query(qtb)\": binary_embedding,\n", + " \"ranking.rerankCount\": depth,\n", + " },\n", + " )\n", + " assert response.is_successful()\n", + " scored_docs = []\n", + " for hit in response.hits:\n", + " doc_id = hit[\"fields\"][\"id\"]\n", + " score = hit[\"relevance\"]\n", + " scored_docs.append(ScoredDoc(qid, doc_id, score))\n", + " return scored_docs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run a test query first.. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "if torch.cuda.is_available():\n", - " device = torch.device(\"cuda\")\n", - " type = torch.bfloat16\n", - "elif torch.backends.mps.is_available():\n", - " device = torch.device(\"mps\")\n", - " type = torch.float32\n", - "else:\n", - " device = torch.device(\"cpu\")\n", - " type = torch.float32" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[ScoredDoc(query_id='float-float', doc_id='4720', score=16.292504370212555), ScoredDoc(query_id='float-float', doc_id='4858', score=13.315170526504517), ScoredDoc(query_id='float-float', doc_id='14686', score=12.212152108550072), ScoredDoc(query_id='float-float', doc_id='4846', score=12.002869427204132), ScoredDoc(query_id='float-float', doc_id='864', score=11.308563649654388)]\n", + "[ScoredDoc(query_id='float-binary', doc_id='4720', score=82.99432492256165), ScoredDoc(query_id='float-binary', doc_id='4858', score=71.45464742183685), ScoredDoc(query_id='float-binary', doc_id='14686', score=68.46699643135071), ScoredDoc(query_id='float-binary', doc_id='4846', score=64.85357594490051), ScoredDoc(query_id='float-binary', doc_id='2161', score=63.85516130924225)]\n", + "[ScoredDoc(query_id='binary-binary', doc_id='4720', score=0.771387243643403), ScoredDoc(query_id='binary-binary', doc_id='4858', score=0.7132036704570055), ScoredDoc(query_id='binary-binary', doc_id='14686', score=0.6979007869958878), ScoredDoc(query_id='binary-binary', doc_id='6087', score=0.6534321829676628), ScoredDoc(query_id='binary-binary', doc_id='2161', score=0.6525899451225996)]\n", + "[ScoredDoc(query_id='phased', doc_id='4720', score=82.99432492256165), ScoredDoc(query_id='phased', doc_id='4858', score=71.45464742183685), ScoredDoc(query_id='phased', doc_id='14686', score=68.46699643135071), ScoredDoc(query_id='phased', doc_id='4846', score=64.85357594490051), ScoredDoc(query_id='phased', doc_id='2161', score=63.85516130924225)]\n" + ] + } + ], + "source": [ + "async with app.asyncio() as session:\n", + " for profile in [\"float-float\", \"float-binary\", \"binary-binary\", \"phased\"]:\n", + " print(\n", + " await get_vespa_response(\n", + " query_embeddings[0], profile, session, profile=profile\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, run through all of the test queries for each of the ranking models." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the base model and the adapter. " - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "500it [11:32, 1.39s/it]\n" + ] + } + ], + "source": [ + "qrels = []\n", + "profiles = [\"float-float\", \"float-binary\", \"binary-binary\", \"phased\"]\n", + "results = {profile: [] for profile in profiles}\n", + "async with app.asyncio(connections=3) as session:\n", + " for row, embedding in zip(tqdm(ds), query_embeddings):\n", + " qrels.append(Qrel(row[\"questionId\"], str(row[\"docId\"]), 1))\n", + " for profile in profiles:\n", + " scored_docs = await get_vespa_response(\n", + " embedding, row[\"questionId\"], session, profile=profile\n", + " )\n", + " results[profile].extend(scored_docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the effectiveness of the 4 different models" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - 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"c8b73c55e2844644a1ad410a4bc202bd", - "18f1f42017324be7bd17ab4612cff888", - "a66e9945421b4f52a0611ff4215ea51c", - "1738ecdd88a34840ae2873a5c65990b5" - ] - }, - "id": "bpvPYA1HnMDp", - "outputId": "4da48909-2eb2-4af2-d1ab-bf43870033f4" - }, - "outputs": [], - "source": [ - "model_name = \"vidore/colpali-v1.2\"\n", - "model = ColPali.from_pretrained(\"vidore/colpaligemma-3b-pt-448-base\", torch_dtype=type).eval()\n", - "model.load_adapter(model_name)\n", - "model = model.eval()\n", - "model.to(device)\n", - "processor = AutoProcessor.from_pretrained(model_name)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "nDCG@5 for float-float: 52.37\n", + "nDCG@5 for float-binary: 51.64\n", + "nDCG@5 for binary-binary: 49.48\n", + "nDCG@5 for phased: 51.70\n" + ] + } + ], + "source": [ + "for profile in profiles:\n", + " score = calc_aggregate([nDCG @ 5], qrels, results[profile])[nDCG @ 5]\n", + " print(f\"nDCG@5 for {profile}: {100*score:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is encouraging as the binary-binary representation is 4x faster than the float-float representation and saves 32x space. We can also largely retain the effectiveness of the float-binary representation by using the phased approach where we re-rank the top 20 pages from the hamming (binary-binary) version using the float-binary representation. Now we can explore the ranking depth and see how the phased approach performs with different ranking depths." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "PUqnrKWLak3O" - }, - "source": [ - "### The ViDoRe Benchmark \n", - "\n", - "We load the DocVQA test set, a subset of the ViDoRe dataset It has 500 pages and a question per page. The task is retrieve the page across the 500 indexed pages. " - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "500it [08:18, 1.00it/s]\n" + ] + } + ], + "source": [ + "results = {\n", + " profile: []\n", + " for profile in [\n", + " \"phased-rerank-count=5\",\n", + " \"phased-rerank-count=10\",\n", + " \"phased-rerank-count=20\",\n", + " \"phased-rerank-count=40\",\n", + " ]\n", + "}\n", + "async with app.asyncio(connections=3) as session:\n", + " for row, embedding in zip(tqdm(ds), query_embeddings):\n", + " qrels.append(Qrel(row[\"questionId\"], str(row[\"docId\"]), 1))\n", + " for count in [5, 10, 20, 40]:\n", + " scored_docs = await get_vespa_response(\n", + " embedding, row[\"questionId\"], session, profile=\"phased\", depth=count\n", + " )\n", + " results[\"phased-rerank-count=\" + str(count)].extend(scored_docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "_-1v-qZ32OgW" - }, - "outputs": [], - "source": [ - "from datasets import load_dataset\n", - "ds = load_dataset(\"vidore/docvqa_test_subsampled\", split=\"test\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "nDCG@5 for phased-rerank-count=5: 50.77\n", + "nDCG@5 for phased-rerank-count=10: 51.58\n", + "nDCG@5 for phased-rerank-count=20: 51.70\n", + "nDCG@5 for phased-rerank-count=40: 51.64\n" + ] + } + ], + "source": [ + "for profile in results.keys():\n", + " score = calc_aggregate([nDCG @ 5], qrels, results[profile])[nDCG @ 5]\n", + " print(f\"nDCG@5 for {profile}: {100*score:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "The binary representation of the patch embeddings reduces the storage by 32x, and using hamming distance instead of dotproduc saves us about 4x in computation compared to the float-float model or the float-binary model (which only saves storage). Using a re-ranking step with only depth 10, we can improve the effectiveness of the binary-binary model to almost match the float-float MaxSim model. The additional re-ranking step only requires that we pass also the float query embedding version without any additional storage overhead. \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00c2c14a88514261b07eb1df9bbc0581": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we use the ColPali model to generate embeddings for the images in the dataset. We use a dataloader to process each image and store the embeddings in a list.\n", - "\n", - "Batch size 4 requires a GPU with 16GB of memory and fits into a T4 GPU. If you have a smaller GPU, you can reduce the batch size to 2. 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null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "07572b0924f14475ae4439728b2124ac": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "07fa4fd379fb4abaa2acbe3b712e6aaa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_87aa782d0ee640b29475bc97c152ad1b", + "IPY_MODEL_c8ecca34fb8240219183e3c379207d99", + "IPY_MODEL_e197d08fdbe6451dbaf0cea1ad3628d9" ], - "source": [ - "dataloader = DataLoader(\n", - " ds['image'],\n", - " batch_size=4,\n", - " shuffle=False,\n", - " collate_fn=lambda x: process_images(processor, x),\n", - ")\n", - "embeddings = []\n", - "for batch_doc in tqdm(dataloader):\n", - " with torch.no_grad():\n", - " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", - " embeddings_doc = model(**batch_doc)\n", - " embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))\n", - " " - ] + "layout": "IPY_MODEL_42a92bd9a6e6445c90346671ac9b01b8" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate embeddings for the queries in the dataset." - ] + "082255bf4243466e9c5f6f158fc2be9b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + 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null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "15a4d3a081fc4c31944cbb06036ed5e7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ea1a6cc73233424dacd2495477c2c013", + "IPY_MODEL_d1af805deb73414ab2caea7e375cab58", + "IPY_MODEL_d0dfb251a9e449d387fb1f4ebd8d545d" ], - "source": [ - "dummy_image = Image.new(\"RGB\", (448, 448), (255, 255, 255))\n", - "dataloader = DataLoader(\n", - " ds['query'],\n", - " batch_size=1,\n", - " shuffle=False,\n", - " collate_fn=lambda x: process_queries(processor, x, dummy_image),\n", - " )\n", - "query_embeddings = []\n", - "for batch_query in tqdm(dataloader):\n", - " with torch.no_grad():\n", - " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", - " embeddings_query = model(**batch_query)\n", - " query_embeddings.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))" - ] + "layout": "IPY_MODEL_d91787870e274a198c6d81976cd3210e" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have all the embeddings. We'll define two helper functions to perform binarization (BQ) and also packing float values\n", - "to shorter hex representation in JSON. Both saves bandwidth and improves feed performance. " - ] + "1738ecdd88a34840ae2873a5c65990b5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import struct\n", - "import numpy as np\n", - "\n", - "\n", - "def binarize_tensor(tensor: torch.Tensor) -> str:\n", - " \"\"\"\n", - " Binarize a floating-point 1-d tensor by thresholding at zero \n", - " and packing the bits into bytes. Returns the hex str representation of the bytes.\n", - " \"\"\"\n", - " if not tensor.is_floating_point():\n", - " raise ValueError(\"Input tensor must be of floating-point type.\")\n", - " return np.packbits(np.where(tensor > 0, 1, 0), axis=0).astype(np.int8).tobytes().hex()\n", - " " - ] + "181fc64b155a43379e653b270d23e07d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def tensor_to_hex_bfloat16(tensor: torch.Tensor) -> str:\n", - " if not tensor.is_floating_point():\n", - " raise ValueError(\"Input tensor must be of float32 type.\")\n", - " def float_to_bfloat16_hex(f: float) -> str:\n", - " packed_float = struct.pack('=f', f)\n", - " bfloat16_bits = struct.unpack('=H', packed_float[2:])[0]\n", - " return format(bfloat16_bits, '04X')\n", - " hex_list = [float_to_bfloat16_hex(float(val)) for val in tensor.flatten()]\n", - " return \"\".join(hex_list)\n" - ] + "18f1f42017324be7bd17ab4612cff888": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Patch Vector pooling\n", - "\n", - "This reduces the number of patch embeddings by a factor of 3, meaning that we go from 1030 patch vectors to 343 patch vectors. This reduces\n", - "both the memory and the number of dotproducts that we need to calculate. This function is not in use in this notebook, but it is included for reference." - ] + "1a6c2da3dc004653ba38a43274e8b1f8": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.cluster.hierarchy import fcluster, linkage\n", - "from typing import Dict, List\n", - "\n", - "def pool_embeddings(embeddings: torch.Tensor, pool_factor=3) -> torch.Tensor:\n", - " \"\"\"\n", - " pool embeddings using hierarchical clustering to reduce the number of patch embeddings.\n", - " Adapted from https://github.com/illuin-tech/vidore-benchmark/blob/main/src/vidore_benchmark/compression/token_pooling.py#L32\n", - " Inspired by https://www.answer.ai/posts/colbert-pooling.html\n", - " \"\"\"\n", - " \n", - " pooled_embeddings = []\n", - " token_length = embeddings.size(0)\n", - "\n", - " if token_length == 1:\n", - " raise ValueError(\"The input tensor must have more than one token.\")\n", - " embeddings.to(device)\n", - "\n", - " similarities = torch.mm(embeddings, embeddings.t())\n", - " if similarities.dtype == torch.bfloat16:\n", - " similarities = similarities.to(torch.float16)\n", - " similarities = 1 - similarities.cpu().numpy()\n", - "\n", - " Z = linkage(similarities, metric=\"euclidean\", method=\"ward\") # noqa: N806\n", - " max_clusters = max(token_length // pool_factor, 1)\n", - " cluster_labels = fcluster(Z, t=max_clusters, criterion=\"maxclust\")\n", - "\n", - " cluster_id_to_indices: Dict[int, torch.Tensor] = {}\n", - "\n", - " with torch.no_grad():\n", - " for cluster_id in range(1, max_clusters + 1):\n", - " cluster_indices = torch.where(torch.tensor(cluster_labels == cluster_id))[0]\n", - " cluster_id_to_indices[cluster_id] = cluster_indices\n", - "\n", - " if cluster_indices.numel() > 0:\n", - " pooled_embedding = embeddings[cluster_indices].mean(dim=0)\n", - " pooled_embedding = torch.nn.functional.normalize(pooled_embedding, p=2, dim=-1)\n", - " pooled_embeddings.append(pooled_embedding)\n", - "\n", - " pooled_embeddings = torch.stack(pooled_embeddings, dim=0)\n", - "\n", - " return pooled_embeddings" - ] + "1e6c9d208cee4004b2947b48cfb990c3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the Vespa feed format, we use hex formats for mixed tensors [doc](https://docs.vespa.ai/en/reference/document-json-format.html#tensor).\n" - ] + "1fab9e005a8b43d384d7cf07ee9f068f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "vespa_docs = []\n", - "\n", - "for row, embedding in zip(ds, embeddings):\n", - " embedding_full = dict()\n", - " embedding_binary = dict()\n", - " # You can experiment with pooling if you want to reduce the number of embeddings\n", - " #pooled_embedding = pool_embeddings(embedding, pool_factor=2) # reduce the number of embeddings by a factor of 2\n", - " for j, emb in enumerate(embedding):\n", - " embedding_full[j] = tensor_to_hex_bfloat16(emb)\n", - " embedding_binary[j] = binarize_tensor(emb)\n", - " vespa_doc = {\n", - " \"id\": row['docId'],\n", - " \"embedding\": embedding_full,\n", - " \"binary_embedding\": embedding_binary\n", - " }\n", - " vespa_docs.append(vespa_doc)" - ] + "20878d8e7b784a1894ca740df0dd8cb7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure Vespa\n", - "[PyVespa](https://pyvespa.readthedocs.io/en/latest/) helps us build the [Vespa application package](https://docs.vespa.ai/en/application-packages.html).\n", - "A Vespa application package consists of configuration files, schemas, models, and code (plugins).\n", - "\n", - "First, we define a [Vespa schema](https://docs.vespa.ai/en/schemas.html) with the fields we want to store and their type. This is a simple\n", - "schema which is all we need to evaluate effectiveness of the model." - ] + "222fca9435b24ae78ecad709b82b62de": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import Schema, Document, Field\n", - "\n", - "colpali_schema = Schema(\n", - " name=\"pdf_page\",\n", - " document=Document(\n", - " fields=[\n", - " Field(name=\"id\", type=\"string\", indexing=[\"summary\", \"attribute\"]),\n", - " Field(\n", - " name=\"embedding\",\n", - " type=\"tensor(patch{}, v[128])\",\n", - " indexing=[\"attribute\"]\n", - " ),\n", - " Field(\n", - " name=\"binary_embedding\",\n", - " type=\"tensor(patch{}, v[16])\",\n", - " indexing=[\"attribute\"]\n", - " )\n", - " ]\n", - " )\n", - ")" - ] + "22819db05f7b48d7883f43101f9e52a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_add907e64ced406c954f20e4c2fd4c2c", + "placeholder": "​", + "style": "IPY_MODEL_513c2edf05ca43e8bcf4938a4e0434fe", + "value": " 4.96G/4.96G [00:48<00:00, 182MB/s]" + } }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import ApplicationPackage\n", - "\n", - "vespa_app_name = \"visionragtest\"\n", - "vespa_application_package = ApplicationPackage(\n", - " name=vespa_app_name, schema=[colpali_schema]\n", - ")" - ] + "24189eaa3a26454ba62a1b12560199ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define how we want to rank the pages. We have 4 ranking models that we want to evaluate. These are all MaxSim variants but with various precision trade-offs.\n", - "\n", - "\n", - "1. **float-float** A regular MaxSim implementation that uses the float representation of both query and page embeddings.\n", - "2. **float-binary** Use the binarized representation of the page embeddings and where we unpack it into float representation. The query representation is still float.\n", - "3. **binary-binary** Use the binarized representation of the doc embeddings and the query embeddings and replaces the dot product with inverted hamming distance.\n", - "4. **phased** This uses the binary-binary in a first-phase, and then re-ranks using the float-binary representation. Only top 20 pages are re-ranked (This can be overriden in the query request as well). " - ] + "243e816f39264b95bc8e0ee980ddfdfd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", - "\n", - "colpali_profile = RankProfile(\n", - " name=\"float-float\",\n", - " # We define both the float and binary query inputs here, the rest of the profiles inherits these inputs\n", - " inputs=[ \n", - " (\"query(qtb)\", \"tensor(querytoken{}, v[16])\"),\n", - " (\"query(qt)\", \"tensor(querytoken{}, v[128])\")\n", - " ],\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " sum(\n", - " query(qt) * cell_cast(attribute(embedding), float), v\n", - " ),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " )\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"max_sim\")\n", - ")\n", - "\n", - "colpali_binary_profile = RankProfile(\n", - " name=\"float-binary\",\n", - " inherits=\"float-float\",\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " sum(\n", - " query(qt) * unpack_bits(attribute(binary_embedding)), v\n", - " ),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " )\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"max_sim\")\n", - ")\n", - "\n", - "colpali_hamming_profile = RankProfile(\n", - " name=\"binary-binary\",\n", - " inherits=\"float-float\",\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " 1/(1+ sum(\n", - " hamming(query(qtb), attribute(binary_embedding)),v\n", - " )),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " )\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"max_sim\")\n", - ")\n", - "\n", - "colpali__phased_hamming_profile = RankProfile(\n", - " name=\"phased\",\n", - " inherits=\"float-float\",\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim_hamming\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " 1/(1+ sum(\n", - " hamming(query(qtb), attribute(binary_embedding)),v\n", - " )),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " ),\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " sum(\n", - " query(qt) * unpack_bits(attribute(binary_embedding)), v\n", - " ),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " )\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"max_sim_hamming\"),\n", - " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=20)\n", - ")\n", - "\n", - "\n", - "colpali_schema.add_rank_profile(colpali_profile)\n", - "colpali_schema.add_rank_profile(colpali_binary_profile)\n", - "colpali_schema.add_rank_profile(colpali_hamming_profile)\n", - "colpali_schema.add_rank_profile(colpali__phased_hamming_profile)\n" - ] + "24464c58a63e4217b60490224c4bd8ee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to Vespa Cloud\n", - "\n", - "With the configured application, we can deploy it to [Vespa Cloud](https://cloud.vespa.ai/en/).\n", - "\n", - "`PyVespa` supports deploying apps to the [development zone](https://cloud.vespa.ai/en/reference/environments#dev-and-perf).\n", - "\n", - "> Note: Deployments to dev and perf expire after 7 days of inactivity, i.e., 7 days after running deploy. This applies to all plans, not only the Free Trial. Use the Vespa Console to extend the expiry period, or redeploy the application to add 7 more days.\n" - ] + "278893eee67845a0a4294fdb887ed264": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To deploy the application to Vespa Cloud we need to create a tenant in the Vespa Cloud:\n", - "\n", - "Create a tenant at [console.vespa-cloud.com](https://console.vespa-cloud.com/) (unless you already have one).\n", - "This step requires a Google or GitHub account, and will start your [free trial](https://cloud.vespa.ai/en/free-trial).\n", - "Make note of the tenant name, it is used in the next steps.\n" - ] + "27d75333ff0b4b92838b299a7433f783": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.deployment import VespaCloud\n", - "import os\n", - "os.environ['TOKENIZERS_PARALLELISM'] = \"false\"\n", - "\n", - "# Replace with your tenant name from the Vespa Cloud Console\n", - "tenant_name = \"vespa-team\" \n", - "\n", - "key = os.getenv(\"VESPA_TEAM_API_KEY\", None)\n", - "if key is not None:\n", - " key = key.replace(r\"\\n\", \"\\n\") # To parse key correctly\n", - "\n", - "vespa_cloud = VespaCloud(\n", - " tenant=tenant_name,\n", - " application=vespa_app_name,\n", - " key_content=key, # Key is only used for CI/CD testing of this notebook. Can be removed if logging in interactively\n", - " application_package=vespa_application_package,\n", - ")" - ] + "292d54e5961e4b03bfbe30394eb4f4a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_96fe2fb513ba405cb018acff742138e9", + "max": 1053, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_839213a9b01041f5bd444cec7a236aa4", + "value": 1053 + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now deploy the app to Vespa Cloud dev zone.\n", - "\n", - "The first deployment typically takes 2 minutes until the endpoint is up." - ] + "29312b3954964fc7b5ae3b178701dbb6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.application import Vespa\n", - "\n", - "app: Vespa = vespa_cloud.deploy()" - ] + "2c9e2aef69ec41ee9a393e44515af223": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7c1dde0348554c5d92b7f0174697981d", + "placeholder": "​", + "style": "IPY_MODEL_bdddf8e0c9784386ac8c79f8a4d0b203", + "value": "preprocessor_config.json: 100%" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example uses the asynchronous feed method and feeds one document at a time. " - ] + "2d6b481dafc6438986b626bd0502d791": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 500/500 [01:13<00:00, 6.77it/s]\n" - ] - } + "2e4f5885c19541d3bf438034c38caeda": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_59332618d960460c989866e04b39fb99", + "max": 17763458, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_d8c4a21878dd44cbb38f42866b13efce", + "value": 17763458 + } + }, + "2e625dcc8e1743bc96aac570e09aa7c0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4682a778dae74457ab322f9e27c1d54a", + "IPY_MODEL_786cfe2477ca420aa0ca7c8985618366", + "IPY_MODEL_379e9902cba9418cbbaf164da2809040" ], - "source": [ - "from vespa.io import VespaResponse\n", - "\n", - "async with app.asyncio(connections=1, total_timeout=180) as session:\n", - " for doc in tqdm(vespa_docs):\n", - " response: VespaResponse = await session.feed_data_point(\n", - " data_id=doc[\"id\"], fields=doc, schema=\"pdf_page\"\n", - " )\n", - " if not response.is_successful():\n", - " print(response.json())\n", - " " - ] + "layout": "IPY_MODEL_29312b3954964fc7b5ae3b178701dbb6" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "j2pUyGjYf4Wv" - }, - "source": [ - "### Run queries and evaluate effectiveness" - ] + "32272e15352d4cafb3dcd0379cffef85": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2c9e2aef69ec41ee9a393e44515af223", + "IPY_MODEL_f94ba19edb6f4086b2ea369214053458", + "IPY_MODEL_e73d858ffafd4a48a74308daa3ccd4c2" + ], + "layout": "IPY_MODEL_86c1cb9e6eb84da6bc36f716c43c6506" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use ir_measures to evaluate the effectiveness of the retrieval model." - ] + "34e6c7d235a7401a92a28fa3a1b30d7d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from ir_measures import calc_aggregate, nDCG, ScoredDoc, Qrel" - ] + "36dbbef072bc4997946b4261d9a4f30d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A simple routine for querying Vespa. Note that we send both vector representations in the query independently\n", - "of the ranking method used, this for simplicity. Not all the ranking models we evaluate needs both representations. " - ] + "376f132d43e04f71b9f9038793911ae4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.io import VespaQueryResponse\n", - "from vespa.application import VespaAsync\n", - "\n", - "async def get_vespa_response(\n", - " embedding: torch.Tensor, \n", - " qid: str, \n", - " session: VespaAsync,\n", - " depth=20,\n", - " profile = \"float-float\") -> List[ScoredDoc]: \n", - " \n", - " # The query tensor api does not support hex formats yet\n", - " float_embedding = {index: vector.tolist() for index, vector in enumerate(embedding)}\n", - " binary_embedding = {index: np.packbits(np.where(vector > 0, 1, 0), axis=0).astype(np.int8).tolist() \n", - " for index, vector in enumerate(embedding)} \n", - " response: VespaQueryResponse = await session.query(\n", - " yql=\"select id from pdf_page where true\", # brute force search, rank all pages\n", - " ranking=profile,\n", - " hits=5,\n", - " timeout=10,\n", - " body={\n", - " \"input.query(qt)\" : float_embedding,\n", - " \"input.query(qtb)\" : binary_embedding,\n", - " \"ranking.rerankCount\": depth\n", - " }\n", - " )\n", - " assert response.is_successful()\n", - " scored_docs = []\n", - " for hit in response.hits:\n", - " doc_id = hit['fields']['id']\n", - " score = hit['relevance']\n", - " scored_docs.append(ScoredDoc(qid, doc_id, score))\n", - " return scored_docs\n" - ] + "379e9902cba9418cbbaf164da2809040": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_055b134c06394f0fbbe4194e4539f49e", + "placeholder": "​", + "style": "IPY_MODEL_9392a6a894fe47e290db7b67a4f08ab4", + "value": " 1.74G/1.74G [00:15<00:00, 179MB/s]" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run a test query first.. " - ] + "3980f62297284bc991552e57057d9e1f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ScoredDoc(query_id='float-float', doc_id='4720', score=16.292504370212555), ScoredDoc(query_id='float-float', doc_id='4858', score=13.315170526504517), ScoredDoc(query_id='float-float', doc_id='14686', score=12.212152108550072), ScoredDoc(query_id='float-float', doc_id='4846', score=12.002869427204132), ScoredDoc(query_id='float-float', doc_id='864', score=11.308563649654388)]\n", - 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"source": [ - "async with app.asyncio() as session:\n", - " for profile in [\"float-float\", \"float-binary\", \"binary-binary\", \"phased\"]:\n", - " print(await get_vespa_response(query_embeddings[0],profile, session, profile=profile))\n", - " " - ] + "layout": "IPY_MODEL_20878d8e7b784a1894ca740df0dd8cb7" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, run through all of the test queries for each of the ranking models." - ] + "42a92bd9a6e6445c90346671ac9b01b8": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - 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"source": [ - "\n", - "qrels = []\n", - "profiles = [\"float-float\", \"float-binary\", \"binary-binary\", \"phased\"]\n", - "results = {profile: [] for profile in profiles}\n", - "async with app.asyncio(connections=3) as session:\n", - " for row, embedding in zip(tqdm(ds), query_embeddings):\n", - " qrels.append(Qrel(row['questionId'], str(row['docId']), 1))\n", - " for profile in profiles:\n", - " scored_docs = await get_vespa_response(embedding, row['questionId'], session, profile=profile)\n", - " results[profile].extend(scored_docs)\n" - ] + "layout": "IPY_MODEL_be38f8dca5fb42d7972a7646f1a62c5e" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the effectiveness of the 4 different models" - ] + "513c2edf05ca43e8bcf4938a4e0434fe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - 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We can also largely retain the effectiveness of the float-binary representation by using the phased approach where we re-rank the top 20 pages from the hamming (binary-binary) version using the float-binary representation. 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about 4x in computation compared to the float-float model or the float-binary model (which only saves storage). Using a re-ranking step with only depth 10, we can improve the effectiveness of the binary-binary model to almost match the float-float MaxSim model. 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// 10\n", + " connections=params.max_connections, timeout=params.num_docs // 10\n", " ) as async_app:\n", " for doc in data:\n", " async with semaphore:\n", diff --git a/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb b/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb index f1662784..f05899b9 100644 --- a/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb +++ b/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb @@ -1,6460 +1,6460 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "XzoiJTAoZobv" - }, - "source": [ - "\n", - " \n", - " \n", - " \"#Vespa\"\n", - "\n", - "\n", - "# PDF-Retrieval using ColQWen2 (ColPali) with Vespa\n", - "\n", - "This notebook is a continuation of our notebooks related to the ColPali models for complex document retrieval.\n", - "\n", - "This notebook demonstrates using the new [ColQWen2](https://huggingface.co/vidore/colqwen2-v0.1) model checkpoint.\n", - "\n", - "> ColQwen is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a Qwen2-VL-2B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository\n", - "\n", - "ColQWen2 is better than the previous ColPali model in the following ways:\n", - "\n", - "- Its more accurate on the ViDoRe dataset (+5 nDCCG@5 points)\n", - "- It's permissive licensed as both the base model and adapter is using open-source licences (Apache 2.0 and MIT)\n", - "- It uses fewer patch embeddings than ColPaliGemma (from 1024 to 768), this reduces both compute and storage.\n", - "\n", - "See also [Scaling ColPali to billions of PDFs with Vespa](https://blog.vespa.ai/scaling-colpali-to-billions/)\n", - "\n", - "The TLDR; of this notebook:\n", - "\n", - "- Generate an image per PDF page using [pdf2image](https://pypi.org/project/pdf2image/)\n", - " and also extract the text using [pypdf](https://pypdf.readthedocs.io/en/stable/user/extract-text.html).\n", - "- For each page image, use ColPali to obtain the visual multi-vector embeddings\n", - "\n", - "Then we store visual embeddings in Vespa as a `int8` tensor, where we use a binary compression technique\n", - "to reduce the storage footprint by 32x compared to float representations. See [Scaling ColPali to billions of PDFs with Vespa](https://blog.vespa.ai/scaling-colpali-to-billions/)\n", - "for details on binarization and using hamming distance for retrieval.\n", - "\n", - "During retrieval time, we use the same ColPali model to generate embeddings for the query and then use Vespa's `nearestNeighbor` query to retrieve the most similar documents\n", - "per query vector token, using binary representation with hamming distance. Then we re-rank the results in two phases:\n", - "\n", - "- In the 0-phase we use hamming distance to retrieve the k closest pages per query token vector representation, this is expressed by using multiple nearestNeighbor query operators in Vespa.\n", - "- The nearestNeighbor operators exposes pages to the first-phase ranking function, which uses an approximate MaxSim using inverted hamming distance insted of cosine similarity. This is done to reduce the number of pages that are re-ranked in the second phase.\n", - "- In the second phase, we perform the full MaxSim operation, using float representations of the embeddings to re-rank the top-k pages from the first phase.\n", - "\n", - "This allows us to scale ColPali to very large collections of PDF pages, while still providing accurate and fast retrieval.\n", - "\n", - "Let us get started.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb)\n", - "\n", - "Install dependencies:\n", - "\n", - "Note that the python pdf2image package requires poppler-utils, see other installation options [here](https://pdf2image.readthedocs.io/en/latest/installation.html#installing-poppler).\n", - "\n", - "For MacOs, the simplest install option is `brew install poppler` if you are using [Homebrew](https://brew.sh/).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!sudo apt-get install poppler-utils -y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now install the required python packages:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "VIly_Pymmbyl" - }, - "outputs": [], - "source": [ - "!pip3 install colpali-engine==0.3.1 pdf2image pypdf pyvespa vespacli requests numpy tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "qKFOvdo5nCVl" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import DataLoader\n", - "from tqdm import tqdm\n", - "from io import BytesIO\n", - "from colpali_engine.models import ColQwen2, ColQwen2Processor" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yGfNhRP4RKBJ" - }, - "source": [ - "### Load the model\n", - "\n", - "We use device map auto to load the model on the available GPU if available, otherwise on the CPU or MPS if available.