Create app.py

app.py

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+import qdrant_client
+
+from llama_index.core import VectorStoreIndex, ServiceContext, SimpleDirectoryReader
+from llama_index.core import load_index_from_storage
+from llama_index.llms.ollama import Ollama
+from llama_index.core import StorageContext
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import Settings
+from llama_index.core import set_global_service_context 
+
+import gradio as gr
+
+DOC_PATH = '/Users/simyinkuan/Documents/rag_llama/ollama-llamaindex-mixtral-python-playground/data/pdf_esg'
+INDEX_PATH = '//Users/simyinkuan/Documents/rag_llama/ollama-llamaindex-mixtral-python-playground/storage'
+Settings.llm = Ollama(model="mistral")
+Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+service_context = ServiceContext.from_defaults(llm=Ollama(model="mistral"),embed_model = embed_model)
+set_global_service_context(service_context)
+
+def construct_index(doc_path=DOC_PATH, index_store=INDEX_PATH, use_cache=False):
+    client = qdrant_client.QdrantClient(path="./qdrant_data")
+    vector_store = QdrantVectorStore(client=client, collection_name="esg")
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+    if use_cache:
+        # rebuild storage context
+        storage_context = StorageContext.from_defaults(persist_dir=index_store)
+        index = load_index_from_storage(storage_context)  # load index
+    else:
+        reader = SimpleDirectoryReader(input_dir="/Users/simyinkuan/Documents/rag_llama/ollama-llamaindex-mixtral-python-playground/data/pdf_esg")
+        documents = reader.load_data()
+        index = VectorStoreIndex.from_documents(documents)
+        index.storage_context.persist(index_store)
+    return None
+
+def qabot(input_text, index_store = INDEX_PATH):
+  
+
+    storage_context = StorageContext.from_defaults(persist_dir=index_store)
+
+    # Load the data
+    index = load_index_from_storage(storage_context)
+
+    query_engine = index.as_query_engine()
+    response = query_engine.query(input_text)
+    return response.response
+
+if __name__ == "__main__":
+    construct_index(DOC_PATH, use_cache=False)
+    # create_index_retriever_query_engine()
+    iface = gr.Interface(fn=qabot, inputs=gr.Textbox(lines=7, label='Enter your query'),
+                         outputs="text",
+                         title="ESG Chatbot")
+    iface.launch(share=True)