mirror of
https://github.com/asg017/sqlite-vec.git
synced 2026-04-25 00:36:56 +02:00
examples + fmt
This commit is contained in:
Alex Garcia
parent
356f75cca7
commit
dd972f6ffe
16 changed files with 1064 additions and 25 deletions
9
examples/nbc-headlines/.gitignore
vendored
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9
examples/nbc-headlines/.gitignore
vendored
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@ -0,0 +1,9 @@
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.venv/
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node_modules/
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*.gguf
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*.dylib
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llama.log
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llamafile-0.8.11
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2024-07-26.json
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articles.json
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8
examples/nbc-headlines/Makefile
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8
examples/nbc-headlines/Makefile
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@ -0,0 +1,8 @@
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deps:
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curl -L https://github.com/asg017/sqlite-rembed/releases/download/v0.0.1-alpha.9/install.sh | sh
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curl -L https://github.com/asg017/sqlite-vec/releases/download/v0.0.1-alpha.37/install.sh | sh
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wget https://github.com/Mozilla-Ocho/llamafile/releases/download/0.8.11/llamafile-0.8.11
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wget https://huggingface.co/leliuga/all-MiniLM-L6-v2-GGUF/resolve/main/all-MiniLM-L6-v2.F16.gguf
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.PHONY: deps
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1
examples/nbc-headlines/README.md
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1
examples/nbc-headlines/README.md
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@ -0,0 +1 @@
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https://www.nbcnews.com/archive/articles/last-seven-days
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31
examples/nbc-headlines/build.sql
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31
examples/nbc-headlines/build.sql
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@ -0,0 +1,31 @@
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.mode box
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.header on
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.bail on
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.load ./vec0
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.load ./rembed0
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insert into rembed_clients(name, options)
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values ('llamafile', 'llamafile');
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create table articles as
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select
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value ->> 'url' as url,
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value ->> 'headline' as headline,
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rembed('llamafile', value ->> 'headline') as headline_embedding
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from json_each(
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readfile('2024-07-26.json')
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);
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select writefile(
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'articles.json',
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json_group_array(
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json_object(
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'id', rowid,
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'url', url,
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'headline', headline,
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'headline_embedding', vec_to_json(headline_embedding)
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)
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)
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)
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from articles;
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34
examples/nbc-headlines/demo.mjs
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34
examples/nbc-headlines/demo.mjs
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@ -0,0 +1,34 @@
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import Database from "better-sqlite3";
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import * as sqliteVec from "sqlite-vec";
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import { pipeline } from "@xenova/transformers";
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const db = new Database("articles.db");
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sqliteVec.load(db);
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const extractor = await pipeline(
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"feature-extraction",
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"Xenova/all-MiniLM-L6-v2"
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);
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const query = "sports";
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const queryEmbedding = await extractor([query], {
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pooling: "mean",
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normalize: true,
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});
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const rows = db
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.prepare(
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`
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select
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article_id,
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headline,
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distance
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from vec_articles
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left join articles on articles.id = vec_articles.article_id
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where headline_embedding match ?
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and k = 8;
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`
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)
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.all(queryEmbedding.data);
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console.log(rows);
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31
examples/nbc-headlines/demo.py
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31
examples/nbc-headlines/demo.py
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import sqlite3
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import sqlite_vec
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from sentence_transformers import SentenceTransformer
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db = sqlite3.connect("articles.db")
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db.enable_load_extension(True)
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sqlite_vec.load(db)
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db.enable_load_extension(False)
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model = SentenceTransformer("all-MiniLM-L6-v2")
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query = "sports"
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query_embedding = model.encode(query)
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results = db.execute(
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"""
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select
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article_id,
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headline,
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distance
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from vec_articles
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left join articles on articles.id = vec_articles.article_id
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where headline_embedding match ?
