Create app.py

app.py

@@ -0,0 +1,64 @@
+from llama_index.core.indices.vector_store.base import VectorStoreIndex
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from langchain_google_genai import ChatGoogleGenerativeAI
+from llama_index.core import Settings
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
+import qdrant_client
+from llama_index.core.indices.query.schema import QueryBundle
+from llama_index.llms.gemini import Gemini
+from llama_index.embeddings.gemini import GeminiEmbedding
+from llama_index.core.memory import ChatMemoryBuffer
+import gradio as gr
+
+def embed_setup():
+  Settings.embed_model = GeminiEmbedding(api_key=os.getenv("GEMINI_API_KEY"), model_name="models/embedding-001")
+  Settings.llm =  Gemini(api_key=os.getenv("GEMINI_API_KEY"), temperature=0.1,model_name="models/gemini-pro")
+
+def qdrant_setup():
+  client = qdrant_client.QdrantClient(
+    os.getenv("QDRANT_URL"),
+    api_key = os.getenv("QDRANT_API_KEY"),
+  )
+  return client
+
+def llm_setup():
+  llm = Gemini(api_key=os.getenv("GEMINI_API_KEY"), temperature=0.6,model_name="models/gemini-pro")
+  return llm
+
+
+def query_index(index, similarity_top_k=3, streaming=True):
+    memory = ChatMemoryBuffer.from_defaults(token_limit=4000)
+    chat_engine = index.as_chat_engine(
+    chat_mode="context",
+    memory=memory,
+    system_prompt = (
+        """You are an AI assistant named Gemini, created by Google. Your task is to provide helpful, accurate, and concise responses to user queries. 
+
+    Context information is below:
+    ----------------
+    {context_str}
+    ----------------
+    Always answer based on the information in the context and be precise
+    Given this context, please respond to the following user query:
+    {query_str}
+
+    Also suggest 3 more questions based on the the context that the user can ask
+    Your response:"""
+    ),)
+    return chat_engine 
+
+def get_response(text,history=None):
+    # Use the initialized query engine to perform the query
+    response = str(chat_engine.chat(text))
+    return response
+
+embed_setup()
+client = qdrant_setup()
+llm = llm_setup()
+vector_store = QdrantVectorStore(client = client,collection_name=os.getenv("COLLECTION_NAME"))
+index = VectorStoreIndex.from_vector_store(llm = llm, vector_store = vector_store)
+chat_engine = query_index(index)  # initialize the query engine
+
+
+t = gr.ChatInterface(get_response, analytics_enabled=True)
+t.launch(debug=True, share=True)