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diff --git a/R2R/r2r/examples/scripts/run_web_multi_rag.py b/R2R/r2r/examples/scripts/run_web_multi_rag.py
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+++ b/R2R/r2r/examples/scripts/run_web_multi_rag.py
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+import fire
+
+from r2r import R2RBuilder, SerperClient, WebSearchPipe
+from r2r.base.abstractions.llm import GenerationConfig
+from r2r.main.assembly.factory_extensions import R2RPipeFactoryWithMultiSearch
+
+
+def run_rag_pipeline(query="Who was Aristotle?"):
+    # Initialize a web search pipe
+    web_search_pipe = WebSearchPipe(serper_client=SerperClient())
+
+    # Define a new synthetic query generation template
+    synthetic_query_generation_template = {
+        "name": "synthetic_query_generation_template",
+        "template": """
+            ### Instruction:
+            Given the following query, write a double newline separated list of up to {num_outputs} advanced queries meant to help answer the original query.
+            DO NOT generate any single query which is likely to require information from multiple distinct documents.
+            EACH single query will be used to carry out a cosine similarity semantic search over distinct indexed documents.
+            FOR EXAMPLE, if asked `how do the key themes of Great Gatsby compare with 1984`, the two queries would be
+            `What are the key themes of Great Gatsby?` and `What are the key themes of 1984?`.
+            Here is the original user query to be transformed into answers:
+
+            ### Query:
+            {message}
+
+            ### Response:
+            """,
+        "input_types": {"num_outputs": "int", "message": "str"},
+    }
+
+    # Build the R2R application with the custom pipeline
+    app = (
+        R2RBuilder()
+        .with_pipe_factory(R2RPipeFactoryWithMultiSearch)
+        .build(
+            # override inputs consumed in building the MultiSearchPipe
+            multi_inner_search_pipe_override=web_search_pipe,
+            query_generation_template_override=synthetic_query_generation_template,
+        )
+    )
+
+    # Run the RAG pipeline through the R2R application
+    result = app.rag(
+        query,
+        rag_generation_config=GenerationConfig(model="gpt-4o"),
+    )
+
+    print(f"Search Results:\n\n{result.search_results}")
+    print(f"RAG Results:\n\n{result.completion}")
+
+
+if __name__ == "__main__":
+    fire.Fire(run_rag_pipeline)