Add semantic search

app.py

@@ -4,6 +4,7 @@ import gradio as gr
 import polars as pl
 
 from app_pr import demo as demo_pr
+from semantic_search import semantic_search
 from table import df_orig
 
 DESCRIPTION = "# ICLR 2025"
@@ -51,6 +52,7 @@ df_main = df_orig.select(
     "Models",
     "Datasets",
     "claimed",
+    "paper_id",
 )
 
 df_main = df_main.rename(
@@ -105,7 +107,10 @@ def update_num_papers(df: pl.DataFrame) -> str:
 
 
 def update_df(
-    title_search_query: str,
+    search_mode: str,
+    search_query: str,
+    candidate_pool_size: int,
+    score_threshold: float,
     presentation_type: str,
     column_names: list[str],
     case_insensitive: bool = True,
@@ -113,13 +118,18 @@ def update_df(
     df = df_main.clone()
     column_names = ["Title", *column_names]
 
-    if title_search_query:
-        if case_insensitive:
-            title_search_query = f"(?i){title_search_query}"
-        try:
-            df = df.filter(pl.col("Title").str.contains(title_search_query))
-        except pl.exceptions.ComputeError as e:
-            raise gr.Error(str(e)) from e
+    if search_query:
+        if search_mode == "Title Search":
+            if case_insensitive:
+                search_query = f"(?i){search_query}"
+            try:
+                df = df.filter(pl.col("Title").str.contains(search_query))
+            except pl.exceptions.ComputeError as e:
+                raise gr.Error(str(e)) from e
+        else:
+            paper_ids = semantic_search(search_query, candidate_pool_size, score_threshold)
+            df = df.filter(pl.col("paper_id").is_in(paper_ids))
+
     if presentation_type != "(ALL)":
         df = df.filter(pl.col("Type").str.contains(presentation_type))
 
@@ -132,22 +142,37 @@ def update_df(
     )
 
 
+def update_search_mode(search_mode: str) -> gr.Accordion:
+    return gr.Accordion(visible=search_mode == "Semantic Search")
+
+
 with gr.Blocks(css_paths="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Accordion(label="Tutorial", open=True):
         gr.Markdown(TUTORIAL)
     with gr.Group():
-        search_title = gr.Textbox(label="Search title")
-        presentation_type = gr.Radio(
-            label="Presentation Type",
-            choices=["(ALL)", "Oral", "Spotlight", "Poster"],
-            value="(ALL)",
-        )
-        column_names = gr.CheckboxGroup(
-            label="Columns",
-            choices=[col for col in COLUMN_INFO if col != "Title"],
-            value=[col for col in DEFAULT_COLUMNS if col != "Title"],
+        search_mode = gr.Radio(
+            label="Search Mode",
+            choices=["Semantic Search", "Title Search"],
+            value="Semantic Search",
+            show_label=False,
         )
+        search_query = gr.Textbox(label="Search", submit_btn=True, show_label=False, placeholder="Enter query here")
+        with gr.Accordion(label="Advanced Search Options", open=False, visible=True) as advanced_search_options:
+            with gr.Row():
+                candidate_pool_size = gr.Slider(label="Candidate Pool Size", minimum=1, maximum=200, step=1, value=100)
+                score_threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.01, value=0.7)
+
+    presentation_type = gr.Radio(
+        label="Presentation Type",
+        choices=["(ALL)", "Oral", "Spotlight", "Poster"],
+        value="(ALL)",
+    )
+    column_names = gr.CheckboxGroup(
+        label="Columns",
+        choices=[col for col in COLUMN_INFO if col != "Title"],
+        value=[col for col in DEFAULT_COLUMNS if col != "Title"],
+    )
 
     num_papers = gr.Textbox(label="Number of papers", value=update_num_papers(df_orig), interactive=False)
 
@@ -164,14 +189,23 @@ with gr.Blocks(css_paths="style.css") as demo:
         column_widths=[COLUMN_INFO[col][1] for col in COLUMN_INFO],
     )
 
