import re import datetime from typing import TypeVar, Dict, List, Tuple import time from itertools import compress import pandas as pd import numpy as np # Model packages import torch.cuda from threading import Thread from transformers import pipeline, TextIteratorStreamer # Alternative model sources #from dataclasses import asdict, dataclass # Langchain functions from langchain.prompts import PromptTemplate from langchain.vectorstores import FAISS from langchain.retrievers import SVMRetriever from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.docstore.document import Document # For keyword extraction (not currently used) #import nltk #nltk.download('wordnet') from nltk.corpus import stopwords from nltk.tokenize import RegexpTokenizer from nltk.stem import WordNetLemmatizer from keybert import KeyBERT # For Name Entity Recognition model #from span_marker import SpanMarkerModel # Not currently used # For BM25 retrieval from gensim.corpora import Dictionary from gensim.models import TfidfModel, OkapiBM25Model from gensim.similarities import SparseMatrixSimilarity import gradio as gr torch.cuda.empty_cache() PandasDataFrame = TypeVar('pd.core.frame.DataFrame') embeddings = None # global variable setup vectorstore = None # global variable setup model_type = None # global variable setup max_memory_length = 0 # How long should the memory of the conversation last? full_text = "" # Define dummy source text (full text) just to enable highlight function to load model = [] # Define empty list for model functions to run tokenizer = [] # Define empty list for model functions to run ## Highlight text constants hlt_chunk_size = 12 hlt_strat = [" ", ". ", "! ", "? ", ": ", "\n\n", "\n", ", "] hlt_overlap = 4 ## Initialise NER model ## ner_model = []#SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-multinerd") # Not currently used ## Initialise keyword model ## # Used to pull out keywords from chat history to add to user queries behind the scenes kw_model = pipeline("feature-extraction", model="sentence-transformers/all-MiniLM-L6-v2") if torch.cuda.is_available(): torch_device = "cuda" gpu_layers = 0 else: torch_device = "cpu" gpu_layers = 0 print("Running on device:", torch_device) threads = 8 #torch.get_num_threads() print("CPU threads:", threads) # Flan Alpaca (small, fast) Model parameters temperature: float = 0.1 top_k: int = 3 top_p: float = 1 repetition_penalty: float = 1.3 flan_alpaca_repetition_penalty: float = 1.3 tinyllama_repetition_penalty: float = 1.5 last_n_tokens: int = 64 max_new_tokens: int = 512 seed: int = 42 reset: bool = False stream: bool = True threads: int = threads batch_size:int = 256 context_length:int = 4096 sample = True class CtransInitConfig_gpu: def __init__(self, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, last_n_tokens=last_n_tokens, max_new_tokens=max_new_tokens, seed=seed, reset=reset, stream=stream, threads=threads, batch_size=batch_size, context_length=context_length, gpu_layers=gpu_layers): self.temperature = temperature self.top_k = top_k self.top_p = top_p self.repetition_penalty = repetition_penalty# repetition_penalty self.last_n_tokens = last_n_tokens self.max_new_tokens = max_new_tokens self.seed = seed self.reset = reset self.stream = stream self.threads = threads self.batch_size = batch_size self.context_length = context_length self.gpu_layers = gpu_layers # self.stop: list[str] = field(default_factory=lambda: [stop_string]) def update_gpu(self, new_value): self.gpu_layers = new_value class CtransInitConfig_cpu(CtransInitConfig_gpu): def __init__(self): super().