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 624, - "referenced_widgets": [ - "63b7d9faffda49adbe8cb927978897ed", - "5b0ab9446d424066bcfb850ec3367c51", - "292d54e5961e4b03bfbe30394eb4f4a5", - "b0c067a5970a490a9fbd2e4130db7717", - "34e6c7d235a7401a92a28fa3a1b30d7d", - "0b2df6b5ff4142f4a73f5c64f68b6f33", - "984fb47b2e6349df9801e8fce333167d", - "96fe2fb513ba405cb018acff742138e9", - "839213a9b01041f5bd444cec7a236aa4", - "a023a3b3ecd94b9e87f62c97166cae4b", - "7ae80928c7ca40e4ada9c4202ff4dcf1", - "07fa4fd379fb4abaa2acbe3b712e6aaa", - "87aa782d0ee640b29475bc97c152ad1b", - "c8ecca34fb8240219183e3c379207d99", - "e197d08fdbe6451dbaf0cea1ad3628d9", - "42a92bd9a6e6445c90346671ac9b01b8", - "63dbac889ca747beae51e1f0608ba1b8", - "3980f62297284bc991552e57057d9e1f", - "66e424d41c304e658b10357591d0c0d5", - "ed729a0d26594df0b39551fb58cab644", - "8e3690b1a39b429e9311fc65a821c450", - "912dd2c6b50a4c1780c30b820684fa8b", - 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"147e865abd9741c8a087b496d9be4f27", - "e309623fc98c46a4b8dc432a7a983583", - "425ae19a5f294ccb8fa2f583dd12b98b", - "895349b8cd09436d8309bc80a13e23cb", - "2e4f5885c19541d3bf438034c38caeda", - "a8a2c6985b3044e6a69bd299a04d3842", - "20878d8e7b784a1894ca740df0dd8cb7", - "36dbbef072bc4997946b4261d9a4f30d", - "61de8364e17b414b95d52e9f8613faea", - "59332618d960460c989866e04b39fb99", - "d8c4a21878dd44cbb38f42866b13efce", - "8084e77687a34d7795c6a5e63dfe03a1", - "a7cf48de4fee4fa29b3c7f40f8a33818", - "4e420de240bf4872804b86b5cb392959", - "6b872cb9ed7d4331aa7c9233cb347a06", - "ce09c48f9cfb4d61a488a59180f72218", - "b6720349abef46f084217dd3905d5001", - "be38f8dca5fb42d7972a7646f1a62c5e", - "df1309c11b464a4fb90ec8b1ce531794", - "24189eaa3a26454ba62a1b12560199ed", - "e03247fa2e004d24bbbe79f88e035546", - "1fab9e005a8b43d384d7cf07ee9f068f", - "6a3e1019955041dc909f6d72edf84c9f", - "5d1b15bb1fda4704ad9de212b7a44d95", - "082255bf4243466e9c5f6f158fc2be9b", - "630e6b3b505441aca8ab027a4c3130f9", - "859a481ee7024e858510dafbde2f99e0", - "c551d36ebf3543cb87fd71922fb08bd4", - "1a6c2da3dc004653ba38a43274e8b1f8", - "243e816f39264b95bc8e0ee980ddfdfd", - "85dbe8aa26d04916b27a494d05574e39", - "c8b73c55e2844644a1ad410a4bc202bd", - "18f1f42017324be7bd17ab4612cff888", - "a66e9945421b4f52a0611ff4215ea51c", - "1738ecdd88a34840ae2873a5c65990b5" - ] - }, - "id": "bpvPYA1HnMDp", - "outputId": "4da48909-2eb2-4af2-d1ab-bf43870033f4" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_section'}\n", - "Loading checkpoint shards: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2/2 [00:05<00:00, 2.90s/it]\n" - ] - } - ], - "source": [ - "model_name = \"vidore/colqwen2-v0.1\"\n", - "\n", - "model = ColQwen2.from_pretrained(\n", - " model_name, torch_dtype=torch.bfloat16, device_map=\"auto\"\n", - ")\n", - "processor = ColQwen2Processor.from_pretrained(model_name)\n", - "model = model.eval()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PUqnrKWLak3O" - }, - "source": [ - "### Working with pdfs\n", - "\n", - "We need to convert a PDF to an array of images. One image per page.\n", - "We use the `pdf2image` library for this task. Secondary, we also extract the text contents of the PDF using `pypdf`.\n", - "\n", - "NOTE: This step requires that you have `poppler` installed on your system. Read more in [pdf2image](https://pdf2image.readthedocs.io/en/latest/installation.html) docs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "_-1v-qZ32OgW" - }, - "outputs": [], - "source": [ - "import requests\n", - "from pdf2image import convert_from_path\n", - "from pypdf import PdfReader\n", - "\n", - "\n", - "def download_pdf(url):\n", - " response = requests.get(url)\n", - " if response.status_code == 200:\n", - " return BytesIO(response.content)\n", - " else:\n", - " raise Exception(f\"Failed to download PDF: Status code {response.status_code}\")\n", - "\n", - "\n", - "def get_pdf_images(pdf_url):\n", - " # Download the PDF\n", - " pdf_file = download_pdf(pdf_url)\n", - " # Save the PDF temporarily to disk (pdf2image requires a file path)\n", - " temp_file = \"temp.pdf\"\n", - " with open(temp_file, \"wb\") as f:\n", - " f.write(pdf_file.read())\n", - " reader = PdfReader(temp_file)\n", - " page_texts = []\n", - " for page_number in range(len(reader.pages)):\n", - " page = reader.pages[page_number]\n", - " text = page.extract_text()\n", - " page_texts.append(text)\n", - " images = convert_from_path(temp_file)\n", - " assert len(images) == len(page_texts)\n", - " return (images, page_texts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define a few sample PDFs to work with. The PDFs are discovered from [this url](https://www.conocophillips.com/company-reports-resources/sustainability-reporting/).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "kZIGixLBRyEi" - }, - "outputs": [], - "source": [ - "sample_pdfs = [\n", - " {\n", - " \"title\": \"ConocoPhillips Sustainability Highlights - Nature (24-0976)\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf\",\n", - " },\n", - " {\n", - " \"title\": \"ConocoPhillips Managing Climate Related Risks\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf\",\n", - " },\n", - " {\n", - " \"title\": \"ConocoPhillips 2023 Sustainability Report\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf\",\n", - " },\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can convert the PDFs to images and also extract the text content.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "YaDInfmT3Tbu" - }, - "outputs": [], - "source": [ - "for pdf in sample_pdfs:\n", - " page_images, page_texts = get_pdf_images(pdf[\"url\"])\n", - " pdf[\"images\"] = page_images\n", - " pdf[\"texts\"] = page_texts" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b3vBUFwATIqk" - }, - "source": [ - "Let us look at the extracted image of the first PDF page. This is the document side input to ColPali, one image per page.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 737 - }, - "id": "DGAXQ-0E3jQS", - "outputId": "6efbad11-5ff4-4eaa-8564-ab399f921b9e" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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cwkuobwwGYRY3eXdhA5Ud8KCQKW38W3dtq8aS6vDdaMmpCB9SZEVGVrdn2FlAXiQKMjHXB5oVCTTa6X/ALnodAIPQ5ry9/GWrCx0y8l1FEjlErOkUaebJi4ZFwjgb12gDahD5OecirMWp6vJdmysbxLBGl1WV2itUYsYplCcEY53HJ6n9af1eS3YXPR6K8xh8Za9dX9hmW3tvNhsZEt3KKLnzVUyEAgucEsBt6FeaePFerh9Xi/ta3NxHHK8TbEMEIWZVGSBvjYKcYkUjPOcA0fVphc9LpGZVxuYDJwMnqa4V/E98vw11fV4Lp3vLMyKk0scbcqR3T5JBz1XGfQEVm3Gsy3er2UcmqR6jp9rrFm0d6EVFV3jlDRkrxwdnuN4B5pKhJ38guem0V5VdeN9XV9RltL5ZE+xX8scckce6B4SAmUAyvfhySeuB0rRuNa8QW2qtpP8Aau9mvbJBcm2QMqTJIXXGMcFBg9eec0/q0luwueiZGcZ59KK8ru/EmsQW9zcebDJf2dnqEaXTWy7mMV1EikjtkHkDgnmruq+JtY0SW/s59QecW+oW8XnpboJjFJEXYKMbMgjqR0z3xR9Wl3C56PSAg4wRz0rm/CWtG+0y2W+1WC8v7nzZVWPbxGrbcfKAMrld3+0T2xXE37Xum6reRRpOLXwtcvqgCDiWKZg20euEa4H4ClGi3Jxb2/r8wuetgg9DQSAMngV5Kuta7ocNtbxzRWss9quo4nKAXNxNKzOh3AswXKrhPm5HtXT+MNXurbUYbE6immWkthPL5rRq3nyjaBF83sxOB8x7dKHh2pJX3C52SurgFWDAjIIOeKWvJ9M1a+0/QoxazW1oyWGlK0siqjFWifcu9gVDcADfwORwTWna+Kr27ktTNrg0+P7LDLCZ7RSb52ldHG0E5xtUfuzzv3dMCm8PJbMLnoisrglWBAOOD3pa8vttam09Xs11aPSbdtS1Np7tkVtsiy7kjO7gblYt6kLx1qzbeLdYfVtIF5OsP2qG1LWUSJvDyLlt6Ph8E9GQkLj5hwaHh5dAuej0VwXgjxJrGs6iq31xC6yWrSz24Kb7aUOAEwoyo5YYc7srkd6z5vFuuZujbXqzXxW/D6asClrMRK5ifpuOSqfeyG38Uvq8uZxC56bRXnN14wv72Z00nUoGjZdNRZkjWQI80rJL9TgDjtW94n1O50ew0u2XUXjuLibymu2WJA2EZiWZhsQnH9056AdwnRkmk+oXOooryYeN9YlsBNNrMNlOuipeRRfZ1P2m48yVdnIz82xRtGDzxitOPxVqj69NCdTjE8erxWi6V5K5eFkjLnON3y7nORwNvNU8NNBc9Gorze18QaydE0O71HXFtIdTkkM14beNVtgqttQZGMsR1bPTAxmqdx441r7HZT/aVguUt7aWWB40RZxJKVLBWy7AqM4XbtzyT0oWGm3owuepsyopZiAoGSSeBS9a8y1fWrq/0DxHHc6vH55tdQjbSfIG6FEDBG3D5hkAHLcNu4xXS+GbvURrGoaXe3n2uO3tbWeKQxKhXzA4K/LwQNgx356mplRcY3uFzqKKKKxGFFFFABRRRQAUUUUAFFFFABRRRQBDd3UFjaTXd1KsUEKF5JG6KoGSaenlhRs2gNyMd6x/F+kya54Q1XTYYo5Z7i2dYlkxgvj5eT05xzXJ3vhnUp79nt9HETS/YzZXHmxj+zFjIMiYB74Y/JkNuwelawhGS1dhHouEQAYVR0A6VXlsbO4voLyWJHuIUdInJ6K2Nwx052r+Ved/EcPNr0Uflo0I02RZPOYLncw/1G4EeaNp5HIyKnXw3fTajDcW2kGMS3FncWt5JIqtZW6Im+ArncCcOCoBB3nPSrVFcqk5WuFz0UhSQxAyOQT2o+T7w28859a84n8Ha4+m38GQVt3itrGMSAmazSXzGU54BYFUw3B8sZ4NWNJ8H3H9o6TJfWLGxglu5jBO8ZEJfyvLwifKBlWYKMgHvS9lC1+YDv1VBwoUc5wKAI8MRtw3UjvXm8Hg/V7G1triysYBqJg1FLlmmwJTIxMKuQckdOn3faorLwXqLXCpLpuzTnv7SdreVolGxIpFkJSM7epTjknvmn7KH839feFz00BFQKAoXoB2oIQsMhSw5GeorzdPCd/brFHdaIup2UQvIraz+0qot985aJwSflGzAyvzLjgVYXw1q0Pi+TUZopZFW9FzHdRyRjEIjA8osR5h5yNuNpznINL2Uf5v6+8LncXVhZ3UtvNcwqz20omiYnG1wpXP5Mw59aX7VaLfCy8yMXLxmYR9yoIBb8yK5/xDaXXiLwvYv/Z1wszSRXD2oeMvHwThlf5JME8qSPXOQK5//hE9Tzbzvo9sLltCuLHdCyjyJTnZ95sgFePlJAyR05ojTTXvSA9HJTGDtwTjHqahhu7Waa4topUaS2IWVB/ASAwB/Ag151rng2/fR7TT9P0mIqumOoeNo9yXbAZZi54zjO9QWyOoqS+8I6mz64bawRXvLi0uGlRo83EaLGJYjk9Sys2G+U55PJpqlC3xf1cLno7CMrltpU469/Sj5Tzxx+leQ6xpt5p0Gl2ckCRKz3k3lag8fkxo+wBVAHlq+csqgnbk4zV+LTlu7jw/pulwz29rqFhEup204IkjhiberOQMbnJdCe+/Pan9XVr8wXPTwqKMBVA+lHyMwb5S2OD3xXM61a3nifwraKNPeF5Lu3lltpXAIjWZS2T7qCce+KwIPBd9p12l1p2nW8U0eo3jRnzAoFs8TiNODkJvK/KOnXiojTi1rKzA9Gyp5yDg4+hpPkB3/KCeN3rXltl4M1ZgY5NN8m0lk09poGeJQTFKxmO1DgjaRySWYdTU174Q1X7NJp8elxyaf9qvXt0Roj5Kvt8rAc7VX7+cAsvGAMmq9jC9uf8Ar7wuellY1IyEBAOOO3elIRtwIU5HzD1HvXjN7bTprMdvqtmbkQxafFPEWDXMkiKu7ydw+ZCThtp+bDc9a3F8I60NS1GR4ZGnk+2kXSyRKsyyqwjQn75xlRtbAXbkGm6CS1kFz0rCHGQpwcD2NLlc44yP0rzGTwDcx29w1rpsKXAsrDyGWQArcxuTKwOeG27ct36ZNSN4R1o6tqUzQyNNI16y3SyRKsqSo4jjJ++cZUbThV2gg0vZQ/m/r7wuehXl1aWVq1zdyRxwxsMu/QEnA/HJFTnaBk4A65NeZ3vgO7/sy5trbS4CkmnWO6HeuJLmKUmQ8nG4pxuPXPWuj8UaXdalaaTBb6WktojkzwYiLRDYQoCufLIBOD1x2FJ04aJSA6kBEyAFGTk47mql5qNpp01pHMdr3k4t49q5y5VmGfQYU15yvgfV5dGl+1WayalFpNnBaSNOC0c8byFirZ4IBX5u9X4/C+ojxHbXD6UjTQ61Jey6mZlzLAyuEXGd3yhlXBGBt461XsoK/vBc9CARAFG1R2A4pNsYBTauMcrjtXm/iHR7vVvFniCG20o3E8lraJa3jSBBaSZkPmDJzxwcrk8Y70//AIRPWf7fvriSKR5HmupUu1eJVkjeNlSMn/WHGVG04UbQQaXsY2u5Bc7jUNUsdNezjuMmS4mENvHHGXYtgngAcAAEk+gq8Am5iNu7oT3rh7fwTFDb+EF/sq28ywkEl8W2sQ3kMpJJ+8d+316A9qxJPB2uyaXc20WniG8+wXUFzdi4X/iYyyMCjZznjBOWwRnA4oVKm/tAepbYthTam0nkYGCaU7OHO35ehPavPfEngVryS/XTtNhES6UkNiA4Xy7gSu5K8/K3zA7vc89aL3wpewXl5b2mkxPo8uopOsEflnav2cKWVHOz/WddwPqBnmkqcH9r+vvA9CwgYnChj37ml+XdjjPWvLF8C6vLocyXVmkmoRaNDb2btOpaO4SSVvlbPBAKfNx6VtL4d1D+0tSb+zgNQnluXg1r7QAUR0IjXAO47cgbSNo27gc0OlBfa/r7wudyNpBxg4Pb1ppEQjKkJs7jjFcd4G0C+0eaeS6tpbUNbRRMhaLbJIu7LgR9ev3mO49xxWOPBN/b+G9GjWxV5orh5dRt08qRp8hwhPmHY+3I4J47cij2UeZrmA79b+0k1eTTOTdQwpcEFeArMygg+uVNSzXlpbPbrLNGjXEnlQgn77YLYH4KT+FcAPDWu2tlPFFZ/aWl0aG1HnXCthkmdmjY5XJ2SYH8Py4Jx1itPB99FPaTzaOssFvrX2qOBzCGSFrfYSFXCLiTDFR6Z5NV7KH8wXPSsIWHCll6eoqnDqNnNq11p0fNzDFHNL8vBVywXnv9xq87h8GeIBHqKAyR30ltdxm9EkapctI2UyV/eHty2NnbNdF4U0OWw17U70aKulWVza28UduJEbDIZN3CkgfeH169c1MqcIpvmuBvyJpeu6VPbN5VxYuzQSKDhcq21l49CCPwq/tQLjaoGc4xXm1v4Ru7G2toX8PRXVnb395JNZo8QW5EjMYZACQDtB24bBGeOgrWk8M6hceFPDOl3qLdSWl1C94rSZHlqGyCT94DIHvinKnFbS0A7LEY5wo3Hr61FHeWs13PbRyo08G0yoOqbhlc15rqHg/WZNLbTY9MSS2T7eLTa8ZMJeTMP3zhV291G4dBirM/hPVFk1eaLTY2urlLCdJxIgMjQsjSxk5yGYoTk8HuaPYw/m/q4XPRyE5zt96p6hpVjqhh+0xkyQP5kUkcjRvGxBGQykEZBI61xq+GL/VdaS61PTAtjLq0l3JbzSq2IjarGu4AkH5x05pmleHNa03xFNqMlrLcTxS3U3nLLCi3SvuMcZP3z1UYbAXbwcUKmlqpagdzYada6bb+RaRCNNzOcksWZjlmJOSSTySatYHPHWmRM7wo0kfluygsmc7T3Ge9PrBtt6jEKqSCVBI5GR0oKq2NwBwcjI6GlopAIUUggqCDwQR1o2KcfKPl6cdKWigBpRGBBVTk5OR3pSqlgxUbh0OORS0UAIFUEkAAnqQOtAVQxYAbj1OOtLRQA0IqjAUAewpWVWGGAI68ilooArLp9ouoSX4gX7VJGsTSdyqlio9OCzfnRbafaWctxJbwqj3EpmlYZJZyACefZR+VWaKd2AhRWXaVBX0I4oKqSCVBI4zilopAJtXJO0ZPBOOtLgZ6UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFb7bF6N+VH22L0b8qoUUxF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQAUUUUAFFFFAFbUL+30yye8umZYIyu9gucZIGfpk02+1Sy0yS1S8uFha6mEEAIPzueg4pdTsU1PSruwk+5cwvEfbIxmuEuDN4p0+AgZutP0dpyP7t3vAH45gf866KNKM1eT06/p+IHeSX0EWoW9izH7ROjyIoGflTGST2+8Pzqzg5xiuM/tm1ubnVPEEiySWkGnwW8axHDFpcSMAcjB+eMZzxSafbXVp4pg0ueGa1tbyymaSA6lJcElWQBgTgofmIyDz+FN4fTXSy/4P5AddaXcF/aR3VrIJIJRlHAIyM47/SpsEV51ZxNpXw3s7vT2nSe5eK3kkNy2EVptrEbiVQ9s44zmtKC0v8AS9UQRW5sLeW2n82GXUzcGQquVdFbkMD1I7HntTlh0m7PZv8AD5/15AdnRWB4RtSNAsb+a5ubi7u7SJ5XmmZh90HAUnC9ew575rfrnnHlk49gCiiipAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqlnpdhp8tzLZ2kUMl0/mTsgx5jc8n8z+dOvrsWNus7IWTzERsHG0MwXd+GaoR+IYJJJkETKY7z7L8zABgASZP90BX/75NNNpWAs22h6XZ6fNp9vYQR2cxJkhC/K+eDkH6CkstC0zTphNa2iJMoKiQszMAccZJJxwOKZ/b1gXt1R5XaeYQqBC4KsVLAkEZAIHWnHXdMWN5DdAKmOdjfNltoK8fMCeMjPNP2ktddwCPQNJha6aOwhUXYKzrj5ZATk5XpyfaksfD+lac8j2tmqPInlszMzts/ugsSQPYcVPc6laWixNPKU8wFlGxicDGSQBkAZGSemaY2s6ckssbXSqYgxckHb8v3sHGCR3A6U/aT2uwLVvbxWttFbwRrHDEoREXoqgYAFSVUn1SytpvJlnxKZBEEVSzFiu4DAHpz9KjGsWUjbIZ0dt6pzlQcttypxhueOOM96hu4F+is6PXdMmjd0ugURQ2SjDcCdoK5Hzc8cZ54qaHUrSe2muEm/dQEiVmRl2EDJBBAPFAFuis5td01Iw7XDDLMuzyn3gqATlcZGAQenQ5plzr1lDNBDHIJpJpoohsztG/GPmxjODnGc4oA1KKpS6tYwXn2SS4CzgoCu0kLu4XJxgZPAz1qI69pgi8z7TlNzLkRuc7fvEccgdz0HrQBpUVl3WvWVvcQwLIJHkmSI7Qdq7hu+9jGcc4znFWJNVsY4xI1wu1kSRcAksrnC4AGTk9AOaALlFUf7Z0/MQ+0j94AR8rcZbaN3Hy/MCPmxyMVGmt2s2qw2EDeY0glJYAgDYQDgkYbk44PGKANKiqbatYLnddxjEby4J52o21j+B4qC71/T7SG5keR2NurMypExJCkBtvGGwSAcdO9AGnRVJtWsUmaJrja6gk7kYAYXdgnGA23nb1x2pf7VsvsCXxmItnIVHKMNxJwMDGTkkY45zQBcorOi17TJ1do7oEIhckow4BwcZHJBIBA5BOKH17TI0V3uduQ5wY33KFIDbhjK4yM5xjNAGjRVM6rYrcNA1wquoJJIIXhdx+bGMhecZ6VEuuac27E7bl2gp5L7juztwuMnIBIx2BoA0aKzZNf0uLZuu1+dVYFUZhhiQucDjJBAB6njrTzrenCONzcja+SPkb5QDtJbj5QDx82OaAL9FZk/iDToI7lvOZzbpI7qkbEny/vheMEg9QOlWZdQt4bWK4kZ1SXGweWxYkjONoGegPbtQBaoqh/bem5YC6VtsayEqrEBWxt5Axk5GB1NSjUbQ2TXnnAQISGZgQVIOCCCMg54xjNAFqisz+3bNriNEf5MyCV3BTyiihjkMB2P4UyXxFYo9sEZ3WaUxHEb71OwuPkxuOQOOPegDWoqiusae7wKtyG88KUZVJU7jhcnGASQQAcUkGqwPpFtqM/7lJ0RgvLHLdFAAyT9BQBforKTxBYtem3MmFKRskm1sHezKAePl5XHOOTinza3aISsTebIJUjK4K5DSCMsCRhgCecZoA0qKzI9ctPIWS4fySzyLtwWwEkKbiQPlGR1OBzUo1nTjNLEbpQ0W/eWBCjZ98biMEjuAaAL1FZ41zT234mfcrKpTyX35IJAC4yeATwOnNPj1axlnjhjuA7SBWUqpKnIJHzYwCQDgE54oAu0VlXmv21nNcQskhe3eBZDsYLiVgoIOOcegqU63pyxJKbjCNu58tvl2nDbhj5cE4O7GKANCiqUGq2s8V5Ll447OR45mkQqBtGSRnqKaNZsGMQE5HmgFcxsOCcKTkfKCeBnGe1AF+is4a7pjSMgu13AMeVYAgMFJBxggMQMjPJqS41W0tZHEs8aLFv8ANLMRs2qGPGOeGB/GgC7RVJdXsWkij84h5Nu0NGwxuOFByPlJxwDjPamy6rBb3VzDPlBCIsMAWLl92AFAyT8vagC/RVEazp5aFVuVbzgCpVSQATtGTj5ckEc45GKik1y0HELea/mxxlcFeHkCbgSPmAJ6j0oA06KrT6ha21zHbyy7ZXwQoUnAJwCSBgAngZxzVV9dsyv+jyCZ/Njj2gFch5Am4EjDAE9RkUAadFZ91rNjaWL3bSl41iklAjUsWVDhsD2JAofWLOLcZZNuGVVUKzOcoG+4Bkcc/TrigDQoqgutac9wkC3SM77cFQSvzLuX5sYGRyOeams9Qtb9Wa2l3hcHO0rkHoRkDIPYjg0AWaKyBr8X2qzt2gdZLi4lgbkYiKEjJ9idoH+8Kks9cs7tIMv5bzYKKQSMEnZlsYBYDIBOeaANOiqj6nZx3n2R5wJuARg4BIyAWxgEjnBOarjXLOZoFtn87zZljPBXAZWIbkcg7Tgjg+tAGnRWbba3aTW9lJK3kvdRxyBMFgu/hQWAwMngZxk1FY+I9Pu7Pz3lEJCNIyuDwA204OMNg4HGeSBQBr0VQXWdPd4kW4+eVmVU8tt2VxuyMZXG4ZzjqKibxBp4WIpJJJ5kqRALC+cvna2MZ2nB56HFAGpRVW31G0urhoIZg8i5ONpAYA4JUkYbB4OM4po1bT2xi7jOY1lxnnYzbQce7cfWgC5RWLceJLW3hlYqzvHb/aTtBKbd5X7+MZ4NadreW96jtbyFgjlGBUqVbrgggEcEH8aAJ6KKKACiiigAooooAKKKKAIL21S9sZ7VyQs0bISOoyMZH061lL4ajR4XW8mV4rJrXcoAJcg/vf8AeG5/++jWhqd49jZebGqGRpI4l3nCguwUE+wzWFdavqDvcRCSCMQ214JDGD87xhNrKc8ff6c4INAFy28OfZpRMLlBL58cxCQ7VO1GQjG4nkMec9fyptn4WhtFiRZxthMflERYbajhgGOTnO0DjA4zioW12/tnis5IYZLmQxBJI0ZlAdHblc5J/dkdRnParuk6pd6ldMskMMMUcEbuuSzF23dD0x8v157UATavpTaosQW4ELJuw/l5Zc4+ZWBBVhj1x6g1Un8Ni4ie3kvX+zZmeJBGNyPLnJLZ5A3NgYHXnOKJdflWBGjhhaV2ulCNJtGYmIHJ6ZwM/WqkviW8UeVHbxyXCJLJIDE642bflIJ+U/MMnJA4POaANO30iRNQF9cXYln87zSEi2L/AKvy8AZPbnrSQ6NLFbQWrXpa2t3jaFBEAQEcMAxzyeAM8fTNVm1jUGhnmit4Nouvs8aAFnAAyWIyNx/2V9zz0q5Jqyp4fbU08qUiASYRiFyQO5GQOc8jOKAKF/4dkfSoLa2nZnhgFv1CMV8xHJU8gN8nFWbLS7oaHc2M8zQvKz7JFIMig92I4LZzkjtimz6nfwTi03aeZwksrSbmCbUCHbjOQx3+pwBnviq0Ou6jezQfZobWOOeYQIJtxZSYBLk4Iz3XH457UAW7Hw+lndG489dzPI5SOLYo3IicDJ/uA9T1qO38ONbJBBHfH7NHLDOUMQ3M8aqv3s8A7AcY696rReINQu1hkghtESRraMiQsSGlXOeOwPbv7VLF4jla0uZJIYRJAkeQGOGZpniOPb5Mj60ATz6Pc3OqXsjXKpZ3Bg3RhMs3l88HPy5OB349Kf8A2JJFDbi1vTFLFHLDvaIOGSRgx4yMEEDB/Q1Rn167SB5JYofKk88RiNmV18qUJyfcHPGMe9Jca3dWP2ua4CzrDdXCokWUOxIi4B5Oen9aALKeGxEI4IrtlskmScRGMFtyoExuz0IGemc9+1JF4emjMMh1DdLbRxR27eQAFEe7G4Z+YkMQentioW17UlhbNrFvRxl9h+5sLZ8vduOCMZGeDnHarWra1LaWcEtosMryQPcYIZgUVQSR045HJPfoaAGzeHftF4t1NcpJKyosxeAENtYsNozhfvEc7uMd+ams9Fe0uraQ3ZeG1WVYYvLAIDkE7mzzjGBwPeqLeI7lEe7NvCbRZfKCKT5hPkebnPT2xj39qsaXeXk+sXK3UsDL9hgmRYGO0bmk7HvwOe+B0oASbwxDNfSXJuZBvuVm2bRgIMlo/ozEsajk8LiWS4eS9LNLFNEH8r58SMGyxJ5xtAA4GKgbxHqK2lrJ9lheWa0N6VQMVCcYTJPB55Y8DjinSeJbg6hd28EduxtfMMsTbt6qse8HOcHJwMDpn2oAsyeGklu7id50Pns0j/ucsJGTYSCTwO+MZ98VozWCy2dtbmQgQPE4bHXyyCPzxWbPqzy6jZQIyonmW7uUb7wkSUlT7fIKot4i1C4XZCLeIubaWOUo20xyTBMYJyeO/HU8d6ANKfw+JYVRLySNl88hgvXzJVkIPI4+Xb15BPSm23hyO3Wf/SOZo50ISMKq+btzgZ7bP1qtdeI7q3t5LkQ2zo32hYY9xDq0RIy/scdhxkdc1cOp3kGrxWdytuI2KoZEVsM5BOAcnaeBgN1657UAVbjwzKS80V0kkqrJ5Ilj5y0Rj2s2T8ozkAD86gs/DM7QeXcOQIZI5IWuCJXLBGQhypGV2txyCOauXWu3FvNO4igMEVw1tsJPmFhHv3emPbHTnParOm6ldXDSx3aQB1torlTESFw4b5Tn0Knn36CgCOLw/HFFsE5GfIJ2xhRmKQydB6kn/wCvUNx4WhmuJJvPUmUuJBJFvBVpGfAGcAjcw5yDnpUdrrt9cvBalLWG8mbnzFYLGNhbnn5s4OCpwQCeMYpNM1yW6vEdlAS7mhQIXyI8wM52+uSv45oAuzaBHNbeQbhwubk5Cj/ltuz+W6pb3TJb22tY3uV3QnL5iykvykcru98jk8+tZD65eSxzXIMQtxYNMI0JDbhKyZDA+gFS3fiO6t4ZbgQ27xt9oWGPcQ6mIkfP7HHYcZHXNAE0fhiGPSnsftDMCINrFBwYgoUkDqDtGRxT7rRZR4cudPt2jMsz7mZFEfVgTt64OBwTnnGagu9bv7QzxNFbNJbSsJZFViuwRq4OwHdj5sEjOMZxzU+ua1LpsCyWyRSsLd7llIJyigdDwAOep9uDQBXg8PTXFkYLyUxjfMB0aRkkQKS7DgtnnPPAAqydEuWvEv21BTepIGD+R8hARkAK7s/xsc56+1VZ/Ed1bRzXT28DWyyTxIikh8xozgk9MHaR046+1WtPvLkanqovp4GW3ghf9yTsUEOScHOOn44BoAhTwsqyW5N4XSJ4pPnjy25H3nac4UMeox+NXE0ho9LsbWO5xLZMrQymPIO0FRuXPPykg8j14rLt9cu7y+tIJlER8+FyYwVDpJHKQCCc4ygOTjPHAptt4kuJp7GzhWDz5obeTy5SzMyyfebdnjaAeDyaANV9GMyzme7aSSdYQ7eWBzG5fge+cfh3qmvhfE5na93SjG1zF8xxKsgLnPzHKAduPSrGmardXd3Ak8cCxXKztH5edy+XIF+bPByDnjGPeqjeJLiOOKd4bcxXG4xqGO6MLKqHf+DZ7YIxz1oAfN4Vjlct9oU7hIjCSHd8jSM+AM4yCzDJyCD0qa+0J5dPljt5QZd9zIgkHykyhgQfpu/Snf2tczaz9ggSAKs8kbu5JO1EjY4A7nzCPbFM1HXJbLU1hjiSSBZIY5cKdwMjYHOQB2OOc+1AFO18NzvDtuH2mKZZYGnIldjsKN5hUjIwRjnIx+FXoNA+z3dpNFcqiwKoIjhCM4APykggbSTnBBx2IqtJq2pS6Xb3UcljGbh7Z0AyxRJJFUhhnng43DHORjvTZNfuYlvTFHDts1mmkEzsTIqyuu1PQ/KfUDIGKANO40j7Reyz/aCqyNA5TZnDRPuHOeh6VRv/AAwL37QpvCI5zKWV4twUuc5XkAMOmTnj0pH12+TfJ5FsyM9zHChcq26InG5icc49setSTapdt4Y1K7jaJLy2SQcxMuxlXPzK3IOCO5HTk0AXP7KzaalbGc+XetIw+TmPeuD359e1V7rw/Fc6gt35i8pEkivFv3BCSMc4B5IOQfwpo1e6F6I3W3MK3SWbgZEhdkDbxzjbz09MnNJLrNympXEQS2Nvb3cNsy7j5reYF+Ydhgt07gHpQBBJ4bneSCA3ubOG2lhixGA0e50ZSefmxsx26e9TTeHWuhcNcXpaW4MhZki2gb40TgZPQID171WTX9QbT47lo7JWNib9lYsAUGPkBz97rlugyOOaP7cv0mlj2JI0160MAETExIIg+GAPJ/Lv6YoAv3GgxT6u1/5ifO0byI8e4kp02nOB0HY9OMU+60hptRN9FdeVKDG6Bo9wDIHXnkZBWQjH0Oar2GsXmoOGEdpbonlLIkrksWdQ3ykcY5GB3welFrql0vhS0vp3hku5kjAIQ7WZyABgck89OPwoAks9FlsJS9vfEGXBud0QJkO9nJXn5c72HfjHfmq0Xhfy5BK17ulUIA5i5bbKsgLnPzElcHp7AVAviS+e3nlW3th9khllmD7sv5cjIQuCduQueScHjmpxql3c6tp+HhjtXvZ4PLVj5h8tJB83Y5K5x2460AX9Q0pr2+guVuBCYtvKx/PgNkgMCMA9CDke1VF8OMI4EN8SLRUS1/dAbFWRH+bn5j+7UduPeifW7iCG9u9lsbe3eWMRFysrMg6+mDjOMcLzntTTq2onURpqiyNz5m0zAMY8eWXHGc54x16EH2oAcfDheOSF70mDyLiCJREAyCYgkk55IxxwKmbRphefbor0JeZzuMOU/wBWqEbc/wCyD1496zz4pnWBLuS2jW2a2EoVcsXbyjIVDDhTxgBgMjnPapYdb1GaWC3+zwJLLMiCRwwXaY3c/LnORsxnIBzmgC1b+Hora2EEdxJtEsEoJAz+6Cgfntz+NP0zSJdNdtt2pjYjMSQ7UwAeQMkKSSCcYHA4FUbjxJNEJY0tlaaCRYZgMkI7y7E47grlvxX1q2usSx6Ob+6hWMRT+XOScBU37S+MnGAckHpg0AMvPDcV3JqcgupYnvVQKygfuGXB3L7kqpP0pR4bt01FbmJkWMNExjaPcQY1CrtOcAYVexPHBFU4fE1xcRbRbRxXI2K0bbnId2OxQBjJKAtjjtyKiXxJe+VJetHEYY7NnMAByZBM0ec54HGe+BQBsyaU73F1tuitrdndPD5YLE7Ahw2eAQB2zx1qvHoUvnwTzX3mTW/lLGRCFGxN3BGeSd55+nFR2ur6hcX8Nm0EMbl5PMd1IyqhDwuSQTvxye2e9O1HWbqynvykMTw2qQ4ByXZ5DgewUdT6+1AEX/CL5S1je93x26QooaLJHlPuBXnCk8A8Hp2pw8MsYIYZL9mS2VhbERAFGLq+W5+bBUDHHGfrUUmv30UYaS3hjEbuJpHU4CrtwSgJZB8xyTnGPQiifxHcQRfaDFbtDIZhGgY708t9pL9ueemMcDmgC9DorJeSXkl2zXMqyh2jTYMuIwCoycYEa+tVrbw0bZvNF4vnBoGDCHAJiLHJG4kk7jk5qWTV7ltZNhbxwbVndGdsk7VjRzgD+I78VXOvXiadb3RjtXN55XkpEWZow5P3hn5seoxk8cUAWtL0CLS7rzY5FZFVkjHl4YBmyctk56AcAe+aiTwxCl+t0LmT5boz7NoxszuEX+6HAb9KiTXrwyW3m28MUTMElc5bBMhQdCSmcAjcCMnBIxmuhBDAFSCD0IPWgDFbw6pg8lbtgjW5gfMeSR5hcEc8ckitOC1EFzeThyxuZRIQR93CKuP/AB39asUUAFFFFABRRRQAUUUUAFFFFADZI45o2jlRXjYYZWGQR7ioRY2axxxi0gCRghF8sYXPXHHGe/rViigCGWztp0ZJraGRWADK6Ag46fl2p8cMUX+riROAvyqBwOg+gp9FAFf7BZebJL9jt/MlBEj+UuXB4OTjmmnTLAwpCbG2MUbbkQxLhT6gY61aooAhltLaeJopreGSNm3MjoCC3rj196SKztoPNEUEaibHmAKMNhQoyPTAAqeigCqdM08wLAbG2MKtuWPyl2g+uMdan8mINuESbt2/O0Z3Yxn644z6U+igCJbaBAAsESgEEAIBgjp+XamPYWUro8lnbu0eShaJSVycnHHHPNWKKAIjbQMoUwREDOAUHc5P5nmgW1uJWlEEXmOcs2wZJxjJP04+lS0UAVP7L0/yPI+wWvk7t/l+Su3d64x1qWeztboILi2hlCHKCSMNt+melTUUARiCFcYhjGG3DCjrjGfrjimW9na2gYW1tDDu+95cYXP1xU9FAEEtjaTJEktrBIkX+rVowQn09Kqpolol4tz+8YrK0yozZVXbO4jjP8R4zjmtGigCtHp1jCmyKzt413BsLEoGQcg9Oo7Ui6bYIkiLZWwST76iJcNznnjnmrVFAFf7DaebLL9lg8yUYkbyxlx6E45pzWls90t01vEbhRgSlBuA+vXuamooAgaztWuDcNbQmcrsMhjG4r0xnripVijQ5WNQdoXIGOB0H0GTTqKAKn9l6f5DQfYLXyWbc0fkrtJ9cY60+Sxs5lZZbSB1YKpDRg5C/dH4dvSrFFAEH2K0wg+ywYRSijyx8qnqBxwD6UfYbTzZZfssHmSjEj+WMuPc96nooAgnsrS5/wBfawS/Nu/eRhucYzz3xxSz2drdFDcW0M2zO3zEDbc+mamooAj8iHAHkx4BLfdHUjBP1IJptvZ2tohS2toYVbqI4woP1xU1FAFaPTrKFQsVnbooYMAsSjkdD06jtVaTQrGS4MpV1UujmJSAhZMbTjHGNo4BA4rSooAYsMSFSsaKVztIUDGTk4+pqIWFmHmcWkG6cYlPljMg/wBrjn8asUUAQw2dtbBRBbwxBc42IFxnGen0H5Uj2drJcLcPbQtOuAJGjBYY5HPWp6KAKw0+yCSoLO3CzHMi+UuHPXnjmnSWNpKUMlrA5jYuhaMHaxOSR6HPNT0UARNbQOpVoImU7jgoCPm+9+eTn1pI7S2htzbxW8SQEEGNUAU568dOamooArpY2kcyTJawLKi7FcRgMq9MA9hTE02zS9kvfs8ZuXYMZWUFgdoXg9uAKt0UAQSWNpMkSS2sDrF/qw0YIT6elJLY2c4kE1pBJ5pBk3xg7yOhPrirFFAEH2K186Ob7ND5sS7Y38sZQegPYUGztTbNbG2h8hiSYvLG0nOenTrzU9FAEKWdrHGY0toVQqVKrGACp6jHpSCytVuTcrbQic9ZRGNx4x169KnooAh+x2v2hrj7ND5zrtaTyxuI9CfSkgs7W2VVgtoYlUllEcYXBPBIxU9FAFdbGzWZZltIBKi7FcRjKr0wD6UsNlaWyhYLWCIBt4CRhcNjGeO+OKnooAia2t3WVXgiYTf60FAd/GPm9eAOtILW3W1NqtvELcqVMQQbCD1GOmKmooAgksrWZZFltoXWQguGjB3EdM+uMcUCytAFAtYAFVkXEY4U9QOOh7ip6KAIYLO2tlVYLaGJVztCIFxnrjHrgU9oYnEgaJGEg2vlQdw9D60+igCr/Zlh5ccf2G22RHdGvlLhT6jjin/YbQSzS/ZYPMmG2VvLGXHox7/jU9FAEEFna2wUQW0MQUkjZGFxnr0+gpo0+yCTILO3CzHMq+UuH+vHNWaKAK39n2WYT9jt8wjER8pf3f8Au8cVPHGkUaxxoqIowqqMAD0Ap1FABRRRQAUUUUAFFFFAD/Jl/wCebflR5Mv/ADzb8q1aKQzK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigAooooAKKKKACimySJFG8kjBUQFmYnAAHU1nHxFoy263B1O18l2KK/mDBYDJGfoaai3siZTjHd2NOiqTavpyXENu19biacBok8wZcHoR65pqa3pcn2jZqFs32f/XYkHyc45/HinyS7C9pDuX6Kh+12/mTR+cm+FQ8i7uUBzgn06H8qiOp2CoXN5AFEQnJ8wf6s9G+nvS5X2HzR7luiq/2+0+2/Y/tMX2nZ5nlbhu2+uPSqieItGkSVk1S0ZYl3SESj5RkDJ/EimoSeyE6kFuzTorNfxBpEaQu+pWqpMCY2MgAYA4OD9a0gQRkHINJxa3Q1KMtmFFFFIoKKKKACiiigAoophlRZViJ+dgSB7DGf5igL2H0UVVudTsLK4gt7q+toJ5zthjllVWkPooJyfwoAtUVTGr6adSOmjULU3wGTbCZfMxjP3c56Urapp6QJO1/aiF0aRZDMu1kX7zA55A7ntQBboqgNc0g6e2oDVLI2SHa1wLhPLB9C2cZpG13SEW2Z9VsVF1zbk3CDzecfLz83PpQBoUVAb21DuhuYQ6Osbr5gyrNyFPoTkYHvUb6pp8WzzL61TfN5C7plG6TpsHPLe3WgC3RWc2v6Ml29o+r2C3Med8JuUDrgZOVzkYAJNMPiTQhAk51rTvJkYokn2pNrMMZAOeSMjj3FAGpRSAggEHIPQio7i5gtLd7i5mjhhjG55JGCqo9STwKAJaKqW2qafeRwSWt9bTJOSIWjlVhIRyQuDzjBzikOraaupDTm1C1F8RkWxmXzCOv3c5oAuUVTttW028eZLXULWd4P9asUysY/wDeweOh6+lOh1KxuYoJYL22ljuGKwukqsJCM5CkHk8Hp6GgC1RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRVL+2dL+0z239pWfnwKXmi89d0ajklhnIA96amt6TJYSX6apZNZxnD3C3CGNT7tnA6igC/RWdJr+jRWcN5Jq9glrMSIpmuUCOf9ls4P4VZa/s1WRmu4AI4xM5Mg+VDnDH0Xg89ODQBYoqkdX0xdQSwOo2gvZF3JbmZfMYdchc5NQ/8JHoYhll/trTvKiYJI/2pMIxzgE54JwfyNAGnRVdb+zeCGdbuBoZ2CwyCQFZCegU5wSfalF7as6ILmEu7MqKJBlmX7wHqR39KAJ6Kzn1/Ro7RLt9WsFtncoszXKBGYdQGzgn2oTX9GeZIU1ewaWRPMRBcoSy4zuAzyMAnPtQBo0VTtNW02/t5Liz1C1uIYv8AWSQzK6pxnkg4HFPTUbGQ2wS9t2N0pa3CyqfOAGSU5+YY54oAs0VSOsaYLWa6Oo2gt4H8uaXz12RvnG1jnAOSOD61LLqFlCkzy3cEaQKGmZpABGD0Lc8A+9AFiiqV1rGmWNtFc3eo2lvbzY8qWWdVV8jI2knB49KH1fTIryOzk1G0S6kUMkLTqHYHoQuckcGgC7RVOx1XTtUV20/ULW7CHDm3mWTaffBOKuUAFFU5NW06LUU0+S/tUvZBlLZplEjD2XOTT31Cyjs5Lx7y3W1jJDzGVQi4ODls4GDx9aALNFZza/oy2UV62r2C2kpKxzm5QI5HUBs4Jqy1/ZpPbwPdwLNcAmCMyANKAMkqM5PHpQBYoqOKeG4VmhlSRVYoSjAgMDgjjuDxipKACiiigAooooAKKKKACiiigCrqVu93pd3bRlQ80LxqW6ZKkDNc9L4XvfsmlQ2+oyRyWqvvmdg5UtHtAUEYK57eldDqRuhpd2bIA3fkv5Of7+Dj9a4+GS5/sy7/ALIl1qS5+yqZhcKxCybhu27+fMxu4Xjp7V0Uea2jOTEcnN7yvp+WuhcsfDuoWWpadPGsawwWkVvIiXTLgqzEnG35xz0OKrf8IrqjWd/aqbeGGW3aKNBMzruLhgVyuUXr8uTyfaoZGuXtdRXSZtVawMMQ3zGTzBKZAG2Fvm+717USTauU1K1nlvY2tYIoTMqORJiVvnG3n5k27ivI59K6Pf3uv6/4c5X7O1rPr6devyOi0rRJNO1TUZnnkuIbmOJVM0hd/l3ZB46cjFc9H4M1RXjDz25j+W1f525tVZWHb73BGPfrUtjqN1aLpt1cpfrYo1zE5xJKGztMZwRu28MBuGfeordPEoeweA3DTrp6iSKZyFLOzjcc8bl+U+uBipSqRbd1/wANoVJ0pJLlfy87P+vQ0j4d1M67/a4u4fN+2eYIdvHk7dmN2M52846Zp9/4eu7jT9chiMIkvblJoTuK4CiPgnHB+Q+tYgtdcnttOtIpL6SZUu1Mr3DxYYSAI7HvxyAeorWawvItcuGa5vpNmneblZn8tp/mBwucehx2pPmTT5lp+jHHlkmuV6+fdakd/wCGdU1CKMi6WCRbKW3cNL5pcs4O0sVzggdQARxXV20fk2sMewJsQLtByBgdM964eaS68mA6nLqyf8S6E2n2XzMtNtO/dt/jzjhuKS7l1+2a4vZDds6WcUU0MQYrueNgXQD+JXC5x2JpSpymkm0OFWFNuSi9f6+X6nfUVV00yHS7Qy7vM8lN+7rnaM596tVyNWdj0E7q4UUUUhhRRRQAVVl/5Cdt/wBc5P5rVqqsv/ITtv8ArnJ/NaqO5E9vmvzLVcN4y8M6rq2t295pltbF/KSJp5ZhtAWTfiSJkYOvcbSrZ713NFSWedx+D9WW6hsDZ2At4tYOpnVxL+/dfNMm3btzvOdhO7G38qoS+B/ENzoK6PJDZRpp+mXllazLOW+0tMAFJXb8gAHPXk+lep0UAeaJ4I1mxu2vTHZ6pNBqS3ixzFYUuU8gxAFVXajpnIOCD14PSUeFdbj1nStRTTbO2WMT+dbWFwI0j8yZXGS6Nv4B3YC5J4xXo1FAHnXjDwTq2s+IJrrTngS2lSK6YtIVb7XAJBFxj7pLJk/7FUrz4ea7q2nWtvNeWlpJDbTzlgnm5vZpTIzDpjbhQG68nivUqKAOI1DwvqGpTareSW1mlzfeHxZcHpcnzN3OPu/OOaxLrwL4g1HStOt1ljs5rW2u4Wad47ncZEjCj/VgbSVPbI9TXqVFAGDFpOpppOm21nqJ0wW9skTwiNZ+QoH3m64xjPeqeveHtU1DQreFruHUrq1vorwR3EYijnVDny22ggeoODyBmuqooA80sfDPifT7uz1b7HZTzpqM10bMTJFtR4PLyXSMBnzyfl6Ad8mpn8Iau9zLZGx0/wAmbV11M6qZf38Y80SbNu3JcY8sHdjb+Vei0UAeS2fgLxNAupJG0EDXVrcWQkacPtSa43lkAQFcIX4JJJxjFaFv4P8AEGhzWoso7LULey1Q30CBvsw2vA8boBhguHbcPXca9KooAbGXaJGkQI5UFlBzg9xnvTqKKACiiigAooooAKKKKACiiigAooooA84vfh3d30viK7muE824lu5LCBQoXdLbiIM743evy5x0PNQQ+A9Zs5HuGWz1KaK+t7xElKxJcokLR+WyquFZSxYNg5IXOMV6dRQB5n/wiOtxX9vqq6RpkrPqFxdSaYbjEUKyQLEBuKEE5Xc2F6k4qIeBfEGnaTcadZrY3K6npf8AZ9w7TNGtoTJK2UG0lkCzFQOD8o9ePUaKAOCg8LanZatPAul6VeWs+pJfDUblz5sSrt+XYBksu3CkNgDGe4OPY+DPEFnoVpaPYQXElnqUV2IprxTHKimTIXEeV++Ou6vVaKAOEXwhqUfg+/t40s4tTl1Eana28THybeRXR1jDYHB2cnA5duKxdO8CeJdOS8EclsXNpM1rIZiSl1ciMTN06KVcj13dq9VooA81T4fanZeGde0CKSzura7SGSzYxiIRyABHBXnGQinOeSWq1feFNVbxdrN5bQJ9ivrQQxlbhUVSIWT5k2EnkjGGH416BRQB594d8F6rpFjrMFzLBcTXulw20My7YwpSNl8tlVQDhmJ38kg4PTmhY/DrVdEm0i+sriK5uNOikW3glO1bYNBICitzuBmcHPHygDtXqFFAHllv8NtVstPn0p7i0v7G8Fk9wGj8rMkMymQkc7i6Zy3GSop8XgTxE0N3pst1a/Y5bu2UXD/vWe1twxQOhxuYkRqRnBAJr1CigDy9PBXiLT7W0WNLLUHs7afT4fMZVKxNIGjkUOjqDtGxlIPAGCan0nwbrul63bTSiKeL+zbe0kkgmSJFZFkB/d7MlRvAXBXpzXpNFAHAeD/CeraddabcXtpY6c2n6V/Z5NrJ5j3L/J87/KowNmQOTljzXSW+l63HcRvN4iaaJWBeP7HGu8dxkdK26KAPOdR8H65Jrd0LSOxNvd6pHqBv5NjSRquz5CrITldh2FWHXnvkHhjX20dbCTT7J1sdXk1GFZLnKXitNI+xht+QgOCDyNyivRqKAPLNQ8Ea7eOb9LS3tZLjUHuXsbW5VBChtjDw7RlSzHlvl/XmlbwD4kb7Befa7BLvSLWzhs4lQsshhwz/ADnlAxLKeDkAV6lRQBzXhSx1XS5NStL2yhS1kvbi6huEuNxcSSlwCm0YwD6muloooAKKKKACiiigAooooAKKKKAK9/cmz066ulUM0MLyBT3wCcfpXNSeMJ4Z3ElpGIfsiyrJvP8ArWiMgQ+xAYZ9q6qaGO4gkglXdHIpR19QRgis+bw/pVxbS28tmrRSrGrqWPIQYXvxitacqa+NXMK0arf7t2KMes6nLrFrai3sxBPai63F23BRtBGMYzluKz4PFuoz6El/HZQPNLNHHFCPMUHdnuygE8dsiuoXT7VJ451hAkih8hGyeE44/QVVtvD+mWkSRQ2xWNJFkRTK7BWXO0jJ4xk1anStrEh0619Jd/08vUyJPFk89vNNYWiyorwhXIZtqum4syqCeDxgVND4hu7zYlrDaNK1ibrcZGKbg+0rnGfXtnPFXv8AhGtI2SqtmE82bz2KOynfz8wIOR1PTHWrFto+n2hUwWyoVhMAwT9wncR19ec9aHOjbRCjTr396X9fcYX/AAlF9b6PY6ld2UTRXiHy1gZmIcqDGpyP4jkexxXUQmQwxmYKJSo3hTkA98VX/suy+xW9n5A+z25RokyflKHK/lirdZzlF/CrG1OE4/E77f8ABCiiiszUKKKKACiiigAooooAKqy/8hO2/wCucn81q1VWX/kJ23/XOT+a1UdyJ7fNfmWqo6vezafpz3FvbNcyqVAiXqckD+tXqr3q3TQAWbxpLuXmQZG3PP6VJZjL4tg8l2ksLxHTPybB8xAycH/9Xb1preLFeaOKDTbtmeRowZF2DIBx78nA9s5pU/4SoRoHNkZGYAkD5VHOSe+en6e9I0XioE+XLabWGTv5YfKowOMdQ350AdHRUNmk0djbpcuHnWNRIw6Fscn86ztR8TaXppKSXHmSj/lnF8x/HsKANeiuFuvH8xJFpZIo7NK2T+Q/xrNk8Z605O2aJPZYh/XNOwHplFcnFrV+YIWMwJaJGOUHUqCasx6/cr9+ONx+VIDo6KyoNdtpCBKrRH1PIrTR0kQMjBlPQg5oAdRWBd6Jqb6lNd2mrvCkrZ8plLBeEHHPH3Scd89snL5NG1GWCJH1mbdHL5gdVAJG0jBx15ORnpgUAblFc3JoeuSthtecKGZlKxgEHOV/+uDn0roxnAycmgBJJEhieWR1SNFLMzHAAHUmuQg+JWjTeTO1pqkOlzyCKHVJrUrbOScD5s5AJ4DEAe9dBr2n/wBreHtS03zvJ+12skHmf3NykZ/DNea6/deIl+FV7oN94b+yfZdMMVxfvcRtbbI0+9GAdxLbRgFRgnk8UAd3rni6x0S/i04Wt9qGoyxmVbSwh82QRg43tyAozxkkZqzoHiOx8R208loJ4pbeQw3FtcxmOWF+u1lPTjn0Nc7/AG1Z+H7XSnGmS3/irUrCFPs0AHmyhFGS7HhEBY5Y+vetLwjoOoadNqur6zJCdV1eZJZ4rfJjhVFCoik8tgdT3NAG9qF/a6Xp9xf3syw2tuhklkboqism08YaVeeEofEsZnFhMP3atH+8di+wKFHVi3AHvXLeIfEOn33jQabrP2uDR9JZJjGLKaQXtxjK5KKR5acHHdsdlql4DEPif4WWOm2FwY9QsJ1uVaaBwgkW4aRASQAwO3B2k4z60Aegabr9rqDXkTxT2dxZhTcQXQCtGrAlWyCVKkA8g9j6VZ0vUYtW06G+gjlSGYFo/NXaWXPDY9COR3wRXm+vyXqajrVzqEUMo8i3W+t7SQlPLVj5NvvIGWkeQljjhBj+LNdrpOr3/wDbkuiarb2sdytqt1FJaMxjZCxUrhhkFSB9QR06UAb9FFFABRRRQAUUUUAc83iyCG5uIp7aQJFIU3xHf0cpyMDB4zjngikbxjZRqZJLa6ERAKOFDbskAjAPBGRx/UYroqKAOdPjLTxM0RgvN6FAyiMEguu4Dg+n+HWtbS9QXVLBbtI2jVmdQrEE4ViueOOcZqwVgg8ydhHHkZeQgDgeprj9Z+J/h3SmaOGV7+ZeNtsMqD/vHj8s00m9jOpVhTV5ux2lFeMX3xk1SUkWOnWtuvYysZG/oKz7T4meKLrU7WJ7yJY5JkVlSBehYA9Rmq9mzleY0b2V2e70V5o3jDWop3X7RGyqxADRD1q7bePbxCBc2kMg7lCVP9amx3HfVj6w2tpcwPpSRSRKrGWOQgb2/hHP/wBb8elQaf4v0q9IR5DbSH+Gbgfn0q5qelvqeySLUbi2woAMLkA8g54PPTFICg1/4m8tdmkQGQL82ZQATgnj5umcD/Oam02fX5tRP9oW0MFqI1PyEHLc5Gck91PQdCOepYPD955q7tYuTGoHy7m+c7iSW+b0x0OM9scU2DwxLDNbu+rXMkcEiOImzt+UDAxnHb9fXmgDoaKKKACqcOp21xqM9jExaaFQz4HA9s+tQ61fPZWqrEGEkzbBIELCMd2IHoP1rK025sLfX1ht2k2G1WMFonyz7ySTkd85yaANVtZjE80UdpdzGJ9jNHFkZwDjOfer0EvnwrJ5bx7h92QYYfUVkW+nX4a7e31KKNJZ3kTy4w/XsxPpjGBVzSL2W+sd86qJo5Gik2fdLKcZHtQAk+sQxXEkMcFzcNF/rDBHuCd8E+vsKf8A2pbNbwTQlphOcRLGOW9evTGOc1j6NFqNxaXBhuktiLmXOYg5d9xyWz0HQYHpVqyM2oWdnfRRwx3EDyI8fIRjkq2COnIzQBoDUYDZPdZYIhKspHzBgcbceueKtKSygkFSRnB6iucj84XCYEcpe6bZyQjS4JJ9SFAwPU5rasrp7jzo5UVZoJPLfacg8Agj8CKALVFFFABRRRQAUUUUAFFFFACMwVSzEAAZJPauZTxVe6gzvomhzX1qjFRcvMsKuR1255I96seNrh7bwbqkkZIbytmR/tEKf0NamlW0dnpNnbxKFSOFFAH0raKjGHO1fWxzzcpVPZxdrK/9X9Cno2vxatJPbPby2d9b4861m+8oPQg9x71pXVzDZWstzcSLHDEpZ3boAK5rVx9m8f6BcR8NcxTwSY/iULuGfxpfHhMmkWVmSfLu7+GGT3UnJH6VXs4ynG2iZPtpRpzvq4/jomvzFj8T6teRfadP8M3M1meUkknSNnHqFPNa2ja3ba3avLAskckT+XNBKNrxOOxFaKqFUKoAAGAB2rlrYfZfiZeRx8Jd6ekzgd2VtoP5UlyTTsrW1G/aU5RcpXTdun4GlqniK3067Sxit7i9v3XeLa2XcwX1YnhR9aXTNV1C9ujFd6JcWKbSwlklRgTxx8p6/wCFcvoOrXBvdXfT9ON9qU97IZWaQIkManagZj9DgCt6z8RXSapDpus6b9hnuM/Z5ElEkcpHVc4GD7GrnS5VZLX11+4zp1+dqTlZemnzdt/n5HQ0UUVynaFFFFABRRRQAUUUUAFVZf8AkJ23/XOT+a1aqrL/AMhO2/65yfzWqjuRPb5r8y1RRRUlhVTUdStdLtTPdSBV6ADksfQCmarqkGkWLXM5z2RB1dvQV5bqWp3Oq3bXFy+T0VR0QegoA09Z8V32plo4mNtbdNiH5mHuf6VgUUVQgorVsPDmqaiA8NqVjP8AHL8o/Xk1tw+ALlh++vokPoiFv54pALD/AMetv/1xj/8AQRTq2x4cKQxot0CURU5TrgY9arTaHdxDKhZB/snn9aAM2pre6mtX3QyFT3HY/UVG6PG5V1KsOoIxTaAOn0/Vo7vEcmI5vTs30rSrhgcHI610Wk6p5+Lec/vR91j/ABf/AF6QzXooooAiuraG9tJrW5jWWCZGjkRujKRgg/hXIx/DyHyoLG717Vr3RoGUx6bPIhjwpBVWYKHdRgYBPYZzXSayboaTP9i8zzuP9Xjft3Ddtz/Ftzj3xWJZQavNqsJDXI06NnKfaZHRyuUxkDknO/AbtigB2qeDF1DxI2vW2talp141qto32XyiDGGLY+dG7n9KlHha6bTbizuPE+tTGV43WfzI0kj2NnClEHDdDnPFU7460mq3wgN9JE/OUBXy0ymQgOVY7d+0qQ2c5B4pol1mMzJGNQaGZWjtC6Eup3rgvnleC2C3Yc80AdjWZ4f0S38OaHb6TaSSvDBu2tKQWO5ixzgDuxrm7eLXonLXz332V2RpRAzPJtJkzjupz5eQvbn1rT0j7edYl+0m98gxoIvP3hseWuc7f3ec7s980AWR4WsG8P3ekTtLNHeM0lxOzYkkkY535HQggY9NoHan6VoA0/UJ9Rub+5v76WJYPOuAg2RqSQoCKAOSST1J+grEtpPE0N1uniuZYUV/lGPm8lSo/wC/rMrfRe1WLK11p7exh1B7sTwXm2WSOc4liKFskjAIDEL07UAdXRXDeV4gm0ZFi+0/bAztIGkmTOI2wMn/AGscL8vStLVhqpWMRT3XliGEsyRN87ZfcCE+Zc/LkrnHHGM0AdPRVbTmmfTLVrhJEmMKmRZGBYNgZBIABP4CrNABRRRQAVz3ijxfYeGLJ5Jcz3Ixtt4yN3OcFv7oODyfTjNYfxN+I9v4G0sQ2+ybWLlT9nhPIQdPMb29B3P418zWvijUYtel1a7me8luSRdrK3+vQ9VPp2x6EDHSu7DYGdaDnsunmZ1HLlajuei+IvGOseJpT9suCltnK20RxGPqO59zWBU00cPlxXNrIZbO4XfDIRzjurejA8Ef0IqJEaR1RFZnY4CqMkn2FRbl0PmavO5vn3Eq1pf/ACF7L/r4j/8AQhXS6V8NPEuqKshtVs4m/ium2n/vkZP6V1OnfB24t7mC4uNZj3RSK+2OAkHBzjJIqHJGtPCVpNNRM+f/AI+Jf99v51HXaXHgGVnd4r9CWJOHjI/rWPe+EtXsgW8gToP4oTu/TrWB9IYdamk+IL/SHAhk3w94XOVP09PwrMIKkgggjgg9qSmB6xo2u2mtQ7oTsmUfPEx+Zf8AEe9aleMW1zNaXCT28jRyocqwr07w9r0etWp3YS6jH7xP6j2pDNmiiikAVXFmg1Br3c3mGIRY7YBJ/rWV4h1C5snhWO5+yxNFK/m+Vv3SLt2R/jljjqduBVaz1DV5NO1a9uFkRokl8mMhSqso4AAG48jueaANJ9GCyyNaXtzaLKxaSOIrtJPUjIOCfapotLht4rWKCSWKO3YttV/9YT13evXNcvPqfiWBYCyyKrgyAusZZVzEMybQRgFnOF5wKJvEGoogL3nlbnAuswgfYv36IFyRzlWbrn7uRwaAOjm0cNPLLbXlxa+ecyrERhj68g4PuKtRWMVvp4s7ctFGF2qyn5h75PeuXTxDqgjMbAPdP5RtYzEUM6eY4ZgD0yig+2fcVE+v3SpY7dXEkcr4nkMSQ+W2zJTcwI69sbh0JoA6uXT4nt4YYy0PkENEydVIGO/Xgn86faWi2qOA7SPI5d3fGWP4ewArG0bVLq71eaBrj7REDP5i+Xj7Oyy7UXI67lycHn5c966GgAooooAKKKKACiiigAooooAzPEWmtq/h2+sEx5k0RCZ/vDkfqBWf4f8AE+n3WlQx3d1Fa3sCCO4gncIyOoweD24romZUUsxCqBkknAFZ1/pmi3kglv7SylkAHzzIpODwOTWsZx5eWRjOnLn54b7amDDcx+I/G9vd2bebp+lRODOPuvK4xhT3wKd4gmi8TeFTe6K5uXs7lZ4wFILNGckAH2Jrpk+x2NsyJ5FvBCPmAwip9ewpqPp9jbK0bW1vA5ypUqisT6djmq9qk00ttv8Agkewk4uMn8V7/pb0KFl4q0W9sVul1G2jG3LpLIFZD3BB5zWX4ff+2vFWo6/ErfYhCtpauwx5gByzD2zWzcaNodxdtLcWFhJcAb2Z4lLY9T/jV9HgSICNo1jUhBtIAHoKXPCKfItxqnUk17Rqy7dTk/D9xaeH9a1jSr6RLaSe6a6geU7VlRgOhPGQcjFLr19bazrei6bp0qXFxDeLdTPEdwhjUHOSOmc4xXQXUekarEI7oWd0gbaBJtfDHsPei2h0jSEMVstnZqTyqbUyR61XtY357a/gR7CfL7O65fx3vb/gjPEF7Np+h3F1bkCVNoUkA9WA7kDv3OK5aTxVqcZH7xP3QkL5SPnbIFG/D+h/5Z7vpniu0nns/szNcSweQTtYyMNpPoc8U3yLAPEnlW2+Mbol2rlR6j0H0rnOsy4takm04GOSFrxruSAKOdqrI4JIz/cQn61iaZ4m1K9+yW81zFAblYnNw6Idm6N2IG1ivJTA3YPXI6V16RWCTrIkdss0uWVlChn9SD360qWlk0DRJb25hY/Miou0keo6dqAOc0XXb7U9WSOaULEI1OI1QLJnd83LbsHaCMA8HrRHr16JLyWSZRHFfC3CsIwip54jJ4bdnaTyQBXTG0tjMsxt4vNUYV9g3AexpPsdrvkf7NDukBDnyxlgeoPrQByU3iTU21BltyjQLKEDKimMgztGC7FsgYHVc8111qJltkFwwaUZ3EY559vanLbwogRYY1QAKFCgAD0qSgAqrL/yE7b/AK5yfzWrVVZf+Qnbf9c5P5rVR3Int81+ZapHdY0Z3IVVGST2FLXL+NtTNppa2cbYkuThsdkHX8+B+dSWch4g1h9Z1JpQSLdPlhX0Hr9TWVRSqrO4RFLMxwAOpNUIms7Oe/uktraMySv0A/mfQV6JonhO00xVluAtxdddzD5V+g/rU/hzQ00axG8A3UgBlf0/2R7CtqlcYUUUUgCiiigCG4tYbpNkyBvQ9x9DXNahpkli24ZeEnhvT2NdXTXRZEZHUMrDBBoA4ilBKsGUkEHII7Va1GyNlclOTG3KH2qpTEdbpt6L21DH/WLw49/WrlcppN19mvkyfkk+Vv6V1dIZk+Jpr+Dw5eSaW5S9CgQuI/M2ksBnb361xtxr/ilxeefHeWLRPcJEILTzRJOkcPloPlOY3YyEHgnpkYrsfFN5fWHhm+udM2fbkQeRvXcu4kAZHcc1yOn+OtRn1Z7i+RbLR/OiTMsRUxqYpg24np++iAB9CPWgCxY614rTXreG8s5ZrSa7uSPKiUbURGAiYnAHzBSrZGckZ45dd3Xilv7TngNyq/b4be2iBQYQvGG48s44L/Plh144rOi8ZeJkiQ3Nqu2ZoljkWA5R/soldXHbJOQfYg9qsReLtWe2upZLuCO8jh3JYfZWLFfLRhLuB4BLHrx0A5FAEOm634kl1BIL65uYpDp4dFNsSPOzNkMBF85G1AcMmcZA5p1nrvix7UCGKW5dYbotJOmxXZUiKshEQJ+ZnAUqOcjJxmpZPEfimLTb+4WAv508ltYn7OGYSCd1BCK2WURqWO7ByOODWx4c8VHWNSnt7h47d3t4JYLdxtkDMhMgweTtYEe2KAI7zVddtrXTpo4ppfMs47m4CwZI8sbpUxjhnDAAeoOKltb7W5NVt7e5juVtXgEE0yRoAs7Lv3DvgcJ0xk881h2PjPXTqPk39p5VtCJDNMsJ5FurCcj/AHnMYX2JqB/Guvx6FDLdItpewzyC6jkttsjLsDoERiA3DbSNwZtp20AbdvfapDd6fFc3dywltBIxkUKWlJPy4EeD24ytXfC19qUitDrDuLh0jeKNxkkbFLNuCqMZbGMZBB5rB1bXfEGnS6g41JTBHeW8MYa2SPYkihiSzHHGcc/jzXd2Ehm0+2laUSs8SsZBj5sjrxx+XFAFiiiigArJ8S+ILTwv4evNYvT+6t0yFB5djwqj3JwK1q+d/wBoDxS13rNr4at5P3NmonuAD1lYfKD9F5/4FXRhaHtqqh06ibseU6/rl94k1u61bUJN9xcPuPoo7KPQAcCs2itTw5oF54n1+00iwXM9w+NxHCL1Zj7AZNfUe7CPZIg6v4X6Pq3iPUbjSbeBn0xxvuJm+7bPj5XH+0em3uPoCPpDw34M0jwzCv2WESXWPnuZRl2+noPYVY8L+GtP8J6FBpWnR7Y4xl5CPmlfuze5/wDrVs18zi8Qq1RyirISow5udrUKKKK5TUKKKKAMnV/D1jrCEyoI58fLMg+b8fWvONV0m60e7MFyvB5SQfdce3+Feu1S1XTINWsXtpx15V+6N2IpgeQVZ0+/m02+ju4Dh0PTsw7g029s5rC8ltZ1xJG2D7+hFQUxHsljeRahZRXUJyki5Ht6irFcL4E1MpPNpsjfK48yP2I6j8ufwruqkZheI9XvtKNutnDHM9yGjiVweZRhgCR22CQ/8BFZ8Xiue4udOMcY+yXWXaTyWOyORisBJ6DOATn1FW/E2v3GkS28NtFatI8E9yXupCiBYgpKggfeO78ACeaztD8Y3Gr+IGsP7OWO2McjqzBlZQixkZJGGOZMEDkUASJrOrpd2kE13bMJp7lGZbcLgRSBB96Qdck8ZPoKksfEGpyxarJPHEBb2r3EOY9ucM4AGGO4fJyflPtzXPweP7+/trQnSbYyTzQqpaKRtvmQvKcJjccBB8w4O72Nba+J9QkN+8VrpzpbNNGtuZys8hiI34UjoRuI9MqT14ALVvr9099qMMrW5MMU0kKxruGEIAywYnPIyGCnPTIGa5W38d6/LFYpP9ktmnmCNJJAqkAwLL91pgAMtwd3I7ZFb1l4pu7u8hIs9PtY5YYLiT7TNskkjmdljC8YLhVBI9W2j1pJfE0txF4c8iytnbVLczSgo0giwI+ML/106npigCpdeNdQSOWKBtPN3C12sySNt8rZcJHEWywA3IxI3EBjjkCq0XjnU7i4wk1qsaWDXBDQBTJIrTLjmXI/1Q+4HHJ5xg1t+G/EkutajLbXNnaKJYpJVaF9xCpKYtsgI6nGR68+ldR5EJKnyo8qML8o4HoKAOe8Ia9da5b3P2iS1uBCIitzaqRG5dAzLjJ+ZScHB7joa6WmoixqFRQqjoAMCnUAFFFFABRRRQAUUUUAZviHT5NW8NappsLKst3aSwIzdAzKQM+3NcFfeFNX8Ty79S0b7LbmOwtJYJLhGZ0ikdpXBU4Awwx3OOgrv9e+0jw9qf2PzPtX2WXyfKzu37DtxjnOcdK4+DVPENhbSLbW11MDcFo/NhmdWUImVBf5xkluvHBwRigDKh8K+I/Oa61fTItUSDUI3ktjMn+nRpbmFZcMdu7OH2tjnPtVn/hGbuG5tbybwdbXdj5E0UekC4jcWjvKW3jednzA4O37uMDIq6t74ris4AthcPPBdOURixEkJSb5nbuchcJx0Tn5si8ur+JZ72aGO2EcRmVEd7ZyUQyKA/YHKEsRng/Q0AcnJ4B8RWtjLcwJHPqkWlw2KDzwBPGyyrLFuPZd8ZBPUoPU1tW/hTVotesoDbp/Y7vbahct5i/LcRQmPZt6nLLE2enymujs7m8ubrQ5L2MxXLwT+agUqNw2jOD09fxrnrG/1qy0WV0ivJNRaXa3nQ3Mm0fORkPwMkKMoCOR04IAMLSvh7qVx4