Solution published: update src/utils.py

src/utils.py

@@ -3,17 +3,15 @@ from typing import List
 
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_qdrant import QdrantVectorStore
+from langchain_chroma import Chroma
 from langchain_ollama import OllamaEmbeddings
 
-# Qdrant collection name
+# Chroma collection name
 COLLECTION_NAME = "faq"
-# Qdrant server URL (default local)
-QDRANT_URL = "http://localhost:6333"
 
 
-def load_faq_to_qdrant(md_dir: str = "data") -> None:
-    """Load all .md files from md_dir into a Qdrant vector store.
+def load_faq_to_chroma(md_dir: str = "data") -> None:
+    """Load all .md files from md_dir into a Chroma vector store.
     The store is created/updated at COLLECTION_NAME.
     """
     loader = TextLoader
@@ -25,21 +23,19 @@ def load_faq_to_qdrant(md_dir: str = "data") -> None:
     splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
     docs = splitter.split_documents(all_docs)
     embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    # Create or update Qdrant collection
-    QdrantVectorStore.from_documents(
+    # Create or update Chroma collection
+    Chroma.from_documents(
         docs,
         embeddings,
         collection_name=COLLECTION_NAME,
-        url=QDRANT_URL,
     )
 
 
 def search_course_docs(query: str, k: int = 3) -> List[str]:
-    """Return top k document snippets from the persisted Qdrant store."""
+    """Return top k document snippets from the persisted Chroma store."""
     embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    store = QdrantVectorStore(
+    store = Chroma(
         collection_name=COLLECTION_NAME,
-        url=QDRANT_URL,
         embedding_function=embeddings,
     )
     results = store.similarity_search(query, k=k)