diff --git a/.github/workflows/notebooks-cloud.yml b/.github/workflows/notebooks-cloud.yml
index 08817b95..de5607ca 100644
--- a/.github/workflows/notebooks-cloud.yml
+++ b/.github/workflows/notebooks-cloud.yml
@@ -26,7 +26,7 @@ jobs:
- name: Set output variable (Make sure it is this quote format - "[path/to/notebook1.ipynb", "path/to/notebook2.ipynb]")
id: set_output
run: |
- notebooks=$(find docs/sphinx/source -name '*cloud.ipynb' ! -name 'mother-of-all-embedding-models-cloud.ipynb' ! -name 'scaling-personal-ai-assistants-with-streaming-mode-cloud.ipynb' | jq -R -s -c 'split("\n")[:-1]')
+ notebooks=$(find docs/sphinx/source -name '*cloud.ipynb' ! -name 'mother-of-all-embedding-models-cloud.ipynb' ! -name 'scaling-personal-ai-assistants-with-streaming-mode-cloud.ipynb' ! -name 'colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb' | jq -R -s -c 'split("\n")[:-1]')
# Print all notebooks echo
echo $notebooks
echo "notebooks=$notebooks" >> $GITHUB_OUTPUT
diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst
index d7b3b1b0..e3154020 100644
--- a/docs/sphinx/source/examples.rst
+++ b/docs/sphinx/source/examples.rst
@@ -25,3 +25,4 @@ Examples
examples/evaluating-with-snowflake-arctic-embed.ipynb
examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.ipynb
examples/colpali-document-retrieval-vision-language-models-cloud.ipynb
+ examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb
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
new file mode 100644
index 00000000..50278d41
--- /dev/null
+++ b/docs/sphinx/source/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.ipynb
@@ -0,0 +1,6211 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XzoiJTAoZobv"
+ },
+ "source": [
+ "\n",
+ " \n",
+ " \n",
+ " ![\"#Vespa\"](\"https://assets.vespa.ai/logos/Vespa-logo-dark-RGB.svg\")
\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 π€ ColPali: Efficient Document Retrieval with Vision Language Models](colpali-document-retrieval-vision-language-models-cloud.ipynb)\n",
+ "\n",
+ "[](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"
+ ]
+ },
+ {
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yGfNhRP4RKBJ"
+ },
+ "source": [
+ "## Load the model\n",
+ "\n"
+ ]
+ },
+ {
+ "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": [
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+ "c551d36ebf3543cb87fd71922fb08bd4",
+ "1a6c2da3dc004653ba38a43274e8b1f8",
+ "243e816f39264b95bc8e0ee980ddfdfd",
+ "85dbe8aa26d04916b27a494d05574e39",
+ "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)"
+ ]
+ },
+ {
+ "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",
+ "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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|ββββββββββ| 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\"))))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generate embeddings for the queries in the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|ββββββββββ| 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 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",
+ " 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"
+ ]
+ },
+ {
+ "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",
+ "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"
+ ]
+ },
+ {
+ "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)\n"
+ ]
+ },
+ {
+ "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",
+ "# 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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|ββββββββββ| 500/500 [01:13<00:00, 6.77it/s]\n"
+ ]
+ }
+ ],
+ "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",
+ " "
+ ]
+ },
+ {
+ "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",
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run a test query first.. "
+ ]
+ },
+ {
+ "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",
+ "[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(await get_vespa_response(query_embeddings[0],profile, session, profile=profile))\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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "500it [11:32, 1.39s/it]\n"
+ ]
+ }
+ ],
+ "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 tqdm(zip(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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Calculate the effectiveness of the 4 different models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "500it [08:18, 1.00it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = {profile: [] for profile in [\"phased-rerank-count=5\", \"phased-rerank-count=10\", \"phased-rerank-count=20\", \"phased-rerank-count=40\"]}\n",
+ "async with app.asyncio(connections=3) as session:\n",
+ " for row, embedding in tqdm(zip(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(embedding, row['questionId'], session, profile=\"phased\", depth=count)\n",
+ " results[\"phased-rerank-count=\" + str(count)].extend(scored_docs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "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"
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diff --git a/docs/sphinx/source/examples/colpali-document-retrieval-vision-language-models-cloud.ipynb b/docs/sphinx/source/examples/colpali-document-retrieval-vision-language-models-cloud.ipynb
index 5bea0ac2..7e07de12 100644
--- a/docs/sphinx/source/examples/colpali-document-retrieval-vision-language-models-cloud.ipynb
+++ b/docs/sphinx/source/examples/colpali-document-retrieval-vision-language-models-cloud.ipynb
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "!pip3 install colpali-engine==0.2.0 pdf2image pypdf pyvespa vespacli requests"
+ "!pip3 install colpali-engine==0.2.2 pdf2image pypdf pyvespa vespacli requests"
]
},
{
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 54790e58..a8cd57ad 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
@@ -76,7 +76,7 @@
},
"outputs": [],
"source": [
- "!pip3 install colpali-engine==0.2.0 pdf2image pypdf pyvespa vespacli requests numpy"
+ "!pip3 install colpali-engine==0.2.2 pdf2image pypdf pyvespa vespacli requests numpy"
]
},
{