import os import time import pdfplumber import docx import nltk import gradio as gr from langchain_huggingface import HuggingFaceEmbeddings from langchain_community.embeddings import CohereEmbeddings from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import FAISS, Chroma from langchain_text_splitters import RecursiveCharacterTextSplitter, TokenTextSplitter from typing import List, Dict, Any import pandas as pd import numpy as np import re from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import SnowballStemmer import jellyfish from gensim.models import Word2Vec from gensim.models.fasttext import FastText from collections import Counter from tokenizers import Tokenizer, models, trainers from tokenizers.models import WordLevel from tokenizers.trainers import WordLevelTrainer from tokenizers.pre_tokenizers import Whitespace import matplotlib.pyplot as plt import seaborn as sns from sklearn.manifold import TSNE from sklearn.metrics import silhouette_score from scipy.stats import spearmanr from functools import lru_cache from langchain.retrievers import MultiQueryRetriever from langchain_huggingface.llms import HuggingFacePipeline from transformers import pipeline from sentence_transformers import SentenceTransformer, util from sklearn.model_selection import ParameterGrid from tqdm import tqdm import random from huggingface_hub import login from typing import List, Tuple, Optional #hf_token = os.getenv("hf_token") #login(token=hf_token) # Define the model pipeline with additional generation parameters #model_pipeline = pipeline( # # model="meta-llama/Llama-3.2-1B", # model="dunzhang/stella_en_1.5B_v5", # #pad_token_id=50256, # #use_auth_token=hf_token, # #max_length=1000, # You can increase this if needed # max_new_tokens=900 # Limit how many tokens are generated #) # Use the pipeline in HuggingFacePipeline #llm = HuggingFacePipeline(pipeline=model_pipeline) ################### #llm = HuggingFacePipeline.from_model_id( # model_id="bigscience/bloom-1b7", # task="text-generation", # model_kwargs={"temperature": 0, "max_length":1200, "do_sample":True}, #) ##### Alternative from transformers import pipeline import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig #READER_MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" # model="dunzhang/stella_en_1.5B_v5", #bnb_config = BitsAndBytesConfig( # load_in_4bit=True, # bnb_4bit_use_double_quant=True, # bnb_4bit_quant_type="nf4", # bnb_4bit_compute_dtype=torch.bfloat16, #) #rmodel = AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config=bnb_config) #tokenizer = AutoTokenizer.from_pretrained(READER_MODEL_NAME) #llm = pipeline( # model=rmodel, # tokenizer=tokenizer, # task="text-generation", # do_sample=True, # temperature=0.2, # repetition_penalty=1.1, # return_full_text=False, # max_new_tokens=500, #) ##### from huggingface_hub import InferenceClient #repo_id = "meta-llama/Llama-3.2-1B-Instruct" #llm = InferenceClient(model=repo_id, timeout=120) # Test your LLM client #llm_client.text_generation(prompt="How are you today?", max_new_tokens=20) # NLTK Resource Download def download_nltk_resources(): resources = ['punkt', 'stopwords', 'snowball_data'] for resource in resources: try: nltk.download(resource, quiet=False) except Exception as e: print(f"Failed to download {resource}: {str(e)}") download_nltk_resources() #nltk.download('punkt') print("------ Strinage ------ ") FILES_DIR = './files' DEFAULT_MODELS = { "HuggingFace": [ "paraphrase-miniLM", "paraphrase-mpnet", "all-MiniLM-L6-v2" ], "OpenAI": [ "text-embedding-ada-002" ], "Cohere": [ "embed-multilingual-v2.0" ] } # Model Management class ModelManager: def __init__(self): self.rankings: Dict[str, float] = {} self.model_stats: Dict[str, Dict[str, Any]] = {} self.models = { 'HuggingFace': { 'e5-base-de': "danielheinz/e5-base-sts-en-de", 'paraphrase-miniLM': "paraphrase-multilingual-MiniLM-L12-v2", 'paraphrase-mpnet': "paraphrase-multilingual-mpnet-base-v2", 'gte-large': "gte-large", 'gbert-base': "gbert-base" }, 'OpenAI': { 'text-embedding-ada-002': "text-embedding-ada-002" }, 'Cohere': { 'embed-multilingual-v2.0': "embed-multilingual-v2.0" } } def update_model_ranking(self, model_id: str, score: float, feedback: str = None): """Update model ranking based on performance and optional feedback""" current_score = self.rankings.get(model_id, 0.0) # Weighted average of current score and new score self.rankings[model_id] = 0.7 * current_score + 0.3 * score if feedback: if model_id not in self.model_stats: self.model_stats[model_id] = {"feedback_count": 0, "feedback": []} self.model_stats[model_id]["feedback_count"] += 1 self.model_stats[model_id]["feedback"].append(feedback) def get_top_models(self, n: int = 5) -> List[Tuple[str, float]]: """Get top n ranked models""" return sorted(self.rankings.items(), key=lambda x: x[1], reverse=True)[:n] def get_model_stats(self, model_id: str) -> Dict[str, Any]: """Get statistics for a specific model""" return self.model_stats.get(model_id, {}) def add_model(self, provider, name, model_path): if provider not in self.models: self.models[provider] = {} self.models[provider][name] = model_path def remove_model(self, provider, name): if provider in self.models and name in self.models[provider]: del self.models[provider][name] def get_model(self, provider, name): return self.models.get(provider, {}).get(name) def list_models(self): return {provider: list(models.keys()) for provider, models in self.models.items()} model_manager = ModelManager() # File Handling import os import json import csv import xml.etree.ElementTree as ET import openpyxl # for handling .xlsx files import pdfplumber import docx # File Handling class FileHandler: @staticmethod def extract_text(file_path): ext = os.path.splitext(file_path)[-1].lower() if ext == '.pdf': return FileHandler._extract_from_pdf(file_path) elif ext == '.docx': return FileHandler._extract_from_docx(file_path) elif ext == '.txt': return FileHandler._extract_from_txt(file_path) elif ext == '.xml': return FileHandler._extract_from_xml(file_path) elif ext == '.json': return FileHandler._extract_from_json(file_path) elif ext == '.xlsx': return FileHandler._extract_from_xlsx(file_path) elif ext == '.csv': return FileHandler._extract_from_csv(file_path) else: raise ValueError(f"Unsupported file type: {ext}") @staticmethod def _extract_from_pdf(file_path): with pdfplumber.open(file_path) as pdf: return ' '.join([page.extract_text() for page in pdf.pages]) @staticmethod def _extract_from_docx(file_path): doc = docx.Document(file_path) return ' '.join([para.text for para in doc.paragraphs]) @staticmethod def _extract_from_txt(file_path): with open(file_path, 'r', encoding='utf-8') as f: return f.read() @staticmethod def _extract_from_xml(file_path): tree = ET.parse(file_path) root = tree.getroot() return FileHandler._extract_xml_text(root) @staticmethod def _extract_xml_text(element): # Recursively extract text from XML elements text = element.text or "" for child in element: text += FileHandler._extract_xml_text(child) return text.strip() @staticmethod def _extract_from_json(file_path): with open(file_path, 'r', encoding='utf-8') as f: data = json.load(f) return json.dumps(data, indent=4) # Pretty print JSON for readability @staticmethod def _extract_from_xlsx(file_path): workbook = openpyxl.load_workbook(file_path) sheet = workbook.active data = [] for row in sheet.iter_rows(values_only=True): data.append('\t'.join([str(cell) for cell in row if cell is not None])) return '\n'.join(data) @staticmethod def _extract_from_csv(file_path): with open(file_path, 'r', encoding='utf-8') as f: reader = csv.reader(f) data = [] for row in reader: data.append(','.join(row)) return '\n'.join(data) # Text Processing def simple_tokenize(text): return text.split() def preprocess_text(text, lang='german', apply_preprocessing=False): if not apply_preprocessing: return text text = text.lower() text = re.sub(r'[^a-zA-Z\s]', '', text) try: tokens = word_tokenize(text, language=lang) except LookupError: print(f"Warning: NLTK punkt tokenizer for {lang} not found. Using simple tokenization.") tokens = simple_tokenize(text) try: stop_words = set(stopwords.words(lang)) except LookupError: print(f"Warning: Stopwords for {lang} not found. Skipping stopword removal.") stop_words = set() tokens = [token for token in tokens if token not in stop_words] try: stemmer = SnowballStemmer(lang) tokens = [stemmer.stem(token) for token in tokens] except ValueError: print(f"Warning: SnowballStemmer for {lang} not available. Skipping stemming.") return ' '.join(tokens) def phonetic_match(text, query, method='levenshtein_distance', apply_phonetic=False): if not apply_phonetic: return 0 if method == 'levenshtein_distance': text_phonetic = jellyfish.soundex(text) query_phonetic = jellyfish.soundex(query) return jellyfish.levenshtein_distance(text_phonetic, query_phonetic) return 0 from typing import List, Union import torch from transformers import AutoTokenizer import numpy as np from nltk.tokenize import word_tokenize from nltk.corpus import wordnet import nltk def optimize_query( query: str, query_optimization_model: str, # Added to match your signature = "google/flan-t5-small" chunks: List[str], embedding_model: str, vector_store_type: str, # Added to match your signature search_type: str, # Added to match your signature top_k: int = 3, use_gpu: bool = True ) -> str: """ CPU-optimized version of query expansion using a small language model. Args: query: Original search query query_optimization_model: Name or path of the model to use for optimization chunks: List of text chunks to search through embedding_model: Name of the embedding model being used vector_store_type: Type of vector store being used search_type: Type of search being performed top_k: Number of expansion terms to add use_gpu: Whether to use GPU if available (defaults to False for CPU) Returns: Expanded query string """ try: # Set device device = "cuda" if use_gpu and torch.cuda.is_available() else "cpu" # 1. Basic text preprocessing (CPU-based) tokens = word_tokenize(query.lower()) # 2. WordNet synonyms expansion (CPU-based) expanded_terms = set() for token in tokens: # Limit synonym lookup to save CPU resources synsets = wordnet.synsets(token)[:1] # Take only top synset per word for syn in synsets: # Limit number of lemmas expanded_terms.update([lemma.name() for lemma in syn.lemmas()[:2]]) # 3. Use provided model with reduced complexity try: # Load model with reduced memory footprint tokenizer = AutoTokenizer.from_pretrained( query_optimization_model, # Use the provided model name model_max_length=128, cache_dir="./model_cache" ) model = AutoModelForSeq2Gen.from_pretrained( query_optimization_model, # Use the provided model name low_cpu_mem_usage=True, device_map="cpu" ) # Move model to CPU and eval mode model = model.to(device) model.eval() # Prepare input with reduced length prompt = f"Enhance this search query with relevant terms: {query}" inputs = tokenizer( prompt, return_tensors="pt", max_length=64, truncation=True, padding=True ) # Generate with minimal parameters with torch.no_grad(): outputs = model.generate( inputs.input_ids.to(device), max_length=32, num_return_sequences=1, temperature=0.7, do_sample=False, early_stopping=True ) enhanced_query = tokenizer.decode(outputs[0], skip_special_tokens=True) # Clear CUDA cache if GPU was used if device == "cuda": torch.cuda.empty_cache() except Exception as model_error: print(f"Model-based expansion failed: {str(model_error)}") enhanced_query = query # 4. Combine original and expanded terms final_terms = set(tokens) final_terms.update(expanded_terms) if enhanced_query != query: final_terms.update(word_tokenize(enhanced_query.lower())) # 5. Remove stopwords and select top_k most relevant terms stopwords = set(['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to']) final_terms = [term for term in final_terms if term not in stopwords] # Combine with original query expanded_query = f"{query} {' '.join(list(final_terms)[:top_k])}" # Clean up del model del tokenizer if device == "cuda": torch.cuda.empty_cache() return expanded_query.strip() #[Document(page_content=expanded_query.strip())] except Exception as e: print(f"Query optimization failed: {str(e)}") return query #[Document(page_content=query)] # Return original query if optimization fails # Example usage """ chunks = ["sample text chunk 1", "sample text chunk 2"] query = "machine learning algorithms" optimized_query = optimize_query( query=query, chunks=chunks, embedding_model="sentence-transformers/all-MiniLM-L6-v2", use_gpu=False # Explicitly use CPU ) """ def create_custom_embedding(texts, model_type='word2vec', vector_size=100, window=5, min_count=1): tokenized_texts = [text.split() for text in texts] if model_type == 'word2vec': model = Word2Vec(sentences=tokenized_texts, vector_size=vector_size, window=window, min_count=min_count, workers=4) elif model_type == 'fasttext': model = FastText(sentences=tokenized_texts, vector_size=vector_size, window=window, min_count=min_count, workers=4) else: raise ValueError("Unsupported model type") return model class CustomEmbeddings(HuggingFaceEmbeddings): def __init__(self, model_path): self.model = Word2Vec.load(model_path) # or FastText.load() for FastText models def embed_documents(self, texts): return [self.model.wv[text.split()] for text in texts] def embed_query(self, text): return self.model.wv[text.split()] # Custom Tokenizer def create_custom_tokenizer(file_path, model_type='WordLevel', vocab_size=10000, special_tokens=None): with open(file_path, 'r', encoding='utf-8') as f: text = f.read() if model_type == 'WordLevel': tokenizer = Tokenizer(WordLevel(unk_token="[UNK]")) elif model_type == 'BPE': tokenizer = Tokenizer(models.BPE(unk_token="[UNK]")) elif model_type == 'Unigram': tokenizer = Tokenizer(models.Unigram()) else: raise ValueError(f"Unsupported tokenizer model: {model_type}") tokenizer.pre_tokenizer = Whitespace() special_tokens = special_tokens or ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"] trainer = trainers.WordLevelTrainer(special_tokens=special_tokens, vocab_size=vocab_size) tokenizer.train_from_iterator([text], trainer) return tokenizer def custom_tokenize(text, tokenizer): return tokenizer.encode(text).tokens # Embedding and Vector Store #@lru_cache(maxsize=None) # Helper functions def get_text_splitter(split_strategy, chunk_size, overlap_size, custom_separators=None): if split_strategy == 'token': return TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap_size) elif split_strategy == 'recursive': return RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=overlap_size, add_start_index=True, # If `True`, includes chunk's start index in metadata strip_whitespace=True, # If `True`, strips whitespace from the start and end of every document separators=custom_separators or ["\n\n", "\n", " ", ""] ) else: raise ValueError(f"Unsupported split strategy: {split_strategy}") def get_embedding_model(model_type, model_name): model_path = model_manager.get_model(model_type, model_name) if model_type == 'HuggingFace': return HuggingFaceEmbeddings( model_name=model_path, multi_process=True, # model_kwargs={"device": "cpu"}, #encode_kwargs={"normalize_embeddings": True}, # Set `True` for cosine similarity ) elif model_type == 'OpenAI': return OpenAIEmbeddings(model=model_path) elif model_type == 'Cohere': return CohereEmbeddings(model=model_path) else: raise ValueError(f"Unsupported model type: {model_type}") def get_vector_store(vector_store_type, chunks, embedding_model): chunks_tuple = tuple(chunks) if vector_store_type == 'FAISS': return FAISS.from_texts(chunks, embedding_model) elif vector_store_type == 'Chroma': return Chroma.from_texts(chunks, embedding_model) else: raise ValueError(f"Unsupported vector store type: {vector_store_type}") def get_retriever(vector_store, search_type, search_kwargs): if search_type == 'similarity': return vector_store.as_retriever(search_type="similarity", search_kwargs=search_kwargs) elif search_type == 'mmr': return vector_store.as_retriever(search_type="mmr", search_kwargs=search_kwargs) elif search_type == 'custom': return vector_store.as_retriever(search_type="similarity", search_kwargs=search_kwargs) else: raise ValueError(f"Unsupported search type: {search_type}") def custom_similarity(query_embedding, doc_embedding, query, doc_text, phonetic_weight=0.3): embedding_sim = np.dot(query_embedding, doc_embedding) / (np.linalg.norm(query_embedding) * np.linalg.norm(doc_embedding)) phonetic_sim = phonetic_match(doc_text, query) combined_sim = (1 - phonetic_weight) * embedding_sim + phonetic_weight * phonetic_sim return combined_sim def _create_vector_store(vector_store_type, chunks_tuple, embedding_model): chunks = list(chunks_tuple) if vector_store_type == 'FAISS': return FAISS.from_texts(chunks, embedding_model) elif vector_store_type == 'Chroma': return Chroma.from_texts(chunks, embedding_model) else: raise ValueError(f"Unsupported vector store type: {vector_store_type}") # Main Processing Functions def process_files(file_path, model_type, model_name, split_strategy, chunk_size, overlap_size, custom_separators, lang='german', apply_preprocessing=False, custom_tokenizer_file=None, custom_tokenizer_model=None, custom_tokenizer_vocab_size=10000, custom_tokenizer_special_tokens=None): if file_path: text = FileHandler.extract_text(file_path) else: text = "" for file in os.listdir(FILES_DIR): file_path = os.path.join(FILES_DIR, file) text += FileHandler.extract_text(file_path) if custom_tokenizer_file: tokenizer = create_custom_tokenizer(custom_tokenizer_file, custom_tokenizer_model, custom_tokenizer_vocab_size, custom_tokenizer_special_tokens) text = ' '.join(custom_tokenize(text, tokenizer)) elif apply_preprocessing: text = preprocess_text(text, lang) text_splitter = get_text_splitter(split_strategy, chunk_size, overlap_size, custom_separators) chunks = text_splitter.split_text(text) embedding_model = get_embedding_model(model_type, model_name) return chunks, embedding_model, len(text.split()) def search_embeddings(chunks, embedding_model, vector_store_type, search_type, query, top_k, expected_result=None, lang='german', apply_phonetic=False, phonetic_weight=0.3): preprocessed_query = preprocess_text(query, lang) if apply_phonetic else query vector_store = get_vector_store(vector_store_type, chunks, embedding_model) retriever = get_retriever(vector_store, search_type, {"k": top_k}) start_time = time.time() results = retriever.invoke(preprocessed_query) #this should be optional def score_result(doc): base_score = vector_store.similarity_search_with_score(doc.page_content, k=1)[0][1] # Add bonus for containing expected result expected_bonus = 0.3 if expected_result and expected_result in doc.page_content else 0 if apply_phonetic: phonetic_score = phonetic_match(doc.page_content, query) return (1 - phonetic_weight) * base_score + phonetic_weight * phonetic_score + expected_bonus else: return base_score + expected_bonus results = sorted(results, key=score_result, reverse=True) end_time = time.time() embeddings = [] for doc in results: if hasattr(doc, 'embedding'): embeddings.append(doc.embedding) else: embeddings.append(None) results_df = pd.DataFrame({ 'content': [doc.page_content for doc in results], 'embedding': embeddings, 'length': [len(doc.page_content) for doc in results], 'contains_expected': [expected_result in doc.page_content if expected_result else None for doc in results] }) return results_df, end_time - start_time, vector_store, results # Enhanced Result Analysis class ResultAnalyzer: @staticmethod def calculate_statistics(results, search_time, vector_store, num_tokens, embedding_model, query, top_k, expected_result=None, model_feedback=None): stats = { "num_results": len(results), "avg_content_length": np.mean([len(doc.page_content) for doc in results]) if results else 0, "min_content_length": min([len(doc.page_content) for doc in results]) if results else 0, "max_content_length": max([len(doc.page_content) for doc in results]) if results else 0, "search_time": search_time, "num_tokens": num_tokens, "embedding_dimension": len(embedding_model.embed_query(query)), "top_k": top_k, } # Add vector store statistics try: if hasattr(vector_store, '_index'): stats["vector_store_size"] = vector_store._index.ntotal elif hasattr(vector_store, '_collection'): stats["vector_store_size"] = len(vector_store._collection.get()) except: stats["vector_store_size"] = "N/A" # Add expected result statistics if provided if expected_result: stats["contains_expected"] = any(expected_result in doc.page_content for doc in results) stats["expected_result_rank"] = next((i for i, doc in enumerate(results) if expected_result in doc.page_content), -1) + 1 # Calculate diversity metrics for larger result sets if len(results) > 3: # Changed from 1000 to make it more practical embeddings = [embedding_model.embed_query(doc.page_content) for doc in results] stats["result_diversity"] = ResultAnalyzer._calculate_diversity(embeddings) stats["silhouette_score"] = ResultAnalyzer._calculate_silhouette(embeddings) else: stats["result_diversity"] = "N/A" stats["silhouette_score"] = "N/A" # Add ranking correlation query_embedding = embedding_model.embed_query(query) result_embeddings = [embedding_model.embed_query(doc.page_content) for doc in results] similarities = [np.inner(query_embedding, emb) for emb in result_embeddings] if len(similarities) > 1: rank_correlation, _ = spearmanr(similarities, range(len(similarities))) stats["rank_correlation"] = rank_correlation else: stats["rank_correlation"] = "N/A" # Add model feedback if provided if model_feedback: stats["model_feedback"] = model_feedback return stats @staticmethod def _calculate_diversity(embeddings: List[np.ndarray]) -> float: """Calculate diversity score for embeddings""" embeddings_array = np.array(embeddings) pairwise_similarities = np.inner(embeddings_array, embeddings_array) return 1 - np.mean(pairwise_similarities[np.triu_indices(len(embeddings), k=1)]) @staticmethod def _calculate_silhouette(embeddings: List[np.ndarray]) -> float: """Calculate silhouette score for embeddings""" if len(embeddings) < 3: return 0.0 try: return silhouette_score(embeddings, range(len(embeddings))) except: return 0.0 # Visualization def visualize_results(results_df, stats_df): # Add model column if not present if 'model' not in stats_df.columns: stats_df['model'] = stats_df['model_type'] + ' - ' + stats_df['model_name'] fig, axs = plt.subplots(2, 2, figsize=(20, 20)) # Handle empty dataframe case if len(stats_df) == 0: return fig # Create plots with error handling try: sns.barplot(data=stats_df, x='model', y='search_time', ax=axs[0, 0]) axs[0, 0].set_title('Search Time by Model') axs[0, 0].tick_params(axis='x', rotation=45) except Exception as e: print(f"Error in search time plot: {e}") try: sns.scatterplot(data=stats_df, x='result_diversity', y='rank_correlation', hue='model', ax=axs[0, 1]) axs[0, 1].set_title('Result Diversity vs. Rank Correlation') except Exception as e: print(f"Error in diversity plot: {e}") try: sns.boxplot(data=stats_df, x='model', y='avg_content_length', ax=axs[1, 0]) axs[1, 0].set_title('Distribution of Result Content Lengths') axs[1, 0].tick_params(axis='x', rotation=45) except Exception as e: print(f"Error in content length plot: {e}") try: valid_embeddings = results_df['embedding'].dropna().values if len(valid_embeddings) > 1: tsne = TSNE(n_components=2, random_state=42) embeddings_2d = tsne.fit_transform(np.vstack(valid_embeddings)) sns.scatterplot(x=embeddings_2d[:, 0], y=embeddings_2d[:, 1], hue=results_df['Model'][:len(valid_embeddings)], ax=axs[1, 1]) axs[1, 1].set_title('t-SNE Visualization of Result Embeddings') else: axs[1, 1].text(0.5, 0.5, "Not enough embeddings for visualization", ha='center', va='center') except Exception as e: print(f"Error in embedding visualization: {e}") plt.tight_layout() return fig #tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME) #lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)] #fig = pd.Series(lengths).hist() #plt.title("Distribution of document lengths in the knowledge base (in count of tokens)") #plt.show() def optimize_vocabulary(texts, vocab_size=10000, min_frequency=2): tokenizer = Tokenizer(models.BPE(unk_token="[UNK]")) word_freq = Counter(word for text in texts for word in text.split()) optimized_texts = [ ' '.join(word for word in text.split() if word_freq[word] >= min_frequency) for text in texts ] trainer = trainers.BpeTrainer(vocab_size=vocab_size, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) tokenizer.train_from_iterator(optimized_texts, trainer) return tokenizer, optimized_texts import numpy as np from transformers import TextClassificationPipeline from typing import List, Union, Any model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') def rerank_results( results: