Create app.py

app.py

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+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import Settings
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.core import SummaryIndex, VectorStoreIndex
+from llama_index.core.tools import QueryEngineTool
+from llama_index.core.query_engine.router_query_engine import RouterQueryEngine
+from llama_index.core.selectors import LLMSingleSelector
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from llama_index.llms.groq import Groq
+from llama_index.core import Settings
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+
+embed_model= HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
+
+GROQ_API_KEY = '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'
+
+llm = Groq(model="llama3-70b-8192", api_key=GROQ_API_KEY)
+
+Settings.llm = llm
+Settings.embed_model = embed_model
+
+from llama_index.core import SimpleDirectoryReader
+
+documents = SimpleDirectoryReader("files").load_data()
+
+from llama_index.core import VectorStoreIndex, StorageContext
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+import qdrant_client
+
+client = qdrant_client.QdrantClient(
+    location=":memory:",
+)
+
+vector_store = QdrantVectorStore(
+    collection_name = "paper",
+    client=client,
+    enable_hybrid=True,
+    batch_size=20,
+)
+
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+index = VectorStoreIndex.from_documents(
+    documents,
+    embed_model=embed_model,
+    storage_context=storage_context,
+)
+
+from llama_index.core.memory import ChatMemoryBuffer
+
+memory = ChatMemoryBuffer.from_defaults(token_limit= 3000)
+
+chat_engine = index.as_chat_engine(
+    chat_mode="context",
+    memory=memory,
+    system_prompt=(
+        "You are an AI assistant who answers the user questions"
+    ),
+)
+
+
+
+def is_greeting(user_input):
+
+    greetings = ["hello", "hi", "hey", "good morning", "good afternoon", "good evening", "greetings"]
+    user_input_lower = user_input.lower().strip()
+    return any(greet in user_input_lower for greet in greetings)
+
+def is_bye(user_input):
+    greetings = ["bye","thanks", "thank you", "thanks a lot", "bye bye", "have a good day"]
+    user_input_lower = user_input.lower().strip()
+    return any(greet in user_input_lower for greet in greetings)
+
+import gradio as gr
+def chat_with_ai(user_input, chat_history):
+    if is_greeting(str(user_input)):
+        response = 'hi,how can i help you?'
+        chat_history.append((user_input, response))
+        return chat_history, ""
+    if is_bye(str(user_input)):
+        response = "you're welcome"
+        chat_history.append((user_input, response))
+        return chat_history, ""
+    response = chat_engine.chat(user_input)
+    full_text = response.response
+
+    references = response.source_nodes
+    ref,pages = [],[]
+    for i in range(len(references)):
+      if references[i].metadata['file_name'] not in ref:
+        ref.append(references[i].metadata['file_name'])
+      pages.append(references[i].metadata['page_label'])
+    complete_response = str(full_text) + "\n\n" + "references:" + str(ref) + "\n\n" + "pages:" + str(pages)
+    if ref !=[] :
+      chat_history.append((user_input, complete_response))
+      ref = []
+    elif ref==[] :
+      chat_history.append((user_input,str(response)))
+
+    return chat_history, ""
+
+
+def gradio_chatbot():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Chat Interface for llama3.1_70B with Groq and llama_index")
+
+        chatbot = gr.Chatbot(label="llamaindex Chatbot")
+        user_input = gr.Textbox(
+            placeholder="Ask a question...", label="Enter your question"
+        )
+
+        submit_button = gr.Button("Send")
+
+        chat_history = gr.State([])
+
+        submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
+
+        user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
+
+    return demo
+
+gradio_chatbot().launch(debug=True)