d0zSb/AE97VoboSTTD7OpTEMqrIhj+ZirshBf5v1rQg8G6pqenwS67o9k+oHXku5g3lyDydqhyCezFc7evTirjeJdX1K6ubQBoTHtby4YW3sVaLeFI5+XLhgfQY71qR6h4gvdL19Z7ZoJo7aX7MIo3VxJ84UKcYbgIQQTyfcUAYTeFNQtNTN7c6BDq9it5fMunl4/lEroY5QrnaflUrg8gNx3qpD4H1+zNrqEFvF9vsLEC1jE4KjdPKzW24/wiKQKCeMqp7V0/na5pLWlrHAZd+1mx504kYuFZS7kmPCZbnj9cwNfeI5pNLjuFkQyNaTSCG0dQdz5lUtk7QgABB65P4AHPReDfFKto+oLBaJPotrZQ20DtmR9igzhXDbV3bmQ5BzsFdh4OttS02C90++0ySCMXlzPFcGWNllWSZ3XAUlgcMOoFdPRQAUUUUAFFFFABVWX/AJCdt/1zk/mtWqqy/wDITtv+ucn81qo7kT2+a/MtV5h4vvDd+IZlBykAES/hyf1Jr0+vGryUz31xMerys35mkiyCum8E6aLvVWupFzHbDI/3z0/qa5mvSPBFuItA83HzTSs2fYcf0oYjpKKKKQwooooAKKKKACiiigChq9sLiwcgfPH86/1rla7kgEEHoa4maPyp5I/7rEfrQIZXZWc32izil7soz9e9cbXTaE+7TgP7rkf1/rTAn1a8fT9MnvFVSIAJJN3/ADzBBc/Xbk1zH/CYXsljctHaxC6jKIkex3y8hLR8LycRYY49a6HX9TGj6HdXxhE3lgARs20MWIUZPYZPJ9M1yN346urG9+yNpVtJcwzPFL5TsyuwaFV2MF+XImAy2ACCM0hmndeJb3dJcWr25tTYR3caPCd4LNtwTvA4/D0461J/b+opq9nalInjlMSyOYgoBcyZywkIU4TgfNk8ZGeKWs+Lm07XbvTRpcM8MSMCSGG4CBpjk7duPlxtznnOKLfxNd+TaRpplhC727XjiZ2t0WNGQYG5PvZcnJ4/OgC3feKrmLUNStbWCJjCm22eVXVJJVKh1LYwR84HHI2tmq0vjK4N1J5K20aQrKWhnBEjshiBiHPDZkYdDkgUsniq9+1zWP8AZFuk0V6tjvmlxGZnDOGBAzt8va3rucL6mmz+K9ml6vqD6bZvc6ZHFIvlyiRXaRRnawGce4GTigCHW/Geqab4jvLKG2ge2gGAzxkc/Z3l5fd1yv3dvIzzxUS+Ob2DS1ubprEvLp9xcxZTyw0qeVsjAEjZJ3njOTxwKuS+KLiG9ijutNtGjYWxnJLpJmaZ4V2oy5ONuSDg4Jx79YthZoiolpAqq+9QIwAG9Rx196AOC1Dx7qdrNfQ7LESJcLHCAvmDYZ1i3EiQAnDcqxQg8cjmtC08W3za7bWMrWMySyxQGKJSs3zw+YZAN7DaDwRz/vevXNZWj+bvtYW80gyZjB346Z9aWK0toXDxW8UbBdoZEAO30+lAE1FFFADZJFiieRyFRFLMT2Ar4j8Q6tJrviPUdVlJLXVw8oz2BPA/AYH4V9eePL06f4B166U4ZbKUKfQlSB+pr4xr2sphpKfyJkFfQ/7PvhhLbR7zxJPH++unNvbkjpGp+Yj6tx/wGvnivtHwRpq6R4G0SyUY8uzjLf7zDc36k1tmdRxpKK6ijub9FFFfPlhRRRQAUUUUAFFFFAHGePNNDRQ6ii/Mp8qT3B6H8/51w1et6/bi60G9iI58osPqOR/KvJKaEW9MuzY6pbXQP+rkBP07/pmvYQcjI6V4melewaVN9o0izlPVoUJ+uKGMlurK0vkVLu1huEVtyrNGHAb1Ge9V45dKbVZLSJrQ6hEvnPEu3zEDcbiOozgc98VS8U+IU8PaWsiqkt9cyCCzgdwoklPTJPRR1J7AGuO8KWlnpHxLuUfUobu8utIjlurrzB+/uGmbOOeAAAAvYAVrCleLkxXOn1jVPBVjcJp+sXGixSgLiC58v5QAQuQegAJxn1NbUNjpxkN5Ba2peaMKZkjXLpjgbh1XGPauV1a1PhHTL3+yPDFxrst/JNcXTs0ZJZjnD5+ZhzgAA8DFXfhzb29r4A0mG1vlvYljb98qlRksSVAPICklcHkYolTiocy/r/IBJPFXgUzQPJq+hmW1+WFmlj3RdsL/AHfwqVm8Fx6Zb35j0UWMrloJRFGUdu5XA5Py9vT2qr45uGisLTRdNSOPU9Zn+yQyBBmJMZlk/wCAoD+JFLPo/wDwj2qaHcadpstzpthZS2YgtwC8RYxlXAJGc7CCevOfWnyQaT7gbkDaNp1hLqVsLK3tJR50lxCFVZAf4iR169fetIHIzXl+l3LxCw0y6s5ZYoL2aRLC3KsZLku8ojySF2Qqw3HON+APu16FpOq2+sWX2m3WRNsjxSRSrh43UkMrD1BFTUpcgF6iiishhRRRQAUUUUAFFFFAFDXLuaw8P6leW5UTQWsssZcZAZVJGR6ZFclbeN719SijeCExLbiGVSdgF4JI0cFucIu8djXdsoZSrAFSMEEcGo3toHVleGNlbIYFQQc9c0AcjP45mNhNcW+mrhYm2s8/HmCB5QMAcrhCM55yOKmuPGrWfmmbTwyITGHin3bpAkbdNvC/vPvc9DxXU+RCBjykx6bR6Y/lxSC2gCbBDGExjbsGMYx/LigDBsPFIvb60tGsGjnuAzJ8+RsUursDjoCqjsfnXpUF9q+oRadZGCSQy3GpT27GOFZH2KZiAASB/Ao+ma6GPT7WG6FzHCqyrH5SkdFTOcAdByB064HpUkVtDCgWONVUO0gHozEkn8ST+dAHHx+L57V5Y7rR1+3xIiTFXCbpSEOOh+X94Ocnp+NWpPGFxDdtFJpaeWjsjutzkjZJHG5A288yjHqAenFdObeBpTKYYzIQFLlRkjrjNKYYj1jQ5/2R9f6D8qAONPj1YYYs2nnloN5Ik2nfs37SMYHGOc9+lWtT8R3n9lW00CfZp21FrOZVZHxtD52lyq8lB1xXS/ZLbfv+zxbsbc7BnHpSyW0E0ZjlgjdCdxVkBGfXFAHHW/jxha2gltUuJpLfc5jk2DzPLZ9uCMAYXruPXjI5qY+NHJzDZrMzJu8vztiqQszNyVzn9yRggcmuqNpbFtxt4i23ZkoM7fT6c9KVbaBFCrDGqqMABAAB6fqfzoALadbq1huEBCyorgHrgjNS0gAAAAAA6AUtABRRRQAVVl/5Cdt/1zk/mtWqqy/8hO2/65yfzWqjuRPb5r8yy33T9K8Vb7x+te1143fwm31G5hPVJWX9aSLK9epeEsf8IzZ49G/9CNeW16N4GuRLobQ5+aGUjHseR/WhiOmooopDCiiigAooooAKKKKACuOv8f2hcY/56H+ddgxCqWPAAya4qV/MmeT+8xNADK6Pw/8A8eUn/XT+grnK6fQ02aap/vMT/T+lMQ/WrqGy0e6nuIY5oVTDxyEBWB4IbIPHPPFctFrmkRmGzGhWqQspQJGoIZGd9xQbMMn7reSSMgZwcV2k0MdxEYpUDocZU+xzUctjazymSWFGfKHcRz8pJX8iT+dIZzj6nHdTxQyaDbyXd1smVHkUhlaKTBLbfvbUZcdOeuM1W+1+HYtIgmh8P2xtlnzDF5KDa5hEoYDGATwv4Z7V0B8OaQYfK+xJs3BsBmGMAqBnPTDEY6YJ4qRtD0t5nlayiLOmxhj5SNu37vT7oAzjOOKAMq4uoJL1dL1DRbTfesXnVz5iPtKKG4Q5OMfe242/Q1RgbRruymvbnw1ZK62MUu0Ij7rZgQBnaMbQrfLjHHFdFHoOmwvE8duUeMkqyyOCckE7jn5ug656U+00fT7G2lt7e1RIpV2uvJ3LjABz2xxjoKAOWh1Tw/bRSXlroNqktirLCdkaEZk2xBWx8ok3FgewJrUuvFLQxi4hs45rNrE3ySifBKgDjbtPOWHf/CtSLRdMhYGOxhUjy+i/3FKp+QJApW0fT2haE2kfltG0RUcfIxyw9gTzQBlP4rCahaWhtVZ7jyyNjvk73ZeAUHQIWOccZxmujqmmlWUYAEG45Q7nYsflYsvJJPBJP41coAKKKKAOO+Ku4/C/X9vX7OPy3LmvkCvtHxvYnUvAuuWijLSWUu0erBSR+oFfF1e7lT/dyXmRLcK+59PKnTbUpjaYUxj0wK+GK+zfAGqLrHgHRL1SCWtER/8AeQbW/VTU5tF8sWETo6KKK8QsKKKKACiiigAooooAgvcCwuM9PKbP5GvGR0Fes+IrkWnh+9kzgmMoPq3H9a8npoQV6z4dz/wjthn/AJ4rXk1exaZD9n0q0hIwUhVT9cUMBmo6PpusRpHqWn2t4iHci3ESyBT6jIrnbXwBpFn40/tqDTNNitltEiihjtlBjmWQt5g4wDggZ68Vtavq0unOkcFobiSSJ5FUNz8rRr0AJP388c8YAOazL7xTc29np0lrZ208t20wYPcNGieUrM3JTIPykYIGD16VUakoqyYyrFo3jDR2urXR9T025sZZXlhbUllaW33HJXKnDqCTjOPSreieHNR8O6fpVhY38UkMc0s2oyTR/PcM+WJTHC/MfyrNPxCd0SeHSyLQyYknmkYLGuyJxu2qdpPmnlsKNvLc0628d3d3cG3h0lS8t0bWCRpWWLeHkBDsU4OIy2F3dQOKbqyasKxtPoU0/jmLXZ5o2t7axa2t4cHckjvl39OQFH51panHezabPFp00cN067UlkGRHngtjuQMkD1xXJr4/lZ7dm0sR28kRJleY7TKPMBRWCFTzHxuKlgwIB6U0eO78aVb376TDsezmvpEM0kZWKMRkhd8Y3MfM4/hOOvNS5N2v0Gatx4Ze0Gjy6K0KTaWJERLknbMkgw+5hyGJAbdg859aveHtJl0mynW5lSW6urmS6naMEIHc5woPOAMDn0zUV1rzwXrolsrW8JgErM5D5lbau1cc44zkjv6VY0LU5NX01LuSHyt4BC7XGAQD/Eoz16jim6kmrMDToooqACiiigAooooAKKKKACufuvGGnwXktrbwXt/LEcS/Y4DIEPoT0zVzxJeyaf4a1G7iOJI4GKH0OMA0zwvYRad4asIIlAzCrue7MwySfxNaxjFR55amE5Tc/ZwdtLkuka9Ya2khtJGEkRxLDKhSSM+6mtB3WNGd2CooyzE4AHrXLa2osPG+g30I2vdmS1nx/GuMrn6GpPHcjnQoLJGK/bruK2cj+6x5/lVeyUpR5dmR7aUYTctXH8ewp8cac5Zraz1K7gUkG4t7Vmj/AD71t6bqdnq9kt3YzrLC3GRwQe4I7Gp4IIraCOCFFSKNQqqowABXMacg074iajZwgLBeWiXZQdA4baT+NFoTT5Vaw+apTcedpp6bbFqXxlpsMzxNBqBZGKnbZyEZBxwcVZ0rxLp+sXklpbC4WaOPzGWaFo/lzjPPvVLxT4lm0ZobO0tZJLu4BKyeUzpEucFiFGSR6CneFE0qSKe7tL5r++c4uriUESZ/ulT9wegqnTj7Pns/L+rEKrN1vZqSffS36nR0UUVzHYFFFFABRRRQAUUUUAFVZf8AkJ23/XOT+a1aqrL/AMhO2/65yfzWqjuRPb5r8y1XmfjKzNrr8kgGEuFEg+vQ/wAv1r0yuc8ZaYb7SPtEa5ltiXGO6/xD+v4VKLPNq6LwdqYsdY8mRsRXI2HPZv4f8PxrnaASDkHBqhHtlFc/4X8QLqtoLedgLyIfMD/GP7w/rXQVIwooooAKKKKACiimTTJBE0sjbVUcmgCjrN0LeyKA/PL8o+neuXqze3b3ty0rcDoq+gqtTEABJAHJNdnaw/Z7WKL+6oB+tc7o1r9ovQ5HyRfMfr2rqKQwopksscMTSyuqRoMszHAA9Sao/wBv6N/0FrH/AMCE/wAaai3siXOMd2aNFICGAIIIPIIqqmqafLdm0jvrZrkdYllUt+Wc0JN7Dckty3RUE17a28yQzXMMcsn3EdwC30FSPLHGUDuql22qCcbj1wPyNFmF0PooopDCiiigAooooARlDqVYAqRgg9xXxR4q0Z/D3irU9KcEfZrhlTPdM5U/ipBr7YrwH9oLwoyXNn4oto/kkAtrvA6MPuMfqMj8BXpZZV5KvI+pMjw2vff2fPFStb3vhe4kAdGNza5PUHh1H0OD+JrwKruj6teaFq9rqlhL5d1bSCRG7fQ+oI4I9DXs4miq1NwJTsfcdFc54K8Y6f410CLUbNgsowtzbk/NC/cH29D3FdHXy0ouDcZbo0CiiipAKKKKACiis3W9Yg0axaaQhpW4ijzyx/woA5rx3qYZodNjb7p8yXH/AI6P5n8q4qpbi4lu7mS4mbdLIxZj71FVCLuj2Zv9XtbYDIeQbv8AdHJ/QV6/XE+BNMIE2pyL1/dxZ/8AHj/T867akxle7tLS5Qm7hikUKQTIoICnBPX3UH8BVO60nQ7rToRdWdjLZQfvY/MRTGmRywzxyCee+ah8V6fb3/hy++0K58q3ldNsjJyEPXBGfoawZlW9tfBWlTqHtLiMSzRno/lwhlU+oyQce1CVzGdRxdrf1exvxWHhzWP9Jht9OvNkpfzEVHw+AM5HfCr+Q9Kk1CPQYLIWGo/2fFbSEuILgoqsd24kA98nOfU1l3NtDpnjvSHs4khF/BPFcJGu0PsAZSQO45GfQ10Vxa2txhrm3hl2jgyIGx+dBUZSaa6oybe18LXt1F9lj0me4hi8uMReWzIgGMADoACR7Z96baaH4UmKw2lhpcptXLBI0RvKY47dvuj8h6VlaLp66xFq2uW0UcD3cUlrp2xAuyIZAfjuzc/QCm+fJazaRLbaZLbzWts1t5cqbBJIwULGD/EAVLFhwApNOxmq0rJtf1/Wp1T2un3V95zw28t3b4G4gFo+4+nXI+tWo40hiSKNQkaAKqqMAAdAKwNA1GySOG1UztLdF5RdSRbVu36syn+QOPlAxwK6Gk0bQlzK4UUUUigooooAKKKKACiiigDN8Q2L6l4e1Czj5kmgZUHq2OP1qt4T1OHU/DlmyMPNhiWKaP8AiR1GCCO3StuuevPDPh7Vr6acxoLoHEzW85Rs/wC0FPX61rGUXHkkYThNT9pDtYp6rKmreOdHsrdg/wDZ++5uWXkJkYUH3z2pfGUiX3h4XthIlydOu47hxEwb7h+YcdwDmtfSbPRdKsm/sz7NHAWw8iSBtze7Z5P1NTWGn6bpay21nHHEJZDI8YbJZiOTgn0q/axTTXT+mZ+wnKMlL7W/lppYsWl7b39nHd20qyQSLuV1PGK5nSpRq3jvU9TtiJLW1tls0kB+V3zubB746VLN4R8LS3RHlpE8rEmGK5ZFc55+UHHWtuzTTtOsI4rT7PBaqDsCMAvHXmlzQiny31K5Ks3HnsktdOv4aFbw/raa5YNMYxBcxSNFPb7smNgcYP8AOskmJviYn2LG4WLC92dPvDZu9/6VdvPD3h/Wbs3TKhuW4aS2nKM2PXaeataTZ6LpNpKum/Z44g3711kDHd/tMTnP1o56cbuN9egvZ1ZcsZ20d79Xbytp5ia7q02lJaeTEJGnlMfKO+AEZuiAk/dx071jHxnNl5PsSeUI9wG5uv2cTff27D124Bz3xXR31pY3yKLsKwhPmA+YVKZBGcgjHBI/Oqw0PRWcEWkB8yPAUH5SuwJkLnH3cLkDpWB1EWs62+mWsbxQLJIyeYQScKNyr0AJPLjgehrPTxVdy291JHZw/wCgxNPch3ZCVDMMKCuQSEY4YDHHrmtH7BoUVpNGzQeSWEMhe4JwwOQuSeCDzilk0nQleKCSG3DjLKrP8zgnJzzlgSM856UAVLfXb+8E7QWy7UuGhQeRKeFdlyTgDHy5+UnGa6Osy2TSLSSS6t5oU8+QhmE+VZ2O4gDOMk88VoGWMZzInDBTz0J7fXmgB9FRC5gaZ4VnjMsYy6BxuUepHami9tGhMwuoTEDguJBtB9M0AT1Vl/5Cdt/1zk/mtWEdJEDowZWGQynINV5f+Qnbf9c5P5rVR3Int81+ZaoIBBBGQeoooqSzy7xNojaRqBaNT9lmJaM/3fVfw/lWJXseoWEGpWb2tyu6N/zU9iPevLdY0W50a68qYbo2P7uUDhh/j7U0IpQzS20yTQyNHIhyrKeRXe6J4zt7pVg1ErBP08z+Bv8AA/pXn1FAHtasrqGVgynkEHINLXj9lqt/px/0S6kjH90HK/keK2ofHOqxjEiW8vuUIP6Giwz0aivPW8e6iRhba2U+uGP9axfFnibWpNPsJob6SCKZWWVYflywPr19e/atKNJ1ZqCZhiK6oU3UavY9B13xXpPh+M/argPPj5beM5c/h2+prgbXx/LrGrNDqKx29rIcQbTxEfRj3z69vpXn7MWYsxLMTkknJNJXrwwFKMWnq31PAqZrWlNSjol0/wAz2IgqxBGCOopURpHVEBZmOABXLeEteN6selXTE3KjFu56yAfwH3Hb8vSvTdK0wWi+bKAZiP8AvkV5NalKlPlke/h68a8FOJZsLNbK1WMcseWPqatUUVibiOiyIUdQysMFSMgiuX1qxtNQ1az0KG1gVHH2m8ZYwCIlPC5/2m4+gNdTWXp2mzW+rapf3DIz3UiiPb/DEq4APvksfxrSnLluzGtDntG2+/p/wdiLxVdyad4U1CeBvLdIdqsv8OSFyPpmsGf7Ja6RbfafD5g0mNo9l2kiiaPkbZCAMjJxnknnmukvLC41NdRsr0w/2dPCEi2Z8wEg7ie3XGKyZ9J17UdPj0i+mshZ/Ks1zGW8yVFIOApGFJxycmtqUoqKTfX+vmYVoylJtLpZaLe/XyYapbTK+uRNZSTtfxqLeVVyo+Tbhm/h2tlsn145ot9ViN59tuEuLgQQZi8qPd5cXQzN/v7Tjqdo6cmtvUrSW+ijtVZUtnb/AEg5+ZkH8I+vQ+2ap3WnX0d5dy6eLYrdwLEwlYr5RUEBhgHIwenHTrSjOLVn/WyKlTlGV47f8O/z/rQ2I5EliSSNgyOAysOhB6GnVBZWq2Vhb2isWWCJYwT3wMVPXO7X0OpXtqFFFFIYUUUUAFUNa0e01/RbvSr6PfbXMZjcdx6Ee4OCPcVfopptO6A+KfFfhm+8I+IbnSL5fmjOY5AMCWM/dYfX9DkVi19g/EHwDY+OtF8iQrDqEALWt1j7h/un1U9/zr5P1zQtR8OatNpmqWzW9zEeVPRh2ZT3B9a+kweLVeNn8SM2rFjwz4o1Xwlq6alpNwY5Rw6Nykq/3WHcfy7V9LeCvi54f8WRx29xKmm6oQAbad8K5/2GPB+nB9q+UKKrE4OnX1ej7gnY+8KK+OdC+I/i3w6ix2GtT+QvSGfEqAegDZx+GK7G2/aE8URIFn0/S5z/AHtjqf0avKnldZP3WmVzH0rRXzTdftCeKZkK29hpduf73lux/VsVueGPHGs+LfD80mpXzPPBcFJFjARSjDKcLj0cfhWNXA1aUOeWw7nreseLLHTFaOFhc3PZEPCn3P8ASvPL/ULnU7pri6kLueAOyj0A7CqtFcgBV7SdMm1bUI7WLIB5d+yL3NRWNjcajdLbWsZeRvyA9Sewr1DRNFg0Wy8qP55W5lkxyx/woAvWttFZ2sdvCu2ONQqipaKKQyrqds97pN5axlQ88DxqW6AspAz+dY114fu30bR1tZoY9T0oIYncExuQmxlPfaRnn6Voarq7aayIlpJcO8TyBUzn5WRTwAT/AB54BOAeDWde+KpLe006W2sEupbxpQES5AVRGrM2GI5Pynggc9cc07kSpqW4+z0zVbrW01fV/ssclvC0Vrb27M6qWxudmIGScAYx0qe7tdZvfCc9pJLapqs0DRtJHuEak8ZGeen61it8QoWWKWHTZTavLsNxLJsRRsicZOCFYiXgMQPlPPSrDeNxFZDUJ9NkTTpLlYIZxKGZgZvKLFANw55wM5HoeKLiVNJNdzpLG0isLC3s4RiKCNY1HsBiqg06S41Oe7vCpUIYbZFOdiEfMx/2iePYAeprFi8f6fLdaXCImA1C5mgVmcKUCSGNWKnB+Z8ADGRn2qpYfEUaiClvpZadnSOIef8AJuYSEh2K/KQsTNgBuoxSuVyrRGrp+jahG2kQXTW4ttKB8t42JaYhCi5BA2/KTnk810dcWfH6slxJFp6vFb6f9tkIuMk/I7FVKqVPKEZLD6V0Gjau+qC6jntTbXVpKI5oxIHXJRXBDYGQVYdhTbuEYKKsjUooopFBRRRQAUUUUAFFFFAEVzG81rNHFIY5HRlVx/CSOD+FeXw+DtTk0D+zYPDVrp19Dp5tp9QF0B9tbchZfk5YSbWyzjK7uO9epyOI42ds7VBJwM8CuGsdW15Hia/tr6MSXQulVkDfunjk/dfLnhWCcHBywoAyLrwVc61LPt8ORaRpk7WcU+nrJGPNCTbpJSEO0YQ4H8R/AU628M+KbUXWoy2qXOq2N9byWjeeoN5HHGYWJOflLRsSQe9bNt4p1mb7P5tmoSS48oslu+WUrGQQM4GC7gnJ+704OIIfFOv22gWjS6dJ9tCLujlhZ2kAjQ5LA43MSTwDjBBHBoAz9N8GanpniXSbl7E3EUenwxzzoLdsXHmSPKxMnzgbpM5Tk1F4c8Darpd54fsrqwgl0eKAzXcbujiGdoDHIm3+JWba3GRkt612tzfTWdzrk0SSTvDBE0MC5O5yGwAPc4rnkfxLcRWWnML1buFp0klkmEW/5UaN2ZAykDcRgdcH0oAyo/AGqReHdKsbK2t7C6Syv4p5Y2VdskoAjJK8nIGMjJAouvB17qkVyLXwxb6NataQ2s1msseLoieNyTsOMKiuAT8x3niugbUNUm0i7sI72ZNYTUEQSi3OFj89BuAIwV2k/hms+41fWnF3JerqNoqNP5UcCtlplEYVQwRsr98rkYbPNAGe3hDxOx1LS/Jgazl+x2UV3PIHElpC8snzqCGJ2skZHfJNXdG0bxHoOoaM02lfbotOhu7IPbTRoPKeSJomCu2cBVK4yT8o9a6h7q4kvGdDKkjaUZBH3D59PWub07V9Xgt9LmiS5uiyN9sLSSSiLiLLsGVTkZf5V9/egCnB4Y1GL7Bqeo+HY9Z86K5e40+R48wTzS+Zvw52n5cITnIAGO9N0vwBq0M5nvDEXh0dLaIbY5cyZnPlh2G9QgkRQwxnHtW5/a+u6nc20KobZBdxedst3BRcybomJPONiEsOPm9CM138Q6+9vFDFYSvMq2sixxoys2Wi3K7scDO5geMYB54IoAwrzwBqFr4Y0e1h04Xt3DbOrhI7VUjkeONSroyhXXK/6wHePU5q+3hDxJLPczvciNJNZsrtrOMRmN1jWDe4Y/OMFGwM87RxzXoOmXEl3pltcTLtlkjDOuwptbuMHkYPFWqAPLLTwfqqLY2g0OG3vrOSd7nWVmTdfKySDHB3kuWUkPgLjvgVmR/D3X7XRDbNYW8shXTTstEhRSISTIGRyVeQEn5jkMMdMYr2aigCjo0L22jWcLwNA8cSqY2WNSpA6Yj+Qf8AAeKkl/5Cdt/1zk/mtWqqy/8AITtv+ucn81qo7kT2+a/MtUUUVJYVBd2lvfW7W9zEskTdVNT0UAeeaz4MurQtNYZuIOuz+Nf8a5dlZGKsCrDggjBFe11Rv9H0/Uh/pVqjt/fxhh+I5p3A8horvLrwDbOSbW8kj/2ZFDD+lZsngLUVPyXNs49yw/pRcRytO1KP7T4WmXq1vKJB9Dx/Vq6VfAuqk8y2q/8AAz/hVhfClvZRTW+qatbxCeI/KODjIUkZ93A+pFaUp8k1LsZV6ftaUod0eRVqaVoF7q0ibBHBAx/4+Lhtie+Cev4V6vpvgnwvpTqzql1MOd1zIGx77en6V06XNoq7UmhCrwAGAxzivRq5ktqa+88ehk7ves/kjl/DOieHfDcYkW+tri9Iw1w8i5HsozwK6X+1dP8A+f23/wC/gpP7U0/ci/a4NzgMoLjJBxgj/vpfzFSm9tAwU3UIZhkDzByOuf1H515c5ym+aTuz26dOFOPLBWREdV08DJvbf/v4KWLU7GeVYobuF5G6Krgk1boqSytf3YsbN7gpvClRjOOpA/rWM3ikxaZq95NZGNtOgecwNJh2C7uoKjGdvBG4c8E4rfmhjuIjHKgdDjKn2OapLommrDcwizj8q5QxyockMhz8vsOTwOOaAOabx8Y9QsLN9ORmuxGR5Uzk/PKYxtDRjONpY528DjNMj+IE0mhRar/Y/wC5kfGRM5WNdhbdI3l/L0AyMrk8sBXWT6VY3M/nzWsby4jG8jnCNvT8m5FUR4S0IWothp0fkhtypubC8Ecc8DBIIHBFAFKy8a2l9qsGnpbyCaW7e3zyVAWIybtwG05xjAPfNSweLIpr9bL7K6zFlTlxjeGIkX/gCgMfUMK1YtI0+Bo2itIkMcpmTauNrlSpYf8AASR9KRtF01pGkNlDvZnctt5LOu1z9SAAaAMe28W/2hp8V3YW0Moa8Fqytc427iNjZVT1VlJHbOKSTxY0Vo9y9rCsYuGgBaWQAbfMJY/u/wDpmemetbv9m2QbcLWIHKNwuOU+6fwpkOk2MDl44ACXMmCxIDHOSATgZ3N09aAJrK4N5YW9y0TwmaNZDG/VMjOD7ip6ZDDHbwRwxIEjjUIijoAOAKfQAUUUUAFFFZOs6VdajLA9tefZjGrDcMk5JU5ABAP3SOc8MaANaue8W+C9G8Z6b9k1W3y6g+TcJxJEfVT6ex4NM/sjxD5r51wCPHy/u+fvE8/hjn2xTZtC1+aOVD4gYI6MoAiAIyTzkc5wR0x0xVRk4vmi7MD508ZfCPxF4TeSeKFtR00ci5t1JKj/AG06r9eR71wFfeHauP8AEXww8JeJmaW80tIblutxanynJ9Tjg/iDXrUM0srVV80S49j4/or3vU/2c4yxbSfEDKO0d1Bu/wDHlI/lXOz/ALPniuMnyr7SpR2PmupP/jld8cdh5faJszyau3+GN95Wu3WnsflvLc7R/tx/OP8Ax0OPxro4P2e/FUhHnX+lRDv+9diP/HK63wv8BDo+rWmpXviBnlt5BII7eDaDjsWYng9OlZYnFYeVOUObcaTGVvaR4Uv9TKySKba3P/LRxyR7Cu6sPDul6aQ0FqpkH/LST5m/Xp+FalfPXLKOmaTaaTbeTax4z95zyzH3NXqKKQBRRRQBja5NaI8cc+nyXkphkfEZAKRqULEEkc52YxzkD0rn7y98P31j5UukpcWlrcbIt9wgy7nBJy2QG3HJbrn3rf15LR5LNLqzvJmlZoUe2fZjcMsrHcDghent9Ky4dd0JokihtLj/AI+Fmjj+UZc7yGyWwB8jcEjtxQBBcyeHHubgXejIpgAnlJdADlY88BvmTAQZwVOz2FOM/hmO9u7iz0lLmaOQzzSQbSAI9khcZb+84OB1IJq/cjSZ9TuRJb3c91FCJzCHJUbsZ2gttDAbc47EetULzVPDc8N3d3kN3A62q3sy5ZWkikwm35W+YHy1BX6etAEMmq+GzLe40mORy/zNvTki4wCxLfu8ySFhnGeT2q/fW/h+20yya60uMR3RihVY2B8sAsytuB6LuY7ge59ajubnw+Ek86C4giW6+z+ej7cSGbdn5WyAHXOSAAM9s1ZI0nVri2sbjTrm8XypkjmuMOoUMFc5LZzkAZxn0oAyWg0FI7w23h0SQ2u6ykjiuVXKBmQgpu6ZLYyO9XdP1vTdItr2C00xbWO2nWOUefHkysyLlstnHzD5j2H0roxptqLN7TyyYXcuwLHJYtuJz1681AuhWS3E0wEx86UTvGZmKeYGDBgucA5UUAT6ZqEeq6dDewqyxygkBsdiRkEZBBxkEcEYNW6htbaKzt0t4F2xJnauc4Gc4HtU1ABRRRQAUUUUAFFFFADZGKROw25UE/McD8TXJab4tnQiLVUT7Q6B/LjTaEHlyPkNuZXU+WQGB+oFdfVIaNpYhaEabZiJm3snkLtLepGOvvQBgTeN1jtHuk0ud4lL4/eoCwSLzXPXsOB6n86mXxjH9oMElhKjxSrHcDzFPl7nCKR/eyWGcdK3zY2hTYbWDbz8vljHIwfzHH0prafZPMkzWdu0qMXRzGCysepBxweB+VAHP2Pitr3ULCGLTPKN4yF2eVciNonkQ8dT8pGO341LqOqajD4e1W+tp4Vms5pdvmw71KL0XAI/Otz7DZgofssGY9pQ+WPl2ghcemASB6ZpUsrdIpohEpjmdnkVvmDE9cg0Ac2/jJLSSe2ntZ55IH8jzo4iqSS7kTGOcDLjueh9sv8A+Ezj/wCgbcgmDzFVsBnbugHcj8/at99OsZZJZJLO3d5l2ys0SkuPRjjkcDrTDpOnHrp9r/q/K/1K/c/u9OntQBy15412Wt5NaWiyXMdpJMm5lCoETc2Wz82GI+Xg9emK2tI8RR6tfz2qWsyCLeBKykK5R9jfr068VfbSdNeJYn0+1aNcFVMKkDAwMDHpxU0VnbQzyTxW8Mc0v+skRAGf6nvQBNRRRQAUUUUAFFFFABVWX/kJ23/XOT+a1apjRK0ySnO5AVH0OM/yFNOzJkrofRRRSKCiiigAooooAKKKKACuX8Vtpi3NidQ06e6b5vKMTEbeRkHHvs/HHpXUUUAcAJfCoJQ2V4QGCiXJYk/MMD5s4AX+Xet5PCuk3oF1JDcfvlVtjyEbeh5x+GetdBtHoKWgDIl8M6VM8UkkDGSKJYUfzGBChWUd/Rjz9PQVX/4Q3RfIaH7O+wgAjzD2x/gPyrfooARFVEVFGFUYA9qWiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIZ7aO4aBpM5hk8xMHvgj+prJHhPTY0jWESQlCjZXacsocAkEEE4dhkj09K3KKAMqPw9YR3pvQjm7YtvmLfM4ZQpB7YwF4/2RVe58I6VdiLzVmPlwrAMSYygUqAfX72fqAe1btFAGSvh6zDsWaR0acz+WwXG4lic4GSPmPBJqbT9FtNMjto7YSBbeN4497lvldgxyT15ArQooAKKKKACiiigAooooAKKKKACiiigAoorLu0vI7iAI1w8XzkmHbncWBUNn+HGR/kUAalFYEF3rrSb5bXYhIHCA4HJzjcOckDr0GaJbzW8NJHZEME2hSBgv8ALk9c4zuAoA36KwBda+r7Taoy/MS23kDc3QA84XbjnnmpVudYitIV+zeZMyElmUcNk9QDxxigDaorAN7rcdu85tFwi7ihXJbgdMHjuf8AChLnUru30+dkkQNI/nLGuMAH5c88/UcUAb9FYZutfSL/AI9IXcqGyOADtBIxk5OWIH+6fUU5LvWljVpLSNmcgAKv3fucnn0L/kKANqisKW51p7S0K2+2Vm/e7VHZ145PAK7uaiF74hELyfZFLkAJHtGM5bJPORn5R+tAHRUVj3MmrQ30rQJ5sLFQilRgcDPcYzk89sDiln/tdkshGyo7IDOVjGFbenv0276ANeisWC71poblp7ONHVF8tV5yx69+fX/Go1vNfKQ7rGFSSoc59Rk457dKAN6iucutT1y1j3NZq48tclU6Oduf4ugyw/4D7jNq3vNYa3uZJbRCyBWiRQVMgzyOTwSMdehODQBs0VhC51pblTJbAxo2HEa8EZ5xzz8oyOnJxSG58QeWZVtYSduRERg5x67vX2oA3qKxpbjWWt4QkCLK8bbiFzhs8dTxxz3yeOKcy6tHNB5bh0EKK4dRgvyWPXrwB7ZoA16K59LnX5vJR4EhLFSzqudvzjOcn0zVmW61lWtRHZRuHjUzHP3GPBHXt1/OgDXornVvPERXf9kjycAKV4HXnrn+6MfU1Yu31f7Lbso2ylyZBGgOBuGBjPPy7u/JxQBtUVzQn19lk8yF1BLN8ijK/INoHJzk5yO2Kt3Musm/jSGMCIYycDachcknOeDu4x0HvQBtUVz89zrzhDHaBC5VtvBx8wypOePlzk80+1uNbmurQXMCxRbsyFF6ja/Uk8YO3tzmgDdorD0+71fzba3uoAdw+eRkwVwATnBIPJwD+lMMuvRea6xrN8xUKygYG5vmGD6beO9AG/RWNPeayhtjFZJJmLfMBx82CSoJPB4A5/vUwXWvb5A9rCAmSNozuwpIAyR1O36ZoA3KK5karrpnMAskEipk5Q4PzdRz/d5+vHWrlxda0rqIrWLHkKzHk/P/ABAfTj86ANqisHz9e2sVhTcckb0GB8pIXAb1wM570Pda95+xbZNiEZbaPnww9+AQT9MUAb1FYdrca3NdWguYFii3ZkKL1G1+pJ452cY5zTWuNbldWFu0QAUMoUEE/Lu7+pOD6CgDeorCNxryIWNvEwIwFCcry3Od3OAFPT+L2pbS51w3EC3FtGI3bMhx9wbV4GD655NAG5RWG91rqyAC2iKFcswXJX5jnAzyQMcZ5p4uta80gWiMuQPmXbjPf7xz6kdunNAGzRWCl9raWrzz2kYZQoWJQcuxOCM54x+tSXP9pf2tCP3n2cEbvLHyngf13fpQBtUVzZGuzGFlMsYRFEm7aCXG3cQO/wDFjscVNqL6vJeo9gsixtACFYAANh85z3+5/nNAG9RXNt/aonYr9s+z+W/lbgC+cDG7HvnGe1Wr271mKcrbWiOgiBLYzluM45+tAG1RWPcyatLc+VCnlRbozvCgkLlN3OevLjHtmo7u51iC9nkjgLWzskcWAHK4YAttGOoLd/4RQBuUVjwSau89q1xEqIXPmrHj5RjAzyc880sz3y6dPHtuXnWU7WTaCULnbzjoFxnAzj3oA16K5l/7aA2l7skna0iqoAPO1gvXGMZHrgdM1fhbV9mpCdMSNk2pRgQPlGB0Hf1757UAa9FYUtxryHcsEbDaTtC9yw9yeBuPvS/atdXy2a1iIZhuVF5UcZ53d/0xQBuUVjWlzqq3UUFxECJDknb/AKtQik8g85JYfWori610kIloqg7gWXGcAEBhz3IyB6Ee9AG9RWMl5rL3kCGyRICwEjN17Zxz064PtTJbjXY/MZbeJ1JIQBOVGeCfm54/nQBuUVjRXuqy2cztaqkqMiYVd2c4LEDPOM469Qaie416EYW3WbdISMqBtXe3yn5v7u05oA3qK5ybUtchnitxaRvIyFs7CFJ25C9eOcjPtk9atSXesrb2hS0RpX3ebxwBnA78HHP4UAbNFYbvqkxsHZJIyUk80IMANldufbGTUAGuTzwSDzY4lKh1baCf9WG47/x/5xQB0dFYFxda4l5MYbbfGDtRSo24y2DnOTkbee2as6fPqrXiRXcI8kRHfKVAJfIxjB6dfyoA1qKKKACiiigAooooAKKKKACs7VJtQh2NYwiT5TuBGecrgdR2zWjRQBjX99qkNwY4LQmPfgSCPf8AKQvbcOc7/wAh+Nb7X4gE+42Y52qUwNq8tnBzk8bea6KigDnluvECrse3UgKAZAmWzxkhc465GM9ADVhjqLHTMiZf3RFxj/np8uM47fe9q2aKAOcxrs8kDjzY0QKJA20EkbAxx3/j/wA4q/eXGrJPEtvawmMltzbi3HGPTHGfyFalFAGDFPrshUtCqlto5QYXkhj1yeMHFMkuvEPlqi20YkKEmRUGAdmeAW7Nx710NFAHPvc69I2w2yxqZgNyDJ2hx1yeBtyc8+lS3lxrDyOlvAUEZfDhQQ3DbeCeeNv4mtuigDEM+uKzMLeNo1JwhX5mGVxzu9Cx/wCA1Ct34h+VjaxndtG0rgDl+eueQE+ma6GigDHurjWo7jFvbQyRl2C7uOAFxk575bn2FMN5rK8raCUBd20oELdc/wARwewH41t0UAYa3etqZjNBAiR7jkKTuwDgDnvx69avp9pvNOUtutp2wSMcr3x1644+tXaKAMK4l1aA3LwQzSzb2Cq23ytuflxznOOD9TntTorrV4wfNtmYK33dgJK56g7uvtjoM1t0UAc8bnX9qotvtJDM7lQdp3EgDnnjAx705bnXZZI1ktliUzLuKDPyhlyCSeBt3c85rfooAxbi81gSFIrIbQ5UnH3hlsEHPptP4n6VXudR1m1MKtbIRK+NwjLbBlhzg88BT+PtXRUUAY1y+spfytbqrwqfkRlADA+XxnPr5hz7U37XrX2K3YWkf2h5MOMcKvvz9fyrbooAwxd69JOi/Y4YkYLkn5ivzYJ69hzj3qs+q64l0kJsVG9m2krwRxgHn1yM9uvSulooAxxdaz9hhcWkTTtvDKeAuOVJ574I+pHaoku9ZV2ZrQuGwE+UDHzYyRnjjJP4Vu0UAYD3PiGNFxbQSFiM/Ljbxn+8c8nH4VYnfVZdQEUa+XbLKpLhRygx3z35zxxj3rXooAxJpNbga6aJUlUyZjDL0UkDjB7Dn3zSJPrj3YLQokaH5gF4cFl4BznIG45rcooAxLm81xJp0isYzGpPlydd3I7Z9CP++W9qiF14hBC/ZYiW3Elh93rgcHtx355roKKAMHU/7X+2qLXzCgAxsAwTjv8Ajn26Uyxl1eK4Bu0uHgLENgAkdfxx06cV0NFAGDPLrXl2axREnylaU4HLbW3AnPHO3t3PpSXVxrrRyCO2CFlKqyAMQRuAON3AJAOewIrfooAwFuddkliWS2WJTMu4oM/KGXIJJ4GN3POadFd6vFciGSAOjzlFZlwVUsxzweQFC+nLVu0UAYc8muR3Fw8CLKikiON1ADDtzntT5LrWfItPLtYzK+7zcjhRnA78cc1s0UAYkd1rslwA1nFFE208/MVBwSOuM9R+GapjVte89YDYKJCjMuUOG4JA68cgf5Irp6KAMWe71pYLYw2iPKysZMjgH+Hvx2NL9p1qOXL28ckSybTsXDMufvj5vTtWzRQBi2dzrE2oRC5tfJtwAXxjrtOR1PGcVFb3uuTQCVrZUUop5iw2Ttzhd3bLfXH579FAHPrea8JzJJZ5XyyDGqjAfB24O7nnGfTJ+tSNca2drLACwDZj2BR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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display\n", - "\n", - "\n", - "def resize_image(image, max_height=800):\n", - " width, height = image.size\n", - " if height > max_height:\n", - " ratio = max_height / height\n", - " new_width = int(width * ratio)\n", - " new_height = int(height * ratio)\n", - " return image.resize((new_width, new_height))\n", - " return image\n", - "\n", - "\n", - "display(resize_image(sample_pdfs[0][\"images\"][0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us also look at the extracted text content of the first PDF page.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Water\n", - "Water sourcing and produced water management are global challenges that require local solutions. We collaborate with \n", - "other users, communities and regulators on solutions and align our actions to protect and conserve water resources.\n", - "Biodiversity \n", - "We manage biodiversity risks and mitigate impacts through the use of the Mitigation Hierarchy, a decision-making \n", - "framework involving prioritized steps to mitigate adverse biodiversity impacts: avoid, minimize, restore and offset. \n", - "Our efforts are designed to reduce impact on biodiversity and contribute to its restoration.Our policies require nature-related risks be assessed in business planning. We disclose our approach \n", - "to governance, strategy, management and performance related to nature. NATURESustainability\n", - "23-1207HAB ITATS CONSE RVED, PROTECTED OR REST ORED\n", - "2 Estimated as the percentage of lease areas overlapping with designated \n", - "protected areas using the World Database on Protected Areas.OVER \n", - "540 ,000\n", - "CUMULATIVE ACRES1\n", - " \n", - "1 Cumulative with varying conservation pr oject start dates \n", - "as early as 2009.on company-owned lands \n", - "and operated assets. UNCONVENTIONAL\n", - "Bakken | Eagle Ford\n", - "Montney | Permian BasinBBL/BOE EUR1\n", - "BBL/BOE2\n", - "CONVENTIONAL/OFFSHORE\n", - "Alaska | APLNG | Ekofisk\n", - "Surmont | Teesside FRESH WATER \n", - "CONSUMPTION \n", - "INTENSITY\n", - "1 Calculated using Enverus data for the average volume of fresh water (bbl) divided by the average estimated ultimate recovery (EUR, BOE) as of April 1, 2024. Intensity value may change as EUR data \n", - "is updated. EUR – estimated ultimate recovery. 2 Calculated using the average volume of fresh water (BBL) divided by the average annual production (BOE).\n", - "24-0976As of Dec. 31, 20230.03%OF LEASE AREAS OVERLAP \n", - "WITH PROTECTED AREAS2\n", - "12PROTECTED AREAS WITHIN \n", - "3 MILES (5 KM) OF FIVE ASSETS\n", - "APLNG | Bakken | Permian Basin \n", - "Montney | Teesside0.06\n", - "0.03\n" - ] - } - ], - "source": [ - "print(sample_pdfs[0][\"texts\"][0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the layout and order of the text is different from the image representation. Note that\n", - "\n", - "- The headlines NATURE and Sustainability have been combined into one word (NATURESustainability).\n", - "- The 0.03% has been converted to 0.03 and order is not preserved in the text representation.\n", - "- The data in the infographics is not represented in the text representation.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we use the ColPali model to generate embeddings of the images.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NRp3P9SlTK97", - "outputId": "b80587ba-4131-45fa-9803-0f42ada54019" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:09<00:00, 9.46s/it]\n", - "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26/26 [06:28<00:00, 14.94s/it]\n", - "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 52/52 [12:57<00:00, 14.95s/it]\n" - ] - } - ], - "source": [ - "for pdf in sample_pdfs:\n", - " page_embeddings = []\n", - " dataloader = DataLoader(\n", - " pdf[\"images\"],\n", - " batch_size=2,\n", - " shuffle=False,\n", - " collate_fn=lambda x: processor.process_images(x),\n", - " )\n", - "\n", - " for batch_doc in tqdm(dataloader):\n", - " with torch.no_grad():\n", - " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", - " embeddings_doc = model(**batch_doc)\n", - " page_embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))\n", - " pdf[\"embeddings\"] = page_embeddings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are done with the document side embeddings, we convert the embeddings to Vespa JSON format so we can store (and index) them in Vespa.\n", - "Details in [Vespa JSON feed format doc](https://docs.vespa.ai/en/reference/document-json-format.html).\n", - "\n", - "We use binary quantization (BQ) of the page level ColPali vector embeddings to reduce their size by 32x.\n", - "\n", - "Read more about binarization of multi-vector representations in the [colbert blog post](https://blog.vespa.ai/announcing-colbert-embedder-in-vespa/).\n", - "\n", - "The binarization step maps 128 dimensional floats to 128 bits, or 16 bytes per vector.\n", - "\n", - "Reducing the size by 32x. On the [DocVQA benchmark](https://huggingface.co/datasets/vidore/docvqa_test_subsampled), binarization results in a small drop in ranking accuracy.\n", - "\n", - "We also demonstrate how to store the image data in Vespa using the [raw](https://docs.vespa.ai/en/reference/schema-reference.html#raw) type for binary data. To encode\n", - "the binary data in JSON, we use base64 encoding.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "import base64\n", - "\n", - "\n", - "def get_base64_image(image):\n", - " buffered = BytesIO()\n", - " image.save(buffered, format=\"JPEG\")\n", - " return str(base64.b64encode(buffered.getvalue()), \"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "FObCnKQQeHQ_" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "vespa_feed = []\n", - "for pdf in sample_pdfs:\n", - " url = pdf[\"url\"]\n", - " title = pdf[\"title\"]\n", - " for page_number, (page_text, embedding, image) in enumerate(\n", - " zip(pdf[\"texts\"], pdf[\"embeddings\"], pdf[\"images\"])\n", - " ):\n", - " base_64_image = get_base64_image(resize_image(image, 640))\n", - " embedding_dict = dict()\n", - " for idx, patch_embedding in enumerate(embedding):\n", - " binary_vector = (\n", - " np.packbits(np.where(patch_embedding > 0, 1, 0))\n", - " .astype(np.int8)\n", - " .tobytes()\n", - " .hex()\n", - " )\n", - " embedding_dict[idx] = binary_vector\n", - " page = {\n", - " \"id\": hash(url + str(page_number)),\n", - " \"url\": url,\n", - " \"title\": title,\n", - " \"page_number\": page_number,\n", - " \"image\": base_64_image,\n", - " \"text\": page_text,\n", - " \"embedding\": embedding_dict,\n", - " }\n", - " vespa_feed.append(page)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure Vespa\n", - "\n", - "[PyVespa](https://pyvespa.readthedocs.io/en/latest/) helps us build the [Vespa application package](https://docs.vespa.ai/en/application-packages.html).\n", - "A Vespa application package consists of configuration files, schemas, models, and code (plugins).\n", - "\n", - "First, we define a [Vespa schema](https://docs.vespa.ai/en/schemas.html) with the fields we want to store and their type.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import Schema, Document, Field, FieldSet, HNSW\n", - "\n", - "colpali_schema = Schema(\n", - " name=\"pdf_page\",\n", - " document=Document(\n", - " fields=[\n", - " Field(\n", - " name=\"id\", type=\"string\", indexing=[\"summary\", \"index\"], match=[\"word\"]\n", - " ),\n", - " Field(name=\"url\", type=\"string\", indexing=[\"summary\", \"index\"]),\n", - " Field(\n", - " name=\"title\",\n", - " type=\"string\",\n", - " indexing=[\"summary\", \"index\"],\n", - " match=[\"text\"],\n", - " index=\"enable-bm25\",\n", - " ),\n", - " Field(name=\"page_number\", type=\"int\", indexing=[\"summary\", \"attribute\"]),\n", - " Field(name=\"image\", type=\"raw\", indexing=[\"summary\"]),\n", - " Field(\n", - " name=\"text\",\n", - " type=\"string\",\n", - " indexing=[\"index\"],\n", - " match=[\"text\"],\n", - " index=\"enable-bm25\",\n", - " ),\n", - " Field(\n", - " name=\"embedding\",\n", - " type=\"tensor(patch{}, v[16])\",\n", - " indexing=[\n", - " \"attribute\",\n", - " \"index\",\n", - " ], # adds HNSW index for candidate retrieval.\n", - " ann=HNSW(\n", - " distance_metric=\"hamming\",\n", - " max_links_per_node=32,\n", - " neighbors_to_explore_at_insert=400,\n", - " ),\n", - " ),\n", - " ]\n", - " ),\n", - " fieldsets=[FieldSet(name=\"default\", fields=[\"title\", \"text\"])],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the `embedding` field which is a tensor field with the type `tensor(patch{}, v[16])`.\n", - "This is the field we use to represent the page level patch embeddings from ColPali.\n", - "\n", - "We also enable [HNSW indexing](https://docs.vespa.ai/en/approximate-nn-hnsw.html)\n", - "for this field to enable fast nearest neighbor search which is used for candidate retrieval.\n", - "\n", - "We use [binary hamming distance](https://docs.vespa.ai/en/nearest-neighbor-search.html#using-binary-embeddings-with-hamming-distance)\n", - "as an approximation of the cosine similarity. Hamming distance is a good approximation\n", - "for binary representations, and it is much faster to compute than cosine similarity/dot product.\n", - "\n", - "The `embedding` field is an example of a mixed tensor where we combine one mapped (sparse) dimensions with a dense dimension.\n", - "\n", - "Read more in [Tensor guide](https://docs.vespa.ai/en/tensor-user-guide.html). We also enable [BM25](https://docs.vespa.ai/en/reference/bm25.html) for the `title` and `texts`Β fields. Notice that the `image` field use type `raw` to store the binary image data, encoded with as a base64 string.\n", - "\n", - "Create the Vespa [application package](https://docs.vespa.ai/en/application-packages):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import ApplicationPackage\n", - "\n", - "vespa_app_name = \"visionrag6\"\n", - "vespa_application_package = ApplicationPackage(\n", - " name=vespa_app_name, schema=[colpali_schema]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define how we want to rank the pages for a query. We use Vespa's support for [BM25](https://docs.vespa.ai/en/reference/bm25.html) for the text, and\n", - "late interaction with Max Sim for the image embeddings.\n", - "\n", - "This means that we use the the text representations as a candidate retrieval phase, then we use the ColPALI embeddings with MaxSim\n", - "to rerank the pages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", - "\n", - "colpali_profile = RankProfile(\n", - " name=\"default\",\n", - " inputs=[(\"query(qt)\", \"tensor(querytoken{}, v[128])\")],\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " sum(\n", - " query(qt) * unpack_bits(attribute(embedding)) , v\n", - " ),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " ),\n", - " Function(name=\"bm25_score\", expression=\"bm25(title) + bm25(text)\"),\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"bm25_score\"),\n", - " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=100),\n", - ")\n", - "colpali_schema.add_rank_profile(colpali_profile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first phase uses a linear combination of BM25 scores for the text fields, and the second phase uses the MaxSim function with the image embeddings. Notice that Vespa supports a `unpack_bits` function to convert the 16 compressed binary vectors to 128-dimensional floats for the MaxSim function. The query input tensor is not compressed and using full float resolution.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy the application to Vespa Cloud\n", - "\n", - "With the configured application, we can deploy it to [Vespa Cloud](https://cloud.vespa.ai/en/).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To deploy the application to Vespa Cloud we need to create a tenant in the Vespa Cloud:\n", - "\n", - "Create a tenant at [console.vespa-cloud.com](https://console.vespa-cloud.com/) (unless you already have one).\n", - "This step requires a Google or GitHub account, and will start your [free trial](https://cloud.vespa.ai/en/free-trial).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.deployment import VespaCloud\n", - "import os\n", - "\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", - "\n", - "# Replace with your tenant name from the Vespa Cloud Console\n", - "tenant_name = \"vespa-team\"\n", - "\n", - "key = os.getenv(\"VESPA_TEAM_API_KEY\", None)\n", - "if key is not None:\n", - " key = key.replace(r\"\\n\", \"\\n\") # To parse key correctly\n", - "\n", - "vespa_cloud = VespaCloud(\n", - " tenant=tenant_name,\n", - " application=vespa_app_name,\n", - " key_content=key, # Key is only used for CI/CD testing of this notebook. Can be removed if logging in interactively\n", - " application_package=vespa_application_package,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now deploy the app to Vespa Cloud dev zone.\n", - "\n", - "The first deployment typically takes 2 minutes until the endpoint is up.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.application import Vespa\n", - "\n", - "app: Vespa = vespa_cloud.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of PDF pages: 155\n" - ] - } - ], - "source": [ - "print(\"Number of PDF pages:\", len(vespa_feed))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Index the documents in Vespa using the Vespa HTTP API.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 155/155 [00:22<00:00, 7.04it/s]\n" - ] - } - ], - "source": [ - "from vespa.io import VespaResponse\n", - "\n", - "async with app.asyncio(connections=1, total_timeout=180) as session:\n", - " for page in tqdm(vespa_feed):\n", - " response: VespaResponse = await session.feed_data_point(\n", - " data_id=page[\"id\"], fields=page, schema=\"pdf_page\"\n", - " )\n", - " if not response.is_successful():\n", - " print(response.json())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j2pUyGjYf4Wv" - }, - "source": [ - "### Querying Vespa\n", - "\n", - "Ok, so now we have indexed the PDF pages in Vespa. Let us now obtain ColPali embeddings for a few text queries and\n", - "use it during ranking of the indexed pdf pages.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can query Vespa with the text query and rerank the results using the ColPali embeddings.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "id": "V2U58J_B5-L6" - }, - "outputs": [], - "source": [ - "queries = [\n", - " \"Percentage of non-fresh water as source?\",\n", - " \"Policies related to nature risk?\",\n", - " \"How much of produced water is recycled?\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain the query embeddings using the ColPali model:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "NxeDd3mcYDpL" - }, - "outputs": [], - "source": [ - "dataloader = DataLoader(\n", - " queries,\n", - " batch_size=1,\n", - " shuffle=False,\n", - " collate_fn=lambda x: processor.process_queries(x),\n", - ")\n", - "qs = []\n", - "for batch_query in dataloader:\n", - " with torch.no_grad():\n", - " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", - " embeddings_query = model(**batch_query)\n", - " qs.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We create a simple routine to display the results. We render the image and the title of the retrieved page/document.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "\n", - "def display_query_results(query, response, hits=5):\n", - " query_time = response.json.get(\"timing\", {}).get(\"searchtime\", -1)\n", - " query_time = round(query_time, 2)\n", - " count = response.json.get(\"root\", {}).get(\"fields\", {}).get(\"totalCount\", 0)\n", - " html_content = f\"