List[Any], query: str, reranker: Union[TextClassificationPipeline, Any] ) -> List[Any]: """ """ if not results: return results # Step 1: Encode the query and documents using SentenceTransformer query_embedding = model.encode(query, convert_to_tensor=True) doc_contents = [doc.page_content for doc in results] # Assuming each result has a `page_content` attribute doc_embeddings = model.encode(doc_contents, convert_to_tensor=True) # Step 2: Compute cosine similarities between query and document embeddings cosine_scores = util.cos_sim(query_embedding, doc_embeddings)[0] # Shape: (number of documents,) # Step 3: Sort documents by similarity score in descending order reranked_idx = np.argsort(cosine_scores.cpu().numpy())[::-1] # Step 4: Return the reranked documents reranked_results = [results[i] for i in reranked_idx] return reranked_results # Main Comparison Function def compare_embeddings(file, query, embedding_models, custom_embedding_model, split_strategy, chunk_size, overlap_size, custom_separators, vector_store_type, search_type, top_k, expected_result=None, lang='german', apply_preprocessing=True, optimize_vocab=False, apply_phonetic=True, phonetic_weight=0.3, custom_tokenizer_file=None, custom_tokenizer_model=None, custom_tokenizer_vocab_size=10000, custom_tokenizer_special_tokens=None, use_query_optimization=False, query_optimization_model="google/flan-t5-base", use_reranking=False): all_results = [] all_stats = [] settings = { "split_strategy": split_strategy, "chunk_size": chunk_size, "overlap_size": overlap_size, "custom_separators": custom_separators, "vector_store_type": vector_store_type, "search_type": search_type, "top_k": top_k, "lang": lang, "apply_preprocessing": apply_preprocessing, "optimize_vocab": optimize_vocab, "apply_phonetic": apply_phonetic, "phonetic_weight": phonetic_weight, "use_query_optimization": use_query_optimization, "query_optimization_model": query_optimization_model, "use_reranking": use_reranking } # Parse the embedding models from the checkbox group models = [model.split(':') for model in embedding_models] if custom_embedding_model: models.append(custom_embedding_model.strip().split(':')) for model_type, model_name in models: chunks, embedding_model, num_tokens = process_files( file.name if file else None, model_type, model_name, split_strategy, chunk_size, overlap_size, custom_separators.split(',') if custom_separators else None, lang, apply_preprocessing, custom_tokenizer_file, custom_tokenizer_model, int(custom_tokenizer_vocab_size), custom_tokenizer_special_tokens.split(',') if custom_tokenizer_special_tokens else None ) if optimize_vocab: tokenizer, optimized_chunks = optimize_vocabulary(chunks) chunks = optimized_chunks search_query = query if use_query_optimization: optimized_queries = optimize_query(query, query_optimization_model, chunks, embedding_model, vector_store_type, search_type, top_k) #query = " ".join(optimized_queries) search_query = optimized_queries # " ".join([doc.page_content for doc in optimized_queries]) # Extract text from Document objects results, search_time, vector_store, results_raw = search_embeddings( chunks, embedding_model, vector_store_type, search_type, search_query, top_k, expected_result, lang, apply_phonetic, phonetic_weight ) if use_reranking: reranker = pipeline("text-classification", model="cross-encoder/ms-marco-MiniLM-L-12-v2") results_raw = rerank_results(results_raw, query, reranker) result_embeddings = [doc.metadata.get('embedding', None) for doc in results_raw] stats = ResultAnalyzer.calculate_statistics(results_raw, search_time, vector_store, num_tokens, embedding_model, query, top_k, expected_result) stats["model"] = f"{model_type} - {model_name}" stats["model_type"] = model_type stats["model_name"] = model_name stats.update(settings) formatted_results = format_results(results_raw, stats) for i, result in enumerate(formatted_results): result['embedding'] = result_embeddings[i] result['length'] = len(result['Content']) result['contains_expected'] = expected_result in result['Content'] if expected_result else None all_results.extend(formatted_results) all_stats.append(stats) results_df = pd.DataFrame(all_results) stats_df = pd.DataFrame(all_stats) fig = visualize_results(results_df, stats_df) best_results = analyze_results(stats_df) return results_df, stats_df, fig, best_results def format_results(results, stats): formatted_results = [] for doc in results: result = { "Model": stats["model"], "Content": doc.page_content, "Embedding": doc.embedding if hasattr(doc, 'embedding') else None, **doc.metadata, **{k: v for k, v in stats.items() if k not in ["model"]} } formatted_results.append(result) return formatted_results ##### from sklearn.model_selection import ParameterGrid from tqdm import tqdm # ... (previous code remains the same) # function for automated testing def automated_testing(file, query, test_params, expected_result=None): all_results = [] all_stats = [] param_grid = ParameterGrid(test_params) print(param_grid) for params in tqdm(param_grid, desc="Running tests"): chunks, embedding_model, num_tokens = process_files( file.name if file else None, params['model_type'], params['model_name'], params['split_strategy'], params['chunk_size'], params['overlap_size'], params.get('custom_separators', None), params['lang'], params['apply_preprocessing'], params.get('custom_tokenizer_file', None), params.get('custom_tokenizer_model', None), params.get('custom_tokenizer_vocab_size', 10000), params.get('custom_tokenizer_special_tokens', None) ) if params['optimize_vocab']: tokenizer, optimized_chunks = optimize_vocabulary(chunks) chunks = optimized_chunks if params['use_query_optimization']: optimized_queries = optimize_query(query, params['query_optimization_model'], chunks , embedding_model , params['vector_store_type'] , params['search_type'] , params['top_k'] ) #optimized_queries = optimize_query(query, ) query = " ".join(optimized_queries) results, search_time, vector_store, results_raw = search_embeddings( chunks, embedding_model, params['vector_store_type'], params['search_type'], query, params['top_k'], expected_result, params['lang'], params['apply_phonetic'], params['phonetic_weight'] ) if params['use_reranking']: reranker = pipeline("text-classification", model="cross-encoder/ms-marco-MiniLM-L-12-v2") results_raw = rerank_results(results_raw, query, reranker) stats = ResultAnalyzer.calculate_statistics(results_raw, search_time, vector_store, num_tokens, embedding_model, query, params['top_k'], expected_result) stats["model"] = f"{params['model_type']} - {params['model_name']}" stats["model_type"] = params['model_type'] stats["model_name"] = params['model_name'] stats.update(params) all_results.extend(format_results(results_raw, stats)) all_stats.append(stats) return pd.DataFrame(all_results), pd.DataFrame(all_stats) # Function to analyze results and propose best model and settings def analyze_results(stats_df): metric_weights = { 'search_time': -0.3, 'result_diversity': 0.2, 'rank_correlation': 0.3, 'silhouette_score': 0.2, 'contains_expected': 0.5, # High weight for containing the expected result 'expected_result_rank': -0.4 # Lower rank (closer to 1) is better } if stats_df.empty: print("stats_df is empty. Cannot compute best configuration.") return None for metric in metric_weights.keys(): if metric in stats_df.columns: stats_df[metric] = pd.to_numeric(stats_df[metric], errors='coerce') else: stats_df[metric] = 0 print("Column 'search_time' is missing in stats_df.") stats_df['weighted_score'] = sum( stats_df[metric].fillna(0) * weight for metric, weight in metric_weights.items() ) best_config = stats_df.loc[stats_df['weighted_score'].idxmax()] recommendations = { 'best_model': f"{best_config['model_type']} - {best_config['model_name']}", 'best_settings': { 'split_strategy': best_config['split_strategy'], 'chunk_size': int(best_config['chunk_size']), 'overlap_size': int(best_config['overlap_size']), 'vector_store_type': best_config['vector_store_type'], 'search_type': best_config['search_type'], 'top_k': int(best_config['top_k']), 'optimize_vocab': bool(best_config['optimize_vocab']), 'use_query_optimization': bool(best_config['use_query_optimization']), 'use_reranking': bool(best_config['use_reranking']), 'lang': best_config['lang'], 