Initial implementation of RAG agent with Qdrant and Tavily: update vectorstore.py

vectorstore.py

@@ -1,31 +1,32 @@
 """
-RAG vector store using ChromaDB and Ollama embeddings.
+RAG vector store using Qdrant and Ollama embeddings.
 """
 
 from pathlib import Path
-from typing import List, Iterable
+from typing import List
 
-import chromadb
+import chromadb  # kept for compatibility if needed
 from langchain.embeddings.ollama import OllamaEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.schema.document import Document
+from langchain.vectorstores import Qdrant
 
 CHROMA_DIR = "./chroma_db"
 EMBED_MODEL = "nomic-embed-text"
 
 
 def create_vectorstore(persist_directory: str = CHROMA_DIR):
-    """Create or load a Chroma vector store.
+    """Create or load a Qdrant vector store.
 
     Parameters
     ----------
     persist_directory:
-        Directory where the Chroma database is stored. If it does not exist, it will be created.
+        Directory where the Qdrant database is stored. If it does not exist, it will be created.
     """
     embeddings = OllamaEmbeddings(model=EMBED_MODEL)
-    client = chromadb.PersistentClient(path=persist_directory)
+    # Qdrant can use a local file store via `path` argument
-    # Use a single collection named "documents"
+    client = Qdrant(persist_directory=persist_directory, embedding_function=embeddings)
-    return client.get_or_create_collection(name="documents", embedding_function=embeddings)
+    return client
 
 
 def load_documents(directory: str, vectorstore) -> None:
@@ -43,4 +44,8 @@ def load_documents(directory: str, vectorstore) -> None:
         docs.extend(text_splitter.create_documents([content], metadata={"source": str(path)}))
 
     if docs:
-        vectorstore.add(documents=docs)
+        # Qdrant expects texts and metadatas lists
+        vectorstore.add_texts(
+            texts=[doc.page_content for doc in docs],
+            metadatas=[doc.metadata for doc in docs],
+        )