import langchain from langchain.embeddings import SentenceTransformerEmbeddings from langchain.document_loaders import UnstructuredPDFLoader,UnstructuredWordDocumentLoader from langchain.indexes import VectorstoreIndexCreator from langchain.vectorstores import FAISS from zipfile import ZipFile import gradio as gr import openpyxl import os import shutil from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter import tiktoken import secrets import time import requests import tempfile from groq import Groq tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo") # create the length function def tiktoken_len(text): tokens = tokenizer.encode( text, disallowed_special=() ) return len(tokens) text_splitter = RecursiveCharacterTextSplitter( chunk_size=800, chunk_overlap=400, length_function=tiktoken_len, separators=["\n\n", "\n", " ", ""] ) embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") foo = Document(page_content='foo is fou!',metadata={"source":'foo source'}) def reset_database(ui_session_id): session_id = f"PDFAISS-{ui_session_id}" if 'drive' in session_id: print("RESET DATABASE: session_id contains 'drive' !!") return None try: shutil.rmtree(session_id) except: print(f'no {session_id} directory present') try: os.remove(f"{session_id}.zip") except: print("no {session_id}.zip present") return None def is_duplicate(split_docs,db): epsilon=0.0 print(f"DUPLICATE: Treating: {split_docs[0].metadata['source'].split('/')[-1]}") for i in range(min(3,len(split_docs))): query = split_docs[i].page_content docs = db.similarity_search_with_score(query,k=1) _ , score = docs[0] epsilon += score print(f"DUPLICATE: epsilon: {epsilon}") return epsilon < 0.1 def merge_split_docs_to_db(split_docs,db,progress,progress_step=0.1): progress(progress_step,desc="merging docs") if len(split_docs)==0: print("MERGE to db: NO docs!!") return filename = split_docs[0].metadata['source'] if is_duplicate(split_docs,db): print(f"MERGE: Document is duplicated: {filename}") return print(f"MERGE: number of split docs: {len(split_docs)}") batch = 10 for i in range(0, len(split_docs), batch): progress(i/len(split_docs),desc=f"added {i} chunks of {len(split_docs)} chunks") db1 = FAISS.from_documents(split_docs[i:i+batch], embeddings) db.merge_from(db1) return db def merge_pdf_to_db(filename,db,progress,progress_step=0.1): progress_step+=0.05 progress(progress_step,'unpacking pdf') doc = UnstructuredPDFLoader(filename).load() doc[0].metadata['source'] = filename.split('/')[-1] split_docs = text_splitter.split_documents(doc) progress_step+=0.3 progress(progress_step,'docx unpacked') return merge_split_docs_to_db(split_docs,db,progress,progress_step) def merge_docx_to_db(filename,db,progress,progress_step=0.1): progress_step+=0.05 progress(progress_step,'unpacking docx') doc = UnstructuredWordDocumentLoader(filename).load() doc[0].metadata['source'] = filename.split('/')[-1] split_docs = text_splitter.split_documents(doc) progress_step+=0.3 progress(progress_step,'docx unpacked') return merge_split_docs_to_db(split_docs,db,progress,progress_step) def merge_txt_to_db(filename,db,progress,progress_step=0.1): progress_step+=0.05 progress(progress_step,'unpacking txt') with open(filename) as f: docs = text_splitter.split_text(f.read()) split_docs = [Document(page_content=doc,metadata={'source':filename.split('/')[-1]}) for doc in docs] progress_step+=0.3 progress(progress_step,'txt unpacked') return merge_split_docs_to_db(split_docs,db,progress,progress_step) def unpack_zip_file(filename,db,progress): with ZipFile(filename, 'r') as zipObj: contents = zipObj.namelist() print(f"unpack zip: contents: {contents}") tmp_directory = filename.split('/')[-1].split('.')[-2] shutil.unpack_archive(filename, tmp_directory) if 'index.faiss' in [item.lower() for item in contents]: db2 = FAISS.load_local(tmp_directory, embeddings, allow_dangerous_deserialization=True) db.merge_from(db2) return db for file in contents: if file.lower().endswith('.docx'): db = merge_docx_to_db(f"{tmp_directory}/{file}",db,progress) if file.lower().endswith('.pdf'): db = merge_pdf_to_db(f"{tmp_directory}/{file}",db,progress) if file.lower().endswith('.txt'): db = merge_txt_to_db(f"{tmp_directory}/{file}",db,progress) return db def add_files_to_zip(session_id): zip_file_name = f"{session_id}.zip" with ZipFile(zip_file_name, "w") as zipObj: for root, dirs, files in os.walk(session_id): for file_name in files: file_path = os.path.join(root, file_name) arcname = os.path.relpath(file_path, session_id) zipObj.write(file_path, arcname) #### UI Functions #### def embed_files(files,ui_session_id,progress=gr.Progress(),progress_step=0.05): if