Create app.py

app.py

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+import chainlit as cl
+import arxiv
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+import os
+
+# Set your OpenAI API key
+os.environ["OPENAI_API_KEY"] = "sk-proj-vFPqdrr801blzZCRBjztT3BlbkFJJJeQVcc62PA40cQ1S9Zv"
+
+# Initialize global variables
+selected_paper = None
+qa_chain = None
+papers = []
+state = "SEARCH"  # Possible states: SEARCH, SELECT, QA
+
+@cl.on_chat_start
+def start():
+    global state
+    state = "SEARCH"
+    cl.Message(content="Welcome! Please enter a search query for arXiv papers.").send()
+
+@cl.on_message
+def main(message: str):
+    global selected_paper, qa_chain, papers, state
+
+    if state == "SEARCH":
+        # Search for papers
+        search = arxiv.Search(
+            query=message,
+            max_results=5,
+            sort_by=arxiv.SortCriterion.Relevance
+        )
+
+        papers = list(search.results())
+        
+        if not papers:
+            cl.Message(content="No papers found. Please try another search query.").send()
+            return
+
+        # Create a numbered list of papers with links
+        paper_list = "\n".join([f"{i+1}. {paper.title} - {paper.authors[0]}\nLink: {paper.entry_id}" for i, paper in enumerate(papers)])
+        cl.Message(content=f"Please select a paper by entering its number:\n\n{paper_list}\n\nEnter the number of the paper you want to select:").send()
+        state = "SELECT"
+
+    elif state == "SELECT":
+        try:
+            selected_index = int(message) - 1
+            if 0 <= selected_index < len(papers):
+                selected_paper = papers[selected_index]
+            else:
+                cl.Message(content="Invalid selection. Please try again.").send()
+                return
+        except ValueError:
+            cl.Message(content="Invalid input. Please enter a number.").send()
+            return
+
+        # Download and process the selected paper
+        paper_text = selected_paper.summary
+
+        # Split the text into chunks
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        chunks = text_splitter.split_text(paper_text)
+
+        # Create embeddings and vector store
+        embeddings = OpenAIEmbeddings()
+        vectorstore = FAISS.from_texts(chunks, embeddings)
+
+        # Create the conversational chain
+        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+        qa_chain = ConversationalRetrievalChain.from_llm(
+            ChatOpenAI(temperature=0),
+            vectorstore.as_retriever(),
+            memory=memory
+        )
+
+        cl.Message(content=f"Selected paper: {selected_paper.title}\nLink: {selected_paper.entry_id}\nYou can now ask questions about this paper. Type 'new search' when you want to search for a different paper.").send()
+        state = "QA"
+
+    elif state == "QA":
+        if message.lower() == "new search":
+            state = "SEARCH"
+            selected_paper = None
+            qa_chain = None
+            papers = []
+            cl.Message(content="Sure! Please enter a new search query for arXiv papers.").send()
+        else:
+            # Answer questions about the selected paper
+            response = qa_chain({"question": message})
+            cl.Message(content=response["answer"]).send()
+
+if __name__ == "__main__":
+    cl.run()