Deploy to HF Space

.env.example

@@ -1,13 +0,0 @@
-VESPA_APP_TOKEN_URL=https://abcde.z.vespa-app.cloud
-VESPA_APP_MTLS_URL=https://fghjkl.z.vespa-app.cloud
-HF_TOKEN=hf_xxxxxxxxxx
-VESPA_CLOUD_SECRET_TOKEN=vespa_cloud_xxxxxxxx
-GEMINI_API_KEY=
-USE_MTLS=false
-VESPA_CLOUD_MTLS_KEY="-----BEGIN PRIVATE KEY-----
-...
------END PRIVATE KEY-----"
-VESPA_CLOUD_MTLS_CERT="-----BEGIN CERTIFICATE-----
-...
------END CERTIFICATE-----"
-HOT_RELOAD=true

.gitattributes

@@ -57,3 +57,5 @@ static/img/colpali_child.png filter=lfs diff=lfs merge=lfs -text
 static/img/vespa-colpali.png filter=lfs diff=lfs merge=lfs -text
 static/img/visual-retrieval-demoapp-arch.png filter=lfs diff=lfs merge=lfs -text
 static/full_images/d40134b18b684815719d4082b0643f0e.jpg filter=lfs diff=lfs merge=lfs -text
+static/full_images/02b30f947c2530e86fcc6f1617b284cb.jpg filter=lfs diff=lfs merge=lfs -text
+static/full_images/de672fe4f18da8c00f3035ce6ac1b7b6.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,15 +0,0 @@
-.sesskey
-.venv/
-__pycache__/
-ipynb_checkpoints/
-.python-version
-.env
-template/
-*.json
-output/
-pdfs/
-colpalidemo/
-src/static/full_images/
-src/static/sim_maps/
-embeddings/
-hf_dataset/

backend/modelmanager.py

@@ -1,24 +0,0 @@
-from .colpali import load_model
-
-
-class ModelManager:
-    _instance = None
-    model = None
-    processor = None
-    use_dummy_model = False
-
-    @staticmethod
-    def get_instance():
-        if ModelManager._instance is None:
-            ModelManager._instance = ModelManager()
-            if not ModelManager.use_dummy_model:
-                ModelManager._instance.initialize_model_and_processor()
-        return ModelManager._instance
-
-    def initialize_model_and_processor(self):
-        if self.model is None or self.processor is None:  # Ensure no reinitialization
-            self.model, self.processor, self.device = load_model()
-            if self.model is None or self.processor is None:
-                print("Failed to initialize model or processor at startup")
-            else:
-                print("Model and processor loaded at startup")

colpali-with-snippets/schemas/pdf_page.sd

@@ -1,254 +0,0 @@
-schema pdf_page {
-    document pdf_page {
-        field id type string {
-            indexing: summary | index
-            match {
-                word
-            }
-        }
-        field url type string {
-            indexing: summary | index
-        }
-        field title type string {
-            indexing: summary | index
-            index: enable-bm25
-            match {
-                text
-            }
-        }
-        field page_number type int {
-            indexing: summary | attribute
-        }
-        field image type raw {
-            indexing: summary
-        }
-        field full_image type raw {
-            indexing: summary
-        }
-        field text type string {
-            indexing: summary | index
-            index: enable-bm25
-            match {
-                text
-            }
-        }
-        field embedding type tensor<int8>(patch{}, v[16]) {
-            indexing: attribute | index
-            attribute {
-                distance-metric: hamming
-            }
-            index {
-                hnsw {
-                    max-links-per-node: 32
-                    neighbors-to-explore-at-insert: 400
-                }
-            }
-        }
-    }
-    fieldset default {
-        fields: title, url, page_number, text
-    }
-
-    document-summary default {
-        from-disk
-
-        summary text {
-            bolding: on
-        }
-
-        summary snippet {
-            source: text
-            dynamic
-        }
-    }
-
-    fieldset image {
-        fields: image
-    }
-
-    rank-profile bm25 {
-
-        inputs {
-            query(qt) tensor<float>(querytoken{}, v[128]) # only used here to generate image similarity map
-        }
-
-        first-phase {
-            expression: bm25(title) + bm25(text)
-        }
-
-    function similarities() {
-            expression  {
-                            sum(
-                                query(qt) * unpack_bits(attribute(embedding)), v
-                            )
-            }
-        }
-        summary-features: similarities
-    }
-
-    rank-profile default {
-        inputs {
-            query(qt) tensor<float>(querytoken{}, v[128])
-        }
-        function max_sim() {
-            expression {
-                
-                                    sum(
-                                        reduce(
-                                            sum(
-                                                query(qt) * unpack_bits(attribute(embedding)) , v
-                                            ),
-                                            max, patch
-                                        ),
-                                        querytoken
-                                    )
-                                
-            }
-        }
-        function bm25_score() {
-            expression {
-                bm25(title) + bm25(text)
-            }
-        }
-        first-phase {
-            expression {
-                bm25_score
-            }
-        }
-        second-phase {
-            rerank-count: 10
-            expression {
-                max_sim
-            }
-        }
-        function similarities() {
-            expression  {
-                            sum(
-                                query(qt) * unpack_bits(attribute(embedding)), v
-                            )
-            }
-        }
-        summary-features: similarities
-    }
-    rank-profile retrieval-and-rerank {
-        inputs {
-            query(rq0) tensor<int8>(v[16])             
-            query(rq1) tensor<int8>(v[16])             
-            query(rq2) tensor<int8>(v[16])             
-            query(rq3) tensor<int8>(v[16])             
-            query(rq4) tensor<int8>(v[16])             
-            query(rq5) tensor<int8>(v[16])             
-            query(rq6) tensor<int8>(v[16])             
-            query(rq7) tensor<int8>(v[16])             
-            query(rq8) tensor<int8>(v[16])             
-            query(rq9) tensor<int8>(v[16])             
-            query(rq10) tensor<int8>(v[16])             
-            query(rq11) tensor<int8>(v[16])             
-            query(rq12) tensor<int8>(v[16])             
-            query(rq13) tensor<int8>(v[16])             
-            query(rq14) tensor<int8>(v[16])             
-            query(rq15) tensor<int8>(v[16])             
-            query(rq16) tensor<int8>(v[16])             
-            query(rq17) tensor<int8>(v[16])             
-            query(rq18) tensor<int8>(v[16])             
-            query(rq19) tensor<int8>(v[16])             
-            query(rq20) tensor<int8>(v[16])             
-            query(rq21) tensor<int8>(v[16])             
-            query(rq22) tensor<int8>(v[16])             
-            query(rq23) tensor<int8>(v[16])             
-            query(rq24) tensor<int8>(v[16])             
-            query(rq25) tensor<int8>(v[16])             
-            query(rq26) tensor<int8>(v[16])             
-            query(rq27) tensor<int8>(v[16])             
-            query(rq28) tensor<int8>(v[16])             
-            query(rq29) tensor<int8>(v[16])             
-            query(rq30) tensor<int8>(v[16])             
-            query(rq31) tensor<int8>(v[16])             
-            query(rq32) tensor<int8>(v[16])             
-            query(rq33) tensor<int8>(v[16])             
-            query(rq34) tensor<int8>(v[16])             
-            query(rq35) tensor<int8>(v[16])             
-            query(rq36) tensor<int8>(v[16])             
-            query(rq37) tensor<int8>(v[16])             
-            query(rq38) tensor<int8>(v[16])             
-            query(rq39) tensor<int8>(v[16])             
-            query(rq40) tensor<int8>(v[16])             
-            query(rq41) tensor<int8>(v[16])             
-            query(rq42) tensor<int8>(v[16])             
-            query(rq43) tensor<int8>(v[16])             
-            query(rq44) tensor<int8>(v[16])             
-            query(rq45) tensor<int8>(v[16])             
-            query(rq46) tensor<int8>(v[16])             
-            query(rq47) tensor<int8>(v[16])             
-            query(rq48) tensor<int8>(v[16])             
-            query(rq49) tensor<int8>(v[16])             
-            query(rq50) tensor<int8>(v[16])             
-            query(rq51) tensor<int8>(v[16])             
-            query(rq52) tensor<int8>(v[16])             
-            query(rq53) tensor<int8>(v[16])             
-            query(rq54) tensor<int8>(v[16])             
-            query(rq55) tensor<int8>(v[16])             
-            query(rq56) tensor<int8>(v[16])             
-            query(rq57) tensor<int8>(v[16])             
-            query(rq58) tensor<int8>(v[16])             
-            query(rq59) tensor<int8>(v[16])             
-            query(rq60) tensor<int8>(v[16])             
-            query(rq61) tensor<int8>(v[16])             
-            query(rq62) tensor<int8>(v[16])             
-            query(rq63) tensor<int8>(v[16])             
-            query(qt) tensor<float>(querytoken{}, v[128])             
-            query(qtb) tensor<int8>(querytoken{}, v[16])             
-        
-        }
-        function max_sim() {
-            expression {
-                
-                                    sum(
-                                        reduce(
-                                            sum(
-                                                query(qt) * unpack_bits(attribute(embedding)) , v
-                                            ),
-                                            max, patch
-                                        ),
-                                        querytoken
-                                    )
-                                
-            }
-        }
-        function max_sim_binary() {
-            expression {
-                
-                                    sum(
-                                      reduce(
-                                        1/(1 + sum(
-                                            hamming(query(qtb), attribute(embedding)) ,v)
-                                        ),
-                                        max,
-                                        patch
-                                      ),
-                                      querytoken
-                                    )
-                                
-            }
-        }
-        first-phase {
-            expression {
-                max_sim_binary
-            }
-        }
-        second-phase {
-            rerank-count: 10
-            expression {
-                max_sim
-            }
-        }
-        function similarities() {
-            expression  {
-                            sum(
-                                query(qt) * unpack_bits(attribute(embedding)), v
-                            )
-            }
-        }
-        summary-features: similarities
-    }
-}

colpali-with-snippets/search/query-profiles/default.xml

@@ -1,2 +0,0 @@
-<query-profile id="default" type="root">
-</query-profile>

colpali-with-snippets/search/query-profiles/types/root.xml

@@ -1,2 +0,0 @@
-<query-profile-type id="root">
-</query-profile-type>

colpali-with-snippets/security/clients.pem

@@ -1,9 +0,0 @@
------BEGIN CERTIFICATE-----
-MIIBODCB36ADAgECAhEAr37yU2TKTDMdQW1txTaMSjAKBggqhkjOPQQDAjAeMRww
-GgYDVQQDExNjbG91ZC52ZXNwYS5leGFtcGxlMB4XDTI0MTAxNzA5NDY1M1oXDTM0
-MTAxNTA5NDY1M1owHjEcMBoGA1UEAxMTY2xvdWQudmVzcGEuZXhhbXBsZTBZMBMG
-ByqGSM49AgEGCCqGSM49AwEHA0IABPQjpb7RFvtnw288EY5eolq2v+0qC0h4JeW5
-jCchXp4KUa5ufqeqyTcAxsfLn3BloPFDJ7Vb2gct9tZONa7xvc4wCgYIKoZIzj0E
-AwIDSAAwRQIgR3wU3NUS02Behd0ojxo5sa5NVi0HhNW8RoAy0UyoGnACIQDWOqq+
-zdKHJDorFuMWeMKKUe0cVQXZV3RvU5ssuXyEnw==
------END CERTIFICATE-----

colpali-with-snippets/services.xml

@@ -1,43 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<services version="1.0">
-    <container id="colpalidemo_container" version="1.0">
-        <search></search>
-        <document-api></document-api>
-        <document-processing></document-processing>
-        <clients>
-            <client id="mtls" permissions="read,write">
-                <certificate file="security/clients.pem" />
-            </client>
-            <client id="token_write" permissions="read,write">
-                <token id="colpalidemo_write" />
-            </client>
-            <client id="token_read" permissions="read">
-                <token id="colpalidemo_read" />
-            </client>
-        </clients>
-        <config name="container.qr-searchers">
-            <tag>
-                <bold>
-                    <open>&lt;strong&gt;</open>
-                    <close>&lt;/strong&gt;</close>
-                </bold>
-                <separator>...</separator>
-            </tag>
-        </config>
-    </container>
-    <content id="colpalidemo_content" version="1.0">
-        <redundancy>1</redundancy>
-        <documents>
-            <document type="pdf_page" mode="index"></document>
-        </documents>
-        <nodes>
-            <node distribution-key="0" hostalias="node1"></node>
-        </nodes>
-        <config name="vespa.config.search.summary.juniperrc">
-            <max_matches>2</max_matches>
-            <length>1000</length>
-            <surround_max>500</surround_max>
-            <min_length>300</min_length>
-        </config>
-    </content>
-</services>

colpali.py

@@ -1,521 +0,0 @@
-#!/usr/bin/env python3
-
-import torch
-from PIL import Image
-import numpy as np
-from typing import cast
-import pprint
-from pathlib import Path
-import base64
-from io import BytesIO
-from typing import Union, Tuple
-import matplotlib
-import re
-
-from colpali_engine.models import ColPali, ColPaliProcessor
-from colpali_engine.utils.torch_utils import get_torch_device
-from einops import rearrange
-from vidore_benchmark.interpretability.plot_utils import plot_similarity_heatmap
-from vidore_benchmark.interpretability.torch_utils import (
-    normalize_similarity_map_per_query_token,
-)
-from vidore_benchmark.interpretability.vit_configs import VIT_CONFIG
-from vidore_benchmark.utils.image_utils import scale_image
-from vespa.application import Vespa
-from vespa.io import VespaQueryResponse
-
-matplotlib.use("Agg")
-
-MAX_QUERY_TERMS = 64
-# OUTPUT_DIR = Path(__file__).parent.parent / "output" / "sim_maps"
-# OUTPUT_DIR.mkdir(exist_ok=True)
-
-COLPALI_GEMMA_MODEL_ID = "vidore--colpaligemma-3b-pt-448-base"
-COLPALI_GEMMA_MODEL_SNAPSHOT = "12c59eb7e23bc4c26876f7be7c17760d5d3a1ffa"
-COLPALI_GEMMA_MODEL_PATH = (
-    Path().home()
-    / f".cache/huggingface/hub/models--{COLPALI_GEMMA_MODEL_ID}/snapshots/{COLPALI_GEMMA_MODEL_SNAPSHOT}"
-)
-COLPALI_MODEL_ID = "vidore--colpali-v1.2"
-COLPALI_MODEL_SNAPSHOT = "9912ce6f8a462d8cf2269f5606eabbd2784e764f"
-COLPALI_MODEL_PATH = (
-    Path().home()
-    / f".cache/huggingface/hub/models--{COLPALI_MODEL_ID}/snapshots/{COLPALI_MODEL_SNAPSHOT}"
-)
-COLPALI_GEMMA_MODEL_NAME = COLPALI_GEMMA_MODEL_ID.replace("--", "/")
-
-
-def load_model() -> Tuple[ColPali, ColPaliProcessor]:
-    model_name = "vidore/colpali-v1.2"
-
-    device = get_torch_device("auto")
-    print(f"Using device: {device}")
-
-    # Load the model
-    model = cast(
-        ColPali,
-        ColPali.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-            device_map=device,
-        ),
-    ).eval()
-
-    # Load the processor
-    processor = cast(ColPaliProcessor, ColPaliProcessor.from_pretrained(model_name))
-    return model, processor
-
-
-def load_vit_config(model):
-    # Load the ViT config
-    print(f"VIT config: {VIT_CONFIG}")
-    vit_config = VIT_CONFIG[COLPALI_GEMMA_MODEL_NAME]
-    return vit_config
-
-
-# Create dummy image
-dummy_image = Image.new("RGB", (448, 448), (255, 255, 255))
-
-
-def gen_similarity_map(
-    model, processor, device, vit_config, query, image: Union[Path, str]
-):
-    # Should take in the b64 image from Vespa query result
-    # And possibly the tensor representing the output_image
-    if isinstance(image, Path):
-        # image is a file path
-        try:
-            image = Image.open(image)
-        except Exception as e:
-            raise ValueError(f"Failed to open image from path: {e}")
-    elif isinstance(image, str):
-        # image is b64 string
-        try:
-            image = Image.open(BytesIO(base64.b64decode(image)))
-        except Exception as e:
-            raise ValueError(f"Failed to open image from b64: {e}")
-
-    # Preview the image
-    scale_image(image, 512)
-    # Preprocess inputs
-    input_text_processed = processor.process_queries([query]).to(device)
-    input_image_processed = processor.process_images([image]).to(device)
-    # Forward passes
-    with torch.no_grad():
-        output_text = model.forward(**input_text_processed)
-        output_image = model.forward(**input_image_processed)
-    # output_image is the tensor that we could get from the Vespa query
-    # Print shape of output_text and output_image
-    # Output image shape: torch.Size([1, 1030, 128])
-    # Remove the special tokens from the output
-    output_image = output_image[
-        :, : processor.image_seq_length, :
-    ]  # (1, n_patches_x * n_patches_y, dim)
-
-    # Rearrange the output image tensor to explicitly represent the 2D grid of patches
-    output_image = rearrange(
-        output_image,
-        "b (h w) c -> b h w c",
-        h=vit_config.n_patch_per_dim,
-        w=vit_config.n_patch_per_dim,
-    )  # (1, n_patches_x, n_patches_y, dim)
-    # Get the similarity map
-    similarity_map = torch.einsum(
-        "bnk,bijk->bnij", output_text, output_image
-    )  # (1, query_tokens, n_patches_x, n_patches_y)
-
-    # Normalize the similarity map
-    similarity_map_normalized = normalize_similarity_map_per_query_token(
-        similarity_map
-    )  # (1, query_tokens, n_patches_x, n_patches_y)
-    # Use this cell output to choose a token using its index
-    query_tokens = processor.tokenizer.tokenize(
-        processor.decode(input_text_processed.input_ids[0])
-    )
-    # Choose a token
-    token_idx = (
-        10  # e.g. if "12: '▁Kazakhstan',", set 12 to choose the token 'Kazakhstan'
-    )
-    selected_token = processor.decode(input_text_processed.input_ids[0, token_idx])
-    # strip whitespace
-    selected_token = selected_token.strip()
-    print(f"Selected token: `{selected_token}`")
-    # Retrieve the similarity map for the chosen token
-    pprint.pprint({idx: val for idx, val in enumerate(query_tokens)})
-    # Resize the image to square
-    input_image_square = image.resize((vit_config.resolution, vit_config.resolution))
-
-    # Plot the similarity map
-    fig, ax = plot_similarity_heatmap(
-        input_image_square,
-        patch_size=vit_config.patch_size,
-        image_resolution=vit_config.resolution,
-        similarity_map=similarity_map_normalized[0, token_idx, :, :],
-    )
-    ax = annotate_plot(ax, selected_token)
-    return fig, ax
-
-
-# def save_figure(fig, filename: str = "similarity_map.png"):
-#     fig.savefig(
-#         OUTPUT_DIR / filename,
-#         bbox_inches="tight",
-#         pad_inches=0,
-#     )
-
-
-def annotate_plot(ax, query, selected_token):
-    # Add the query text
-    ax.set_title(query, fontsize=18)
-    # Add annotation with selected token
-    ax.annotate(
-        f"Selected token:`{selected_token}`",
-        xy=(0.5, 0.95),
-        xycoords="axes fraction",
-        ha="center",
-        va="center",
-        fontsize=18,
-        color="black",
-        bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="black", lw=1),
-    )
-    return ax
-
-
-def gen_similarity_map_new(
-    processor: ColPaliProcessor,
-    model: ColPali,
-    device,
-    vit_config,
-    query: str,
-    query_embs: torch.Tensor,
-    token_idx_map: dict,
-    token_to_show: str,
-    image: Union[Path, str],
-):
-    if isinstance(image, Path):
-        # image is a file path
-        try:
-            image = Image.open(image)
-        except Exception as e:
-            raise ValueError(f"Failed to open image from path: {e}")
-    elif isinstance(image, str):
-        # image is b64 string
-        try:
-            image = Image.open(BytesIO(base64.b64decode(image)))
-        except Exception as e:
-            raise ValueError(f"Failed to open image from b64: {e}")
-    token_idx = token_idx_map[token_to_show]
-    print(f"Selected token: `{token_to_show}`")
-    # strip whitespace
-    # Preview the image
-    # scale_image(image, 512)
-    # Preprocess inputs
-    input_image_processed = processor.process_images([image]).to(device)
-    # Forward passes
-    with torch.no_grad():
-        output_image = model.forward(**input_image_processed)
-    # output_image is the tensor that we could get from the Vespa query
-    # Print shape of output_text and output_image
-    # Output image shape: torch.Size([1, 1030, 128])
-    # Remove the special tokens from the output
-    print(f"Output image shape before dim: {output_image.shape}")
-    output_image = output_image[
-        :, : processor.image_seq_length, :
-    ]  # (1, n_patches_x * n_patches_y, dim)
-    print(f"Output image shape after dim: {output_image.shape}")
-    # Rearrange the output image tensor to explicitly represent the 2D grid of patches
-    output_image = rearrange(
-        output_image,
-        "b (h w) c -> b h w c",
-        h=vit_config.n_patch_per_dim,
-        w=vit_config.n_patch_per_dim,
-    )  # (1, n_patches_x, n_patches_y, dim)
-    # Get the similarity map
-    print(f"Query embs shape: {query_embs.shape}")
-    # Add 1 extra dim to start of query_embs
-    query_embs = query_embs.unsqueeze(0).to(device)
-    print(f"Output image shape: {output_image.shape}")
-    similarity_map = torch.einsum(
-        "bnk,bijk->bnij", query_embs, output_image
-    )  # (1, query_tokens, n_patches_x, n_patches_y)
-    print(f"Similarity map shape: {similarity_map.shape}")
-    # Normalize the similarity map
-    similarity_map_normalized = normalize_similarity_map_per_query_token(
-        similarity_map
-    )  # (1, query_tokens, n_patches_x, n_patches_y)
-    print(f"Similarity map normalized shape: {similarity_map_normalized.shape}")
-    # Use this cell output to choose a token using its index
-    input_image_square = image.resize((vit_config.resolution, vit_config.resolution))
-
-    # Plot the similarity map
-    fig, ax = plot_similarity_heatmap(
-        input_image_square,
-        patch_size=vit_config.patch_size,
-        image_resolution=vit_config.resolution,
-        similarity_map=similarity_map_normalized[0, token_idx, :, :],
-    )
-    ax = annotate_plot(ax, query, token_to_show)
-    # save the figure
-    # save_figure(fig, f"similarity_map_{token_to_show}.png")
-    return fig, ax
-
-
-def get_query_embeddings_and_token_map(
-    processor, model, query, image
-) -> Tuple[torch.Tensor, dict]:
-    inputs = processor.process_queries([query]).to(model.device)
-    with torch.no_grad():
-        embeddings_query = model(**inputs)
-        q_emb = embeddings_query.to("cpu")[0]  # Extract the single embedding
-    # Use this cell output to choose a token using its index
-    query_tokens = processor.tokenizer.tokenize(processor.decode(inputs.input_ids[0]))
-    # reverse key, values in dictionary
-    print(query_tokens)
-    token_to_idx = {val: idx for idx, val in enumerate(query_tokens)}
-    return q_emb, token_to_idx
-
-
-def format_query_results(query, response, hits=5) -> dict:
-    query_time = response.json.get("timing", {}).get("searchtime", -1)
-    query_time = round(query_time, 2)
-    count = response.json.get("root", {}).get("fields", {}).get("totalCount", 0)
-    result_text = f"Query text: '{query}', query time {query_time}s, count={count}, top results:\n"
-    print(result_text)
-    return response.json
-
-
-async def query_vespa_default(
-    app: Vespa,
-    query: str,
-    q_emb: torch.Tensor,
-    hits: int = 3,
-    timeout: str = "10s",
-    **kwargs,
-) -> dict:
-    async with app.asyncio(connections=1, total_timeout=120) as session:
-        query_embedding = format_q_embs(q_emb)
-        response: VespaQueryResponse = await session.query(
-            body={
-                "yql": "select id,title,url,image,page_number,text from pdf_page where userQuery();",
-                "ranking": "default",
-                "query": query,
-                "timeout": timeout,
-                "hits": hits,
-                "input.query(qt)": query_embedding,
-                "presentation.timing": True,
-                **kwargs,
-            },
-        )
-        assert response.is_successful(), response.json
-    return format_query_results(query, response)
-
-
-def float_to_binary_embedding(float_query_embedding: dict) -> dict:
-    binary_query_embeddings = {}
-    for k, v in float_query_embedding.items():
-        binary_vector = (
-            np.packbits(np.where(np.array(v) > 0, 1, 0)).astype(np.int8).tolist()
-        )
-        binary_query_embeddings[k] = binary_vector
-        if len(binary_query_embeddings) >= MAX_QUERY_TERMS:
-            print(f"Warning: Query has more than {MAX_QUERY_TERMS} terms. Truncating.")
-            break
-    return binary_query_embeddings
-
-
-def create_nn_query_strings(
-    binary_query_embeddings: dict, target_hits_per_query_tensor: int = 20
-) -> Tuple[str, dict]:
-    # Query tensors for nearest neighbor calculations
-    nn_query_dict = {}
-    for i in range(len(binary_query_embeddings)):
-        nn_query_dict[f"input.query(rq{i})"] = binary_query_embeddings[i]
-    nn = " OR ".join(
-        [
-            f"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))"
-            for i in range(len(binary_query_embeddings))
-        ]
-    )
-    return nn, nn_query_dict
-
-
-def format_q_embs(q_embs: torch.Tensor) -> dict:
-    float_query_embedding = {k: v.tolist() for k, v in enumerate(q_embs)}
-    return float_query_embedding
-
-
-async def query_vespa_nearest_neighbor(
-    app: Vespa,
-    query: str,
-    q_emb: torch.Tensor,
-    target_hits_per_query_tensor: int = 20,
-    hits: int = 3,
-    timeout: str = "10s",
-    **kwargs,
-) -> dict:
-    # Hyperparameter for speed vs. accuracy
-    async with app.asyncio(connections=1, total_timeout=180) as session:
-        float_query_embedding = format_q_embs(q_emb)
-        binary_query_embeddings = float_to_binary_embedding(float_query_embedding)
-
-        # Mixed tensors for MaxSim calculations
-        query_tensors = {
-            "input.query(qtb)": binary_query_embeddings,
-            "input.query(qt)": float_query_embedding,
-        }
-        nn_string, nn_query_dict = create_nn_query_strings(
-            binary_query_embeddings, target_hits_per_query_tensor
-        )
-        query_tensors.update(nn_query_dict)
-        response: VespaQueryResponse = await session.query(
-            body={
-                **query_tensors,
-                "presentation.timing": True,
-                "yql": f"select id,title,text,url,image,page_number from pdf_page where {nn_string}",
-                "ranking.profile": "retrieval-and-rerank",
-                "timeout": timeout,
-                "hits": hits,
-                **kwargs,
-            },
-        )
-        assert response.is_successful(), response.json
-    return format_query_results(query, response)
-
-
-def is_special_token(token: str) -> bool:
-    # Pattern for tokens that start with '<', numbers, whitespace, or single characters
-    pattern = re.compile(r"^<.*$|^\d+$|^\s+$|^.$")
-    if pattern.match(token):
-        return True
-    return False
-
-
-async def get_result_from_query(
-    app: Vespa,
-    processor: ColPaliProcessor,
-    model: ColPali,
-    query: str,
-    nn=False,
-    gen_sim_map=False,
-):
-    # Get the query embeddings and token map
-    print(query)
-    q_embs, token_to_idx = get_query_embeddings_and_token_map(
-        processor, model, query, dummy_image
-    )
-    print(token_to_idx)
-    # Use the token map to choose a token randomly for now
-    # Dynamically select a token containing 'water'
-
-    if nn:
-        result = await query_vespa_nearest_neighbor(app, query, q_embs)
-    else:
-        result = await query_vespa_default(app, query, q_embs)
-    # Print score, title id and text of the results
-    for idx, child in enumerate(result["root"]["children"]):
-        print(
-            f"Result {idx+1}: {child['relevance']}, {child['fields']['title']}, {child['fields']['id']}"
-        )
-
-    if gen_sim_map:
-        for single_result in result["root"]["children"]:
-            img = single_result["fields"]["image"]
-            for token in token_to_idx:
-                if is_special_token(token):
-                    print(f"Skipping special token: {token}")
-                    continue
-                fig, ax = gen_similarity_map_new(
-                    processor,
-                    model,
-                    model.device,
-                    load_vit_config(model),
-                    query,
-                    q_embs,
-                    token_to_idx,
-                    token,
-                    img,
-                )
-                sim_map = base64.b64encode(fig.canvas.tostring_rgb()).decode("utf-8")
-                single_result["fields"][f"sim_map_{token}"] = sim_map
-    return result
-
-
-def get_result_dummy(query: str, nn: bool = False):
-    result = {}
-    result["timing"] = {}
-    result["timing"]["querytime"] = 0.23700000000000002
-    result["timing"]["summaryfetchtime"] = 0.001
-    result["timing"]["searchtime"] = 0.23900000000000002
-    result["root"] = {}
-    result["root"]["id"] = "toplevel"
-    result["root"]["relevance"] = 1
-    result["root"]["fields"] = {}
-    result["root"]["fields"]["totalCount"] = 59
-    result["root"]["coverage"] = {}
-    result["root"]["coverage"]["coverage"] = 100
-    result["root"]["coverage"]["documents"] = 155
-    result["root"]["coverage"]["full"] = True
-    result["root"]["coverage"]["nodes"] = 1
-    result["root"]["coverage"]["results"] = 1
-    result["root"]["coverage"]["resultsFull"] = 1
-    result["root"]["children"] = []
-    elt0 = {}
-    elt0["id"] = "index:colpalidemo_content/0/424c85e7dece761d226f060f"
-    elt0["relevance"] = 2354.050122871995
-    elt0["source"] = "colpalidemo_content"
-    elt0["fields"] = {}
-    elt0["fields"]["id"] = "a767cb1868be9a776cd56b768347b089"
-    elt0["fields"]["url"] = (
-        "https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf"
-    )
-    elt0["fields"]["title"] = "ConocoPhillips 2023 Sustainability Report"
-    elt0["fields"]["page_number"] = 50
-    elt0["fields"]["image"] = "empty for now - is base64 encoded image"
-    result["root"]["children"].append(elt0)
-    elt1 = {}
-    elt1["id"] = "index:colpalidemo_content/0/b927c4979f0beaf0d7fab8e9"
-    elt1["relevance"] = 2313.7529950886965
-    elt1["source"] = "colpalidemo_content"
-    elt1["fields"] = {}
-    elt1["fields"]["id"] = "9f2fc0aa02c9561adfaa1451c875658f"
-    elt1["fields"]["url"] = (
-        "https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf"
-    )
-    elt1["fields"]["title"] = "ConocoPhillips Managing Climate Related Risks"
-    elt1["fields"]["page_number"] = 44
-    elt1["fields"]["image"] = "empty for now - is base64 encoded image"
-    result["root"]["children"].append(elt1)
-    elt2 = {}
-    elt2["id"] = "index:colpalidemo_content/0/9632d72238829d6afefba6c9"
-    elt2["relevance"] = 2312.230182081461
-    elt2["source"] = "colpalidemo_content"
-    elt2["fields"] = {}
-    elt2["fields"]["id"] = "d638ded1ddcb446268b289b3f65430fd"
-    elt2["fields"]["url"] = (
-        "https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf"
-    )
-    elt2["fields"]["title"] = (
-        "ConocoPhillips Sustainability Highlights - Nature (24-0976)"
-    )
-    elt2["fields"]["page_number"] = 0
-    elt2["fields"]["image"] = "empty for now - is base64 encoded image"
-    result["root"]["children"].append(elt2)
-    return result
-
-
-if __name__ == "__main__":
-    model, processor = load_model()
-    vit_config = load_vit_config(model)
-    query = "How many percent of source water is fresh water?"
-    image_filepath = (
-        Path(__file__).parent.parent
-        / "static"
-        / "assets"
-        / "ConocoPhillips Sustainability Highlights - Nature (24-0976).png"
-    )
-    gen_similarity_map(
-        model, processor, model.device, vit_config, query=query, image=image_filepath
-    )
-    result = get_result_dummy("dummy query")
-    print(result)
-    print("Done")