__init__() self.gpu_layers = 0 gpu_config = CtransInitConfig_gpu() cpu_config = CtransInitConfig_cpu() #@dataclass #class CtransGenGenerationConfig: # top_k: int = top_k # top_p: float = top_p # temperature: float = temperature # repetition_penalty: float = tinyllama_repetition_penalty # last_n_tokens: int = last_n_tokens # seed: int = seed # batch_size:int = batch_size # threads: int = threads # reset: bool = True class CtransGenGenerationConfig: def __init__(self, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, last_n_tokens=last_n_tokens, seed=seed, threads=threads, batch_size=batch_size, reset=True ): self.temperature = temperature self.top_k = top_k self.top_p = top_p self.repetition_penalty = repetition_penalty# repetition_penalty self.last_n_tokens = last_n_tokens self.seed = seed self.threads = threads self.batch_size = batch_size self.reset = reset # Vectorstore funcs def docs_to_faiss_save(docs_out:PandasDataFrame, embeddings=embeddings): print(f"> Total split documents: {len(docs_out)}") vectorstore_func = FAISS.from_documents(documents=docs_out, embedding=embeddings) ''' #with open("vectorstore.pkl", "wb") as f: #pickle.dump(vectorstore, f) ''' #if Path(save_to).exists(): # vectorstore_func.save_local(folder_path=save_to) #else: # os.mkdir(save_to) # vectorstore_func.save_local(folder_path=save_to) global vectorstore vectorstore = vectorstore_func out_message = "Document processing complete" #print(out_message) #print(f"> Saved to: {save_to}") return out_message # Prompt functions def base_prompt_templates(model_type = "Flan Alpaca (small, fast)"): #EXAMPLE_PROMPT = PromptTemplate( # template="\nCONTENT:\n\n{page_content}\n\nSOURCE: {source}\n\n", # input_variables=["page_content", "source"], #) CONTENT_PROMPT = PromptTemplate( template="{page_content}\n\n",#\n\nSOURCE: {source}\n\n", input_variables=["page_content"] ) # The main prompt: instruction_prompt_template_alpaca_quote = """### Instruction: Quote directly from the SOURCE below that best answers the QUESTION. Only quote full sentences in the correct order. If you cannot find an answer, start your response with "My best guess is: ". CONTENT: {summaries} QUESTION: {question} Response:""" instruction_prompt_template_alpaca = """### Instruction: ### User: Answer the QUESTION using information from the following CONTENT. CONTENT: {summaries} QUESTION: {question} Response:""" instruction_prompt_template_openllama = """Answer the QUESTION using information from the following CONTENT. QUESTION - {question} CONTENT - {summaries} Answer:""" instruction_prompt_template_platypus = """### Instruction: Answer the QUESTION using information from the following CONTENT. CONTENT: {summaries} QUESTION: {question} ### Response:""" instruction_prompt_template_wizard_orca_quote = """### HUMAN: Quote text from the CONTENT to answer the QUESTION below. CONTENT - {summaries} QUESTION - {question} ### RESPONSE: """ instruction_prompt_template_wizard_orca = """### HUMAN: Answer the QUESTION below based on the CONTENT. Only refer to CONTENT that directly answers the question. CONTENT - {summaries} QUESTION - {question} ### RESPONSE: """ instruction_prompt_template_orca = """ ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: Answer the QUESTION with a short response using information from the following CONTENT. QUESTION: {question} CONTENT: {summaries} ### Response:""" instruction_prompt_template_orca_quote = """ ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: Quote text from the CONTENT to answer the QUESTION below. QUESTION: {question} CONTENT: {summaries} ### Response: """ instruction_prompt_template_orca_rev = """ ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: Answer the QUESTION with a short response using information from the following CONTENT. QUESTION: {question} CONTENT: {summaries} ### Response:""" instruction_prompt_mistral_orca = """<|im_start|>system\n You are an AI assistant that follows instruction extremely well. Help as much as you can. <|im_start|>user\n Answer the QUESTION using information from the following CONTENT. Respond with short answers that