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and k = 8;
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""",
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[query_embedding]
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).fetchall()
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for (article_id, headline, distance) in results:
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print(article_id, headline, distance)
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54
examples/nbc-headlines/demo.sql
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54
examples/nbc-headlines/demo.sql
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@ -0,0 +1,54 @@
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.load vec0
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.header on
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.bail on
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.timer on
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create temp table raw_articles as
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select
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value ->> 'id' as id,
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value ->> 'url' as url,
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value ->> 'headline' as headline,
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value ->> 'headline_embedding' as headline_embedding
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from json_each(
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readfile('articles.json')
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);
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create table articles(
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id integer primary key,
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headline text,
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url text
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);
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insert into articles(id, headline, url)
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select id, headline, url from temp.raw_articles;
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select * from articles limit 5;
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create virtual table vec_articles using vec0(
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article_id integer primary key,
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headline_embedding float[384]
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);
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insert into vec_articles(article_id, headline_embedding)
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select id, headline_embedding from temp.raw_articles;
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select * from articles limit 5;
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select article_id, vec_to_json(headline_embedding) from articles limit 5;
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.param set :query 'sports'
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.load ./rembed0
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insert into rembed_clients values ('all-MiniLM-L6-v2', 'llamafile');
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.mode qbox -ww
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select
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article_id,
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--headline,
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distance
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from vec_articles
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--left join articles on articles.id = vec_articles.article_id
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where headline_embedding match rembed('all-MiniLM-L6-v2', :query)
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and k = 10;
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54
examples/nbc-headlines/index.html
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54
examples/nbc-headlines/index.html
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<html>
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<body>
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<h1>sqlite-vec articles.db demo</h1>
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<script type="module">
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import { default as init } from "https://cdn.jsdelivr.net/npm/sqlite-vec-wasm-demo@latest/sqlite3.mjs";
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import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers";
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const sqlite3 = await init();
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const extractor = await pipeline(
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"feature-extraction",
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"Xenova/all-MiniLM-L6-v2",
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);
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const dbContents = await fetch("articles.db").then((r) => r.arrayBuffer());
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const db = new sqlite3.oo1.DB();
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const p = sqlite3.wasm.allocFromTypedArray(dbContents);
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const rc = sqlite3.capi.sqlite3_deserialize(
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db.pointer,