colpalidemo/schemas/pdf_page.sd

@@ -1,204 +0,0 @@
-schema pdf_page {
-    document pdf_page {
-        field id type string {
-            indexing: summary | index
-            match {
-                word
-            }
-        }
-        field url type string {
-            indexing: summary | index
-        }
-        field title type string {
-            indexing: summary | index
-            index: enable-bm25
-            match {
-                text
-            }
-        }
-        field page_number type int {
-            indexing: summary | attribute
-        }
-        field image type raw {
-            indexing: summary
-        }
-        field full_image type raw {
-            indexing: summary
-        }
-        field text type string {
-            indexing: summary | index
-            index: enable-bm25
-            match {
-                text
-            }
-        }
-        field embedding type tensor<int8>(patch{}, v[16]) {
-            indexing: attribute | index
-            attribute {
-                distance-metric: hamming
-            }
-            index {
-                hnsw {
-                    max-links-per-node: 32
-                    neighbors-to-explore-at-insert: 400
-                }
-            }
-        }
-    }
-    fieldset default {
-        fields: title, url, page_number, text
-    }
-    fieldset image {
-        fields: image
-    }
-    rank-profile default {
-        inputs {
-            query(qt) tensor<float>(querytoken{}, v[128])             
-        
-        }
-        function max_sim() {
-            expression {
-                
-                                    sum(
-                                        reduce(
-                                            sum(
-                                                query(qt) * unpack_bits(attribute(embedding)) , v
-                                            ),
-                                            max, patch
-                                        ),
-                                        querytoken
-                                    )
-                                
-            }
-        }
-        function bm25_score() {
-            expression {
-                bm25(title) + bm25(text)
-            }
-        }
-        first-phase {
-            expression {
-                bm25_score
-            }
-        }
-        second-phase {
-            rerank-count: 10
-            expression {
-                max_sim
-            }
-        }
-    }
-    rank-profile retrieval-and-rerank {
-        inputs {
-            query(rq0) tensor<int8>(v[16])             
-            query(rq1) tensor<int8>(v[16])             
-            query(rq2) tensor<int8>(v[16])             
-            query(rq3) tensor<int8>(v[16])             
-            query(rq4) tensor<int8>(v[16])             
-            query(rq5) tensor<int8>(v[16])             
-            query(rq6) tensor<int8>(v[16])             
-            query(rq7) tensor<int8>(v[16])             
-            query(rq8) tensor<int8>(v[16])             
-            query(rq9) tensor<int8>(v[16])             
-            query(rq10) tensor<int8>(v[16])             
-            query(rq11) tensor<int8>(v[16])             
-            query(rq12) tensor<int8>(v[16])             
-            query(rq13) tensor<int8>(v[16])             
-            query(rq14) tensor<int8>(v[16])             
-            query(rq15) tensor<int8>(v[16])             
-            query(rq16) tensor<int8>(v[16])             
-            query(rq17) tensor<int8>(v[16])             
-            query(rq18) tensor<int8>(v[16])             
-            query(rq19) tensor<int8>(v[16])             
-            query(rq20) tensor<int8>(v[16])             
-            query(rq21) tensor<int8>(v[16])             
-            query(rq22) tensor<int8>(v[16])             
-            query(rq23) tensor<int8>(v[16])             
-            query(rq24) tensor<int8>(v[16])             
-            query(rq25) tensor<int8>(v[16])             
-            query(rq26) tensor<int8>(v[16])             
-            query(rq27) tensor<int8>(v[16])             
-            query(rq28) tensor<int8>(v[16])             
-            query(rq29) tensor<int8>(v[16])             
-            query(rq30) tensor<int8>(v[16])             
-            query(rq31) tensor<int8>(v[16])             
-            query(rq32) tensor<int8>(v[16])             
-            query(rq33) tensor<int8>(v[16])             
-            query(rq34) tensor<int8>(v[16])             
-            query(rq35) tensor<int8>(v[16])             
-            query(rq36) tensor<int8>(v[16])             
-            query(rq37) tensor<int8>(v[16])             
-            query(rq38) tensor<int8>(v[16])             
-            query(rq39) tensor<int8>(v[16])             
-            query(rq40) tensor<int8>(v[16])             
-            query(rq41) tensor<int8>(v[16])             
-            query(rq42) tensor<int8>(v[16])             
-            query(rq43) tensor<int8>(v[16])             
-            query(rq44) tensor<int8>(v[16])             
-            query(rq45) tensor<int8>(v[16])             
-            query(rq46) tensor<int8>(v[16])             
-            query(rq47) tensor<int8>(v[16])             
-            query(rq48) tensor<int8>(v[16])             
-            query(rq49) tensor<int8>(v[16])             
-            query(rq50) tensor<int8>(v[16])             
-            query(rq51) tensor<int8>(v[16])             
-            query(rq52) tensor<int8>(v[16])             
-            query(rq53) tensor<int8>(v[16])             
-            query(rq54) tensor<int8>(v[16])             
-            query(rq55) tensor<int8>(v[16])             
-            query(rq56) tensor<int8>(v[16])             
-            query(rq57) tensor<int8>(v[16])             
-            query(rq58) tensor<int8>(v[16])             
-            query(rq59) tensor<int8>(v[16])             
-            query(rq60) tensor<int8>(v[16])             
-            query(rq61) tensor<int8>(v[16])             
-            query(rq62) tensor<int8>(v[16])             
-            query(rq63) tensor<int8>(v[16])             
-            query(qt) tensor<float>(querytoken{}, v[128])             
-            query(qtb) tensor<int8>(querytoken{}, v[16])             
-        
-        }
-        function max_sim() {
-            expression {
-                
-                                    sum(
-                                        reduce(
-                                            sum(
-                                                query(qt) * unpack_bits(attribute(embedding)) , v
-                                            ),
-                                            max, patch
-                                        ),
-                                        querytoken
-                                    )
-                                
-            }
-        }
-        function max_sim_binary() {
-            expression {
-                
-                                    sum(
-                                      reduce(
-                                        1/(1 + sum(
-                                            hamming(query(qtb), attribute(embedding)) ,v)
-                                        ),
-                                        max,
-                                        patch
-                                      ),
-                                      querytoken
-                                    )
-                                
-            }
-        }
-        first-phase {
-            expression {
-                max_sim_binary
-            }
-        }
-        second-phase {
-            rerank-count: 10
-            expression {
-                max_sim
-            }
-        }
-    }
-}

colpalidemo/search/query-profiles/default.xml

@@ -1,2 +0,0 @@
-<query-profile id="default" type="root">
-</query-profile>

colpalidemo/search/query-profiles/types/root.xml

@@ -1,2 +0,0 @@
-<query-profile-type id="root">
-</query-profile-type>

colpalidemo/services.xml

@@ -1,28 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<services version="1.0">
-    <container id="colpalidemo_container" version="1.0">
-        <search></search>
-        <document-api></document-api>
-        <document-processing></document-processing>
-        <clients>
-            <client id="mtls" permissions="read,write">
-                <certificate file="security/clients.pem"/>
-            </client>
-            <client id="token_write" permissions="read,write">
-                <token id="colpalidemo_write"/>
-            </client>
-            <client id="token_read" permissions="read">
-                <token id="colpalidemo_read"/>
-            </client>
-        </clients>
-    </container>
-    <content id="colpalidemo_content" version="1.0">
-        <redundancy>1</redundancy>
-        <documents>
-            <document type="pdf_page" mode="index"></document>
-        </documents>
-        <nodes>
-            <node distribution-key="0" hostalias="node1"></node>
-        </nodes>
-    </content>
-</services>

deploy_vespa_app.py

@@ -1,208 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-from vespa.package import (
-    ApplicationPackage,
-    Field,
-    Schema,
-    Document,
-    HNSW,
-    RankProfile,
-    Function,
-    AuthClient,
-    Parameter,
-    FieldSet,
-    SecondPhaseRanking,
-)
-from vespa.deployment import VespaCloud
-import os
-from pathlib import Path
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Deploy Vespa application")
-    parser.add_argument("--tenant_name", required=True, help="Vespa Cloud tenant name")
-    parser.add_argument(
-        "--vespa_application_name", required=True, help="Vespa application name"
-    )
-    parser.add_argument(
-        "--token_id_write", required=True, help="Vespa Cloud token ID for write access"
-    )
-    parser.add_argument(
-        "--token_id_read", required=True, help="Vespa Cloud token ID for read access"
-    )
-
-    args = parser.parse_args()
-    tenant_name = args.tenant_name
-    vespa_app_name = args.vespa_application_name
-    token_id_write = args.token_id_write
-    token_id_read = args.token_id_read
-
-    # Define the Vespa schema
-    colpali_schema = Schema(
-        name="pdf_page",
-        document=Document(
-            fields=[
-                Field(
-                    name="id",
-                    type="string",
-                    indexing=["summary", "index"],
-                    match=["word"],
-                ),
-                Field(name="url", type="string", indexing=["summary", "index"]),
-                Field(
-                    name="title",
-                    type="string",
-                    indexing=["summary", "index"],
-                    match=["text"],
-                    index="enable-bm25",
-                ),
-                Field(
-                    name="page_number", type="int", indexing=["summary", "attribute"]
-                ),
-                Field(name="image", type="raw", indexing=["summary"]),
-                Field(name="full_image", type="raw", indexing=["summary"]),
-                Field(
-                    name="text",
-                    type="string",
-                    indexing=["summary", "index"],
-                    match=["text"],
-                    index="enable-bm25",
-                ),
-                Field(
-                    name="embedding",
-                    type="tensor<int8>(patch{}, v[16])",
-                    indexing=[
-                        "attribute",
-                        "index",
-                    ],  # adds HNSW index for candidate retrieval.
-                    ann=HNSW(
-                        distance_metric="hamming",
-                        max_links_per_node=32,
-                        neighbors_to_explore_at_insert=400,
-                    ),
-                ),
-            ]
-        ),
-        fieldsets=[
-            FieldSet(name="default", fields=["title", "url", "page_number", "text"]),
-            FieldSet(name="image", fields=["image"]),
-        ],
-    )
-
-    # Define rank profiles
-    colpali_profile = RankProfile(
-        name="default",
-        inputs=[("query(qt)", "tensor<float>(querytoken{}, v[128])")],
-        functions=[
-            Function(
-                name="max_sim",
-                expression="""
-                    sum(
-                        reduce(
-                            sum(
-                                query(qt) * unpack_bits(attribute(embedding)) , v
-                            ),
-                            max, patch
-                        ),
-                        querytoken
-                    )
-                """,
-            ),
-            Function(name="bm25_score", expression="bm25(title) + bm25(text)"),
-        ],
-        first_phase="bm25_score",
-        second_phase=SecondPhaseRanking(expression="max_sim", rerank_count=10),
-    )
-    colpali_schema.add_rank_profile(colpali_profile)
-
-    # Add retrieval-and-rerank rank profile
-    input_query_tensors = []
-    MAX_QUERY_TERMS = 64
-    for i in range(MAX_QUERY_TERMS):
-        input_query_tensors.append((f"query(rq{i})", "tensor<int8>(v[16])"))
-
-    input_query_tensors.append(("query(qt)", "tensor<float>(querytoken{}, v[128])"))
-    input_query_tensors.append(("query(qtb)", "tensor<int8>(querytoken{}, v[16])"))
-
-    colpali_retrieval_profile = RankProfile(
-        name="retrieval-and-rerank",
-        inputs=input_query_tensors,
-        functions=[
-            Function(
-                name="max_sim",
-                expression="""
-                    sum(
-                        reduce(
-                            sum(
-                                query(qt) * unpack_bits(attribute(embedding)) , v
-                            ),
-                            max, patch
-                        ),
-                        querytoken
-                    )
-                """,
-            ),
-            Function(
-                name="max_sim_binary",
-                expression="""
-                    sum(
-                      reduce(
-                        1/(1 + sum(
-                            hamming(query(qtb), attribute(embedding)) ,v)
-                        ),
-                        max,
-                        patch
-                      ),
-                      querytoken
-                    )
-                """,
-            ),
-        ],
-        first_phase="max_sim_binary",
-        second_phase=SecondPhaseRanking(expression="max_sim", rerank_count=10),
-    )
-    colpali_schema.add_rank_profile(colpali_retrieval_profile)
-
-    # Create the Vespa application package
-    vespa_application_package = ApplicationPackage(
-        name=vespa_app_name,
-        schema=[colpali_schema],
-        auth_clients=[
-            AuthClient(
-                id="mtls",  # Note that you still need to include the mtls client.
-                permissions=["read", "write"],
-                parameters=[Parameter("certificate", {"file": "security/clients.pem"})],
-            ),
-            AuthClient(
-                id="token_write",
-                permissions=["read", "write"],
-                parameters=[Parameter("token", {"id": token_id_write})],
-            ),
-            AuthClient(
-                id="token_read",
-                permissions=["read"],
-                parameters=[Parameter("token", {"id": token_id_read})],
-            ),
-        ],
-    )
-    vespa_team_api_key = os.getenv("VESPA_TEAM_API_KEY")
-    # Deploy the application to Vespa Cloud
-    vespa_cloud = VespaCloud(
-        tenant=tenant_name,
-        application=vespa_app_name,
-        key_content=vespa_team_api_key,
-        application_root="colpali-with-snippets",
-        #application_package=vespa_application_package,
-    )
-
-    #app = vespa_cloud.deploy()
-    vespa_cloud.deploy_from_disk("default", "colpali-with-snippets")
-
-    # Output the endpoint URL
-    endpoint_url = vespa_cloud.get_token_endpoint()
-    print(f"Application deployed. Token endpoint URL: {endpoint_url}")
-
-
-if __name__ == "__main__":
-    main()

feed_vespa.py

@@ -1,209 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-from io import BytesIO
-from typing import cast
-import os
-import json
-import hashlib
-
-from colpali_engine.models import ColPali, ColPaliProcessor
-from colpali_engine.utils.torch_utils import get_torch_device
-from vidore_benchmark.utils.image_utils import scale_image, get_base64_image
-import requests
-from pdf2image import convert_from_path
-from pypdf import PdfReader
-import numpy as np
-from vespa.application import Vespa
-from vespa.io import VespaResponse
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Feed data into Vespa application")
-    parser.add_argument(
-        "--application_name",
-        required=True,
-        default="colpalidemo",
-        help="Vespa application name",
-    )
-    parser.add_argument(
-        "--vespa_schema_name",
-        required=True,
-        default="pdf_page",
-        help="Vespa schema name",
-    )
-    args = parser.parse_args()
-
-    vespa_app_url = os.getenv("VESPA_APP_URL")
-    vespa_cloud_secret_token = os.getenv("VESPA_CLOUD_SECRET_TOKEN")
-    # Set application and schema names
-    application_name = args.application_name
-    schema_name = args.vespa_schema_name
-    # Instantiate Vespa connection using token
-    app = Vespa(url=vespa_app_url, vespa_cloud_secret_token=vespa_cloud_secret_token)
-    app.get_application_status()
-    model_name = "vidore/colpali-v1.2"
-
-    device = get_torch_device("auto")
-    print(f"Using device: {device}")
-
-    # Load the model
-    model = cast(
-        ColPali,
-        ColPali.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-            device_map=device,
-        ),
-    ).eval()
-
-    # Load the processor
-    processor = cast(ColPaliProcessor, ColPaliProcessor.from_pretrained(model_name))
-
-    # Define functions to work with PDFs
-    def download_pdf(url):
-        response = requests.get(url)
-        if response.status_code == 200:
-            return BytesIO(response.content)
-        else:
-            raise Exception(
-                f"Failed to download PDF: Status code {response.status_code}"
-            )
-
-    def get_pdf_images(pdf_url):
-        # Download the PDF
-        pdf_file = download_pdf(pdf_url)
-        # Save the PDF temporarily to disk (pdf2image requires a file path)
-        temp_file = "temp.pdf"
-        with open(temp_file, "wb") as f:
-            f.write(pdf_file.read())
-        reader = PdfReader(temp_file)
-        page_texts = []
-        for page_number in range(len(reader.pages)):
-            page = reader.pages[page_number]
-            text = page.extract_text()
-            page_texts.append(text)
-        images = convert_from_path(temp_file)
-        assert len(images) == len(page_texts)
-        return (images, page_texts)
-
-    # Define sample PDFs
-    sample_pdfs = [
-        {
-            "title": "ConocoPhillips Sustainability Highlights - Nature (24-0976)",
-            "url": "https://static.conocophillips.com/files/resources/24-0976-sustainability-highlights_nature.pdf",
-        },
-        {
-            "title": "ConocoPhillips Managing Climate Related Risks",
-            "url": "https://static.conocophillips.com/files/resources/conocophillips-2023-managing-climate-related-risks.pdf",
-        },
-        {
-            "title": "ConocoPhillips 2023 Sustainability Report",
-            "url": "https://static.conocophillips.com/files/resources/conocophillips-2023-sustainability-report.pdf",
-        },
-    ]
-
-    # Check if vespa_feed.json exists
-    if os.path.exists("vespa_feed.json"):
-        print("Loading vespa_feed from vespa_feed.json")
-        with open("vespa_feed.json", "r") as f:
-            vespa_feed_saved = json.load(f)
-        vespa_feed = []
-        for doc in vespa_feed_saved:
-            put_id = doc["put"]
-            fields = doc["fields"]
-            # Extract document_id from put_id
-            # Format: 'id:application_name:schema_name::document_id'
-            parts = put_id.split("::")
-            document_id = parts[1] if len(parts) > 1 else ""
-            page = {"id": document_id, "fields": fields}
-            vespa_feed.append(page)
-    else:
-        print("Generating vespa_feed")
-        # Process PDFs
-        for pdf in sample_pdfs:
-            page_images, page_texts = get_pdf_images(pdf["url"])
-            pdf["images"] = page_images
-            pdf["texts"] = page_texts
-
-        # Generate embeddings
-        for pdf in sample_pdfs:
-            page_embeddings = []
-            dataloader = DataLoader(
-                pdf["images"],
-                batch_size=2,
-                shuffle=False,
-                collate_fn=lambda x: processor.process_images(x),
-            )
-            for batch_doc in tqdm(dataloader):
-                with torch.no_grad():
-                    batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
-                    embeddings_doc = model(**batch_doc)
-                    page_embeddings.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
-            pdf["embeddings"] = page_embeddings
-
-        # Prepare Vespa feed
-        vespa_feed = []
-        for pdf in sample_pdfs:
-            url = pdf["url"]
-            title = pdf["title"]
-            for page_number, (page_text, embedding, image) in enumerate(
-                zip(pdf["texts"], pdf["embeddings"], pdf["images"])
-            ):
-                base_64_image = get_base64_image(
-                    scale_image(image, 640), add_url_prefix=False
-                )
-                base_64_full_image = get_base64_image(image, add_url_prefix=False)
-                embedding_dict = dict()
-                for idx, patch_embedding in enumerate(embedding):
-                    binary_vector = (
-                        np.packbits(np.where(patch_embedding > 0, 1, 0))
-                        .astype(np.int8)
-                        .tobytes()
-                        .hex()
-                    )
-                    embedding_dict[idx] = binary_vector
-                # id_hash should be md5 hash of url and page_number
-                id_hash = hashlib.md5(f"{url}_{page_number}".encode()).hexdigest()
-                page = {
-                    "id": id_hash,
-                    "fields": {
-                        "id": id_hash,
-                        "url": url,
-                        "title": title,
-                        "page_number": page_number,
-                        "image": base_64_image,
-                        "full_image": base_64_full_image,
-                        "text": page_text,
-                        "embedding": embedding_dict,
-                    },
-                }
-                vespa_feed.append(page)
-
-        # Save vespa_feed to vespa_feed.json in the specified format
-        vespa_feed_to_save = []
-        for page in vespa_feed:
-            document_id = page["id"]
-            put_id = f"id:{application_name}:{schema_name}::{document_id}"
-            vespa_feed_to_save.append({"put": put_id, "fields": page["fields"]})
-        with open("vespa_feed.json", "w") as f:
-            json.dump(vespa_feed_to_save, f)
-
-    def callback(response: VespaResponse, id: str):
-        if not response.is_successful():
-            print(
-                f"Failed to feed document {id} with status code {response.status_code}: Reason {response.get_json()}"
-            )
-
-    # Feed data into Vespa
-    app.feed_iterable(vespa_feed, schema=schema_name, callback=callback)
-
-
-if __name__ == "__main__":
-    main()

hello.py

@@ -1,17 +0,0 @@
-from fasthtml.common import *
-from importlib.util import find_spec
-
-# Run find_spec for all the modules (imports will be removed by ruff if not used. This is just to check if the modules are available, and should be removed)ß
-for module in ["torch", "einops", "PIL", "vidore_benchmark", "colpali_engine"]:
-    spec = find_spec(module)
-    assert spec is not None, f"Module {module} not found"
-
-app, rt = fast_app()
-
-
-@rt("/")
-def get():
-    return Div(P("Hello World!"), hx_get="/change")
-
-
-serve()