Query text: '{query}', query time {query_time}s, count={count}, top results:

\"\n", - "\n", - " for i, hit in enumerate(response.hits[:hits]):\n", - " title = hit[\"fields\"][\"title\"]\n", - " url = hit[\"fields\"][\"url\"]\n", - " page = hit[\"fields\"][\"page_number\"]\n", - " image = hit[\"fields\"][\"image\"]\n", - " score = hit[\"relevance\"]\n", - "\n", - " html_content += f\"

PDF Result {i + 1}

\"\n", - " html_content += f'

Title: {title}, page {page+1} with score {score:.2f}

'\n", - " html_content += (\n", - " f''\n", - " )\n", - "\n", - " display(HTML(html_content))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Query Vespa with the queries and display the results, here we are using the `default` rank profile.\n", - "\n", - "Note that we retrieve using textual representation with `userInput(@userQuery)`, this means that we use the BM25 ranking for the extracted text in the first ranking phase and then re-rank the top-k pages using the ColPali embeddings.\n", - "\n", - "Later in this notebook we will use Vespa's support for approximate nearest neighbor search (`nearestNeighbor`) to retrieve directly using the ColPali embeddings.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Query text: 'Percentage of non-fresh water as source?', query time 0.15s, count=233, top results:

PDF Result 1

Title: ConocoPhillips Sustainability Highlights - Nature (24-0976), page 1 with score 74.30