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"main",
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p,
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dbContents.byteLength,
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dbContents.byteLength,
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sqlite3.capi.SQLITE_DESERIALIZE_FREEONCLOSE,
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);
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db.checkRc(rc);
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const query = "sports";
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const queryEmbedding = await extractor([query], {
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pooling: "mean",
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normalize: true,
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});
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const rows = db
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.selectObjects(
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`
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select
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article_id,
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headline,
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distance
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from vec_articles
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left join articles on articles.id = vec_articles.article_id
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where headline_embedding match ?
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and k = 8;
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`,
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queryEmbedding.data.buffer,
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);
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const output = document.body.appendChild(document.createElement('pre'));
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output.innerText = JSON.stringify(rows, null, 2);
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</script>
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</body>
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</html>
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801
examples/nbc-headlines/package-lock.json
generated
Normal file
801
examples/nbc-headlines/package-lock.json
generated
Normal file
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@ -0,0 +1,801 @@
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{
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"name": "nbc-headlines",
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"version": "1.0.0",
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"lockfileVersion": 3,
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"requires": true,
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"packages": {
|
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"": {
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"name": "nbc-headlines",
|
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"version": "1.0.0",
|
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"license": "ISC",
|
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"dependencies": {
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"@xenova/transformers": "^2.17.2",
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"better-sqlite3": "^11.1.2",
|
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"sqlite-vec": "^0.0.1-alpha.37"
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}
|
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},
|
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"node_modules/@huggingface/jinja": {
|
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"version": "0.2.2",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=18"
|
||||
}
|
||||
},
|
||||
"node_modules/@protobufjs/aspromise": {
|
||||
"version": "1.1.2",
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"resolved": "https://registry.npmjs.org/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
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|
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||||
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|
||||
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||||
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||||
"funding": [
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{
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"type": "github",
|
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"url": "https://github.com/sponsors/feross"
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},
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{
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||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
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{
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||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
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}
|
||||
],
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/better-sqlite3": {
|
||||
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|
||||
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"optionalDependencies": {
|
||||
"sqlite-vec-darwin-arm64": "0.0.1-alpha.37",
|
||||
"sqlite-vec-darwin-x64": "0.0.1-alpha.37",
|
||||
"sqlite-vec-linux-x64": "0.0.1-alpha.37",
|
||||
"sqlite-vec-windows-x64": "0.0.1-alpha.37"
|
||||
}
|
||||
},
|
||||