prepare_feed_deploy.py

@@ -1,956 +0,0 @@
-# # Visual PDF Retrieval - demo application
-#
-# In this notebook, we will prepare the Vespa backend application for our visual retrieval demo.
-# We will use ColPali as the model to extract patch vectors from images of pdf pages.
-# At query time, we use MaxSim to retrieve and/or (based on the configuration) rank the page results.
-#
-# To see the application in action, visit TODO:
-#
-# The web application is written in FastHTML, meaning the complete application is written in python.
-#
-# The steps we will take in this notebook are:
-#
-# 0. Setup and configuration
-# 1. Download the data
-# 2. Prepare the data
-# 3. Generate queries for evaluation and typeahead search suggestions
-# 4. Deploy the Vespa application
-# 5. Create the Vespa application
-# 6. Feed the data to the Vespa application
-#
-# All the steps that are needed to provision the Vespa application, including feeding the data, can be done from this notebook.
-# We have tried to make it easy for others to run this notebook, to create your own PDF Enterprise Search application using Vespa.
-#
-
-# ## 0. Setup and Configuration
-#
-
-# +
-import os
-import asyncio
-import json
-from typing import Tuple
-import hashlib
-import numpy as np
-
-# Vespa
-from vespa.package import (
-    ApplicationPackage,
-    Field,
-    Schema,
-    Document,
-    HNSW,
-    RankProfile,
-    Function,
-    FieldSet,
-    SecondPhaseRanking,
-    Summary,
-    DocumentSummary,
-)
-from vespa.deployment import VespaCloud
-from vespa.application import Vespa
-from vespa.io import VespaResponse
-
-# Google Generative AI
-import google.generativeai as genai
-
-# Torch and other ML libraries
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-from pdf2image import convert_from_path
-from pypdf import PdfReader
-
-# ColPali model and processor
-from colpali_engine.models import ColPali, ColPaliProcessor
-from colpali_engine.utils.torch_utils import get_torch_device
-from vidore_benchmark.utils.image_utils import scale_image, get_base64_image
-
-# Other utilities
-from bs4 import BeautifulSoup
-import httpx
-from urllib.parse import urljoin, urlparse
-
-# Load environment variables
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Avoid warning from huggingface tokenizers
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-# -
-
-# ### Create a free trial in Vespa Cloud
-#
-# Create a tenant from [here](https://vespa.ai/free-trial/).
-# The trial includes $300 credit.
-# Take note of your tenant name.
-#
-
-VESPA_TENANT_NAME = "vespa-team"
-
-# Here, set your desired application name. (Will be created in later steps)
-# Note that you can not have hyphen `-` or underscore `_` in the application name.
-#
-
-VESPA_APPLICATION_NAME = "colpalidemo"
-VESPA_SCHEMA_NAME = "pdf_page"
-
-# Next, you need to create some tokens for feeding data, and querying the application.
-# We recommend separate tokens for feeding and querying, (the former with write permission, and the latter with read permission).
-# The tokens can be created from the [Vespa Cloud console](https://console.vespa-cloud.com/) in the 'Account' -> 'Tokens' section.
-#
-
-VESPA_TOKEN_ID_WRITE = "colpalidemo_write"
-
-# We also need to set the value of the write token to be able to feed data to the Vespa application.
-#
-
-VESPA_CLOUD_SECRET_TOKEN = os.getenv("VESPA_CLOUD_SECRET_TOKEN") or input(
-    "Enter Vespa cloud secret token: "
-)
-
-# We will also use the Gemini API to create sample queries for our images.
-# You can also use other VLM's to create these queries.
-# Create a Gemini API key from [here](https://aistudio.google.com/app/apikey).
-#
-
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") or input(
-    "Enter Google Generative AI API key: "
-)
-
-# +
-MODEL_NAME = "vidore/colpali-v1.2"
-
-# Configure Google Generative AI
-genai.configure(api_key=GEMINI_API_KEY)
-
-# Set device for Torch
-device = get_torch_device("auto")
-print(f"Using device: {device}")
-
-# Load the ColPali model and processor
-model = ColPali.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-    device_map=device,
-).eval()
-
-processor = ColPaliProcessor.from_pretrained(MODEL_NAME)
-# -
-
-# ## 1. Download PDFs
-#
-# We are going to use public reports from the Norwegian Government Pension Fund Global (also known as the Oil Fund).
-# The fund puts transparency at the forefront and publishes reports on its investments, holdings, and returns, as well as its strategy and governance.
-#
-# These reports are the ones we are going to use for this showcase.
-# Here are some sample images:
-#
-# ![Sample1](./static/img/gfpg-sample-1.png)
-# ![Sample2](./static/img/gfpg-sample-2.png)
-#
-
-# As we can see, a lot of the information is in the form of tables, charts and numbers.
-# These are not easily extractable using pdf-readers or OCR tools.
-#
-
-# +
-import requests
-
-url = "https://www.nbim.no/en/publications/reports/"
-response = requests.get(url)
-response.raise_for_status()
-html_content = response.text
-
-# Parse with BeautifulSoup
-soup = BeautifulSoup(html_content, "html.parser")
-
-links = []
-url_to_year = {}
-
-# Find all 'div's with id starting with 'year-'
-for year_div in soup.find_all("div", id=lambda x: x and x.startswith("year-")):
-    year_id = year_div.get("id", "")
-    year = year_id.replace("year-", "")
-
-    # Within this div, find all 'a' elements with the specific classes
-    for a_tag in year_div.select("a.button.button--download-secondary[href]"):
-        href = a_tag["href"]
-        full_url = urljoin(url, href)
-        links.append(full_url)
-        url_to_year[full_url] = year
-links, url_to_year
-# -
-
-# Limit the number of PDFs to download
-NUM_PDFS = 2  # Set to None to download all PDFs
-links = links[:NUM_PDFS] if NUM_PDFS else links
-links
-
-# +
-from nest_asyncio import apply
-from typing import List
-
-apply()
-
-max_attempts = 3
-
-
-async def download_pdf(session, url, filename):
-    attempt = 0
-    while attempt < max_attempts:
-        try:
-            response = await session.get(url)
-            response.raise_for_status()
-
-            # Use Content-Disposition header to get the filename if available
-            content_disposition = response.headers.get("Content-Disposition")
-            if content_disposition:
-                import re
-
-                fname = re.findall('filename="(.+)"', content_disposition)
-                if fname:
-                    filename = fname[0]
-
-            # Ensure the filename is safe to use on the filesystem
-            safe_filename = filename.replace("/", "_").replace("\\", "_")
-            if not safe_filename or safe_filename == "_":
-                print(f"Invalid filename: {filename}")
-                continue
-
-            filepath = os.path.join("pdfs", safe_filename)
-            with open(filepath, "wb") as f:
-                f.write(response.content)
-            print(f"Downloaded {safe_filename}")
-            return filepath
-        except Exception as e:
-            print(f"Error downloading {filename}: {e}")
-            print(f"Retrying ({attempt})...")
-            await asyncio.sleep(1)  # Wait a bit before retrying
-            attempt += 1
-    return None
-
-
-async def download_pdfs(links: List[str]) -> List[dict]:
-    """Download PDFs from a list of URLs. Add the filename to the dictionary."""
-    async with httpx.AsyncClient() as client:
-        tasks = []
-
-        for idx, link in enumerate(links):
-            # Try to get the filename from the URL
-            path = urlparse(link).path
-            filename = os.path.basename(path)
-
-            # If filename is empty,skip
-            if not filename:
-                continue
-            tasks.append(download_pdf(client, link, filename))
-
-        # Run the tasks concurrently
-        paths = await asyncio.gather(*tasks)
-        pdf_files = [
-            {"url": link, "path": path} for link, path in zip(links, paths) if path
-        ]
-        return pdf_files
-
-
-# Create the pdfs directory if it doesn't exist
-os.makedirs("pdfs", exist_ok=True)
-# Now run the download_pdfs function with the URL
-pdfs = asyncio.run(download_pdfs(links))
-# -
-
-pdfs
-
-# ## 2. Convert PDFs to Images
-#
-
-
-# +
-def get_pdf_images(pdf_path):
-    reader = PdfReader(pdf_path)
-    page_texts = []
-    for page_number in range(len(reader.pages)):
-        page = reader.pages[page_number]
-        text = page.extract_text()
-        page_texts.append(text)
-    images = convert_from_path(pdf_path)
-    # Convert to PIL images
-    assert len(images) == len(page_texts)
-    return images, page_texts
-
-
-pdf_folder = "pdfs"
-pdf_pages = []
-for pdf in tqdm(pdfs):
-    pdf_file = pdf["path"]
-    title = os.path.splitext(os.path.basename(pdf_file))[0]
-    images, texts = get_pdf_images(pdf_file)
-    for page_no, (image, text) in enumerate(zip(images, texts)):
-        pdf_pages.append(
-            {
-                "title": title,
-                "year": int(url_to_year[pdf["url"]]),
-                "url": pdf["url"],
-                "path": pdf_file,
-                "image": image,
-                "text": text,
-                "page_no": page_no,
-            }
-        )
-# -
-
-len(pdf_pages)
-
-# +
-from collections import Counter
-
-# Print the length of the text fields - mean, max and min
-text_lengths = [len(page["text"]) for page in pdf_pages]
-print(f"Mean text length: {np.mean(text_lengths)}")
-print(f"Max text length: {np.max(text_lengths)}")
-print(f"Min text length: {np.min(text_lengths)}")
-print(f"Median text length: {np.median(text_lengths)}")
-print(f"Number of text with length == 0: {Counter(text_lengths)[0]}")
-# -
-
-# ## 3. Generate Queries
-#
-# In this step, we want to generate queries for each page image.
-# These will be useful for 2 reasons:
-#
-# 1. We can use these queries as typeahead suggestions in the search bar.
-# 2. We can use the queries to generate an evaluation dataset. See [Improving Retrieval with LLM-as-a-judge](https://blog.vespa.ai/improving-retrieval-with-llm-as-a-judge/) for a deeper dive into this topic.
-#
-# The prompt for generating queries is taken from [this](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html#an-update-retrieval-focused-prompt) wonderful blog post by Daniel van Strien.
-#
-# We will use the Gemini API to generate these queries, with `gemini-1.5-flash-8b` as the model.
-#
-
-# +
-from pydantic import BaseModel
-
-
-class GeneratedQueries(BaseModel):
-    broad_topical_question: str
-    broad_topical_query: str
-    specific_detail_question: str
-    specific_detail_query: str
-    visual_element_question: str
-    visual_element_query: str
-
-
-def get_retrieval_prompt() -> Tuple[str, GeneratedQueries]:
-    prompt = (
-        prompt
-    ) = """You are an investor, stock analyst and financial expert. You will be presented an image of a document page from a report published by the Norwegian Government Pension Fund Global (GPFG). The report may be annual or quarterly reports, or policy reports, on topics such as responsible investment, risk etc.
-Your task is to generate retrieval queries and questions that you would use to retrieve this document (or ask based on this document) in a large corpus.
-Please generate 3 different types of retrieval queries and questions.
-A retrieval query is a keyword based query, made up of 2-5 words, that you would type into a search engine to find this document.
-A question is a natural language question that you would ask, for which the document contains the answer.
-The queries should be of the following types:
-1. A broad topical query: This should cover the main subject of the document.
-2. A specific detail query: This should cover a specific detail or aspect of the document.
-3. A visual element query: This should cover a visual element of the document, such as a chart, graph, or image.
-
-Important guidelines:
-- Ensure the queries are relevant for retrieval tasks, not just describing the page content.
-- Use a fact-based natural language style for the questions.
-- Frame the queries as if someone is searching for this document in a large corpus.
-- Make the queries diverse and representative of different search strategies.
-
-Format your response as a JSON object with the structure of the following example:
-{
-    "broad_topical_question": "What was the Responsible Investment Policy in 2019?",
-    "broad_topical_query": "responsible investment policy 2019",
-    "specific_detail_question": "What is the percentage of investments in renewable energy?",
-    "specific_detail_query": "renewable energy investments percentage",
-    "visual_element_question": "What is the trend of total holding value over time?",
-    "visual_element_query": "total holding value trend"
-}
-
-If there are no relevant visual elements, provide an empty string for the visual element question and query.
-Here is the document image to analyze:
-Generate the queries based on this image and provide the response in the specified JSON format.
-Only return JSON. Don't return any extra explanation text. """
-
-    return prompt, GeneratedQueries
-
-
-prompt_text, pydantic_model = get_retrieval_prompt()
-
-# +
-gemini_model = genai.GenerativeModel("gemini-1.5-flash-8b")
-
-
-def generate_queries(image, prompt_text, pydantic_model):
-    try:
-        response = gemini_model.generate_content(
-            [image, "\n\n", prompt_text],
-            generation_config=genai.GenerationConfig(
-                response_mime_type="application/json",
-                response_schema=pydantic_model,
-            ),
-        )
-        queries = json.loads(response.text)
-    except Exception as _e:
-        queries = {
-            "broad_topical_question": "",
-            "broad_topical_query": "",
-            "specific_detail_question": "",
-            "specific_detail_query": "",
-            "visual_element_question": "",
-            "visual_element_query": "",
-        }
-    return queries
-
-
-# -
-
-for pdf in tqdm(pdf_pages):
-    image = pdf.get("image")
-    pdf["queries"] = generate_queries(image, prompt_text, pydantic_model)
-
-pdf_pages[46]["image"]
-
-pdf_pages[46]["queries"]
-
-# +
-# Generate queries async - keeping for now as we probably need when applying to the full dataset
-# import asyncio
-# from tenacity import retry, stop_after_attempt, wait_exponential
-# import google.generativeai as genai
-# from tqdm.asyncio import tqdm_asyncio
-
-# max_in_flight = 200  # Maximum number of concurrent requests
-
-
-# async def generate_queries_for_image_async(model, image, semaphore):
-#     @retry(stop=stop_after_attempt(3), wait=wait_exponential(), reraise=True)
-#     async def _generate():
-#         async with semaphore:
-#             result = await model.generate_content_async(
-#                 [image, "\n\n", prompt_text],
-#                 generation_config=genai.GenerationConfig(
-#                     response_mime_type="application/json",
-#                     response_schema=pydantic_model,
-#                 ),
-#             )
-#             return json.loads(result.text)
-
-#     try:
-#         return await _generate()
-#     except Exception as e:
-#         print(f"Error generating queries for image: {e}")
-#         return None  # Return None or handle as needed
-
-
-# async def enrich_pdfs():
-#     gemini_model = genai.GenerativeModel("gemini-1.5-flash-8b")
-#     semaphore = asyncio.Semaphore(max_in_flight)
-#     tasks = []
-#     for pdf in pdf_pages:
-#         pdf["queries"] = []
-#         image = pdf.get("image")
-#         if image:
-#             task = generate_queries_for_image_async(gemini_model, image, semaphore)
-#             tasks.append((pdf, task))
-
-#     # Run the tasks concurrently using asyncio.gather()
-#     for pdf, task in tqdm_asyncio(tasks):
-#         result = await task
-#         if result:
-#             pdf["queries"] = result
-#     return pdf_pages
-
-
-# pdf_pages = asyncio.run(enrich_pdfs())
-
-# +
-# write title, url, page_no, text, queries, not image to JSON
-with open("output/pdf_pages.json", "w") as f:
-    to_write = [{k: v for k, v in pdf.items() if k != "image"} for pdf in pdf_pages]
-    json.dump(to_write, f, indent=2)
-
-# with open("pdfs/pdf_pages.json", "r") as f:
-#     saved_pdf_pages = json.load(f)
-# for pdf, saved_pdf in zip(pdf_pages, saved_pdf_pages):
-#     pdf.update(saved_pdf)
-# -
-
-# ## 4. Generate embeddings
-#
-# Now that we have the queries, we can use the ColPali model to generate embeddings for each page image.
-#
-
-
-def generate_embeddings(images, model, processor, batch_size=2) -> np.ndarray:
-    """
-    Generate embeddings for a list of images.
-    Move to CPU only once per batch.
-
-    Args:
-        images (List[PIL.Image]): List of PIL images.
-        model (nn.Module): The model to generate embeddings.
-        processor: The processor to preprocess images.
-        batch_size (int, optional): Batch size for processing. Defaults to 64.
-
-    Returns:
-        np.ndarray: Embeddings for the images, shape
-                    (len(images), processor.max_patch_length (1030 for ColPali), model.config.hidden_size (Patch embedding dimension - 128 for ColPali)).
-    """
-    embeddings_list = []
-
-    def collate_fn(batch):
-        # Batch is a list of images
-        return processor.process_images(batch)  # Should return a dict of tensors
-
-    dataloader = DataLoader(
-        images,
-        shuffle=False,
-        collate_fn=collate_fn,
-    )
-
-    for batch_doc in tqdm(dataloader, desc="Generating embeddings"):
-        with torch.no_grad():
-            # Move batch to the device
-            batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
-            embeddings_batch = model(**batch_doc)
-            embeddings_list.append(torch.unbind(embeddings_batch.to("cpu"), dim=0))
-    # Concatenate all embeddings and create a numpy array
-    all_embeddings = np.concatenate(embeddings_list, axis=0)
-    return all_embeddings
-
-
-# Generate embeddings for all images
-images = [pdf["image"] for pdf in pdf_pages]
-embeddings = generate_embeddings(images, model, processor)
-
-embeddings.shape
-
-# ## 5. Prepare Data on Vespa Format
-#
-# Now, that we have all the data we need, all that remains is to make sure it is in the right format for Vespa.
-#
-
-
-def float_to_binary_embedding(float_query_embedding: dict) -> dict:
-    """Utility function to convert float query embeddings to binary query embeddings."""
-    binary_query_embeddings = {}
-    for k, v in float_query_embedding.items():
-        binary_vector = (
-            np.packbits(np.where(np.array(v) > 0, 1, 0)).astype(np.int8).tolist()
-        )
-        binary_query_embeddings[k] = binary_vector
-    return binary_query_embeddings
-
-
-vespa_feed = []
-for pdf, embedding in zip(pdf_pages, embeddings):
-    url = pdf["url"]
-    year = pdf["year"]
-    title = pdf["title"]
-    image = pdf["image"]
-    text = pdf.get("text", "")
-    page_no = pdf["page_no"]
-    query_dict = pdf["queries"]
-    questions = [v for k, v in query_dict.items() if "question" in k and v]
-    queries = [v for k, v in query_dict.items() if "query" in k and v]
-    base_64_image = get_base64_image(
-        scale_image(image, 32), add_url_prefix=False
-    )  # Scaled down image to return fast on search (~1kb)
-    base_64_full_image = get_base64_image(image, add_url_prefix=False)
-    embedding_dict = {k: v for k, v in enumerate(embedding)}
-    binary_embedding = float_to_binary_embedding(embedding_dict)
-    # id_hash should be md5 hash of url and page_number
-    id_hash = hashlib.md5(f"{url}_{page_no}".encode()).hexdigest()
-    page = {
-        "id": id_hash,
-        "fields": {
-            "id": id_hash,
-            "url": url,
-            "title": title,
-            "year": year,
-            "page_number": page_no,
-            "blur_image": base_64_image,
-            "full_image": base_64_full_image,
-            "text": text,
-            "embedding": binary_embedding,
-            "queries": queries,
-            "questions": questions,
-        },
-    }
-    vespa_feed.append(page)
-
-# +
-# We will prepare the Vespa feed data, including the embeddings and the generated queries
-
-
-# Save vespa_feed to vespa_feed.json
-os.makedirs("output", exist_ok=True)
-with open("output/vespa_feed.json", "w") as f:
-    vespa_feed_to_save = []
-    for page in vespa_feed:
-        document_id = page["id"]
-        put_id = f"id:{VESPA_APPLICATION_NAME}:{VESPA_SCHEMA_NAME}::{document_id}"
-        vespa_feed_to_save.append({"put": put_id, "fields": page["fields"]})
-    json.dump(vespa_feed_to_save, f)
-
-# +
-# import json
-
-# with open("output/vespa_feed.json", "r") as f:
-#     vespa_feed = json.load(f)
-# -
-
-len(vespa_feed)
-
-# ## 5. Prepare Vespa Application
-#
-
-# +
-# Define the Vespa schema
-colpali_schema = Schema(
-    name=VESPA_SCHEMA_NAME,
-    document=Document(
-        fields=[
-            Field(
-                name="id",
-                type="string",
-                indexing=["summary", "index"],
-                match=["word"],
-            ),
-            Field(name="url", type="string", indexing=["summary", "index"]),
-            Field(name="year", type="int", indexing=["summary", "attribute"]),
-            Field(
-                name="title",
-                type="string",
-                indexing=["summary", "index"],
-                match=["text"],
-                index="enable-bm25",
-            ),
-            Field(name="page_number", type="int", indexing=["summary", "attribute"]),
-            Field(name="blur_image", type="raw", indexing=["summary"]),
-            Field(name="full_image", type="raw", indexing=["summary"]),
-            Field(
-                name="text",
-                type="string",
-                indexing=["summary", "index"],
-                match=["text"],
-                index="enable-bm25",
-            ),
-            Field(
-                name="embedding",
-                type="tensor<int8>(patch{}, v[16])",
-                indexing=[
-                    "attribute",
-                    "index",
-                ],
-                ann=HNSW(
-                    distance_metric="hamming",
-                    max_links_per_node=32,
-                    neighbors_to_explore_at_insert=400,
-                ),
-            ),
-            Field(
-                name="questions",
-                type="array<string>",
-                indexing=["summary", "attribute"],
-                summary=Summary(fields=["matched-elements-only"]),
-            ),
-            Field(
-                name="queries",
-                type="array<string>",
-                indexing=["summary", "attribute"],
-                summary=Summary(fields=["matched-elements-only"]),
-            ),
-        ]
-    ),
-    fieldsets=[
-        FieldSet(
-            name="default",
-            fields=["title", "url", "blur_image", "page_number", "text"],
-        ),
-        FieldSet(
-            name="image",
-            fields=["full_image"],
-        ),
-    ],
-    document_summaries=[
-        DocumentSummary(
-            name="default",
-            summary_fields=[
-                Summary(
-                    name="text",
-                    fields=[("bolding", "on")],
-                ),
-                Summary(
-                    name="snippet",
-                    fields=[("source", "text"), "dynamic"],
-                ),
-            ],
-            from_disk=True,
-        ),
-        DocumentSummary(
-            name="suggestions",
-            summary_fields=[
-                Summary(name="questions"),
-            ],
-            from_disk=True,
-        ),
-    ],
-)
-
-# Define similarity functions used in all rank profiles
-mapfunctions = [
-    Function(
-        name="similarities",  # computes similarity scores between each query token and image patch
-        expression="""
-                sum(
-                    query(qt) * unpack_bits(attribute(embedding)), v
-                )
-            """,
-    ),
-    Function(
-        name="normalized",  # normalizes the similarity scores to [-1, 1]
-        expression="""
-                (similarities - reduce(similarities, min)) / (reduce((similarities - reduce(similarities, min)), max)) * 2 - 1
-            """,
-    ),
-    Function(
-        name="quantized",  # quantizes the normalized similarity scores to signed 8-bit integers [-128, 127]
-        expression="""
-                cell_cast(normalized * 127.999, int8)
-            """,
-    ),
-]
-
-# Define the 'bm25' rank profile
-colpali_bm25_profile = RankProfile(
-    name="bm25",
-    inputs=[("query(qt)", "tensor<float>(querytoken{}, v[128])")],
-    first_phase="bm25(title) + bm25(text)",
-    functions=mapfunctions,
-)
-
-
-# A function to create an inherited rank profile which also returns quantized similarity scores
-def with_quantized_similarity(rank_profile: RankProfile) -> RankProfile:
-    return RankProfile(
-        name=f"{rank_profile.name}_sim",
-        first_phase=rank_profile.first_phase,
-        inherits=rank_profile.name,
-        summary_features=["quantized"],
-    )
-
-
-colpali_schema.add_rank_profile(colpali_bm25_profile)
-colpali_schema.add_rank_profile(with_quantized_similarity(colpali_bm25_profile))
-
-# Update the 'default' rank profile
-colpali_profile = RankProfile(
-    name="default",
-    inputs=[("query(qt)", "tensor<float>(querytoken{}, v[128])")],
-    first_phase="bm25_score",
-    second_phase=SecondPhaseRanking(expression="max_sim", rerank_count=10),
-    functions=mapfunctions
-    + [
-        Function(
-            name="max_sim",
-            expression="""
-                sum(
-                    reduce(
-                        sum(
-                            query(qt) * unpack_bits(attribute(embedding)), v
-                        ),
-                        max, patch
-                    ),
-                    querytoken
-                )
-            """,
-        ),
-        Function(name="bm25_score", expression="bm25(title) + bm25(text)"),
-    ],
-)
-colpali_schema.add_rank_profile(colpali_profile)
-colpali_schema.add_rank_profile(with_quantized_similarity(colpali_profile))
-
-# Update the 'retrieval-and-rerank' rank profile
-input_query_tensors = []
-MAX_QUERY_TERMS = 64
-for i in range(MAX_QUERY_TERMS):
-    input_query_tensors.append((f"query(rq{i})", "tensor<int8>(v[16])"))
-
-input_query_tensors.extend(
-    [
-        ("query(qt)", "tensor<float>(querytoken{}, v[128])"),
-        ("query(qtb)", "tensor<int8>(querytoken{}, v[16])"),
-    ]
-)
-
-colpali_retrieval_profile = RankProfile(
-    name="retrieval-and-rerank",
-    inputs=input_query_tensors,
-    first_phase="max_sim_binary",
-    second_phase=SecondPhaseRanking(expression="max_sim", rerank_count=10),
-    functions=mapfunctions
-    + [
-        Function(
-            name="max_sim",
-            expression="""
-                sum(
-                    reduce(
-                        sum(
-                            query(qt) * unpack_bits(attribute(embedding)), v
-                        ),
-                        max, patch
-                    ),
-                    querytoken
-                )
-            """,
-        ),
-        Function(
-            name="max_sim_binary",
-            expression="""
-                sum(
-                    reduce(
-                        1 / (1 + sum(
-                            hamming(query(qtb), attribute(embedding)), v)
-                        ),
-                        max, patch
-                    ),
-                    querytoken
-                )
-            """,
-        ),
-    ],
-)
-colpali_schema.add_rank_profile(colpali_retrieval_profile)
-colpali_schema.add_rank_profile(with_quantized_similarity(colpali_retrieval_profile))
-
-# +
-from vespa.configuration.services import (
-    services,
-    container,
-    search,
-    document_api,
-    document_processing,
-    clients,
-    client,
-    config,
-    content,
-    redundancy,
-    documents,
-    node,
-    certificate,
-    token,
-    document,
-    nodes,
-)
-from vespa.configuration.vt import vt
-from vespa.package import ServicesConfiguration
-
-service_config = ServicesConfiguration(
-    application_name=VESPA_APPLICATION_NAME,
-    services_config=services(
-        container(
-            search(),
-            document_api(),
-            document_processing(),
-            clients(
-                client(
-                    certificate(file="security/clients.pem"),
-                    id="mtls",
-                    permissions="read,write",
-                ),
-                client(
-                    token(id=f"{VESPA_TOKEN_ID_WRITE}"),
-                    id="token_write",
-                    permissions="read,write",
-                ),
-            ),
-            config(
-                vt("tag")(
-                    vt("bold")(
-                        vt("open", "<strong>"),
-                        vt("close", "</strong>"),
-                    ),
-                    vt("separator", "..."),
-                ),
-                name="container.qr-searchers",
-            ),
-            id=f"{VESPA_APPLICATION_NAME}_container",
-            version="1.0",
-        ),
-        content(
-            redundancy("1"),
-            documents(document(type="pdf_page", mode="index")),
-            nodes(node(distribution_key="0", hostalias="node1")),
-            config(
-                vt("max_matches", "2", replace_underscores=False),
-                vt("length", "1000"),
-                vt("surround_max", "500", replace_underscores=False),
-                vt("min_length", "300", replace_underscores=False),
-                name="vespa.config.search.summary.juniperrc",
-            ),
-            id=f"{VESPA_APPLICATION_NAME}_content",
-            version="1.0",
-        ),
-        version="1.0",
-    ),
-)
-# -
-
-# Create the Vespa application package
-vespa_application_package = ApplicationPackage(
-    name=VESPA_APPLICATION_NAME,
-    schema=[colpali_schema],
-    services_config=service_config,
-)
-
-# ## 6. Deploy Vespa Application
-#
-
-VESPA_TEAM_API_KEY = os.getenv("VESPA_TEAM_API_KEY") or input(
-    "Enter Vespa team API key: "
-)
-
-# +
-vespa_cloud = VespaCloud(
-    tenant=VESPA_TENANT_NAME,
-    application=VESPA_APPLICATION_NAME,
-    key_content=VESPA_TEAM_API_KEY,
-    application_package=vespa_application_package,
-)
-
-# Deploy the application
-vespa_cloud.deploy()
-
-# Output the endpoint URL
-endpoint_url = vespa_cloud.get_token_endpoint()
-print(f"Application deployed. Token endpoint URL: {endpoint_url}")
-# -
-
-# Make sure to take note of the token endpoint_url.
-# You need to put this in your `.env` file - `VESPA_APP_URL=https://abcd.vespa-app.cloud` - to access the Vespa application from your web application.
-#
-
-# ## 8. Feed Data to Vespa
-#
-
-# Instantiate Vespa connection using token
-app = Vespa(url=endpoint_url, vespa_cloud_secret_token=VESPA_CLOUD_SECRET_TOKEN)
-app.get_application_status()
-
-
-# +
-def callback(response: VespaResponse, id: str):
-    if not response.is_successful():
-        print(
-            f"Failed to feed document {id} with status code {response.status_code}: Reason {response.get_json()}"
-        )
-
-
-# Feed data into Vespa asynchronously
-app.feed_async_iterable(vespa_feed, schema=VESPA_SCHEMA_NAME, callback=callback)