directly answer the question. CONTENT: {summaries} QUESTION: {question}\n Answer:<|im_end|>""" instruction_prompt_tinyllama_orca = """<|im_start|>system\n You are an AI assistant that follows instruction extremely well. Help as much as you can. <|im_start|>user\n Answer the QUESTION using information from the following CONTENT. Only quote text that directly answers the question and nothing more. If you can't find an answer to the question, respond with "Sorry, I can't find an answer to that question.". CONTENT: {summaries} QUESTION: {question}\n Answer:<|im_end|>""" instruction_prompt_marx = """ ### HUMAN: Answer the QUESTION using information from the following CONTENT. CONTENT: {summaries} QUESTION: {question} ### RESPONSE: """ if model_type == "Flan Alpaca (small, fast)": INSTRUCTION_PROMPT=PromptTemplate(template=instruction_prompt_template_alpaca, input_variables=['question', 'summaries']) elif model_type == "Mistral Open Orca (larger, slow)": INSTRUCTION_PROMPT=PromptTemplate(template=instruction_prompt_mistral_orca, input_variables=['question', 'summaries']) return INSTRUCTION_PROMPT, CONTENT_PROMPT def generate_expanded_prompt(inputs: Dict[str, str], instruction_prompt, content_prompt, extracted_memory, vectorstore, embeddings): # , question = inputs["question"] chat_history = inputs["chat_history"] new_question_kworded = adapt_q_from_chat_history(question, chat_history, extracted_memory) # new_question_keywords, docs_keep_as_doc, doc_df, docs_keep_out = hybrid_retrieval(new_question_kworded, vectorstore, embeddings, k_val = 10, out_passages = 2, vec_score_cut_off = 1, vec_weight = 1, bm25_weight = 1, svm_weight = 1)#, #vectorstore=globals()["vectorstore"], embeddings=globals()["embeddings"]) # Expand the found passages to the neighbouring context docs_keep_as_doc, doc_df = get_expanded_passages(vectorstore, docs_keep_out, width=3) if docs_keep_as_doc == []: {"answer": "I'm sorry, I couldn't find a relevant answer to this question.", "sources":"I'm sorry, I couldn't find a relevant source for this question."} # Build up sources content to add to user display doc_df['meta_clean'] = [f"{' '.join(f'{k}: {v}' for k, v in d.items() if k != 'page_section')}" for d in doc_df['metadata']] doc_df['content_meta'] = doc_df['meta_clean'].astype(str) + ".

" + doc_df['page_content'].astype(str) modified_page_content = [f" SOURCE {i+1} - {word}" for i, word in enumerate(doc_df['page_content'])] docs_content_string = ''.join(modified_page_content) sources_docs_content_string = '

'.join(doc_df['content_meta'])#.replace(" "," ")#.strip() instruction_prompt_out = instruction_prompt.format(question=new_question_kworded, summaries=docs_content_string) print('Final prompt is: ') print(instruction_prompt_out) return instruction_prompt_out, sources_docs_content_string, new_question_kworded def create_full_prompt(user_input, history, extracted_memory, vectorstore, embeddings, model_type): if not user_input.strip(): return history, "", "Respond with 'Please enter a question.' RESPONSE:" #if chain_agent is None: # history.append((user_input, "Please click the button to submit the Huggingface API key before using the chatbot (top right)")) # return history, history, "", "" print("\n==== date/time: " + str(datetime.datetime.now()) + " ====") print("User input: " + user_input) history = history or [] # Create instruction prompt instruction_prompt, content_prompt = base_prompt_templates(model_type=model_type) instruction_prompt_out, docs_content_string, new_question_kworded =\ generate_expanded_prompt({"question": user_input, "chat_history": history}, #vectorstore, instruction_prompt, content_prompt, extracted_memory, vectorstore, embeddings) history.append(user_input) print("Output history is:") print(history) return history, docs_content_string, instruction_prompt_out # Chat functions def