Initial commit: RAG system for control theory Q&A

Ollama + FAISS based retrieval-augmented generation system that indexes
Wikipedia articles on automatic control theory and answers questions in Russian.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

rag/query.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+RAG query: search indexed documents and generate answer via Ollama.
+Usage: python rag/query.py "ваш вопрос"
+"""
+
+import os
+import sys
+import pickle
+from pathlib import Path
+
+import requests
+import numpy as np
+import faiss
+
+OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://192.168.0.47:11434")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "bge-m3")
+GENERATE_MODEL = os.getenv("GENERATE_MODEL", "qwen3.5:9b")
+TOP_K = 5
+
+STORE_DIR = Path(__file__).resolve().parent / "store"
+INDEX_PATH = STORE_DIR / "faiss.index"
+CHUNKS_PATH = STORE_DIR / "chunks.pkl"
+META_PATH = STORE_DIR / "meta.pkl"
+
+SYSTEM_PROMPT = """Ты — эксперт по теории автоматического управления и электротехнике.
+Отвечай на вопросы, опираясь ТОЛЬКО на предоставленный контекст.
+Если в контексте нет информации для ответа, скажи об этом.
+Отвечай на русском языке, точно и по существу.
+Указывай источники, из которых взята информация."""
+
+
+def load_index():
+    if not INDEX_PATH.exists():
+        print("Index not found! Run: python rag/index.py")
+        sys.exit(1)
+    index = faiss.read_index(str(INDEX_PATH))
+    with open(CHUNKS_PATH, "rb") as f:
+        chunks = pickle.load(f)
+    with open(META_PATH, "rb") as f:
+        meta = pickle.load(f)
+    return index, chunks, meta
+
+
+def get_embedding(text: str) -> np.ndarray:
+    resp = requests.post(
+        f"{OLLAMA_HOST}/api/embed",
+        json={"model": EMBED_MODEL, "input": [text]},
+        timeout=60,
+    )
+    resp.raise_for_status()
+    return np.array(resp.json()["embeddings"], dtype=np.float32)
+
+
+def search(query: str, index, chunks, meta, k: int = TOP_K):
+    q_emb = get_embedding(query)
+    faiss.normalize_L2(q_emb)
+    scores, indices = index.search(q_emb, k)
+    results = []
+    for score, idx in zip(scores[0], indices[0]):
+        if idx < 0:
+            continue
+        results.append({
+            "chunk": chunks[idx],
+            "meta": meta[idx],
+            "score": float(score),
+        })
+    return results
+
+
+def generate(query: str, context_chunks: list[dict]) -> str:
+    context_parts = []
+    for i, r in enumerate(context_chunks, 1):
+        url = r["meta"]["url"]
+        context_parts.append(f"[Источник {i}] ({url})\n{r['chunk']}")
+    context = "\n\n---\n\n".join(context_parts)
+
+    prompt = f"""Контекст из документов:
+
+{context}
+
+---
+
+Вопрос: {query}
+
+Ответ:"""
+
+    resp = requests.post(
+        f"{OLLAMA_HOST}/api/generate",
+        json={
+            "model": GENERATE_MODEL,
+            "system": SYSTEM_PROMPT,
+            "prompt": prompt,
+            "stream": False,
+            "think": False,
+            "options": {"temperature": 0.3, "num_predict": 2048},
+        },
+        timeout=300,
+    )
+    resp.raise_for_status()
+    return resp.json()["response"]
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python rag/query.py \"ваш вопрос\"")
+        sys.exit(1)
+
+    query = " ".join(sys.argv[1:])
+    print(f"Query: {query}\n")
+
+    index, chunks, meta = load_index()
+    print(f"Index: {index.ntotal} vectors")
+
+    results = search(query, index, chunks, meta)
+    print(f"Top-{len(results)} results:\n")
+    for i, r in enumerate(results, 1):
+        print(f"  [{i}] score={r['score']:.4f}  {r['meta']['url']}")
+        print(f"      {r['chunk'][:120]}...\n")
+
+    print("Generating answer...\n")
+    answer = generate(query, results)
+    print("=" * 60)
+    print(answer)
+    print("=" * 60)
+
+    print("\nSources:")
+    seen = set()
+    for r in results:
+        url = r["meta"]["url"]
+        if url not in seen:
+            seen.add(url)
+            print(f"  - {url}")
+
+
+if __name__ == "__main__":
+    main()