from abc import ABC, abstractmethod from typing import Any, List, Dict, Optional, Union from concurrent.futures import ThreadPoolExecutor, as_completed import json, time # from optimum.intel import IPEXModel from .prompts import * from .config import * from .utils import * from .models import * from functools import partial from .model_loader import * import math import numpy as np import re from bs4 import BeautifulSoup from lxml import html, etree from dataclasses import dataclass class ExtractionStrategy(ABC): """ Abstract base class for all extraction strategies. """ def __init__(self, input_format: str = "markdown", **kwargs): """ Initialize the extraction strategy. Args: input_format: Content format to use for extraction. Options: "markdown" (default), "html", "fit_markdown" **kwargs: Additional keyword arguments """ self.input_format = input_format self.DEL = "<|DEL|>" self.name = self.__class__.__name__ self.verbose = kwargs.get("verbose", False) @abstractmethod def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]: """ Extract meaningful blocks or chunks from the given HTML. :param url: The URL of the webpage. :param html: The HTML content of the webpage. :return: A list of extracted blocks or chunks. """ pass def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: """ Process sections of text in parallel by default. :param url: The URL of the webpage. :param sections: List of sections (strings) to process. :return: A list of processed JSON blocks. """ extracted_content = [] with ThreadPoolExecutor() as executor: futures = [executor.submit(self.extract, url, section, **kwargs) for section in sections] for future in as_completed(futures): extracted_content.extend(future.result()) return extracted_content class NoExtractionStrategy(ExtractionStrategy): """ A strategy that does not extract any meaningful content from the HTML. It simply returns the entire HTML as a single block. """ def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]: """ Extract meaningful blocks or chunks from the given HTML. """ return [{"index": 0, "content": html}] def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: return [{"index": i, "tags": [], "content": section} for i, section in enumerate(sections)] ####################################################### # Strategies using LLM-based extraction for text data # ####################################################### class LLMExtractionStrategy(ExtractionStrategy): """ A strategy that uses an LLM to extract meaningful content from the HTML. Attributes: provider: The provider to use for extraction. It follows the format /, e.g., "ollama/llama3.3". api_token: The API token for the provider. instruction: The instruction to use for the LLM model. schema: Pydantic model schema for structured data. extraction_type: "block" or "schema". chunk_token_threshold: Maximum tokens per chunk. overlap_rate: Overlap between chunks. word_token_rate: Word to token conversion rate. apply_chunking: Whether to apply chunking. base_url: The base URL for the API request. api_base: The base URL for the API request. extra_args: Additional arguments for the API request, such as temprature, max_tokens, etc. verbose: Whether to print verbose output. usages: List of individual token usages. total_usage: Accumulated token usage. """ def __init__(self, provider: str = DEFAULT_PROVIDER, api_token: Optional[str] = None, instruction:str = None, schema:Dict = None, extraction_type = "block", **kwargs): """ Initialize the strategy with clustering parameters. Args: provider: The provider to use for extraction. It follows the format /, e.g., "ollama/llama3.3". api_token: The API token for the provider. instruction: The instruction to use for the LLM model. schema: Pydantic model schema for structured data. extraction_type: "block" or "schema". chunk_token_threshold: Maximum tokens per chunk. overlap_rate: Overlap between chunks. word_token_rate: Word to token conversion rate. apply_chunking: Whether to apply chunking. base_url: The base URL for the API request. api_base: The base URL for the API request. extra_args: Additional arguments for the API request, such as temprature, max_tokens, etc. verbose: Whether to print verbose output. usages: List of individual token usages. total_usage: Accumulated token usage. """ super().