produce_streaming_answer_chatbot(history, full_prompt, model_type): #print("Model type is: ", model_type) #if not full_prompt.strip(): # if history is None: # history = [] # return history if model_type == "Flan Alpaca (small, fast)": # Get the model and tokenizer, and tokenize the user text. model_inputs = tokenizer(text=full_prompt, return_tensors="pt", return_attention_mask=False).to(torch_device) # return_attention_mask=False was added # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread. streamer = TextIteratorStreamer(tokenizer, timeout=120., skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( model_inputs, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=sample, repetition_penalty=repetition_penalty, top_p=top_p, temperature=temperature, top_k=top_k ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() # Pull the generated text from the streamer, and update the model output. start = time.time() NUM_TOKENS=0 print('-'*4+'Start Generation'+'-'*4) history[-1][1] = "" for new_text in streamer: if new_text == None: new_text = "" history[-1][1] += new_text NUM_TOKENS+=1 yield history time_generate = time.time() - start print('\n') print('-'*4+'End Generation'+'-'*4) print(f'Num of generated tokens: {NUM_TOKENS}') print(f'Time for complete generation: {time_generate}s') print(f'Tokens per secound: {NUM_TOKENS/time_generate}') print(f'Time per token: {(time_generate/NUM_TOKENS)*1000}ms') elif model_type == "Mistral Open Orca (larger, slow)": tokens = model.tokenize(full_prompt) gen_config = CtransGenGenerationConfig() print(vars(gen_config)) # Pull the generated text from the streamer, and update the model output. start = time.time() NUM_TOKENS=0 print('-'*4+'Start Generation'+'-'*4) history[-1][1] = "" for new_text in model.generate(tokens, **vars(gen_config)): #CtransGen_generate(prompt=full_prompt)#, config=CtransGenGenerationConfig()): # #top_k=top_k, temperature=temperature, repetition_penalty=repetition_penalty, if new_text == None: new_text = "" history[-1][1] += model.detokenize(new_text) #new_text NUM_TOKENS+=1 yield history time_generate = time.time() - start print('\n') print('-'*4+'End Generation'+'-'*4) print(f'Num of generated tokens: {NUM_TOKENS}') print(f'Time for complete generation: {time_generate}s') print(f'Tokens per secound: {NUM_TOKENS/time_generate}') print(f'Time per token: {(time_generate/NUM_TOKENS)*1000}ms') # Chat helper functions def adapt_q_from_chat_history(question, chat_history, extracted_memory, keyword_model=""):#keyword_model): # new_question_keywords, chat_history_str, chat_history_first_q, chat_history_first_ans, max_memory_length = _get_chat_history(chat_history) if chat_history_str: # Keyword extraction is now done in the add_inputs_to_history function #remove_q_stopwords(str(chat_history_first_q) + " " + str(chat_history_first_ans)) new_question_kworded = str(extracted_memory) + ". " + question #+ " " + new_question_keywords #extracted_memory + " " + question else: new_question_kworded = question #new_question_keywords #print("Question output is: " + new_question_kworded) return new_question_kworded def create_doc_df(docs_keep_out): # Extract content and metadata from 'winning' passages. content=[] meta=[] meta_url=[] page_section=[] score=[] for item in docs_keep_out: content.append(item[0].page_content) meta.append(item[0].metadata) meta_url.append(item[0].metadata['source']) page_section.append(item[0].metadata['page_section']) score.append(item[1]) # Create df from 'winning' passages doc_df = pd.DataFrame(list(zip(content, meta, page_section, meta_url, score)), columns =['page_content', 'metadata', 'page_section', 'meta_url', 'score']) docs_content = doc_df['page_content'].astype(str) doc_df['full_url'] = "https://" + doc_df['meta_url'] return doc_df def