"node_modules/sqlite-vec-darwin-arm64": {
|
||||
"version": "0.0.1-alpha.37",
|
||||
"resolved": "https://registry.npmjs.org/sqlite-vec-darwin-arm64/-/sqlite-vec-darwin-arm64-0.0.1-alpha.37.tgz",
|
||||
"integrity": "sha512-3cPdW8JbNVZ08bwsCMsAVv0atZsvRY6Co3bgbiOVjwduBYNd3cTCC6Q+ICjz+H4hTEMZNqGdIWEeCTLgbvnEPw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT OR Apache",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
]
|
||||
},
|
||||
"node_modules/sqlite-vec-darwin-x64": {
|
||||
"version": "0.0.1-alpha.37",
|
||||
"resolved": "https://registry.npmjs.org/sqlite-vec-darwin-x64/-/sqlite-vec-darwin-x64-0.0.1-alpha.37.tgz",
|
||||
"integrity": "sha512-/56xiUbONFw+g3x/UlzGP2ykMDkrQ10trZVxk2Mmshd1y1QOqCoJoWzzcFm3OdaHI/Gz9vOJdCA8Py1QAr7Xqg==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT OR Apache",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
]
|
||||
},
|
||||
"node_modules/sqlite-vec-linux-x64": {
|
||||
"version": "0.0.1-alpha.37",
|
||||
"resolved": "https://registry.npmjs.org/sqlite-vec-linux-x64/-/sqlite-vec-linux-x64-0.0.1-alpha.37.tgz",
|
||||
"integrity": "sha512-BEyesm7Vo4EzE+ZQR1CY4M+lHNQufG26q1rkT5LKv6bIyPpebd+zs+hC0ndJmiNjLDNqqsbCsLQoTfLpGG2Urg==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT OR Apache",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/sqlite-vec-windows-x64": {
|
||||
"version": "0.0.1-alpha.37",
|
||||
"resolved": "https://registry.npmjs.org/sqlite-vec-windows-x64/-/sqlite-vec-windows-x64-0.0.1-alpha.37.tgz",
|
||||
"integrity": "sha512-jPSrUQNuFXy0Y6weSGS3ivU6pZp9nrvHHP3rseXFHuVj+pYK7gN6h1bC20J9Ch/BVz+9S2cVouoO/HvV90+E8w==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT OR Apache",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"windows"
|
||||
]
|
||||
},
|
||||
"node_modules/streamx": {
|
||||
"version": "2.18.0",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"fast-fifo": "^1.3.2",
|
||||
"queue-tick": "^1.0.1",
|
||||
"text-decoder": "^1.1.0"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"bare-events": "^2.2.0"
|
||||
}
|
||||
},
|
||||
"node_modules/string_decoder": {
|
||||
"version": "1.3.0",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"safe-buffer": "~5.2.0"
|
||||
}
|
||||
},
|
||||
"node_modules/strip-json-comments": {
|
||||
"version": "2.0.1",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=0.10.0"
|
||||
}
|
||||
},
|
||||
"node_modules/tar-fs": {
|
||||
"version": "2.1.1",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"chownr": "^1.1.1",
|
||||
"mkdirp-classic": "^0.5.2",
|
||||
"pump": "^3.0.0",
|
||||
"tar-stream": "^2.1.4"
|
||||
}
|
||||
},
|
||||
"node_modules/tar-stream": {
|
||||
"version": "2.2.0",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"bl": "^4.0.3",
|
||||
"end-of-stream": "^1.4.1",
|
||||
"fs-constants": "^1.0.0",
|
||||
"inherits": "^2.0.3",
|
||||
"readable-stream": "^3.1.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/text-decoder": {
|
||||
"version": "1.1.1",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"b4a": "^1.6.4"
|
||||
}
|
||||
},
|
||||
"node_modules/tunnel-agent": {
|
||||
"version": "0.6.0",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"safe-buffer": "^5.0.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/undici-types": {
|
||||
"version": "5.26.5",
|
||||
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
|
||||
"integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/util-deprecate": {
|
||||
"version": "1.0.2",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/wrappy": {
|
||||
"version": "1.0.2",
|
||||
"license": "ISC"
|
||||
}
|
||||
}
|
||||
}
|
||||
17
examples/nbc-headlines/package.json
Normal file
17
examples/nbc-headlines/package.json
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
{
|
||||
"name": "nbc-headlines",
|
||||
"version": "1.0.0",
|
||||
"main": "demo.js",
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1"
|
||||
},
|
||||
"keywords": [],
|
||||
"author": "",
|
||||
"license": "ISC",
|
||||
"description": "",
|
||||
"dependencies": {
|
||||
"@xenova/transformers": "^2.17.2",
|
||||
"better-sqlite3": "^11.1.2",
|
||||
"sqlite-vec": "^0.0.1-alpha.37"
|
||||
}
|
||||
}
|
||||
|
|
@ -7,7 +7,7 @@ db.loadExtension("../../dist/vec0");
|
|||
|
||||
const { sqlite_version, vec_version } = db
|
||||
.prepare(
|
||||
"select sqlite_version() as sqlite_version, vec_version() as vec_version;"
|
||||
"select sqlite_version() as sqlite_version, vec_version() as vec_version;",
|
||||
)
|
||||
.get();
|
||||
|
||||
|
|
@ -25,7 +25,7 @@ const query = [0.3, 0.3, 0.3, 0.3];
|
|||
db.exec("CREATE VIRTUAL TABLE vec_items USING vec0(embedding float[4])");
|
||||
|
||||
const insertStmt = db.prepare(
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, vec_f32(?))"
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, vec_f32(?))",
|
||||
);
|
||||
|
||||
const insertVectors = db.transaction((items) => {
|
||||
|
|
@ -46,7 +46,7 @@ const rows = db
|
|||
WHERE embedding MATCH ?
|
||||
ORDER BY distance
|
||||
LIMIT 3
|
||||
`
|
||||
`,
|
||||
)
|
||||
.all(new Float32Array(query));
|
||||
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ const query = [0.3, 0.3, 0.3, 0.3];
|
|||
db.exec("CREATE VIRTUAL TABLE vec_items USING vec0(embedding float[4])");
|
||||
|
||||
const insertStmt = db.prepare(
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, ?)"
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, ?)",
|
||||
);
|
||||
|
||||
const insertVectors = db.transaction((items) => {
|
||||
|
|
@ -44,7 +44,7 @@ const rows = db
|
|||
WHERE embedding MATCH ?