__init__(**kwargs) self.provider = provider self.api_token = api_token or PROVIDER_MODELS.get(provider, "no-token") or os.getenv("OPENAI_API_KEY") self.instruction = instruction self.extract_type = extraction_type self.schema = schema if schema: self.extract_type = "schema" self.chunk_token_threshold = kwargs.get("chunk_token_threshold", CHUNK_TOKEN_THRESHOLD) self.overlap_rate = kwargs.get("overlap_rate", OVERLAP_RATE) self.word_token_rate = kwargs.get("word_token_rate", WORD_TOKEN_RATE) self.apply_chunking = kwargs.get("apply_chunking", True) self.base_url = kwargs.get("base_url", None) self.api_base = kwargs.get("api_base", kwargs.get("base_url", None)) self.extra_args = kwargs.get("extra_args", {}) if not self.apply_chunking: self.chunk_token_threshold = 1e9 self.verbose = kwargs.get("verbose", False) self.usages = [] # Store individual usages self.total_usage = TokenUsage() # Accumulated usage if not self.api_token: raise ValueError("API token must be provided for LLMExtractionStrategy. Update the config.py or set OPENAI_API_KEY environment variable.") def extract(self, url: str, ix:int, html: str) -> List[Dict[str, Any]]: """ Extract meaningful blocks or chunks from the given HTML using an LLM. How it works: 1. Construct a prompt with variables. 2. Make a request to the LLM using the prompt. 3. Parse the response and extract blocks or chunks. Args: url: The URL of the webpage. ix: Index of the block. html: The HTML content of the webpage. Returns: A list of extracted blocks or chunks. """ if self.verbose: # print("[LOG] Extracting blocks from URL:", url) print(f"[LOG] Call LLM for {url} - block index: {ix}") variable_values = { "URL": url, "HTML": escape_json_string(sanitize_html(html)), } prompt_with_variables = PROMPT_EXTRACT_BLOCKS if self.instruction: variable_values["REQUEST"] = self.instruction prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION if self.extract_type == "schema" and self.schema: variable_values["SCHEMA"] = json.dumps(self.schema, indent=2) prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION for variable in variable_values: prompt_with_variables = prompt_with_variables.replace( "{" + variable + "}", variable_values[variable] ) response = perform_completion_with_backoff( self.provider, prompt_with_variables, self.api_token, base_url=self.api_base or self.base_url, extra_args = self.extra_args ) # , json_response=self.extract_type == "schema") # Track usage usage = TokenUsage( completion_tokens=response.usage.completion_tokens, prompt_tokens=response.usage.prompt_tokens, total_tokens=response.usage.total_tokens, completion_tokens_details=response.usage.completion_tokens_details.__dict__ if response.usage.completion_tokens_details else {}, prompt_tokens_details=response.usage.prompt_tokens_details.__dict__ if response.usage.prompt_tokens_details else {} ) self.usages.append(usage) # Update totals self.total_usage.completion_tokens += usage.completion_tokens self.total_usage.prompt_tokens += usage.prompt_tokens self.total_usage.total_tokens += usage.total_tokens try: blocks = extract_xml_data(["blocks"], response.choices[0].message.content)['blocks'] blocks = json.loads(blocks) for block in blocks: block['error'] = False except Exception as e: parsed, unparsed = split_and_parse_json_objects(response.choices[0].message.content) blocks = parsed if unparsed: blocks.append({ "index": 0, "error": True, "tags": ["error"], "content": unparsed }) if self.verbose: print("[LOG] Extracted", len(blocks), "blocks from URL:", url, "block index:", ix) return blocks def _merge(self, documents, chunk_token_threshold, overlap): """ Merge documents into sections based on chunk_token_threshold and overlap. """ chunks = [] sections = [] total_tokens = 0 # Calculate the total tokens across all documents for document in documents: total_tokens += len(document.split(' ')) * self.word_token_rate # Calculate the number of sections needed num_sections = math.floor(total_tokens / chunk_token_threshold) if num_sections < 1: num_sections = 1 # Ensure there is at least one section adjusted_chunk_threshold = total_tokens / num_sections total_token_so_far = 0 current_chunk = [] for document in documents: tokens = document.split(' ') token_count = len(tokens) * self.word_token_rate if total_token_so_far + token_count <= adjusted_chunk_threshold: current_chunk.extend(tokens) total_token_so_far += token_count else: # Ensure to handle the last section properly if len(sections) == num_sections - 1: current_chunk.extend(tokens) continue # Add overlap if specified if overlap > 0 and current_chunk: overlap_tokens = current_chunk[-overlap:] current_chunk.extend(overlap_tokens) sections.append(' '.join(current_chunk)) current_chunk = tokens total_token_so_far = token_count # Add the last chunk if current_chunk: sections.append(' '.join(current_chunk)) return sections def run(self, url: str, sections: List[str]) -> List[Dict[str, Any]]: """ Process sections sequentially with a delay for rate limiting issues, specifically for LLMExtractionStrategy. Args: url: The URL of the webpage. sections: List of sections (strings) to process. Returns: A list of extracted blocks or chunks. """ merged_sections = self._merge( sections, self.chunk_token_threshold, overlap= int(self.chunk_token_threshold * self.overlap_rate) ) extracted_content = [] if self.provider.startswith("groq/"): # Sequential processing