hybrid_retrieval(new_question_kworded, vectorstore, embeddings, k_val, out_passages, vec_score_cut_off, vec_weight, bm25_weight, svm_weight): # ,vectorstore, embeddings #vectorstore=globals()["vectorstore"] #embeddings=globals()["embeddings"] docs = vectorstore.similarity_search_with_score(new_question_kworded, k=k_val) print("Docs from similarity search:") print(docs) # Keep only documents with a certain score docs_len = [len(x[0].page_content) for x in docs] docs_scores = [x[1] for x in docs] # Only keep sources that are sufficiently relevant (i.e. similarity search score below threshold below) score_more_limit = pd.Series(docs_scores) < vec_score_cut_off docs_keep = list(compress(docs, score_more_limit)) if docs_keep == []: docs_keep_as_doc = [] docs_content = [] docs_url = [] return docs_keep_as_doc, docs_content, docs_url # Only keep sources that are at least 100 characters long length_more_limit = pd.Series(docs_len) >= 100 docs_keep = list(compress(docs_keep, length_more_limit)) if docs_keep == []: docs_keep_as_doc = [] docs_content = [] docs_url = [] return docs_keep_as_doc, docs_content, docs_url docs_keep_as_doc = [x[0] for x in docs_keep] docs_keep_length = len(docs_keep_as_doc) if docs_keep_length == 1: content=[] meta_url=[] score=[] for item in docs_keep: content.append(item[0].page_content) meta_url.append(item[0].metadata['source']) score.append(item[1]) # Create df from 'winning' passages doc_df = pd.DataFrame(list(zip(content, meta_url, score)), columns =['page_content', 'meta_url', 'score']) docs_content = doc_df['page_content'].astype(str) docs_url = doc_df['meta_url'] return docs_keep_as_doc, docs_content, docs_url # Check for if more docs are removed than the desired output if out_passages > docs_keep_length: out_passages = docs_keep_length k_val = docs_keep_length vec_rank = [*range(1, docs_keep_length+1)] vec_score = [(docs_keep_length/x)*vec_weight for x in vec_rank] # 2nd level check on retrieved docs with BM25 content_keep=[] for item in docs_keep: content_keep.append(item[0].page_content) corpus = corpus = [doc.lower().split() for doc in content_keep] dictionary = Dictionary(corpus) bm25_model = OkapiBM25Model(dictionary=dictionary) bm25_corpus = bm25_model[list(map(dictionary.doc2bow, corpus))] bm25_index = SparseMatrixSimilarity(bm25_corpus, num_docs=len(corpus), num_terms=len(dictionary), normalize_queries=False, normalize_documents=False) query = new_question_kworded.lower().split() tfidf_model = TfidfModel(dictionary=dictionary, smartirs='bnn') # Enforce binary weighting of queries tfidf_query = tfidf_model[dictionary.doc2bow(query)] similarities = np.array(bm25_index[tfidf_query]) #print(similarities) temp = similarities.argsort() ranks = np.arange(len(similarities))[temp.argsort()][::-1] # Pair each index with its corresponding value pairs = list(zip(ranks, docs_keep_as_doc)) # Sort the pairs by the indices pairs.sort() # Extract the values in the new order bm25_result = [value for ranks, value in pairs] bm25_rank=[] bm25_score = [] for vec_item in docs_keep: x = 0 for bm25_item in bm25_result: x = x + 1 if bm25_item.page_content == vec_item[0].page_content: bm25_rank.append(x) bm25_score.append((docs_keep_length/x)*bm25_weight) # 3rd level check on retrieved docs with SVM retriever svm_retriever = SVMRetriever.from_texts(content_keep, embeddings, k = k_val) svm_result = svm_retriever.get_relevant_documents(new_question_kworded) svm_rank=[] svm_score = [] for vec_item in docs_keep: x = 0 for svm_item in svm_result: x = x + 1 if svm_item.page_content == vec_item[0].page_content: svm_rank.append(x) svm_score.append((docs_keep_length/x)*svm_weight) ## Calculate final score based on three ranking methods final_score = [a + b + c for a, b, c in zip(vec_score, bm25_score, svm_score)] final_rank = [sorted(final_score, reverse=True).index(x)+1 for x in final_score] # Force final_rank to increment by 1 each time