pyproject.toml

@@ -1,118 +0,0 @@
-[project]
-name = "visual-retrieval-colpali"
-version = "0.1.0"
-description = "Visual retrieval with ColPali"
-readme = "README.md"
-requires-python = ">=3.10, <3.13" 
-license = { text = "Apache-2.0" }
-dependencies = [
-    "python-fasthtml",
-    "huggingface-hub",
-    "pyvespa>=0.50.0",
-    "vespacli",
-    "torch",
-    "vidore-benchmark[interpretability]>=4.0.0,<5.0.0",
-    "colpali-engine",
-    "einops",
-    "pypdf",
-    "setuptools",
-    "python-dotenv",
-    "shad4fast>=1.2.1",
-    "google-generativeai>=0.7.2",
-    "spacy",
-    "pip"
-]
-
-# dev-dependencies
-[project.optional-dependencies]
-dev = [
-    "ruff",
-    "python-dotenv",
-    "huggingface_hub[cli]"
-]
-feed = [
-    "ipykernel",
-    "jupytext",
-    "pydantic",
-    "beautifulsoup4",
-    "pdf2image",
-    "google-generativeai"
-]
-[tool.ruff]
-# Exclude a variety of commonly ignored directories.
-exclude = [
-    ".bzr",
-    ".direnv",
-    ".eggs",
-    ".git",
-    ".git-rewrite",
-    ".hg",
-    ".ipynb_checkpoints",
-    ".mypy_cache",
-    ".nox",
-    ".pants.d",
-    ".pyenv",
-    ".pytest_cache",
-    ".pytype",
-    ".ruff_cache",
-    ".svn",
-    ".tox",
-    ".venv",
-    ".vscode",
-    "__pypackages__",
-    "_build",
-    "buck-out",
-    "build",
-    "dist",
-    "node_modules",
-    "site-packages",
-    "venv",
-]
-
-# Same as Black.
-line-length = 88
-indent-width = 4
-
-# Assume Python 3.8
-target-version = "py38"
-
-[tool.ruff.lint]
-# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
-# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
-# McCabe complexity (`C901`) by default.
-select = ["E4", "E7", "E9", "F"]
-ignore = []
-
-# Allow fix for all enabled rules (when `--fix`) is provided.
-fixable = ["ALL"]
-unfixable = []
-
-# Allow unused variables when underscore-prefixed.
-dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
-
-[tool.ruff.format]
-# Like Black, use double quotes for strings.
-quote-style = "double"
-
-# Like Black, indent with spaces, rather than tabs.
-indent-style = "space"
-
-# Like Black, respect magic trailing commas.
-skip-magic-trailing-comma = false
-
-# Like Black, automatically detect the appropriate line ending.
-line-ending = "auto"
-
-# Enable auto-formatting of code examples in docstrings. Markdown,
-# reStructuredText code/literal blocks and doctests are all supported.
-#
-# This is currently disabled by default, but it is planned for this
-# to be opt-out in the future.
-docstring-code-format = false
-
-# Set the line length limit used when formatting code snippets in
-# docstrings.
-#
-# This only has an effect when the `docstring-code-format` setting is
-# enabled.
-docstring-code-line-length = "dynamic"

query_vespa.py

@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import torch
-from torch.utils.data import DataLoader
-from PIL import Image
-import numpy as np
-from typing import cast
-import asyncio
-
-from colpali_engine.models import ColPali, ColPaliProcessor
-from colpali_engine.utils.torch_utils import get_torch_device
-from vespa.application import Vespa
-from vespa.io import VespaQueryResponse
-from dotenv import load_dotenv
-from pathlib import Path
-
-MAX_QUERY_TERMS = 64
-SAVEDIR = Path(__file__) / "output" / "images"
-load_dotenv()
-
-
-def process_queries(processor, queries, image):
-    inputs = processor(
-        images=[image] * len(queries), text=queries, return_tensors="pt", padding=True
-    )
-    return inputs
-
-
-def display_query_results(query, response, hits=5):
-    query_time = response.json.get("timing", {}).get("searchtime", -1)
-    query_time = round(query_time, 2)
-    count = response.json.get("root", {}).get("fields", {}).get("totalCount", 0)
-    result_text = f"Query text: '{query}', query time {query_time}s, count={count}, top results:\n"
-
-    for i, hit in enumerate(response.hits[:hits]):
-        title = hit["fields"]["title"]
-        url = hit["fields"]["url"]
-        page = hit["fields"]["page_number"]
-        image = hit["fields"]["image"]
-        _id = hit["id"]
-        score = hit["relevance"]
-
-        result_text += f"\nPDF Result {i + 1}\n"
-        result_text += f"Title: {title}, page {page+1} with score {score:.2f}\n"
-        result_text += f"URL: {url}\n"
-        result_text += f"ID: {_id}\n"
-        # Optionally, save or display the image
-        # img_data = base64.b64decode(image)
-        # img_path = SAVEDIR / f"{title}.png"
-        # with open(f"{img_path}", "wb") as f:
-        #     f.write(img_data)
-    print(result_text)
-
-
-async def query_vespa_default(app, queries, qs):
-    async with app.asyncio(connections=1, total_timeout=120) as session:
-        for idx, query in enumerate(queries):
-            query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}
-            response: VespaQueryResponse = await session.query(
-                yql="select documentid,title,url,image,page_number from pdf_page where userInput(@userQuery)",
-                ranking="default",
-                userQuery=query,
-                timeout=120,
-                hits=3,
-                body={"input.query(qt)": query_embedding, "presentation.timing": True},
-            )
-            assert response.is_successful()
-            display_query_results(query, response)
-
-
-async def query_vespa_nearest_neighbor(app, queries, qs):
-    # Using nearestNeighbor for retrieval
-    target_hits_per_query_tensor = (
-        20  # this is a hyper parameter that can be tuned for speed versus accuracy
-    )
-    async with app.asyncio(connections=1, total_timeout=180) as session:
-        for idx, query in enumerate(queries):
-            float_query_embedding = {k: v.tolist() for k, v in enumerate(qs[idx])}
-            binary_query_embeddings = dict()
-            for k, v in float_query_embedding.items():
-                binary_vector = (
-                    np.packbits(np.where(np.array(v) > 0, 1, 0))
-                    .astype(np.int8)
-                    .tolist()
-                )
-                binary_query_embeddings[k] = binary_vector
-                if len(binary_query_embeddings) >= MAX_QUERY_TERMS:
-                    print(
-                        f"Warning: Query has more than {MAX_QUERY_TERMS} terms. Truncating."
-                    )
-                    break
-
-            # The mixed tensors used in MaxSim calculations
-            # We use both binary and float representations
-            query_tensors = {
-                "input.query(qtb)": binary_query_embeddings,
-                "input.query(qt)": float_query_embedding,
-            }
-            # The query tensors used in the nearest neighbor calculations
-            for i in range(0, len(binary_query_embeddings)):
-                query_tensors[f"input.query(rq{i})"] = binary_query_embeddings[i]
-            nn = []
-            for i in range(0, len(binary_query_embeddings)):
-                nn.append(
-                    f"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))"
-                )
-            # We use an OR operator to combine the nearest neighbor operator
-            nn = " OR ".join(nn)
-            response: VespaQueryResponse = await session.query(
-                body={
-                    **query_tensors,
-                    "presentation.timing": True,
-                    "yql": f"select documentid, title, url, image, page_number from pdf_page where {nn}",
-                    "ranking.profile": "retrieval-and-rerank",
-                    "timeout": 120,
-                    "hits": 3,
-                },
-            )
-            assert response.is_successful(), response.json
-            display_query_results(query, response)
-
-
-def main():
-    vespa_app_url = os.environ.get(
-        "VESPA_APP_URL"
-    )  # Ensure this is set to your Vespa app URL
-    vespa_cloud_secret_token = os.environ.get("VESPA_CLOUD_SECRET_TOKEN")
-    if not vespa_app_url or not vespa_cloud_secret_token:
-        raise ValueError(
-            "Please set the VESPA_APP_URL and VESPA_CLOUD_SECRET_TOKEN environment variables"
-        )
-    # Instantiate Vespa connection
-    app = Vespa(url=vespa_app_url, vespa_cloud_secret_token=vespa_cloud_secret_token)
-    status_resp = app.get_application_status()
-    if status_resp.status_code != 200:
-        print(f"Failed to connect to Vespa at {vespa_app_url}")
-        return
-    else:
-        print(f"Connected to Vespa at {vespa_app_url}")
-    # Load the model
-    device = get_torch_device("auto")
-    print(f"Using device: {device}")
-
-    model_name = "vidore/colpali-v1.2"
-    processor_name = "google/paligemma-3b-mix-448"
-
-    model = cast(
-        ColPali,
-        ColPali.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-            device_map=device,
-        ),
-    ).eval()
-
-    processor = cast(ColPaliProcessor, ColPaliProcessor.from_pretrained(processor_name))
-
-    # Create dummy image
-    dummy_image = Image.new("RGB", (448, 448), (255, 255, 255))
-
-    # Define queries
-    queries = [
-        "Percentage of non-fresh water as source?",
-        "Policies related to nature risk?",
-        "How much of produced water is recycled?",
-    ]
-
-    # Obtain query embeddings
-    dataloader = DataLoader(
-        queries,
-        batch_size=1,
-        shuffle=False,
-        collate_fn=lambda x: process_queries(processor, x, dummy_image),
-    )
-    qs = []
-    for batch_query in dataloader:
-        with torch.no_grad():
-            batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
-            embeddings_query = model(**batch_query)
-            qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
-
-    # Perform queries using default rank profile
-    print("Performing queries using default rank profile:")
-    asyncio.run(query_vespa_default(app, queries, qs))
-
-    # Perform queries using nearestNeighbor
-    print("Performing queries using nearestNeighbor:")
-    asyncio.run(query_vespa_nearest_neighbor(app, queries, qs))
-
-
-if __name__ == "__main__":
-    main()

ruff.toml

@@ -1,77 +0,0 @@
-# Exclude a variety of commonly ignored directories.
-exclude = [
-    ".bzr",
-    ".direnv",
-    ".eggs",
-    ".git",
-    ".git-rewrite",
-    ".hg",
-    ".ipynb_checkpoints",
-    ".mypy_cache",
-    ".nox",
-    ".pants.d",
-    ".pyenv",
-    ".pytest_cache",
-    ".pytype",
-    ".ruff_cache",
-    ".svn",
-    ".tox",
-    ".venv",
-    ".vscode",
-    "__pypackages__",
-    "_build",
-    "buck-out",
-    "build",
-    "dist",
-    "node_modules",
-    "site-packages",
-    "venv",
-]
-
-# Same as Black.
-line-length = 88
-indent-width = 4
-
-# Assume Python 3.8
-target-version = "py38"
-
-[lint]
-# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
-# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
-# McCabe complexity (`C901`) by default.
-select = ["E4", "E7", "E9", "F"]
-ignore = []
-
-# Allow fix for all enabled rules (when `--fix`) is provided.
-fixable = ["ALL"]
-unfixable = []
-
-# Allow unused variables when underscore-prefixed.
-dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
-
-[format]
-# Like Black, use double quotes for strings.
-quote-style = "double"
-
-# Like Black, indent with spaces, rather than tabs.
-indent-style = "space"
-
-# Like Black, respect magic trailing commas.
-skip-magic-trailing-comma = false
-
-# Like Black, automatically detect the appropriate line ending.
-line-ending = "auto"
-
-# Enable auto-formatting of code examples in docstrings. Markdown,
-# reStructuredText code/literal blocks and doctests are all supported.
-#
-# This is currently disabled by default, but it is planned for this
-# to be opt-out in the future.
-docstring-code-format = false
-
-# Set the line length limit used when formatting code snippets in
-# docstrings.
-#
-# This only has an effect when the `docstring-code-format` setting is
-# enabled.
-docstring-code-line-length = "dynamic"

src/README.md

@@ -1,19 +0,0 @@
----
-title: ColPali 🤝 Vespa - Visual Retrieval
-short_description: Visual Retrieval with ColPali and Vespa
-emoji: 👀
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 4.44.0
-app_file: main.py
-pinned: false
-license: apache-2.0
-suggested_hardware: t4-small
-models:
-  - vidore/colpaligemma-3b-pt-448-base
-  - vidore/colpali-v1.2
-preload_from_hub:
-  - vidore/colpaligemma-3b-pt-448-base config.json,model-00001-of-00002.safetensors,model-00002-of-00002.safetensors,model.safetensors.index.json,preprocessor_config.json,special_tokens_map.json,tokenizer.json,tokenizer_config.json 12c59eb7e23bc4c26876f7be7c17760d5d3a1ffa
-  - vidore/colpali-v1.2 adapter_config.json,adapter_model.safetensors,preprocessor_config.json,special_tokens_map.json,tokenizer.json,tokenizer_config.json 9912ce6f8a462d8cf2269f5606eabbd2784e764f
----

src/backend/cache.py

@@ -1,26 +0,0 @@
-from collections import OrderedDict
-
-
-# Initialize LRU Cache
-class LRUCache:
-    def __init__(self, max_size=20):
-        self.max_size = max_size
-        self.cache = OrderedDict()
-
-    def get(self, key):
-        if key in self.cache:
-            self.cache.move_to_end(key)
-            return self.cache[key]
-        return None
-
-    def set(self, key, value):
-        if key in self.cache:
-            self.cache.move_to_end(key)
-        else:
-            if len(self.cache) >= self.max_size:
-                self.cache.popitem(last=False)
-        self.cache[key] = value
-
-    def delete(self, key):
-        if key in self.cache:
-            del self.cache[key]

src/backend/colpali.py

@@ -1,281 +0,0 @@
-import torch
-from PIL import Image
-import numpy as np
-from typing import Generator, Tuple, List, Union, Dict
-from pathlib import Path
-import base64
-from io import BytesIO
-import re
-import io
-import matplotlib.cm as cm
-
-from colpali_engine.models import ColPali, ColPaliProcessor
-from colpali_engine.utils.torch_utils import get_torch_device
-from vidore_benchmark.interpretability.torch_utils import (
-    normalize_similarity_map_per_query_token,
-)
-from functools import lru_cache
-import logging
-
-
-class SimMapGenerator:
-    """
-    Generates similarity maps based on query embeddings and image patches using the ColPali model.
-    """
-
-    colormap = cm.get_cmap("viridis")  # Preload colormap for efficiency
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        model_name: str = "vidore/colpali-v1.2",
-        n_patch: int = 32,
-    ):
-        """
-        Initializes the SimMapGenerator class with a specified model and patch dimension.
-
-        Args:
-            model_name (str): The model name for loading the ColPali model.
-            n_patch (int): The number of patches per dimension.
-        """
-        self.model_name = model_name
-        self.n_patch = n_patch
-        self.device = get_torch_device("auto")
-        self.logger = logger
-        self.logger.info(f"Using device: {self.device}")
-        self.model, self.processor = self.load_model()
-
-    def load_model(self) -> Tuple[ColPali, ColPaliProcessor]:
-        """
-        Loads the ColPali model and processor.
-
-        Returns:
-            Tuple[ColPali, ColPaliProcessor]: Loaded model and processor.
-        """
-        model = ColPali.from_pretrained(
-            self.model_name,
-            torch_dtype=torch.bfloat16,  # Note that the embeddings created during feed were float32 -> binarized, yet setting this seem to produce the most similar results both locally (mps) and HF (Cuda)
-            device_map=self.device,
-        ).eval()
-
-        processor = ColPaliProcessor.from_pretrained(self.model_name)
-        return model, processor
-
-    def gen_similarity_maps(
-        self,
-        query: str,
-        query_embs: torch.Tensor,
-        token_idx_map: Dict[int, str],
-        images: List[Union[Path, str]],
-        vespa_sim_maps: List[Dict],
-    ) -> Generator[Tuple[int, str, str], None, None]:
-        """
-        Generates similarity maps for the provided images and query, and returns base64-encoded blended images.
-
-        Args:
-            query (str): The query string.
-            query_embs (torch.Tensor): Query embeddings tensor.
-            token_idx_map (dict): Mapping from indices to tokens.
-            images (List[Union[Path, str]]): List of image paths or base64-encoded strings.
-            vespa_sim_maps (List[Dict]): List of Vespa similarity maps.
-
-        Yields:
-            Tuple[int, str, str]: A tuple containing the image index, selected token, and base64-encoded image.
-        """
-        processed_images, original_images, original_sizes = [], [], []
-        for img in images:
-            img_pil = self._load_image(img)
-            original_images.append(img_pil.copy())
-            original_sizes.append(img_pil.size)
-            processed_images.append(img_pil)
-
-        vespa_sim_map_tensor = self._prepare_similarity_map_tensor(
-            query_embs, vespa_sim_maps
-        )
-        similarity_map_normalized = normalize_similarity_map_per_query_token(
-            vespa_sim_map_tensor
-        )
-
-        for idx, img in enumerate(original_images):
-            for token_idx, token in token_idx_map.items():
-                if self.should_filter_token(token):
-                    continue
-
-                sim_map = similarity_map_normalized[idx, token_idx, :, :]
-                blended_img_base64 = self._blend_image(
-                    img, sim_map, original_sizes[idx]
-                )
-                yield idx, token, token_idx, blended_img_base64
-
-    def _load_image(self, img: Union[Path, str]) -> Image:
-        """
-        Loads an image from a file path or a base64-encoded string.
-
-        Args:
-            img (Union[Path, str]): The image to load.
-
-        Returns:
-            Image: The loaded PIL image.
-        """
-        try:
-            if isinstance(img, Path):
-                return Image.open(img).convert("RGB")
-            elif isinstance(img, str):
-                return Image.open(BytesIO(base64.b64decode(img))).convert("RGB")
-        except Exception as e:
-            raise ValueError(f"Failed to load image: {e}")
-
-    def _prepare_similarity_map_tensor(
-        self, query_embs: torch.Tensor, vespa_sim_maps: List[Dict]
-    ) -> torch.Tensor:
-        """
-        Prepares a similarity map tensor from Vespa similarity maps.
-
-        Args:
-            query_embs (torch.Tensor): Query embeddings tensor.
-            vespa_sim_maps (List[Dict]): List of Vespa similarity maps.
-
-        Returns:
-            torch.Tensor: The prepared similarity map tensor.
-        """
-        vespa_sim_map_tensor = torch.zeros(
-            (len(vespa_sim_maps), query_embs.size(1), self.n_patch, self.n_patch)
-        )
-        for idx, vespa_sim_map in enumerate(vespa_sim_maps):
-            for cell in vespa_sim_map["quantized"]["cells"]:
-                patch = int(cell["address"]["patch"])
-                query_token = int(cell["address"]["querytoken"])
-                value = cell["value"]
-                if hasattr(self.processor, "image_seq_length"):
-                    image_seq_length = self.processor.image_seq_length
-                else:
-                    image_seq_length = 1024
-
-                if patch >= image_seq_length:
-                    continue
-                vespa_sim_map_tensor[
-                    idx,
-                    query_token,
-                    patch // self.n_patch,
-                    patch % self.n_patch,
-                ] = value
-        return vespa_sim_map_tensor
-
-    def _blend_image(
-        self, img: Image, sim_map: torch.Tensor, original_size: Tuple[int, int]
-    ) -> str:
-        """
-        Blends an image with a similarity map and encodes it to base64.
-
-        Args:
-            img (Image): The original image.
-            sim_map (torch.Tensor): The similarity map tensor.
-            original_size (Tuple[int, int]): The original size of the image.
-
-        Returns:
-            str: The base64-encoded blended image.
-        """
-        SCALING_FACTOR = 8
-        sim_map_resolution = (
-            max(32, int(original_size[0] / SCALING_FACTOR)),
-            max(32, int(original_size[1] / SCALING_FACTOR)),
-        )
-
-        sim_map_np = sim_map.cpu().float().numpy()
-        sim_map_img = Image.fromarray(sim_map_np).resize(
-            sim_map_resolution, resample=Image.BICUBIC
-        )
-        sim_map_resized_np = np.array(sim_map_img, dtype=np.float32)
-        sim_map_normalized = self._normalize_sim_map(sim_map_resized_np)
-
-        heatmap = self.colormap(sim_map_normalized)
-        heatmap_img = Image.fromarray((heatmap * 255).astype(np.uint8)).convert("RGBA")
-
-        buffer = io.BytesIO()
-        heatmap_img.save(buffer, format="PNG")
-        return base64.b64encode(buffer.getvalue()).decode("utf-8")
-
-    @staticmethod
-    def _normalize_sim_map(sim_map: np.ndarray) -> np.ndarray:
-        """
-        Normalizes a similarity map to range [0, 1].
-
-        Args:
-            sim_map (np.ndarray): The similarity map.
-
-        Returns:
-            np.ndarray: The normalized similarity map.
-        """
-        sim_map_min, sim_map_max = sim_map.min(), sim_map.max()
-        if sim_map_max - sim_map_min > 1e-6:
-            return (sim_map - sim_map_min) / (sim_map_max - sim_map_min)
-        return np.zeros_like(sim_map)
-
-    @staticmethod
-    def should_filter_token(token: str) -> bool:
-        """
-        Determines if a token should be filtered out based on predefined patterns.
-
-        The function filters out tokens that:
-
-            - Start with '<' (e.g., '<bos>')
-            - Consist entirely of whitespace
-            - Are purely punctuation (excluding tokens that contain digits or start with '▁')
-            - Start with an underscore '_'
-            - Exactly match the word 'Question'
-            - Are exactly the single character '▁'
-
-        Output of test:
-            Token: '2'         | False
-            Token: '0'         | False
-            Token: '2'         | False
-            Token: '3'         | False
-            Token: '▁2'        | False
-            Token: '▁hi'       | False
-            Token: 'norwegian' | False
-            Token: 'unlisted'  | False
-            Token: '<bos>'     | True
-            Token: 'Question'  | True
-            Token: ':'         | True
-            Token: '<pad>'     | True
-            Token: '\n'        | True
-            Token: '▁'         | True
-            Token: '?'         | True
-            Token: ')'         | True
-            Token: '%'         | True
-            Token: '/)'        | True
-
-
-        Args:
-            token (str): The token to check.
-
-        Returns:
-            bool: True if the token should be filtered out, False otherwise.
-        """
-        pattern = re.compile(
-            r"^<.*$|^\s+$|^(?!.*\d)(?!▁)[^\w\s]+$|^_.*$|^Question$|^▁$"
-        )
-        return bool(pattern.match(token))
-
-    @lru_cache(maxsize=128)
-    def get_query_embeddings_and_token_map(
-        self, query: str
-    ) -> Tuple[torch.Tensor, dict]:
-        """
-        Retrieves query embeddings and a token index map.
-
-        Args:
-            query (str): The query string.
-
-        Returns:
-            Tuple[torch.Tensor, dict]: Query embeddings and token index map.
-        """
-        inputs = self.processor.process_queries([query]).to(self.model.device)
-        with torch.no_grad():
-            q_emb = self.model(**inputs).to("cpu")[0]
-
-        query_tokens = self.processor.tokenizer.tokenize(
-            self.processor.decode(inputs.input_ids[0])
-        )
-        idx_to_token = {idx: token for idx, token in enumerate(query_tokens)}
-        return q_emb, idx_to_token

src/backend/stopwords.py

@@ -1,18 +0,0 @@
-import spacy
-import os
-
-# Download the model if it is not already present
-if not spacy.util.is_package("en_core_web_sm"):
-    spacy.cli.download("en_core_web_sm")
-nlp = spacy.load("en_core_web_sm")
-
-
-# It would be possible to remove bolding for stopwords without removing them from the query,
-# but that would require a java plugin which we didn't want to complicate this sample app with.
-def filter(text):
-    doc = nlp(text)
-    tokens = [token.text for token in doc if not token.is_stop]
-    if len(tokens) == 0:
-        # if we remove all the words we don't have a query at all, so use the original
-        return text
-    return " ".join(tokens)