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 72.69

PDF Result 3

Title: ConocoPhillips Managing Climate Related Risks, page 45 with score 72.01

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Query text: 'Policies related to nature risk?', query time 0.08s, count=200, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 44 with score 77.48

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 42 with score 77.15

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 41 with score 72.63

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Query text: 'How much of produced water is recycled?', query time 0.08s, count=243, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 92 with score 84.52

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 82.96

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 87 with score 80.30

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from vespa.io import VespaQueryResponse\n", - "\n", - "async with app.asyncio(connections=1, total_timeout=120) as session:\n", - " for idx, query in enumerate(queries):\n", - " query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}\n", - " response: VespaQueryResponse = await session.query(\n", - " yql=\"select title,url,image,page_number from pdf_page where userInput(@userQuery)\",\n", - " ranking=\"default\",\n", - " userQuery=query,\n", - " timeout=120,\n", - " hits=3,\n", - " body={\"input.query(qt)\": query_embedding, \"presentation.timing\": True},\n", - " )\n", - " assert response.is_successful()\n", - " display_query_results(query, response)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using nearestNeighbor for retrieval\n", - "\n", - "In the above example, we used the ColPali embeddings in ranking, but using the text query for retrieval.\n", - "This is a reasonable approach for text-heavy documents where the text representation is the most important and where ColPali embeddings are used to\n", - "re-rank the top-k documents from the text retrieval phase.\n", - "\n", - "In some cases, the ColPali embeddings are the most important and we want\n", - "to demonstrate how we can use HNSW indexing with binary hamming distance to retrieve the most similar pages to a query and\n", - "then have two steps of re-ranking using the ColPali embeddings.\n", - "\n", - "All the phases here are executed locally inside the Vespa content node(s) so that no vector data needs\n", - "to cross the network.\n", - "\n", - "Let us add a new rank-profile to the schema, the `nearestNeighbor` operator takes a query tensor and a field tensor as argument and\n", - "we need to define the query tensors types in the rank-profile.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", - "\n", - "input_query_tensors = []\n", - "MAX_QUERY_TERMS = 64\n", - "for i in range(MAX_QUERY_TERMS):\n", - " input_query_tensors.append((f\"query(rq{i})\", \"tensor(v[16])\"))\n", - "\n", - "input_query_tensors.append((\"query(qt)\", \"tensor(querytoken{}, v[128])\"))\n", - "input_query_tensors.append((\"query(qtb)\", \"tensor(querytoken{}, v[16])\"))\n", - "\n", - "colpali_retrieval_profile = RankProfile(\n", - " name=\"retrieval-and-rerank\",\n", - " inputs=input_query_tensors,\n", - " functions=[\n", - " Function(\n", - " name=\"max_sim\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " sum(\n", - " query(qt) * unpack_bits(attribute(embedding)) , v\n", - " ),\n", - " max, patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " ),\n", - " Function(\n", - " name=\"max_sim_binary\",\n", - " expression=\"\"\"\n", - " sum(\n", - " reduce(\n", - " 1/(1 + sum(\n", - " hamming(query(qtb), attribute(embedding)) ,v)\n", - " ),\n", - " max,\n", - " patch\n", - " ),\n", - " querytoken\n", - " )\n", - " \"\"\",\n", - " ),\n", - " ],\n", - " first_phase=FirstPhaseRanking(expression=\"max_sim_binary\"),\n", - " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=10),\n", - ")\n", - "colpali_schema.add_rank_profile(colpali_retrieval_profile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define two functions, one for the first phase and one for the second phase. Instead of the float representations, we use the binary representations with inverted hamming distance in the first phase. Now, we need to re-deploy the application to Vespa Cloud.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vespa.application import Vespa\n", - "\n", - "app: Vespa = vespa_cloud.deploy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can query Vespa with the text queries and use the `nearestNeighbor` operator to retrieve the most similar pages to the query and pass the different query tensors.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Query text: 'Percentage of non-fresh water as source?', query time 0.1s, count=173, top results:

PDF Result 1

Title: ConocoPhillips Sustainability Highlights - Nature (24-0976), page 1 with score 74.30

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 72.69

PDF Result 3

Title: ConocoPhillips Managing Climate Related Risks, page 45 with score 72.01

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Query text: 'Policies related to nature risk?', query time 0.07s, count=173, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 44 with score 77.48

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 42 with score 77.15

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 41 with score 72.63

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Query text: 'How much of produced water is recycled?', query time 0.04s, count=118, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 92 with score 84.52

