Create app.py

app.py

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+# https://python.langchain.com/docs/tutorials/rag/
+import gradio as gr
+from langchain import hub
+from langchain_chroma import Chroma
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_mistralai import MistralAIEmbeddings
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_mistralai import ChatMistralAI
+from langchain_community.document_loaders import PyPDFLoader
+import requests
+from pathlib import Path
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.retrievers import ArxivRetriever
+import bs4
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from urllib.parse import urljoin
+
+rate_limiter = InMemoryRateLimiter(
+    requests_per_second=0.1,  # <-- MistralAI free. We can only make a request once every second
+    check_every_n_seconds=0.01,  # Wake up every 100 ms to check whether allowed to make a request,
+    max_bucket_size=10,  # Controls the maximum burst size.
+)
+"""
+# get data
+urlsfile = open("urls.txt")
+urls = urlsfile.readlines()
+urls = [url.replace("\n","") for url in urls]
+urlsfile.close()
+
+# Load, chunk and index the contents of the blog.
+loader = WebBaseLoader(urls)
+docs = loader.load()
+
+# load arxiv papers 
+arxivfile = open("arxiv.txt")
+arxivs = arxivfile.readlines()
+arxivs = [arxiv.replace("\n","") for arxiv in arxivs]
+arxivfile.close()
+
+retriever = ArxivRetriever(
+    load_max_docs=2,
+    get_ful_documents=True,
+)
+
+for arxiv in arxivs: 
+    doc = retriever.invoke(arxiv) 
+    doc[0].metadata['Published'] = str(doc[0].metadata['Published'])
+    docs.append(doc[0])
+    
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+def RAG(llm, docs, embeddings):
+
+    # Split text
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(docs)
+
+    # Create vector store
+    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
+
+    # Retrieve and generate using the relevant snippets of the documents
+    retriever = vectorstore.as_retriever()
+
+    # Prompt basis example for RAG systems
+    prompt = hub.pull("rlm/rag-prompt")
+
+    # Create the chain
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    return rag_chain
+
+# LLM model
+llm = ChatMistralAI(model="mistral-large-latest", rate_limiter=rate_limiter)
+
+# Embeddings
+embed_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
+# embed_model = "nvidia/NV-Embed-v2"
+embeddings = HuggingFaceInstructEmbeddings(model_name=embed_model)
+# embeddings = MistralAIEmbeddings()
+
+# RAG chain
+rag_chain = RAG(llm, docs, embeddings)
+
+def handle_prompt(message, history):
+    try:
+        # Stream output
+        out=""
+        for chunk in rag_chain.stream(message):
+            out += chunk
+            yield out
+    except:
+        raise gr.Error("Requests rate limit exceeded")
+"""
+
+def handle_prompt(message, history): 
+    print(message)
+
+
+greetingsmessage = "Hi, I'm your personal arXiv reader. Input the arXiv number of the paper:"
+
+demo = gr.ChatInterface(handle_prompt, type="messages", title="ChangBot", theme=gr.themes.Soft(), description=greetingsmessage)
+
+demo.launch()
+
+example_questions = [
+                    "Tell me more about SimBIG",
+                    "How can you constrain neutrino mass with galaxies?", 
+                    "What is the DESI BGS?",    
+                    "What is SEDflow?", 
+                    "What are normalizing flows?"
+                     ]