ui_session_id not in os.environ['users'].split(', '): return "README.md", "" print(files) progress(progress_step,desc="Starting...") split_docs=[] if len(ui_session_id)==0: ui_session_id = secrets.token_urlsafe(16) session_id = f"PDFAISS-{ui_session_id}" try: db = FAISS.load_local(session_id,embeddings, allow_dangerous_deserialization=True) except: print(f"SESSION: {session_id} database does not exist, create a FAISS db") db = FAISS.from_documents([foo], embeddings) db.save_local(session_id) print(f"SESSION: {session_id} database created") print("EMBEDDED, before embeddeding: ",session_id,len(db.index_to_docstore_id)) for file_id,file in enumerate(files): print("ID : ", file_id, "FILE : ", file) file_type = file.name.split('.')[-1].lower() source = file.name.split('/')[-1] print(f"current file: {source}") progress(file_id/len(files),desc=f"Treating {source}") if file_type == 'pdf': db2 = merge_pdf_to_db(file.name,db,progress) if file_type == 'txt': db2 = merge_txt_to_db(file.name,db,progress) if file_type == 'docx': db2 = merge_docx_to_db(file.name,db,progress) if file_type == 'zip': db2 = unpack_zip_file(file.name,db,progress) if db2 != None: db = db2 db.save_local(session_id) ### move file to store ### progress(progress_step, desc = 'moving file to store') directory_path = f"{session_id}/store/" if not os.path.exists(directory_path): os.makedirs(directory_path) try: shutil.move(file.name, directory_path) except: pass ### load the updated db and zip it ### progress(progress_step, desc = 'loading db') db = FAISS.load_local(session_id,embeddings, allow_dangerous_deserialization=True) print("EMBEDDED, after embeddeding: ",session_id,len(db.index_to_docstore_id)) progress(progress_step, desc = 'zipping db for download') add_files_to_zip(session_id) print(f"EMBEDDED: db zipped") progress(progress_step, desc = 'db zipped') return f"{session_id}.zip", ui_session_id, "" def add_to_db(references,ui_session_id): files = store_files(references) return embed_files(files,ui_session_id) def export_files(references): files = store_files(references, ret_names=True) #paths = [file.name for file in files] return files def display_docs(docs): output_str = '' for i, doc in enumerate(docs): source = doc.metadata['source'].split('/')[-1] output_str += f"Ref: {i+1}\n{repr(doc.page_content)}\nSource: {source}\n*§*§*\n" return output_str def display_docs_modal(docs): output_list = [] for i, doc in enumerate(docs): source = doc.metadata['source'].split('/')[-1] output_str.append(f"Ref: {i+1}\n{repr(doc.page_content)}\nSource: {source}\n*§*§*\n") return output_list def ask_llm(system, user_input): messages = [ { "role": "system", "content": system }, { "role": "user", "content": user_input, } ] client = Groq(api_key=os.environ["GROQ_KEY"]) chat_completion = client.chat.completions.create( messages=messages, model='mixtral-8x7b-32768', ) return chat_completion.choices[0].message.content def ask_llm_stream(system, user_input): llm_response = "" client = Groq(api_key=os.environ["GROQ_KEY"]) if user_input is None or user_input == "": user_input = "What is the introduction of the document about?" messages = [ { "role": "system", "content": system }, { "role": "user", "content": user_input, } ] stream = client.chat.completions.create( messages=messages, model="mixtral-8x7b-32768", temperature=0.5, max_tokens=1024, top_p=1, stop=None, stream=True, ) for chunk in stream: llm_response += str(chunk.choices[0].delta.content) if chunk.choices[0].delta.content is not None else "" yield llm_response def ask_gpt(query, ui_session_id, history): if ui_session_id not in os.environ['users'].split(', '): return "Please Login", "", "" session_id = f"PDFAISS-{ui_session_id}" try: db = FAISS.load_local(session_id,embeddings, allow_dangerous_deserialization=True) print("ASKGPT after loading",session_id,len(db.index_to_docstore_id)) except: print(f"SESSION: {session_id} database does not exist") return f"SESSION: {session_id} database does not exist","","" docs = db.similarity_search(query, k=5) documents = "\n\n*-*-*-*-*-*\n\n".join(f"Content: {doc.page_content}\n" for doc in docs) system = f"# Instructions\nTake a deep breath and resonate step by step.\nYou are a helpful standard assistant. Your have only one mission and that consists in answering to the user input based on the **provided documents**. If the answer to the question that is asked by the user isn't contained in the **provided documents**, say so but **don't make up an answer**. I chose you because you can say 'I don't know' so please don't do like the other LLMs and don't define acronyms that aren\'t present in the following **PROVIDED DOCUMENTS** double check if it is present before answering. If some of the information can be useful for the user you can tell him.