add file

vector_store.py

@@ -1,14 +1,10 @@
-from langchain_qdrant import QdrantVectorStore
+from langchain_community.vectorstores import Chroma
 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")
-        )
+class ChromaStore:
+    def __init__(self, collection_name="rag_collection"):
+        self.client = Chroma(embedding_function=OllamaEmbeddings(model="nomic-embed-text"), collection_name=collection_name)
         # ensure collection exists
         if not self.client.collection_exists:
             self.client.create_collection()