src/backend/testquery.py

@@ -1,3013 +0,0 @@
-import torch
-
-token_to_idx = {
-    "<bos>": 0,
-    "Question": 1,
-    ":": 2,
-    "▁Percentage": 3,
-    "▁of": 4,
-    "▁non": 5,
-    "-": 6,
-    "fresh": 7,
-    "▁water": 8,
-    "▁as": 9,
-    "▁source": 10,
-    "?": 11,
-    "<pad>": 21,
-    "\n": 22,
-}
-idx_to_token = {v: k for k, v in token_to_idx.items()}
-q_embs = torch.tensor(
-    [
-        [
-            1.6547e-01,
-            -1.8838e-02,
-            1.0150e-01,
-            -2.2643e-02,
-            5.0652e-02,
-            3.2039e-02,
-            6.8322e-02,
-            2.7134e-02,
-            7.2197e-03,
-            -4.2341e-02,
-            -5.8006e-02,
-            -1.1389e-01,
-            7.0041e-02,
-            -6.8021e-02,
-            2.4681e-02,
-            5.3306e-02,
-            4.1714e-02,
-            6.2021e-02,
-            1.3488e-01,
-            3.4943e-02,
-            3.8032e-02,
-            -3.2724e-02,
-            -1.2960e-01,
-            1.1453e-02,
-            -2.6477e-02,
-            3.5219e-02,
-            -7.6606e-02,
-            2.2387e-01,
-            -3.4888e-02,
-            -4.0333e-02,
-            1.4128e-01,
-            4.2248e-02,
-            -1.2664e-01,
-            -7.8376e-02,
-            -2.0356e-02,
-            4.2198e-02,
-            -7.0776e-02,
-            1.3965e-02,
-            4.2442e-03,
-            7.1987e-02,
-            8.5172e-04,
-            -6.4878e-02,
-            -1.8954e-01,
-            -8.6171e-02,
-            9.1983e-02,
-            -9.3358e-02,
-            2.2704e-01,
-            1.3102e-02,
-            6.5327e-02,
-            2.4815e-02,
-            -1.4533e-01,
-            5.8823e-02,
-            -6.1434e-02,
-            5.2004e-02,
-            -8.4065e-02,
-            1.6298e-01,
-            8.1965e-02,
-            2.6553e-02,
-            -1.2377e-01,
-            -5.3495e-02,
-            -3.4537e-02,
-            5.1438e-02,
-            8.2665e-03,
-            7.9407e-02,
-            5.8799e-02,
-            -3.5538e-02,
-            1.9870e-01,
-            6.2459e-02,
-            1.6154e-01,
-            7.2921e-02,
-            -9.7275e-02,
-            3.0933e-02,
-            -1.0579e-02,
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-)

src/backend/vespa_app.py

@@ -1,496 +0,0 @@
-import os
-import time
-from typing import Any, Dict, Tuple
-import asyncio
-import numpy as np
-import torch
-from dotenv import load_dotenv
-from vespa.application import Vespa
-from vespa.io import VespaQueryResponse
-from .colpali import SimMapGenerator
-import backend.stopwords
-import logging
-
-
-class VespaQueryClient:
-    MAX_QUERY_TERMS = 64
-    VESPA_SCHEMA_NAME = "pdf_page"
-    SELECT_FIELDS = "id,title,url,blur_image,page_number,snippet,text"
-
-    def __init__(self, logger: logging.Logger):
-        """
-        Initialize the VespaQueryClient by loading environment variables and establishing a connection to the Vespa application.
-        """
-        load_dotenv()
-        self.logger = logger
-
-        if os.environ.get("USE_MTLS") == "true":
-            self.logger.info("Connected using mTLS")
-            mtls_key = os.environ.get("VESPA_CLOUD_MTLS_KEY")
-            mtls_cert = os.environ.get("VESPA_CLOUD_MTLS_CERT")
-
-            self.vespa_app_url = os.environ.get("VESPA_APP_MTLS_URL")
-            if not self.vespa_app_url:
-                raise ValueError(
-                    "Please set the VESPA_APP_MTLS_URL environment variable"
-                )
-
-            if not mtls_cert or not mtls_key:
-                raise ValueError(
-                    "USE_MTLS was true, but VESPA_CLOUD_MTLS_KEY and VESPA_CLOUD_MTLS_CERT were not set"
-                )
-
-            # write the key and cert to a file
-            mtls_key_path = "/tmp/vespa-data-plane-private-key.pem"
-            with open(mtls_key_path, "w") as f:
-                f.write(mtls_key)
-
-            mtls_cert_path = "/tmp/vespa-data-plane-public-cert.pem"
-            with open(mtls_cert_path, "w") as f:
-                f.write(mtls_cert)
-
-            # Instantiate Vespa connection
-            self.app = Vespa(
-                url=self.vespa_app_url, key=mtls_key_path, cert=mtls_cert_path
-            )
-        else:
-            self.logger.info("Connected using token")
-            self.vespa_app_url = os.environ.get("VESPA_APP_TOKEN_URL")
-            if not self.vespa_app_url:
-                raise ValueError(
-                    "Please set the VESPA_APP_TOKEN_URL environment variable"
-                )
-
-            self.vespa_cloud_secret_token = os.environ.get("VESPA_CLOUD_SECRET_TOKEN")
-
-            if not self.vespa_cloud_secret_token:
-                raise ValueError(
-                    "Please set the VESPA_CLOUD_SECRET_TOKEN environment variable"
-                )
-
-            # Instantiate Vespa connection
-            self.app = Vespa(
-                url=self.vespa_app_url,
-                vespa_cloud_secret_token=self.vespa_cloud_secret_token,
-            )
-
-        self.app.wait_for_application_up()
-        self.logger.info(f"Connected to Vespa at {self.vespa_app_url}")
-
-    def get_fields(self, sim_map: bool = False):
-        if not sim_map:
-            return self.SELECT_FIELDS
-        else:
-            return "summaryfeatures"
-
-    def format_query_results(
-        self, query: str, response: VespaQueryResponse, hits: int = 5
-    ) -> dict:
-        """
-        Format the Vespa query results.
-
-        Args:
-            query (str): The query text.
-            response (VespaQueryResponse): The response from Vespa.
-            hits (int, optional): Number of hits to display. Defaults to 5.
-
-        Returns:
-            dict: The JSON content of the response.
-        """
-        query_time = response.json.get("timing", {}).get("searchtime", -1)
-        query_time = round(query_time, 2)
-        count = response.json.get("root", {}).get("fields", {}).get("totalCount", 0)
-        result_text = f"Query text: '{query}', query time {query_time}s, count={count}, top results:\n"
-        self.logger.debug(result_text)
-        return response.json
-
-    async def query_vespa_default(
-        self,
-        query: str,
-        q_emb: torch.Tensor,
-        hits: int = 3,
-        timeout: str = "10s",
-        sim_map: bool = False,
-        **kwargs,
-    ) -> dict:
-        """
-        Query Vespa using the default ranking profile.
-
-        Args:
-            query (str): The query text.
-            q_emb (torch.Tensor): Query embeddings.
-            hits (int, optional): Number of hits to retrieve. Defaults to 3.
-            timeout (str, optional): Query timeout. Defaults to "10s".
-
-        Returns:
-            dict: The formatted query results.
-        """
-        async with self.app.asyncio(connections=1) as session:
-            query_embedding = self.format_q_embs(q_emb)
-
-            start = time.perf_counter()
-            response: VespaQueryResponse = await session.query(
-                body={
-                    "yql": (
-                        f"select {self.get_fields(sim_map=sim_map)} from {self.VESPA_SCHEMA_NAME} where userQuery();"
-                    ),
-                    "ranking": self.get_rank_profile("default", sim_map),
-                    "query": query,
-                    "timeout": timeout,
-                    "hits": hits,
-                    "input.query(qt)": query_embedding,
-                    "presentation.timing": True,
-                    **kwargs,
-                },
-            )
-            assert response.is_successful(), response.json
-            stop = time.perf_counter()
-            self.logger.debug(
-                f"Query time + data transfer took: {stop - start} s, Vespa reported searchtime was "
-                f"{response.json.get('timing', {}).get('searchtime', -1)} s"
-            )
-        return self.format_query_results(query, response)
-
-    async def query_vespa_bm25(
-        self,
-        query: str,
-        q_emb: torch.Tensor,
-        hits: int = 3,
-        timeout: str = "10s",
-        sim_map: bool = False,
-        **kwargs,
-    ) -> dict:
-        """
-        Query Vespa using the BM25 ranking profile.
-
-        Args:
-            query (str): The query text.
-            q_emb (torch.Tensor): Query embeddings.
-            hits (int, optional): Number of hits to retrieve. Defaults to 3.
-            timeout (str, optional): Query timeout. Defaults to "10s".
-
-        Returns:
-            dict: The formatted query results.
-        """
-        async with self.app.asyncio(connections=1) as session:
-            query_embedding = self.format_q_embs(q_emb)
-
-            start = time.perf_counter()
-            response: VespaQueryResponse = await session.query(
-                body={
-                    "yql": (
-                        f"select {self.get_fields(sim_map=sim_map)} from {self.VESPA_SCHEMA_NAME} where userQuery();"
-                    ),
-                    "ranking": self.get_rank_profile("bm25", sim_map),
-                    "query": query,
-                    "timeout": timeout,
-                    "hits": hits,
-                    "input.query(qt)": query_embedding,
-                    "presentation.timing": True,
-                    **kwargs,
-                },
-            )
-            assert response.is_successful(), response.json
-            stop = time.perf_counter()
-            self.logger.debug(
-                f"Query time + data transfer took: {stop - start} s, Vespa reported searchtime was "
-                f"{response.json.get('timing', {}).get('searchtime', -1)} s"
-            )
-        return self.format_query_results(query, response)
-
-    def float_to_binary_embedding(self, float_query_embedding: dict) -> dict:
-        """
-        Convert float query embeddings to binary embeddings.
-
-        Args:
-            float_query_embedding (dict): Dictionary of float embeddings.
-
-        Returns:
-            dict: Dictionary of binary embeddings.
-        """
-        binary_query_embeddings = {}
-        for key, vector in float_query_embedding.items():
-            binary_vector = (
-                np.packbits(np.where(np.array(vector) > 0, 1, 0))
-                .astype(np.int8)
-                .tolist()
-            )
-            binary_query_embeddings[key] = binary_vector
-            if len(binary_query_embeddings) >= self.MAX_QUERY_TERMS:
-                self.logger.warning(
-                    f"Warning: Query has more than {self.MAX_QUERY_TERMS} terms. Truncating."
-                )
-                break
-        return binary_query_embeddings
-
-    def create_nn_query_strings(
-        self, binary_query_embeddings: dict, target_hits_per_query_tensor: int = 20
-    ) -> Tuple[str, dict]:
-        """
-        Create nearest neighbor query strings for Vespa.
-
-        Args:
-            binary_query_embeddings (dict): Binary query embeddings.
-            target_hits_per_query_tensor (int, optional): Target hits per query tensor. Defaults to 20.
-
-        Returns:
-            Tuple[str, dict]: Nearest neighbor query string and query tensor dictionary.
-        """
-        nn_query_dict = {}
-        for i in range(len(binary_query_embeddings)):
-            nn_query_dict[f"input.query(rq{i})"] = binary_query_embeddings[i]
-        nn = " OR ".join(
-            [
-                f"({{targetHits:{target_hits_per_query_tensor}}}nearestNeighbor(embedding,rq{i}))"
-                for i in range(len(binary_query_embeddings))
-            ]
-        )
-        return nn, nn_query_dict
-
-    def format_q_embs(self, q_embs: torch.Tensor) -> dict:
-        """
-        Convert query embeddings to a dictionary of lists.
-
-        Args:
-            q_embs (torch.Tensor): Query embeddings tensor.
-
-        Returns:
-            dict: Dictionary where each key is an index and value is the embedding list.
-        """
-        return {idx: emb.tolist() for idx, emb in enumerate(q_embs)}
-
-    async def get_result_from_query(
-        self,
-        query: str,
-        q_embs: torch.Tensor,
-        ranking: str,
-        idx_to_token: dict,
-    ) -> Dict[str, Any]:
-        """
-        Get query results from Vespa based on the ranking method.
-
-        Args:
-            query (str): The query text.
-            q_embs (torch.Tensor): Query embeddings.
-            ranking (str): The ranking method to use.
-            idx_to_token (dict): Index to token mapping.
-
-        Returns:
-            Dict[str, Any]: The query results.
-        """
-
-        # Remove stopwords from the query to avoid visual emphasis on irrelevant words (e.g., "the", "and", "of")
-        query = backend.stopwords.filter(query)
-
-        rank_method = ranking.split("_")[0]
-        sim_map: bool = len(ranking.split("_")) > 1 and ranking.split("_")[1] == "sim"
-        if rank_method == "nn+colpali":
-            result = await self.query_vespa_nearest_neighbor(
-                query, q_embs, sim_map=sim_map
-            )
-        elif rank_method == "bm25+colpali":
-            result = await self.query_vespa_default(query, q_embs, sim_map=sim_map)
-        elif rank_method == "bm25":
-            result = await self.query_vespa_bm25(query, q_embs, sim_map=sim_map)
-        else:
-            raise ValueError(f"Unsupported ranking: {rank_method}")
-        if "root" not in result or "children" not in result["root"]:
-            result["root"] = {"children": []}
-            return result
-        for single_result in result["root"]["children"]:
-            self.logger.debug(single_result["fields"].keys())
-        return result
-
-    def get_sim_maps_from_query(
-        self, query: str, q_embs: torch.Tensor, ranking: str, idx_to_token: dict
-    ):
-        """
-        Get similarity maps from Vespa based on the ranking method.
-
-        Args:
-            query (str): The query text.
-            q_embs (torch.Tensor): Query embeddings.
-            ranking (str): The ranking method to use.
-            idx_to_token (dict): Index to token mapping.
-
-        Returns:
-            Dict[str, Any]: The query results.
-        """
-        # Get the result by calling asyncio.run
-        result = asyncio.run(
-            self.get_result_from_query(query, q_embs, ranking, idx_to_token)
-        )
-        vespa_sim_maps = []
-        for single_result in result["root"]["children"]:
-            vespa_sim_map = single_result["fields"].get("summaryfeatures", None)
-            if vespa_sim_map is not None:
-                vespa_sim_maps.append(vespa_sim_map)
-            else:
-                raise ValueError("No sim_map found in Vespa response")
-        return vespa_sim_maps
-
-    async def get_full_image_from_vespa(self, doc_id: str) -> str:
-        """
-        Retrieve the full image from Vespa for a given document ID.
-
-        Args:
-            doc_id (str): The document ID.
-
-        Returns:
-            str: The full image data.
-        """
-        async with self.app.asyncio(connections=1) as session:
-            start = time.perf_counter()
-            response: VespaQueryResponse = await session.query(
-                body={
-                    "yql": f'select full_image from {self.VESPA_SCHEMA_NAME} where id contains "{doc_id}"',
-                    "ranking": "unranked",
-                    "presentation.timing": True,
-                },
-            )
-            assert response.is_successful(), response.json
-            stop = time.perf_counter()
-            self.logger.debug(
-                f"Getting image from Vespa took: {stop - start} s, Vespa reported searchtime was "
-                f"{response.json.get('timing', {}).get('searchtime', -1)} s"
-            )
-        return response.json["root"]["children"][0]["fields"]["full_image"]
-
-    def get_results_children(self, result: VespaQueryResponse) -> list:
-        return result["root"]["children"]
-
-    def results_to_search_results(
-        self, result: VespaQueryResponse, idx_to_token: dict
-    ) -> list:
-        # Initialize sim_map_ fields in the result
-        fields_to_add = [
-            f"sim_map_{token}_{idx}"
-            for idx, token in idx_to_token.items()
-            if not SimMapGenerator.should_filter_token(token)
-        ]
-        for child in result["root"]["children"]:
-            for sim_map_key in fields_to_add:
-                child["fields"][sim_map_key] = None
-        return self.get_results_children(result)
-
-    async def get_suggestions(self, query: str) -> list:
-        async with self.app.asyncio(connections=1) as session:
-            start = time.perf_counter()
-            yql = f'select questions from {self.VESPA_SCHEMA_NAME} where questions matches (".*{query}.*")'
-            response: VespaQueryResponse = await session.query(
-                body={
-                    "yql": yql,
-                    "query": query,
-                    "ranking": "unranked",
-                    "presentation.timing": True,
-                    "presentation.summary": "suggestions",
-                },
-            )
-            assert response.is_successful(), response.json
-            stop = time.perf_counter()
-            self.logger.debug(
-                f"Getting suggestions from Vespa took: {stop - start} s, Vespa reported searchtime was "
-                f"{response.json.get('timing', {}).get('searchtime', -1)} s"
-            )
-            search_results = (
-                response.json["root"]["children"]
-                if "root" in response.json and "children" in response.json["root"]
-                else []
-            )
-            questions = [
-                result["fields"]["questions"]
-                for result in search_results
-                if "questions" in result["fields"]
-            ]
-            for q in questions:
-                print(q)
-            unique_questions = set([item for sublist in questions for item in sublist])
-
-            # remove an artifact from our data generation
-            if "string" in unique_questions:
-                unique_questions.remove("string")
-
-            return list(unique_questions)
-
-    def get_rank_profile(self, ranking: str, sim_map: bool) -> str:
-        if sim_map:
-            return f"{ranking}_sim"
-        else:
-            return ranking
-
-    async def query_vespa_nearest_neighbor(
-        self,
-        query: str,
-        q_emb: torch.Tensor,
-        target_hits_per_query_tensor: int = 20,
-        hits: int = 3,
-        timeout: str = "10s",
-        sim_map: bool = False,
-        **kwargs,
-    ) -> dict:
-        """
-        Query Vespa using nearest neighbor search with mixed tensors for MaxSim calculations.
-
-        Args:
-            query (str): The query text.
-            q_emb (torch.Tensor): Query embeddings.
-            target_hits_per_query_tensor (int, optional): Target hits per query tensor. Defaults to 20.
-            hits (int, optional): Number of hits to retrieve. Defaults to 3.
-            timeout (str, optional): Query timeout. Defaults to "10s".
-
-        Returns:
-            dict: The formatted query results.
-        """
-        async with self.app.asyncio(connections=1) as session:
-            float_query_embedding = self.format_q_embs(q_emb)
-            binary_query_embeddings = self.float_to_binary_embedding(
-                float_query_embedding
-            )
-
-            # Mixed tensors for MaxSim calculations
-            query_tensors = {
-                "input.query(qtb)": binary_query_embeddings,
-                "input.query(qt)": float_query_embedding,
-            }
-            nn_string, nn_query_dict = self.create_nn_query_strings(
-                binary_query_embeddings, target_hits_per_query_tensor
-            )
-            query_tensors.update(nn_query_dict)
-            response: VespaQueryResponse = await session.query(
-                body={
-                    **query_tensors,
-                    "presentation.timing": True,
-                    "yql": (
-                        f"select {self.get_fields(sim_map=sim_map)} from {self.VESPA_SCHEMA_NAME} where {nn_string} or userQuery()"
-                    ),
-                    "ranking.profile": self.get_rank_profile(
-                        "retrieval-and-rerank", sim_map
-                    ),
-                    "timeout": timeout,
-                    "hits": hits,
-                    "query": query,
-                    **kwargs,
-                },
-            )
-            assert response.is_successful(), response.json
-        return self.format_query_results(query, response)
-
-    async def keepalive(self) -> bool:
-        """
-        Query Vespa to keep the connection alive.
-
-        Returns:
-            bool: True if the connection is alive.
-        """
-        async with self.app.asyncio(connections=1) as session:
-            response: VespaQueryResponse = await session.query(
-                body={
-                    "yql": f"select title from {self.VESPA_SCHEMA_NAME} where true limit 1;",
-                    "ranking": "unranked",
-                    "query": "keepalive",
-                    "timeout": "3s",
-                    "hits": 1,
-                },
-            )
-            assert response.is_successful(), response.json
-        return True