+    search_mode.change(
+        fn=update_search_mode,
+        inputs=search_mode,
+        outputs=advanced_search_options,
+    )
+
     inputs = [
-        search_title,
+        search_mode,
+        search_query,
+        candidate_pool_size,
+        score_threshold,
         presentation_type,
         column_names,
     ]
     gr.on(
         triggers=[
-            search_title.submit,
+            search_query.submit,
             presentation_type.input,
             column_names.input,
         ],

pyproject.toml

@@ -11,6 +11,10 @@ dependencies = [
     "hf-transfer>=0.1.9",
     "polars>=1.27.1",
     "loguru>=0.7.3",
+    "torch==2.4.0",
+    "sentence-transformers>=4.1.0",
+    "spaces>=0.35.0",
+    "faiss-cpu>=1.10.0",
 ]
 
 [tool.ruff]

requirements.txt

@@ -46,6 +46,8 @@ dill==0.3.8
     #   multiprocess
 exceptiongroup==1.2.2
     # via anyio
+faiss-cpu==1.10.0
+    # via iclr2025 (pyproject.toml)
 fastapi==0.115.12
     # via gradio
 ffmpy==0.5.0
@@ -54,7 +56,10 @@ filelock==3.18.0
     # via
     #   datasets
     #   huggingface-hub
-frozenlist==1.5.0
+    #   torch
+    #   transformers
+    #   triton
+frozenlist==1.6.0
     # via
     #   aiohttp
     #   aiosignal
@@ -63,10 +68,12 @@ fsspec==2024.12.0
     #   datasets
     #   gradio-client
     #   huggingface-hub
+    #   torch
 gradio==5.25.2
     # via
     #   iclr2025 (pyproject.toml)
     #   gradio-modal
+    #   spaces
 gradio-client==1.8.0
     # via gradio
 gradio-modal==0.0.4
@@ -86,11 +93,15 @@ httpx==0.28.1
     #   gradio
     #   gradio-client
     #   safehttpx
+    #   spaces
 huggingface-hub==0.30.2
     # via
     #   datasets
     #   gradio
     #   gradio-client
+    #   sentence-transformers
+    #   tokenizers
+    #   transformers
 idna==3.10
     # via
     #   anyio
@@ -100,7 +111,11 @@ idna==3.10
 itsdangerous==2.2.0
     # via gradio
 jinja2==3.1.6
-    # via gradio
+    # via
+    #   gradio
+    #   torch
+joblib==1.4.2
+    # via scikit-learn
 loguru==0.7.3
     # via iclr2025 (pyproject.toml)
 markdown-it-py==3.0.0
@@ -111,37 +126,83 @@ markupsafe==3.0.2
     #   jinja2
 mdurl==0.1.2
     # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
 multidict==6.4.3
     # via
     #   aiohttp
     #   yarl
 multiprocess==0.70.16
     # via datasets
+networkx==3.4.2
+    # via torch
 numpy==2.2.4
     # via
     #   datasets
+    #   faiss-cpu
     #   gradio
     #   pandas
+    #   scikit-learn
+    #   scipy
+    #   transformers
+nvidia-cublas-cu12==12.1.3.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-nccl-cu12==2.20.5
+    # via torch
+nvidia-nvjitlink-cu12==12.8.93
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
 orjson==3.10.16
     # via gradio
 packaging==24.2
     # via
     #   datasets
+    #   faiss-cpu
     #   gradio
     #   gradio-client
     #   huggingface-hub
+    #   spaces
+    #   transformers
 pandas==2.2.3
     # via
     #   datasets
     #   gradio
 pillow==11.2.1
-    # via gradio
+    # via
+    #   gradio
+    #   sentence-transformers
 polars==1.27.1
     # via iclr2025 (pyproject.toml)
 propcache==0.3.1
     # via
     #   aiohttp
     #   yarl
+psutil==5.9.8
+    # via spaces
 pyarrow==19.0.1
     # via datasets
 pycparser==2.22
@@ -150,6 +211,7 @@ pydantic==2.11.3
     # via
     #   fastapi
     #   gradio
+    #   spaces
 pydantic-core==2.33.1
     # via pydantic
 pydub==0.25.1
@@ -167,34 +229,67 @@ pyyaml==6.0.2
     #   datasets
     #   gradio
     #   huggingface-hub
+    #   transformers
+regex==2024.11.6
+    # via transformers
 requests==2.32.3
     # via
     #   datasets
     #   huggingface-hub
+    #   spaces
+    #   transformers
 rich==14.0.0
     # via typer
-ruff==0.11.5
+ruff==0.11.6
     # via gradio
 safehttpx==0.1.6
     # via gradio
+safetensors==0.5.3
+    # via transformers
+scikit-learn==1.6.1
+    # via sentence-transformers
+scipy==1.15.2
+    # via
+    #   scikit-learn
+    #   sentence-transformers
 semantic-version==2.10.0
     # via gradio
+sentence-transformers==4.1.0
+    # via iclr2025 (pyproject.toml)
 shellingham==1.5.4
     # via typer
 six==1.17.0
     # via python-dateutil
 sniffio==1.3.1
     # via anyio
+spaces==0.35.0
+    # via iclr2025 (pyproject.toml)
 starlette==0.46.2
     # via
     #   fastapi
     #   gradio
+sympy==1.13.3
+    # via torch
+threadpoolctl==3.6.0
+    # via scikit-learn
+tokenizers==0.21.1
+    # via transformers
 tomlkit==0.13.2
     # via gradio
+torch==2.4.0
+    # via
+    #   iclr2025 (pyproject.toml)
+    #   sentence-transformers
 tqdm==4.67.1
     # via
     #   datasets
     #   huggingface-hub
+    #   sentence-transformers
+    #   transformers
+transformers==4.51.3
+    # via sentence-transformers
+triton==3.0.0
+    # via torch
 typer==0.15.2
     # via gradio
 typing-extensions==4.13.2
@@ -208,6 +303,9 @@ typing-extensions==4.13.2
     #   pydantic
     #   pydantic-core
     #   rich
+    #   sentence-transformers
+    #   spaces
+    #   torch
     #   typer
     #   typing-inspection
     #   uvicorn
@@ -223,5 +321,5 @@ websockets==15.0.1
     # via gradio-client
 xxhash==3.5.0
     # via datasets
-yarl==1.19.0
+yarl==1.20.0
     # via aiohttp

