Create app.py

app.py

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+import gradio as gr
+import ollama
+import bs4
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import OllamaEmbeddings
+
+# Check if user has inputted a URL or uploaded a document and load, split, and retrieve documents
+def load_and_retrieve(url, document):
+    
+    # If user has inputted a URL
+    if url:
+        loader = WebBaseLoader(
+            web_paths=(url,),
+            bs_kwargs=dict() 
+        )
+        docs = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)
+        splits = text_splitter.split_documents(docs)
+        embeddings = OllamaEmbeddings(model="nomic-embed-text")
+        vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
+        return vectorstore.as_retriever()
+    
+    # If user has uploaded a document
+    if document:
+        loader = PyPDFLoader(document)
+        docs = loader.load_and_split()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)
+        splits = text_splitter.split_documents(docs)
+        embeddings = OllamaEmbeddings(model="nomic-embed-text")
+        vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
+        return vectorstore.as_retriever()
+
+# Function to format documents
+def format_docs(docs):
+    # Return the page content of each document
+    return "\n\n".join(doc.page_content for doc in docs)
+
+# Function that defines the RAG chain
+def rag_chain(url = False, document = False, question = ''):
+    retriever = load_and_retrieve(url, document)
+    retrieved_docs = retriever.invoke(question)
+    formatted_context = format_docs(retrieved_docs)
+    formatted_prompt = f"Question: {question}\n\nContext: {formatted_context}"
+    print("==============")
+    print(formatted_prompt)
+    print("==============")
+    response = ollama.chat(model='llama3', messages=[{'role': 'user', 'content': formatted_prompt}])
+    return response['message']['content']
+
+# Gradio interface
+iface = gr.Interface(
+    fn=rag_chain,
+    inputs=["text", "file", "text"],
+    outputs="text",
+    title="RAG Chain Question Answering",
+    description="Enter a URL or upload a document and a query to get answers from the RAG chain."
+)
+
+# Launch the app
+iface.launch(share=True)