Create rag_gradio_app.py

rag_gradio_app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import torch
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from sentence_transformers import SentenceTransformer
+import openai
+
+# Load pre-trained embedding model
+model_name = 'intfloat/e5-small'
+embedding_model = HuggingFaceEmbeddings(model_name=model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load ChromaDB
+persist_directory = './docs/chroma/'
+vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)
+
+# OpenAI API Key
+openai.api_key = 'your-api-key'
+
+def retrieve_documents(question, k=5):
+    """Retrieve top K relevant documents from ChromaDB"""
+    docs = vectordb.similarity_search(question, k=k)
+    return [doc.page_content for doc in docs]
+
+def generate_response(question, context):
+    """Generate response using OpenAI GPT-4"""
+    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()
+
+def rag_pipeline(question):
+    """Full RAG Pipeline - Retrieve Docs & Generate Response"""
+    retrieved_docs = retrieve_documents(question, k=5)
+    context = " ".join(retrieved_docs)
+    response = generate_response(question, context)
+    return response, retrieved_docs
+
+def gradio_interface(question):
+    response, retrieved_docs = rag_pipeline(question)
+    return response, "\n\n".join(retrieved_docs)
+
+# Create Gradio App
+iface = gr.Interface(
+    fn=gradio_interface,
+    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 along with the AI-generated response."
+)
+
+iface.launch()