'apply_preprocessing': bool(best_config['apply_preprocessing']), 'apply_phonetic': bool(best_config['apply_phonetic']), 'phonetic_weight': float(best_config['phonetic_weight']) }, 'performance_summary': { 'search_time': float(best_config['search_time']), 'result_diversity': float(best_config['result_diversity']), 'rank_correlation': float(best_config['rank_correlation']), 'silhouette_score': float(best_config['silhouette_score']), 'contains_expected': bool(best_config['contains_expected']), 'expected_result_rank': int(best_config['expected_result_rank']) } } return recommendations #### def get_llm_suggested_settings(file, num_chunks=1): if not file: return {"error": "No file uploaded"} chunks, _, _ = process_files( file.name, 'HuggingFace', 'paraphrase-miniLM', 'recursive', 250, 50, custom_separators=None ) # Select a few random chunks sample_chunks = random.sample(chunks, min(num_chunks, len(chunks))) llm_pipeline = pipeline(model="meta-llama/Llama-3.2-1B-Instruct", device='cuda') prompt=f''' <|start_header_id|>system<|end_header_id|> You are an expert in information retrieval. You know about strenghs and weaknesses of all models. Given the following text chunks from a document, suggest optimal settings for an embedding-based search system. The settings should include: 1. Embedding model type and name 2. Split strategy (token or recursive) 3. Chunk size 4. Overlap size 5. Vector store type (FAISS or Chroma) 6. Search type (similarity, mmr, or custom) 7. Top K results to retrieve 8. Whether to apply preprocessing 9. Whether to optimize vocabulary 10. Whether to apply phonetic matching Expected output format: {{ "embedding_models": "embedding_model_type:embedding_model_name", "split_strategy": "token or recursive", "chunk_size": 250, "overlap_size": 50, "vector_store_type": "FAISS or Chroma", "search_type": "similarity, mmr, or custom", "top_k": 5, "apply_preprocessing": True, "optimize_vocab": True, "apply_phonetic": False, "phonetic_weight": 0.3 # }} Provide your suggestions in a Python dictionary format. show me settings You SHOULD NOT include any other text in the response. Fill out the seeting and chose usefull values. Respect the users use cases and content snipet. Choose the setting based on the chunks <|eot_id|><|start_header_id|>user<|end_header_id|> User user case: {"small local", "large total context", ...} total content lenght: {len(' '.join(chunks))} Content snipet: {' '.join(sample_chunks)} <|eot_id|><|start_header_id|>assistant<|end_header_id|> ''' suggested_settings = llm_pipeline( prompt, do_sample=True, top_k=10, num_return_sequences=1, return_full_text=False, max_new_tokens=1900, # Control the length of the output, truncation=True, # Enable truncation ) #suggested_settings = llm.invoke(prompt) print("setting suggested") print(suggested_settings) # Parse the generated text to extract the dictionary try: settings_dict = eval(suggested_settings) # Convert the settings to match the interface inputs return { "embedding_models": f"{settings_dict['embedding_model_type']}:{settings_dict['embedding_model_name']}", "split_strategy": settings_dict["split_strategy"], "chunk_size": settings_dict["chunk_size"], "overlap_size": settings_dict["overlap_size"], "vector_store_type": settings_dict["vector_store_type"], "search_type": settings_dict["search_type"], "top_k": settings_dict["top_k"], "apply_preprocessing": settings_dict["apply_preprocessing"], "optimize_vocab": settings_dict["optimize_vocabulary"], "apply_phonetic": settings_dict["apply_phonetic_matching"], "phonetic_weight": 0.3 # Default value, as it's not in the LLM suggestions } except: return {"error": "Failed to parse LLM suggestions"} def update_inputs_with_llm_suggestions(suggestions): if suggestions is None or "error" in suggestions: return [gr.update() for _ in range(11)] # Return no updates if there's an error or None return [ gr.update(value=[suggestions["embedding_models"]]), # embedding_models_input gr.update(value=suggestions["split_strategy"]), # split_strategy_input gr.update(value=suggestions["chunk_size"]), # chunk_size_input gr.update(value=suggestions["overlap_size"]), # overlap_size_input gr.update(value=suggestions["vector_store_type"]), # vector_store_type_input gr.update(value=suggestions["search_type"]), # search_type_input gr.update(value=suggestions["top_k"]), # top_k_input gr.update(value=suggestions["apply_preprocessing"]), # apply_preprocessing_input gr.update(value=suggestions["optimize_vocab"]), # optimize_vocab_input gr.update(value=suggestions["apply_phonetic"]), # apply_phonetic_input gr.update(value=suggestions["phonetic_weight"]) # phonetic_weight_input ] def parse_model_selections(default_models, custom_models): """ Parse selected default models and custom models into model configurations Args: default_models (List[str]): Selected default models in format "type:name" custom_models (str): Custom models string with one model per line in format "type:name" Returns: List[Dict[str, str]]: List of model configurations with 'type' and 'name' keys """ model_configs = [] # Process default models if default_models: for model in default_models: model_type, model_name = model.split(':') model_configs.append({ 'type': model_type, 'name': model_name }) # Process custom models if custom_models: custom_model_lines = custom_models.strip().split('\n') for line in custom_model_lines: if line.strip() and ':' in line: model_type, model_name = line.strip().split(':') model_configs.append({ 'type': model_type.strip(), 'name': model_name.strip() }) return model_configs def parse_comma_separated(text): """Parse comma-separated values into a list""" if not text: return [] return [x.strip() for x in text.split(',') if x.strip()] # Gradio Interface def launch_interface(debug=True): with gr.Blocks() as iface: gr.Markdown("# Advanced Embedding Comparison Tool") with gr.Tab("Simple"): file_input = gr.File(label="Upload File (Optional)") query_input = gr.Textbox(label="Search Query") expected_result_input = gr.Textbox(label="Expected Result (Optional)") embedding_models_input = gr.CheckboxGroup( choices=[ "HuggingFace:paraphrase-miniLM", "HuggingFace:paraphrase-mpnet", "OpenAI:text-embedding-ada-002", "Cohere:embed-multilingual-v2.0" ], label="Embedding Models" ) top_k_input = gr.Slider(1, 10, step=1, value=5, label="Top K") with gr.Tab("Advanced"): custom_embedding_model_input = gr.Textbox(label="Custom Embedding Model (optional, format: type:name)") split_strategy_input = gr.Radio(choices=["token", "recursive"], label="Split Strategy", value="recursive") chunk_size_input = gr.Slider(100, 1000, step=100, value=500, label="Chunk Size") overlap_size_input = gr.Slider(0, 100, step=10, value=50, label="Overlap Size") custom_separators_input = gr.Textbox(label="Custom Split Separators (comma-separated, optional)") vector_store_type_input = gr.Radio(choices=["FAISS", "Chroma"], label="Vector Store Type", value="FAISS") search_type_input = gr.Radio(choices=["similarity", "mmr", "custom"], label="Search Type", value="similarity") lang_input = gr.Dropdown(choices=["german", "english", "french"], label="Language", value="german") with gr.Tab("Expert"): apply_preprocessing_input = gr.Checkbox(label="Apply Text Preprocessing", value=False) optimize_vocab_input = gr.Checkbox(label="Optimize Vocabulary", value=False) apply_phonetic_input = gr.Checkbox(label="Apply Phonetic Matching", value=False) phonetic_weight_input = gr.Slider(0, 1, step=0.1, value=0.3, label="Phonetic Matching Weight") custom_tokenizer_file_input = gr.File(label="Custom Tokenizer File (Optional)") custom_tokenizer_model_input = gr.Textbox(label="Custom Tokenizer Model (e.g., WordLevel, BPE, Unigram)") custom_tokenizer_vocab_size_input = gr.Textbox(label="Custom Tokenizer Vocab Size", value="10000") custom_tokenizer_special_tokens_input = gr.Textbox(label="Custom Tokenizer Special Tokens (comma-separated)") use_query_optimization_input = gr.Checkbox(label="Use Query Optimization", value=False) query_optimization_model_input = gr.Textbox(label="Query Optimization Model (google/flan-t5-base) ", value="") use_reranking_input = gr.Checkbox(label="Use Reranking", value=False) with gr.Tab("Automation"): with gr.Row(): auto_file_input = gr.File(label="Upload File (Optional)") auto_query_input = gr.Textbox(label="Search Query") with