src/frontend/app.py

@@ -1,644 +0,0 @@
-from typing import Optional
-from urllib.parse import quote_plus
-
-from fasthtml.components import H1, H2, H3, Br, Div, Form, Img, NotStr, P, Span, Strong
-from fasthtml.xtend import A, Script
-from lucide_fasthtml import Lucide
-from shad4fast import Badge, Button, Input, Label, RadioGroup, RadioGroupItem, Separator
-
-# JavaScript to check the input value and enable/disable the search button and radio buttons
-check_input_script = Script(
-    """
-        window.onload = function() {
-            const input = document.getElementById('search-input');
-            const button = document.querySelector('[data-button="search-button"]');
-            const radioGroupItems = document.querySelectorAll('button[data-ref="radio-item"]');  // Get all radio buttons
-            
-            function checkInputValue() {
-                const isInputEmpty = input.value.trim() === "";
-                button.disabled = isInputEmpty;  // Disable the submit button
-                radioGroupItems.forEach(item => {
-                    item.disabled = isInputEmpty;  // Disable/enable the radio buttons
-                });
-            }
-
-            input.addEventListener('input', checkInputValue);  // Listen for input changes
-            checkInputValue();  // Initial check when the page loads
-        };
-    """
-)
-
-# JavaScript to handle the image swapping, reset button, and active class toggling
-image_swapping = Script(
-    """
-    document.addEventListener('click', function (e) {
-        if (e.target.classList.contains('sim-map-button') || e.target.classList.contains('reset-button')) {
-            const imgContainer = e.target.closest('.relative'); 
-            const overlayContainer = imgContainer.querySelector('.overlay-container');
-            const newSrc = e.target.getAttribute('data-image-src');
-    
-            // If it's a reset button, remove the overlay image
-            if (e.target.classList.contains('reset-button')) {
-                overlayContainer.innerHTML = '';  // Clear the overlay container, showing only the full image
-            } else {
-                // Create a new overlay image
-                const img = document.createElement('img');
-                img.src = newSrc;
-                img.classList.add('overlay-image', 'absolute', 'top-0', 'left-0', 'w-full', 'h-full');
-                overlayContainer.innerHTML = '';  // Clear any previous overlay
-                overlayContainer.appendChild(img);  // Add the new overlay image
-            }
-    
-            // Toggle active class on buttons
-            const activeButton = document.querySelector('.sim-map-button.active');
-            if (activeButton) {
-                activeButton.classList.remove('active');
-            }
-            if (e.target.classList.contains('sim-map-button')) {
-                e.target.classList.add('active');
-            }
-        }
-    });
-    """
-)
-
-toggle_text_content = Script(
-    """
-    function toggleTextContent(idx) {
-        const textColumn = document.getElementById(`text-column-${idx}`);
-        const imageTextColumns = document.getElementById(`image-text-columns-${idx}`);
-        const toggleButton = document.getElementById(`toggle-button-${idx}`);
-    
-        if (textColumn.classList.contains('md-grid-text-column')) {
-          // Hide the text column
-          textColumn.classList.remove('md-grid-text-column');
-          imageTextColumns.classList.remove('grid-image-text-columns');
-          toggleButton.innerText = `Show Text`;
-        } else {
-          // Show the text column
-          textColumn.classList.add('md-grid-text-column');
-          imageTextColumns.classList.add('grid-image-text-columns');
-          toggleButton.innerText = `Hide Text`;
-        }
-    }
-    """
-)
-
-autocomplete_script = Script(
-    """
-    document.addEventListener('DOMContentLoaded', function() {
-        const input = document.querySelector('#search-input');
-        const awesomplete = new Awesomplete(input, { minChars: 1, maxItems: 5 });
-
-        input.addEventListener('input', function() {
-            if (this.value.length >= 1) {
-                // Use template literals to insert the input value dynamically in the query parameter
-                fetch(`/suggestions?query=${encodeURIComponent(this.value)}`)
-                    .then(response => response.json())
-                    .then(data => {
-                        // Update the Awesomplete list dynamically with fetched suggestions
-                        awesomplete.list = data.suggestions;
-                    })
-                    .catch(err => console.error('Error fetching suggestions:', err));
-            }
-        });
-    });
-    """
-)
-
-dynamic_elements_scrollbars = Script(
-    """
-    (function () {
-        const { applyOverlayScrollbars, getScrollbarTheme } = OverlayScrollbarsManager;
-
-        function applyScrollbarsToDynamicElements() {
-            const scrollbarTheme = getScrollbarTheme();
-
-            // Apply scrollbars to dynamically loaded result-text-full and result-text-snippet elements
-            const resultTextFullElements = document.querySelectorAll('[id^="result-text-full"]');
-            const resultTextSnippetElements = document.querySelectorAll('[id^="result-text-snippet"]');
-
-            resultTextFullElements.forEach(element => {
-                applyOverlayScrollbars(element, scrollbarTheme);
-            });
-
-            resultTextSnippetElements.forEach(element => {
-                applyOverlayScrollbars(element, scrollbarTheme);
-            });
-        }
-
-        // Apply scrollbars after dynamic content is loaded (e.g., after search results)
-        applyScrollbarsToDynamicElements();
-
-        // Observe changes in the 'dark' class to adjust the theme dynamically if needed
-        const observer = new MutationObserver(applyScrollbarsToDynamicElements);
-        observer.observe(document.documentElement, { attributes: true, attributeFilter: ['class'] });
-    })();
-    """
-)
-
-submit_form_on_radio_change = Script(
-    """
-    document.addEventListener('click', function (e) {
-        // if target has data-ref="radio-item" and type is button
-        if (e.target.getAttribute('data-ref') === 'radio-item' && e.target.type === 'button') {
-            console.log('Radio button clicked');
-            const form = e.target.closest('form');
-            form.submit();
-        }
-    });
-    """
-)
-
-
-def SearchBox(with_border=False, query_value="", ranking_value="nn+colpali"):
-    grid_cls = "grid gap-2 items-center p-3 bg-muted w-full"
-
-    if with_border:
-        grid_cls = "grid gap-2 p-3 rounded-md border border-input bg-muted w-full ring-offset-background focus-within:outline-none focus-within:ring-2 focus-within:ring-ring focus-within:ring-offset-2 focus-within:border-input"
-
-    return Form(
-        Div(
-            Lucide(
-                icon="search", cls="absolute left-2 top-2 text-muted-foreground z-10"
-            ),
-            Input(
-                placeholder="Enter your search query...",
-                name="query",
-                value=query_value,
-                id="search-input",
-                cls="text-base pl-10 border-transparent ring-offset-transparent ring-0 focus-visible:ring-transparent bg-white dark:bg-background awesomplete",
-                data_list="#suggestions",
-                style="font-size: 1rem",
-                autofocus=True,
-            ),
-            cls="relative",
-        ),
-        Div(
-            Div(
-                Span("Ranking by:", cls="text-muted-foreground text-xs font-semibold"),
-                RadioGroup(
-                    Div(
-                        RadioGroupItem(value="nn+colpali", id="nn+colpali"),
-                        Label("ColPali", htmlFor="ColPali"),
-                        cls="flex items-center space-x-2",
-                    ),
-                    Div(
-                        RadioGroupItem(value="bm25", id="bm25"),
-                        Label("BM25", htmlFor="BM25"),
-                        cls="flex items-center space-x-2",
-                    ),
-                    Div(
-                        RadioGroupItem(value="bm25+colpali", id="bm25+colpali"),
-                        Label("Hybrid ColPali + BM25", htmlFor="Hybrid ColPali + BM25"),
-                        cls="flex items-center space-x-2",
-                    ),
-                    name="ranking",
-                    default_value=ranking_value,
-                    cls="grid-flow-col gap-x-5 text-muted-foreground",
-                    # Submit form when radio button is clicked
-                ),
-                cls="grid grid-flow-col items-center gap-x-3 border border-input px-3 rounded-sm",
-            ),
-            Button(
-                Lucide(icon="arrow-right", size="21"),
-                size="sm",
-                type="submit",
-                data_button="search-button",
-                disabled=True,
-            ),
-            cls="flex justify-between",
-        ),
-        check_input_script,
-        autocomplete_script,
-        submit_form_on_radio_change,
-        action=f"/search?query={quote_plus(query_value)}&ranking={quote_plus(ranking_value)}",
-        method="GET",
-        hx_get="/fetch_results",  # As the component is a form, input components query and ranking are sent as query parameters automatically, see https://htmx.org/docs/#parameters
-        hx_trigger="load",
-        hx_target="#search-results",
-        hx_swap="outerHTML",
-        hx_indicator="#loading-indicator",
-        cls=grid_cls,
-    )
-
-
-def SampleQueries():
-    sample_queries = [
-        "Total amount of fixed salaries paid in 2023?",
-        "Proportion of female new hires 2021-2023?",
-        "Number of internship applications trend 2021-2023",
-        "Gender balance at level 4 or above in NY office 2023?",
-        "What percentage of the funds unlisted real estate investments were in Switzerland 2023?",
-        "child jumping over puddle",
-        "hula hoop kid",
-    ]
-
-    query_badges = []
-    for query in sample_queries:
-        query_badges.append(
-            A(
-                Badge(
-                    Div(
-                        Lucide(
-                            icon="text-search", size="18", cls="text-muted-foreground"
-                        ),
-                        Span(query, cls="text-base font-normal"),
-                        cls="flex gap-2 items-center",
-                    ),
-                    variant="outline",
-                    cls="text-base font-normal text-muted-foreground hover:border-black dark:hover:border-white",
-                ),
-                href=f"/search?query={quote_plus(query)}",
-                cls="no-underline",
-            )
-        )
-
-    return Div(*query_badges, cls="grid gap-2 justify-items-center")
-
-
-def Hero():
-    return Div(
-        H1(
-            "Vespa.ai + ColPali",
-            cls="text-5xl md:text-7xl font-bold tracking-wide md:tracking-wider bg-clip-text text-transparent bg-gradient-to-r from-black to-slate-700 dark:from-white dark:to-slate-300 animate-fade-in",
-        ),
-        P(
-            "Efficient Document Retrieval with Vision Language Models",
-            cls="text-lg md:text-2xl text-muted-foreground md:tracking-wide",
-        ),
-        cls="grid gap-5 text-center",
-    )
-
-
-def Home():
-    return Div(
-        Div(
-            Hero(),
-            SearchBox(with_border=True),
-            SampleQueries(),
-            cls="grid gap-8 content-start mt-[13vh]",
-        ),
-        cls="grid w-full h-full max-w-screen-md gap-4 mx-auto",
-    )
-
-
-def AboutThisDemo():
-    return Div(
-        Div(
-            Div(
-                H1(
-                    "Vespa.ai + ColPali",
-                    cls="text-5xl font-bold tracking-wide md:tracking-wider",
-                ),
-                P(
-                    "Efficient Document Retrieval with Vision Language Models",
-                    cls="text-lg text-muted-foreground md:tracking-wide",
-                ),
-                Div(
-                    Img(
-                        src="/static/img/vespa-colpali.png",
-                        alt="Vespa and ColPali",
-                        cls="object-contain h-[377px]",
-                    ),
-                    cls="grid justify-center",
-                ),
-                Div(
-                    P(
-                        "This is a demo application showcasing the integration of Vespa.ai and ColPali for visual retrieval of documents.",
-                        cls="text-base",
-                    ),
-                    P(
-                        "The application uses a combination of neural networks and traditional search algorithms to retrieve relevant documents based on visual and textual queries.",
-                        cls="text-base",
-                    ),
-                    cls="grid gap-2 text-center",
-                ),
-                cls="grid gap-5 text-center",
-            ),
-            cls="grid gap-8 content-start mt-[8vh]",
-        ),
-        cls="grid w-full h-full max-w-screen-md gap-4 mx-auto",
-    )
-
-
-def Search(request, search_results=[]):
-    query_value = request.query_params.get("query", "").strip()
-    ranking_value = request.query_params.get("ranking", "nn+colpali")
-    return Div(
-        Div(
-            Div(
-                SearchBox(query_value=query_value, ranking_value=ranking_value),
-                Div(
-                    LoadingMessage(),
-                    id="search-results",  # This will be replaced by the search results
-                ),
-                cls="grid",
-            ),
-            cls="grid",
-        ),
-    )
-
-
-def LoadingMessage(display_text="Retrieving search results"):
-    return Div(
-        Lucide(icon="loader-circle", cls="size-5 mr-1.5 animate-spin"),
-        Span(display_text, cls="text-base text-center"),
-        cls="p-10 text-muted-foreground flex items-center justify-center",
-        id="loading-indicator",
-    )
-
-
-def LoadingSkeleton():
-    return Div(
-        Div(cls="h-5 bg-muted"),
-        Div(cls="h-5 bg-muted"),
-        Div(cls="h-5 bg-muted"),
-        cls="grid gap-2 animate-pulse",
-    )
-
-
-def SimMapButtonReady(query_id, idx, token, token_idx, img_src):
-    return Button(
-        token.replace("\u2581", ""),
-        size="sm",
-        data_image_src=img_src,
-        id=f"sim-map-button-{query_id}-{idx}-{token_idx}-{token}",
-        cls="sim-map-button pointer-events-auto font-mono text-xs h-5 rounded-none px-2",
-    )
-
-
-def SimMapButtonPoll(query_id, idx, token, token_idx):
-    return Button(
-        Lucide(icon="loader-circle", size="15", cls="animate-spin"),
-        size="sm",
-        disabled=True,
-        hx_get=f"/get_sim_map?query_id={query_id}&idx={idx}&token={token}&token_idx={token_idx}",
-        hx_trigger="every 0.5s",
-        hx_swap="outerHTML",
-        cls="pointer-events-auto text-xs h-5 rounded-none px-2",
-    )
-
-
-def SearchInfo(search_time, total_count):
-    return (
-        Div(
-            Span(
-                "Retrieved ",
-                Strong(total_count),
-                Span(" results"),
-                Span(" in "),
-                Strong(f"{search_time:.3f}"),  # 3 significant digits
-                Span(" seconds."),
-            ),
-            cls="grid bg-background border-t text-sm text-center p-3",
-        ),
-    )
-
-
-def SearchResult(
-    results: list,
-    query: str,
-    query_id: Optional[str] = None,
-    search_time: float = 0,
-    total_count: int = 0,
-):
-    if not results:
-        return Div(
-            P(
-                "No results found for your query.",
-                cls="text-muted-foreground text-base text-center",
-            ),
-            cls="grid p-10",
-        )
-
-    doc_ids = []
-    # Otherwise, display the search results
-    result_items = []
-    for idx, result in enumerate(results):
-        fields = result["fields"]  # Extract the 'fields' part of each result
-        doc_id = fields["id"]
-        doc_ids.append(doc_id)
-        blur_image_base64 = f"data:image/jpeg;base64,{fields['blur_image']}"
-
-        sim_map_fields = {
-            key: value
-            for key, value in fields.items()
-            if key.startswith(
-                "sim_map_"
-            )  # filtering is done before creating with 'should_filter_token'-function
-        }
-
-        # Generate buttons for the sim_map fields
-        sim_map_buttons = []
-        for key, value in sim_map_fields.items():
-            token = key.split("_")[-2]
-            token_idx = int(key.split("_")[-1])
-            if value is not None:
-                sim_map_base64 = f"data:image/jpeg;base64,{value}"
-                sim_map_buttons.append(
-                    SimMapButtonReady(
-                        query_id=query_id,
-                        idx=idx,
-                        token=token,
-                        token_idx=token_idx,
-                        img_src=sim_map_base64,
-                    )
-                )
-            else:
-                sim_map_buttons.append(
-                    SimMapButtonPoll(
-                        query_id=query_id,
-                        idx=idx,
-                        token=token,
-                        token_idx=token_idx,
-                    )
-                )
-
-        # Add "Reset Image" button to restore the full image
-        reset_button = Button(
-            "Reset",
-            variant="outline",
-            size="sm",
-            data_image_src=blur_image_base64,
-            cls="reset-button pointer-events-auto font-mono text-xs h-5 rounded-none px-2",
-        )
-
-        tokens_icon = Lucide(icon="images", size="15")
-
-        # Add "Tokens" button - this has no action, just a placeholder
-        tokens_button = Button(
-            tokens_icon,
-            "Tokens",
-            size="sm",
-            cls="tokens-button flex gap-[3px] font-bold pointer-events-none font-mono text-xs h-5 rounded-none px-2",
-        )
-
-        result_items.append(
-            Div(
-                Div(
-                    Div(
-                        Lucide(icon="file-text"),
-                        H2(fields["title"], cls="text-xl md:text-2xl font-semibold"),
-                        Separator(orientation="vertical"),
-                        Badge(
-                            f"Relevance score: {result['relevance']:.4f}",
-                            cls="flex gap-1.5 items-center justify-center",
-                        ),
-                        cls="flex items-center gap-2",
-                    ),
-                    Div(
-                        Button(
-                            "Hide Text",
-                            size="sm",
-                            id=f"toggle-button-{idx}",
-                            onclick=f"toggleTextContent({idx})",
-                            cls="hidden md:block",
-                        ),
-                    ),
-                    cls="flex flex-wrap items-center justify-between bg-background px-3 py-4",
-                ),
-                Div(
-                    Div(
-                        Div(
-                            tokens_button,
-                            *sim_map_buttons,
-                            reset_button,
-                            cls="flex flex-wrap gap-px w-full pointer-events-none",
-                        ),
-                        Div(
-                            Div(
-                                Div(
-                                    Img(
-                                        src=blur_image_base64,
-                                        hx_get=f"/full_image?doc_id={doc_id}",
-                                        style="backdrop-filter: blur(5px);",
-                                        hx_trigger="load",
-                                        hx_swap="outerHTML",
-                                        alt=fields["title"],
-                                        cls="result-image w-full h-full object-contain",
-                                    ),
-                                    Div(
-                                        cls="overlay-container absolute top-0 left-0 w-full h-full pointer-events-none"
-                                    ),
-                                    cls="relative w-full h-full",
-                                ),
-                                cls="grid bg-muted p-2",
-                            ),
-                            cls="block",
-                        ),
-                        id=f"image-column-{idx}",
-                        cls="image-column relative bg-background px-3 py-5 grid-image-column",
-                    ),
-                    Div(
-                        Div(
-                            A(
-                                Lucide(icon="external-link", size="18"),
-                                f"PDF Source (Page {fields['page_number'] + 1})",
-                                href=f"{fields['url']}#page={fields['page_number'] + 1}",
-                                target="_blank",
-                                cls="flex items-center gap-1.5 font-mono bold text-sm",
-                            ),
-                            cls="flex items-center justify-end",
-                        ),
-                        Div(
-                            Div(
-                                Div(
-                                    Div(
-                                        Div(
-                                            H3(
-                                                "Dynamic summary",
-                                                cls="text-base font-semibold",
-                                            ),
-                                            P(
-                                                NotStr(fields.get("snippet", "")),
-                                                cls="text-highlight text-muted-foreground",
-                                            ),
-                                            cls="grid grid-rows-[auto_0px] content-start gap-y-3",
-                                        ),
-                                        id=f"result-text-snippet-{idx}",
-                                        cls="grid gap-y-3 p-8 border border-dashed",
-                                    ),
-                                    Div(
-                                        Div(
-                                            Div(
-                                                H3(
-                                                    "Full text",
-                                                    cls="text-base font-semibold",
-                                                ),
-                                                Div(
-                                                    P(
-                                                        NotStr(fields.get("text", "")),
-                                                        cls="text-highlight text-muted-foreground",
-                                                    ),
-                                                    Br(),
-                                                ),
-                                                cls="grid grid-rows-[auto_0px] content-start gap-y-3",
-                                            ),
-                                            id=f"result-text-full-{idx}",
-                                            cls="grid gap-y-3 p-8 border border-dashed",
-                                        ),
-                                        Div(
-                                            cls="absolute inset-x-0 bottom-0 bg-gradient-to-t from-[#fcfcfd] dark:from-[#1c2024] pt-[7%]"
-                                        ),
-                                        cls="relative grid",
-                                    ),
-                                    cls="grid grid-rows-[1fr_1fr] xl:grid-rows-[1fr_2fr] gap-y-8 p-8 text-sm",
-                                ),
-                                cls="grid bg-background",
-                            ),
-                            cls="grid bg-muted p-2",
-                        ),
-                        id=f"text-column-{idx}",
-                        cls="text-column relative bg-background px-3 py-5 hidden md-grid-text-column",
-                    ),
-                    id=f"image-text-columns-{idx}",
-                    cls="relative grid grid-cols-1 border-t grid-image-text-columns",
-                ),
-                cls="grid grid-cols-1 grid-rows-[auto_auto_1fr]",
-            ),
-        )
-
-    return [
-        Div(
-            SearchInfo(search_time, total_count),
-            *result_items,
-            image_swapping,
-            toggle_text_content,
-            dynamic_elements_scrollbars,
-            id="search-results",
-            cls="grid grid-cols-1 gap-px bg-border min-h-0",
-        ),
-        Div(
-            ChatResult(query_id=query_id, query=query, doc_ids=doc_ids),
-            hx_swap_oob="true",
-            id="chat_messages",
-        ),
-    ]
-
-
-def ChatResult(query_id: str, query: str, doc_ids: Optional[list] = None):
-    messages = Div(LoadingSkeleton())
-
-    if doc_ids:
-        messages = Div(
-            LoadingSkeleton(),
-            hx_ext="sse",
-            sse_connect=f"/get-message?query_id={query_id}&doc_ids={','.join(doc_ids)}&query={quote_plus(query)}",
-            sse_swap="message",
-            sse_close="close",
-            hx_swap="innerHTML",
-        )
-
-    return Div(
-        Div("AI-response (Gemini-8B)", cls="text-xl font-semibold p-5"),
-        Div(
-            Div(
-                messages,
-            ),
-            id="chat-messages",
-            cls="overflow-auto min-h-0 grid items-end px-5",
-        ),
-        id="chat_messages",
-        cls="h-full grid grid-rows-[auto_1fr_auto] min-h-0 gap-3",
-    )

src/frontend/layout.py

@@ -1,171 +0,0 @@
-from fasthtml.components import Body, Div, Header, Img, Nav, Title
-from fasthtml.xtend import A, Script
-from lucide_fasthtml import Lucide
-from shad4fast import Button, Separator
-
-layout_script = Script(
-    """
-    document.addEventListener("DOMContentLoaded", function () {
-          const main = document.querySelector('main');
-          const aside = document.querySelector('aside');
-          const body = document.body;
-        
-          if (main && aside && main.nextElementSibling === aside) {
-            // If we have both main and aside, adjust the layout for larger screens
-            body.classList.remove('grid-cols-1'); // Remove single-column layout
-            body.classList.add('md:grid-cols-[minmax(0,_45fr)_minmax(0,_15fr)]'); // Two-column layout on larger screens
-          } else if (main) {
-            // If only main, keep it full width
-            body.classList.add('grid-cols-1');
-          }
-    });
-    """
-)
-
-overlay_scrollbars_manager = Script(
-    """
-    (function () {
-        const { OverlayScrollbars } = OverlayScrollbarsGlobal;
-
-        function getPreferredTheme() {
-            return localStorage.theme === 'dark' || (!('theme' in localStorage) && window.matchMedia('(prefers-color-scheme: dark)').matches)
-                ? 'dark'
-                : 'light';
-        }
-
-        function applyOverlayScrollbars(element, scrollbarTheme) {
-            // Destroy existing OverlayScrollbars instance if it exists
-            const instance = OverlayScrollbars(element);
-            if (instance) {
-                instance.destroy();
-            }
-
-            // Reinitialize OverlayScrollbars with the correct theme and settings
-            OverlayScrollbars(element, {
-                overflow: {
-                    x: 'hidden',
-                    y: 'scroll'
-                },
-                scrollbars: {
-                    theme: scrollbarTheme,
-                    visibility: 'auto',
-                    autoHide: 'leave',
-                    autoHideDelay: 800
-                }
-            });
-        }
-
-        // Function to get the current scrollbar theme (light or dark)
-        function getScrollbarTheme() {
-            const isDarkMode = getPreferredTheme() === 'dark';
-            return isDarkMode ? 'os-theme-light' : 'os-theme-dark';  // Light theme in dark mode, dark theme in light mode
-        }
-
-        // Expose the common functions globally for reuse
-        window.OverlayScrollbarsManager = {
-            applyOverlayScrollbars: applyOverlayScrollbars,
-            getScrollbarTheme: getScrollbarTheme
-        };
-    })();
-    """
-)
-
-static_elements_scrollbars = Script(
-    """
-    (function () {
-        const { applyOverlayScrollbars, getScrollbarTheme } = OverlayScrollbarsManager;
-
-        function applyScrollbarsToStaticElements() {
-            const mainElement = document.querySelector('main');
-            const chatMessagesElement = document.querySelector('#chat-messages');
-
-            const scrollbarTheme = getScrollbarTheme();
-
-            if (mainElement) {
-                applyOverlayScrollbars(mainElement, scrollbarTheme);
-            }
-
-            if (chatMessagesElement) {
-                applyOverlayScrollbars(chatMessagesElement, scrollbarTheme);
-            }
-        }
-
-        // Apply the scrollbars on page load
-        applyScrollbarsToStaticElements();
-
-        // Observe changes in the 'dark' class on the <html> element to adjust the theme dynamically
-        const observer = new MutationObserver(applyScrollbarsToStaticElements);
-        observer.observe(document.documentElement, { attributes: true, attributeFilter: ['class'] });
-    })();
-    """
-)
-
-
-def Logo():
-    return Div(
-        Img(
-            src="https://assets.vespa.ai/logos/vespa-logo-black.svg",
-            alt="Vespa Logo",
-            cls="h-full dark:hidden",
-        ),
-        Img(
-            src="https://assets.vespa.ai/logos/vespa-logo-white.svg",
-            alt="Vespa Logo Dark Mode",
-            cls="h-full hidden dark:block",
-        ),
-        cls="h-[27px]",
-    )
-
-
-def ThemeToggle(variant="ghost", cls=None, **kwargs):
-    return Button(
-        Lucide("sun", cls="dark:flex hidden"),
-        Lucide("moon", cls="dark:hidden"),
-        variant=variant,
-        size="icon",
-        cls=f"theme-toggle {cls}",
-        **kwargs,
-    )
-
-
-def Links():
-    return Nav(
-        A(
-            Button("About this demo?", variant="link"),
-            href="/about-this-demo",
-        ),
-        Separator(orientation="vertical"),
-        A(
-            Button(Lucide(icon="github"), size="icon", variant="ghost"),
-            href="https://github.com/vespa-engine/vespa",
-            target="_blank",
-        ),
-        A(
-            Button(Lucide(icon="slack"), size="icon", variant="ghost"),
-            href="https://slack.vespa.ai",
-            target="_blank",
-        ),
-        Separator(orientation="vertical"),
-        ThemeToggle(),
-        cls="flex items-center space-x-2",
-    )
-
-
-def Layout(*c, is_home=False, **kwargs):
-    return (
-        Title("Visual Retrieval ColPali"),
-        Body(
-            Header(
-                A(Logo(), href="/"),
-                Links(),
-                cls="min-h-[55px] h-[55px] w-full flex items-center justify-between px-4",
-            ),
-            *c,
-            **kwargs,
-            data_is_home=str(is_home).lower(),
-            cls="grid grid-rows-[minmax(0,55px)_minmax(0,1fr)] min-h-0",
-        ),
-        layout_script,
-        overlay_scrollbars_manager,
-        static_elements_scrollbars,
-    )

src/globals.css

@@ -1,293 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-
-@layer base {
-    :root {
-        --background: 240 20% 99%; /* 1 */
-        --foreground: 210 13% 13%; /* 12 */
-        --card: 240 20% 99%; /* 1 */
-        --card-foreground: 210 13% 13%; /* 12 */
-        --popover: 240 20% 99%; /* 1 */
-        --popover-foreground: 210 13% 13%; /* 12 */
-        --primary: 210 13% 13%; /* 12 */
-        --primary-foreground: 240 20% 98%; /* 2 */
-        --secondary: 240 11% 95%; /* 3 */
-        --secondary-foreground: 210 13% 13%; /* 12 */
-        --muted: 240 11% 95%; /* 3 */
-        --muted-foreground: 220 6% 40%; /* 11 */
-        --accent: 240 11% 95%; /* 3 */
-        --accent-foreground: 210 13% 13%; /* 12 */
-        --destructive: 358 75% 59%; /* 9 - red */
-        --destructive-foreground: 240 20% 98%; /* 2 */
-        --border: 240 10% 86%; /* 6 */
-        --input: 240 10% 86%; /* 6 */
-        --ring: 210 13% 13%; /* 12 */
-        --chart-1: 10 78% 54%; /* 9 - tomato */
-        --chart-2: 173 80% 36%; /* 9 - teal */
-        --chart-3: 206 100% 50%; /* 9 - blue */
-        --chart-4: 42 100% 62%; /* 9 - amber */
-        --chart-5: 23 93% 53%; /* 9 - orange */
-    }
-
-    .dark {
-        --background: 240 6% 7%; /* 1 */
-        --foreground: 220 9% 94%; /* 12 */
-        --card: 240 6% 7%; /* 1 */
-        --card-foreground: 220 9% 94%; /* 12 */
-        --popover: 240 6% 7%; /* 1 */
-        --popover-foreground: 220 9% 94%; /* 12 */
-        --primary: 220 9% 94%; /* 12 */
-        --primary-foreground: 220 6% 10%; /* 2 */
-        --secondary: 225 6% 14%; /* 3 */
-        --secondary-foreground: 220 9% 94%; /* 12 */
-        --muted: 225 6% 14%; /* 3 */
-        --muted-foreground: 216 7% 71%; /* 11 */
-        --accent: 225 6% 14%; /* 3 */
-        --accent-foreground: 220 9% 94%; /* 12 */
-        --destructive: 358 75% 59%; /* 9 - red */
-        --destructive-foreground: 220 9% 94%; /* 12 */
-        --border: 213 8% 23%; /* 6 */
-        --input: 213 8% 23%; /* 6 */
-        --ring: 220 9% 94%; /* 12 */
-        --chart-1: 10 78% 54%; /* 9 - tomato */
-        --chart-2: 173 80% 36%; /* 9 - teal */
-        --chart-3: 206 100% 50%; /* 9 - blue */
-        --chart-4: 42 100% 62%; /* 9 - amber */
-        --chart-5: 23 93% 53%; /* 9 - orange */
-    }
-}
-
-@layer base {
-    :root:has(.no-bg-scroll) {
-        overflow: hidden;
-    }
-
-    * {
-        @apply border-border;
-    }
-
-    body {
-        @apply bg-background text-foreground antialiased min-h-screen;
-        font-feature-settings: "rlig" 1, "calt" 1;
-    }
-}
-
-@layer utilities {
-
-    /* Hide scrollbar for Chrome, Safari and Opera */
-    .no-scrollbar::-webkit-scrollbar {
-        display: none;
-    }
-
-    /* Hide scrollbar for IE, Edge and Firefox */
-    .no-scrollbar {
-        -webkit-overflow-scrolling: touch;
-        -ms-overflow-style: none;
-        /* IE and Edge */
-        scrollbar-width: none;
-        /* Firefox */
-    }
-}
-
-@keyframes slideInFromTop {
-    from {
-        transform: translateY(-100%);
-    }
-
-    to {
-        transform: translateY(0);
-    }
-}
-
-@keyframes slideInFromBottom {
-    from {
-        transform: translateY(100%);
-    }
-
-    to {
-        transform: translateY(0);
-    }
-}
-
-.toast {
-    animation-duration: 0.2s;
-    animation-fill-mode: forwards;
-}
-
-@media (max-width: 640px) {
-    .toast {
-        animation-name: slideInFromTop;
-    }
-}
-
-@media (min-width: 641px) {
-    .toast {
-        animation-name: slideInFromBottom;
-    }
-}
-
-@keyframes fade-in {
-    from {
-        opacity: 0;
-    }
-    to {
-        opacity: 1;
-    }
-}
-
-@keyframes slide-up {
-    from {
-        transform: translateY(20px);
-        opacity: 0;
-    }
-    to {
-        transform: translateY(0);
-        opacity: 1;
-    }
-}
-
-.animate-fade-in {
-    animation: fade-in 1s ease-out forwards;
-}
-
-.animate-slide-up {
-    animation: slide-up 1s ease-out forwards;
-}
-
-.sim-map-button.active {
-    background-color: #61D790;
-    color: #2E2F27;
-
-    &:hover {
-        background-color: #61D790;
-    }
-}
-
-.text-highlight strong {
-    color: black;
-
-    .dark & {
-        color: white;
-    }
-}
-
-.tokens-button {
-    background-color: #B7E2F1;
-    color: #2E2F27;
-}
-
-.overlay-image {
-    opacity: 0.5;
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: 10;
-}
-
-header {
-    grid-column: 1/-1;
-}
-
-body {
-    &[data-is-home="true"] {
-        background: radial-gradient(circle at 50% 100%, #fcfcfd, #fcfcfd, #fdfdfe, #fdfdfe, #fefefe, #fefefe, #ffffff, #ffffff);
-
-        .dark & {
-            background: radial-gradient(circle at 50% 50%, #272a2d, #242629, #212326, #1e1f22, #1b1c1e, #18181b, #151517, #111113);
-        }
-    }
-}
-
-main {
-    overflow: auto;
-}
-
-aside {
-    overflow: auto;
-}
-
-.scroll-container {
-    padding-right: 10px;
-}
-
-.question-message {
-    background-color: #61D790;
-    color: #2E2F27;
-}
-
-.grid-image-text-columns {
-    @apply md:grid-cols-2 md:col-span-2
-}
-
-.grid-image-column {
-    @apply grid grid-rows-subgrid row-span-2 content-start;
-}
-
-.md-grid-text-column {
-    @apply md:grid md:grid-rows-subgrid md:row-span-2 md:content-start;
-}
-
-#search-input[aria-expanded="true"] {
-    border-top: 1px solid hsl(var(--input));
-    border-left: 1px solid hsl(var(--input));
-    border-right: 1px solid hsl(var(--input));
-    border-bottom: none;
-    border-bottom-left-radius: 0;
-    border-bottom-right-radius: 0;
-}
-
-.awesomplete {
-    width: 100%;
-}
-
-.awesomplete > ul {
-    @apply text-sm space-y-1;
-    margin: 0;
-    border-top: none;
-    border-left: 1px solid hsl(var(--input));
-    border-right: 1px solid hsl(var(--input));
-    border-bottom: 1px solid hsl(var(--input));
-    border-radius: 0 0 calc(var(--radius) - 2px) calc(var(--radius) - 2px);
-    background: white;
-
-    .dark & {
-        background: hsl(var(--background));
-    }
-
-    box-shadow: none;
-    text-shadow: none;
-}
-
-.awesomplete > ul:before {
-    display: none;
-}
-
-.awesomplete > ul > li:hover {
-    background-color: #B7E2F1;
-    color: #2E2F27;
-}
-
-.awesomplete > ul > li[aria-selected="true"] {
-    background-color: #B7E2F1;
-    color: #2E2F27;
-}
-
-.awesomplete mark {
-    background-color: #61D790;
-    color: #2E2F27;
-}
-
-.awesomplete li:hover mark {
-    background-color: #61D790;
-    color: #2E2F27;
-}
-
-.awesomplete li[aria-selected="true"] mark {
-    background-color: #61D790;
-    color: #2E2F27;
-}
-

