Create app.py

app.py

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+import gradio as gr
+import openai
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+
+# Set API Key
+openai.api_key = "your-openai-api-key"
+
+# Load embedding model
+model_name = "intfloat/e5-small"
+embedding_model = HuggingFaceEmbeddings(model_name=model_name)
+
+# Load ChromaDB
+persist_directory = "./docs/chroma/"
+vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)
+
+# Define RAG function
+def rag_pipeline(question):
+    """Retrieve relevant documents and generate AI response"""
+    retrieved_docs = vectordb.similarity_search(question, k=5)
+    context = " ".join([doc.page_content for doc in retrieved_docs])
+    
+    # Generate AI response
+    full_prompt = f"Context: {context}\\n\\nQuestion: {question}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role": "user", "content": full_prompt}],
+        max_tokens=300,
+        temperature=0.7
+    )
+    
+    return response['choices'][0]['message']['content'].strip(), retrieved_docs
+
+# Gradio UI
+iface = gr.Interface(
+    fn=rag_pipeline,
+    inputs=gr.Textbox(label="Enter your question"),
+    outputs=[gr.Textbox(label="Generated Response"), gr.Textbox(label="Retrieved Documents")],
+    title="RAG-Based Question Answering System",
+    description="Enter a question and retrieve relevant documents with AI-generated response."
+)
+
+# Launch Gradio app
+if __name__ == "__main__":
+    iface.launch()