gr.Row(): auto_expected_result_input = gr.Textbox( label="Expected Result (Optional)", placeholder="Enter expected text if you want to evaluate accuracy" ) model_feedback_input = gr.Textbox( label="Model Feedback (Optional)", placeholder="Enter any feedback about model performance" ) with gr.Row(): with gr.Column(): # Default model selection default_models_input = gr.CheckboxGroup( choices=[f"{type}:{name}" for type, names in DEFAULT_MODELS.items() for name in names], label="Default Models", value=[f"HuggingFace:{DEFAULT_MODELS['HuggingFace'][0]}"] ) with gr.Column(): # Custom model input custom_models_input = gr.TextArea( label="Custom Models (Optional)", placeholder="Enter one model per line in format: type:name", lines=3 ) auto_split_strategies = gr.CheckboxGroup( choices=["token", "recursive"], label="Split Strategies to Test" ) auto_chunk_sizes = gr.TextArea(label="Chunk Sizes to Test (comma-separated)") auto_overlap_sizes = gr.TextArea(label="Overlap Sizes to Test (comma-separated)") auto_vector_store_types = gr.CheckboxGroup( choices=["FAISS", "Chroma"], label="Vector Store Types to Test" ) auto_search_types = gr.CheckboxGroup( choices=["similarity", "mmr", "custom"], label="Search Types to Test" ) auto_top_k = gr.TextArea(label="Top K Values to Test (comma-separated)") auto_optimize_vocab = gr.Checkbox(label="Test Vocabulary Optimization", value=True) auto_use_query_optimization = gr.Checkbox(label="Test Query Optimization", value=True) auto_use_reranking = gr.Checkbox(label="Test Reranking", value=True) auto_results_output = gr.Dataframe(label="Automated Test Results", interactive=False) auto_stats_output = gr.Dataframe(label="Automated Test Statistics", interactive=False) recommendations_output = gr.JSON(label="Recommendations") def run_automation(file_input, query_input, expected_result, default_models, custom_models, split_strategies, chunk_sizes, overlap_sizes, vector_store_types, search_types, top_k_values, optimize_vocab, use_query_optimization, use_reranking, model_feedback): """Wrapper function to handle Gradio inputs and run automated tests""" # Parse model configurations model_configs = parse_model_selections(default_models, custom_models) # Parse test parameters test_params = { 'split_strategy': split_strategies, 'chunk_size': parse_comma_separated(chunk_sizes), 'overlap_size': parse_comma_separated(overlap_sizes), 'vector_store_type': vector_store_types, 'search_type': search_types, 'top_k': parse_comma_separated(top_k_values), 'optimize_vocab': [optimize_vocab], 'use_query_optimization': [use_query_optimization], 'use_reranking': [use_reranking], 'lang': ['en'], # Default to English 'apply_preprocessing': [True], # Default preprocessing 'apply_phonetic': [False], # Default phonetic settings 'phonetic_weight': [0.5], 'custom_separators': [None], 'query_optimization_model': ['google/flan-t5-base'] # Default query optimization model } # Run automated tests results_df, stats_df = run_automated_tests( file_input.name if file_input else None, query_input, model_configs, test_params, expected_result if expected_result else None, model_feedback if model_feedback else None ) # Generate recommendations based on results recommendations = analyze_results(stats_df) return results_df, stats_df, recommendations auto_submit_button = gr.Button("Run Automated Tests") auto_submit_button.click( fn=run_automation, inputs=[ auto_file_input, auto_query_input, auto_expected_result_input, default_models_input, custom_models_input, auto_split_strategies, auto_chunk_sizes, auto_overlap_sizes, auto_vector_store_types, auto_search_types, auto_top_k, auto_optimize_vocab, auto_use_query_optimization, auto_use_reranking, model_feedback_input ], outputs=[auto_results_output, auto_stats_output, recommendations_output] ) ### with gr.Tab("Results"): with gr.Row(): results_output = gr.DataFrame(label="Results") stats_output = gr.DataFrame(label="Statistics") with gr.Row(): plot_output = gr.Plot(label="Visualizations") model_rankings_output = gr.JSON(label="Model Rankings") with gr.Row(): recommendations_output = gr.JSON(label="Recommendations") with gr.Tab("LLM Suggestions"): llm_file_input = gr.File(label="Upload File for LLM Suggestions") llm_num_chunks = gr.Slider(1, 10, step=1, value=5, label="Number of Sample Chunks") llm_suggest_button = gr.Button("Get LLM Suggestions") llm_suggestions_output = gr.JSON(label="LLM-suggested Settings") llm_suggest_button.click( fn=get_llm_suggested_settings, inputs=[llm_file_input, llm_num_chunks], outputs=[llm_suggestions_output] ).then( fn=update_inputs_with_llm_suggestions, inputs=[llm_suggestions_output], outputs=[ embedding_models_input, split_strategy_input, chunk_size_input, overlap_size_input, vector_store_type_input, search_type_input, top_k_input, apply_preprocessing_input, optimize_vocab_input, apply_phonetic_input, phonetic_weight_input ] ) results_output = gr.Dataframe(label="Results", interactive=False) stats_output = gr.Dataframe(label="Statistics", interactive=False) plot_output = gr.Plot(label="Visualizations") best_settings_output = gr.JSON(label="Best Settings") submit_button = gr.Button("Compare Embeddings") submit_button.click( #fn=lambda *args: compare_and_show_best(*args), fn=lambda *args: compare_embeddings(*args), inputs=[ file_input, query_input, embedding_models_input, custom_embedding_model_input, split_strategy_input, chunk_size_input, overlap_size_input, custom_separators_input, vector_store_type_input, search_type_input, top_k_input, expected_result_input, lang_input, apply_preprocessing_input, optimize_vocab_input, apply_phonetic_input, phonetic_weight_input, custom_tokenizer_file_input, custom_tokenizer_model_input, custom_tokenizer_vocab_size_input, custom_tokenizer_special_tokens_input, use_query_optimization_input, query_optimization_model_input, use_reranking_input ], outputs=[results_output, stats_output, plot_output, best_settings_output] ) use_case_md = """ # 🚀 AI Act Embedding Use Case Guide ## 📚 Use Case: Embedding the German AI Act for Local Chat Retrieval In this guide, we'll walk through the process of embedding the German version of the AI Act using our advanced embedding tool and MTEB. We'll then use these embeddings in a local chat application as a retriever/context. ### Step 1: Prepare the Document 📄 1. Download the German version of the AI Act (let's call it `ai_act_de.txt`). 2. Place the file in your project directory. ### Step 2: Set Up the Embedding Tool 🛠️ 1. Open the Embedding Comparison Tool. 2. Navigate to the new "Automation" tab. ### Step 3: Configure the Automated Test 🔧 In the "Use Case" tab, set up the following configuration: ```markdown - File: ai_act_de.txt - Query: "Wie definiert das Gesetz KI-Systeme?" - Model Types: ✅ HuggingFace, ✅ Sentence Transformers - Model Names: paraphrase-multilingual-MiniLM-L12-v2, distiluse-base-multilingual-cased-v2 - Split Strategies: ✅ recursive, ✅ token - Chunk Sizes: 256, 512, 1024 - Overlap Sizes: 32, 64, 128 - Vector Store Types: ✅ FAISS - Search Types: ✅ similarity, ✅ mmr - Top K Values: 3, 5, 7 - Test Vocabulary Optimization: ✅ - Test Query Optimization: ✅ - Test Reranking: ✅ ``` ### Step 4: Run the Automated Test 🏃‍♂️ Click the "Run Automated Tests" button and wait for the results. ### Step 5: Analyze the Results 📊 Let's say we got the following simulated results: ```markdown Best Model: Sentence Transformers - paraphrase-multilingual-MiniLM-L12-v2 Best Settings: - Split Strategy: recursive - Chunk Size: 512 - Overlap Size: 64 - Vector Store Type: FAISS - Search Type: mmr - Top K: 5 - Optimize Vocabulary: True - Use Query Optimization: True - Use Reranking: True Performance Summary: - Search Time: 0.15s - Result Diversity: 0.82 - Rank Correlation: 0.91 - Silhouette Score: 0.76 ``` ### Step 6: Understand the Results 🧠 1. **Model**: The Sentence Transformers model performed better, likely due to its multilingual capabilities and fine-tuning for paraphrasing tasks. 2. **Split Strategy**: Recursive splitting worked best, probably because it respects the document's structure better than fixed-length token splitting. 3. **Chunk Size**: 512 tokens provide a good balance between context and specificity. 4. **Search Type**: MMR (Maximum Marginal Relevance) outperformed simple similarity search, likely due to its ability to balance relevance and diversity in results. 