src/icons.py

@@ -1 +0,0 @@
-ICONS = {"chevrons-right": "<path d=\"m6 17 5-5-5-5\"></path><path d=\"m13 17 5-5-5-5\"></path>", "moon": "<path d=\"M12 3a6 6 0 0 0 9 9 9 9 0 1 1-9-9Z\"></path>", "sun": "<circle cx=\"12\" cy=\"12\" r=\"4\"></circle><path d=\"M12 2v2\"></path><path d=\"M12 20v2\"></path><path d=\"m4.93 4.93 1.41 1.41\"></path><path d=\"m17.66 17.66 1.41 1.41\"></path><path d=\"M2 12h2\"></path><path d=\"M20 12h2\"></path><path d=\"m6.34 17.66-1.41 1.41\"></path><path d=\"m19.07 4.93-1.41 1.41\"></path>", "github": "<path d=\"M15 22v-4a4.8 4.8 0 0 0-1-3.5c3 0 6-2 6-5.5.08-1.25-.27-2.48-1-3.5.28-1.15.28-2.35 0-3.5 0 0-1 0-3 1.5-2.64-.5-5.36-.5-8 0C6 2 5 2 5 2c-.3 1.15-.3 2.35 0 3.5A5.403 5.403 0 0 0 4 9c0 3.5 3 5.5 6 5.5-.39.49-.68 1.05-.85 1.65-.17.6-.22 1.23-.15 1.85v4\"></path><path d=\"M9 18c-4.51 2-5-2-7-2\"></path>", "slack": "<rect height=\"8\" rx=\"1.5\" width=\"3\" x=\"13\" y=\"2\"></rect><path d=\"M19 8.5V10h1.5A1.5 1.5 0 1 0 19 8.5\"></path><rect height=\"8\" rx=\"1.5\" width=\"3\" x=\"8\" y=\"14\"></rect><path d=\"M5 15.5V14H3.5A1.5 1.5 0 1 0 5 15.5\"></path><rect height=\"3\" rx=\"1.5\" width=\"8\" x=\"14\" y=\"13\"></rect><path d=\"M15.5 19H14v1.5a1.5 1.5 0 1 0 1.5-1.5\"></path><rect height=\"3\" rx=\"1.5\" width=\"8\" x=\"2\" y=\"8\"></rect><path d=\"M8.5 5H10V3.5A1.5 1.5 0 1 0 8.5 5\"></path>", "settings": "<path d=\"M12.22 2h-.44a2 2 0 0 0-2 2v.18a2 2 0 0 1-1 1.73l-.43.25a2 2 0 0 1-2 0l-.15-.08a2 2 0 0 0-2.73.73l-.22.38a2 2 0 0 0 .73 2.73l.15.1a2 2 0 0 1 1 1.72v.51a2 2 0 0 1-1 1.74l-.15.09a2 2 0 0 0-.73 2.73l.22.38a2 2 0 0 0 2.73.73l.15-.08a2 2 0 0 1 2 0l.43.25a2 2 0 0 1 1 1.73V20a2 2 0 0 0 2 2h.44a2 2 0 0 0 2-2v-.18a2 2 0 0 1 1-1.73l.43-.25a2 2 0 0 1 2 0l.15.08a2 2 0 0 0 2.73-.73l.22-.39a2 2 0 0 0-.73-2.73l-.15-.08a2 2 0 0 1-1-1.74v-.5a2 2 0 0 1 1-1.74l.15-.09a2 2 0 0 0 .73-2.73l-.22-.38a2 2 0 0 0-2.73-.73l-.15.08a2 2 0 0 1-2 0l-.43-.25a2 2 0 0 1-1-1.73V4a2 2 0 0 0-2-2z\"></path><circle cx=\"12\" cy=\"12\" r=\"3\"></circle>", "arrow-right": "<path d=\"M5 12h14\"></path><path d=\"m12 5 7 7-7 7\"></path>", "search": "<circle cx=\"11\" cy=\"11\" r=\"8\"></circle><path d=\"m21 21-4.3-4.3\"></path>", "file-search": "<path d=\"M14 2v4a2 2 0 0 0 2 2h4\"></path><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"></path><path d=\"m9 18-1.5-1.5\"></path><circle cx=\"5\" cy=\"14\" r=\"3\"></circle>", "message-circle-question": "<path d=\"M7.9 20A9 9 0 1 0 4 16.1L2 22Z\"></path><path d=\"M9.09 9a3 3 0 0 1 5.83 1c0 2-3 3-3 3\"></path><path d=\"M12 17h.01\"></path>", "text-search": "<path d=\"M21 6H3\"></path><path d=\"M10 12H3\"></path><path d=\"M10 18H3\"></path><circle cx=\"17\" cy=\"15\" r=\"3\"></circle><path d=\"m21 19-1.9-1.9\"></path>", "maximize": "<path d=\"M8 3H5a2 2 0 0 0-2 2v3\"></path><path d=\"M21 8V5a2 2 0 0 0-2-2h-3\"></path><path d=\"M3 16v3a2 2 0 0 0 2 2h3\"></path><path d=\"M16 21h3a2 2 0 0 0 2-2v-3\"></path>", "expand": "<path d=\"m21 21-6-6m6 6v-4.8m0 4.8h-4.8\"></path><path d=\"M3 16.2V21m0 0h4.8M3 21l6-6\"></path><path d=\"M21 7.8V3m0 0h-4.8M21 3l-6 6\"></path><path d=\"M3 7.8V3m0 0h4.8M3 3l6 6\"></path>", "fullscreen": "<path d=\"M3 7V5a2 2 0 0 1 2-2h2\"></path><path d=\"M17 3h2a2 2 0 0 1 2 2v2\"></path><path d=\"M21 17v2a2 2 0 0 1-2 2h-2\"></path><path d=\"M7 21H5a2 2 0 0 1-2-2v-2\"></path><rect height=\"8\" rx=\"1\" width=\"10\" x=\"7\" y=\"8\"></rect>", "images": "<path d=\"M18 22H4a2 2 0 0 1-2-2V6\"></path><path d=\"m22 13-1.296-1.296a2.41 2.41 0 0 0-3.408 0L11 18\"></path><circle cx=\"12\" cy=\"8\" r=\"2\"></circle><rect height=\"16\" rx=\"2\" width=\"16\" x=\"6\" y=\"2\"></rect>", "circle": "<circle cx=\"12\" cy=\"12\" r=\"10\"></circle>", "loader-circle": "<path d=\"M21 12a9 9 0 1 1-6.219-8.56\"></path>", "file-text": "<path d=\"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z\"></path><path d=\"M14 2v4a2 2 0 0 0 2 2h4\"></path><path d=\"M10 9H8\"></path><path d=\"M16 13H8\"></path><path d=\"M16 17H8\"></path>", "file-question": "<path d=\"M12 17h.01\"></path><path d=\"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7z\"></path><path d=\"M9.1 9a3 3 0 0 1 5.82 1c0 2-3 3-3 3\"></path>", "external-link": "<path d=\"M15 3h6v6\"></path><path d=\"M10 14 21 3\"></path><path d=\"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6\"></path>"}

src/main.py

@@ -1,420 +0,0 @@
-import asyncio
-import base64
-import os
-import time
-import uuid
-import logging
-import sys
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
-
-import google.generativeai as genai
-from fastcore.parallel import threaded
-from fasthtml.common import (
-    Aside,
-    Div,
-    FileResponse,
-    HighlightJS,
-    Img,
-    JSONResponse,
-    Link,
-    Main,
-    P,
-    RedirectResponse,
-    Script,
-    StreamingResponse,
-    fast_app,
-    serve,
-)
-from PIL import Image
-from shad4fast import ShadHead
-from vespa.application import Vespa
-
-from backend.colpali import SimMapGenerator
-from backend.vespa_app import VespaQueryClient
-from frontend.app import (
-    AboutThisDemo,
-    ChatResult,
-    Home,
-    Search,
-    SearchBox,
-    SearchResult,
-    SimMapButtonPoll,
-    SimMapButtonReady,
-)
-from frontend.layout import Layout
-
-highlight_js_theme_link = Link(id="highlight-theme", rel="stylesheet", href="")
-highlight_js_theme = Script(src="/static/js/highlightjs-theme.js")
-highlight_js = HighlightJS(
-    langs=["python", "javascript", "java", "json", "xml"],
-    dark="github-dark",
-    light="github",
-)
-
-overlayscrollbars_link = Link(
-    rel="stylesheet",
-    href="https://cdnjs.cloudflare.com/ajax/libs/overlayscrollbars/2.10.0/styles/overlayscrollbars.min.css",
-    type="text/css",
-)
-overlayscrollbars_js = Script(
-    src="https://cdnjs.cloudflare.com/ajax/libs/overlayscrollbars/2.10.0/browser/overlayscrollbars.browser.es5.min.js"
-)
-awesomplete_link = Link(
-    rel="stylesheet",
-    href="https://cdnjs.cloudflare.com/ajax/libs/awesomplete/1.1.7/awesomplete.min.css",
-    type="text/css",
-)
-awesomplete_js = Script(
-    src="https://cdnjs.cloudflare.com/ajax/libs/awesomplete/1.1.7/awesomplete.min.js"
-)
-sselink = Script(src="https://unpkg.com/[email protected]/sse.js")
-
-# Get log level from environment variable, default to INFO
-LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper()
-# Configure logger
-logger = logging.getLogger("vespa_app")
-handler = logging.StreamHandler(sys.stdout)
-handler.setFormatter(
-    logging.Formatter(
-        "%(levelname)s: \t %(asctime)s \t %(message)s",
-        datefmt="%Y-%m-%d %H:%M:%S",
-    )
-)
-logger.addHandler(handler)
-logger.setLevel(getattr(logging, LOG_LEVEL))
-
-app, rt = fast_app(
-    htmlkw={"cls": "grid h-full"},
-    pico=False,
-    hdrs=(
-        highlight_js,
-        highlight_js_theme_link,
-        highlight_js_theme,
-        overlayscrollbars_link,
-        overlayscrollbars_js,
-        awesomplete_link,
-        awesomplete_js,
-        sselink,
-        ShadHead(tw_cdn=False, theme_handle=True),
-    ),
-)
-vespa_app: Vespa = VespaQueryClient(logger=logger)
-thread_pool = ThreadPoolExecutor()
-# Gemini config
-
-genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
-GEMINI_SYSTEM_PROMPT = """If the user query is a question, try your best to answer it based on the provided images. 
-If the user query can not be interpreted as a question, or if the answer to the query can not be inferred from the images,
-answer with the exact phrase "I am sorry, I can't find enough relevant information on these pages to answer your question.".
-Your response should be HTML formatted, but only simple tags, such as <b>. <p>, <i>, <br> <ul> and <li> are allowed. No HTML tables.
-This means that newlines will be replaced with <br> tags, bold text will be enclosed in <b> tags, and so on.
-Do NOT include backticks (`) in your response. Only simple HTML tags and text.
-"""
-gemini_model = genai.GenerativeModel(
-    "gemini-1.5-flash-8b", system_instruction=GEMINI_SYSTEM_PROMPT
-)
-STATIC_DIR = Path("static")
-IMG_DIR = STATIC_DIR / "full_images"
-SIM_MAP_DIR = STATIC_DIR / "sim_maps"
-os.makedirs(IMG_DIR, exist_ok=True)
-os.makedirs(SIM_MAP_DIR, exist_ok=True)
-
-
-@app.on_event("startup")
-def load_model_on_startup():
-    app.sim_map_generator = SimMapGenerator(logger=logger)
-    return
-
-
-@app.on_event("startup")
-async def keepalive():
-    asyncio.create_task(poll_vespa_keepalive())
-    return
-
-
-def generate_query_id(query, ranking_value):
-    hash_input = (query + ranking_value).encode("utf-8")
-    return hash(hash_input)
-
-
-@rt("/static/{filepath:path}")
-def serve_static(filepath: str):
-    return FileResponse(STATIC_DIR / filepath)
-
-
-@rt("/")
-def get(session):
-    if "session_id" not in session:
-        session["session_id"] = str(uuid.uuid4())
-    return Layout(Main(Home()), is_home=True)
-
-
-@rt("/about-this-demo")
-def get():
-    return Layout(Main(AboutThisDemo()))
-
-
-@rt("/search")
-def get(request, query: str = "", ranking: str = "nn+colpali"):
-    logger.info(f"/search: Fetching results for query: {query}, ranking: {ranking}")
-
-    # Always render the SearchBox first
-    if not query:
-        # Show SearchBox and a message for missing query
-        return Layout(
-            Main(
-                Div(
-                    SearchBox(query_value=query, ranking_value=ranking),
-                    Div(
-                        P(
-                            "No query provided. Please enter a query.",
-                            cls="text-center text-muted-foreground",
-                        ),
-                        cls="p-10",
-                    ),
-                    cls="grid",
-                )
-            )
-        )
-    # Generate a unique query_id based on the query and ranking value
-    query_id = generate_query_id(query, ranking)
-    # Show the loading message if a query is provided
-    return Layout(
-        Main(Search(request), data_overlayscrollbars_initialize=True, cls="border-t"),
-        Aside(
-            ChatResult(query_id=query_id, query=query),
-            cls="border-t border-l hidden md:block",
-        ),
-    )  # Show SearchBox and Loading message initially
-
-
-@rt("/fetch_results")
-async def get(session, request, query: str, ranking: str):
-    if "hx-request" not in request.headers:
-        return RedirectResponse("/search")
-
-    # Get the hash of the query and ranking value
-    query_id = generate_query_id(query, ranking)
-    logger.info(f"Query id in /fetch_results: {query_id}")
-    # Run the embedding and query against Vespa app
-    start_inference = time.perf_counter()
-    q_embs, idx_to_token = app.sim_map_generator.get_query_embeddings_and_token_map(
-        query
-    )
-    end_inference = time.perf_counter()
-    logger.info(
-        f"Inference time for query_id: {query_id} \t {end_inference - start_inference:.2f} seconds"
-    )
-
-    start = time.perf_counter()
-    # Fetch real search results from Vespa
-    result = await vespa_app.get_result_from_query(
-        query=query,
-        q_embs=q_embs,
-        ranking=ranking,
-        idx_to_token=idx_to_token,
-    )
-    end = time.perf_counter()
-    logger.info(
-        f"Search results fetched in {end - start:.2f} seconds. Vespa search time: {result['timing']['searchtime']}"
-    )
-    search_time = result["timing"]["searchtime"]
-    # Safely get total_count with a default of 0
-    total_count = result.get("root", {}).get("fields", {}).get("totalCount", 0)
-
-    search_results = vespa_app.results_to_search_results(result, idx_to_token)
-
-    get_and_store_sim_maps(
-        query_id=query_id,
-        query=query,
-        q_embs=q_embs,
-        ranking=ranking,
-        idx_to_token=idx_to_token,
-        doc_ids=[result["fields"]["id"] for result in search_results],
-    )
-    return SearchResult(search_results, query, query_id, search_time, total_count)
-
-
-def get_results_children(result):
-    search_results = (
-        result["root"]["children"]
-        if "root" in result and "children" in result["root"]
-        else []
-    )
-    return search_results
-
-
-async def poll_vespa_keepalive():
-    while True:
-        await asyncio.sleep(5)
-        await vespa_app.keepalive()
-        logger.debug(f"Vespa keepalive: {time.time()}")
-
-
-@threaded
-def get_and_store_sim_maps(
-    query_id, query: str, q_embs, ranking, idx_to_token, doc_ids
-):
-    ranking_sim = ranking + "_sim"
-    vespa_sim_maps = vespa_app.get_sim_maps_from_query(
-        query=query,
-        q_embs=q_embs,
-        ranking=ranking_sim,
-        idx_to_token=idx_to_token,
-    )
-    img_paths = [IMG_DIR / f"{doc_id}.jpg" for doc_id in doc_ids]
-    # All images should be downloaded, but best to wait 5 secs
-    max_wait = 5
-    start_time = time.time()
-    while (
-        not all([os.path.exists(img_path) for img_path in img_paths])
-        and time.time() - start_time < max_wait
-    ):
-        time.sleep(0.2)
-    if not all([os.path.exists(img_path) for img_path in img_paths]):
-        logger.warning(f"Images not ready in 5 seconds for query_id: {query_id}")
-        return False
-    sim_map_generator = app.sim_map_generator.gen_similarity_maps(
-        query=query,
-        query_embs=q_embs,
-        token_idx_map=idx_to_token,
-        images=img_paths,
-        vespa_sim_maps=vespa_sim_maps,
-    )
-    for idx, token, token_idx, blended_img_base64 in sim_map_generator:
-        with open(SIM_MAP_DIR / f"{query_id}_{idx}_{token_idx}.png", "wb") as f:
-            f.write(base64.b64decode(blended_img_base64))
-        logger.debug(
-            f"Sim map saved to disk for query_id: {query_id}, idx: {idx}, token: {token}"
-        )
-    return True
-
-
-@app.get("/get_sim_map")
-async def get_sim_map(query_id: str, idx: int, token: str, token_idx: int):
-    """
-    Endpoint that each of the sim map button polls to get the sim map image
-    when it is ready. If it is not ready, returns a SimMapButtonPoll, that
-    continues to poll every 1 second.
-    """
-    sim_map_path = SIM_MAP_DIR / f"{query_id}_{idx}_{token_idx}.png"
-    if not os.path.exists(sim_map_path):
-        logger.debug(
-            f"Sim map not ready for query_id: {query_id}, idx: {idx}, token: {token}"
-        )
-        return SimMapButtonPoll(
-            query_id=query_id, idx=idx, token=token, token_idx=token_idx
-        )
-    else:
-        return SimMapButtonReady(
-            query_id=query_id,
-            idx=idx,
-            token=token,
-            token_idx=token_idx,
-            img_src=sim_map_path,
-        )
-
-
-@app.get("/full_image")
-async def full_image(doc_id: str):
-    """
-    Endpoint to get the full quality image for a given result id.
-    """
-    img_path = IMG_DIR / f"{doc_id}.jpg"
-    if not os.path.exists(img_path):
-        image_data = await vespa_app.get_full_image_from_vespa(doc_id)
-        # image data is base 64 encoded string. Save it to disk as jpg.
-        with open(img_path, "wb") as f:
-            f.write(base64.b64decode(image_data))
-        logger.debug(f"Full image saved to disk for doc_id: {doc_id}")
-    else:
-        with open(img_path, "rb") as f:
-            image_data = base64.b64encode(f.read()).decode("utf-8")
-    return Img(
-        src=f"data:image/jpeg;base64,{image_data}",
-        alt="something",
-        cls="result-image w-full h-full object-contain",
-    )
-
-
-@rt("/suggestions")
-async def get_suggestions(query: str = ""):
-    """Endpoint to get suggestions as user types in the search box"""
-    query = query.lower().strip()
-
-    if query:
-        suggestions = await vespa_app.get_suggestions(query)
-        if len(suggestions) > 0:
-            return JSONResponse({"suggestions": suggestions})
-
-    return JSONResponse({"suggestions": []})
-
-
-async def message_generator(query_id: str, query: str, doc_ids: list):
-    """Generator function to yield SSE messages for chat response"""
-    images = []
-    num_images = 3  # Number of images before firing chat request
-    max_wait = 10  # seconds
-    start_time = time.time()
-    # Check if full images are ready on disk
-    while (
-        len(images) < min(num_images, len(doc_ids))
-        and time.time() - start_time < max_wait
-    ):
-        images = []
-        for idx in range(num_images):
-            image_filename = IMG_DIR / f"{doc_ids[idx]}.jpg"
-            if not os.path.exists(image_filename):
-                logger.debug(
-                    f"Message generator: Full image not ready for query_id: {query_id}, idx: {idx}"
-                )
-                continue
-            else:
-                logger.debug(
-                    f"Message generator: image ready for query_id: {query_id}, idx: {idx}"
-                )
-                images.append(Image.open(image_filename))
-        if len(images) < num_images:
-            await asyncio.sleep(0.2)
-
-    # yield message with number of images ready
-    yield f"event: message\ndata: Generating response based on {len(images)} images...\n\n"
-    if not images:
-        yield "event: message\ndata: Failed to send images to Gemini-8B!\n\n"
-        yield "event: close\ndata: \n\n"
-        return
-
-    # If newlines are present in the response, the connection will be closed.
-    def replace_newline_with_br(text):
-        return text.replace("\n", "<br>")
-
-    response_text = ""
-    async for chunk in await gemini_model.generate_content_async(
-        images + ["\n\n Query: ", query], stream=True
-    ):
-        if chunk.text:
-            response_text += chunk.text
-            response_text = replace_newline_with_br(response_text)
-            yield f"event: message\ndata: {response_text}\n\n"
-            await asyncio.sleep(0.1)
-    yield "event: close\ndata: \n\n"
-
-
-@app.get("/get-message")
-async def get_message(query_id: str, query: str, doc_ids: str):
-    return StreamingResponse(
-        message_generator(query_id=query_id, query=query, doc_ids=doc_ids.split(",")),
-        media_type="text/event-stream",
-    )
-
-
-@rt("/app")
-def get():
-    return Layout(Main(Div(P(f"Connected to Vespa at {vespa_app.url}"), cls="p-4")))
-
-
-if __name__ == "__main__":
-    HOT_RELOAD = os.getenv("HOT_RELOAD", "False").lower() == "true"
-    logger.info(f"Starting app with hot reload: {HOT_RELOAD}")
-    serve(port=7860, reload=HOT_RELOAD)