with a delay for ix, section in enumerate(merged_sections): extract_func = partial(self.extract, url) extracted_content.extend(extract_func(ix, sanitize_input_encode(section))) time.sleep(0.5) # 500 ms delay between each processing else: # Parallel processing using ThreadPoolExecutor # extract_func = partial(self.extract, url) # for ix, section in enumerate(merged_sections): # extracted_content.append(extract_func(ix, section)) with ThreadPoolExecutor(max_workers=4) as executor: extract_func = partial(self.extract, url) futures = [executor.submit(extract_func, ix, sanitize_input_encode(section)) for ix, section in enumerate(merged_sections)] for future in as_completed(futures): try: extracted_content.extend(future.result()) except Exception as e: if self.verbose: print(f"Error in thread execution: {e}") # Add error information to extracted_content extracted_content.append({ "index": 0, "error": True, "tags": ["error"], "content": str(e) }) return extracted_content def show_usage(self) -> None: """Print a detailed token usage report showing total and per-request usage.""" print("\n=== Token Usage Summary ===") print(f"{'Type':<15} {'Count':>12}") print("-" * 30) print(f"{'Completion':<15} {self.total_usage.completion_tokens:>12,}") print(f"{'Prompt':<15} {self.total_usage.prompt_tokens:>12,}") print(f"{'Total':<15} {self.total_usage.total_tokens:>12,}") print("\n=== Usage History ===") print(f"{'Request #':<10} {'Completion':>12} {'Prompt':>12} {'Total':>12}") print("-" * 48) for i, usage in enumerate(self.usages, 1): print(f"{i:<10} {usage.completion_tokens:>12,} {usage.prompt_tokens:>12,} {usage.total_tokens:>12,}") ####################################################### # Strategies using clustering for text data extraction # ####################################################### class CosineStrategy(ExtractionStrategy): """ Extract meaningful blocks or chunks from the given HTML using cosine similarity. How it works: 1. Pre-filter documents using embeddings and semantic_filter. 2. Perform clustering using cosine similarity. 3. Organize texts by their cluster labels, retaining order. 4. Filter clusters by word count. 5. Extract meaningful blocks or chunks from the filtered clusters. Attributes: semantic_filter (str): A keyword filter for document filtering. word_count_threshold (int): Minimum number of words per cluster. max_dist (float): The maximum cophenetic distance on the dendrogram to form clusters. linkage_method (str): The linkage method for hierarchical clustering. top_k (int): Number of top categories to extract. model_name (str): The name of the sentence-transformers model. sim_threshold (float): The similarity threshold for clustering. """ def __init__(self, semantic_filter = None, word_count_threshold=10, max_dist=0.2, linkage_method='ward', top_k=3, model_name = 'sentence-transformers/all-MiniLM-L6-v2', sim_threshold = 0.3, **kwargs): """ Initialize the strategy with clustering parameters. Args: semantic_filter (str): A keyword filter for document filtering. word_count_threshold (int): Minimum number of words per cluster. max_dist (float): The maximum cophenetic distance on the dendrogram to form clusters. linkage_method (str): The linkage method for hierarchical clustering. top_k (int): Number of top categories to extract. """ super().__init__(**kwargs) import numpy as np self.semantic_filter = semantic_filter self.word_count_threshold = word_count_threshold self.max_dist = max_dist self.linkage_method = linkage_method self.top_k = top_k self.sim_threshold = sim_threshold self.timer = time.time() self.verbose = kwargs.get("verbose", False) self.buffer_embeddings = np.array([]) self.get_embedding_method = "direct" self.device = get_device() # import torch # self.device = torch.device('cpu') self.default_batch_size = calculate_batch_size(self.device) if self.verbose: print(f"[LOG] Loading Extraction Model for {self.device.type} device.") # if False and self.device.type == "cpu": # self.model = load_onnx_all_MiniLM_l6_v2() # self.tokenizer = self.model.tokenizer # self.get_embedding_method = "direct" # else: self.tokenizer, self.model = load_HF_embedding_model(model_name) self.model.to(self.device) self.model.eval() self.get_embedding_method = "batch" self.buffer_embeddings = np.array([]) # if model_name == "bert-base-uncased": # self.tokenizer, self.model = load_bert_base_uncased() # self.model.eval() # Ensure the model is in evaluation mode # self.get_embedding_method = "batch" # elif model_name == "BAAI/bge-small-en-v1.5": # self.tokenizer, self.model = load_bge_small_en_v1_5() # self.model.eval() # Ensure the model is in evaluation mode # self.get_embedding_method = "batch" # elif model_name == "sentence-transformers/all-MiniLM-L6-v2": # self.model = load_onnx_all_MiniLM_l6_v2() # self.tokenizer = self.model.tokenizer # self.get_embedding_method = "direct" if self.verbose: print(f"[LOG] Loading Multilabel Classifier for {self.device.type} device.") self.nlp, _ = load_text_multilabel_classifier() # self.default_batch_size = 16 if self.device.type == 'cpu' else 64 if self.verbose: print(f"[LOG] Model loaded {model_name}, models/reuters, took " + str(time.time() - self.timer) + " seconds") def filter_documents_embeddings(self, documents: List[str], semantic_filter: str, at_least_k: int = 20) -> List[str]: """ Filter and sort documents based on the cosine similarity of their embeddings with the semantic_filter embedding. Args: documents (List[str]): A list of document texts. semantic_filter (str): A keyword filter for document filtering. at_least_k (int): The minimum number of documents to return. Returns: List[str]: A list of filtered and sorted document texts. """ if not semantic_filter: return documents if len(documents) < at_least_k: at_least_k = len(documents) // 2 from sklearn.metrics.pairwise import cosine_similarity # Compute embedding for the keyword filter query_embedding = self.get_embeddings([semantic_filter])[0] # Compute embeddings for the documents document_embeddings = self.get_embeddings(documents) # Calculate cosine similarity between the query embedding and document embeddings similarities = cosine_similarity([query_embedding], document_embeddings).flatten() # Filter documents based on the similarity threshold filtered_docs = [(doc, sim) for doc, sim in zip(documents, similarities) if sim >= self.sim_threshold] # If the number of filtered documents is less than at_least_k, sort remaining documents by similarity if len(filtered_docs) < at_least_k: remaining_docs = [(doc, sim) for doc, sim in zip(documents, similarities) if sim < self.sim_threshold] remaining_docs.sort(key=lambda x: x[1], reverse=True) filtered_docs.extend(remaining_docs[:at_least_k - len(filtered_docs)]) # Extract the document texts from the tuples filtered_docs = [doc for doc, _ in filtered_docs] return filtered_docs[:at_least_k] def get_embeddings(self, sentences: List[str], batch_size=None, bypass_buffer=False): """ Get BERT embeddings for a list of sentences. Args: sentences (List[str]): A list of text chunks (sentences). Returns: NumPy array of embeddings. """ # if self.buffer_embeddings.any() and not bypass_buffer: # return self.buffer_embeddings if self.device.type in [ "cpu", "gpu", "cuda", "mps"]: import torch # Tokenize sentences and convert to tensor if batch_size is None: batch_size = self.default_batch_size all_embeddings = [] for i in range(0, len(sentences), batch_size): batch_sentences = sentences[i:i + batch_size] encoded_input = self.tokenizer(batch_sentences, padding=True, truncation=True, return_tensors='pt') encoded_input = {key: tensor.to(self.device) for key, tensor in encoded_input.items()} # Ensure no gradients are calculated with torch.no_grad(): model_output = self.model(**encoded_input) # Get embeddings from the last hidden state (mean pooling) embeddings = model_output.last_hidden_state.mean(dim=1).cpu().numpy() all_embeddings.append(embeddings) self.buffer_embeddings = np.vstack(all_embeddings) elif self.device.type == "cpu": # self.buffer_embeddings = self.model(sentences) if batch_size is None: batch_size = self.default_batch_size all_embeddings = [] for i in range(0, len(sentences), batch_size): batch_sentences = sentences[i:i + batch_size] embeddings = self.model(batch_sentences) all_embeddings.append(embeddings) self.buffer_embeddings = np.vstack(all_embeddings) return self.buffer_embeddings def hierarchical_clustering(self, sentences: List[str], embeddings = None): """ Perform hierarchical clustering on sentences and return cluster labels. Args: sentences (List[str]): A list of text chunks (sentences). Returns: NumPy array of cluster labels. """ # Get embeddings from scipy.cluster.hierarchy import linkage, fcluster from scipy.spatial.distance import pdist self.timer = time.time() embeddings = self.get_embeddings(sentences, bypass_buffer=True) # print(f"[LOG] 🚀 Embeddings computed in {time.time() - self.timer:.2f} seconds") # Compute pairwise cosine distances distance_matrix = pdist(embeddings, 'cosine') # Perform agglomerative clustering respecting order linked = linkage(distance_matrix, method=self.linkage_method) # Form flat clusters labels = fcluster(linked, self.max_dist, criterion='distance') return labels def filter_clusters_by_word_count(self, clusters: Dict[int, List[str]]) -> Dict[int, List[str]]: """ Filter clusters to remove those with a word count below the threshold. Args: clusters (Dict[int, List[str]]): Dictionary of clusters. Returns: Dict[int, List[str]]: Filtered dictionary of clusters. """ filtered_clusters = {} for cluster_id, texts in clusters.items(): # Concatenate texts for analysis full_text = " ".join(texts) # Count words word_count = len(full_text.split()) # Keep