From 60a799daf5f7c4eb4c97bb62f5a06e321a47b992 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A0=D0=B8=D0=BD=D0=B0=D1=80=20=D0=9C=D0=B8=D1=80=D0=B7?=
 =?UTF-8?q?=D0=B0=D0=B3=D0=B8=D1=82=D0=BE=D0=B2?= <mirzagitov03@mail.ru>
Date: Wed, 10 Jun 2026 13:30:30 +0000
Subject: [PATCH] =?UTF-8?q?Initial=20submission=20of=20FAQ-=D0=B1=D0=BE?=
 =?UTF-8?q?=D1=82:=20add=20src/utils.py?=
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---
 src/utils.py | 42 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 src/utils.py

diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000..9e3afb9
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,42 @@
+import os
+from pathlib import Path
+from langchain_ollama import OllamaEmbeddings
+from langchain_chroma import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+CHROMA_PATH = Path("./chroma_faq")
+
+
+def load_faq_to_chroma(md_dir: str = "data"):
+    """Load all .md files from md_dir into a persistent Chroma store.
+    The function will create or update the store at CHROMA_PATH.
+    """
+    # Ensure directory exists
+    Path(md_dir).mkdir(parents=True, exist_ok=True)
+    # Gather all markdown files
+    md_files = list(Path(md_dir).glob("*.md"))
+    if not md_files:
+        raise FileNotFoundError(f"No .md files found in {md_dir}")
+
+    # Read and split documents
+    texts = []
+    for md_file in md_files:
+        text = md_file.read_text(encoding="utf-8")
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        texts.extend(splitter.split_text(text))
+
+    # Create embeddings
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+
+    # Persist to Chroma
+    chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=embeddings)
+    chroma.add_texts(texts)
+    chroma.persist()
+    return chroma
+
+
+def search_course_docs(query: str, k: int = 3):
+    """Search the persistent Chroma store for the top k documents matching query."""
+    chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=OllamaEmbeddings(model="nomic-embed-text"))
+    retriever = chroma.as_retriever(search_kwargs={"k": k})
+    return retriever.get_relevant_documents(query)