1. 介绍
目前用到三个插件：
sqlite-ai：使用sql来操作llm
sqlite-vec：类似faiss的向量检索插件
sqlite-rag：基于sqlite的混合rag插件
2. sqlite-ai
使用pip进行安装，下面是使用示例：
import importlib.resources
import sqlite3
conn = sqlite3.connect("example.db")
ext_path = importlib.resources.files("sqliteai.binaries.cpu") / "ai"
conn.enable_load_extension(True)
conn.load_extension(str(ext_path))
conn.enable_load_extension(False)
conn.execute("SELECT llm_model_load('../MiniCPM4-0.5B-bf16.gguf', 'n_predict=4096,n_gpu_layers=99');")
conn.execute("SELECT llm_context_create('n_ctx=512,n_threads=6,n_batch=128');")
conn.execute("SELECT llm_chat_respond('你是谁?replay within 10 words');").fetchone()[0]3. sqlite-vec
使用pip进行安装，下面是使用示例：
import sqlite3
import sqlite_vec
import sqlite_lembed
from typing import List
db = sqlite3.connect(":memory:")
db.enable_load_extension(True)
sqlite_vec.load(db)
sqlite_lembed.load(db)
db.enable_load_extension(False)
db.executef"""INSERT INTO temp.lembed_models(name, model) select 'default', lembed_model_from_file('m3e-base.f16.gguf')""")
db.execute("""create table articles (headline text);""")
db.execute("""create virtual table vec_articles using vec0(headline_embeddings float[768]);""")
db.execute(f"""insert into articles VALUES ('{text[:500]}');""")
db.execute("""insert into vec_articles(rowid, headline_embeddings) select rowid, lembed(headline) from articles;""")
db.execute(f"""with matches as (SELECT rowid,distance FROM vec_articles WHERE headline_embeddings MATCH lembed('天气') and k = 2 ORDER BY distance)
select headline,100/(1+distance) from matches left join articles on articles.rowid = matches.rowid;""").fetchall()