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 82.96

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 87 with score 80.30

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from vespa.io import VespaQueryResponse\n", - "\n", - "target_hits_per_query_tensor = (\n", - " 20 # this is a hyper parameter that can be tuned for speed versus accuracy\n", - ")\n", - "async with app.asyncio(connections=1, total_timeout=180) as session:\n", - " for idx, query in enumerate(queries):\n", - " float_query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}\n", - " binary_query_embeddings = dict()\n", - " for k, v in float_query_embedding.items():\n", - " binary_query_embeddings[k] = (\n", - " np.packbits(np.where(np.array(v) > 0, 1, 0)).astype(np.int8).tolist()\n", - " )\n", - "\n", - " # The mixed tensors used in MaxSim calculations\n", - " # We use both binary and float representations\n", - " query_tensors = {\n", - " \"input.query(qtb)\": binary_query_embeddings,\n", - " \"input.query(qt)\": float_query_embedding,\n", - " }\n", - " # The query tensors used in the nearest neighbor calculations\n", - " for i in range(0, len(binary_query_embeddings)):\n", - " query_tensors[f\"input.query(rq{i})\"] = binary_query_embeddings[i]\n", - " nn = []\n", - " for i in range(0, len(binary_query_embeddings)):\n", - " nn.append(\n", - " f\"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))\"\n", - " )\n", - " # We use a OR operator to combine the nearest neighbor operator\n", - " nn = \" OR \".join(nn)\n", - " response: VespaQueryResponse = await session.query(\n", - " yql=f\"select title, url, image, page_number from pdf_page where {nn}\",\n", - " ranking=\"retrieval-and-rerank\",\n", - " timeout=120,\n", - " hits=3,\n", - " body={**query_tensors, \"presentation.timing\": True},\n", - " )\n", - " assert response.is_successful()\n", - " display_query_results(query, response)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Depending on the scale, we can evaluate changing different number of targetHits per nearestNeighbor operator and the ranking depths in the two phases.\n", - "We can also parallelize the ranking phases by using more threads per query request to reduce latency.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this notebook, we have demonstrated how to represent the new ColQwen2 in Vespa.\n", - "We have generated embeddings for images of PDF pages using ColQwen2 and stored the embeddings in Vespa using [mixed tensors](https://docs.vespa.ai/en/tensor-user-guide.html).\n", - "\n", - "We demonstrated how to store the base64 encoded image using the `raw` Vespa field type, plus meta data like title and url.\n", - "We have demonstrated how to retrieve relevant pages for a query using the embeddings generated by ColPali.\n", - "\n", - "This notebook can be extended to include more complex ranking models, more complex queries, and more complex data structures, including metadata and other fields which can be filtered on or used for ranking.\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - 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It is a Qwen2-VL-2B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository\n", + "\n", + "ColQWen2 is better than the previous ColPali model in the following ways:\n", + "\n", + "- Its more accurate on the ViDoRe dataset (+5 nDCCG@5 points)\n", + "- It's permissive licensed as both the base model and adapter is using open-source licences (Apache 2.0 and MIT)\n", + "- It uses fewer patch embeddings than ColPaliGemma (from 1024 to 768), this reduces both compute and storage.\n", + "\n", + "See also [Scaling ColPali to billions of PDFs with Vespa](https://blog.vespa.ai/scaling-colpali-to-billions/)\n", + "\n", + "The TLDR; of this notebook:\n", + "\n", + "- Generate an image per PDF page using [pdf2image](https://pypi.org/project/pdf2image/)\n", + " and also extract the text using [pypdf](https://pypdf.readthedocs.io/en/stable/user/extract-text.html).\n", + "- For each page image, use ColPali to obtain the visual multi-vector embeddings\n", + "\n", + "Then we store visual embeddings in Vespa as a `int8` tensor, where we use a binary compression technique\n", + "to reduce the storage footprint by 32x compared to float representations. See [Scaling ColPali to billions of PDFs with Vespa](https://blog.vespa.ai/scaling-colpali-to-billions/)\n", + "for details on binarization and using hamming distance for retrieval.\n", + "\n", + "During retrieval time, we use the same ColPali model to generate embeddings for the query and then use Vespa's `nearestNeighbor` query to retrieve the most similar documents\n", + "per query vector token, using binary representation with hamming distance. Then we re-rank the results in two phases:\n", + "\n", + "- In the 0-phase we use hamming distance to retrieve the k closest pages per query token vector representation, this is expressed by using multiple nearestNeighbor query operators in Vespa.\n", + "- The nearestNeighbor operators exposes pages to the first-phase ranking function, which uses an approximate MaxSim using inverted hamming distance insted of cosine similarity. This is done to reduce the number of pages that are re-ranked in the second phase.\n", + "- In the second phase, we perform the full MaxSim operation, using float representations of the embeddings to re-rank the top-k pages from the first phase.\n", + "\n", + "This allows us to scale ColPali to very large collections of PDF pages, while still providing accurate and fast retrieval.\n", + "\n", + "Let us get started.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.ipynb)\n", + "\n", + "Install dependencies:\n", + "\n", + "Note that the python pdf2image package requires poppler-utils, see other installation options [here](https://pdf2image.readthedocs.io/en/latest/installation.html#installing-poppler).\n", + "\n", + "For MacOs, the simplest install option is `brew install poppler` if you are using [Homebrew](https://brew.sh/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!sudo apt-get install poppler-utils -y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now install the required python packages:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VIly_Pymmbyl" + }, + "outputs": [], + "source": [ + "!pip3 install colpali-engine==0.3.1 pdf2image pypdf pyvespa vespacli requests numpy tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "qKFOvdo5nCVl" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import tqdm\n", + "from io import BytesIO\n", + "from colpali_engine.models import ColQwen2, ColQwen2Processor" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yGfNhRP4RKBJ" + }, + "source": [ + "### Load the model\n", + "\n", + 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] + } + ], + "source": [ + "model_name = \"vidore/colqwen2-v0.1\"\n", + "\n", + "model = ColQwen2.from_pretrained(\n", + " model_name, torch_dtype=torch.bfloat16, device_map=\"auto\"\n", + ")\n", + "processor = ColQwen2Processor.from_pretrained(model_name)\n", + "model = model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PUqnrKWLak3O" + }, + "source": [ + "### Working with pdfs\n", + "\n", + "We need to convert a PDF to an array of images. One image per page.\n", + "We use the `pdf2image` library for this task. Secondary, we also extract the text contents of the PDF using `pypdf`.\n", + "\n", + "NOTE: This step requires that you have `poppler` installed on your system. Read more in [pdf2image](https://pdf2image.readthedocs.io/en/latest/installation.html) docs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "_-1v-qZ32OgW" + }, + "outputs": [], + "source": [ + "import requests\n", + "from pdf2image import convert_from_path\n", + "from pypdf import PdfReader\n", + "\n", + "\n", + "def download_pdf(url):\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " return BytesIO(response.content)\n", + " else:\n", + " raise Exception(f\"Failed to download PDF: Status code {response.status_code}\")\n", + "\n", + "\n", + "def get_pdf_images(pdf_url):\n", + " # Download the PDF\n", + " pdf_file = download_pdf(pdf_url)\n", + " # Save the PDF temporarily to disk (pdf2image requires a file path)\n", + " temp_file = \"temp.pdf\"\n", + " with open(temp_file, \"wb\") as f:\n", + " f.write(pdf_file.read())\n", + " reader = PdfReader(temp_file)\n", + " page_texts = []\n", + " for page_number in range(len(reader.pages)):\n", + " page = reader.pages[page_number]\n", + " text = page.extract_text()\n", + " page_texts.append(text)\n", + " images = convert_from_path(temp_file)\n", + " assert len(images) == len(page_texts)\n", + " return (images, page_texts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define a few sample PDFs to work with. The PDFs are discovered from [this url](https://www.conocophillips.com/company-reports-resources/sustainability-reporting/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "kZIGixLBRyEi" + }, + "outputs": [], + "source": [ + "sample_pdfs = [\n", + " {\n", + " \"title\": \"ConocoPhillips Sustainability Highlights - Nature (24-0976)\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf\",\n", + " },\n", + " {\n", + " \"title\": \"ConocoPhillips Managing Climate Related Risks\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf\",\n", + " },\n", + " {\n", + " \"title\": \"ConocoPhillips 2023 Sustainability Report\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can convert the PDFs to images and also extract the text content.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "YaDInfmT3Tbu" + }, + "outputs": [], + "source": [ + "for pdf in sample_pdfs:\n", + " page_images, page_texts = get_pdf_images(pdf[\"url\"])\n", + " pdf[\"images\"] = page_images\n", + " pdf[\"texts\"] = page_texts" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b3vBUFwATIqk" + }, + "source": [ + "Let us look at the extracted image of the first PDF page. This is the document side input to ColPali, one image per page.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 737 + }, + "id": "DGAXQ-0E3jQS", + "outputId": "6efbad11-5ff4-4eaa-8564-ab399f921b9e" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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cwkuobwwGYRY3eXdhA5Ud8KCQKW38W3dtq8aS6vDdaMmpCB9SZEVGVrdn2FlAXiQKMjHXB5oVCTTa6X/ALnodAIPQ5ry9/GWrCx0y8l1FEjlErOkUaebJi4ZFwjgb12gDahD5OecirMWp6vJdmysbxLBGl1WV2itUYsYplCcEY53HJ6n9af1eS3YXPR6K8xh8Za9dX9hmW3tvNhsZEt3KKLnzVUyEAgucEsBt6FeaePFerh9Xi/ta3NxHHK8TbEMEIWZVGSBvjYKcYkUjPOcA0fVphc9LpGZVxuYDJwMnqa4V/E98vw11fV4Lp3vLMyKk0scbcqR3T5JBz1XGfQEVm3Gsy3er2UcmqR6jp9rrFm0d6EVFV3jlDRkrxwdnuN4B5pKhJ38guem0V5VdeN9XV9RltL5ZE+xX8scckce6B4SAmUAyvfhySeuB0rRuNa8QW2qtpP8Aau9mvbJBcm2QMqTJIXXGMcFBg9eec0/q0luwueiZGcZ59KK8ru/EmsQW9zcebDJf2dnqEaXTWy7mMV1EikjtkHkDgnmruq+JtY0SW/s59QecW+oW8XnpboJjFJEXYKMbMgjqR0z3xR9Wl3C56PSAg4wRz0rm/CWtG+0y2W+1WC8v7nzZVWPbxGrbcfKAMrld3+0T2xXE37Xum6reRRpOLXwtcvqgCDiWKZg20euEa4H4ClGi3Jxb2/r8wuetgg9DQSAMngV5Kuta7ocNtbxzRWss9quo4nKAXNxNKzOh3AswXKrhPm5HtXT+MNXurbUYbE6immWkthPL5rRq3nyjaBF83sxOB8x7dKHh2pJX3C52SurgFWDAjIIOeKWvJ9M1a+0/QoxazW1oyWGlK0siqjFWifcu9gVDcADfwORwTWna+Kr27ktTNrg0+P7LDLCZ7RSb52ldHG0E5xtUfuzzv3dMCm8PJbMLnoisrglWBAOOD3pa8vttam09Xs11aPSbdtS1Np7tkVtsiy7kjO7gblYt6kLx1qzbeLdYfVtIF5OsP2qG1LWUSJvDyLlt6Ph8E9GQkLj5hwaHh5dAuej0VwXgjxJrGs6iq31xC6yWrSz24Kb7aUOAEwoyo5YYc7srkd6z5vFuuZujbXqzXxW/D6asClrMRK5ifpuOSqfeyG38Uvq8uZxC56bRXnN14wv72Z00nUoGjZdNRZkjWQI80rJL9TgDjtW94n1O50ew0u2XUXjuLibymu2WJA2EZiWZhsQnH9056AdwnRkmk+oXOooryYeN9YlsBNNrMNlOuipeRRfZ1P2m48yVdnIz82xRtGDzxitOPxVqj69NCdTjE8erxWi6V5K5eFkjLnON3y7nORwNvNU8NNBc9Gorze18QaydE0O71HXFtIdTkkM14beNVtgqttQZGMsR1bPTAxmqdx441r7HZT/aVguUt7aWWB40RZxJKVLBWy7AqM4XbtzyT0oWGm3owuepsyopZiAoGSSeBS9a8y1fWrq/0DxHHc6vH55tdQjbSfIG6FEDBG3D5hkAHLcNu4xXS+GbvURrGoaXe3n2uO3tbWeKQxKhXzA4K/LwQNgx356mplRcY3uFzqKKKKxGFFFFABRRRQAUUUUAFFFFABRRRQBDd3UFjaTXd1KsUEKF5JG6KoGSaenlhRs2gNyMd6x/F+kya54Q1XTYYo5Z7i2dYlkxgvj5eT05xzXJ3vhnUp79nt9HETS/YzZXHmxj+zFjIMiYB74Y/JkNuwelawhGS1dhHouEQAYVR0A6VXlsbO4voLyWJHuIUdInJ6K2Nwx052r+Ved/EcPNr0Uflo0I02RZPOYLncw/1G4EeaNp5HIyKnXw3fTajDcW2kGMS3FncWt5JIqtZW6Im+ArncCcOCoBB3nPSrVFcqk5WuFz0UhSQxAyOQT2o+T7w28859a84n8Ha4+m38GQVt3itrGMSAmazSXzGU54BYFUw3B8sZ4NWNJ8H3H9o6TJfWLGxglu5jBO8ZEJfyvLwifKBlWYKMgHvS9lC1+YDv1VBwoUc5wKAI8MRtw3UjvXm8Hg/V7G1triysYBqJg1FLlmmwJTIxMKuQckdOn3faorLwXqLXCpLpuzTnv7SdreVolGxIpFkJSM7epTjknvmn7KH839feFz00BFQKAoXoB2oIQsMhSw5GeorzdPCd/brFHdaIup2UQvIraz+0qot985aJwSflGzAyvzLjgVYXw1q0Pi+TUZopZFW9FzHdRyRjEIjA8osR5h5yNuNpznINL2Uf5v6+8LncXVhZ3UtvNcwqz20omiYnG1wpXP5Mw59aX7VaLfCy8yMXLxmYR9yoIBb8yK5/xDaXXiLwvYv/Z1wszSRXD2oeMvHwThlf5JME8qSPXOQK5//hE9Tzbzvo9sLltCuLHdCyjyJTnZ95sgFePlJAyR05ojTTXvSA9HJTGDtwTjHqahhu7Waa4topUaS2IWVB/ASAwB/Ag151rng2/fR7TT9P0mIqumOoeNo9yXbAZZi54zjO9QWyOoqS+8I6mz64bawRXvLi0uGlRo83EaLGJYjk9Sys2G+U55PJpqlC3xf1cLno7CMrltpU469/Sj5Tzxx+leQ6xpt5p0Gl2ckCRKz3k3lag8fkxo+wBVAHlq+csqgnbk4zV+LTlu7jw/pulwz29rqFhEup204IkjhiberOQMbnJdCe+/Pan9XVr8wXPTwqKMBVA+lHyMwb5S2OD3xXM61a3nifwraKNPeF5Lu3lltpXAIjWZS2T7qCce+KwIPBd9p12l1p2nW8U0eo3jRnzAoFs8TiNODkJvK/KOnXiojTi1rKzA9Gyp5yDg4+hpPkB3/KCeN3rXltl4M1ZgY5NN8m0lk09poGeJQTFKxmO1DgjaRySWYdTU174Q1X7NJp8elxyaf9qvXt0Roj5Kvt8rAc7VX7+cAsvGAMmq9jC9uf8Ar7wuellY1IyEBAOOO3elIRtwIU5HzD1HvXjN7bTprMdvqtmbkQxafFPEWDXMkiKu7ydw+ZCThtp+bDc9a3F8I60NS1GR4ZGnk+2kXSyRKsyyqwjQn75xlRtbAXbkGm6CS1kFz0rCHGQpwcD2NLlc44yP0rzGTwDcx29w1rpsKXAsrDyGWQArcxuTKwOeG27ct36ZNSN4R1o6tqUzQyNNI16y3SyRKsqSo4jjJ++cZUbThV2gg0vZQ/m/r7wuehXl1aWVq1zdyRxwxsMu/QEnA/HJFTnaBk4A65NeZ3vgO7/sy5trbS4CkmnWO6HeuJLmKUmQ8nG4pxuPXPWuj8UaXdalaaTBb6WktojkzwYiLRDYQoCufLIBOD1x2FJ04aJSA6kBEyAFGTk47mql5qNpp01pHMdr3k4t49q5y5VmGfQYU15yvgfV5dGl+1WayalFpNnBaSNOC0c8byFirZ4IBX5u9X4/C+ojxHbXD6UjTQ61Jey6mZlzLAyuEXGd3yhlXBGBt461XsoK/vBc9CARAFG1R2A4pNsYBTauMcrjtXm/iHR7vVvFniCG20o3E8lraJa3jSBBaSZkPmDJzxwcrk8Y70//AIRPWf7fvriSKR5HmupUu1eJVkjeNlSMn/WHGVG04UbQQaXsY2u5Bc7jUNUsdNezjuMmS4mENvHHGXYtgngAcAAEk+gq8Am5iNu7oT3rh7fwTFDb+EF/sq28ywkEl8W2sQ3kMpJJ+8d+316A9qxJPB2uyaXc20WniG8+wXUFzdi4X/iYyyMCjZznjBOWwRnA4oVKm/tAepbYthTam0nkYGCaU7OHO35ehPavPfEngVryS/XTtNhES6UkNiA4Xy7gSu5K8/K3zA7vc89aL3wpewXl5b2mkxPo8uopOsEflnav2cKWVHOz/WddwPqBnmkqcH9r+vvA9CwgYnChj37ml+XdjjPWvLF8C6vLocyXVmkmoRaNDb2btOpaO4SSVvlbPBAKfNx6VtL4d1D+0tSb+zgNQnluXg1r7QAUR0IjXAO47cgbSNo27gc0OlBfa/r7wudyNpBxg4Pb1ppEQjKkJs7jjFcd4G0C+0eaeS6tpbUNbRRMhaLbJIu7LgR9ev3mO49xxWOPBN/b+G9GjWxV5orh5dRt08qRp8hwhPmHY+3I4J47cij2UeZrmA79b+0k1eTTOTdQwpcEFeArMygg+uVNSzXlpbPbrLNGjXEnlQgn77YLYH4KT+FcAPDWu2tlPFFZ/aWl0aG1HnXCthkmdmjY5XJ2SYH8Py4Jx1itPB99FPaTzaOssFvrX2qOBzCGSFrfYSFXCLiTDFR6Z5NV7KH8wXPSsIWHCll6eoqnDqNnNq11p0fNzDFHNL8vBVywXnv9xq87h8GeIBHqKAyR30ltdxm9EkapctI2UyV/eHty2NnbNdF4U0OWw17U70aKulWVza28UduJEbDIZN3CkgfeH169c1MqcIpvmuBvyJpeu6VPbN5VxYuzQSKDhcq21l49CCPwq/tQLjaoGc4xXm1v4Ru7G2toX8PRXVnb395JNZo8QW5EjMYZACQDtB24bBGeOgrWk8M6hceFPDOl3qLdSWl1C94rSZHlqGyCT94DIHvinKnFbS0A7LEY5wo3Hr61FHeWs13PbRyo08G0yoOqbhlc15rqHg/WZNLbTY9MSS2T7eLTa8ZMJeTMP3zhV291G4dBirM/hPVFk1eaLTY2urlLCdJxIgMjQsjSxk5yGYoTk8HuaPYw/m/q4XPRyE5zt96p6hpVjqhh+0xkyQP5kUkcjRvGxBGQykEZBI61xq+GL/VdaS61PTAtjLq0l3JbzSq2IjarGu4AkH5x05pmleHNa03xFNqMlrLcTxS3U3nLLCi3SvuMcZP3z1UYbAXbwcUKmlqpagdzYada6bb+RaRCNNzOcksWZjlmJOSSTySatYHPHWmRM7wo0kfluygsmc7T3Ge9PrBtt6jEKqSCVBI5GR0oKq2NwBwcjI6GlopAIUUggqCDwQR1o2KcfKPl6cdKWigBpRGBBVTk5OR3pSqlgxUbh0OORS0UAIFUEkAAnqQOtAVQxYAbj1OOtLRQA0IqjAUAewpWVWGGAI68ilooArLp9ouoSX4gX7VJGsTSdyqlio9OCzfnRbafaWctxJbwqj3EpmlYZJZyACefZR+VWaKd2AhRWXaVBX0I4oKqSCVBI4zilopAJtXJO0ZPBOOtLgZ6UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFb7bF6N+VH22L0b8qoUUxF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQBf+2xejflR9ti9G/KqFFAF/7bF6N+VH22L0b8qoUUAX/tsXo35UfbYvRvyqhRQAUUUUAFFFFAFbUL+30yye8umZYIyu9gucZIGfpk02+1Sy0yS1S8uFha6mEEAIPzueg4pdTsU1PSruwk+5cwvEfbIxmuEuDN4p0+AgZutP0dpyP7t3vAH45gf866KNKM1eT06/p+IHeSX0EWoW9izH7ROjyIoGflTGST2+8Pzqzg5xiuM/tm1ubnVPEEiySWkGnwW8axHDFpcSMAcjB+eMZzxSafbXVp4pg0ueGa1tbyymaSA6lJcElWQBgTgofmIyDz+FN4fTXSy/4P5AddaXcF/aR3VrIJIJRlHAIyM47/SpsEV51ZxNpXw3s7vT2nSe5eK3kkNy2EVptrEbiVQ9s44zmtKC0v8AS9UQRW5sLeW2n82GXUzcGQquVdFbkMD1I7HntTlh0m7PZv8AD5/15AdnRWB4RtSNAsb+a5ubi7u7SJ5XmmZh90HAUnC9ew575rfrnnHlk49gCiiipAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqlnpdhp8tzLZ2kUMl0/mTsgx5jc8n8z+dOvrsWNus7IWTzERsHG0MwXd+GaoR+IYJJJkETKY7z7L8zABgASZP90BX/75NNNpWAs22h6XZ6fNp9vYQR2cxJkhC/K+eDkH6CkstC0zTphNa2iJMoKiQszMAccZJJxwOKZ/b1gXt1R5XaeYQqBC4KsVLAkEZAIHWnHXdMWN5DdAKmOdjfNltoK8fMCeMjPNP2ktddwCPQNJha6aOwhUXYKzrj5ZATk5XpyfaksfD+lac8j2tmqPInlszMzts/ugsSQPYcVPc6laWixNPKU8wFlGxicDGSQBkAZGSemaY2s6ckssbXSqYgxckHb8v3sHGCR3A6U/aT2uwLVvbxWttFbwRrHDEoREXoqgYAFSVUn1SytpvJlnxKZBEEVSzFiu4DAHpz9KjGsWUjbIZ0dt6pzlQcttypxhueOOM96hu4F+is6PXdMmjd0ugURQ2SjDcCdoK5Hzc8cZ54qaHUrSe2muEm/dQEiVmRl2EDJBBAPFAFuis5td01Iw7XDDLMuzyn3gqATlcZGAQenQ5plzr1lDNBDHIJpJpoohsztG/GPmxjODnGc4oA1KKpS6tYwXn2SS4CzgoCu0kLu4XJxgZPAz1qI69pgi8z7TlNzLkRuc7fvEccgdz0HrQBpUVl3WvWVvcQwLIJHkmSI7Qdq7hu+9jGcc4znFWJNVsY4xI1wu1kSRcAksrnC4AGTk9AOaALlFUf7Z0/MQ+0j94AR8rcZbaN3Hy/MCPmxyMVGmt2s2qw2EDeY0glJYAgDYQDgkYbk44PGKANKiqbatYLnddxjEby4J52o21j+B4qC71/T7SG5keR2NurMypExJCkBtvGGwSAcdO9AGnRVJtWsUmaJrja6gk7kYAYXdgnGA23nb1x2pf7VsvsCXxmItnIVHKMNxJwMDGTkkY45zQBcorOi17TJ1do7oEIhckow4BwcZHJBIBA5BOKH17TI0V3uduQ5wY33KFIDbhjK4yM5xjNAGjRVM6rYrcNA1wquoJJIIXhdx+bGMhecZ6VEuuac27E7bl2gp5L7juztwuMnIBIx2BoA0aKzZNf0uLZuu1+dVYFUZhhiQucDjJBAB6njrTzrenCONzcja+SPkb5QDtJbj5QDx82OaAL9FZk/iDToI7lvOZzbpI7qkbEny/vheMEg9QOlWZdQt4bWK4kZ1SXGweWxYkjONoGegPbtQBaoqh/bem5YC6VtsayEqrEBWxt5Axk5GB1NSjUbQ2TXnnAQISGZgQVIOCCCMg54xjNAFqisz+3bNriNEf5MyCV3BTyiihjkMB2P4UyXxFYo9sEZ3WaUxHEb71OwuPkxuOQOOPegDWoqiusae7wKtyG88KUZVJU7jhcnGASQQAcUkGqwPpFtqM/7lJ0RgvLHLdFAAyT9BQBforKTxBYtem3MmFKRskm1sHezKAePl5XHOOTinza3aISsTebIJUjK4K5DSCMsCRhgCecZoA0qKzI9ctPIWS4fySzyLtwWwEkKbiQPlGR1OBzUo1nTjNLEbpQ0W/eWBCjZ98biMEjuAaAL1FZ41zT234mfcrKpTyX35IJAC4yeATwOnNPj1axlnjhjuA7SBWUqpKnIJHzYwCQDgE54oAu0VlXmv21nNcQskhe3eBZDsYLiVgoIOOcegqU63pyxJKbjCNu58tvl2nDbhj5cE4O7GKANCiqUGq2s8V5Ll447OR45mkQqBtGSRnqKaNZsGMQE5HmgFcxsOCcKTkfKCeBnGe1AF+is4a7pjSMgu13AMeVYAgMFJBxggMQMjPJqS41W0tZHEs8aLFv8ANLMRs2qGPGOeGB/GgC7RVJdXsWkij84h5Nu0NGwxuOFByPlJxwDjPamy6rBb3VzDPlBCIsMAWLl92AFAyT8vagC/RVEazp5aFVuVbzgCpVSQATtGTj5ckEc45GKik1y0HELea/mxxlcFeHkCbgSPmAJ6j0oA06KrT6ha21zHbyy7ZXwQoUnAJwCSBgAngZxzVV9dsyv+jyCZ/Njj2gFch5Am4EjDAE9RkUAadFZ91rNjaWL3bSl41iklAjUsWVDhsD2JAofWLOLcZZNuGVVUKzOcoG+4Bkcc/TrigDQoqgutac9wkC3SM77cFQSvzLuX5sYGRyOeams9Qtb9Wa2l3hcHO0rkHoRkDIPYjg0AWaKyBr8X2qzt2gdZLi4lgbkYiKEjJ9idoH+8Kks9cs7tIMv5bzYKKQSMEnZlsYBYDIBOeaANOiqj6nZx3n2R5wJuARg4BIyAWxgEjnBOarjXLOZoFtn87zZljPBXAZWIbkcg7Tgjg+tAGnRWbba3aTW9lJK3kvdRxyBMFgu/hQWAwMngZxk1FY+I9Pu7Pz3lEJCNIyuDwA204OMNg4HGeSBQBr0VQXWdPd4kW4+eVmVU8tt2VxuyMZXG4ZzjqKibxBp4WIpJJJ5kqRALC+cvna2MZ2nB56HFAGpRVW31G0urhoIZg8i5ONpAYA4JUkYbB4OM4po1bT2xi7jOY1lxnnYzbQce7cfWgC5RWLceJLW3hlYqzvHb/aTtBKbd5X7+MZ4NadreW96jtbyFgjlGBUqVbrgggEcEH8aAJ6KKKACiiigAooooAKKKKAIL21S9sZ7VyQs0bISOoyMZH061lL4ajR4XW8mV4rJrXcoAJcg/vf8AeG5/++jWhqd49jZebGqGRpI4l3nCguwUE+wzWFdavqDvcRCSCMQ214JDGD87xhNrKc8ff6c4INAFy28OfZpRMLlBL58cxCQ7VO1GQjG4nkMec9fyptn4WhtFiRZxthMflERYbajhgGOTnO0DjA4zioW12/tnis5IYZLmQxBJI0ZlAdHblc5J/dkdRnParuk6pd6ldMskMMMUcEbuuSzF23dD0x8v157UATavpTaosQW4ELJuw/l5Zc4+ZWBBVhj1x6g1Un8Ni4ie3kvX+zZmeJBGNyPLnJLZ5A3NgYHXnOKJdflWBGjhhaV2ulCNJtGYmIHJ6ZwM/WqkviW8UeVHbxyXCJLJIDE642bflIJ+U/MMnJA4POaANO30iRNQF9cXYln87zSEi2L/AKvy8AZPbnrSQ6NLFbQWrXpa2t3jaFBEAQEcMAxzyeAM8fTNVm1jUGhnmit4Nouvs8aAFnAAyWIyNx/2V9zz0q5Jqyp4fbU08qUiASYRiFyQO5GQOc8jOKAKF/4dkfSoLa2nZnhgFv1CMV8xHJU8gN8nFWbLS7oaHc2M8zQvKz7JFIMig92I4LZzkjtimz6nfwTi03aeZwksrSbmCbUCHbjOQx3+pwBnviq0Ou6jezQfZobWOOeYQIJtxZSYBLk4Iz3XH457UAW7Hw+lndG489dzPI5SOLYo3IicDJ/uA9T1qO38ONbJBBHfH7NHLDOUMQ3M8aqv3s8A7AcY696rReINQu1hkghtESRraMiQsSGlXOeOwPbv7VLF4jla0uZJIYRJAkeQGOGZpniOPb5Mj60ATz6Pc3OqXsjXKpZ3Bg3RhMs3l88HPy5OB349Kf8A2JJFDbi1vTFLFHLDvaIOGSRgx4yMEEDB/Q1Rn167SB5JYofKk88RiNmV18qUJyfcHPGMe9Jca3dWP2ua4CzrDdXCokWUOxIi4B5Oen9aALKeGxEI4IrtlskmScRGMFtyoExuz0IGemc9+1JF4emjMMh1DdLbRxR27eQAFEe7G4Z+YkMQentioW17UlhbNrFvRxl9h+5sLZ8vduOCMZGeDnHarWra1LaWcEtosMryQPcYIZgUVQSR045HJPfoaAGzeHftF4t1NcpJKyosxeAENtYsNozhfvEc7uMd+ams9Fe0uraQ3ZeG1WVYYvLAIDkE7mzzjGBwPeqLeI7lEe7NvCbRZfKCKT5hPkebnPT2xj39qsaXeXk+sXK3UsDL9hgmRYGO0bmk7HvwOe+B0oASbwxDNfSXJuZBvuVm2bRgIMlo/ozEsajk8LiWS4eS9LNLFNEH8r58SMGyxJ5xtAA4GKgbxHqK2lrJ9lheWa0N6VQMVCcYTJPB55Y8DjinSeJbg6hd28EduxtfMMsTbt6qse8HOcHJwMDpn2oAsyeGklu7id50Pns0j/ucsJGTYSCTwO+MZ98VozWCy2dtbmQgQPE4bHXyyCPzxWbPqzy6jZQIyonmW7uUb7wkSUlT7fIKot4i1C4XZCLeIubaWOUo20xyTBMYJyeO/HU8d6ANKfw+JYVRLySNl88hgvXzJVkIPI4+Xb15BPSm23hyO3Wf/SOZo50ISMKq+btzgZ7bP1qtdeI7q3t5LkQ2zo32hYY9xDq0RIy/scdhxkdc1cOp3kGrxWdytuI2KoZEVsM5BOAcnaeBgN1657UAVbjwzKS80V0kkqrJ5Ilj5y0Rj2s2T8ozkAD86gs/DM7QeXcOQIZI5IWuCJXLBGQhypGV2txyCOauXWu3FvNO4igMEVw1tsJPmFhHv3emPbHTnParOm6ldXDSx3aQB1torlTESFw4b5Tn0Knn36CgCOLw/HFFsE5GfIJ2xhRmKQydB6kn/wCvUNx4WhmuJJvPUmUuJBJFvBVpGfAGcAjcw5yDnpUdrrt9cvBalLWG8mbnzFYLGNhbnn5s4OCpwQCeMYpNM1yW6vEdlAS7mhQIXyI8wM52+uSv45oAuzaBHNbeQbhwubk5Cj/ltuz+W6pb3TJb22tY3uV3QnL5iykvykcru98jk8+tZD65eSxzXIMQtxYNMI0JDbhKyZDA+gFS3fiO6t4ZbgQ27xt9oWGPcQ6mIkfP7HHYcZHXNAE0fhiGPSnsftDMCINrFBwYgoUkDqDtGRxT7rRZR4cudPt2jMsz7mZFEfVgTt64OBwTnnGagu9bv7QzxNFbNJbSsJZFViuwRq4OwHdj5sEjOMZxzU+ua1LpsCyWyRSsLd7llIJyigdDwAOep9uDQBXg8PTXFkYLyUxjfMB0aRkkQKS7DgtnnPPAAqydEuWvEv21BTepIGD+R8hARkAK7s/xsc56+1VZ/Ed1bRzXT28DWyyTxIikh8xozgk9MHaR046+1WtPvLkanqovp4GW3ghf9yTsUEOScHOOn44BoAhTwsqyW5N4XSJ4pPnjy25H3nac4UMeox+NXE0ho9LsbWO5xLZMrQymPIO0FRuXPPykg8j14rLt9cu7y+tIJlER8+FyYwVDpJHKQCCc4ygOTjPHAptt4kuJp7GzhWDz5obeTy5SzMyyfebdnjaAeDyaANV9GMyzme7aSSdYQ7eWBzG5fge+cfh3qmvhfE5na93SjG1zF8xxKsgLnPzHKAduPSrGmardXd3Ak8cCxXKztH5edy+XIF+bPByDnjGPeqjeJLiOOKd4bcxXG4xqGO6MLKqHf+DZ7YIxz1oAfN4Vjlct9oU7hIjCSHd8jSM+AM4yCzDJyCD0qa+0J5dPljt5QZd9zIgkHykyhgQfpu/Snf2tczaz9ggSAKs8kbu5JO1EjY4A7nzCPbFM1HXJbLU1hjiSSBZIY5cKdwMjYHOQB2OOc+1AFO18NzvDtuH2mKZZYGnIldjsKN5hUjIwRjnIx+FXoNA+z3dpNFcqiwKoIjhCM4APykggbSTnBBx2IqtJq2pS6Xb3UcljGbh7Z0AyxRJJFUhhnng43DHORjvTZNfuYlvTFHDts1mmkEzsTIqyuu1PQ/KfUDIGKANO40j7Reyz/aCqyNA5TZnDRPuHOeh6VRv/AAwL37QpvCI5zKWV4twUuc5XkAMOmTnj0pH12+TfJ5FsyM9zHChcq26InG5icc49setSTapdt4Y1K7jaJLy2SQcxMuxlXPzK3IOCO5HTk0AXP7KzaalbGc+XetIw+TmPeuD359e1V7rw/Fc6gt35i8pEkivFv3BCSMc4B5IOQfwpo1e6F6I3W3MK3SWbgZEhdkDbxzjbz09MnNJLrNympXEQS2Nvb3cNsy7j5reYF+Ydhgt07gHpQBBJ4bneSCA3ubOG2lhixGA0e50ZSefmxsx26e9TTeHWuhcNcXpaW4MhZki2gb40TgZPQID171WTX9QbT47lo7JWNib9lYsAUGPkBz97rlugyOOaP7cv0mlj2JI0160MAETExIIg+GAPJ/Lv6YoAv3GgxT6u1/5ifO0byI8e4kp02nOB0HY9OMU+60hptRN9FdeVKDG6Bo9wDIHXnkZBWQjH0Oar2GsXmoOGEdpbonlLIkrksWdQ3ykcY5GB3welFrql0vhS0vp3hku5kjAIQ7WZyABgck89OPwoAks9FlsJS9vfEGXBud0QJkO9nJXn5c72HfjHfmq0Xhfy5BK17ulUIA5i5bbKsgLnPzElcHp7AVAviS+e3nlW3th9khllmD7sv5cjIQuCduQueScHjmpxql3c6tp+HhjtXvZ4PLVj5h8tJB83Y5K5x2460AX9Q0pr2+guVuBCYtvKx/PgNkgMCMA9CDke1VF8OMI4EN8SLRUS1/dAbFWRH+bn5j+7UduPeifW7iCG9u9lsbe3eWMRFysrMg6+mDjOMcLzntTTq2onURpqiyNz5m0zAMY8eWXHGc54x16EH2oAcfDheOSF70mDyLiCJREAyCYgkk55IxxwKmbRphefbor0JeZzuMOU/wBWqEbc/wCyD1496zz4pnWBLuS2jW2a2EoVcsXbyjIVDDhTxgBgMjnPapYdb1GaWC3+zwJLLMiCRwwXaY3c/LnORsxnIBzmgC1b+Hora2EEdxJtEsEoJAz+6Cgfntz+NP0zSJdNdtt2pjYjMSQ7UwAeQMkKSSCcYHA4FUbjxJNEJY0tlaaCRYZgMkI7y7E47grlvxX1q2usSx6Ob+6hWMRT+XOScBU37S+MnGAckHpg0AMvPDcV3JqcgupYnvVQKygfuGXB3L7kqpP0pR4bt01FbmJkWMNExjaPcQY1CrtOcAYVexPHBFU4fE1xcRbRbRxXI2K0bbnId2OxQBjJKAtjjtyKiXxJe+VJetHEYY7NnMAByZBM0ec54HGe+BQBsyaU73F1tuitrdndPD5YLE7Ahw2eAQB2zx1qvHoUvnwTzX3mTW/lLGRCFGxN3BGeSd55+nFR2ur6hcX8Nm0EMbl5PMd1IyqhDwuSQTvxye2e9O1HWbqynvykMTw2qQ4ByXZ5DgewUdT6+1AEX/CL5S1je93x26QooaLJHlPuBXnCk8A8Hp2pw8MsYIYZL9mS2VhbERAFGLq+W5+bBUDHHGfrUUmv30UYaS3hjEbuJpHU4CrtwSgJZB8xyTnGPQiifxHcQRfaDFbtDIZhGgY708t9pL9ueemMcDmgC9DorJeSXkl2zXMqyh2jTYMuIwCoycYEa+tVrbw0bZvNF4vnBoGDCHAJiLHJG4kk7jk5qWTV7ltZNhbxwbVndGdsk7VjRzgD+I78VXOvXiadb3RjtXN55XkpEWZow5P3hn5seoxk8cUAWtL0CLS7rzY5FZFVkjHl4YBmyctk56AcAe+aiTwxCl+t0LmT5boz7NoxszuEX+6HAb9KiTXrwyW3m28MUTMElc5bBMhQdCSmcAjcCMnBIxmuhBDAFSCD0IPWgDFbw6pg8lbtgjW5gfMeSR5hcEc8ckitOC1EFzeThyxuZRIQR93CKuP/AB39asUUAFFFFABRRRQAUUUUAFFFFADZI45o2jlRXjYYZWGQR7ioRY2axxxi0gCRghF8sYXPXHHGe/rViigCGWztp0ZJraGRWADK6Ag46fl2p8cMUX+riROAvyqBwOg+gp9FAFf7BZebJL9jt/MlBEj+UuXB4OTjmmnTLAwpCbG2MUbbkQxLhT6gY61aooAhltLaeJopreGSNm3MjoCC3rj196SKztoPNEUEaibHmAKMNhQoyPTAAqeigCqdM08wLAbG2MKtuWPyl2g+uMdan8mINuESbt2/O0Z3Yxn644z6U+igCJbaBAAsESgEEAIBgjp+XamPYWUro8lnbu0eShaJSVycnHHHPNWKKAIjbQMoUwREDOAUHc5P5nmgW1uJWlEEXmOcs2wZJxjJP04+lS0UAVP7L0/yPI+wWvk7t/l+Su3d64x1qWeztboILi2hlCHKCSMNt+melTUUARiCFcYhjGG3DCjrjGfrjimW9na2gYW1tDDu+95cYXP1xU9FAEEtjaTJEktrBIkX+rVowQn09Kqpolol4tz+8YrK0yozZVXbO4jjP8R4zjmtGigCtHp1jCmyKzt413BsLEoGQcg9Oo7Ui6bYIkiLZWwST76iJcNznnjnmrVFAFf7DaebLL9lg8yUYkbyxlx6E45pzWls90t01vEbhRgSlBuA+vXuamooAgaztWuDcNbQmcrsMhjG4r0xnripVijQ5WNQdoXIGOB0H0GTTqKAKn9l6f5DQfYLXyWbc0fkrtJ9cY60+Sxs5lZZbSB1YKpDRg5C/dH4dvSrFFAEH2K0wg+ywYRSijyx8qnqBxwD6UfYbTzZZfssHmSjEj+WMuPc96nooAgnsrS5/wBfawS/Nu/eRhucYzz3xxSz2drdFDcW0M2zO3zEDbc+mamooAj8iHAHkx4BLfdHUjBP1IJptvZ2tohS2toYVbqI4woP1xU1FAFaPTrKFQsVnbooYMAsSjkdD06jtVaTQrGS4MpV1UujmJSAhZMbTjHGNo4BA4rSooAYsMSFSsaKVztIUDGTk4+pqIWFmHmcWkG6cYlPljMg/wBrjn8asUUAQw2dtbBRBbwxBc42IFxnGen0H5Uj2drJcLcPbQtOuAJGjBYY5HPWp6KAKw0+yCSoLO3CzHMi+UuHPXnjmnSWNpKUMlrA5jYuhaMHaxOSR6HPNT0UARNbQOpVoImU7jgoCPm+9+eTn1pI7S2htzbxW8SQEEGNUAU568dOamooArpY2kcyTJawLKi7FcRgMq9MA9hTE02zS9kvfs8ZuXYMZWUFgdoXg9uAKt0UAQSWNpMkSS2sDrF/qw0YIT6elJLY2c4kE1pBJ5pBk3xg7yOhPrirFFAEH2K186Ob7ND5sS7Y38sZQegPYUGztTbNbG2h8hiSYvLG0nOenTrzU9FAEKWdrHGY0toVQqVKrGACp6jHpSCytVuTcrbQic9ZRGNx4x169KnooAh+x2v2hrj7ND5zrtaTyxuI9CfSkgs7W2VVgtoYlUllEcYXBPBIxU9FAFdbGzWZZltIBKi7FcRjKr0wD6UsNlaWyhYLWCIBt4CRhcNjGeO+OKnooAia2t3WVXgiYTf60FAd/GPm9eAOtILW3W1NqtvELcqVMQQbCD1GOmKmooAgksrWZZFltoXWQguGjB3EdM+uMcUCytAFAtYAFVkXEY4U9QOOh7ip6KAIYLO2tlVYLaGJVztCIFxnrjHrgU9oYnEgaJGEg2vlQdw9D60+igCr/Zlh5ccf2G22RHdGvlLhT6jjin/YbQSzS/ZYPMmG2VvLGXHox7/jU9FAEEFna2wUQW0MQUkjZGFxnr0+gpo0+yCTILO3CzHMq+UuH+vHNWaKAK39n2WYT9jt8wjER8pf3f8Au8cVPHGkUaxxoqIowqqMAD0Ap1FABRRRQAUUUUAFFFFAD/Jl/wCebflR5Mv/ADzb8q1aKQzK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv8Azzb8q1aKAMryZf8Anm35UeTL/wA82/KtWigDK8mX/nm35UeTL/zzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/wDPNvyrVooAyvJl/wCebflR5Mv/ADzb8q1aKAMryZf+ebflR5Mv/PNvyrVooAyvJl/55t+VHky/882/KtWigDK8mX/nm35UeTL/AM82/KtWigDK8mX/AJ5t+VHky/8APNvyrVooAyvJl/55t+VHky/882/KtWigAooooAKKKKACimySJFG8kjBUQFmYnAAHU1nHxFoy263B1O18l2KK/mDBYDJGfoaai3siZTjHd2NOiqTavpyXENu19biacBok8wZcHoR65pqa3pcn2jZqFs32f/XYkHyc45/HinyS7C9pDuX6Kh+12/mTR+cm+FQ8i7uUBzgn06H8qiOp2CoXN5AFEQnJ8wf6s9G+nvS5X2HzR7luiq/2+0+2/Y/tMX2nZ5nlbhu2+uPSqieItGkSVk1S0ZYl3SESj5RkDJ/EimoSeyE6kFuzTorNfxBpEaQu+pWqpMCY2MgAYA4OD9a0gQRkHINJxa3Q1KMtmFFFFIoKKKKACiiigAoophlRZViJ+dgSB7DGf5igL2H0UVVudTsLK4gt7q+toJ5zthjllVWkPooJyfwoAtUVTGr6adSOmjULU3wGTbCZfMxjP3c56Urapp6QJO1/aiF0aRZDMu1kX7zA55A7ntQBboqgNc0g6e2oDVLI2SHa1wLhPLB9C2cZpG13SEW2Z9VsVF1zbk3CDzecfLz83PpQBoUVAb21DuhuYQ6Osbr5gyrNyFPoTkYHvUb6pp8WzzL61TfN5C7plG6TpsHPLe3WgC3RWc2v6Ml29o+r2C3Med8JuUDrgZOVzkYAJNMPiTQhAk51rTvJkYokn2pNrMMZAOeSMjj3FAGpRSAggEHIPQio7i5gtLd7i5mjhhjG55JGCqo9STwKAJaKqW2qafeRwSWt9bTJOSIWjlVhIRyQuDzjBzikOraaupDTm1C1F8RkWxmXzCOv3c5oAuUVTttW028eZLXULWd4P9asUysY/wDeweOh6+lOh1KxuYoJYL22ljuGKwukqsJCM5CkHk8Hp6GgC1RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRVL+2dL+0z239pWfnwKXmi89d0ajklhnIA96amt6TJYSX6apZNZxnD3C3CGNT7tnA6igC/RWdJr+jRWcN5Jq9glrMSIpmuUCOf9ls4P4VZa/s1WRmu4AI4xM5Mg+VDnDH0Xg89ODQBYoqkdX0xdQSwOo2gvZF3JbmZfMYdchc5NQ/8JHoYhll/trTvKiYJI/2pMIxzgE54JwfyNAGnRVdb+zeCGdbuBoZ2CwyCQFZCegU5wSfalF7as6ILmEu7MqKJBlmX7wHqR39KAJ6Kzn1/Ro7RLt9WsFtncoszXKBGYdQGzgn2oTX9GeZIU1ewaWRPMRBcoSy4zuAzyMAnPtQBo0VTtNW02/t5Liz1C1uIYv8AWSQzK6pxnkg4HFPTUbGQ2wS9t2N0pa3CyqfOAGSU5+YY54oAs0VSOsaYLWa6Oo2gt4H8uaXz12RvnG1jnAOSOD61LLqFlCkzy3cEaQKGmZpABGD0Lc8A+9AFiiqV1rGmWNtFc3eo2lvbzY8qWWdVV8jI2knB49KH1fTIryOzk1G0S6kUMkLTqHYHoQuckcGgC7RVOx1XTtUV20/ULW7CHDm3mWTaffBOKuUAFFU5NW06LUU0+S/tUvZBlLZplEjD2XOTT31Cyjs5Lx7y3W1jJDzGVQi4ODls4GDx9aALNFZza/oy2UV62r2C2kpKxzm5QI5HUBs4Jqy1/ZpPbwPdwLNcAmCMyANKAMkqM5PHpQBYoqOKeG4VmhlSRVYoSjAgMDgjjuDxipKACiiigAooooAKKKKACiiigCrqVu93pd3bRlQ80LxqW6ZKkDNc9L4XvfsmlQ2+oyRyWqvvmdg5UtHtAUEYK57eldDqRuhpd2bIA3fkv5Of7+Dj9a4+GS5/sy7/ALIl1qS5+yqZhcKxCybhu27+fMxu4Xjp7V0Uea2jOTEcnN7yvp+WuhcsfDuoWWpadPGsawwWkVvIiXTLgqzEnG35xz0OKrf8IrqjWd/aqbeGGW3aKNBMzruLhgVyuUXr8uTyfaoZGuXtdRXSZtVawMMQ3zGTzBKZAG2Fvm+717USTauU1K1nlvY2tYIoTMqORJiVvnG3n5k27ivI59K6Pf3uv6/4c5X7O1rPr6devyOi0rRJNO1TUZnnkuIbmOJVM0hd/l3ZB46cjFc9H4M1RXjDz25j+W1f525tVZWHb73BGPfrUtjqN1aLpt1cpfrYo1zE5xJKGztMZwRu28MBuGfeordPEoeweA3DTrp6iSKZyFLOzjcc8bl+U+uBipSqRbd1/wANoVJ0pJLlfy87P+vQ0j4d1M67/a4u4fN+2eYIdvHk7dmN2M52846Zp9/4eu7jT9chiMIkvblJoTuK4CiPgnHB+Q+tYgtdcnttOtIpL6SZUu1Mr3DxYYSAI7HvxyAeorWawvItcuGa5vpNmneblZn8tp/mBwucehx2pPmTT5lp+jHHlkmuV6+fdakd/wCGdU1CKMi6WCRbKW3cNL5pcs4O0sVzggdQARxXV20fk2sMewJsQLtByBgdM964eaS68mA6nLqyf8S6E2n2XzMtNtO/dt/jzjhuKS7l1+2a4vZDds6WcUU0MQYrueNgXQD+JXC5x2JpSpymkm0OFWFNuSi9f6+X6nfUVV00yHS7Qy7vM8lN+7rnaM596tVyNWdj0E7q4UUUUhhRRRQAVVl/5Cdt/wBc5P5rVqqsv/ITtv8ArnJ/NaqO5E9vmvzLVcN4y8M6rq2t295pltbF/KSJp5ZhtAWTfiSJkYOvcbSrZ713NFSWedx+D9WW6hsDZ2At4tYOpnVxL+/dfNMm3btzvOdhO7G38qoS+B/ENzoK6PJDZRpp+mXllazLOW+0tMAFJXb8gAHPXk+lep0UAeaJ4I1mxu2vTHZ6pNBqS3ixzFYUuU8gxAFVXajpnIOCD14PSUeFdbj1nStRTTbO2WMT+dbWFwI0j8yZXGS6Nv4B3YC5J4xXo1FAHnXjDwTq2s+IJrrTngS2lSK6YtIVb7XAJBFxj7pLJk/7FUrz4ea7q2nWtvNeWlpJDbTzlgnm5vZpTIzDpjbhQG68nivUqKAOI1DwvqGpTareSW1mlzfeHxZcHpcnzN3OPu/OOaxLrwL4g1HStOt1ljs5rW2u4Wad47ncZEjCj/VgbSVPbI9TXqVFAGDFpOpppOm21nqJ0wW9skTwiNZ+QoH3m64xjPeqeveHtU1DQreFruHUrq1vorwR3EYijnVDny22ggeoODyBmuqooA80sfDPifT7uz1b7HZTzpqM10bMTJFtR4PLyXSMBnzyfl6Ad8mpn8Iau9zLZGx0/wAmbV11M6qZf38Y80SbNu3JcY8sHdjb+Vei0UAeS2fgLxNAupJG0EDXVrcWQkacPtSa43lkAQFcIX4JJJxjFaFv4P8AEGhzWoso7LULey1Q30CBvsw2vA8boBhguHbcPXca9KooAbGXaJGkQI5UFlBzg9xnvTqKKACiiigAooooAKKKKACiiigAooooA84vfh3d30viK7muE824lu5LCBQoXdLbiIM743evy5x0PNQQ+A9Zs5HuGWz1KaK+t7xElKxJcokLR+WyquFZSxYNg5IXOMV6dRQB5n/wiOtxX9vqq6RpkrPqFxdSaYbjEUKyQLEBuKEE5Xc2F6k4qIeBfEGnaTcadZrY3K6npf8AZ9w7TNGtoTJK2UG0lkCzFQOD8o9ePUaKAOCg8LanZatPAul6VeWs+pJfDUblz5sSrt+XYBksu3CkNgDGe4OPY+DPEFnoVpaPYQXElnqUV2IprxTHKimTIXEeV++Ou6vVaKAOEXwhqUfg+/t40s4tTl1Eana28THybeRXR1jDYHB2cnA5duKxdO8CeJdOS8EclsXNpM1rIZiSl1ciMTN06KVcj13dq9VooA81T4fanZeGde0CKSzura7SGSzYxiIRyABHBXnGQinOeSWq1feFNVbxdrN5bQJ9ivrQQxlbhUVSIWT5k2EnkjGGH416BRQB594d8F6rpFjrMFzLBcTXulw20My7YwpSNl8tlVQDhmJ38kg4PTmhY/DrVdEm0i+sriK5uNOikW3glO1bYNBICitzuBmcHPHygDtXqFFAHllv8NtVstPn0p7i0v7G8Fk9wGj8rMkMymQkc7i6Zy3GSop8XgTxE0N3pst1a/Y5bu2UXD/vWe1twxQOhxuYkRqRnBAJr1CigDy9PBXiLT7W0WNLLUHs7afT4fMZVKxNIGjkUOjqDtGxlIPAGCan0nwbrul63bTSiKeL+zbe0kkgmSJFZFkB/d7MlRvAXBXpzXpNFAHAeD/CeraddabcXtpY6c2n6V/Z5NrJ5j3L/J87/KowNmQOTljzXSW+l63HcRvN4iaaJWBeP7HGu8dxkdK26KAPOdR8H65Jrd0LSOxNvd6pHqBv5NjSRquz5CrITldh2FWHXnvkHhjX20dbCTT7J1sdXk1GFZLnKXitNI+xht+QgOCDyNyivRqKAPLNQ8Ea7eOb9LS3tZLjUHuXsbW5VBChtjDw7RlSzHlvl/XmlbwD4kb7Befa7BLvSLWzhs4lQsshhwz/ADnlAxLKeDkAV6lRQBzXhSx1XS5NStL2yhS1kvbi6huEuNxcSSlwCm0YwD6muloooAKKKKACiiigAooooAKKKKAK9/cmz066ulUM0MLyBT3wCcfpXNSeMJ4Z3ElpGIfsiyrJvP8ArWiMgQ+xAYZ9q6qaGO4gkglXdHIpR19QRgis+bw/pVxbS28tmrRSrGrqWPIQYXvxitacqa+NXMK0arf7t2KMes6nLrFrai3sxBPai63F23BRtBGMYzluKz4PFuoz6El/HZQPNLNHHFCPMUHdnuygE8dsiuoXT7VJ451hAkih8hGyeE44/QVVtvD+mWkSRQ2xWNJFkRTK7BWXO0jJ4xk1anStrEh0619Jd/08vUyJPFk89vNNYWiyorwhXIZtqum4syqCeDxgVND4hu7zYlrDaNK1ibrcZGKbg+0rnGfXtnPFXv8AhGtI2SqtmE82bz2KOynfz8wIOR1PTHWrFto+n2hUwWyoVhMAwT9wncR19ec9aHOjbRCjTr396X9fcYX/AAlF9b6PY6ld2UTRXiHy1gZmIcqDGpyP4jkexxXUQmQwxmYKJSo3hTkA98VX/suy+xW9n5A+z25RokyflKHK/lirdZzlF/CrG1OE4/E77f8ABCiiiszUKKKKACiiigAooooAKqy/8hO2/wCucn81q1VWX/kJ23/XOT+a1UdyJ7fNfmWqo6vezafpz3FvbNcyqVAiXqckD+tXqr3q3TQAWbxpLuXmQZG3PP6VJZjL4tg8l2ksLxHTPybB8xAycH/9Xb1preLFeaOKDTbtmeRowZF2DIBx78nA9s5pU/4SoRoHNkZGYAkD5VHOSe+en6e9I0XioE+XLabWGTv5YfKowOMdQ350AdHRUNmk0djbpcuHnWNRIw6Fscn86ztR8TaXppKSXHmSj/lnF8x/HsKANeiuFuvH8xJFpZIo7NK2T+Q/xrNk8Z605O2aJPZYh/XNOwHplFcnFrV+YIWMwJaJGOUHUqCasx6/cr9+ONx+VIDo6KyoNdtpCBKrRH1PIrTR0kQMjBlPQg5oAdRWBd6Jqb6lNd2mrvCkrZ8plLBeEHHPH3Scd89snL5NG1GWCJH1mbdHL5gdVAJG0jBx15ORnpgUAblFc3JoeuSthtecKGZlKxgEHOV/+uDn0roxnAycmgBJJEhieWR1SNFLMzHAAHUmuQg+JWjTeTO1pqkOlzyCKHVJrUrbOScD5s5AJ4DEAe9dBr2n/wBreHtS03zvJ+12skHmf3NykZ/DNea6/deIl+FV7oN94b+yfZdMMVxfvcRtbbI0+9GAdxLbRgFRgnk8UAd3rni6x0S/i04Wt9qGoyxmVbSwh82QRg43tyAozxkkZqzoHiOx8R208loJ4pbeQw3FtcxmOWF+u1lPTjn0Nc7/AG1Z+H7XSnGmS3/irUrCFPs0AHmyhFGS7HhEBY5Y+vetLwjoOoadNqur6zJCdV1eZJZ4rfJjhVFCoik8tgdT3NAG9qF/a6Xp9xf3syw2tuhklkboqism08YaVeeEofEsZnFhMP3atH+8di+wKFHVi3AHvXLeIfEOn33jQabrP2uDR9JZJjGLKaQXtxjK5KKR5acHHdsdlql4DEPif4WWOm2FwY9QsJ1uVaaBwgkW4aRASQAwO3B2k4z60Aegabr9rqDXkTxT2dxZhTcQXQCtGrAlWyCVKkA8g9j6VZ0vUYtW06G+gjlSGYFo/NXaWXPDY9COR3wRXm+vyXqajrVzqEUMo8i3W+t7SQlPLVj5NvvIGWkeQljjhBj+LNdrpOr3/wDbkuiarb2sdytqt1FJaMxjZCxUrhhkFSB9QR06UAb9FFFABRRRQAUUUUAc83iyCG5uIp7aQJFIU3xHf0cpyMDB4zjngikbxjZRqZJLa6ERAKOFDbskAjAPBGRx/UYroqKAOdPjLTxM0RgvN6FAyiMEguu4Dg+n+HWtbS9QXVLBbtI2jVmdQrEE4ViueOOcZqwVgg8ydhHHkZeQgDgeprj9Z+J/h3SmaOGV7+ZeNtsMqD/vHj8s00m9jOpVhTV5ux2lFeMX3xk1SUkWOnWtuvYysZG/oKz7T4meKLrU7WJ7yJY5JkVlSBehYA9Rmq9mzleY0b2V2e70V5o3jDWop3X7RGyqxADRD1q7bePbxCBc2kMg7lCVP9amx3HfVj6w2tpcwPpSRSRKrGWOQgb2/hHP/wBb8elQaf4v0q9IR5DbSH+Gbgfn0q5qelvqeySLUbi2woAMLkA8g54PPTFICg1/4m8tdmkQGQL82ZQATgnj5umcD/Oam02fX5tRP9oW0MFqI1PyEHLc5Gck91PQdCOepYPD955q7tYuTGoHy7m+c7iSW+b0x0OM9scU2DwxLDNbu+rXMkcEiOImzt+UDAxnHb9fXmgDoaKKKACqcOp21xqM9jExaaFQz4HA9s+tQ61fPZWqrEGEkzbBIELCMd2IHoP1rK025sLfX1ht2k2G1WMFonyz7ySTkd85yaANVtZjE80UdpdzGJ9jNHFkZwDjOfer0EvnwrJ5bx7h92QYYfUVkW+nX4a7e31KKNJZ3kTy4w/XsxPpjGBVzSL2W+sd86qJo5Gik2fdLKcZHtQAk+sQxXEkMcFzcNF/rDBHuCd8E+vsKf8A2pbNbwTQlphOcRLGOW9evTGOc1j6NFqNxaXBhuktiLmXOYg5d9xyWz0HQYHpVqyM2oWdnfRRwx3EDyI8fIRjkq2COnIzQBoDUYDZPdZYIhKspHzBgcbceueKtKSygkFSRnB6iucj84XCYEcpe6bZyQjS4JJ9SFAwPU5rasrp7jzo5UVZoJPLfacg8Agj8CKALVFFFABRRRQAUUUUAFFFFACMwVSzEAAZJPauZTxVe6gzvomhzX1qjFRcvMsKuR1255I96seNrh7bwbqkkZIbytmR/tEKf0NamlW0dnpNnbxKFSOFFAH0raKjGHO1fWxzzcpVPZxdrK/9X9Cno2vxatJPbPby2d9b4861m+8oPQg9x71pXVzDZWstzcSLHDEpZ3boAK5rVx9m8f6BcR8NcxTwSY/iULuGfxpfHhMmkWVmSfLu7+GGT3UnJH6VXs4ynG2iZPtpRpzvq4/jomvzFj8T6teRfadP8M3M1meUkknSNnHqFPNa2ja3ba3avLAskckT+XNBKNrxOOxFaKqFUKoAAGAB2rlrYfZfiZeRx8Jd6ekzgd2VtoP5UlyTTsrW1G/aU5RcpXTdun4GlqniK3067Sxit7i9v3XeLa2XcwX1YnhR9aXTNV1C9ujFd6JcWKbSwlklRgTxx8p6/wCFcvoOrXBvdXfT9ON9qU97IZWaQIkManagZj9DgCt6z8RXSapDpus6b9hnuM/Z5ElEkcpHVc4GD7GrnS5VZLX11+4zp1+dqTlZemnzdt/n5HQ0UUVynaFFFFABRRRQAUUUUAFVZf8AkJ23/XOT+a1aqrL/AMhO2/65yfzWqjuRPb5r8y1RRRUlhVTUdStdLtTPdSBV6ADksfQCmarqkGkWLXM5z2RB1dvQV5bqWp3Oq3bXFy+T0VR0QegoA09Z8V32plo4mNtbdNiH5mHuf6VgUUVQgorVsPDmqaiA8NqVjP8AHL8o/Xk1tw+ALlh++vokPoiFv54pALD/AMetv/1xj/8AQRTq2x4cKQxot0CURU5TrgY9arTaHdxDKhZB/snn9aAM2pre6mtX3QyFT3HY/UVG6PG5V1KsOoIxTaAOn0/Vo7vEcmI5vTs30rSrhgcHI610Wk6p5+Lec/vR91j/ABf/AF6QzXooooAiuraG9tJrW5jWWCZGjkRujKRgg/hXIx/DyHyoLG717Vr3RoGUx6bPIhjwpBVWYKHdRgYBPYZzXSayboaTP9i8zzuP9Xjft3Ddtz/Ftzj3xWJZQavNqsJDXI06NnKfaZHRyuUxkDknO/AbtigB2qeDF1DxI2vW2talp141qto32XyiDGGLY+dG7n9KlHha6bTbizuPE+tTGV43WfzI0kj2NnClEHDdDnPFU7460mq3wgN9JE/OUBXy0ymQgOVY7d+0qQ2c5B4pol1mMzJGNQaGZWjtC6Eup3rgvnleC2C3Yc80AdjWZ4f0S38OaHb6TaSSvDBu2tKQWO5ixzgDuxrm7eLXonLXz332V2RpRAzPJtJkzjupz5eQvbn1rT0j7edYl+0m98gxoIvP3hseWuc7f3ec7s980AWR4WsG8P3ekTtLNHeM0lxOzYkkkY535HQggY9NoHan6VoA0/UJ9Rub+5v76WJYPOuAg2RqSQoCKAOSST1J+grEtpPE0N1uniuZYUV/lGPm8lSo/wC/rMrfRe1WLK11p7exh1B7sTwXm2WSOc4liKFskjAIDEL07UAdXRXDeV4gm0ZFi+0/bAztIGkmTOI2wMn/AGscL8vStLVhqpWMRT3XliGEsyRN87ZfcCE+Zc/LkrnHHGM0AdPRVbTmmfTLVrhJEmMKmRZGBYNgZBIABP4CrNABRRRQAVz3ijxfYeGLJ5Jcz3Ixtt4yN3OcFv7oODyfTjNYfxN+I9v4G0sQ2+ybWLlT9nhPIQdPMb29B3P418zWvijUYtel1a7me8luSRdrK3+vQ9VPp2x6EDHSu7DYGdaDnsunmZ1HLlajuei+IvGOseJpT9suCltnK20RxGPqO59zWBU00cPlxXNrIZbO4XfDIRzjurejA8Ef0IqJEaR1RFZnY4CqMkn2FRbl0PmavO5vn3Eq1pf/ACF7L/r4j/8AQhXS6V8NPEuqKshtVs4m/ium2n/vkZP6V1OnfB24t7mC4uNZj3RSK+2OAkHBzjJIqHJGtPCVpNNRM+f/AI+Jf99v51HXaXHgGVnd4r9CWJOHjI/rWPe+EtXsgW8gToP4oTu/TrWB9IYdamk+IL/SHAhk3w94XOVP09PwrMIKkgggjgg9qSmB6xo2u2mtQ7oTsmUfPEx+Zf8AEe9aleMW1zNaXCT28jRyocqwr07w9r0etWp3YS6jH7xP6j2pDNmiiikAVXFmg1Br3c3mGIRY7YBJ/rWV4h1C5snhWO5+yxNFK/m+Vv3SLt2R/jljjqduBVaz1DV5NO1a9uFkRokl8mMhSqso4AAG48jueaANJ9GCyyNaXtzaLKxaSOIrtJPUjIOCfapotLht4rWKCSWKO3YttV/9YT13evXNcvPqfiWBYCyyKrgyAusZZVzEMybQRgFnOF5wKJvEGoogL3nlbnAuswgfYv36IFyRzlWbrn7uRwaAOjm0cNPLLbXlxa+ecyrERhj68g4PuKtRWMVvp4s7ctFGF2qyn5h75PeuXTxDqgjMbAPdP5RtYzEUM6eY4ZgD0yig+2fcVE+v3SpY7dXEkcr4nkMSQ+W2zJTcwI69sbh0JoA6uXT4nt4YYy0PkENEydVIGO/Xgn86faWi2qOA7SPI5d3fGWP4ewArG0bVLq71eaBrj7REDP5i+Xj7Oyy7UXI67lycHn5c966GgAooooAKKKKACiiigAooooAzPEWmtq/h2+sEx5k0RCZ/vDkfqBWf4f8AE+n3WlQx3d1Fa3sCCO4gncIyOoweD24romZUUsxCqBkknAFZ1/pmi3kglv7SylkAHzzIpODwOTWsZx5eWRjOnLn54b7amDDcx+I/G9vd2bebp+lRODOPuvK4xhT3wKd4gmi8TeFTe6K5uXs7lZ4wFILNGckAH2Jrpk+x2NsyJ5FvBCPmAwip9ewpqPp9jbK0bW1vA5ypUqisT6djmq9qk00ttv8Agkewk4uMn8V7/pb0KFl4q0W9sVul1G2jG3LpLIFZD3BB5zWX4ff+2vFWo6/ErfYhCtpauwx5gByzD2zWzcaNodxdtLcWFhJcAb2Z4lLY9T/jV9HgSICNo1jUhBtIAHoKXPCKfItxqnUk17Rqy7dTk/D9xaeH9a1jSr6RLaSe6a6geU7VlRgOhPGQcjFLr19bazrei6bp0qXFxDeLdTPEdwhjUHOSOmc4xXQXUekarEI7oWd0gbaBJtfDHsPei2h0jSEMVstnZqTyqbUyR61XtY357a/gR7CfL7O65fx3vb/gjPEF7Np+h3F1bkCVNoUkA9WA7kDv3OK5aTxVqcZH7xP3QkL5SPnbIFG/D+h/5Z7vpniu0nns/szNcSweQTtYyMNpPoc8U3yLAPEnlW2+Mbol2rlR6j0H0rnOsy4takm04GOSFrxruSAKOdqrI4JIz/cQn61iaZ4m1K9+yW81zFAblYnNw6Idm6N2IG1ivJTA3YPXI6V16RWCTrIkdss0uWVlChn9SD360qWlk0DRJb25hY/Miou0keo6dqAOc0XXb7U9WSOaULEI1OI1QLJnd83LbsHaCMA8HrRHr16JLyWSZRHFfC3CsIwip54jJ4bdnaTyQBXTG0tjMsxt4vNUYV9g3AexpPsdrvkf7NDukBDnyxlgeoPrQByU3iTU21BltyjQLKEDKimMgztGC7FsgYHVc8111qJltkFwwaUZ3EY559vanLbwogRYY1QAKFCgAD0qSgAqrL/yE7b/AK5yfzWrVVZf+Qnbf9c5P5rVR3Int81+ZapHdY0Z3IVVGST2FLXL+NtTNppa2cbYkuThsdkHX8+B+dSWch4g1h9Z1JpQSLdPlhX0Hr9TWVRSqrO4RFLMxwAOpNUIms7Oe/uktraMySv0A/mfQV6JonhO00xVluAtxdddzD5V+g/rU/hzQ00axG8A3UgBlf0/2R7CtqlcYUUUUgCiiigCG4tYbpNkyBvQ9x9DXNahpkli24ZeEnhvT2NdXTXRZEZHUMrDBBoA4ilBKsGUkEHII7Va1GyNlclOTG3KH2qpTEdbpt6L21