diff --git a/templates/rag-mongo/README.md b/templates/rag-mongo/README.md
index ab0d1e4c1dd..d17eb5bd5b9 100644
--- a/templates/rag-mongo/README.md
+++ b/templates/rag-mongo/README.md
@@ -40,6 +40,13 @@ from rag_mongo import chain as rag_mongo_chain
 add_routes(app, rag_mongo_chain, path="/rag-mongo")
 ```
 
+If you want to set up an ingestion pipeline, you can add the following code to your `server.py` file:
+```python
+from rag_mongo import ingest as rag_mongo_ingest
+
+add_routes(app, rag_mongo_ingest, path="/rag-mongo-ingest")
+```
+
 (Optional) Let's now configure LangSmith. 
 LangSmith will help us trace, monitor and debug LangChain applications. 
 LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
diff --git a/templates/rag-mongo/rag_mongo/chain.py b/templates/rag-mongo/rag_mongo/chain.py
index 455c4a3421f..c75159881ba 100644
--- a/templates/rag-mongo/rag_mongo/chain.py
+++ b/templates/rag-mongo/rag_mongo/chain.py
@@ -1,11 +1,17 @@
 import os
 
 from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
 from langchain.pydantic_v1 import BaseModel
 from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.schema.runnable import (
+    RunnableLambda,
+    RunnableParallel,
+    RunnablePassthrough,
+)
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import MongoDBAtlasVectorSearch
 from pymongo import MongoClient
 
@@ -54,3 +60,24 @@ class Question(BaseModel):
 
 
 chain = chain.with_types(input_type=Question)
+
+
+def _ingest(url: str) -> dict:
+    loader = PyPDFLoader(url)
+    data = loader.load()
+
+    # Split docs
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+    docs = text_splitter.split_documents(data)
+
+    # Insert the documents in MongoDB Atlas Vector Search
+    _ = MongoDBAtlasVectorSearch.from_documents(
+        documents=docs,
+        embedding=OpenAIEmbeddings(disallowed_special=()),
+        collection=MONGODB_COLLECTION,
+        index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
+    )
+    return {}
+
+
+ingest = RunnableLambda(_ingest)