src/output.css

@@ -1,2893 +0,0 @@
-*, ::before, ::after {
-  --tw-border-spacing-x: 0;
-  --tw-border-spacing-y: 0;
-  --tw-translate-x: 0;
-  --tw-translate-y: 0;
-  --tw-rotate: 0;
-  --tw-skew-x: 0;
-  --tw-skew-y: 0;
-  --tw-scale-x: 1;
-  --tw-scale-y: 1;
-  --tw-pan-x:  ;
-  --tw-pan-y:  ;
-  --tw-pinch-zoom:  ;
-  --tw-scroll-snap-strictness: proximity;
-  --tw-gradient-from-position:  ;
-  --tw-gradient-via-position:  ;
-  --tw-gradient-to-position:  ;
-  --tw-ordinal:  ;
-  --tw-slashed-zero:  ;
-  --tw-numeric-figure:  ;
-  --tw-numeric-spacing:  ;
-  --tw-numeric-fraction:  ;
-  --tw-ring-inset:  ;
-  --tw-ring-offset-width: 0px;
-  --tw-ring-offset-color: #fff;
-  --tw-ring-color: rgb(59 130 246 / 0.5);
-  --tw-ring-offset-shadow: 0 0 #0000;
-  --tw-ring-shadow: 0 0 #0000;
-  --tw-shadow: 0 0 #0000;
-  --tw-shadow-colored: 0 0 #0000;
-  --tw-blur:  ;
-  --tw-brightness:  ;
-  --tw-contrast:  ;
-  --tw-grayscale:  ;
-  --tw-hue-rotate:  ;
-  --tw-invert:  ;
-  --tw-saturate:  ;
-  --tw-sepia:  ;
-  --tw-drop-shadow:  ;
-  --tw-backdrop-blur:  ;
-  --tw-backdrop-brightness:  ;
-  --tw-backdrop-contrast:  ;
-  --tw-backdrop-grayscale:  ;
-  --tw-backdrop-hue-rotate:  ;
-  --tw-backdrop-invert:  ;
-  --tw-backdrop-opacity:  ;
-  --tw-backdrop-saturate:  ;
-  --tw-backdrop-sepia:  ;
-  --tw-contain-size:  ;
-  --tw-contain-layout:  ;
-  --tw-contain-paint:  ;
-  --tw-contain-style:  ;
-}
-
-::backdrop {
-  --tw-border-spacing-x: 0;
-  --tw-border-spacing-y: 0;
-  --tw-translate-x: 0;
-  --tw-translate-y: 0;
-  --tw-rotate: 0;
-  --tw-skew-x: 0;
-  --tw-skew-y: 0;
-  --tw-scale-x: 1;
-  --tw-scale-y: 1;
-  --tw-pan-x:  ;
-  --tw-pan-y:  ;
-  --tw-pinch-zoom:  ;
-  --tw-scroll-snap-strictness: proximity;
-  --tw-gradient-from-position:  ;
-  --tw-gradient-via-position:  ;
-  --tw-gradient-to-position:  ;
-  --tw-ordinal:  ;
-  --tw-slashed-zero:  ;
-  --tw-numeric-figure:  ;
-  --tw-numeric-spacing:  ;
-  --tw-numeric-fraction:  ;
-  --tw-ring-inset:  ;
-  --tw-ring-offset-width: 0px;
-  --tw-ring-offset-color: #fff;
-  --tw-ring-color: rgb(59 130 246 / 0.5);
-  --tw-ring-offset-shadow: 0 0 #0000;
-  --tw-ring-shadow: 0 0 #0000;
-  --tw-shadow: 0 0 #0000;
-  --tw-shadow-colored: 0 0 #0000;
-  --tw-blur:  ;
-  --tw-brightness:  ;
-  --tw-contrast:  ;
-  --tw-grayscale:  ;
-  --tw-hue-rotate:  ;
-  --tw-invert:  ;
-  --tw-saturate:  ;
-  --tw-sepia:  ;
-  --tw-drop-shadow:  ;
-  --tw-backdrop-blur:  ;
-  --tw-backdrop-brightness:  ;
-  --tw-backdrop-contrast:  ;
-  --tw-backdrop-grayscale:  ;
-  --tw-backdrop-hue-rotate:  ;
-  --tw-backdrop-invert:  ;
-  --tw-backdrop-opacity:  ;
-  --tw-backdrop-saturate:  ;
-  --tw-backdrop-sepia:  ;
-  --tw-contain-size:  ;
-  --tw-contain-layout:  ;
-  --tw-contain-paint:  ;
-  --tw-contain-style:  ;
-}
-
-/*
-! tailwindcss v3.4.13 | MIT License | https://tailwindcss.com
-*/
-
-/*
-1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
-2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
-*/
-
-*,
-::before,
-::after {
-  box-sizing: border-box;
-  /* 1 */
-  border-width: 0;
-  /* 2 */
-  border-style: solid;
-  /* 2 */
-  border-color: #e5e7eb;
-  /* 2 */
-}
-
-::before,
-::after {
-  --tw-content: '';
-}
-
-/*
-1. Use a consistent sensible line-height in all browsers.
-2. Prevent adjustments of font size after orientation changes in iOS.
-3. Use a more readable tab size.
-4. Use the user's configured `sans` font-family by default.
-5. Use the user's configured `sans` font-feature-settings by default.
-6. Use the user's configured `sans` font-variation-settings by default.
-7. Disable tap highlights on iOS
-*/
-
-html,
-:host {
-  line-height: 1.5;
-  /* 1 */
-  -webkit-text-size-adjust: 100%;
-  /* 2 */
-  -moz-tab-size: 4;
-  /* 3 */
-  -o-tab-size: 4;
-     tab-size: 4;
-  /* 3 */
-  font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
-  /* 4 */
-  font-feature-settings: normal;
-  /* 5 */
-  font-variation-settings: normal;
-  /* 6 */
-  -webkit-tap-highlight-color: transparent;
-  /* 7 */
-}
-
-/*
-1. Remove the margin in all browsers.
-2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
-*/
-
-body {
-  margin: 0;
-  /* 1 */
-  line-height: inherit;
-  /* 2 */
-}
-
-/*
-1. Add the correct height in Firefox.
-2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
-3. Ensure horizontal rules are visible by default.
-*/
-
-hr {
-  height: 0;
-  /* 1 */
-  color: inherit;
-  /* 2 */
-  border-top-width: 1px;
-  /* 3 */
-}
-
-/*
-Add the correct text decoration in Chrome, Edge, and Safari.
-*/
-
-abbr:where([title]) {
-  -webkit-text-decoration: underline dotted;
-          text-decoration: underline dotted;
-}
-
-/*
-Remove the default font size and weight for headings.
-*/
-
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-  font-size: inherit;
-  font-weight: inherit;
-}
-
-/*
-Reset links to optimize for opt-in styling instead of opt-out.
-*/
-
-a {
-  color: inherit;
-  text-decoration: inherit;
-}
-
-/*
-Add the correct font weight in Edge and Safari.
-*/
-
-b,
-strong {
-  font-weight: bolder;
-}
-
-/*
-1. Use the user's configured `mono` font-family by default.
-2. Use the user's configured `mono` font-feature-settings by default.
-3. Use the user's configured `mono` font-variation-settings by default.
-4. Correct the odd `em` font sizing in all browsers.
-*/
-
-code,
-kbd,
-samp,
-pre {
-  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-  /* 1 */
-  font-feature-settings: normal;
-  /* 2 */
-  font-variation-settings: normal;
-  /* 3 */
-  font-size: 1em;
-  /* 4 */
-}
-
-/*
-Add the correct font size in all browsers.
-*/
-
-small {
-  font-size: 80%;
-}
-
-/*
-Prevent `sub` and `sup` elements from affecting the line height in all browsers.
-*/
-
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
-}
-
-sub {
-  bottom: -0.25em;
-}
-
-sup {
-  top: -0.5em;
-}
-
-/*
-1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
-2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
-3. Remove gaps between table borders by default.
-*/
-
-table {
-  text-indent: 0;
-  /* 1 */
-  border-color: inherit;
-  /* 2 */
-  border-collapse: collapse;
-  /* 3 */
-}
-
-/*
-1. Change the font styles in all browsers.
-2. Remove the margin in Firefox and Safari.
-3. Remove default padding in all browsers.
-*/
-
-button,
-input,
-optgroup,
-select,
-textarea {
-  font-family: inherit;
-  /* 1 */
-  font-feature-settings: inherit;
-  /* 1 */
-  font-variation-settings: inherit;
-  /* 1 */
-  font-size: 100%;
-  /* 1 */
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-.\[\&\>svg\]\:text-destructive>svg {
-  color: hsl(var(--destructive));
-}
-
-.\[\&\>svg\]\:text-foreground>svg {
-  color: hsl(var(--foreground));
-}
-
-.\[\&\>svg\~\*\]\:pl-7>svg~* {
-  padding-left: 1.75rem;
-}
-
-.\[\&\>tr\]\:last\:border-b-0:last-child>tr {
-  border-bottom-width: 0px;
-}
-
-.\[\&_p\]\:leading-relaxed p {
-  line-height: 1.625;
-}
-
-.\[\&_tr\:last-child\]\:border-0 tr:last-child {
-  border-width: 0px;
-}
-
-.\[\&_tr\]\:border-b tr {
-  border-bottom-width: 1px;
-}
-

src/requirements.txt

@@ -1,540 +0,0 @@
-# This file was autogenerated by uv via the following command:
-#    uv pip compile pyproject.toml -o src/requirements.txt
-accelerate==0.34.2
-    # via peft
-aiohappyeyeballs==2.4.3
-    # via aiohttp
-aiohttp==3.10.9
-    # via
-    #   datasets
-    #   fsspec
-    #   pyvespa
-aiosignal==1.3.1
-    # via aiohttp
-annotated-types==0.7.0
-    # via pydantic
-anyio==4.6.0
-    # via
-    #   httpx
-    #   starlette
-    #   watchfiles
-async-timeout==4.0.3
-    # via aiohttp
-attrs==24.2.0
-    # via aiohttp
-beautifulsoup4==4.12.3
-    # via python-fasthtml
-blis==0.7.11
-    # via thinc
-cachetools==5.5.0
-    # via google-auth
-catalogue==2.0.10
-    # via
-    #   spacy
-    #   srsly
-    #   thinc
-certifi==2024.8.30
-    # via
-    #   httpcore
-    #   httpx
-    #   requests
-cffi==1.17.1
-    # via cryptography
-charset-normalizer==3.3.2
-    # via requests
-click==8.1.7
-    # via
-    #   typer
-    #   uvicorn
-cloudpathlib==0.20.0
-    # via weasel
-colpali-engine==0.3.1
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   vidore-benchmark
-confection==0.1.5
-    # via
-    #   thinc
-    #   weasel
-contourpy==1.3.0
-    # via matplotlib
-cryptography==43.0.1
-    # via pyvespa
-cycler==0.12.1
-    # via matplotlib
-cymem==2.0.8
-    # via
-    #   preshed
-    #   spacy
-    #   thinc
-datasets==2.21.0
-    # via
-    #   mteb
-    #   vidore-benchmark
-dill==0.3.8
-    # via
-    #   datasets
-    #   multiprocess
-docker==7.1.0
-    # via pyvespa
-einops==0.8.0
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   vidore-benchmark
-eval-type-backport==0.2.0
-    # via mteb
-exceptiongroup==1.2.2
-    # via anyio
-fastcore==1.7.11
-    # via
-    #   fastlite
-    #   python-fasthtml
-    #   pyvespa
-    #   sqlite-minutils
-fastlite==0.0.11
-    # via python-fasthtml
-filelock==3.16.1
-    # via
-    #   datasets
-    #   huggingface-hub
-    #   torch
-    #   transformers
-fonttools==4.54.1
-    # via matplotlib
-frozenlist==1.4.1
-    # via
-    #   aiohttp
-    #   aiosignal
-fsspec==2024.6.1
-    # via
-    #   datasets
-    #   huggingface-hub
-    #   torch
-google-ai-generativelanguage==0.6.10
-    # via google-generativeai
-google-api-core==2.21.0
-    # via
-    #   google-ai-generativelanguage
-    #   google-api-python-client
-    #   google-generativeai
-google-api-python-client==2.149.0
-    # via google-generativeai
-google-auth==2.35.0
-    # via
-    #   google-ai-generativelanguage
-    #   google-api-core
-    #   google-api-python-client
-    #   google-auth-httplib2
-    #   google-generativeai
-google-auth-httplib2==0.2.0
-    # via google-api-python-client
-google-generativeai==0.8.3
-    # via visual-retrieval-colpali (pyproject.toml)
-googleapis-common-protos==1.65.0
-    # via
-    #   google-api-core
-    #   grpcio-status
-gputil==1.4.0
-    # via
-    #   colpali-engine
-    #   vidore-benchmark
-grpcio==1.67.0
-    # via
-    #   google-api-core
-    #   grpcio-status
-grpcio-status==1.67.0
-    # via google-api-core
-h11==0.14.0
-    # via
-    #   httpcore
-    #   uvicorn
-h2==4.1.0
-    # via httpx
-hpack==4.0.0
-    # via h2
-httpcore==1.0.6
-    # via httpx
-httplib2==0.22.0
-    # via
-    #   google-api-python-client
-    #   google-auth-httplib2
-httptools==0.6.1
-    # via uvicorn
-httpx==0.27.2
-    # via
-    #   python-fasthtml
-    #   pyvespa
-huggingface-hub==0.25.1
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   accelerate
-    #   datasets
-    #   peft
-    #   sentence-transformers
-    #   tokenizers
-    #   transformers
-hyperframe==6.0.1
-    # via h2
-idna==3.10
-    # via
-    #   anyio
-    #   httpx
-    #   requests
-    #   yarl
-itsdangerous==2.2.0
-    # via python-fasthtml
-jinja2==3.1.4
-    # via
-    #   pyvespa
-    #   spacy
-    #   torch
-joblib==1.4.2
-    # via scikit-learn
-kiwisolver==1.4.7
-    # via matplotlib
-langcodes==3.4.1
-    # via spacy
-language-data==1.2.0
-    # via langcodes
-loguru==0.7.2
-    # via vidore-benchmark
-lucide-fasthtml==0.0.9
-    # via shad4fast
-lxml==5.3.0
-    # via
-    #   lucide-fasthtml
-    #   pyvespa
-marisa-trie==1.2.1
-    # via language-data
-markdown-it-py==3.0.0
-    # via rich
-markupsafe==2.1.5
-    # via jinja2
-matplotlib==3.9.2
-    # via
-    #   seaborn
-    #   vidore-benchmark
-mdurl==0.1.2
-    # via markdown-it-py
-mpmath==1.3.0
-    # via sympy
-mteb==1.15.3
-    # via vidore-benchmark
-multidict==6.1.0
-    # via
-    #   aiohttp
-    #   yarl
-multiprocess==0.70.16
-    # via datasets
-murmurhash==1.0.10
-    # via
-    #   preshed
-    #   spacy
-    #   thinc
-networkx==3.3
-    # via torch
-numpy==1.26.4
-    # via
-    #   accelerate
-    #   blis
-    #   colpali-engine
-    #   contourpy
-    #   datasets
-    #   matplotlib
-    #   mteb
-    #   pandas
-    #   peft
-    #   pyarrow
-    #   scikit-learn
-    #   scipy
-    #   seaborn
-    #   spacy
-    #   thinc
-    #   transformers
-    #   vidore-benchmark
-oauthlib==3.2.2
-    # via python-fasthtml
-packaging==24.1
-    # via
-    #   accelerate
-    #   datasets
-    #   fastcore
-    #   huggingface-hub
-    #   matplotlib
-    #   peft
-    #   spacy
-    #   thinc
-    #   transformers
-    #   weasel
-pandas==2.2.3
-    # via
-    #   datasets
-    #   seaborn
-pdf2image==1.17.0
-    # via vidore-benchmark
-peft==0.11.1
-    # via
-    #   colpali-engine
-    #   vidore-benchmark
-pillow==10.4.0
-    # via
-    #   colpali-engine
-    #   matplotlib
-    #   pdf2image
-    #   sentence-transformers
-    #   vidore-benchmark
-pip==24.3.1
-    # via visual-retrieval-colpali (pyproject.toml)
-polars==1.9.0
-    # via mteb
-preshed==3.0.9
-    # via
-    #   spacy
-    #   thinc
-proto-plus==1.24.0
-    # via
-    #   google-ai-generativelanguage
-    #   google-api-core
-protobuf==5.28.3
-    # via
-    #   google-ai-generativelanguage
-    #   google-api-core
-    #   google-generativeai
-    #   googleapis-common-protos
-    #   grpcio-status
-    #   proto-plus
-psutil==6.0.0
-    # via
-    #   accelerate
-    #   peft
-pyarrow==17.0.0
-    # via datasets
-pyasn1==0.6.1
-    # via
-    #   pyasn1-modules
-    #   rsa
-pyasn1-modules==0.4.1
-    # via google-auth
-pycparser==2.22
-    # via cffi
-pydantic==2.9.2
-    # via
-    #   confection
-    #   google-generativeai
-    #   mteb
-    #   spacy
-    #   thinc
-    #   weasel
-pydantic-core==2.23.4
-    # via pydantic
-pygments==2.18.0
-    # via rich
-pyparsing==3.1.4
-    # via
-    #   httplib2
-    #   matplotlib
-pypdf==5.0.1
-    # via visual-retrieval-colpali (pyproject.toml)
-python-dateutil==2.9.0.post0
-    # via
-    #   matplotlib
-    #   pandas
-    #   python-fasthtml
-    #   pyvespa
-python-dotenv==1.0.1
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   uvicorn
-    #   vidore-benchmark
-python-fasthtml==0.6.9
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   lucide-fasthtml
-    #   shad4fast
-python-multipart==0.0.12
-    # via python-fasthtml
-pytrec-eval-terrier==0.5.6
-    # via mteb
-pytz==2024.2
-    # via pandas
-pyvespa==0.50.0
-    # via visual-retrieval-colpali (pyproject.toml)
-pyyaml==6.0.2
-    # via
-    #   accelerate
-    #   datasets
-    #   huggingface-hub
-    #   peft
-    #   transformers
-    #   uvicorn
-regex==2024.9.11
-    # via transformers
-requests==2.32.3
-    # via
-    #   colpali-engine
-    #   datasets
-    #   docker
-    #   google-api-core
-    #   huggingface-hub
-    #   lucide-fasthtml
-    #   mteb
-    #   pyvespa
-    #   requests-toolbelt
-    #   spacy
-    #   transformers
-    #   weasel
-requests-toolbelt==1.0.0
-    # via pyvespa
-rich==13.9.2
-    # via
-    #   mteb
-    #   typer
-rsa==4.9
-    # via google-auth
-safetensors==0.4.5
-    # via
-    #   accelerate
-    #   peft
-    #   transformers
-scikit-learn==1.5.2
-    # via
-    #   mteb
-    #   sentence-transformers
-scipy==1.14.1
-    # via
-    #   mteb
-    #   scikit-learn
-    #   sentence-transformers
-seaborn==0.13.2
-    # via vidore-benchmark
-sentence-transformers==3.1.1
-    # via
-    #   mteb
-    #   vidore-benchmark
-sentencepiece==0.2.0
-    # via vidore-benchmark
-setuptools==75.1.0
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   marisa-trie
-    #   spacy
-    #   thinc
-shad4fast==1.2.1
-    # via visual-retrieval-colpali (pyproject.toml)
-shellingham==1.5.4
-    # via typer
-six==1.16.0
-    # via python-dateutil
-smart-open==7.0.5
-    # via weasel
-sniffio==1.3.1
-    # via
-    #   anyio
-    #   httpx
-soupsieve==2.6
-    # via beautifulsoup4
-spacy==3.7.5
-    # via visual-retrieval-colpali (pyproject.toml)
-spacy-legacy==3.0.12
-    # via spacy
-spacy-loggers==1.0.5
-    # via spacy
-sqlite-minutils==3.37.0.post3
-    # via fastlite
-srsly==2.4.8
-    # via
-    #   confection
-    #   spacy
-    #   thinc
-    #   weasel
-starlette==0.39.2
-    # via python-fasthtml
-sympy==1.13.3
-    # via torch
-tenacity==9.0.0
-    # via pyvespa
-thinc==8.2.5
-    # via spacy
-threadpoolctl==3.5.0
-    # via scikit-learn
-tokenizers==0.20.0
-    # via transformers
-torch==2.4.1
-    # via
-    #   visual-retrieval-colpali (pyproject.toml)
-    #   accelerate
-    #   colpali-engine
-    #   mteb
-    #   peft
-    #   sentence-transformers
-    #   vidore-benchmark
-tqdm==4.66.5
-    # via
-    #   datasets
-    #   google-generativeai
-    #   huggingface-hub
-    #   mteb
-    #   peft
-    #   sentence-transformers
-    #   spacy
-    #   transformers
-transformers==4.45.1
-    # via
-    #   colpali-engine
-    #   peft
-    #   sentence-transformers
-    #   vidore-benchmark
-typer==0.12.5
-    # via
-    #   spacy
-    #   vidore-benchmark
-    #   weasel
-typing-extensions==4.12.2
-    # via
-    #   anyio
-    #   cloudpathlib
-    #   google-generativeai
-    #   huggingface-hub
-    #   mteb
-    #   multidict
-    #   pydantic
-    #   pydantic-core
-    #   pypdf
-    #   pyvespa
-    #   rich
-    #   torch
-    #   typer
-    #   uvicorn
-tzdata==2024.2
-    # via pandas
-uritemplate==4.1.1
-    # via google-api-python-client
-urllib3==2.2.3
-    # via
-    #   docker
-    #   requests
-uvicorn==0.31.0
-    # via python-fasthtml
-uvloop==0.20.0
-    # via uvicorn
-vespacli==8.391.23
-    # via visual-retrieval-colpali (pyproject.toml)
-vidore-benchmark==4.0.0
-    # via visual-retrieval-colpali (pyproject.toml)
-wasabi==1.1.3
-    # via
-    #   spacy
-    #   thinc
-    #   weasel
-watchfiles==0.24.0
-    # via uvicorn
-weasel==0.4.1
-    # via spacy
-websockets==13.1
-    # via uvicorn
-wrapt==1.16.0
-    # via smart-open
-xxhash==3.5.0
-    # via datasets
-yarl==1.13.1
-    # via aiohttp

src/static/js/highlightjs-theme.js

@@ -1,23 +0,0 @@
-(function () {
-    function getPreferredTheme() {
-        if (localStorage.theme === 'dark' || (!('theme' in localStorage) && window.matchMedia('(prefers-color-scheme: dark)').matches)) {
-            return 'dark';
-        }
-        return 'light';
-    }
-
-    function syncHighlightTheme() {
-        const link = document.getElementById('highlight-theme');
-        const preferredTheme = getPreferredTheme();
-        link.href = preferredTheme === 'dark' ?
-            'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.4.0/styles/github-dark.min.css' :
-            'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.4.0/styles/github.min.css';
-    }
-
-    // Apply the correct theme immediately
-    syncHighlightTheme();
-
-    // Observe changes in the 'dark' class on the <html> element
-    const observer = new MutationObserver(syncHighlightTheme);
-    observer.observe(document.documentElement, {attributes: true, attributeFilter: ['class']});
-})();

src/tailwind.config.js

@@ -1,243 +0,0 @@
-function filterDefault(values) {
-    return Object.fromEntries(
-        Object.entries(values).filter(([key]) => key !== "DEFAULT"),
-    );
-}
-
-/** @type {import('tailwindcss').Config} */
-export default {
-    darkMode: ["selector"],
-    content: [
-        "./**/*.py",
-        "./.venv/lib/python3.12/site-packages/shad4fast/**/*.{py,js}",
-    ],
-    theme: {
-        container: {
-            center: true,
-            padding: "2rem",
-            screens: {
-                "2xl": "1400px",
-            },
-        },
-        extend: {
-            animation: {
-                "accordion-down": "accordion-down 0.2s ease-out",
-                "accordion-up": "accordion-up 0.2s ease-out",
-            },
-            animationDelay: ({theme}) => ({
-                ...theme("transitionDelay"),
-            }),
-            animationDuration: ({theme}) => ({
-                0: "0ms",
-                ...theme("transitionDuration"),
-            }),
-            animationTimingFunction: ({theme}) => ({
-                ...theme("transitionTimingFunction"),
-            }),
-            animationFillMode: {
-                none: "none",
-                forwards: "forwards",
-                backwards: "backwards",
-                both: "both",
-            },
-            animationDirection: {
-                normal: "normal",
-                reverse: "reverse",
-                alternate: "alternate",
-                "alternate-reverse": "alternate-reverse",
-            },
-            animationOpacity: ({theme}) => ({
-                DEFAULT: 0,
-                ...theme("opacity"),
-            }),
-            animationTranslate: ({theme}) => ({
-                DEFAULT: "100%",
-                ...theme("translate"),
-            }),
-            animationScale: ({theme}) => ({
-                DEFAULT: 0,
-                ...theme("scale"),
-            }),
-            animationRotate: ({theme}) => ({
-                DEFAULT: "30deg",
-                ...theme("rotate"),
-            }),
-            animationRepeat: {
-                0: "0",
-                1: "1",
-                infinite: "infinite",
-            },
-            keyframes: {
-                enter: {
-                    from: {
-                        opacity: "var(--tw-enter-opacity, 1)",
-                        transform:
-                            "translate3d(var(--tw-enter-translate-x, 0), var(--tw-enter-translate-y, 0), 0) scale3d(var(--tw-enter-scale, 1), var(--tw-enter-scale, 1), var(--tw-enter-scale, 1)) rotate(var(--tw-enter-rotate, 0))",
-                    },
-                },
-                exit: {
-                    to: {
-                        opacity: "var(--tw-exit-opacity, 1)",
-                        transform:
-                            "translate3d(var(--tw-exit-translate-x, 0), var(--tw-exit-translate-y, 0), 0) scale3d(var(--tw-exit-scale, 1), var(--tw-exit-scale, 1), var(--tw-exit-scale, 1)) rotate(var(--tw-exit-rotate, 0))",
-                    },
-                },
-            },
-            colors: {
-                border: "hsl(var(--border))",
-                input: "hsl(var(--input))",
-                ring: "hsl(var(--ring))",
-                background: "hsl(var(--background))",
-                foreground: "hsl(var(--foreground))",
-                primary: {
-                    DEFAULT: "hsl(var(--primary))",
-                    foreground: "hsl(var(--primary-foreground))",
-                },
-                secondary: {
-                    DEFAULT: "hsl(var(--secondary))",
-                    foreground: "hsl(var(--secondary-foreground))",
-                },
-                destructive: {
-                    DEFAULT: "hsl(var(--destructive))",
-                    foreground: "hsl(var(--destructive-foreground))",
-                },
-                muted: {
-                    DEFAULT: "hsl(var(--muted))",
-                    foreground: "hsl(var(--muted-foreground))",
-                },
-                accent: {
-                    DEFAULT: "hsl(var(--accent))",
-                    foreground: "hsl(var(--accent-foreground))",
-                },
-                popover: {
-                    DEFAULT: "hsl(var(--popover))",
-                    foreground: "hsl(var(--popover-foreground))",
-                },
-                card: {
-                    DEFAULT: "hsl(var(--card))",
-                    foreground: "hsl(var(--card-foreground))",
-                },
-            },
-            borderRadius: {
-                lg: `var(--radius)`,
-                md: `calc(var(--radius) - 2px)`,
-                sm: "calc(var(--radius) - 4px)",
-            },
-        },
-    },
-    plugins: [
-        function ({addUtilities, matchUtilities, theme}) {
-            addUtilities({
-                "@keyframes enter": theme("keyframes.enter"),
-                "@keyframes exit": theme("keyframes.exit"),
-                ".animate-in": {
-                    animationName: "enter",
-                    animationDuration: theme("animationDuration.DEFAULT"),
-                    "--tw-enter-opacity": "initial",
-                    "--tw-enter-scale": "initial",
-                    "--tw-enter-rotate": "initial",
-                    "--tw-enter-translate-x": "initial",
-                    "--tw-enter-translate-y": "initial",
-                },
-                ".animate-out": {
-                    animationName: "exit",
-                    animationDuration: theme("animationDuration.DEFAULT"),
-                    "--tw-exit-opacity": "initial",
-                    "--tw-exit-scale": "initial",
-                    "--tw-exit-rotate": "initial",
-                    "--tw-exit-translate-x": "initial",
-                    "--tw-exit-translate-y": "initial",
-                },
-            });
-
-            matchUtilities(
-                {
-                    "fade-in": (value) => ({"--tw-enter-opacity": value}),
-                    "fade-out": (value) => ({"--tw-exit-opacity": value}),
-                },
-                {values: theme("animationOpacity")},
-            );
-
-            matchUtilities(
-                {
-                    "zoom-in": (value) => ({"--tw-enter-scale": value}),
-                    "zoom-out": (value) => ({"--tw-exit-scale": value}),
-                },
-                {values: theme("animationScale")},
-            );
-
-            matchUtilities(
-                {
-                    "spin-in": (value) => ({"--tw-enter-rotate": value}),
-                    "spin-out": (value) => ({"--tw-exit-rotate": value}),
-                },
-                {values: theme("animationRotate")},
-            );
-
-            matchUtilities(
-                {
-                    "slide-in-from-top": (value) => ({
-                        "--tw-enter-translate-y": `-${value}`,
-                    }),
-                    "slide-in-from-bottom": (value) => ({
-                        "--tw-enter-translate-y": value,
-                    }),
-                    "slide-in-from-left": (value) => ({
-                        "--tw-enter-translate-x": `-${value}`,
-                    }),
-                    "slide-in-from-right": (value) => ({
-                        "--tw-enter-translate-x": value,
-                    }),
-                    "slide-out-to-top": (value) => ({
-                        "--tw-exit-translate-y": `-${value}`,
-                    }),
-                    "slide-out-to-bottom": (value) => ({
-                        "--tw-exit-translate-y": value,
-                    }),
-                    "slide-out-to-left": (value) => ({
-                        "--tw-exit-translate-x": `-${value}`,
-                    }),
-                    "slide-out-to-right": (value) => ({
-                        "--tw-exit-translate-x": value,
-                    }),
-                },
-                {values: theme("animationTranslate")},
-            );
-
-            matchUtilities(
-                {duration: (value) => ({animationDuration: value})},
-                {values: filterDefault(theme("animationDuration"))},
-            );
-
-            matchUtilities(
-                {delay: (value) => ({animationDelay: value})},
-                {values: theme("animationDelay")},
-            );
-
-            matchUtilities(
-                {ease: (value) => ({animationTimingFunction: value})},
-                {values: filterDefault(theme("animationTimingFunction"))},
-            );
-
-            addUtilities({
-                ".running": {animationPlayState: "running"},
-                ".paused": {animationPlayState: "paused"},
-            });
-
-            matchUtilities(
-                {"fill-mode": (value) => ({animationFillMode: value})},
-                {values: theme("animationFillMode")},
-            );
-
-            matchUtilities(
-                {direction: (value) => ({animationDirection: value})},
-                {values: theme("animationDirection")},
-            );
-
-            matchUtilities(
-                {repeat: (value) => ({animationIterationCount: value})},
-                {values: theme("animationRepeat")},
-            );
-        },
-    ],
-};

src/tailwindcss

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:327703a4646081906e11d116ff4e8e43076466c3d269282bbe612555b9fe0c58
-size 47351504