\nFinish your response by **ONE** follow up question that the provided documents could answer.\n\nThe documents are separated by the string \'*-*-*-*-*-*\'. Do not provide any explanations or details.\n\n# **Provided documents**: {documents}." gen = ask_llm_stream(system, query) last_value="" displayable_docs = display_docs(docs) while True: try: last_value = next(gen) yield last_value, displayable_docs, history + f"[query]\n{query}\n[answer]\n{last_value}\n[references]\n{displayable_docs}\n\n" except StopIteration as e: break history += f"[query]\n{query}\n[answer]\n{last_value}\n[references]\n{displayable_docs}\n\n" return last_value, displayable_docs, history def auth_user(ui_session_id): if ui_session_id in os.environ['users'].split(', '): return gr.Textbox(label='Username', visible=False), gr.File(file_count="multiple", file_types=[".txt", ".pdf",".zip",".docx"], visible=True), gr.Button("Reset AI Knowledge", visible=True), gr.Markdown(label='AI Answer', visible=True), gr.Textbox(placeholder="Type your question", label="Question ❔", scale=9, visible=True), gr.Button("▶", scale=1, visible=True), gr.Textbox(label='Sources', show_copy_button=True, visible=True), gr.File(label="Zipped database", visible=True), gr.Textbox(label='History', show_copy_button=True, visible=True) else: return gr.Textbox(label='Username', visible=True), gr.File(file_count="multiple", file_types=[".txt", ".pdf",".zip",".docx"], visible=False), gr.Button("Reset AI Knowledge", visible=False), gr.Markdown(label='AI Answer', visible=False), gr.Textbox(placeholder="Type your question", label="Question ❔", scale=9, visible=False), gr.Button("▶", scale=1, visible=False), gr.Textbox(label='Sources', show_copy_button=True, visible=False), gr.File(label="Zipped database", visible=False), gr.Textbox(label='History', show_copy_button=True, visible=False) def display_info0(documents): try: gr.Info(documents.split("\n*§*§*\n")[0]) except Exception as e: gr.Info("No Document") def display_info1(documents): try: gr.Info(documents.split("\n*§*§*\n")[1]) except Exception as e: gr.Info("No Document") def display_info2(documents): try: gr.Info(documents.split("\n*§*§*\n")[2]) except Exception as e: gr.Info("No Document") def display_info3(documents): try: gr.Info(documents.split("\n*§*§*\n")[3]) except Exception as e: gr.Info("No Document") def display_info4(documents): try: gr.Info(documents.split("\n*§*§*\n")[4]) except Exception as e: gr.Info("No Document") with gr.Blocks() as demo: gr.Markdown("# Enrich an LLM knowledge with your own documents 🧠🤖") with gr.Column(): tb_session_id = gr.Textbox(label='Username') docs_input = gr.File(file_count="multiple", file_types=[".txt", ".pdf",".zip",".docx"], visible=False) btn_reset_db = gr.Button("Reset AI Knowledge", visible=False) with gr.Column(): answer_output = gr.Markdown(label='AI Answer', visible=False) with gr.Row(): query_input = gr.Textbox(placeholder="Type your question", label="Question ❔", scale=9, visible=False) btn_askGPT = gr.Button("▶", scale=1, visible=False) with gr.Row(): btn1 = gr.Button("Ref 1") btn2 = gr.Button("Ref 2") btn3 = gr.Button("Ref 3") btn4 = gr.Button("Ref 4") btn5 = gr.Button("Ref 5") tb_sources = gr.Textbox(label='Sources', show_copy_button=True, visible=False) with gr.Accordion("Download your knowledge base and see your conversation history", open=False): db_output = gr.File(label="Zipped database", visible=False) tb_history = gr.Textbox(label='History', show_copy_button=True, visible=False, interactive=False) tb_session_id.submit(auth_user, inputs=tb_session_id, outputs=[tb_session_id, docs_input, btn_reset_db, answer_output, query_input, btn_askGPT, tb_sources, db_output, tb_history]) docs_input.upload(embed_files, inputs=[docs_input,tb_session_id], outputs=[db_output,tb_session_id, query_input]) btn_reset_db.click(reset_database,inputs=[tb_session_id],outputs=[db_output]) btn_askGPT.click(ask_gpt, inputs=[query_input, tb_session_id, tb_history], outputs=[answer_output, tb_sources, tb_history]) query_input.submit(ask_gpt, inputs=[query_input, tb_session_id, tb_history], outputs=[answer_output, tb_sources, tb_history]) btn1.click(display_info0, inputs=tb_sources, outputs=None) btn2.click(display_info1, inputs=tb_sources, outputs=None) btn3.click(display_info2, inputs=tb_sources, outputs=None) btn4.click(display_info3, inputs=tb_sources, outputs=None) btn5.click(display_info4, inputs=tb_sources, outputs=None) demo.launch(debug=False,share=False)