DH/WLw49/WrlcppN19mvkyfkk+Vv6V1dIZk+Jpr+Dw5eSaW5S9CgQuI/M2ksBnb361xtxr/ilxeefHeWLRPcJEILTzRJOkcPloPlOY3YyEHgnpkYrsfFN5fWHhm+udM2fbkQeRvXcu4kAZHcc1yOn+OtRn1Z7i+RbLR/OiTMsRUxqYpg24np++iAB9CPWgCxY614rTXreG8s5ZrSa7uSPKiUbURGAiYnAHzBSrZGckZ45dd3Xilv7TngNyq/b4be2iBQYQvGG48s44L/Plh144rOi8ZeJkiQ3Nqu2ZoljkWA5R/soldXHbJOQfYg9qsReLtWe2upZLuCO8jh3JYfZWLFfLRhLuB4BLHrx0A5FAEOm634kl1BIL65uYpDp4dFNsSPOzNkMBF85G1AcMmcZA5p1nrvix7UCGKW5dYbotJOmxXZUiKshEQJ+ZnAUqOcjJxmpZPEfimLTb+4WAv508ltYn7OGYSCd1BCK2WURqWO7ByOODWx4c8VHWNSnt7h47d3t4JYLdxtkDMhMgweTtYEe2KAI7zVddtrXTpo4ppfMs47m4CwZI8sbpUxjhnDAAeoOKltb7W5NVt7e5juVtXgEE0yRoAs7Lv3DvgcJ0xk881h2PjPXTqPk39p5VtCJDNMsJ5FurCcj/AHnMYX2JqB/Guvx6FDLdItpewzyC6jkttsjLsDoERiA3DbSNwZtp20AbdvfapDd6fFc3dywltBIxkUKWlJPy4EeD24ytXfC19qUitDrDuLh0jeKNxkkbFLNuCqMZbGMZBB5rB1bXfEGnS6g41JTBHeW8MYa2SPYkihiSzHHGcc/jzXd2Ehm0+2laUSs8SsZBj5sjrxx+XFAFiiiigArJ8S+ILTwv4evNYvT+6t0yFB5djwqj3JwK1q+d/wBoDxS13rNr4at5P3NmonuAD1lYfKD9F5/4FXRhaHtqqh06ibseU6/rl94k1u61bUJN9xcPuPoo7KPQAcCs2itTw5oF54n1+00iwXM9w+NxHCL1Zj7AZNfUe7CPZIg6v4X6Pq3iPUbjSbeBn0xxvuJm+7bPj5XH+0em3uPoCPpDw34M0jwzCv2WESXWPnuZRl2+noPYVY8L+GtP8J6FBpWnR7Y4xl5CPmlfuze5/wDrVs18zi8Qq1RyirISow5udrUKKKK5TUKKKKAMnV/D1jrCEyoI58fLMg+b8fWvONV0m60e7MFyvB5SQfdce3+Feu1S1XTINWsXtpx15V+6N2IpgeQVZ0+/m02+ju4Dh0PTsw7g029s5rC8ltZ1xJG2D7+hFQUxHsljeRahZRXUJyki5Ht6irFcL4E1MpPNpsjfK48yP2I6j8ufwruqkZheI9XvtKNutnDHM9yGjiVweZRhgCR22CQ/8BFZ8Xiue4udOMcY+yXWXaTyWOyORisBJ6DOATn1FW/E2v3GkS28NtFatI8E9yXupCiBYgpKggfeO78ACeaztD8Y3Gr+IGsP7OWO2McjqzBlZQixkZJGGOZMEDkUASJrOrpd2kE13bMJp7lGZbcLgRSBB96Qdck8ZPoKksfEGpyxarJPHEBb2r3EOY9ucM4AGGO4fJyflPtzXPweP7+/trQnSbYyTzQqpaKRtvmQvKcJjccBB8w4O72Nba+J9QkN+8VrpzpbNNGtuZys8hiI34UjoRuI9MqT14ALVvr9099qMMrW5MMU0kKxruGEIAywYnPIyGCnPTIGa5W38d6/LFYpP9ktmnmCNJJAqkAwLL91pgAMtwd3I7ZFb1l4pu7u8hIs9PtY5YYLiT7TNskkjmdljC8YLhVBI9W2j1pJfE0txF4c8iytnbVLczSgo0giwI+ML/106npigCpdeNdQSOWKBtPN3C12sySNt8rZcJHEWywA3IxI3EBjjkCq0XjnU7i4wk1qsaWDXBDQBTJIrTLjmXI/1Q+4HHJ5xg1t+G/EkutajLbXNnaKJYpJVaF9xCpKYtsgI6nGR68+ldR5EJKnyo8qML8o4HoKAOe8Ia9da5b3P2iS1uBCIitzaqRG5dAzLjJ+ZScHB7joa6WmoixqFRQqjoAMCnUAFFFFABRRRQAUUUUAZviHT5NW8NappsLKst3aSwIzdAzKQM+3NcFfeFNX8Ty79S0b7LbmOwtJYJLhGZ0ikdpXBU4Awwx3OOgrv9e+0jw9qf2PzPtX2WXyfKzu37DtxjnOcdK4+DVPENhbSLbW11MDcFo/NhmdWUImVBf5xkluvHBwRigDKh8K+I/Oa61fTItUSDUI3ktjMn+nRpbmFZcMdu7OH2tjnPtVn/hGbuG5tbybwdbXdj5E0UekC4jcWjvKW3jednzA4O37uMDIq6t74ris4AthcPPBdOURixEkJSb5nbuchcJx0Tn5si8ur+JZ72aGO2EcRmVEd7ZyUQyKA/YHKEsRng/Q0AcnJ4B8RWtjLcwJHPqkWlw2KDzwBPGyyrLFuPZd8ZBPUoPU1tW/hTVotesoDbp/Y7vbahct5i/LcRQmPZt6nLLE2enymujs7m8ubrQ5L2MxXLwT+agUqNw2jOD09fxrnrG/1qy0WV0ivJNRaXa3nQ3Mm0fORkPwMkKMoCOR04IAMLSvh7qVx4d0zSb/AE97VoboSTTD7OpTEMqrIhj+ZirshBf5v1rQg8G6pqenwS67o9k+oHXku5g3lyDydqhyCezFc7evTirjeJdX1K6ubQBoTHtby4YW3sVaLeFI5+XLhgfQY71qR6h4gvdL19Z7ZoJo7aX7MIo3VxJ84UKcYbgIQQTyfcUAYTeFNQtNTN7c6BDq9it5fMunl4/lEroY5QrnaflUrg8gNx3qpD4H1+zNrqEFvF9vsLEC1jE4KjdPKzW24/wiKQKCeMqp7V0/na5pLWlrHAZd+1mx504kYuFZS7kmPCZbnj9cwNfeI5pNLjuFkQyNaTSCG0dQdz5lUtk7QgABB65P4AHPReDfFKto+oLBaJPotrZQ20DtmR9igzhXDbV3bmQ5BzsFdh4OttS02C90++0ySCMXlzPFcGWNllWSZ3XAUlgcMOoFdPRQAUUUUAFFFFABVWX/AJCdt/1zk/mtWqqy/wDITtv+ucn81qo7kT2+a/MtV5h4vvDd+IZlBykAES/hyf1Jr0+vGryUz31xMerys35mkiyCum8E6aLvVWupFzHbDI/3z0/qa5mvSPBFuItA83HzTSs2fYcf0oYjpKKKKQwooooAKKKKACiiigChq9sLiwcgfPH86/1rla7kgEEHoa4maPyp5I/7rEfrQIZXZWc32izil7soz9e9cbXTaE+7TgP7rkf1/rTAn1a8fT9MnvFVSIAJJN3/ADzBBc/Xbk1zH/CYXsljctHaxC6jKIkex3y8hLR8LycRYY49a6HX9TGj6HdXxhE3lgARs20MWIUZPYZPJ9M1yN346urG9+yNpVtJcwzPFL5TsyuwaFV2MF+XImAy2ACCM0hmndeJb3dJcWr25tTYR3caPCd4LNtwTvA4/D0461J/b+opq9nalInjlMSyOYgoBcyZywkIU4TgfNk8ZGeKWs+Lm07XbvTRpcM8MSMCSGG4CBpjk7duPlxtznnOKLfxNd+TaRpplhC727XjiZ2t0WNGQYG5PvZcnJ4/OgC3feKrmLUNStbWCJjCm22eVXVJJVKh1LYwR84HHI2tmq0vjK4N1J5K20aQrKWhnBEjshiBiHPDZkYdDkgUsniq9+1zWP8AZFuk0V6tjvmlxGZnDOGBAzt8va3rucL6mmz+K9ml6vqD6bZvc6ZHFIvlyiRXaRRnawGce4GTigCHW/Geqab4jvLKG2ge2gGAzxkc/Z3l5fd1yv3dvIzzxUS+Ob2DS1ubprEvLp9xcxZTyw0qeVsjAEjZJ3njOTxwKuS+KLiG9ijutNtGjYWxnJLpJmaZ4V2oy5ONuSDg4Jx79YthZoiolpAqq+9QIwAG9Rx196AOC1Dx7qdrNfQ7LESJcLHCAvmDYZ1i3EiQAnDcqxQg8cjmtC08W3za7bWMrWMySyxQGKJSs3zw+YZAN7DaDwRz/vevXNZWj+bvtYW80gyZjB346Z9aWK0toXDxW8UbBdoZEAO30+lAE1FFFADZJFiieRyFRFLMT2Ar4j8Q6tJrviPUdVlJLXVw8oz2BPA/AYH4V9eePL06f4B166U4ZbKUKfQlSB+pr4xr2sphpKfyJkFfQ/7PvhhLbR7zxJPH++unNvbkjpGp+Yj6tx/wGvnivtHwRpq6R4G0SyUY8uzjLf7zDc36k1tmdRxpKK6ijub9FFFfPlhRRRQAUUUUAFFFFAHGePNNDRQ6ii/Mp8qT3B6H8/51w1et6/bi60G9iI58osPqOR/KvJKaEW9MuzY6pbXQP+rkBP07/pmvYQcjI6V4melewaVN9o0izlPVoUJ+uKGMlurK0vkVLu1huEVtyrNGHAb1Ge9V45dKbVZLSJrQ6hEvnPEu3zEDcbiOozgc98VS8U+IU8PaWsiqkt9cyCCzgdwoklPTJPRR1J7AGuO8KWlnpHxLuUfUobu8utIjlurrzB+/uGmbOOeAAAAvYAVrCleLkxXOn1jVPBVjcJp+sXGixSgLiC58v5QAQuQegAJxn1NbUNjpxkN5Ba2peaMKZkjXLpjgbh1XGPauV1a1PhHTL3+yPDFxrst/JNcXTs0ZJZjnD5+ZhzgAA8DFXfhzb29r4A0mG1vlvYljb98qlRksSVAPICklcHkYolTiocy/r/IBJPFXgUzQPJq+hmW1+WFmlj3RdsL/AHfwqVm8Fx6Zb35j0UWMrloJRFGUdu5XA5Py9vT2qr45uGisLTRdNSOPU9Zn+yQyBBmJMZlk/wCAoD+JFLPo/wDwj2qaHcadpstzpthZS2YgtwC8RYxlXAJGc7CCevOfWnyQaT7gbkDaNp1hLqVsLK3tJR50lxCFVZAf4iR169fetIHIzXl+l3LxCw0y6s5ZYoL2aRLC3KsZLku8ojySF2Qqw3HON+APu16FpOq2+sWX2m3WRNsjxSRSrh43UkMrD1BFTUpcgF6iiishhRRRQAUUUUAFFFFAFDXLuaw8P6leW5UTQWsssZcZAZVJGR6ZFclbeN719SijeCExLbiGVSdgF4JI0cFucIu8djXdsoZSrAFSMEEcGo3toHVleGNlbIYFQQc9c0AcjP45mNhNcW+mrhYm2s8/HmCB5QMAcrhCM55yOKmuPGrWfmmbTwyITGHin3bpAkbdNvC/vPvc9DxXU+RCBjykx6bR6Y/lxSC2gCbBDGExjbsGMYx/LigDBsPFIvb60tGsGjnuAzJ8+RsUursDjoCqjsfnXpUF9q+oRadZGCSQy3GpT27GOFZH2KZiAASB/Ao+ma6GPT7WG6FzHCqyrH5SkdFTOcAdByB064HpUkVtDCgWONVUO0gHozEkn8ST+dAHHx+L57V5Y7rR1+3xIiTFXCbpSEOOh+X94Ocnp+NWpPGFxDdtFJpaeWjsjutzkjZJHG5A288yjHqAenFdObeBpTKYYzIQFLlRkjrjNKYYj1jQ5/2R9f6D8qAONPj1YYYs2nnloN5Ik2nfs37SMYHGOc9+lWtT8R3n9lW00CfZp21FrOZVZHxtD52lyq8lB1xXS/ZLbfv+zxbsbc7BnHpSyW0E0ZjlgjdCdxVkBGfXFAHHW/jxha2gltUuJpLfc5jk2DzPLZ9uCMAYXruPXjI5qY+NHJzDZrMzJu8vztiqQszNyVzn9yRggcmuqNpbFtxt4i23ZkoM7fT6c9KVbaBFCrDGqqMABAAB6fqfzoALadbq1huEBCyorgHrgjNS0gAAAAAA6AUtABRRRQAVVl/5Cdt/1zk/mtWqqy/8hO2/65yfzWqjuRPb5r8yy33T9K8Vb7x+te1143fwm31G5hPVJWX9aSLK9epeEsf8IzZ49G/9CNeW16N4GuRLobQ5+aGUjHseR/WhiOmooopDCiiigAooooAKKKKACuOv8f2hcY/56H+ddgxCqWPAAya4qV/MmeT+8xNADK6Pw/8A8eUn/XT+grnK6fQ02aap/vMT/T+lMQ/WrqGy0e6nuIY5oVTDxyEBWB4IbIPHPPFctFrmkRmGzGhWqQspQJGoIZGd9xQbMMn7reSSMgZwcV2k0MdxEYpUDocZU+xzUctjazymSWFGfKHcRz8pJX8iT+dIZzj6nHdTxQyaDbyXd1smVHkUhlaKTBLbfvbUZcdOeuM1W+1+HYtIgmh8P2xtlnzDF5KDa5hEoYDGATwv4Z7V0B8OaQYfK+xJs3BsBmGMAqBnPTDEY6YJ4qRtD0t5nlayiLOmxhj5SNu37vT7oAzjOOKAMq4uoJL1dL1DRbTfesXnVz5iPtKKG4Q5OMfe242/Q1RgbRruymvbnw1ZK62MUu0Ij7rZgQBnaMbQrfLjHHFdFHoOmwvE8duUeMkqyyOCckE7jn5ug656U+00fT7G2lt7e1RIpV2uvJ3LjABz2xxjoKAOWh1Tw/bRSXlroNqktirLCdkaEZk2xBWx8ok3FgewJrUuvFLQxi4hs45rNrE3ySifBKgDjbtPOWHf/CtSLRdMhYGOxhUjy+i/3FKp+QJApW0fT2haE2kfltG0RUcfIxyw9gTzQBlP4rCahaWhtVZ7jyyNjvk73ZeAUHQIWOccZxmujqmmlWUYAEG45Q7nYsflYsvJJPBJP41coAKKKKAOO+Ku4/C/X9vX7OPy3LmvkCvtHxvYnUvAuuWijLSWUu0erBSR+oFfF1e7lT/dyXmRLcK+59PKnTbUpjaYUxj0wK+GK+zfAGqLrHgHRL1SCWtER/8AeQbW/VTU5tF8sWETo6KKK8QsKKKKACiiigAooooAgvcCwuM9PKbP5GvGR0Fes+IrkWnh+9kzgmMoPq3H9a8npoQV6z4dz/wjthn/AJ4rXk1exaZD9n0q0hIwUhVT9cUMBmo6PpusRpHqWn2t4iHci3ESyBT6jIrnbXwBpFn40/tqDTNNitltEiihjtlBjmWQt5g4wDggZ68Vtavq0unOkcFobiSSJ5FUNz8rRr0AJP388c8YAOazL7xTc29np0lrZ208t20wYPcNGieUrM3JTIPykYIGD16VUakoqyYyrFo3jDR2urXR9T025sZZXlhbUllaW33HJXKnDqCTjOPSreieHNR8O6fpVhY38UkMc0s2oyTR/PcM+WJTHC/MfyrNPxCd0SeHSyLQyYknmkYLGuyJxu2qdpPmnlsKNvLc0628d3d3cG3h0lS8t0bWCRpWWLeHkBDsU4OIy2F3dQOKbqyasKxtPoU0/jmLXZ5o2t7axa2t4cHckjvl39OQFH51panHezabPFp00cN067UlkGRHngtjuQMkD1xXJr4/lZ7dm0sR28kRJleY7TKPMBRWCFTzHxuKlgwIB6U0eO78aVb376TDsezmvpEM0kZWKMRkhd8Y3MfM4/hOOvNS5N2v0Gatx4Ze0Gjy6K0KTaWJERLknbMkgw+5hyGJAbdg859aveHtJl0mynW5lSW6urmS6naMEIHc5woPOAMDn0zUV1rzwXrolsrW8JgErM5D5lbau1cc44zkjv6VY0LU5NX01LuSHyt4BC7XGAQD/Eoz16jim6kmrMDToooqACiiigAooooAKKKKACufuvGGnwXktrbwXt/LEcS/Y4DIEPoT0zVzxJeyaf4a1G7iOJI4GKH0OMA0zwvYRad4asIIlAzCrue7MwySfxNaxjFR55amE5Tc/ZwdtLkuka9Ya2khtJGEkRxLDKhSSM+6mtB3WNGd2CooyzE4AHrXLa2osPG+g30I2vdmS1nx/GuMrn6GpPHcjnQoLJGK/bruK2cj+6x5/lVeyUpR5dmR7aUYTctXH8ewp8cac5Zraz1K7gUkG4t7Vmj/AD71t6bqdnq9kt3YzrLC3GRwQe4I7Gp4IIraCOCFFSKNQqqowABXMacg074iajZwgLBeWiXZQdA4baT+NFoTT5Vaw+apTcedpp6bbFqXxlpsMzxNBqBZGKnbZyEZBxwcVZ0rxLp+sXklpbC4WaOPzGWaFo/lzjPPvVLxT4lm0ZobO0tZJLu4BKyeUzpEucFiFGSR6CneFE0qSKe7tL5r++c4uriUESZ/ulT9wegqnTj7Pns/L+rEKrN1vZqSffS36nR0UUVzHYFFFFABRRRQAUUUUAFVZf8AkJ23/XOT+a1aqrL/AMhO2/65yfzWqjuRPb5r8y1XmfjKzNrr8kgGEuFEg+vQ/wAv1r0yuc8ZaYb7SPtEa5ltiXGO6/xD+v4VKLPNq6LwdqYsdY8mRsRXI2HPZv4f8PxrnaASDkHBqhHtlFc/4X8QLqtoLedgLyIfMD/GP7w/rXQVIwooooAKKKKACiimTTJBE0sjbVUcmgCjrN0LeyKA/PL8o+neuXqze3b3ty0rcDoq+gqtTEABJAHJNdnaw/Z7WKL+6oB+tc7o1r9ovQ5HyRfMfr2rqKQwopksscMTSyuqRoMszHAA9Sao/wBv6N/0FrH/AMCE/wAaai3siXOMd2aNFICGAIIIPIIqqmqafLdm0jvrZrkdYllUt+Wc0JN7Dckty3RUE17a28yQzXMMcsn3EdwC30FSPLHGUDuql22qCcbj1wPyNFmF0PooopDCiiigAooooARlDqVYAqRgg9xXxR4q0Z/D3irU9KcEfZrhlTPdM5U/ipBr7YrwH9oLwoyXNn4oto/kkAtrvA6MPuMfqMj8BXpZZV5KvI+pMjw2vff2fPFStb3vhe4kAdGNza5PUHh1H0OD+JrwKruj6teaFq9rqlhL5d1bSCRG7fQ+oI4I9DXs4miq1NwJTsfcdFc54K8Y6f410CLUbNgsowtzbk/NC/cH29D3FdHXy0ouDcZbo0CiiipAKKKKACiis3W9Yg0axaaQhpW4ijzyx/woA5rx3qYZodNjb7p8yXH/AI6P5n8q4qpbi4lu7mS4mbdLIxZj71FVCLuj2Zv9XtbYDIeQbv8AdHJ/QV6/XE+BNMIE2pyL1/dxZ/8AHj/T867akxle7tLS5Qm7hikUKQTIoICnBPX3UH8BVO60nQ7rToRdWdjLZQfvY/MRTGmRywzxyCee+ah8V6fb3/hy++0K58q3ldNsjJyEPXBGfoawZlW9tfBWlTqHtLiMSzRno/lwhlU+oyQce1CVzGdRxdrf1exvxWHhzWP9Jht9OvNkpfzEVHw+AM5HfCr+Q9Kk1CPQYLIWGo/2fFbSEuILgoqsd24kA98nOfU1l3NtDpnjvSHs4khF/BPFcJGu0PsAZSQO45GfQ10Vxa2txhrm3hl2jgyIGx+dBUZSaa6oybe18LXt1F9lj0me4hi8uMReWzIgGMADoACR7Z96baaH4UmKw2lhpcptXLBI0RvKY47dvuj8h6VlaLp66xFq2uW0UcD3cUlrp2xAuyIZAfjuzc/QCm+fJazaRLbaZLbzWts1t5cqbBJIwULGD/EAVLFhwApNOxmq0rJtf1/Wp1T2un3V95zw28t3b4G4gFo+4+nXI+tWo40hiSKNQkaAKqqMAAdAKwNA1GySOG1UztLdF5RdSRbVu36syn+QOPlAxwK6Gk0bQlzK4UUUUigooooAKKKKACiiigDN8Q2L6l4e1Czj5kmgZUHq2OP1qt4T1OHU/DlmyMPNhiWKaP8AiR1GCCO3StuuevPDPh7Vr6acxoLoHEzW85Rs/wC0FPX61rGUXHkkYThNT9pDtYp6rKmreOdHsrdg/wDZ++5uWXkJkYUH3z2pfGUiX3h4XthIlydOu47hxEwb7h+YcdwDmtfSbPRdKsm/sz7NHAWw8iSBtze7Z5P1NTWGn6bpay21nHHEJZDI8YbJZiOTgn0q/axTTXT+mZ+wnKMlL7W/lppYsWl7b39nHd20qyQSLuV1PGK5nSpRq3jvU9TtiJLW1tls0kB+V3zubB746VLN4R8LS3RHlpE8rEmGK5ZFc55+UHHWtuzTTtOsI4rT7PBaqDsCMAvHXmlzQiny31K5Ks3HnsktdOv4aFbw/raa5YNMYxBcxSNFPb7smNgcYP8AOskmJviYn2LG4WLC92dPvDZu9/6VdvPD3h/Wbs3TKhuW4aS2nKM2PXaeataTZ6LpNpKum/Z44g3711kDHd/tMTnP1o56cbuN9egvZ1ZcsZ20d79Xbytp5ia7q02lJaeTEJGnlMfKO+AEZuiAk/dx071jHxnNl5PsSeUI9wG5uv2cTff27D124Bz3xXR31pY3yKLsKwhPmA+YVKZBGcgjHBI/Oqw0PRWcEWkB8yPAUH5SuwJkLnH3cLkDpWB1EWs62+mWsbxQLJIyeYQScKNyr0AJPLjgehrPTxVdy291JHZw/wCgxNPch3ZCVDMMKCuQSEY4YDHHrmtH7BoUVpNGzQeSWEMhe4JwwOQuSeCDzilk0nQleKCSG3DjLKrP8zgnJzzlgSM856UAVLfXb+8E7QWy7UuGhQeRKeFdlyTgDHy5+UnGa6Osy2TSLSSS6t5oU8+QhmE+VZ2O4gDOMk88VoGWMZzInDBTz0J7fXmgB9FRC5gaZ4VnjMsYy6BxuUepHami9tGhMwuoTEDguJBtB9M0AT1Vl/5Cdt/1zk/mtWEdJEDowZWGQynINV5f+Qnbf9c5P5rVR3Int81+ZaoIBBBGQeoooqSzy7xNojaRqBaNT9lmJaM/3fVfw/lWJXseoWEGpWb2tyu6N/zU9iPevLdY0W50a68qYbo2P7uUDhh/j7U0IpQzS20yTQyNHIhyrKeRXe6J4zt7pVg1ErBP08z+Bv8AA/pXn1FAHtasrqGVgynkEHINLXj9lqt/px/0S6kjH90HK/keK2ofHOqxjEiW8vuUIP6Giwz0aivPW8e6iRhba2U+uGP9axfFnibWpNPsJob6SCKZWWVYflywPr19e/atKNJ1ZqCZhiK6oU3UavY9B13xXpPh+M/argPPj5beM5c/h2+prgbXx/LrGrNDqKx29rIcQbTxEfRj3z69vpXn7MWYsxLMTkknJNJXrwwFKMWnq31PAqZrWlNSjol0/wAz2IgqxBGCOopURpHVEBZmOABXLeEteN6selXTE3KjFu56yAfwH3Hb8vSvTdK0wWi+bKAZiP8AvkV5NalKlPlke/h68a8FOJZsLNbK1WMcseWPqatUUVibiOiyIUdQysMFSMgiuX1qxtNQ1az0KG1gVHH2m8ZYwCIlPC5/2m4+gNdTWXp2mzW+rapf3DIz3UiiPb/DEq4APvksfxrSnLluzGtDntG2+/p/wdiLxVdyad4U1CeBvLdIdqsv8OSFyPpmsGf7Ja6RbfafD5g0mNo9l2kiiaPkbZCAMjJxnknnmukvLC41NdRsr0w/2dPCEi2Z8wEg7ie3XGKyZ9J17UdPj0i+mshZ/Ks1zGW8yVFIOApGFJxycmtqUoqKTfX+vmYVoylJtLpZaLe/XyYapbTK+uRNZSTtfxqLeVVyo+Tbhm/h2tlsn145ot9ViN59tuEuLgQQZi8qPd5cXQzN/v7Tjqdo6cmtvUrSW+ijtVZUtnb/AEg5+ZkH8I+vQ+2ap3WnX0d5dy6eLYrdwLEwlYr5RUEBhgHIwenHTrSjOLVn/WyKlTlGV47f8O/z/rQ2I5EliSSNgyOAysOhB6GnVBZWq2Vhb2isWWCJYwT3wMVPXO7X0OpXtqFFFFIYUUUUAFUNa0e01/RbvSr6PfbXMZjcdx6Ee4OCPcVfopptO6A+KfFfhm+8I+IbnSL5fmjOY5AMCWM/dYfX9DkVi19g/EHwDY+OtF8iQrDqEALWt1j7h/un1U9/zr5P1zQtR8OatNpmqWzW9zEeVPRh2ZT3B9a+kweLVeNn8SM2rFjwz4o1Xwlq6alpNwY5Rw6Nykq/3WHcfy7V9LeCvi54f8WRx29xKmm6oQAbad8K5/2GPB+nB9q+UKKrE4OnX1ej7gnY+8KK+OdC+I/i3w6ix2GtT+QvSGfEqAegDZx+GK7G2/aE8URIFn0/S5z/AHtjqf0avKnldZP3WmVzH0rRXzTdftCeKZkK29hpduf73lux/VsVueGPHGs+LfD80mpXzPPBcFJFjARSjDKcLj0cfhWNXA1aUOeWw7nreseLLHTFaOFhc3PZEPCn3P8ASvPL/ULnU7pri6kLueAOyj0A7CqtFcgBV7SdMm1bUI7WLIB5d+yL3NRWNjcajdLbWsZeRvyA9Sewr1DRNFg0Wy8qP55W5lkxyx/woAvWttFZ2sdvCu2ONQqipaKKQyrqds97pN5axlQ88DxqW6AspAz+dY114fu30bR1tZoY9T0oIYncExuQmxlPfaRnn6Voarq7aayIlpJcO8TyBUzn5WRTwAT/AB54BOAeDWde+KpLe006W2sEupbxpQES5AVRGrM2GI5Pynggc9cc07kSpqW4+z0zVbrW01fV/ssclvC0Vrb27M6qWxudmIGScAYx0qe7tdZvfCc9pJLapqs0DRtJHuEak8ZGeen61it8QoWWKWHTZTavLsNxLJsRRsicZOCFYiXgMQPlPPSrDeNxFZDUJ9NkTTpLlYIZxKGZgZvKLFANw55wM5HoeKLiVNJNdzpLG0isLC3s4RiKCNY1HsBiqg06S41Oe7vCpUIYbZFOdiEfMx/2iePYAeprFi8f6fLdaXCImA1C5mgVmcKUCSGNWKnB+Z8ADGRn2qpYfEUaiClvpZadnSOIef8AJuYSEh2K/KQsTNgBuoxSuVyrRGrp+jahG2kQXTW4ttKB8t42JaYhCi5BA2/KTnk810dcWfH6slxJFp6vFb6f9tkIuMk/I7FVKqVPKEZLD6V0Gjau+qC6jntTbXVpKI5oxIHXJRXBDYGQVYdhTbuEYKKsjUooopFBRRRQAUUUUAFFFFAEVzG81rNHFIY5HRlVx/CSOD+FeXw+DtTk0D+zYPDVrp19Dp5tp9QF0B9tbchZfk5YSbWyzjK7uO9epyOI42ds7VBJwM8CuGsdW15Hia/tr6MSXQulVkDfunjk/dfLnhWCcHBywoAyLrwVc61LPt8ORaRpk7WcU+nrJGPNCTbpJSEO0YQ4H8R/AU628M+KbUXWoy2qXOq2N9byWjeeoN5HHGYWJOflLRsSQe9bNt4p1mb7P5tmoSS48oslu+WUrGQQM4GC7gnJ+704OIIfFOv22gWjS6dJ9tCLujlhZ2kAjQ5LA43MSTwDjBBHBoAz9N8GanpniXSbl7E3EUenwxzzoLdsXHmSPKxMnzgbpM5Tk1F4c8Darpd54fsrqwgl0eKAzXcbujiGdoDHIm3+JWba3GRkt612tzfTWdzrk0SSTvDBE0MC5O5yGwAPc4rnkfxLcRWWnML1buFp0klkmEW/5UaN2ZAykDcRgdcH0oAyo/AGqReHdKsbK2t7C6Syv4p5Y2VdskoAjJK8nIGMjJAouvB17qkVyLXwxb6NataQ2s1msseLoieNyTsOMKiuAT8x3niugbUNUm0i7sI72ZNYTUEQSi3OFj89BuAIwV2k/hms+41fWnF3JerqNoqNP5UcCtlplEYVQwRsr98rkYbPNAGe3hDxOx1LS/Jgazl+x2UV3PIHElpC8snzqCGJ2skZHfJNXdG0bxHoOoaM02lfbotOhu7IPbTRoPKeSJomCu2cBVK4yT8o9a6h7q4kvGdDKkjaUZBH3D59PWub07V9Xgt9LmiS5uiyN9sLSSSiLiLLsGVTkZf5V9/egCnB4Y1GL7Bqeo+HY9Z86K5e40+R48wTzS+Zvw52n5cITnIAGO9N0vwBq0M5nvDEXh0dLaIbY5cyZnPlh2G9QgkRQwxnHtW5/a+u6nc20KobZBdxedst3BRcybomJPONiEsOPm9CM138Q6+9vFDFYSvMq2sixxoys2Wi3K7scDO5geMYB54IoAwrzwBqFr4Y0e1h04Xt3DbOrhI7VUjkeONSroyhXXK/6wHePU5q+3hDxJLPczvciNJNZsrtrOMRmN1jWDe4Y/OMFGwM87RxzXoOmXEl3pltcTLtlkjDOuwptbuMHkYPFWqAPLLTwfqqLY2g0OG3vrOSd7nWVmTdfKySDHB3kuWUkPgLjvgVmR/D3X7XRDbNYW8shXTTstEhRSISTIGRyVeQEn5jkMMdMYr2aigCjo0L22jWcLwNA8cSqY2WNSpA6Yj+Qf8AAeKkl/5Cdt/1zk/mtWqqy/8AITtv+ucn81qo7kT2+a/MtUUUVJYVBd2lvfW7W9zEskTdVNT0UAeeaz4MurQtNYZuIOuz+Nf8a5dlZGKsCrDggjBFe11Rv9H0/Uh/pVqjt/fxhh+I5p3A8horvLrwDbOSbW8kj/2ZFDD+lZsngLUVPyXNs49yw/pRcRytO1KP7T4WmXq1vKJB9Dx/Vq6VfAuqk8y2q/8AAz/hVhfClvZRTW+qatbxCeI/KODjIUkZ93A+pFaUp8k1LsZV6ftaUod0eRVqaVoF7q0ibBHBAx/4+Lhtie+Cev4V6vpvgnwvpTqzql1MOd1zIGx77en6V06XNoq7UmhCrwAGAxzivRq5ktqa+88ehk7ves/kjl/DOieHfDcYkW+tri9Iw1w8i5HsozwK6X+1dP8A+f23/wC/gpP7U0/ci/a4NzgMoLjJBxgj/vpfzFSm9tAwU3UIZhkDzByOuf1H515c5ym+aTuz26dOFOPLBWREdV08DJvbf/v4KWLU7GeVYobuF5G6Krgk1boqSytf3YsbN7gpvClRjOOpA/rWM3ikxaZq95NZGNtOgecwNJh2C7uoKjGdvBG4c8E4rfmhjuIjHKgdDjKn2OapLommrDcwizj8q5QxyockMhz8vsOTwOOaAOabx8Y9QsLN9ORmuxGR5Uzk/PKYxtDRjONpY528DjNMj+IE0mhRar/Y/wC5kfGRM5WNdhbdI3l/L0AyMrk8sBXWT6VY3M/nzWsby4jG8jnCNvT8m5FUR4S0IWothp0fkhtypubC8Ecc8DBIIHBFAFKy8a2l9qsGnpbyCaW7e3zyVAWIybtwG05xjAPfNSweLIpr9bL7K6zFlTlxjeGIkX/gCgMfUMK1YtI0+Bo2itIkMcpmTauNrlSpYf8AASR9KRtF01pGkNlDvZnctt5LOu1z9SAAaAMe28W/2hp8V3YW0Moa8Fqytc427iNjZVT1VlJHbOKSTxY0Vo9y9rCsYuGgBaWQAbfMJY/u/wDpmemetbv9m2QbcLWIHKNwuOU+6fwpkOk2MDl44ACXMmCxIDHOSATgZ3N09aAJrK4N5YW9y0TwmaNZDG/VMjOD7ip6ZDDHbwRwxIEjjUIijoAOAKfQAUUUUAFFFZOs6VdajLA9tefZjGrDcMk5JU5ABAP3SOc8MaANaue8W+C9G8Z6b9k1W3y6g+TcJxJEfVT6ex4NM/sjxD5r51wCPHy/u+fvE8/hjn2xTZtC1+aOVD4gYI6MoAiAIyTzkc5wR0x0xVRk4vmi7MD508ZfCPxF4TeSeKFtR00ci5t1JKj/AG06r9eR71wFfeHauP8AEXww8JeJmaW80tIblutxanynJ9Tjg/iDXrUM0srVV80S49j4/or3vU/2c4yxbSfEDKO0d1Bu/wDHlI/lXOz/ALPniuMnyr7SpR2PmupP/jld8cdh5faJszyau3+GN95Wu3WnsflvLc7R/tx/OP8Ax0OPxro4P2e/FUhHnX+lRDv+9diP/HK63wv8BDo+rWmpXviBnlt5BII7eDaDjsWYng9OlZYnFYeVOUObcaTGVvaR4Uv9TKySKba3P/LRxyR7Cu6sPDul6aQ0FqpkH/LST5m/Xp+FalfPXLKOmaTaaTbeTax4z95zyzH3NXqKKQBRRRQBja5NaI8cc+nyXkphkfEZAKRqULEEkc52YxzkD0rn7y98P31j5UukpcWlrcbIt9wgy7nBJy2QG3HJbrn3rf15LR5LNLqzvJmlZoUe2fZjcMsrHcDghent9Ky4dd0JokihtLj/AI+Fmjj+UZc7yGyWwB8jcEjtxQBBcyeHHubgXejIpgAnlJdADlY88BvmTAQZwVOz2FOM/hmO9u7iz0lLmaOQzzSQbSAI9khcZb+84OB1IJq/cjSZ9TuRJb3c91FCJzCHJUbsZ2gttDAbc47EetULzVPDc8N3d3kN3A62q3sy5ZWkikwm35W+YHy1BX6etAEMmq+GzLe40mORy/zNvTki4wCxLfu8ySFhnGeT2q/fW/h+20yya60uMR3RihVY2B8sAsytuB6LuY7ge59ajubnw+Ek86C4giW6+z+ej7cSGbdn5WyAHXOSAAM9s1ZI0nVri2sbjTrm8XypkjmuMOoUMFc5LZzkAZxn0oAyWg0FI7w23h0SQ2u6ykjiuVXKBmQgpu6ZLYyO9XdP1vTdItr2C00xbWO2nWOUefHkysyLlstnHzD5j2H0roxptqLN7TyyYXcuwLHJYtuJz1681AuhWS3E0wEx86UTvGZmKeYGDBgucA5UUAT6ZqEeq6dDewqyxygkBsdiRkEZBBxkEcEYNW6htbaKzt0t4F2xJnauc4Gc4HtU1ABRRRQAUUUUAFFFFADZGKROw25UE/McD8TXJab4tnQiLVUT7Q6B/LjTaEHlyPkNuZXU+WQGB+oFdfVIaNpYhaEabZiJm3snkLtLepGOvvQBgTeN1jtHuk0ud4lL4/eoCwSLzXPXsOB6n86mXxjH9oMElhKjxSrHcDzFPl7nCKR/eyWGcdK3zY2hTYbWDbz8vljHIwfzHH0prafZPMkzWdu0qMXRzGCysepBxweB+VAHP2Pitr3ULCGLTPKN4yF2eVciNonkQ8dT8pGO341LqOqajD4e1W+tp4Vms5pdvmw71KL0XAI/Otz7DZgofssGY9pQ+WPl2ghcemASB6ZpUsrdIpohEpjmdnkVvmDE9cg0Ac2/jJLSSe2ntZ55IH8jzo4iqSS7kTGOcDLjueh9sv8A+Ezj/wCgbcgmDzFVsBnbugHcj8/at99OsZZJZJLO3d5l2ys0SkuPRjjkcDrTDpOnHrp9r/q/K/1K/c/u9OntQBy15412Wt5NaWiyXMdpJMm5lCoETc2Wz82GI+Xg9emK2tI8RR6tfz2qWsyCLeBKykK5R9jfr068VfbSdNeJYn0+1aNcFVMKkDAwMDHpxU0VnbQzyTxW8Mc0v+skRAGf6nvQBNRRRQAUUUUAFFFFABVWX/kJ23/XOT+a1apjRK0ySnO5AVH0OM/yFNOzJkrofRRRSKCiiigAooooAKKKKACuX8Vtpi3NidQ06e6b5vKMTEbeRkHHvs/HHpXUUUAcAJfCoJQ2V4QGCiXJYk/MMD5s4AX+Xet5PCuk3oF1JDcfvlVtjyEbeh5x+GetdBtHoKWgDIl8M6VM8UkkDGSKJYUfzGBChWUd/Rjz9PQVX/4Q3RfIaH7O+wgAjzD2x/gPyrfooARFVEVFGFUYA9qWiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIZ7aO4aBpM5hk8xMHvgj+prJHhPTY0jWESQlCjZXacsocAkEEE4dhkj09K3KKAMqPw9YR3pvQjm7YtvmLfM4ZQpB7YwF4/2RVe58I6VdiLzVmPlwrAMSYygUqAfX72fqAe1btFAGSvh6zDsWaR0acz+WwXG4lic4GSPmPBJqbT9FtNMjto7YSBbeN4497lvldgxyT15ArQooAKKKKACiiigAooooAKKKKACiiigAoorLu0vI7iAI1w8XzkmHbncWBUNn+HGR/kUAalFYEF3rrSb5bXYhIHCA4HJzjcOckDr0GaJbzW8NJHZEME2hSBgv8ALk9c4zuAoA36KwBda+r7Taoy/MS23kDc3QA84XbjnnmpVudYitIV+zeZMyElmUcNk9QDxxigDaorAN7rcdu85tFwi7ihXJbgdMHjuf8AChLnUru30+dkkQNI/nLGuMAH5c88/UcUAb9FYZutfSL/AI9IXcqGyOADtBIxk5OWIH+6fUU5LvWljVpLSNmcgAKv3fucnn0L/kKANqisKW51p7S0K2+2Vm/e7VHZ145PAK7uaiF74hELyfZFLkAJHtGM5bJPORn5R+tAHRUVj3MmrQ30rQJ5sLFQilRgcDPcYzk89sDiln/tdkshGyo7IDOVjGFbenv0276ANeisWC71poblp7ONHVF8tV5yx69+fX/Go1vNfKQ7rGFSSoc59Rk457dKAN6iucutT1y1j3NZq48tclU6Oduf4ugyw/4D7jNq3vNYa3uZJbRCyBWiRQVMgzyOTwSMdehODQBs0VhC51pblTJbAxo2HEa8EZ5xzz8oyOnJxSG58QeWZVtYSduRERg5x67vX2oA3qKxpbjWWt4QkCLK8bbiFzhs8dTxxz3yeOKcy6tHNB5bh0EKK4dRgvyWPXrwB7ZoA16K59LnX5vJR4EhLFSzqudvzjOcn0zVmW61lWtRHZRuHjUzHP3GPBHXt1/OgDXornVvPERXf9kjycAKV4HXnrn+6MfU1Yu31f7Lbso2ylyZBGgOBuGBjPPy7u/JxQBtUVzQn19lk8yF1BLN8ijK/INoHJzk5yO2Kt3Musm/jSGMCIYycDachcknOeDu4x0HvQBtUVz89zrzhDHaBC5VtvBx8wypOePlzk80+1uNbmurQXMCxRbsyFF6ja/Uk8YO3tzmgDdorD0+71fzba3uoAdw+eRkwVwATnBIPJwD+lMMuvRea6xrN8xUKygYG5vmGD6beO9AG/RWNPeayhtjFZJJmLfMBx82CSoJPB4A5/vUwXWvb5A9rCAmSNozuwpIAyR1O36ZoA3KK5karrpnMAskEipk5Q4PzdRz/d5+vHWrlxda0rqIrWLHkKzHk/P/ABAfTj86ANqisHz9e2sVhTcckb0GB8pIXAb1wM570Pda95+xbZNiEZbaPnww9+AQT9MUAb1FYdrca3NdWguYFii3ZkKL1G1+pJ452cY5zTWuNbldWFu0QAUMoUEE/Lu7+pOD6CgDeorCNxryIWNvEwIwFCcry3Od3OAFPT+L2pbS51w3EC3FtGI3bMhx9wbV4GD655NAG5RWG91rqyAC2iKFcswXJX5jnAzyQMcZ5p4uta80gWiMuQPmXbjPf7xz6kdunNAGzRWCl9raWrzz2kYZQoWJQcuxOCM54x+tSXP9pf2tCP3n2cEbvLHyngf13fpQBtUVzZGuzGFlMsYRFEm7aCXG3cQO/wDFjscVNqL6vJeo9gsixtACFYAANh85z3+5/nNAG9RXNt/aonYr9s+z+W/lbgC+cDG7HvnGe1Wr271mKcrbWiOgiBLYzluM45+tAG1RWPcyatLc+VCnlRbozvCgkLlN3OevLjHtmo7u51iC9nkjgLWzskcWAHK4YAttGOoLd/4RQBuUVjwSau89q1xEqIXPmrHj5RjAzyc880sz3y6dPHtuXnWU7WTaCULnbzjoFxnAzj3oA16K5l/7aA2l7skna0iqoAPO1gvXGMZHrgdM1fhbV9mpCdMSNk2pRgQPlGB0Hf1757UAa9FYUtxryHcsEbDaTtC9yw9yeBuPvS/atdXy2a1iIZhuVF5UcZ53d/0xQBuUVjWlzqq3UUFxECJDknb/AKtQik8g85JYfWori610kIloqg7gWXGcAEBhz3IyB6Ee9AG9RWMl5rL3kCGyRICwEjN17Zxz064PtTJbjXY/MZbeJ1JIQBOVGeCfm54/nQBuUVjRXuqy2cztaqkqMiYVd2c4LEDPOM469Qaie416EYW3WbdISMqBtXe3yn5v7u05oA3qK5ybUtchnitxaRvIyFs7CFJ25C9eOcjPtk9atSXesrb2hS0RpX3ebxwBnA78HHP4UAbNFYbvqkxsHZJIyUk80IMANldufbGTUAGuTzwSDzY4lKh1baCf9WG47/x/5xQB0dFYFxda4l5MYbbfGDtRSo24y2DnOTkbee2as6fPqrXiRXcI8kRHfKVAJfIxjB6dfyoA1qKKKACiiigAooooAKKKKACs7VJtQh2NYwiT5TuBGecrgdR2zWjRQBjX99qkNwY4LQmPfgSCPf8AKQvbcOc7/wAh+Nb7X4gE+42Y52qUwNq8tnBzk8bea6KigDnluvECrse3UgKAZAmWzxkhc465GM9ADVhjqLHTMiZf3RFxj/np8uM47fe9q2aKAOcxrs8kDjzY0QKJA20EkbAxx3/j/wA4q/eXGrJPEtvawmMltzbi3HGPTHGfyFalFAGDFPrshUtCqlto5QYXkhj1yeMHFMkuvEPlqi20YkKEmRUGAdmeAW7Nx710NFAHPvc69I2w2yxqZgNyDJ2hx1yeBtyc8+lS3lxrDyOlvAUEZfDhQQ3DbeCeeNv4mtuigDEM+uKzMLeNo1JwhX5mGVxzu9Cx/wCA1Ct34h+VjaxndtG0rgDl+eueQE+ma6GigDHurjWo7jFvbQyRl2C7uOAFxk575bn2FMN5rK8raCUBd20oELdc/wARwewH41t0UAYa3etqZjNBAiR7jkKTuwDgDnvx69avp9pvNOUtutp2wSMcr3x1644+tXaKAMK4l1aA3LwQzSzb2Cq23ytuflxznOOD9TntTorrV4wfNtmYK33dgJK56g7uvtjoM1t0UAc8bnX9qotvtJDM7lQdp3EgDnnjAx705bnXZZI1ktliUzLuKDPyhlyCSeBt3c85rfooAxbi81gSFIrIbQ5UnH3hlsEHPptP4n6VXudR1m1MKtbIRK+NwjLbBlhzg88BT+PtXRUUAY1y+spfytbqrwqfkRlADA+XxnPr5hz7U37XrX2K3YWkf2h5MOMcKvvz9fyrbooAwxd69JOi/Y4YkYLkn5ivzYJ69hzj3qs+q64l0kJsVG9m2krwRxgHn1yM9uvSulooAxxdaz9hhcWkTTtvDKeAuOVJ574I+pHaoku9ZV2ZrQuGwE+UDHzYyRnjjJP4Vu0UAYD3PiGNFxbQSFiM/Ljbxn+8c8nH4VYnfVZdQEUa+XbLKpLhRygx3z35zxxj3rXooAxJpNbga6aJUlUyZjDL0UkDjB7Dn3zSJPrj3YLQokaH5gF4cFl4BznIG45rcooAxLm81xJp0isYzGpPlydd3I7Z9CP++W9qiF14hBC/ZYiW3Elh93rgcHtx355roKKAMHU/7X+2qLXzCgAxsAwTjv8Ajn26Uyxl1eK4Bu0uHgLENgAkdfxx06cV0NFAGDPLrXl2axREnylaU4HLbW3AnPHO3t3PpSXVxrrRyCO2CFlKqyAMQRuAON3AJAOewIrfooAwFuddkliWS2WJTMu4oM/KGXIJJ4GN3POadFd6vFciGSAOjzlFZlwVUsxzweQFC+nLVu0UAYc8muR3Fw8CLKikiON1ADDtzntT5LrWfItPLtYzK+7zcjhRnA78cc1s0UAYkd1rslwA1nFFE208/MVBwSOuM9R+GapjVte89YDYKJCjMuUOG4JA68cgf5Irp6KAMWe71pYLYw2iPKysZMjgH+Hvx2NL9p1qOXL28ckSybTsXDMufvj5vTtWzRQBi2dzrE2oRC5tfJtwAXxjrtOR1PGcVFb3uuTQCVrZUUop5iw2Ttzhd3bLfXH579FAHPrea8JzJJZ5XyyDGqjAfB24O7nnGfTJ+tSNca2drLACwDZj2BR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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display\n", + "\n", + "\n", + "def resize_image(image, max_height=800):\n", + " width, height = image.size\n", + " if height > max_height:\n", + " ratio = max_height / height\n", + " new_width = int(width * ratio)\n", + " new_height = int(height * ratio)\n", + " return image.resize((new_width, new_height))\n", + " return image\n", + "\n", + "\n", + "display(resize_image(sample_pdfs[0][\"images\"][0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also look at the extracted text content of the first PDF page.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Water\n", + "Water sourcing and produced water management are global challenges that require local solutions. We collaborate with \n", + "other users, communities and regulators on solutions and align our actions to protect and conserve water resources.\n", + "Biodiversity \n", + "We manage biodiversity risks and mitigate impacts through the use of the Mitigation Hierarchy, a decision-making \n", + "framework involving prioritized steps to mitigate adverse biodiversity impacts: avoid, minimize, restore and offset. \n", + "Our efforts are designed to reduce impact on biodiversity and contribute to its restoration.Our policies require nature-related risks be assessed in business planning. We disclose our approach \n", + "to governance, strategy, management and performance related to nature. NATURESustainability\n", + "23-1207HAB ITATS CONSE RVED, PROTECTED OR REST ORED\n", + "2 Estimated as the percentage of lease areas overlapping with designated \n", + "protected areas using the World Database on Protected Areas.OVER \n", + "540 ,000\n", + "CUMULATIVE ACRES1\n", + " \n", + "1 Cumulative with varying conservation pr oject start dates \n", + "as early as 2009.on company-owned lands \n", + "and operated assets. UNCONVENTIONAL\n", + "Bakken | Eagle Ford\n", + "Montney | Permian BasinBBL/BOE EUR1\n", + "BBL/BOE2\n", + "CONVENTIONAL/OFFSHORE\n", + "Alaska | APLNG | Ekofisk\n", + "Surmont | Teesside FRESH WATER \n", + "CONSUMPTION \n", + "INTENSITY\n", + "1 Calculated using Enverus data for the average volume of fresh water (bbl) divided by the average estimated ultimate recovery (EUR, BOE) as of April 1, 2024. Intensity value may change as EUR data \n", + "is updated. EUR – estimated ultimate recovery. 2 Calculated using the average volume of fresh water (BBL) divided by the average annual production (BOE).\n", + "24-0976As of Dec. 31, 20230.03%OF LEASE AREAS OVERLAP \n", + "WITH PROTECTED AREAS2\n", + "12PROTECTED AREAS WITHIN \n", + "3 MILES (5 KM) OF FIVE ASSETS\n", + "APLNG | Bakken | Permian Basin \n", + "Montney | Teesside0.06\n", + "0.03\n" + ] + } + ], + "source": [ + "print(sample_pdfs[0][\"texts\"][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the layout and order of the text is different from the image representation. Note that\n", + "\n", + "- The headlines NATURE and Sustainability have been combined into one word (NATURESustainability).\n", + "- The 0.03% has been converted to 0.03 and order is not preserved in the text representation.\n", + "- The data in the infographics is not represented in the text representation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ColPali model to generate embeddings of the images.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NRp3P9SlTK97", + "outputId": "b80587ba-4131-45fa-9803-0f42ada54019" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:09<00:00, 9.46s/it]\n", + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26/26 [06:28<00:00, 14.94s/it]\n", + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 52/52 [12:57<00:00, 14.95s/it]\n" + ] + } + ], + "source": [ + "for pdf in sample_pdfs:\n", + " page_embeddings = []\n", + " dataloader = DataLoader(\n", + " pdf[\"images\"],\n", + " batch_size=2,\n", + " shuffle=False,\n", + " collate_fn=lambda x: processor.process_images(x),\n", + " )\n", + "\n", + " for batch_doc in tqdm(dataloader):\n", + " with torch.no_grad():\n", + " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", + " embeddings_doc = model(**batch_doc)\n", + " page_embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))\n", + " pdf[\"embeddings\"] = page_embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are done with the document side embeddings, we convert the embeddings to Vespa JSON format so we can store (and index) them in Vespa.\n", + "Details in [Vespa JSON feed format doc](https://docs.vespa.ai/en/reference/document-json-format.html).\n", + "\n", + "We use binary quantization (BQ) of the page level ColPali vector embeddings to reduce their size by 32x.\n", + "\n", + "Read more about binarization of multi-vector representations in the [colbert blog post](https://blog.vespa.ai/announcing-colbert-embedder-in-vespa/).\n", + "\n", + "The binarization step maps 128 dimensional floats to 128 bits, or 16 bytes per vector.\n", + "\n", + "Reducing the size by 32x. On the [DocVQA benchmark](https://huggingface.co/datasets/vidore/docvqa_test_subsampled), binarization results in a small drop in ranking accuracy.\n", + "\n", + "We also demonstrate how to store the image data in Vespa using the [raw](https://docs.vespa.ai/en/reference/schema-reference.html#raw) type for binary data. To encode\n", + "the binary data in JSON, we use base64 encoding.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "\n", + "\n", + "def get_base64_image(image):\n", + " buffered = BytesIO()\n", + " image.save(buffered, format=\"JPEG\")\n", + " return str(base64.b64encode(buffered.getvalue()), \"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "FObCnKQQeHQ_" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "vespa_feed = []\n", + "for pdf in sample_pdfs:\n", + " url = pdf[\"url\"]\n", + " title = pdf[\"title\"]\n", + " for page_number, (page_text, embedding, image) in enumerate(\n", + " zip(pdf[\"texts\"], pdf[\"embeddings\"], pdf[\"images\"])\n", + " ):\n", + " base_64_image = get_base64_image(resize_image(image, 640))\n", + " embedding_dict = dict()\n", + " for idx, patch_embedding in enumerate(embedding):\n", + " binary_vector = (\n", + " np.packbits(np.where(patch_embedding > 0, 1, 0))\n", + " .astype(np.int8)\n", + " .tobytes()\n", + " .hex()\n", + " )\n", + " embedding_dict[idx] = binary_vector\n", + " page = {\n", + " \"id\": hash(url + str(page_number)),\n", + " \"url\": url,\n", + " \"title\": title,\n", + " \"page_number\": page_number,\n", + " \"image\": base_64_image,\n", + " \"text\": page_text,\n", + " \"embedding\": embedding_dict,\n", + " }\n", + " vespa_feed.append(page)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure Vespa\n", + "\n", + "[PyVespa](https://pyvespa.readthedocs.io/en/latest/) helps us build the [Vespa application package](https://docs.vespa.ai/en/application-packages.html).\n", + "A Vespa application package consists of configuration files, schemas, models, and code (plugins).\n", + "\n", + "First, we define a [Vespa schema](https://docs.vespa.ai/en/schemas.html) with the fields we want to store and their type.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import Schema, Document, Field, FieldSet, HNSW\n", + "\n", + "colpali_schema = Schema(\n", + " name=\"pdf_page\",\n", + " document=Document(\n", + " fields=[\n", + " Field(\n", + " name=\"id\", type=\"string\", indexing=[\"summary\", \"index\"], match=[\"word\"]\n", + " ),\n", + " Field(name=\"url\", type=\"string\", indexing=[\"summary\", \"index\"]),\n", + " Field(\n", + " name=\"title\",\n", + " type=\"string\",\n", + " indexing=[\"summary\", \"index\"],\n", + " match=[\"text\"],\n", + " index=\"enable-bm25\",\n", + " ),\n", + " Field(name=\"page_number\", type=\"int\", indexing=[\"summary\", \"attribute\"]),\n", + " Field(name=\"image\", type=\"raw\", indexing=[\"summary\"]),\n", + " Field(\n", + " name=\"text\",\n", + " type=\"string\",\n", + " indexing=[\"index\"],\n", + " match=[\"text\"],\n", + " index=\"enable-bm25\",\n", + " ),\n", + " Field(\n", + " name=\"embedding\",\n", + " type=\"tensor(patch{}, v[16])\",\n", + " indexing=[\n", + " \"attribute\",\n", + " \"index\",\n", + " ], # adds HNSW index for candidate retrieval.\n", + " ann=HNSW(\n", + " distance_metric=\"hamming\",\n", + " max_links_per_node=32,\n", + " neighbors_to_explore_at_insert=400,\n", + " ),\n", + " ),\n", + " ]\n", + " ),\n", + " fieldsets=[FieldSet(name=\"default\", fields=[\"title\", \"text\"])],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the `embedding` field which is a tensor field with the type `tensor(patch{}, v[16])`.\n", + "This is the field we use to represent the page level patch embeddings from ColPali.\n", + "\n", + "We also enable [HNSW indexing](https://docs.vespa.ai/en/approximate-nn-hnsw.html)\n", + "for this field to enable fast nearest neighbor search which is used for candidate retrieval.\n", + "\n", + "We use [binary hamming distance](https://docs.vespa.ai/en/nearest-neighbor-search.html#using-binary-embeddings-with-hamming-distance)\n", + "as an approximation of the cosine similarity. Hamming distance is a good approximation\n", + "for binary representations, and it is much faster to compute than cosine similarity/dot product.\n", + "\n", + "The `embedding` field is an example of a mixed tensor where we combine one mapped (sparse) dimensions with a dense dimension.\n", + "\n", + "Read more in [Tensor guide](https://docs.vespa.ai/en/tensor-user-guide.html). We also enable [BM25](https://docs.vespa.ai/en/reference/bm25.html) for the `title` and `texts`Β fields. Notice that the `image` field use type `raw` to store the binary image data, encoded with as a base64 string.\n", + "\n", + "Create the Vespa [application package](https://docs.vespa.ai/en/application-packages):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import ApplicationPackage\n", + "\n", + "vespa_app_name = \"visionrag6\"\n", + "vespa_application_package = ApplicationPackage(\n", + " name=vespa_app_name, schema=[colpali_schema]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define how we want to rank the pages for a query. We use Vespa's support for [BM25](https://docs.vespa.ai/en/reference/bm25.html) for the text, and\n", + "late interaction with Max Sim for the image embeddings.\n", + "\n", + "This means that we use the the text representations as a candidate retrieval phase, then we use the ColPALI embeddings with MaxSim\n", + "to rerank the pages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", + "\n", + "colpali_profile = RankProfile(\n", + " name=\"default\",\n", + " inputs=[(\"query(qt)\", \"tensor(querytoken{}, v[128])\")],\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " sum(\n", + " query(qt) * unpack_bits(attribute(embedding)) , v\n", + " ),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " ),\n", + " Function(name=\"bm25_score\", expression=\"bm25(title) + bm25(text)\"),\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"bm25_score\"),\n", + " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=100),\n", + ")\n", + "colpali_schema.add_rank_profile(colpali_profile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first phase uses a linear combination of BM25 scores for the text fields, and the second phase uses the MaxSim function with the image embeddings. Notice that Vespa supports a `unpack_bits` function to convert the 16 compressed binary vectors to 128-dimensional floats for the MaxSim function. The query input tensor is not compressed and using full float resolution.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deploy the application to Vespa Cloud\n", + "\n", + "With the configured application, we can deploy it to [Vespa Cloud](https://cloud.vespa.ai/en/).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To deploy the application to Vespa Cloud we need to create a tenant in the Vespa Cloud:\n", + "\n", + "Create a tenant at [console.vespa-cloud.com](https://console.vespa-cloud.com/) (unless you already have one).\n", + "This step requires a Google or GitHub account, and will start your [free trial](https://cloud.vespa.ai/en/free-trial).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.deployment import VespaCloud\n", + "import os\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "\n", + "# Replace with your tenant name from the Vespa Cloud Console\n", + "tenant_name = \"vespa-team\"\n", + "\n", + "key = os.getenv(\"VESPA_TEAM_API_KEY\", None)\n", + "if key is not None:\n", + " key = key.replace(r\"\\n\", \"\\n\") # To parse key correctly\n", + "\n", + "vespa_cloud = VespaCloud(\n", + " tenant=tenant_name,\n", + " application=vespa_app_name,\n", + " key_content=key, # Key is only used for CI/CD testing of this notebook. Can be removed if logging in interactively\n", + " application_package=vespa_application_package,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now deploy the app to Vespa Cloud dev zone.\n", + "\n", + "The first deployment typically takes 2 minutes until the endpoint is up.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.application import Vespa\n", + "\n", + "app: Vespa = vespa_cloud.deploy()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of PDF pages: 155\n" + ] + } + ], + "source": [ + "print(\"Number of PDF pages:\", len(vespa_feed))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index the documents in Vespa using the Vespa HTTP API.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 155/155 [00:22<00:00, 7.04it/s]\n" + ] + } + ], + "source": [ + "from vespa.io import VespaResponse\n", + "\n", + "async with app.asyncio(connections=1, timeout=180) as session:\n", + " for page in tqdm(vespa_feed):\n", + " response: VespaResponse = await session.feed_data_point(\n", + " data_id=page[\"id\"], fields=page, schema=\"pdf_page\"\n", + " )\n", + " if not response.is_successful():\n", + " print(response.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j2pUyGjYf4Wv" + }, + "source": [ + "### Querying Vespa\n", + "\n", + "Ok, so now we have indexed the PDF pages in Vespa. Let us now obtain ColPali embeddings for a few text queries and\n", + "use it during ranking of the indexed pdf pages.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can query Vespa with the text query and rerank the results using the ColPali embeddings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "V2U58J_B5-L6" + }, + "outputs": [], + "source": [ + "queries = [\n", + " \"Percentage of non-fresh water as source?\",\n", + " \"Policies related to nature risk?\",\n", + " \"How much of produced water is recycled?\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain the query embeddings using the ColPali model:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "NxeDd3mcYDpL" + }, + "outputs": [], + "source": [ + "dataloader = DataLoader(\n", + " queries,\n", + " batch_size=1,\n", + " shuffle=False,\n", + " collate_fn=lambda x: processor.process_queries(x),\n", + ")\n", + "qs = []\n", + "for batch_query in dataloader:\n", + " with torch.no_grad():\n", + " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", + " embeddings_query = model(**batch_query)\n", + " qs.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a simple routine to display the results. We render the image and the title of the retrieved page/document.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, HTML\n", + "\n", + "\n", + "def display_query_results(query, response, hits=5):\n", + " query_time = response.json.get(\"timing\", {}).get(\"searchtime\", -1)\n", + " query_time = round(query_time, 2)\n", + " count = response.json.get(\"root\", {}).get(\"fields\", {}).get(\"totalCount\", 0)\n", + " html_content = f\"

