Solution published to Gitea.: update load_documents.py

load_documents.py

@@ -1,13 +1,12 @@
-# Utility script to load documents from a directory into the Chroma vector store.
-#
-# The script walks through the specified directory, reads all .txt files, splits them into chunks using
-# `RecursiveCharacterTextSplitter`, embeds the chunks with `OllamaEmbeddings`, and stores them in the
-# local Chroma collection via the helper functions defined in :mod:`main`.
+#!/usr/bin/env python
+# Load documents from a directory into the Qdrant vector store.
 #
 # Usage:
 #     python load_documents.py /path/to/docs
 #
-# The script prints the number of documents added.
+# The script walks through the specified directory, reads all .txt files, splits them into chunks using
+# `RecursiveCharacterTextSplitter`, embeds the chunks with `OllamaEmbeddings`, and stores them in the
+# local Qdrant collection via the helper functions defined in :mod:`main`.
 
 import os
 import sys
@@ -48,8 +47,7 @@ def load_documents_from_dir(directory: str) -> int:
         documents = chunk_document(content, title)
         ids = [str(uuid4()) for _ in documents]
         embeddings = embedding.embed_documents([doc.page_content for doc in documents])
-        collection = vector_store.get_collection(name="rag_memory")
-        collection.add(ids=ids, documents=[doc.page_content for doc in documents], embeddings=embeddings, metadatas=[doc.metadata for doc in documents])
+        vector_store.add_documents(documents, ids=ids, embeddings=embeddings)
         total_chunks += len(documents)
     return total_chunks