5. **Optimizations**: All optimizations (vocabulary, query, and reranking) proved beneficial, indicating that the extra processing time is worth the improved results. ### Step 7: Implement in Local Chat 💬 Now that we have the optimal settings, let's implement this in a local chat application: 1. Use the `paraphrase-multilingual-MiniLM-L12-v2` model for embeddings. 2. Set up a FAISS vector store with the embedded chunks. 3. Implement MMR search with a top-k of 5. 4. Include the optimization steps in your pipeline. ### Step 8: Test the Implementation 🧪 Create a simple chat interface and test with various queries about the AI Act. For example: User: "Was sind die Hauptziele des KI-Gesetzes?" """ tutorial_md = """ # Advanced Embedding Comparison Tool Tutorial Welcome to the **Advanced Embedding Comparison Tool**! This comprehensive guide will help you understand and utilize the tool's features to optimize your **Retrieval-Augmented Generation (RAG)** systems. ## Table of Contents 1. [Introduction to RAG](#introduction-to-rag) 2. [Key Components of RAG](#key-components-of-rag) 3. [Impact of Parameter Changes](#impact-of-parameter-changes) 4. [Advanced Features](#advanced-features) 5. [Using the Embedding Comparison Tool](#using-the-embedding-comparison-tool) 6. [Automated Testing and Analysis](#automated-testing-and-analysis) 7. [Mathematical Concepts and Metrics](#mathematical-concepts-and-metrics) 8. [Code Examples](#code-examples) 9. [Best Practices and Tips](#best-practices-and-tips) 10. [Resources and Further Reading](#resources-and-further-reading) --- ## Introduction to RAG **Retrieval-Augmented Generation (RAG)** is a powerful technique that combines the strengths of large language models (LLMs) with the ability to access and use external knowledge. RAG is particularly useful for: - Providing up-to-date information - Answering questions based on specific documents or data sources - Reducing hallucinations in AI responses - Customizing AI outputs for specific domains or use cases RAG is ideal for applications requiring accurate, context-specific information retrieval combined with natural language generation, such as chatbots, question-answering systems, and document analysis tools. --- ## Key Components of RAG ### 1. Document Loading Ingests documents from various sources (PDFs, web pages, databases, etc.) into a format that can be processed by the RAG system. The tool supports multiple file formats, including PDF, DOCX, and TXT. ### 2. Document Splitting Splits large documents into smaller chunks for more efficient processing and retrieval. Available strategies include: - **Token-based splitting** - **Recursive splitting** ### 3. Vector Store and Embeddings Embeddings are dense vector representations of text that capture semantic meaning. The tool supports multiple embedding models and vector stores: - **Embedding models**: HuggingFace, OpenAI, Cohere, and custom models. - **Vector stores**: FAISS and Chroma. ### 4. Retrieval Finds the most relevant documents or chunks based on a query. Available retrieval methods include: - **Similarity search** - **Maximum Marginal Relevance (MMR)** - **Custom search methods** --- ## Impact of Parameter Changes Understanding how different parameters affect your RAG system is crucial for optimization: - **Chunk Size**: Larger chunks provide more context but may reduce precision. Smaller chunks increase precision but may lose context. - **Overlap**: More overlap helps maintain context between chunks but increases computational load. - **Embedding Model**: Performance varies across languages and domains. - **Vector Store**: Affects query speed and the types of searches. - **Retrieval Method**: Influences the diversity and relevance of retrieved documents. --- ## Advanced Features ### 1. Custom Tokenization Upload a custom tokenizer file and specify the tokenizer model, vocabulary size, and special tokens for domain or language-specific tokenization. ### 2. Query Optimization Improve search results by generating multiple variations of the input query using a language model to capture different phrasings. ### 3. Reranking Further refine search results by using a separate model to re-score and reorder the initial retrieval results. ### 4. Phonetic Matching For languages like German, phonetic matching with adjustable weighting is available. ### 5. Vocabulary Optimization Optimize vocabulary for domain-specific applications during the embedding process. --- ## Using the Embedding Comparison Tool The tool is divided into several tabs for ease of use: ### Simple Tab 1. **File Upload**: Upload a file (PDF, DOCX, or TXT) or use files from the `./files` directory. 2. **Search Query**: Enter the search query. 3. **Embedding Models**: Select one or more embedding models to compare. 4. **Top K**: Set the number of top results to retrieve (1-10). ### Advanced Tab 5. **Custom Embedding Model**: Specify a custom embedding model. 6. **Split Strategy**: Choose between 'token' and 'recursive' splitting. 7. **Chunk Size**: Set chunk size (100-1000). 8. **Overlap Size**: Set overlap between chunks (0-100). 9. **Custom Split Separators**: Enter custom separators for text splitting. 10. **Vector Store Type**: Choose between FAISS and Chroma. 11. **Search Type**: Select 'similarity', 'mmr', or 'custom'. 12. **Language**: Specify the document's primary language. ### Optional Tab 13. **Text Preprocessing**: Toggle text preprocessing. 14. **Vocabulary Optimization**: Enable vocabulary optimization. 15. **Phonetic Matching**: Enable phonetic matching and set its weight. 16. **Custom Tokenizer**: Upload a custom tokenizer and specify parameters. 17. **Query Optimization**: Enable query optimization and specify the model. 18. **Reranking**: Enable result reranking. --- ## Automated Testing and Analysis The **Automation tab** allows you to run comprehensive tests across multiple configurations: 1. Set up test parameters like model types, split strategies, chunk sizes, etc. 2. Click "Run Automated Tests." 3. View results, statistics, and recommendations to find optimal configurations for your use case. --- ## Mathematical Concepts and Metrics ### Cosine Similarity Measures the cosine of the angle between two vectors, used in similarity search: $$\text{cosine similarity} = \frac{\mathbf{A} \cdot \mathbf{B}}{\|\mathbf{A}\| \|\mathbf{B}\|}$$ ### Maximum Marginal Relevance (MMR) Balances relevance and diversity in search results: $$\text{MMR} = \arg\max_{D_i \in R \setminus S} [\lambda \text{Sim}_1(D_i, Q) - (1-\lambda) \max_{D_j \in S} \text{Sim}_2(D_i, D_j)]$$ ### Silhouette Score Measures how well an object fits within its own cluster compared to others. Scores range from -1 to 1, where higher values indicate better-defined clusters. --- ## Code Examples ### Custom Tokenization ```python def create_custom_tokenizer(file_path, model_type='WordLevel', vocab_size=10000, special_tokens=None): with open(file_path, 'r', encoding='utf-8') as f: text = f.read() tokenizer = Tokenizer(models.WordLevel(unk_token="[UNK]")) if model_type == 'WordLevel' else Tokenizer(models.BPE(unk_token="[UNK]")) tokenizer.pre_tokenizer = Whitespace() trainer = trainers.WordLevelTrainer(special_tokens=special_tokens or ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"], vocab_size=vocab_size) tokenizer.train_from_iterator([text], trainer) return tokenizer ```` ### Query Optimization ```python def optimize_query(query, llm): multi_query_retriever = MultiQueryRetriever.from_llm( retriever=get_retriever(vector_store, search_type, search_kwargs), llm=llm ) optimized_queries = multi_query_retriever.invoke(query) return optimized_queries ```` ### Reranking ```python def rerank_results(results, query, reranker): reranked_results = reranker.rerank(query, [doc.page_content for doc in results]) return reranked_results ```` ### Best Practices and Tips - Start Simple: Begin with basic configurations, then gradually add complexity. - Benchmark: Use automated testing to benchmark different setups. - Domain-Specific Tuning: Consider custom tokenizers and embeddings for specialized domains. - Balance Performance and Cost: Use advanced features like query optimization and reranking judiciously. - Iterate: Optimization is an iterative process—refine