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app.py

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+import streamlit as st 
+import llama_index 
+import logging
+import sys
+import os 
+import wikipedia
+from llama_index import VectorStoreIndex, SimpleDirectoryReader
+from llama_index.indices.query.query_transform import HyDEQueryTransform
+from llama_index.query_engine.transform_query_engine import TransformQueryEngine
+# from llama_index.indices.vector_store import ChatGPTRetrievalPluginIndex
+from llama_index.readers import ChatGPTRetrievalPluginReader
+from IPython.display import Markdown, display
+
+def get_wikipedia_document(topic):
+    wiki_wiki = wikipedia.Wikipedia('en')
+
+    page = wiki_wiki.page(topic)
+    if page.exists():
+        return page.text
+    else:
+        return None
+    
+def write_string_to_file(text, filename):
+    with open(filename, 'w') as file:
+        file.write(text)
+
+
+def ui(): 
+
+    api_key = st.text_input('Enter your OpenAI key here: ')
+    if api_key is not None:
+        os.environ["OPENAI_API_KEY"] = api_key
+        logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+        logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+        topic_name= st.text_input('Enter your topic for Wikipedia: ')
+        if topic_name is not None:
+            page_object = wikipedia.page(topic_name)
+            content = page_object.content
+            filename = f"./Data/{topic_name}.txt"
+            write_string_to_file(content, filename)
+
+            documents = SimpleDirectoryReader(f'./Data/{topic_name}.txt"').load_data()
+            index = VectorStoreIndex.from_documents(documents)
+
+
+            query_str= st.text_input('Enter your query for the document: ')
+            if query_str is not None:
+                query_engine = index.as_query_engine()
+                response = query_engine.query(query_str)
+                hyde = HyDEQueryTransform(include_original=True)
+                hyde_query_engine = TransformQueryEngine(query_engine, hyde)
+
+                response = hyde_query_engine.query(query_str)
+                query_bundle = hyde(query_str)
+                hyde_doc = query_bundle.embedding_strs[0]
+                st.text(hyde_doc)
+            
+
+
+if __name__=="__main__":
+    ui()
+
+
+
+
+
+
+
+
+

requirements.txt

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+streamlit 
+llama_index 
+openai 
+tiktoken 
+chromadb 
+wikipedia