| 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") |
|
|
| |
| def tiktoken_len(text): |
| tokens = tokenizer.encode( |
| text, |
| disallowed_special=() |
| ) |
| return len(tokens) |
|
|
| text_splitter = RecursiveCharacterTextSplitter( |
| chunk_size=750, |
| chunk_overlap=350, |
| length_function=tiktoken_len, |
| separators=["\n\n", "\n", " ", ""] |
| ) |
|
|
| embeddings = SentenceTransformerEmbeddings(model_name="all-mpnet-base-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) |
|
|
| |
|
|
| 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) |
| |
| 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 |
|
|
| |
| 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) |
| |
| 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 hide_source(): |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
|
|
|
|
| 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="llama3-70b-8192", |
| ) |
| 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): |
| print(f"before: {os.environ['prompts']}") |
| os.environ['prompts'] += ', ' + query |
| print(f"after: {os.environ['prompts']}") |
|
|
| 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=4) |
|
|
| 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) |
| yn_display = len(docs)*[True]+(5-len(docs))*[False] |
| |
| 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", gr.Button("Ref 1", visible=yn_display[0]), gr.Button("Ref 2", visible=yn_display[1]), gr.Button("Ref 3", visible=yn_display[2]), gr.Button("Ref 4", visible=yn_display[3]), gr.Button("Ref 5", visible=yn_display[4]) |
| 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, gr.Button("Ref 1", visible=yn_display[0]), gr.Button("Ref 2", visible=yn_display[1]), gr.Button("Ref 3", visible=yn_display[2]), gr.Button("Ref 4", visible=yn_display[3]), gr.Button("Ref 5", visible=yn_display[4]) |
|
|
|
|
| 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: |
| return gr.Markdown(value=documents.split("\n*§*§*\n")[0], label='Source', visible=True), gr.Button('Hide', visible=True) |
| except Exception as e: |
| gr.Info("No Document") |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
|
|
| def display_info1(documents): |
| try: |
| return gr.Markdown(value=documents.split("\n*§*§*\n")[1], label='Source', visible=True), gr.Button('Hide', visible=True) |
| except Exception as e: |
| gr.Info("No Document") |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
| |
| def display_info2(documents): |
| try: |
| return gr.Markdown(value=documents.split("\n*§*§*\n")[2], label='Source', visible=True), gr.Button('Hide', visible=True) |
| except Exception as e: |
| gr.Info("No Document") |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
| |
| def display_info3(documents): |
| try: |
| return gr.Markdown(value=documents.split("\n*§*§*\n")[3], label='Source', visible=True), gr.Button('Hide', visible=True) |
| except Exception as e: |
| gr.Info("No Document") |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
| |
| def display_info4(documents): |
| try: |
| return gr.Markdown(value=documents.split("\n*§*§*\n")[4], label='Source', visible=True), gr.Button('Hide', visible=True) |
| except Exception as e: |
| gr.Info("No Document") |
| return gr.Markdown(label='Source', visible=False), gr.Button('Hide', visible=False) |
|
|
| 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) |
| btn_hide_source = gr.Button('Hide', visible=False) |
| md_ref = gr.Markdown(label='Source', 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", visible=False) |
| btn2 = gr.Button("Ref 2", visible=False) |
| btn3 = gr.Button("Ref 3", visible=False) |
| btn4 = gr.Button("Ref 4", visible=False) |
| btn5 = gr.Button("Ref 5", visible=False) |
| |
| |
| 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, btn1, btn2, btn3, btn4, btn5]) |
| query_input.submit(ask_gpt, inputs=[query_input, tb_session_id, tb_history], outputs=[answer_output, tb_sources, tb_history, btn1, btn2, btn3, btn4, btn5]) |
|
|
| btn1.click(display_info0, inputs=tb_sources, outputs=[md_ref, btn_hide_source]) |
| btn2.click(display_info1, inputs=tb_sources, outputs=[md_ref, btn_hide_source]) |
| btn3.click(display_info2, inputs=tb_sources, outputs=[md_ref, btn_hide_source]) |
| btn4.click(display_info3, inputs=tb_sources, outputs=[md_ref, btn_hide_source]) |
| btn5.click(display_info4, inputs=tb_sources, outputs=[md_ref, btn_hide_source]) |
| btn_hide_source.click(hide_source, inputs=None, outputs=[md_ref, btn_hide_source]) |
| |
|
|
|
|
| demo.launch(debug=False,share=False) |