semantic_search.py

@@ -0,0 +1,34 @@
+import datasets
+import numpy as np
+import scipy.spatial
+import scipy.special
+import spaces
+from sentence_transformers import CrossEncoder, SentenceTransformer
+
+from table import BASE_REPO_ID
+
+ds = datasets.load_dataset(BASE_REPO_ID, split="train")
+ds = ds.rename_column("submission_number", "paper_id")
+ds.add_faiss_index(column="embedding")
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
+
+
+@spaces.GPU(duration=5)
+def semantic_search(query: str, candidate_pool_size: int = 100, score_threshold: float = 0.7) -> list[int]:
+    query_vec = model.encode(query)
+    _, retrieved_data = ds.get_nearest_examples("embedding", query_vec, k=candidate_pool_size)
+
+    rerank_inputs = [
+        [query, f"{title}\n{abstract}"]
+        for title, abstract in zip(retrieved_data["title"], retrieved_data["abstract"], strict=True)
+    ]
+    rerank_scores = reranker.predict(rerank_inputs)
+    sorted_indices = np.argsort(rerank_scores)[::-1]
+
+    return [
+        retrieved_data["paper_id"][i]
+        for i in sorted_indices
+        if scipy.special.expit(rerank_scores[i]) >= score_threshold
+    ]

uv.lock

@@ -450,6 +450,38 @@ wheels = [
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 ]
 
+[[package]]
+name = "faiss-cpu"
+version = "1.10.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+]
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+]
+
 [[package]]
 name = "fastapi"
 version = "0.115.12"
@@ -742,21 +774,29 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "datasets" },
+    { name = "faiss-cpu" },
     { name = "gradio", extra = ["oauth"] },
     { name = "gradio-modal" },
     { name = "hf-transfer" },
     { name = "loguru" },
     { name = "polars" },
+    { name = "sentence-transformers" },
+    { name = "spaces" },
+    { name = "torch" },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "datasets", specifier = ">=3.5.0" },
+    { name = "faiss-cpu", specifier = ">=1.10.0" },
     { name = "gradio", extras = ["oauth"], specifier = ">=5.25.2" },
     { name = "gradio-modal", specifier = ">=0.0.4" },
     { name = "hf-transfer", specifier = ">=0.1.9" },
     { name = "loguru", specifier = ">=0.7.3" },
     { name = "polars", specifier = ">=1.27.1" },
+    { name = "sentence-transformers", specifier = ">=4.1.0" },
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