Query text: '{query}', query time {query_time}s, count={count}, top results:

\"\n", + "\n", + " for i, hit in enumerate(response.hits[:hits]):\n", + " title = hit[\"fields\"][\"title\"]\n", + " url = hit[\"fields\"][\"url\"]\n", + " page = hit[\"fields\"][\"page_number\"]\n", + " image = hit[\"fields\"][\"image\"]\n", + " score = hit[\"relevance\"]\n", + "\n", + " html_content += f\"

PDF Result {i + 1}

\"\n", + " html_content += f'

Title: {title}, page {page+1} with score {score:.2f}

'\n", + " html_content += (\n", + " f''\n", + " )\n", + "\n", + " display(HTML(html_content))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query Vespa with the queries and display the results, here we are using the `default` rank profile.\n", + "\n", + "Note that we retrieve using textual representation with `userInput(@userQuery)`, this means that we use the BM25 ranking for the extracted text in the first ranking phase and then re-rank the top-k pages using the ColPali embeddings.\n", + "\n", + "Later in this notebook we will use Vespa's support for approximate nearest neighbor search (`nearestNeighbor`) to retrieve directly using the ColPali embeddings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Query text: 'Percentage of non-fresh water as source?', query time 0.15s, count=233, top results:

PDF Result 1

Title: ConocoPhillips Sustainability Highlights - Nature (24-0976), page 1 with score 74.30

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 72.69

PDF Result 3

Title: ConocoPhillips Managing Climate Related Risks, page 45 with score 72.01

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Query text: 'Policies related to nature risk?', query time 0.08s, count=200, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 44 with score 77.48

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 42 with score 77.15

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 41 with score 72.63

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Query text: 'How much of produced water is recycled?', query time 0.08s, count=243, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 92 with score 84.52

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 82.96

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 87 with score 80.30

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from vespa.io import VespaQueryResponse\n", + "\n", + "async with app.asyncio(connections=1, timeout=120) as session:\n", + " for idx, query in enumerate(queries):\n", + " query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}\n", + " response: VespaQueryResponse = await session.query(\n", + " yql=\"select title,url,image,page_number from pdf_page where userInput(@userQuery)\",\n", + " ranking=\"default\",\n", + " userQuery=query,\n", + " timeout=120,\n", + " hits=3,\n", + " body={\"input.query(qt)\": query_embedding, \"presentation.timing\": True},\n", + " )\n", + " assert response.is_successful()\n", + " display_query_results(query, response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using nearestNeighbor for retrieval\n", + "\n", + "In the above example, we used the ColPali embeddings in ranking, but using the text query for retrieval.\n", + "This is a reasonable approach for text-heavy documents where the text representation is the most important and where ColPali embeddings are used to\n", + "re-rank the top-k documents from the text retrieval phase.\n", + "\n", + "In some cases, the ColPali embeddings are the most important and we want\n", + "to demonstrate how we can use HNSW indexing with binary hamming distance to retrieve the most similar pages to a query and\n", + "then have two steps of re-ranking using the ColPali embeddings.\n", + "\n", + "All the phases here are executed locally inside the Vespa content node(s) so that no vector data needs\n", + "to cross the network.\n", + "\n", + "Let us add a new rank-profile to the schema, the `nearestNeighbor` operator takes a query tensor and a field tensor as argument and\n", + "we need to define the query tensors types in the rank-profile.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.package import RankProfile, Function, FirstPhaseRanking, SecondPhaseRanking\n", + "\n", + "input_query_tensors = []\n", + "MAX_QUERY_TERMS = 64\n", + "for i in range(MAX_QUERY_TERMS):\n", + " input_query_tensors.append((f\"query(rq{i})\", \"tensor(v[16])\"))\n", + "\n", + "input_query_tensors.append((\"query(qt)\", \"tensor(querytoken{}, v[128])\"))\n", + "input_query_tensors.append((\"query(qtb)\", \"tensor(querytoken{}, v[16])\"))\n", + "\n", + "colpali_retrieval_profile = RankProfile(\n", + " name=\"retrieval-and-rerank\",\n", + " inputs=input_query_tensors,\n", + " functions=[\n", + " Function(\n", + " name=\"max_sim\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " sum(\n", + " query(qt) * unpack_bits(attribute(embedding)) , v\n", + " ),\n", + " max, patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " ),\n", + " Function(\n", + " name=\"max_sim_binary\",\n", + " expression=\"\"\"\n", + " sum(\n", + " reduce(\n", + " 1/(1 + sum(\n", + " hamming(query(qtb), attribute(embedding)) ,v)\n", + " ),\n", + " max,\n", + " patch\n", + " ),\n", + " querytoken\n", + " )\n", + " \"\"\",\n", + " ),\n", + " ],\n", + " first_phase=FirstPhaseRanking(expression=\"max_sim_binary\"),\n", + " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=10),\n", + ")\n", + "colpali_schema.add_rank_profile(colpali_retrieval_profile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define two functions, one for the first phase and one for the second phase. Instead of the float representations, we use the binary representations with inverted hamming distance in the first phase. Now, we need to re-deploy the application to Vespa Cloud.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vespa.application import Vespa\n", + "\n", + "app: Vespa = vespa_cloud.deploy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can query Vespa with the text queries and use the `nearestNeighbor` operator to retrieve the most similar pages to the query and pass the different query tensors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Query text: 'Percentage of non-fresh water as source?', query time 0.1s, count=173, top results:

PDF Result 1

Title: ConocoPhillips Sustainability Highlights - Nature (24-0976), page 1 with score 74.30

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 72.69

PDF Result 3

Title: ConocoPhillips Managing Climate Related Risks, page 45 with score 72.01

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Query text: 'Policies related to nature risk?', query time 0.07s, count=173, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 44 with score 77.48

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 42 with score 77.15

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 41 with score 72.63

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Query text: 'How much of produced water is recycled?', query time 0.04s, count=118, top results:

PDF Result 1

Title: ConocoPhillips 2023 Sustainability Report, page 92 with score 84.52

PDF Result 2

Title: ConocoPhillips 2023 Sustainability Report, page 51 with score 82.96

PDF Result 3

Title: ConocoPhillips 2023 Sustainability Report, page 87 with score 80.30

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used in the nearest neighbor calculations\n", + " for i in range(0, len(binary_query_embeddings)):\n", + " query_tensors[f\"input.query(rq{i})\"] = binary_query_embeddings[i]\n", + " nn = []\n", + " for i in range(0, len(binary_query_embeddings)):\n", + " nn.append(\n", + " f\"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))\"\n", + " )\n", + " # We use a OR operator to combine the nearest neighbor operator\n", + " nn = \" OR \".join(nn)\n", + " response: VespaQueryResponse = await session.query(\n", + " yql=f\"select title, url, image, page_number from pdf_page where {nn}\",\n", + " ranking=\"retrieval-and-rerank\",\n", + " timeout=120,\n", + " hits=3,\n", + " body={**query_tensors, \"presentation.timing\": True},\n", + " )\n", + " assert response.is_successful()\n", + " display_query_results(query, response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on the scale, we can evaluate changing different number of targetHits per nearestNeighbor operator and the ranking depths in the two phases.\n", + "We can also parallelize the ranking phases by using more threads per query request to reduce latency.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we have demonstrated how to represent the new ColQwen2 in Vespa.\n", + "We have generated embeddings for images of PDF pages using ColQwen2 and stored the embeddings in Vespa using [mixed tensors](https://docs.vespa.ai/en/tensor-user-guide.html).\n", + "\n", + "We demonstrated how to store the base64 encoded image using the `raw` Vespa field type, plus meta data like title and url.\n", + "We have demonstrated how to retrieve relevant pages for a query using the embeddings generated by ColPali.\n", + "\n", + "This notebook can be extended to include more complex ranking models, more complex queries, and more complex data structures, including metadata and other fields which can be filtered on or used for ranking.\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "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": "3.11.10" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00c2c14a88514261b07eb1df9bbc0581": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": 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"nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/sphinx/source/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb b/docs/sphinx/source/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb index f096320f..dce1da04 100644 --- a/docs/sphinx/source/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb +++ b/docs/sphinx/source/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb @@ -99,7 +99,10 @@ "from io import BytesIO\n", "\n", "from colpali_engine.models.paligemma_colbert_architecture import ColPali\n", - "from colpali_engine.utils.colpali_processing_utils import process_images, process_queries\n", + "from colpali_engine.utils.colpali_processing_utils import (\n", + " process_images,\n", + " process_queries,\n", + ")\n", "from colpali_engine.utils.image_utils import scale_image, get_base64_image" ] }, @@ -129,14 +132,14 @@ "outputs": [], "source": [ "if torch.cuda.is_available():\n", - " device = torch.device(\"cuda\")\n", - " type = torch.bfloat16\n", + " device = torch.device(\"cuda\")\n", + " type = torch.bfloat16\n", "elif torch.backends.mps.is_available():\n", - " device = torch.device(\"mps\")\n", - " type = torch.float32\n", + " device = torch.device(\"mps\")\n", + " type = torch.float32\n", "else:\n", - " device = torch.device(\"cpu\")\n", - " type = torch.float32" + " device = torch.device(\"cpu\")\n", + " type = torch.float32" ] }, { @@ -327,7 +330,9 @@ "outputs": [], "source": [ "model_name = \"vidore/colpali-v1.2\"\n", - "model = ColPali.from_pretrained(\"vidore/colpaligemma-3b-pt-448-base\", torch_dtype=type).eval()\n", + "model = ColPali.from_pretrained(\n", + " \"vidore/colpaligemma-3b-pt-448-base\", torch_dtype=type\n", + ").eval()\n", "model.load_adapter(model_name)\n", "model = model.eval()\n", "model.to(device)\n", @@ -360,6 +365,7 @@ "from pdf2image import convert_from_path\n", "from pypdf import PdfReader\n", "\n", + "\n", "def download_pdf(url):\n", " response = requests.get(url)\n", " if response.status_code == 200:\n", @@ -367,6 +373,7 @@ " else:\n", " raise Exception(f\"Failed to download PDF: Status code {response.status_code}\")\n", "\n", + "\n", "def get_pdf_images(pdf_url):\n", " # Download the PDF\n", " pdf_file = download_pdf(pdf_url)\n", @@ -401,18 +408,18 @@ "outputs": [], "source": [ "sample_pdfs = [\n", - " {\n", - " \"title\": \"ConocoPhillips Sustainability Highlights - Nature (24-0976)\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf\"\n", - " },\n", - " {\n", - " \"title\": \"ConocoPhillips Managing Climate Related Risks\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf\"\n", - " },\n", - " {\n", - " \"title\": \"ConocoPhillips 2023 Sustainability Report\",\n", - " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf\" \n", - " }\n", + " {\n", + " \"title\": \"ConocoPhillips Sustainability Highlights - Nature (24-0976)\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf\",\n", + " },\n", + " {\n", + " \"title\": \"ConocoPhillips Managing Climate Related Risks\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf\",\n", + " },\n", + " {\n", + " \"title\": \"ConocoPhillips 2023 Sustainability Report\",\n", + " \"url\": \"https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf\",\n", + " },\n", "]" ] }, @@ -432,9 +439,9 @@ "outputs": [], "source": [ "for pdf in sample_pdfs:\n", - " page_images, page_texts = get_pdf_images(pdf['url'])\n", - " pdf['images'] = page_images\n", - " pdf['texts'] = page_texts\n" + " page_images, page_texts = get_pdf_images(pdf[\"url\"])\n", + " pdf[\"images\"] = page_images\n", + " pdf[\"texts\"] = page_texts" ] }, { @@ -472,7 +479,8 @@ ], "source": [ "from IPython.display import display\n", - "display(scale_image(sample_pdfs[0]['images'][0],720))" + "\n", + "display(scale_image(sample_pdfs[0][\"images\"][0], 720))" ] }, { @@ -529,7 +537,7 @@ } ], "source": [ - "print(sample_pdfs[0]['texts'][0])" + "print(sample_pdfs[0][\"texts\"][0])" ] }, { @@ -572,21 +580,20 @@ } ], "source": [ - "\n", "for pdf in sample_pdfs:\n", - " page_embeddings = []\n", - " dataloader = DataLoader(\n", - " pdf['images'],\n", + " page_embeddings = []\n", + " dataloader = DataLoader(\n", + " pdf[\"images\"],\n", " batch_size=2,\n", " shuffle=False,\n", " collate_fn=lambda x: process_images(processor, x),\n", " )\n", - " for batch_doc in tqdm(dataloader):\n", - " with torch.no_grad():\n", - " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", - " embeddings_doc = model(**batch_doc)\n", - " page_embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))\n", - " pdf['embeddings'] = page_embeddings" + " for batch_doc in tqdm(dataloader):\n", + " with torch.no_grad():\n", + " batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}\n", + " embeddings_doc = model(**batch_doc)\n", + " page_embeddings.extend(list(torch.unbind(embeddings_doc.to(\"cpu\"))))\n", + " pdf[\"embeddings\"] = page_embeddings" ] }, { @@ -613,26 +620,34 @@ "outputs": [], "source": [ "import numpy as np\n", + "\n", "vespa_feed = []\n", "for pdf in sample_pdfs:\n", - " url = pdf['url']\n", - " title = pdf['title']\n", - " for page_number, (page_text, embedding, image) in enumerate(zip(pdf['texts'], pdf['embeddings'], pdf['images'])):\n", - " base_64_image = get_base64_image(scale_image(image,640),add_url_prefix=False)\n", - " embedding_dict = dict()\n", - " for idx, patch_embedding in enumerate(embedding):\n", - " binary_vector = np.packbits(np.where(patch_embedding > 0, 1, 0)).astype(np.int8).tobytes().hex()\n", - " embedding_dict[idx] = binary_vector \n", - " page = {\n", - " \"id\": hash(url + str(page_number)),\n", - " \"url\": url,\n", - " \"title\": title,\n", - " \"page_number\": page_number,\n", - " \"image\": base_64_image,\n", - " \"text\": page_text,\n", - " \"embedding\": embedding_dict\n", - " }\n", - " vespa_feed.append(page)" + " url = pdf[\"url\"]\n", + " title = pdf[\"title\"]\n", + " for page_number, (page_text, embedding, image) in enumerate(\n", + " zip(pdf[\"texts\"], pdf[\"embeddings\"], pdf[\"images\"])\n", + " ):\n", + " base_64_image = get_base64_image(scale_image(image, 640), add_url_prefix=False)\n", + " embedding_dict = dict()\n", + " for idx, patch_embedding in enumerate(embedding):\n", + " binary_vector = (\n", + " np.packbits(np.where(patch_embedding > 0, 1, 0))\n", + " .astype(np.int8)\n", + " .tobytes()\n", + " .hex()\n", + " )\n", + " embedding_dict[idx] = binary_vector\n", + " page = {\n", + " \"id\": hash(url + str(page_number)),\n", + " \"url\": url,\n", + " \"title\": title,\n", + " \"page_number\": page_number,\n", + " \"image\": base_64_image,\n", + " \"text\": page_text,\n", + " \"embedding\": embedding_dict,\n", + " }\n", + " vespa_feed.append(page)" ] }, { @@ -652,29 +667,49 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "from vespa.package import Schema, Document, Field, FieldSet, HNSW\n", "\n", "colpali_schema = Schema(\n", " name=\"pdf_page\",\n", " document=Document(\n", " fields=[\n", - " Field(name=\"id\", type=\"string\", indexing=[\"summary\", \"index\"], match=[\"word\"]),\n", + " Field(\n", + " name=\"id\", type=\"string\", indexing=[\"summary\", \"index\"], match=[\"word\"]\n", + " ),\n", " Field(name=\"url\", type=\"string\", indexing=[\"summary\", \"index\"]),\n", - " Field(name=\"title\", type=\"string\", indexing=[\"summary\", \"index\"], match=[\"text\"], index=\"enable-bm25\"),\n", + " Field(\n", + " name=\"title\",\n", + " type=\"string\",\n", + " indexing=[\"summary\", \"index\"],\n", + " match=[\"text\"],\n", + " index=\"enable-bm25\",\n", + " ),\n", " Field(name=\"page_number\", type=\"int\", indexing=[\"summary\", \"attribute\"]),\n", " Field(name=\"image\", type=\"raw\", indexing=[\"summary\"]),\n", - " Field(name=\"text\", type=\"string\", indexing=[\"index\"], match=[\"text\"], index=\"enable-bm25\"),\n", + " Field(\n", + " name=\"text\",\n", + " type=\"string\",\n", + " indexing=[\"index\"],\n", + " match=[\"text\"],\n", + " index=\"enable-bm25\",\n", + " ),\n", " Field(\n", " name=\"embedding\",\n", " type=\"tensor(patch{}, v[16])\",\n", - " indexing=[\"attribute\", \"index\"], # adds HNSW index for candidate retrieval.\n", - " ann=HNSW(distance_metric=\"hamming\", max_links_per_node=32, neighbors_to_explore_at_insert=400), \n", - " )\n", + " indexing=[\n", + " \"attribute\",\n", + " \"index\",\n", + " ], # adds HNSW index for candidate retrieval.\n", + " ann=HNSW(\n", + " distance_metric=\"hamming\",\n", + " max_links_per_node=32,\n", + " neighbors_to_explore_at_insert=400,\n", + " ),\n", + " ),\n", " ]\n", " ),\n", - " fieldsets=[FieldSet(name=\"default\", fields=[\"title\", \"text\"])]\n", - ")\n" + " fieldsets=[FieldSet(name=\"default\", fields=[\"title\", \"text\"])],\n", + ")" ] }, { @@ -749,12 +784,10 @@ " )\n", " \"\"\",\n", " ),\n", - " Function(\n", - " name=\"bm25_score\", expression=\"bm25(title) + bm25(text)\"\n", - " )\n", + " Function(name=\"bm25_score\", expression=\"bm25(title) + bm25(text)\"),\n", " ],\n", " first_phase=FirstPhaseRanking(expression=\"bm25_score\"),\n", - " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=100)\n", + " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=100),\n", ")\n", "colpali_schema.add_rank_profile(colpali_profile)" ] @@ -794,10 +827,10 @@ "from vespa.deployment import VespaCloud\n", "import os\n", "\n", - "os.environ['TOKENIZERS_PARALLELISM'] = \"false\"\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", "\n", "# Replace with your tenant name from the Vespa Cloud Console\n", - "tenant_name = \"vespa-team\" \n", + "tenant_name = \"vespa-team\"\n", "\n", "key = os.getenv(\"VESPA_TEAM_API_KEY\", None)\n", "if key is not None:\n", @@ -863,10 +896,10 @@ "source": [ "from vespa.io import VespaResponse\n", "\n", - "async with app.asyncio(connections=1, total_timeout=180) as session:\n", + "async with app.asyncio(connections=1, timeout=180) as session:\n", " for page in vespa_feed:\n", " response: VespaResponse = await session.feed_data_point(\n", - " data_id=page['id'], fields=page, schema=\"pdf_page\"\n", + " data_id=page[\"id\"], fields=page, schema=\"pdf_page\"\n", " )\n", " if not response.is_successful():\n", " print(response.json())" @@ -910,8 +943,11 @@ }, "outputs": [], "source": [ - "queries = [\"Percentage of non-fresh water as source?\", \n", - " \"Policies related to nature risk?\", \"How much of produced water is recycled?\"]" + "queries = [\n", + " \"Percentage of non-fresh water as source?\",\n", + " \"Policies related to nature risk?\",\n", + " \"How much of produced water is recycled?\",\n", + "]" ] }, { @@ -930,17 +966,17 @@ "outputs": [], "source": [ "dataloader = DataLoader(\n", - " queries,\n", - " batch_size=1,\n", - " shuffle=False,\n", - " collate_fn=lambda x: process_queries(processor, x, dummy_image),\n", - " )\n", + " queries,\n", + " batch_size=1,\n", + " shuffle=False,\n", + " collate_fn=lambda x: process_queries(processor, x, dummy_image),\n", + ")\n", "qs = []\n", "for batch_query in dataloader:\n", - " with torch.no_grad():\n", - " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", - " embeddings_query = model(**batch_query)\n", - " qs.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))\n" + " with torch.no_grad():\n", + " batch_query = {k: v.to(model.device) for k, v in batch_query.items()}\n", + " embeddings_query = model(**batch_query)\n", + " qs.extend(list(torch.unbind(embeddings_query.to(\"cpu\"))))" ] }, { @@ -958,24 +994,25 @@ "source": [ "from IPython.display import display, HTML\n", "\n", + "\n", "def display_query_results(query, response, hits=5):\n", - " \n", - " \n", - " query_time = response.json.get('timing', {}).get('searchtime', -1)\n", + " query_time = response.json.get(\"timing\", {}).get(\"searchtime\", -1)\n", " query_time = round(query_time, 2)\n", - " count = response.json.get('root', {}).get('fields', {}).get('totalCount', 0)\n", - " html_content = f'

Query text: \\'{query}\\', query time {query_time}s, count={count}, top results:

'\n", - " \n", - " for i, hit in enumerate(response.hits[:hits]): \n", - " title = hit['fields']['title']\n", - " url = hit['fields']['url']\n", - " page = hit['fields']['page_number']\n", - " image = hit['fields']['image']\n", - " score = hit['relevance']\n", - " \n", - " html_content += f'

PDF Result {i + 1}

'\n", + " count = response.json.get(\"root\", {}).get(\"fields\", {}).get(\"totalCount\", 0)\n", + " html_content = f\"

Query text: '{query}', query time {query_time}s, count={count}, top results:

\"\n", + "\n", + " for i, hit in enumerate(response.hits[:hits]):\n", + " title = hit[\"fields\"][\"title\"]\n", + " url = hit[\"fields\"][\"url\"]\n", + " page = hit[\"fields\"][\"page_number\"]\n", + " image = hit[\"fields\"][\"image\"]\n", + " score = hit[\"relevance\"]\n", + "\n", + " html_content += f\"

PDF Result {i + 1}

\"\n", " html_content += f'

Title: {title}, page {page+1} with score {score:.2f}

'\n", - " html_content += f''\n", + " html_content += (\n", + " f''\n", + " )\n", "\n", " display(HTML(html_content))" ] @@ -1036,7 +1073,7 @@ "source": [ "from vespa.io import VespaQueryResponse\n", "\n", - "async with app.asyncio(connections=1, total_timeout=120) as session:\n", + "async with app.asyncio(connections=1, timeout=120) as session:\n", " for idx, query in enumerate(queries):\n", " query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}\n", " response: VespaQueryResponse = await session.query(\n", @@ -1045,10 +1082,7 @@ " userQuery=query,\n", " timeout=120,\n", " hits=3,\n", - " body={\n", - " \"input.query(qt)\": query_embedding,\n", - " \"presentation.timing\": True\n", - " },\n", + " body={\"input.query(qt)\": query_embedding, \"presentation.timing\": True},\n", " )\n", " assert response.is_successful()\n", " display_query_results(query, response)" @@ -1085,7 +1119,7 @@ "\n", "input_query_tensors = []\n", "MAX_QUERY_TERMS = 64\n", - "for i in range(MAX_QUERY_TERMS): \n", + "for i in range(MAX_QUERY_TERMS):\n", " input_query_tensors.append((f\"query(rq{i})\", \"tensor(v[16])\"))\n", "\n", "input_query_tensors.append((\"query(qt)\", \"tensor(querytoken{}, v[128])\"))\n", @@ -1123,10 +1157,10 @@ " querytoken\n", " )\n", " \"\"\",\n", - " )\n", + " ),\n", " ],\n", " first_phase=FirstPhaseRanking(expression=\"max_sim_binary\"),\n", - " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=10)\n", + " second_phase=SecondPhaseRanking(expression=\"max_sim\", rerank_count=10),\n", ")\n", "colpali_schema.add_rank_profile(colpali_retrieval_profile)" ] @@ -1200,40 +1234,44 @@ ], "source": [ "from vespa.io import VespaQueryResponse\n", - "target_hits_per_query_tensor = 20 # this is a hyper parameter that can be tuned for speed versus accuracy\n", - "async with app.asyncio(connections=1, total_timeout=180) as session:\n", + "\n", + "target_hits_per_query_tensor = (\n", + " 20 # this is a hyper parameter that can be tuned for speed versus accuracy\n", + ")\n", + "async with app.asyncio(connections=1, timeout=180) as session:\n", " for idx, query in enumerate(queries):\n", " float_query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}\n", " binary_query_embeddings = dict()\n", " for k, v in float_query_embedding.items():\n", - " binary_query_embeddings[k] = np.packbits(np.where(np.array(v) > 0, 1, 0)).astype(np.int8).tolist()\n", - " \n", + " binary_query_embeddings[k] = (\n", + " np.packbits(np.where(np.array(v) > 0, 1, 0)).astype(np.int8).tolist()\n", + " )\n", + "\n", " # The mixed tensors used in MaxSim calculations\n", - " # We use both binary and float representations \n", - " query_tensors={\n", - " \"input.query(qtb)\": binary_query_embeddings,\n", - " \"input.query(qt)\": float_query_embedding\n", + " # We use both binary and float representations\n", + " query_tensors = {\n", + " \"input.query(qtb)\": binary_query_embeddings,\n", + " \"input.query(qt)\": float_query_embedding,\n", " }\n", " # The query tensors used in the nearest neighbor calculations\n", - " for i in range(0,len(binary_query_embeddings)):\n", + " for i in range(0, len(binary_query_embeddings)):\n", " query_tensors[f\"input.query(rq{i})\"] = binary_query_embeddings[i]\n", " nn = []\n", - " for i in range(0,len(binary_query_embeddings)):\n", - " nn.append(f\"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))\")\n", + " for i in range(0, len(binary_query_embeddings)):\n", + " nn.append(\n", + " f\"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))\"\n", + " )\n", " # We use a OR operator to combine the nearest neighbor operator\n", " nn = \" OR \".join(nn)\n", " response: VespaQueryResponse = await session.query(\n", " yql=f\"select title, url, image, page_number from pdf_page where {nn}\",\n", " ranking=\"retrieval-and-rerank\",\n", " timeout=120,\n", - " hits=3,\n", - " body={\n", - " **query_tensors,\n", - " \"presentation.timing\": True\n", - " }\n", + " hits=3,\n", + " body={**query_tensors, \"presentation.timing\": True},\n", " )\n", " assert response.is_successful()\n", - " display_query_results(query, response)\n" + " display_query_results(query, response)" ] }, { @@ -6418,4 +6456,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file