Update app.py

app.py

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+import os
+from dotenv import load_dotenv
+from gradio.components import upload_button
+from llama_index.llms.groq import Groq
+from llama_index.llms.openai import OpenAI
+from llama_index.core import Settings
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.core.node_parser import SentenceSplitter
+from llama_parse import LlamaParse
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.core.retrievers import VectorIndexRetriever
+from llama_index.core import get_response_synthesizer
+from llama_index.core.query_engine import RetrieverQueryEngine
+from llama_index.core.postprocessor import SimilarityPostprocessor
+#from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import gradio as gr
+import shutil
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')   
+#GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+LLAMAINDEX_API_KEY = os.getenv('LLAMAINDEX_API_KEY')
+
+# llm = Groq(model="llama-3.1-70b-versatile", api_key=GROQ_API_KEY)
+llm = OpenAI(model="gpt-4o-mini",api_key = OPENAI_API_KEY)
+# response = llm.complete("Explain the importance of low latency LLMs")
+# response.text
+Settings.llm = llm
+
+# set up embedding model
+# embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+embed_model = OpenAIEmbedding()
+Settings.embed_model = embed_model
+
+# create splitter
+splitter = SentenceSplitter(chunk_size=10000, chunk_overlap=100)
+Settings.transformations = [splitter]
+
+def upload_file(file_ls):
+    try:
+        shutil.rmtree('./data')
+    except:
+        pass
+    UPLOAD_FOLDER = './data'
+    if not os.path.exists(UPLOAD_FOLDER):
+        os.mkdir(UPLOAD_FOLDER)
+    for file in file_ls:
+        shutil.copy(file, UPLOAD_FOLDER)
+    gr.Info("File uploaded")
+
+def process_documents():
+    # create parser
+    parser = LlamaParse(
+        api_key=LLAMAINDEX_API_KEY, 
+        result_type="markdown",  # "markdown" and "text" are available
+        verbose=True,
+    )
+
+    filename_fn = lambda filename: {"file_name": filename}
+    required_exts = [".pdf",".docx"]
+    file_extractor = {".pdf": parser}
+    reader = SimpleDirectoryReader(
+        input_dir="./data",
+        file_extractor=file_extractor,
+        required_exts=required_exts,
+        recursive=True,
+        file_metadata=filename_fn
+    )
+    documents = reader.load_data()
+    len_docs = len(documents)
+    print("index creating with `%d` documents", len(documents))
+    global index
+    index = VectorStoreIndex.from_documents(documents, embed_model=embed_model, transformations=[splitter])
+    index.storage_context.persist(persist_dir="./vectordb")
+    return f"Processed {len_docs} documents successfully.{len_docs}"
+
+def query_index(query_input):
+    # set up retriever
+    retriever = VectorIndexRetriever(
+        index=index,
+        similarity_top_k = 15,
+        #vector_store_query_mode="mmr",
+        #vector_store_kwargs={"mmr_threshold": 0.4}
+    )
+
+    # set up response synthesizer
+    # response_synthesizer = get_response_synthesizer()
+
+    # setting up query engine
+    query_engine = RetrieverQueryEngine(
+        retriever = retriever,
+        node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.53)],
+        response_synthesizer=get_response_synthesizer(response_mode="tree_summarize",verbose=True) 
+    )
+    # print(query_engine.get_prompts())
+
+    output = query_engine.query(query_input)
+    return output.response
+# source_nodes_list = output.source_nodes
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# RAG with Llamaindex")
+    
+    upload_button = gr.UploadButton("Click to upload a file", file_count="multiple")
+    upload_button.upload(upload_file, upload_button)
+    # File upload interface
+    # with gr.Row():
+    #     docs = gr.Files(label="Upload Documents", file_types=[".txt", ".pdf"])
+    
+    # Process button
+    process_button = gr.Button("Process Documents")
+    
+    # Output for document processing
+    process_output = gr.Textbox(label="Processing Output")
+    
+    # Query interface
+    query_input = gr.Textbox(label="Enter your query")
+    query_button = gr.Button("Submit Query")
+    query_output = gr.Textbox(label="Response")
+
+    # Create Gradio interface for document upload
+    # upload_interface = gr.Interface(
+    #     fn=process_documents,
+    #     inputs=gr.inputs.File(file_count="multiple"),
+    #     outputs="text",
+    #     title="Upload Documents",
+    #     description="Upload text files to index them for querying."
+    # )
+    # # Linking the processing function
+    process_button.click(fn=process_documents, inputs=None, outputs=process_output)
+    
+    # Linking the query function
+    query_button.click(fn=query_index, inputs=query_input, outputs=query_output)
+
+# Run the interface
+if __name__ == "__main__":
+    demo.launch()