diff --git a/extensions/superbig/requirements.txt b/extensions/superbig/requirements.txt
new file mode 100644
index 00000000..20535c51
--- /dev/null
+++ b/extensions/superbig/requirements.txt
@@ -0,0 +1,3 @@
+chromadb==0.3.18
+posthog==2.4.2
+sentence_transformers==2.2.2
diff --git a/extensions/superbig/script.py b/extensions/superbig/script.py
new file mode 100644
index 00000000..dafc63e5
--- /dev/null
+++ b/extensions/superbig/script.py
@@ -0,0 +1,145 @@
+import datetime
+import re
+import textwrap
+
+import chromadb
+import gradio as gr
+import posthog
+import torch
+from chromadb.config import Settings
+from modules import shared
+from sentence_transformers import SentenceTransformer
+
+print('Intercepting all calls to posthog :)')
+posthog.capture = lambda *args, **kwargs: None
+
+
+class Collecter():
+    def __init__(self):
+        pass
+
+    def add(self, texts: list[str]):
+        pass
+
+    def get(self, search_strings: list[str], n_results: int) -> list[str]:
+        pass
+
+    def clear(self):
+        pass
+
+
+class Embedder():
+    def __init__(self):
+        pass
+
+    def embed(self, text: str) -> list[torch.Tensor]:
+        pass
+
+
+class ChromaCollector(Collecter):
+    def __init__(self, embedder: Embedder):
+        super().__init__()
+        self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
+        self.embedder = embedder
+        self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed)
+        self.ids = []
+
+    def add(self, texts: list[str]):
+        self.ids = [f"id{i}" for i in range(len(texts))]
+        self.collection.add(documents=texts, ids=self.ids)
+
+    def get(self, search_strings: list[str], n_results: int) -> list[str]:
+        result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['documents'][0]
+        return result
+
+    def clear(self):
+        self.collection.delete(ids=self.ids)
+
+
+class SentenceTransformerEmbedder(Embedder):
+    def __init__(self) -> None:
+        self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+        self.embed = self.model.encode
+
+
+embedder = SentenceTransformerEmbedder()
+collector = ChromaCollector(embedder)
+global_data = ''
+
+
+def feed_data_into_collector(corpus):
+    global collector
+    chunk_len = 700
+    data_chunks = [corpus[i:i + chunk_len] for i in range(0, len(corpus), chunk_len)]
+    collector.clear()
+    collector.add(data_chunks)
+
+
+def input_modifier(string):
+
+    # Find the user input
+    pattern = re.compile(r"<\|begin-user-input\|>(.*?)<\|end-user-input\|>")
+    match = re.search(pattern, string)
+    if match:
+        user_input = match.group(1)
+    else:
+        user_input = ''
+
+    # Get the 5 most similar chunks
+    feed_data_into_collector(global_data)
+    results = collector.get(user_input, n_results=5)
+
+    # Make the replacements
+    string = string.replace('<|begin-user-input|>', '')
+    string = string.replace('<|end-user-input|>', '')
+    string = string.replace('<|injection-point|>', '\n'.join(results))
+
+    return string
+
+
+def ui():
+    gr.Markdown(textwrap.dedent("""
+
+    *This extension is currently experimental and under development.*
+
+    ## How to use it
+
+    1) Paste your input text (of whatever length) into the text box below.
+    2) Click on the "Apply" button located below the text box
+    3) In your prompt, enter your question between <|begin-user-input|> and <|end-user-input|>, and specify the injection point with <|injection-point|>
+
+    ## How it works
+
+    In the background, the 5 chunks in the input text most similar to the user input will be placed at the injection point, and the special tokens above will be removed. Then the text generation will proceed as usual.
+
+    ## Example
+
+    For your convenience, you can use the following prompt as a starting point (for Alpaca models):
+
+    ```
+    Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+    ### Instruction:
+    You are ArxivGPT, trained on millions of Arxiv papers. You always answer the question, even if full context isn't provided to you. The following are snippets from an Arxiv paper. Use the snippets to answer the question. Think about it step by step
+
+    <|injection-point|>
+
+    ### Input:
+    <|begin-user-input|>What datasets are mentioned in the paper above?<|end-user-input|>
+
+    ### Response:
+    ```
+
+    """))
+    if shared.is_chat():
+        # Chat mode has to be handled differently, probably using a custom_generate_chat_prompt
+        pass
+    else:
+        data_input = gr.Textbox(lines=20, label='Input data', info='Paste your input data below and then click on Apply before generating.')
+        with gr.Row():
+            update = gr.Button('Apply')
+            last_updated = gr.Markdown()
+
+        update.click(
+            lambda x: globals().update(global_data=x), data_input, None).then(
+            lambda: "Last updated on " + str(datetime.datetime.now()), None, last_updated, show_progress=False)