final_rank = list(pd.Series(final_rank).rank(method='first')) #print("final rank: " + str(final_rank)) #print("out_passages: " + str(out_passages)) best_rank_index_pos = [] for x in range(1,out_passages+1): try: best_rank_index_pos.append(final_rank.index(x)) except IndexError: # catch the error pass # Adjust best_rank_index_pos to best_rank_pos_series = pd.Series(best_rank_index_pos) docs_keep_out = [docs_keep[i] for i in best_rank_index_pos] # Keep only 'best' options docs_keep_as_doc = [x[0] for x in docs_keep_out] # Make df of best options doc_df = create_doc_df(docs_keep_out) return docs_keep_as_doc, doc_df, docs_keep_out def get_expanded_passages(vectorstore, docs, width): """ Extracts expanded passages based on given documents and a width for context. Parameters: - vectorstore: The primary data source. - docs: List of documents to be expanded. - width: Number of documents to expand around a given document for context. Returns: - expanded_docs: List of expanded Document objects. - doc_df: DataFrame representation of expanded_docs. """ from collections import defaultdict def get_docs_from_vstore(vectorstore): vector = vectorstore.docstore._dict return list(vector.items()) def extract_details(docs_list): docs_list_out = [tup[1] for tup in docs_list] content = [doc.page_content for doc in docs_list_out] meta = [doc.metadata for doc in docs_list_out] return ''.join(content), meta[0], meta[-1] def get_parent_content_and_meta(vstore_docs, width, target): #target_range = range(max(0, target - width), min(len(vstore_docs), target + width + 1)) target_range = range(max(0, target), min(len(vstore_docs), target + width + 1)) # Now only selects extra passages AFTER the found passage parent_vstore_out = [vstore_docs[i] for i in target_range] content_str_out, meta_first_out, meta_last_out = [], [], [] for _ in parent_vstore_out: content_str, meta_first, meta_last = extract_details(parent_vstore_out) content_str_out.append(content_str) meta_first_out.append(meta_first) meta_last_out.append(meta_last) return content_str_out, meta_first_out, meta_last_out def merge_dicts_except_source(d1, d2): merged = {} for key in d1: if key != "source": merged[key] = str(d1[key]) + " to " + str(d2[key]) else: merged[key] = d1[key] # or d2[key], based on preference return merged def merge_two_lists_of_dicts(list1, list2): return [merge_dicts_except_source(d1, d2) for d1, d2 in zip(list1, list2)] # Step 1: Filter vstore_docs vstore_docs = get_docs_from_vstore(vectorstore) doc_sources = {doc.metadata['source'] for doc, _ in docs} vstore_docs = [(k, v) for k, v in vstore_docs if v.metadata.get('source') in doc_sources] # Step 2: Group by source and proceed vstore_by_source = defaultdict(list) for k, v in vstore_docs: vstore_by_source[v.metadata['source']].append((k, v)) expanded_docs = [] for doc, score in docs: search_source = doc.metadata['source'] search_section = doc.metadata['page_section'] parent_vstore_meta_section = [doc.metadata['page_section'] for _, doc in vstore_by_source[search_source]] search_index = parent_vstore_meta_section.index(search_section) if search_section in parent_vstore_meta_section else -1 content_str, meta_first, meta_last = get_parent_content_and_meta(vstore_by_source[search_source], width, search_index) meta_full = merge_two_lists_of_dicts(meta_first, meta_last) expanded_doc = (Document(page_content=content_str[0], metadata=meta_full[0]), score) expanded_docs.append(expanded_doc) doc_df = create_doc_df(expanded_docs) # Assuming you've defined the 'create_doc_df' function elsewhere return expanded_docs, doc_df def highlight_found_text(search_text: str, full_text: str, hlt_chunk_size:int=hlt_chunk_size, hlt_strat:List=hlt_strat, hlt_overlap:int=hlt_overlap) -> str: """ Highlights occurrences of search_text within full_text. Parameters: - search_text (str): The text to be searched for within full_text. - full_text (str): The text within which search_text occurrences will be highlighted. Returns: - str: A string with occurrences of search_text highlighted. Example: >>> highlight_found_text("world", "Hello, world! This is a test. Another world awaits.") 