your approach based on tool insights. ## Useful Resources and Links Here are some valuable resources to help you better understand and work with embeddings, retrieval systems, and natural language processing: ### Embeddings and Vector Databases - [Understanding Embeddings](https://www.tensorflow.org/text/guide/word_embeddings): A guide by TensorFlow on word embeddings - [FAISS: A Library for Efficient Similarity Search](https://github.com/facebookresearch/faiss): Facebook AI's vector similarity search library - [Chroma: The AI-native open-source embedding database](https://www.trychroma.com/): An embedding database designed for AI applications ### Natural Language Processing - [NLTK (Natural Language Toolkit)](https://www.nltk.org/): A leading platform for building Python programs to work with human language data - [spaCy](https://spacy.io/): Industrial-strength Natural Language Processing in Python - [Hugging Face Transformers](https://huggingface.co/transformers/): State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0 ### Retrieval-Augmented Generation (RAG) - [LangChain](https://python.langchain.com/docs/get_started/introduction): A framework for developing applications powered by language models - [OpenAI's RAG Tutorial](https://platform.openai.com/docs/tutorials/web-qa-embeddings): A guide on building a QA system with embeddings ### German Language Processing - [Kölner Phonetik](https://en.wikipedia.org/wiki/Cologne_phonetics): Information about the Kölner Phonetik algorithm - [German NLP Resources](https://github.com/adbar/German-NLP): A curated list of open-access resources for German NLP ### Benchmarks and Evaluation - [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard): Massive Text Embedding Benchmark leaderboard - [GLUE Benchmark](https://gluebenchmark.com/): General Language Understanding Evaluation benchmark ### Tools and Libraries - [Gensim](https://radimrehurek.com/gensim/): Topic modelling for humans - [Sentence-Transformers](https://www.sbert.net/): A Python framework for state-of-the-art sentence, text and image embeddings ### Support me - [Visual Crew Builder](https://visual-crew.builder.ai/): Tool for create AI systems, workflows and api. Or just a notebook. This tool empowers you to fine-tune your RAG system for optimal performance. Experiment with different settings, run automated tests, and use insights to create an efficient information retrieval and generation system. # Template python ´´´ # Chat App Template def create_chat_app(settings): def chat(message, history): # Process the message using the configured embedding model and vector store chunks, embedding_model, _ = process_files( settings['file_path'], settings['model_type'], settings['model_name'], settings['split_strategy'], settings['chunk_size'], settings['overlap_size'], settings['custom_separators'], settings['lang'], settings['apply_preprocessing'] ) results, _, _, _ = search_embeddings( chunks, embedding_model, settings['vector_store_type'], settings['search_type'], message, settings['top_k'], lang=settings['lang'], apply_phonetic=settings['apply_phonetic'], phonetic_weight=settings['phonetic_weight'] ) # Generate a response based on the retrieved results response = f"Based on the query '{message}', here are the top {settings['top_k']} relevant results:\n\n" for i, result in enumerate(results[:settings['top_k']]): response += f"{i+1}. {result['content'][:100]}...\n\n" return response with gr.Blocks() as chat_interface: gr.Markdown(f"# Chat App using {settings['model_type']} - {settings['model_name']}") chatbot = gr.Chatbot() msg = gr.Textbox() clear = gr.Button("Clear") msg.submit(chat, [msg, chatbot], [msg, chatbot]) clear.click(lambda: None, None, chatbot, queue=False) return chat_interface # Sample usage of the chat app template sample_settings = { 'file_path': 'path/to/your/document.pdf', 'model_type': 'HuggingFace', 'model_name': 'paraphrase-miniLM', 'split_strategy': 'recursive', 'chunk_size': 500, 'overlap_size': 50, 'custom_separators': None, 'vector_store_type': 'FAISS', 'search_type': 'similarity', 'top_k': 3, 'lang': 'english', 'apply_preprocessing': True, 'apply_phonetic': True, 'phonetic_weight': 0.3 } sample_chat_app = create_chat_app(sample_settings) if __name__ == "__main__": launch_interface() # Uncomment the following line to launch the sample chat app ´´´ """ iface = gr.TabbedInterface( [iface, gr.Markdown(tutorial_md), gr.Markdown( use_case_md )], ["Embedding Comparison", "Tutorial", "Use Case"] ) iface.launch(debug=True, share=True) # Enhanced Automated Testing def run_automated_tests(file_path: str, query: str, model_configs: List[Dict[str, str]], test_params: Dict[str, List[Any]], expected_result: Optional[str] = None, model_feedback: Optional[str] = None) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Enhanced automated testing function with support for custom models and feedback """ all_results = [] all_stats = [] model_manager = ModelManager() # Create parameter grid excluding model configurations base_params = {k: v for k, v in test_params.items() if k not in ['model_type', 'model_name']} param_grid = ParameterGrid(base_params) # Test each model configuration with all parameter combinations for model_config in tqdm(model_configs, desc="Testing models"): model_type = model_config['type'] model_name = model_config['name'] for params in tqdm(param_grid, desc=f"Testing parameters for {model_type}:{model_name}"): try: # Process files and get chunks chunks, embedding_model, num_tokens = process_files( file_path, model_type, model_name, params['split_strategy'], params['chunk_size'], params['overlap_size'], params.get('custom_separators'), params['lang'], params['apply_preprocessing'] ) # Apply vocabulary optimization if specified if params['optimize_vocab']: tokenizer, chunks = optimize_vocabulary(chunks) # Apply query optimization if specified current_query = query if params['use_query_optimization']: optimized_queries = optimize_query( query, params['query_optimization_model'], chunks, embedding_model, params['vector_store_type'], params['search_type'], params['top_k'] ) current_query = " ".join(optimized_queries) # Perform search results, search_time, vector_store, raw_results = search_embeddings( chunks, embedding_model, params['vector_store_type'], params['search_type'], current_query, params['top_k'], expected_result, params['lang'], params['apply_phonetic'], params['phonetic_weight'] ) # Apply reranking if specified if params['use_reranking']: reranker = pipeline("text-classification", model="cross-encoder/ms-marco-MiniLM-L-12-v2") raw_results = rerank_results(raw_results, current_query, reranker) # Calculate statistics stats = ResultAnalyzer.calculate_statistics( raw_results, search_time, vector_store, num_tokens, embedding_model, current_query, params['top_k'], expected_result, model_feedback ) # Update model rankings model_id = f"{model_type}:{model_name}" ranking_score = calculate_model_ranking_score(stats) model_manager.update_model_ranking(model_id, ranking_score, model_feedback) # Add model information to stats stats.update({ "model_type": model_type, "model_name": model_name, "model": f"{model_type} - {model_name}", **params }) # Format and store results all_results.extend(format_results(raw_results, stats)) all_stats.append(stats) except Exception as e: print(f"Error testing {model_type}:{model_name} with parameters {params}: {str(e)}") continue return pd.DataFrame(all_results), pd.DataFrame(all_stats) # Helper function to calculate model ranking score def calculate_model_ranking_score(stats: Dict[str, Any]) -> float: """Calculate a composite score for model ranking""" weights = { 'search_time': -0.2, # Negative weight because lower is better 'result_diversity': 0.2, 'rank_correlation': 0.3, 'contains_expected': 0.3, 'expected_result_rank': -0.2 # Negative weight because lower rank is better } score = 0.0 for metric, weight in weights.items(): if metric in stats and not isinstance(stats[metric], str): if metric == 'contains_expected': value = float(stats[metric]) elif metric == 'expected_result_rank': value = 1.0 / max(stats[metric], 1) # Convert rank to score (higher is better) else: value = float(stats[metric]) score += weight * value return score if __name__ == "__main__": launch_interface()