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app.py

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+import gradio as gr
+import os
+import tempfile
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+
+# Persistent vectorstore directory
+PERSIST_DIRECTORY = "./chroma_db"
+
+# Store chat histories
+chat_histories = {}
+
+def get_session_history(session_id: str) -> BaseChatMessageHistory:
+    if session_id not in chat_histories:
+        chat_histories[session_id] = ChatMessageHistory()
+    return chat_histories[session_id]
+
+def process_files(api_key, model_name, session_id, files, question):
+    if not api_key:
+        return "Please enter your Groq API key"
+    
+    # Initialize LLM
+    llm = ChatGroq(groq_api_key=api_key, model_name=model_name)
+    
+    # Process PDFs
+    documents = []
+    for file in files:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+            tmp.write(file)
+            tmp_path = tmp.name
+        
+        loader = PyPDFLoader(tmp_path)
+        docs = loader.load()
+        documents.extend(docs)
+        os.unlink(tmp_path)  # Clean up temp file
+
+    # Split and embed
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    splits = text_splitter.split_documents(documents)
+    
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    vectorstore = Chroma.from_documents(splits, embedding=embeddings, persist_directory=PERSIST_DIRECTORY)
+    retriever = vectorstore.as_retriever()
+
+    # Setup RAG chain
+    rag_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        return_source_documents=True,
+    )
+
+    # Get chat history
+    chat_history = get_session_history(session_id)
+    
+    # Process question
+    response = rag_chain({
+        "question": question,
+        "chat_history": [(msg.content if msg.type == "human" else "Assistant: " + msg.content)
+                         for msg in chat_history.messages]
+    })
+
+    # Update history
+    chat_history.add_user_message(question)
+    chat_history.add_ai_message(response["answer"])
+    
+    # Format response
+    output = f"**Assistant:** {response['answer']}\n\n---\n**Chat History:**\n"
+    for msg in chat_history.messages[-6:]:  # Show last 3 exchanges
+        output += f"{msg.type.capitalize()}: {msg.content}\n"
+    
+    return output
+
+# Gradio Interface
+with gr.Blocks(title="RAG PDF Chat") as demo:
+    gr.Markdown("## 📚 Conversational RAG with PDF Uploads")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            api_key = gr.Textbox(
+                label="Groq API Key", 
+                type="password",
+                placeholder="Enter your API key"
+            )
+            model = gr.Dropdown(
+                label="LLM Model",
+                choices=[
+                    "qwen-2.5-32b", 
+                    "deepseek-r1-distill-llama-70b", 
+                    "gemma2-9b-it",
+                    "mixtral-8x7b-32768", 
+                    "llama-3.3-70b-versatile", 
+                    "Gemma2-9b-It"
+                ],
+                value="mixtral-8x7b-32768"
+            )
+            session_id = gr.Textbox(
+                label="Session ID",
+                value="default_session"
+            )
+            
+        with gr.Column(scale=2):
+            file_input = gr.File(
+                label="Upload PDFs",
+                file_types=[".pdf"],
+                file_count="multiple"
+            )
+            question = gr.Textbox(
+                label="Your Question",
+                placeholder="Ask about the uploaded documents..."
+            )
+            submit_btn = gr.Button("Submit")
+            output = gr.Markdown()
+    
+    submit_btn.click(
+        fn=process_files,
+        inputs=[api_key, model, session_id, file_input, question],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirement.txt

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+gradio
+langchain
+langchain-community
+langchain-groq
+pypdf
+chromadb
+sentence-transformers