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| import os | |
| import json | |
| import gradio as gr | |
| from llama_index.core import ( | |
| VectorStoreIndex, | |
| download_loader, | |
| StorageContext | |
| ) | |
| from dotenv import load_dotenv, find_dotenv | |
| import chromadb | |
| from llama_index.llms.mistralai import MistralAI | |
| from llama_index.embeddings.mistralai import MistralAIEmbedding | |
| from llama_index.vector_stores.chroma import ChromaVectorStore | |
| from llama_index.core.indices.service_context import ServiceContext | |
| TITLE = "RIZOA-AUCHAN Chatbot Demo" | |
| DESCRIPTION = "Example of an assistant with Gradio, coupling with function calling and Mistral AI via its API" | |
| PLACEHOLDER = ( | |
| "Vous pouvez me posez une question sur ce contexte, appuyer sur Entrée pour valider" | |
| ) | |
| PLACEHOLDER_URL = "Extract text from this url" | |
| llm_model = "mistral-medium" | |
| load_dotenv() | |
| env_api_key = os.environ.get("MISTRAL_API_KEY") | |
| query_engine = None | |
| # Define LLMs | |
| llm = MistralAI(api_key=env_api_key, model=llm_model) | |
| embed_model = MistralAIEmbedding(model_name="mistral-embed", api_key=env_api_key) | |
| # create client and a new collection | |
| db = chromadb.PersistentClient(path="./chroma_db") | |
| chroma_collection = db.get_or_create_collection("quickstart") | |
| # set up ChromaVectorStore and load in data | |
| vector_store = ChromaVectorStore(chroma_collection=chroma_collection) | |
| storage_context = StorageContext.from_defaults(vector_store=vector_store) | |
| service_context = ServiceContext.from_defaults( | |
| chunk_size=1024, llm=llm, embed_model=embed_model | |
| ) | |
| PDFReader = download_loader("PDFReader") | |
| loader = PDFReader() | |
| index = VectorStoreIndex( | |
| [], service_context=service_context, storage_context=storage_context | |
| ) | |
| query_engine = index.as_query_engine(similarity_top_k=5) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Image(value=".\img\logo_rizoa_auchan.jpg", | |
| height=250, | |
| width=250, | |
| container=False, | |
| show_download_button=False | |
| ) | |
| with gr.Column(scale=4): | |
| gr.Markdown( | |
| """ | |
| # Bienvenue au Chatbot FAIR-PLAI | |
| Ce chatbot est un assistant numérique, médiateur des vendeurs-acheteurs | |
| """ | |
| ) | |
| # gr.Markdown(""" ### 1 / Extract data from PDF """) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # input_file = gr.File( | |
| # label="Load a pdf", | |
| # file_types=[".pdf"], | |
| # file_count="single", | |
| # type="filepath", | |
| # interactive=True, | |
| # ) | |
| # file_msg = gr.Textbox( | |
| # label="Loaded documents:", container=False, visible=False | |
| # ) | |
| # input_file.upload( | |
| # fn=load_document, | |
| # inputs=[ | |
| # input_file, | |
| # ], | |
| # outputs=[file_msg], | |
| # concurrency_limit=20, | |
| # ) | |
| # file_btn = gr.Button(value="Encode file ✅", interactive=True) | |
| # btn_msg = gr.Textbox(container=False, visible=False) | |
| # with gr.Row(): | |
| # db_list = gr.Markdown(value=get_documents_in_db) | |
| # delete_btn = gr.Button(value="Empty db 🗑️", interactive=True, scale=0) | |
| # file_btn.click( | |
| # load_file, | |
| # inputs=[input_file], | |
| # outputs=[file_msg, btn_msg, db_list], | |
| # show_progress="full", | |
| # ) | |
| # delete_btn.click(empty_db, outputs=[db_list], show_progress="minimal") | |
| gr.Markdown(""" ### Ask a question """) | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox(placeholder=PLACEHOLDER) | |
| clear = gr.ClearButton([msg, chatbot]) | |
| def respond(message, chat_history): | |
| response = query_engine.query(message) | |
| chat_history.append((message, str(response))) | |
| return chat_history | |
| msg.submit(respond, [msg, chatbot], [chatbot]) | |
| demo.title = TITLE | |
| if __name__ == "__main__": | |
| demo.launch() | |