Update rag_tools

rag_tools.py

@@ -3,6 +3,7 @@ from qdrant_client import QdrantClient
 from langchain.embeddings.ollama import OllamaEmbeddings
 from langchain.vectorstores.qdrant import QdrantVectorStore
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import Document
 
 # Initialize embeddings and vector store
 embeddings = OllamaEmbeddings(model="nomic-embed-text")
@@ -10,7 +11,9 @@ client = QdrantClient(host="localhost", port=6333)
 collection_name = "knowledge_base"
 # Ensure collection exists
 if not client.has_collection(collection_name):
-    client.create_collection(name=collection_name, vectors_config={"size": embeddings.embed_query(["test"]).shape[1], "distance": "Cosine"})
+    # Determine embedding dimension by embedding a dummy text
+    dim = embeddings.embed_query(["test"])[0].shape[0]
+    client.create_collection(name=collection_name, vectors_config={"size": dim, "distance": "Cosine"})
 vector_store = QdrantVectorStore(client=client, collection_name=collection_name, embedding=embeddings)
 
 # Text splitter