diff --git a/backend/scripts/populate_db.py b/backend/scripts/populate_db.py
new file mode 100644
index 0000000..72d40f2
--- /dev/null
+++ b/backend/scripts/populate_db.py
@@ -0,0 +1,52 @@
+import os
+import sys
+from pathlib import Path
+
+# Add backend directory to path so we can import src
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+
+from dotenv import load_dotenv
+load_dotenv()
+
+from src.rag.ingest import process_file
+from src.rag.store import ingest_documents
+from src.mongo.vector_store import is_file_processed, log_processed_file
+
+def populate_from_dataset(dataset_dir):
+    dataset_path = Path(dataset_dir)
+    if not dataset_path.exists():
+        print(f"Dataset directory not found: {dataset_dir}")
+        return
+
+    print(f"Scanning {dataset_dir}...")
+    
+    total_chunks = 0
+    files_processed = 0
+
+    for file_path in dataset_path.glob('*'):
+        if file_path.is_file() and file_path.suffix.lower() in ['.csv', '.pdf']:
+            if is_file_processed(file_path.name):
+                print(f"Skipping {file_path.name} (already processed)")
+                continue
+
+            print(f"Processing {file_path.name}...")
+            try:
+                chunks = process_file(str(file_path))
+                if chunks:
+                    count = ingest_documents(chunks)
+                    print(f"  Ingested {count} chunks.")
+                    if count > 0:
+                        log_processed_file(file_path.name)
+                        total_chunks += count
+                        files_processed += 1
+                else:
+                    print("  No text found/extracted.")
+            except Exception as e:
+                print(f"  Error processing file: {e}")
+
+    print(f"\nFinished! Processed {files_processed} files. Total chunks ingested: {total_chunks}")
+
+if __name__ == "__main__":
+    # Assuming run from backend/
+    dataset_dir = os.path.join(os.path.dirname(__file__), '../dataset')
+    populate_from_dataset(dataset_dir)
diff --git a/backend/src/mongo/connection.py b/backend/src/mongo/connection.py
new file mode 100644
index 0000000..d868d23
--- /dev/null
+++ b/backend/src/mongo/connection.py
@@ -0,0 +1,8 @@
+import os
+from pymongo import MongoClient
+
+def get_mongo_client():
+    uri = os.environ.get("MONGO_URI")
+    if not uri:
+        raise ValueError("MONGO_URI environment variable not set")
+    return MongoClient(uri)
diff --git a/backend/src/mongo/vector_store.py b/backend/src/mongo/vector_store.py
new file mode 100644
index 0000000..c1162f6
--- /dev/null
+++ b/backend/src/mongo/vector_store.py
@@ -0,0 +1,49 @@
+from .connection import get_mongo_client
+
+def insert_rag_documents(documents, collection_name="rag_documents", db_name="vectors_db"):
+    client = get_mongo_client()
+    db = client.get_database(db_name)
+    collection = db[collection_name]
+    
+    if documents:
+        result = collection.insert_many(documents)
+        return len(result.inserted_ids)
+    return 0
+
+def search_rag_documents(query_embedding, collection_name="rag_documents", db_name="vectors_db", num_results=5):
+    client = get_mongo_client()
+    db = client.get_database(db_name)
+    collection = db[collection_name]
+    
+    pipeline = [
+        {
+            "$vectorSearch": {
+                "index": "vector_index",
+                "path": "embedding",
+                "queryVector": query_embedding,
+                "numCandidates": num_results * 10,
+                "limit": num_results
+            }
+        },
+        {
+            "$project": {
+                "_id": 0,
+                "text": 1,
+                "score": { "$meta": "vectorSearchScore" }
+            }
+        }
+    ]
+    
+    return list(collection.aggregate(pipeline))
+
+def is_file_processed(filename, log_collection="ingested_files", db_name="vectors_db"):
+    client = get_mongo_client()
+    db = client.get_database(db_name)
+    collection = db[log_collection]
+    return collection.find_one({"filename": filename}) is not None
+
+def log_processed_file(filename, log_collection="ingested_files", db_name="vectors_db"):
+    client = get_mongo_client()
+    db = client.get_database(db_name)
+    collection = db[log_collection]
+    collection.insert_one({"filename": filename, "processed_at": 1}) # keeping it simple
diff --git a/backend/src/rag/store.py b/backend/src/rag/store.py
index b09af99..1ba8628 100644
--- a/backend/src/rag/store.py
+++ b/backend/src/rag/store.py
@@ -1,18 +1,7 @@
-import os
-from pymongo import MongoClient
-from .embeddings import get_embeddings_batch
-
-def get_mongo_client():
-    uri = os.environ.get("MONGO_URI")
-    if not uri:
-        raise ValueError("MONGO_URI environment variable not set")
-    return MongoClient(uri)
+from .embeddings import get_embeddings_batch, get_embedding
+from ..mongo.vector_store import insert_rag_documents, search_rag_documents
 
 def ingest_documents(text_chunks, collection_name="rag_documents"):
-    client = get_mongo_client()
-    db = client.get_database("vectors_db")
-    collection = db[collection_name]
-    
     embeddings = get_embeddings_batch(text_chunks)
     
     documents = []
@@ -22,37 +11,8 @@ def ingest_documents(text_chunks, collection_name="rag_documents"):
             "embedding": embedding
         })
     
-    if documents:
-        collection.insert_many(documents)
-        return len(documents)
-    return 0
+    return insert_rag_documents(documents, collection_name=collection_name)
 
 def vector_search(query_text, collection_name="rag_documents", num_results=5):
-    from .embeddings import get_embedding
     query_embedding = get_embedding(query_text)
-    
-    client = get_mongo_client()
-    db = client.get_database("vectors_db")
-    collection = db[collection_name]
-    
-    pipeline = [
-        {
-            "$vectorSearch": {
-                "index": "vector_index",
-                "path": "embedding",
-                "queryVector": query_embedding,
-                "numCandidates": num_results * 10,
-                "limit": num_results
-            }
-        },
-        {
-            "$project": {
-                "_id": 0,
-                "text": 1,
-                "score": { "$meta": "vectorSearchScore" }
-            }
-        }
-    ]
-    
-    results = list(collection.aggregate(pipeline))
-    return results
+    return search_rag_documents(query_embedding, collection_name=collection_name, num_results=num_results)