clusters with word count above the threshold if word_count >= self.word_count_threshold: filtered_clusters[cluster_id] = texts return filtered_clusters def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]: """ Extract clusters from HTML content using hierarchical clustering. Args: url (str): The URL of the webpage. html (str): The HTML content of the webpage. Returns: List[Dict[str, Any]]: A list of processed JSON blocks. """ # Assume `html` is a list of text chunks for this strategy t = time.time() text_chunks = html.split(self.DEL) # Split by lines or paragraphs as needed # Pre-filter documents using embeddings and semantic_filter text_chunks = self.filter_documents_embeddings(text_chunks, self.semantic_filter) if not text_chunks: return [] # Perform clustering labels = self.hierarchical_clustering(text_chunks) # print(f"[LOG] 🚀 Clustering done in {time.time() - t:.2f} seconds") # Organize texts by their cluster labels, retaining order t = time.time() clusters = {} for index, label in enumerate(labels): clusters.setdefault(label, []).append(text_chunks[index]) # Filter clusters by word count filtered_clusters = self.filter_clusters_by_word_count(clusters) # Convert filtered clusters to a sorted list of dictionaries cluster_list = [{"index": int(idx), "tags" : [], "content": " ".join(filtered_clusters[idx])} for idx in sorted(filtered_clusters)] if self.verbose: print(f"[LOG] 🚀 Assign tags using {self.device}") if self.device.type in ["gpu", "cuda", "mps", "cpu"]: labels = self.nlp([cluster['content'] for cluster in cluster_list]) for cluster, label in zip(cluster_list, labels): cluster['tags'] = label # elif self.device.type == "cpu": # # Process the text with the loaded model # texts = [cluster['content'] for cluster in cluster_list] # # Batch process texts # docs = self.nlp.pipe(texts, disable=["tagger", "parser", "ner", "lemmatizer"]) # for doc, cluster in zip(docs, cluster_list): # tok_k = self.top_k # top_categories = sorted(doc.cats.items(), key=lambda x: x[1], reverse=True)[:tok_k] # cluster['tags'] = [cat for cat, _ in top_categories] # for cluster in cluster_list: # doc = self.nlp(cluster['content']) # tok_k = self.top_k # top_categories = sorted(doc.cats.items(), key=lambda x: x[1], reverse=True)[:tok_k] # cluster['tags'] = [cat for cat, _ in top_categories] if self.verbose: print(f"[LOG] 🚀 Categorization done in {time.time() - t:.2f} seconds") return cluster_list def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: """ Process sections using hierarchical clustering. Args: url (str): The URL of the webpage. sections (List[str]): List of sections (strings) to process. Returns: """ # This strategy processes all sections together return self.extract(url, self.DEL.join(sections), **kwargs) ####################################################### # New extraction strategies for JSON-based extraction # ####################################################### class JsonElementExtractionStrategy(ExtractionStrategy): """ Abstract base class for extracting structured JSON from HTML content. How it works: 1. Parses HTML content using the `_parse_html` method. 2. Uses a schema to define base selectors, fields, and transformations. 3. Extracts data hierarchically, supporting nested fields and lists. 4. Handles computed fields with expressions or functions. Attributes: DEL (str): Delimiter used to combine HTML sections. Defaults to '\n'. schema (Dict[str, Any]): The schema defining the extraction rules. verbose (bool): Enables verbose logging for debugging purposes. Methods: extract(url, html_content, *q, **kwargs): Extracts structured data from HTML content. _extract_item(element, fields): Extracts fields from a single element. _extract_single_field(element, field): Extracts a single field based on its type. _apply_transform(value, transform): Applies a transformation to a value. _compute_field(item, field): Computes a field value using an expression or function. run(url, sections, *q, **kwargs): Combines HTML sections and runs the extraction strategy. Abstract Methods: _parse_html(html_content): Parses raw HTML into a structured format (e.g., BeautifulSoup or lxml). _get_base_elements(parsed_html, selector): Retrieves base elements using a selector. _get_elements(element, selector): Retrieves child elements using a selector. _get_element_text(element): Extracts text content from an element. _get_element_html(element): Extracts raw HTML from an element. _get_element_attribute(element, attribute): Extracts an attribute's value from an element. """ DEL = '\n' def __init__(self, schema: Dict[str, Any], **kwargs): """ Initialize the JSON element extraction strategy with a schema. Args: schema (Dict[str, Any]): The schema defining the extraction rules. """ super().