Add app

Dockerfile

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+FROM python:3.11
+
+# Set up a new user named "user" with user ID 1000 for permission
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Upgreade pip
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY --chown=user requirements.txt requirements.txt
+
+# Install requirements
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user app.py app.py
+
+COPY --chown=user data/ data/
+
+ENTRYPOINT ["solara", "run", "app.py", "--host=0.0.0.0", "--port", "7860"]

app.py

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+import sqlite3
+import sqlite_vec
+
+from datasets import load_dataset
+
+dataset = load_dataset("m3hrdadfi/recipe_nlg_lite")
+recipe_names = dataset["train"]["name"]
+
+from sentence_transformers import SentenceTransformer
+
+tfm_base = SentenceTransformer("all-MiniLM-L6-v2")
+X_tfm = tfm_base.encode(recipe_names)
+n_feats = X_tfm.shape[1]
+
+import polars as pl
+import solara
+
+@solara.component
+def Display_Full(query,db,limit):
+    with db:
+        rows_orig = db.execute(
+            f"""
+              SELECT
+                rowid,
+                distance
+              FROM vec_sents
+              WHERE embedding MATCH ?
+              ORDER BY distance
+              LIMIT {limit}
+            """,
+            [sqlite_vec.serialize_float32(query)],
+        ).fetchall()
+    df1 = pl.DataFrame({"results": [recipe_names[rowid] for rowid in [dict(row)["rowid"] for row in rows_orig]]})
+    with solara.Column():
+        solara.Markdown("## Full precision")
+        solara.DataFrame(df1, items_per_page=10)
+
+@solara.component
+def Display_Binary(query,db,limit):
+    with db:
+        rows_bin = db.execute(
+            f"""
+              SELECT
+                rowid,
+                distance
+              FROM bin_vec_sents
+              WHERE embedding MATCH vec_quantize_binary(?)
+              ORDER BY distance
+              LIMIT {limit}
+            """,
+            [sqlite_vec.serialize_float32(query)],
+        ).fetchall()
+    df2 = pl.DataFrame({"results": [recipe_names[rowid] for rowid in [dict(row)["rowid"] for row in rows_bin]]})
+    with solara.Column():
+        solara.Markdown("## Binary quantization")
+        solara.DataFrame(df2, items_per_page=10)
+
+
+@solara.component
+def Page():
+    with solara.Column(margin=10):
+        with solara.Head():
+            solara.Title("Recipe finder")
+        solara.Markdown("# Recipe finder")
+        solara.Markdown("I built this tool to help me get a feeling of binary embedding quantization in [sqlite-vec](https://alexgarcia.xyz/sqlite-vec/). For any given text, it gives the top 10 results. The dataset I'm using is [m3hrdadfi/recipe_nlg_lite](https://hf.co/datasets/m3hrdadfi/recipe_nlg_lite) which consists of 6,119 recipes. Inspired by [Exploring SQLite-vec](https://www.youtube.com/watch?v=wYU66AjRIAc) by [@fishnets88](https://twitter.com/fishnets88)")
+        q = solara.use_reactive("I would like to have some vegetable soup")
+        solara.InputText("Enter a query", value=q, continuous_update=True)
+        query = tfm_base.encode([q.value])[0]
+        limit = 10
+        db = sqlite3.connect(":memory:")
+        db.enable_load_extension(True)
+        sqlite_vec.load(db)
+        db.enable_load_extension(False)
+        db.row_factory = sqlite3.Row
+
+        db.execute(f"create virtual table vec_sents using vec0(embedding float[{n_feats}])")
+        
+        with db:
+            for i, item in enumerate([{"vector": x} for i, x in enumerate(X_tfm)]):
+                db.execute(
+                    "INSERT INTO vec_sents(rowid, embedding) VALUES (?, ?)",
+                    [i, sqlite_vec.serialize_float32(item["vector"])],
+                )
+        
+        db.execute(f"create virtual table bin_vec_sents using vec0(embedding bit[{n_feats}])")
+        
+        with db:
+            for i, item in enumerate([{"vector": x} for i, x in enumerate(X_tfm)]):
+                db.execute(
+                        "INSERT INTO bin_vec_sents(rowid, embedding) VALUES (?, vec_quantize_binary(?))",
+                        [i, sqlite_vec.serialize_float32(item["vector"])],
+                    )
+    
+    
+    
+        with solara.Row():
+            Display_Full(query,db,limit)
+            Display_Binary(query,db,limit)
+

requirements.txt

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+solara==1.40.0
+sqlite-vec==0.1.3
+datasets==3.0.1
+sentence-transformers==3.2.0
+polars==1.9.0