Create app.py

app.py

@@ -0,0 +1,80 @@
+import os
+import chainlit as cl
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQA
+
+# Optional: Switch to Groq API
+USE_GROQ = False  # Set to True if using Groq
+
+if USE_GROQ:
+    import httpx
+
+def load_pdf(file_path):
+    loader = PyPDFLoader(file_path)
+    pages = loader.load_and_split()
+    return pages
+
+@cl.on_chat_start
+async def start():
+    files = await cl.AskFileMessage(
+        content="📄 Please upload a PDF file to begin.",
+        accept=["application/pdf"],
+        max_size_mb=10
+    ).send()
+
+    file = files[0]
+    file_path = file.path
+
+    pages = load_pdf(file_path)
+
+    # Create vector store
+    embeddings = OpenAIEmbeddings()
+    vectordb = FAISS.from_documents(pages, embeddings)
+
+    retriever = vectordb.as_retriever()
+
+    if USE_GROQ:
+        # Custom RAG using Groq API
+        cl.user_session.set("retriever", retriever)
+    else:
+        # Standard LangChain RetrievalQA with OpenAI
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=ChatOpenAI(temperature=0.2),
+            retriever=retriever
+        )
+        cl.user_session.set("chain", qa_chain)
+
+@cl.on_message
+async def chat(msg: cl.Message):
+    if USE_GROQ:
+        retriever = cl.user_session.get("retriever")
+        docs = retriever.get_relevant_documents(msg.content)
+        context = "\n".join([doc.page_content for doc in docs][:3])
+
+        headers = {
+            "Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}",
+            "Content-Type": "application/json"
+        }
+
+        body = {
+            "messages": [
+                {"role": "system", "content": f"You are a helpful assistant answering questions from a PDF. Use this context:\n{context}"},
+                {"role": "user", "content": msg.content}
+            ],
+            "model": "llama3-8b-8192",
+            "temperature": 0.5
+        }
+
+        async with httpx.AsyncClient() as client:
+            res = await client.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=body)
+
+        reply = res.json()["choices"][0]["message"]["content"]
+        await cl.Message(content=reply).send()
+
+    else:
+        chain = cl.user_session.get("chain")
+        response = chain.run(msg.content)
+        await cl.Message(content=response).send()