add vector_store

vector_store.py

@@ -0,0 +1,21 @@
+from langchain_qdrant import QdrantVectorStore
+from langchain_ollama import OllamaEmbeddings
+from langchain.schema import Document
+
+class QdrantStore:
+    def __init__(self, host="localhost", port=6333, collection_name="rag_collection"):
+        self.client = QdrantVectorStore(
+            url=f"http://{host}:{port}",
+            collection_name=collection_name,
+            embeddings=OllamaEmbeddings(model="nomic-embed-text")
+        )
+        # ensure collection exists
+        if not self.client.collection_exists:
+            self.client.create_collection()
+
+    def add_documents(self, docs):
+        # docs: list of Document
+        self.client.add_documents(docs)
+
+    def search(self, query, limit=5):
+        return self.client.similarity_search(query, k=limit)