diff --git a/examples/deep_research_demo/deep_research_demo.py b/examples/deep_research_demo/deep_research_demo.py
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+++ b/examples/deep_research_demo/deep_research_demo.py
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+import os
+
+import pandas as pd
+
+import lotus
+from lotus import WebSearchCorpus, parse_pdf, web_search
+from lotus.models import LM
+
+os.environ["SERPAPI_API_KEY"] = ""
+os.environ["OPENAI_API_KEY"] = ""
+
+lm = LM(model="gpt-4o-mini")
+
+lotus.settings.configure(lm=lm)
+
+"""
+Creating a deep research pipeline with LOTUS is as simple as two commands -- `web_search` and `sem_agg`. 
+We can use `web_search` to pull a list of relevant articles from Google. From here, we can use `sem_agg` to 
+condense the articles into a more concise response. This directly makes use of Internet sources to make the 
+most accurate and up-to-date response. 
+"""
+
+question = "Why do attention-based models like Transformers scale better than CNNs or RNNs for large datasets?"
+# Pull the first five relevant articles from Google
+df_google = web_search(WebSearchCorpus.GOOGLE, question, 5)[["title", "snippet"]]
+print(f"Results from Google\n{df_google}")
+
+# Condense or summarize the articles
+df_agg_result = df_google.sem_agg(
+    "Summarize each {snippet} into a brief paragraph, answering the question: " + question
+)
+print(f"Summarized results\n{df_agg_result._output[0]}")
+
+"""
+We can also use `web_search` with arXiv to pull a list of relevant research papers for more accurate research.
+Let's try to answer the same question once again, but using both Google and arXiv.
+"""
+
+df_arxiv = web_search(WebSearchCorpus.ARXIV, question, 5)[
+    ["title", "abstract"]
+]  # Working with arXiv, we parse the `abstract` of a paper instead of `snippet`.
+print(f"Results from arXiv\n{df_arxiv}")
+
+df_arxiv = df_arxiv.rename(columns={"abstract": "snippet"})
+df_altogether = pd.concat([df_google, df_arxiv])
+df_joint_agg_result = df_altogether.sem_agg(
+    "Summarize each {snippet} into a brief paragraph, answering the question: " + question
+)
+print(f"Summarized results\n{df_joint_agg_result._output[0]}")
+
+"""
+Sometimes, less is more. Not all these sources will be equally relevant to the question. Now, let's expand to more than just `web_search` and `sem_agg` 
+to see if we can filter by relevance. Let's only take the top 5 results out of a combined
+DataFrame of fifty Google and arXiv results. This will give us a good blend of 
+diverse, specific, and relevant sources.
+"""
+
+df_google_twentyfive = web_search(WebSearchCorpus.GOOGLE, question, 25)[["title", "snippet"]]
+df_arxiv_twentyfive = web_search(WebSearchCorpus.ARXIV, question, 25)[["title", "abstract"]]
+df_arxiv_twentyfive = df_arxiv_twentyfive.rename(columns={"abstract": "snippet"})
+
+df_twentyfive = pd.concat([df_google_twentyfive, df_arxiv_twentyfive])
+"""
+Use `sem_topk` to filter by relevance. We'll use the `quick` method, which allows us to quickly sort by relevance. 
+We set `return_stats` to False because we do not require the specific statistics that were measured for scoring.
+"""
+df_twentyfive = df_twentyfive.sem_topk(
+    "Which {snippet} is most relevant to the question and unique?", K=5, method="quick", return_stats=False
+)
+
+summarized_answer = df_twentyfive.sem_agg(
+    "Summarize each {snippet} into a brief paragraph, answering the question: " + question
+)._output[0]
+print(f"Summarized results\n{summarized_answer}")
+
+"""
+A key advantage to using LOTUS, compared to LLM chats, is also that you possess all the data that you've used to answer the question.
+We can continue using `df_twentyfive` to answer follow-up questionss, extracting different pieces of information or builiding on our
+repository of context. Now, let's make use of the `parse_pdf` method to also pass in a textbook relevant to this question. 
+"""
+
+
+"""
+Even with unusual formatting or interesting PDF layouts and over multiple pages, `parse_pdf`can accurately parse text rapdily. 
+We can also simply pass in the URL of the PDF to parse it, so you do not even need to download the file.
+"""
+attention_is_all_you_need = parse_pdf("https://arxiv.org/pdf/1706.03762", per_page=False).rename(
+    columns={"file_path": "title", "content": "snippet"}
+)
+the_transformer = parse_pdf("https://web.stanford.edu/~jurafsky/slp3/9.pdf", per_page=False).rename(
+    columns={"file_path": "title", "content": "snippet"}
+)
+
+print(f"Parsed 'Attention is All You Need'\n{attention_is_all_you_need}")
+print(f"Parsed Jurafsky's 'Speech and Language Processing'\n{the_transformer}")
+df_combined = pd.concat([df_twentyfive, attention_is_all_you_need, the_transformer])
+
+answer_with_textbooks = df_combined.sem_agg(
+    "Summarize each {snippet} into a brief paragraph, answering the question: " + question
+)._output[0]
+print(f"Answer with textbooks\n{answer_with_textbooks}")