ADK python , RAG Data add

embed.py

import glob
import os
from langchain_text_splitters import RecursiveCharacterTextSplitter
import ollama
import psycopg2
from pgvector.psycopg2 import register_vector

# PostgreSQL 接続
conn = psycopg2.connect(
    dbname="mydb",
    user="root",
    password="admin",
    host="localhost",
    port=5432
)
folder_path = "./data"
# pgvector を psycopg2 に登録
register_vector(conn)
data = []

# スプリッターの初期化
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,      # 1チャンクあたりの文字数（目安）
    chunk_overlap=20,    # 前後のチャンクと重複させる文字数（文脈維持のため）
    length_function=len, # 長さを測る関数（通常はlen）
    separators=["\n\n", "\n", " ", ""] # 分割する優先順位
    # is_separator_regex=False,
)

contData = []
for file_path in glob.glob(os.path.join(folder_path, "*.txt")):
    with open(file_path, "r", encoding="utf-8") as f:
        targetContent = f.read()
        # 分割実行
        chunks = text_splitter.create_documents([targetContent])

        for i, chunk in enumerate(chunks):
            print(f"Chunk {i}: {chunk.page_content}")
            data.append({
                "filename": os.path.basename(file_path),
                "content": chunk.page_content,
            })
            contData.append(chunk.page_content)
            embedding = ollama.embeddings(
                model="qwen3-embedding:0.6b",
                prompt= chunk.page_content
            )

            vec = embedding["embedding"]
            print(len(vec))
            print(vec[:5])

            # SQL へ挿入
            with conn.cursor() as cur:
                cur.execute(
                    "INSERT INTO documents (content, embedding) VALUES (%s, %s)",
                    (chunk.page_content, vec)
                )
                conn.commit()


#print(data)