'Hello, world! This is a test. Another world awaits.' """ def extract_text_from_input(text, i=0): if isinstance(text, str): return text.replace(" ", " ").strip() elif isinstance(text, list): return text[i][0].replace(" ", " ").strip() else: return "" def extract_search_text_from_input(text): if isinstance(text, str): return text.replace(" ", " ").strip() elif isinstance(text, list): return text[-1][1].replace(" ", " ").strip() else: return "" full_text = extract_text_from_input(full_text) search_text = extract_search_text_from_input(search_text) text_splitter = RecursiveCharacterTextSplitter( chunk_size=hlt_chunk_size, separators=hlt_strat, chunk_overlap=hlt_overlap, ) sections = text_splitter.split_text(search_text) found_positions = {} for x in sections: text_start_pos = 0 while text_start_pos != -1: text_start_pos = full_text.find(x, text_start_pos) if text_start_pos != -1: found_positions[text_start_pos] = text_start_pos + len(x) text_start_pos += 1 # Combine overlapping or adjacent positions sorted_starts = sorted(found_positions.keys()) combined_positions = [] if sorted_starts: current_start, current_end = sorted_starts[0], found_positions[sorted_starts[0]] for start in sorted_starts[1:]: if start <= (current_end + 10): current_end = max(current_end, found_positions[start]) else: combined_positions.append((current_start, current_end)) current_start, current_end = start, found_positions[start] combined_positions.append((current_start, current_end)) # Construct pos_tokens pos_tokens = [] prev_end = 0 for start, end in combined_positions: if end-start > 15: # Only combine if there is a significant amount of matched text. Avoids picking up single words like 'and' etc. pos_tokens.append(full_text[prev_end:start]) pos_tokens.append('' + full_text[start:end] + '') prev_end = end pos_tokens.append(full_text[prev_end:]) return "".join(pos_tokens) # # Chat history functions def clear_chat(chat_history_state, sources, chat_message, current_topic): chat_history_state = [] sources = '' chat_message = '' current_topic = '' return chat_history_state, sources, chat_message, current_topic def _get_chat_history(chat_history: List[Tuple[str, str]], max_memory_length:int = max_memory_length): # Limit to last x interactions only if (not chat_history) | (max_memory_length == 0): chat_history = [] if len(chat_history) > max_memory_length: chat_history = chat_history[-max_memory_length:] #print(chat_history) first_q = "" first_ans = "" for human_s, ai_s in chat_history: first_q = human_s first_ans = ai_s #print("Text to keyword extract: " + first_q + " " + first_ans) break conversation = "" for human_s, ai_s in chat_history: human = f"Human: " + human_s ai = f"Assistant: " + ai_s conversation += "\n" + "\n".join([human, ai]) return conversation, first_q, first_ans, max_memory_length def add_inputs_answer_to_history(user_message, history, current_topic): if history is None: history = [("","")] #history.append((user_message, [-1])) chat_history_str, chat_history_first_q, chat_history_first_ans, max_memory_length = _get_chat_history(history) # Only get the keywords for the first question and response, or do it every time if over 'max_memory_length' responses in the conversation if (len(history) == 1) | (len(history) > max_memory_length): #print("History after appending is:") #print(history) first_q_and_first_ans = str(chat_history_first_q) + " " + str(chat_history_first_ans) #ner_memory = remove_q_ner_extractor(first_q_and_first_ans) keywords = keybert_keywords(first_q_and_first_ans, n = 8, kw_model=kw_model) #keywords.append(ner_memory) # Remove