|
||||
ORDER BY distance
|
||||
LIMIT 5
|
||||
`
|
||||
`,
|
||||
)
|
||||
.all([new Uint8Array(new Float32Array(query).buffer)]);
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,7 @@
|
|||
package main
|
||||
|
||||
import (
|
||||
"bytes"
|
||||
"database/sql"
|
||||
"encoding/binary"
|
||||
"fmt"
|
||||
"log"
|
||||
|
||||
|
|
@ -11,15 +9,6 @@ import (
|
|||
_ "github.com/mattn/go-sqlite3"
|
||||
)
|
||||
|
||||
|
||||
func serializeFloat32(vector []float32) ([]byte, error) {
|
||||
buf := new(bytes.Buffer)
|
||||
err := binary.Write(buf, binary.LittleEndian, vector)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
return buf.Bytes(), nil
|
||||
}
|
||||
func main() {
|
||||
sqlite_vec.Auto()
|
||||
db, err := sql.Open("sqlite3", ":memory:")
|
||||
|
|
@ -51,7 +40,7 @@ func main() {
|
|||
q := []float32{0.3, 0.3, 0.3, 0.3}
|
||||
|
||||
for id, values := range items {
|
||||
v, err := serializeFloat32(values)
|
||||
v, err := sqlite_vec.SerializeFloat32(values)
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
|
|
@ -61,7 +50,7 @@ func main() {
|
|||
}
|
||||
}
|
||||
|
||||
query, err := serializeFloat32(q)
|
||||
query, err := sqlite_vec.SerializeFloat32(q)
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ sqliteVec.load(db);
|
|||
|
||||
const { sqlite_version, vec_version } = db
|
||||
.prepare(
|
||||
"select sqlite_version() as sqlite_version, vec_version() as vec_version;"
|
||||
"select sqlite_version() as sqlite_version, vec_version() as vec_version;",
|
||||
)
|
||||
.get();
|
||||
|
||||
|
|
@ -24,7 +24,7 @@ const query = [0.3, 0.3, 0.3, 0.3];
|
|||
db.exec("CREATE VIRTUAL TABLE vec_items USING vec0(embedding float[4])");
|
||||
|
||||
const insertStmt = db.prepare(
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, ?)"
|
||||
"INSERT INTO vec_items(rowid, embedding) VALUES (?, ?)",
|
||||
);
|
||||
|
||||
const insertVectors = db.transaction((items) => {
|
||||
|
|
@ -45,7 +45,7 @@ const rows = db
|
|||
WHERE embedding MATCH ?
|
||||
ORDER BY distance
|
||||
LIMIT 3
|
||||
`
|
||||
`,
|
||||
)
|
||||
.all(new Float32Array(query));
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,9 @@
|
|||
# `sqlite-vec` statically compiled in the SQLite CLI
|
||||
|
||||
You can compile your own version of the `sqlite3` CLI with `sqlite-vec` builtin. The process is not well documented, but the special `SQLITE_EXTRA_INIT` compile option can be used to "inject" code at initialization time. See the `Makefile` at the root of this project for some more info.
|
||||
You can compile your own version of the `sqlite3` CLI with `sqlite-vec` builtin.
|
||||
The process is not well documented, but the special `SQLITE_EXTRA_INIT` compile
|
||||
option can be used to "inject" code at initialization time. See the `Makefile`
|
||||
at the root of this project for some more info.
|
||||
|
||||
The `core_init.c` file here demonstrates auto-loading the `sqlite-vec` entrypoints at startup.
|
||||
The `core_init.c` file here demonstrates auto-loading the `sqlite-vec`
|
||||
entrypoints at startup.
|
||||
|
|
|
|||
|
|
@ -1,5 +1,11 @@
|
|||
# `sqlite-vec` statically compiled into WASM builds
|
||||
|
||||
You can compile your own version of SQLite's WASM build with `sqlite-vec` builtin. Dynamically loading SQLite extensions is not supported in the official WASM build yet, but you can statically compile extensions in. It's not well documented, but the `sqlite3_wasm_extra_init` option in the SQLite `ext/wasm` Makefile allows you to inject your own code at initialization time. See the `Makefile` at the room of the project for more info.
|
||||
You can compile your own version of SQLite's WASM build with `sqlite-vec`
|
||||
builtin. Dynamically loading SQLite extensions is not supported in the official
|
||||
WASM build yet, but you can statically compile extensions in. It's not well
|
||||
documented, but the `sqlite3_wasm_extra_init` option in the SQLite `ext/wasm`
|
||||
Makefile allows you to inject your own code at initialization time. See the
|
||||
`Makefile` at the room of the project for more info.
|
||||
|
||||
The `wasm.c` file here demonstrates auto-loading the `sqlite-vec` entrypoints at startup.
|
||||
The `wasm.c` file here demonstrates auto-loading the `sqlite-vec` entrypoints at
|
||||
startup.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue