add vector_store

vector_store.py

@@ -0,0 +1,13 @@
+from langchain_qdrant import QdrantStore
+from langchain_ollama import OllamaEmbeddings
+
+class QdrantStore:
+    def __init__(self, url="http://localhost:6333", collection_name="rag_collection"):
+        self.client = QdrantStore(url=url, collection_name=collection_name)
+        self.embeddings = OllamaEmbeddings(model="nomic-embed-text")
+
+    def add_documents(self, docs):
+        self.client.add_documents(docs, embeddings=self.embeddings)
+
+    def search(self, query, limit=5):
+        return self.client.search(query, limit=limit, embeddings=self.embeddings)