duplicate words while preserving order ordered_tokens = set() result = [] for word in keywords: if word not in ordered_tokens: ordered_tokens.add(word) result.append(word) extracted_memory = ' '.join(result) else: extracted_memory=current_topic print("Extracted memory is:") print(extracted_memory) return history, extracted_memory # Keyword functions def remove_q_stopwords(question): # Remove stopwords from question. Not used at the moment # Prepare keywords from question by removing stopwords text = question.lower() # Remove numbers text = re.sub('[0-9]', '', text) tokenizer = RegexpTokenizer(r'\w+') text_tokens = tokenizer.tokenize(text) #text_tokens = word_tokenize(text) tokens_without_sw = [word for word in text_tokens if not word in stopwords] # Remove duplicate words while preserving order ordered_tokens = set() result = [] for word in tokens_without_sw: if word not in ordered_tokens: ordered_tokens.add(word) result.append(word) new_question_keywords = ' '.join(result) return new_question_keywords def remove_q_ner_extractor(question): predict_out = ner_model.predict(question) predict_tokens = [' '.join(v for k, v in d.items() if k == 'span') for d in predict_out] # Remove duplicate words while preserving order ordered_tokens = set() result = [] for word in predict_tokens: if word not in ordered_tokens: ordered_tokens.add(word) result.append(word) new_question_keywords = ' '.join(result).lower() return new_question_keywords def apply_lemmatize(text, wnl=WordNetLemmatizer()): def prep_for_lemma(text): # Remove numbers text = re.sub('[0-9]', '', text) print(text) tokenizer = RegexpTokenizer(r'\w+') text_tokens = tokenizer.tokenize(text) #text_tokens = word_tokenize(text) return text_tokens tokens = prep_for_lemma(text) def lem_word(word): if len(word) > 3: out_word = wnl.lemmatize(word) else: out_word = word return out_word return [lem_word(token) for token in tokens] def keybert_keywords(text, n, kw_model): tokens_lemma = apply_lemmatize(text) lemmatised_text = ' '.join(tokens_lemma) keywords_text = KeyBERT(model=kw_model).extract_keywords(lemmatised_text, stop_words='english', top_n=n, keyphrase_ngram_range=(1, 1)) keywords_list = [item[0] for item in keywords_text] return keywords_list # Gradio functions def turn_off_interactivity(user_message, history): return gr.update(value="", interactive=False), history + [[user_message, None]] def restore_interactivity(): return gr.update(interactive=True) def update_message(dropdown_value): return gr.Textbox.update(value=dropdown_value) def hide_block(): return gr.Radio.update(visible=False) # Vote function def vote(data: gr.LikeData, chat_history, instruction_prompt_out, model_type): import os import pandas as pd chat_history_last = str(str(chat_history[-1][0]) + " - " + str(chat_history[-1][1])) response_df = pd.DataFrame(data={"thumbs_up":data.liked, "chosen_response":data.value, "input_prompt":instruction_prompt_out, "chat_history":chat_history_last, "model_type": model_type, "date_time": pd.Timestamp.now()}, index=[0]) if data.liked: print("You upvoted this response: " + data.value) if os.path.isfile("thumbs_up_data.csv"): existing_thumbs_up_df = pd.read_csv("thumbs_up_data.csv") thumbs_up_df_concat = pd.concat([existing_thumbs_up_df, response_df], ignore_index=True).drop("Unnamed: 0",axis=1, errors="ignore") thumbs_up_df_concat.to_csv("thumbs_up_data.csv") else: response_df.to_csv("thumbs_up_data.csv") else: print("You downvoted this response: " + data.value) if os.path.isfile("thumbs_down_data.csv"): existing_thumbs_down_df = pd.read_csv("thumbs_down_data.csv") thumbs_down_df_concat = pd.concat([existing_thumbs_down_df, response_df], ignore_index=True).drop("Unnamed: 0",axis=1, errors="ignore") thumbs_down_df_concat.to_csv("thumbs_down_data.csv") else: response_df.to_csv("thumbs_down_data.csv")