feat: local AI server scaffolding (FastAPI, RAG, embeddings). Port policy (>=26000), README/API docs, scripts.

scripts/embed_ollama.py

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+#!/usr/bin/env python3
+import argparse
+import json
+import os
+from pathlib import Path
+from typing import List, Dict, Any
+
+import requests
+
+
+def chunk_text(text: str, max_chars: int = 1200, overlap: int = 200) -> List[str]:
+    chunks: List[str] = []
+    start = 0
+    n = len(text)
+    while start < n:
+        end = min(start + max_chars, n)
+        chunk = text[start:end].strip()
+        if chunk:
+            chunks.append(chunk)
+        if end == n:
+            break
+        start = max(0, end - overlap)
+    return chunks
+
+
+def embed_texts_ollama(texts: List[str], model: str = "nomic-embed-text", host: str = "http://localhost:11434") -> List[List[float]]:
+    url = f"{host}/api/embeddings"
+    vectors: List[List[float]] = []
+    for t in texts:
+        resp = requests.post(url, json={"model": model, "prompt": t}, timeout=120)
+        resp.raise_for_status()
+        data = resp.json()
+        vectors.append(data["embedding"])  # type: ignore[index]
+    return vectors
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Build simple vector index using Ollama embeddings")
+    parser.add_argument("--text", default=None, help="Path to extracted .txt; default = first in data/")
+    parser.add_argument("--model", default="nomic-embed-text", help="Ollama embedding model name")
+    parser.add_argument("--host", default="http://localhost:11434", help="Ollama host")
+    parser.add_argument("--out", default="data/index.jsonl", help="Output JSONL path")
+    parser.add_argument("--max-chars", type=int, default=1200, help="Max characters per chunk")
+    parser.add_argument("--overlap", type=int, default=200, help="Characters overlap between chunks")
+    args = parser.parse_args()
+
+    data_dir = Path("data")
+    if args.text:
+        text_path = Path(args.text)
+    else:
+        txts = sorted(data_dir.glob("*.txt"))
+        if not txts:
+            raise SystemExit("data/*.txt가 없습니다. 먼저 scripts/pdf_stats.py로 PDF를 추출하세요.")
+        text_path = txts[0]
+
+    text = text_path.read_text(encoding="utf-8")
+    chunks = chunk_text(text, max_chars=args.max_chars, overlap=args.overlap)
+
+    vectors = embed_texts_ollama(chunks, model=args.model, host=args.host)
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with out_path.open("w", encoding="utf-8") as f:
+        for i, (chunk, vec) in enumerate(zip(chunks, vectors)):
+            row: Dict[str, Any] = {
+                "id": f"{text_path.stem}:{i}",
+                "text": chunk,
+                "vector": vec,
+                "source": text_path.name,
+            }
+            f.write(json.dumps(row, ensure_ascii=False) + "\n")
+
+    meta = {
+        "source_text": str(text_path),
+        "embedding_model": args.model,
+        "host": args.host,
+        "chunks": len(chunks),
+        "index_path": str(out_path),
+    }
+    print(json.dumps(meta, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()
+