Create app.py

app.py

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+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain_community.graphs.networkx_graph import NetworkxEntityGraph
+from langchain.chains import GraphQAChain
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Pinecone
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain import PromptTemplate
+from neo4j import GraphDatabase
+import networkx as nx
+import pinecone
+import os
+
+# RAG Setup
+text_path = r"C:\Users\USER\Downloads\RAG_langchain\text_chunks.txt"
+loader = TextLoader(text_path, encoding='utf-8')
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=3000, chunk_overlap=4)
+docs = text_splitter.split_documents(documents)
+embeddings = HuggingFaceEmbeddings()
+
+pinecone.init(
+    api_key=os.getenv('PINECONE_API_KEY', '6396a319-9bc0-49b2-97ba-400e96eff377'),
+    environment='gcp-starter'
+)
+
+index_name = "langchain-demo"
+if index_name not in pinecone.list_indexes():
+    pinecone.create_index(name=index_name, metric="cosine", dimension=768)
+    docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)
+else:
+    docsearch = Pinecone.from_existing_index(index_name, embeddings)
+
+rag_llm = ChatGroq(
+    model="Llama3-8b-8192",
+    temperature=0,
+    max_tokens=None,
+    timeout=None,
+    max_retries=5,
+    groq_api_key='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'
+)
+
+rag_prompt = PromptTemplate(
+    template="""
+    You are a Thai rice assistant that gives concise and direct answers. 
+    Do not explain the process, 
+    just provide the answer,
+    provide the answer only in Thai."
+
+    Context: {context}
+    Question: {question}
+    Answer: 
+    """,
+    input_variables=["context", "question"]
+)
+
+rag_chain = (
+    {"context": docsearch.as_retriever(), "question": RunnablePassthrough()}
+    | rag_prompt
+    | rag_llm
+    | StrOutputParser()
+)
+
+graphrag_llm = ChatGroq(
+    model="Llama3-8b-8192",
+    temperature=0,
+    max_tokens=None,
+    timeout=None,
+    max_retries=5,
+    groq_api_key='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'
+)
+
+uri = "neo4j+s://46084f1a.databases.neo4j.io"
+user = "neo4j"
+password = "FwnX0ige_QYJk8eEYSXSF0l081mWWGIS7TFg6t8rLZc"
+driver = GraphDatabase.driver(uri, auth=(user, password))
+
+def fetch_nodes(tx):
+    query = "MATCH (n) RETURN id(n) AS id, labels(n) AS labels"
+    result = tx.run(query)
+    return result.data()
+
+def fetch_relationships(tx):
+    query = "MATCH (n)-[r]->(m) RETURN id(n) AS source, id(m) AS target, type(r) AS relation"
+    result = tx.run(query)
+    return result.data()
+
+def populate_networkx_graph():
+    G = nx.Graph()
+    with driver.session() as session:
+        nodes = session.read_transaction(fetch_nodes)
+        relationships = session.read_transaction(fetch_relationships)
+        for node in nodes:
+            G.add_node(node['id'], labels=node['labels'])
+        for relationship in relationships:
+            G.add_edge(
+                relationship['source'],
+                relationship['target'],
+                relation=relationship['relation']
+            )
+    return G
+
+networkx_graph = populate_networkx_graph()
+graph = NetworkxEntityGraph()
+graph._graph = networkx_graph
+
+graphrag_chain = GraphQAChain.from_llm(
+    llm=graphrag_llm,
+    graph=graph,
+    verbose=True
+)
+
+def get_rag_response(question):
+    response = rag_chain.invoke(question)
+    return response
+
+def get_graphrag_response(question):
+    system_prompt = "You are a Thai rice assistant that gives concise and direct answers. Do not explain the process, just provide the answer, provide the answer only in Thai."
+    formatted_question = f"System Prompt: {system_prompt}\n\nQuestion: {question}"
+    response = graphrag_chain.run(formatted_question)
+    return response
+
+def compare_models(question):
+    rag_response = get_rag_response(question)
+    graphrag_response = get_graphrag_response(question)
+    return rag_response, graphrag_response
+
+def store_feedback(feedback, question, rag_response, graphrag_response):
+    print("Storing feedback...")
+    print(f"Question: {question}")
+    print(f"RAG Response: {rag_response}")
+    print(f"GraphRAG Response: {graphrag_response}")
+    print(f"User Feedback: {feedback}")
+    
+    with open("feedback.txt", "a", encoding='utf-8') as f:
+        f.write(f"Question: {question}\n")
+        f.write(f"RAG Response: {rag_response}\n")
+        f.write(f"GraphRAG Response: {graphrag_response}\n")
+        f.write(f"User Feedback: {feedback}\n\n")
+
+def handle_feedback(feedback, question, rag_response, graphrag_response):
+    store_feedback(feedback, question, rag_response, graphrag_response)
+    return "Feedback stored successfully!"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Thai Rice Assistant A/B Testing")
+
+    with gr.Row():
+        with gr.Column():
+            question_input = gr.Textbox(label="Ask a question about Thai rice:")
+            submit_btn = gr.Button("Get Answers")
+
+        with gr.Column():
+            rag_output = gr.Textbox(label="Model A", interactive=False)
+            graphrag_output = gr.Textbox(label="Model B", interactive=False)
+
+    with gr.Row():
+        with gr.Column():
+            choice = gr.Radio(["A is better", "B is better", "Tie", "Both Bad"], label="Which response is better?")
+            send_feedback_btn = gr.Button("Send Feedback")
+
+    def on_submit(question):
+        rag_response, graphrag_response = compare_models(question)
+        return rag_response, graphrag_response
+
+    def on_feedback(feedback):
+        question = question_input.value
+        rag_response = rag_output.value
+        graphrag_response = graphrag_output.value
+        return handle_feedback(feedback, question, rag_response, graphrag_response)
+
+    submit_btn.click(on_submit, inputs=[question_input], outputs=[rag_output, graphrag_output])
+    send_feedback_btn.click(on_feedback, inputs=[choice], outputs=[])
+
+demo.launch(share=True)