__init__(**kwargs) self.schema = schema self.verbose = kwargs.get('verbose', False) def extract(self, url: str, html_content: str, *q, **kwargs) -> List[Dict[str, Any]]: """ Extract structured data from HTML content. How it works: 1. Parses the HTML content using the `_parse_html` method. 2. Identifies base elements using the schema's base selector. 3. Extracts fields from each base element using `_extract_item`. Args: url (str): The URL of the page being processed. html_content (str): The raw HTML content to parse and extract. *q: Additional positional arguments. **kwargs: Additional keyword arguments for custom extraction. Returns: List[Dict[str, Any]]: A list of extracted items, each represented as a dictionary. """ parsed_html = self._parse_html(html_content) base_elements = self._get_base_elements(parsed_html, self.schema['baseSelector']) results = [] for element in base_elements: # Extract base element attributes item = {} if 'baseFields' in self.schema: for field in self.schema['baseFields']: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value # Extract child fields field_data = self._extract_item(element, self.schema['fields']) item.update(field_data) if item: results.append(item) return results @abstractmethod def _parse_html(self, html_content: str): """Parse HTML content into appropriate format""" pass @abstractmethod def _get_base_elements(self, parsed_html, selector: str): """Get all base elements using the selector""" pass @abstractmethod def _get_elements(self, element, selector: str): """Get child elements using the selector""" pass def _extract_field(self, element, field): try: if field['type'] == 'nested': nested_elements = self._get_elements(element, field['selector']) nested_element = nested_elements[0] if nested_elements else None return self._extract_item(nested_element, field['fields']) if nested_element else {} if field['type'] == 'list': elements = self._get_elements(element, field['selector']) return [self._extract_list_item(el, field['fields']) for el in elements] if field['type'] == 'nested_list': elements = self._get_elements(element, field['selector']) return [self._extract_item(el, field['fields']) for el in elements] return self._extract_single_field(element, field) except Exception as e: if self.verbose: print(f"Error extracting field {field['name']}: {str(e)}") return field.get('default') def _extract_single_field(self, element, field): """ Extract a single field based on its type. How it works: 1. Selects the target element using the field's selector. 2. Extracts the field value based on its type (e.g., text, attribute, regex). 3. Applies transformations if defined in the schema. Args: element: The base element to extract the field from. field (Dict[str, Any]): The field definition in the schema. Returns: Any: The extracted field value. """ if 'selector' in field: selected = self._get_elements(element, field['selector']) if not selected: return field.get('default') selected = selected[0] else: selected = element value = None if field['type'] == 'text': value = self._get_element_text(selected) elif field['type'] == 'attribute': value = self._get_element_attribute(selected, field['attribute']) elif field['type'] == 'html': value = self._get_element_html(selected) elif field['type'] == 'regex': text = self._get_element_text(selected) match = re.search(field['pattern'], text) value = match.group(1) if match else None if 'transform' in field: value = self._apply_transform(value, field['transform']) return value if value is not None else field.get('default') def _extract_list_item(self, element, fields): item = {} for field in fields: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value return item def _extract_item(self, element, fields): """ Extracts fields from a given element. How it works: 1. Iterates through the fields defined in the schema. 2. Handles computed, single, and nested field types. 3. Updates the item dictionary with extracted field values. Args: element: The base element to extract fields from. fields (List[Dict[str, Any]]): The list of fields to extract. Returns: Dict[str, Any]: A dictionary representing the extracted item. """ item = {} for field in fields: if field['type'] == 'computed': value = self._compute_field(item, field) else: value = self._extract_field(element, field) if value is not None: item[field['name']] = value return item def _apply_transform(self, value, transform): """ Apply a transformation to a value. How it works: 1. Checks the transformation type (e.g., `lowercase`, `strip`). 2. Applies the transformation to the value. 3. Returns the transformed value. Args: value (str): The value to transform. transform (str): The type of transformation to apply. Returns: str: The transformed value. """ if transform == 'lowercase': return value.lower() elif transform == 'uppercase': return value.upper() elif transform == 'strip': return value.strip() return value def _compute_field(self, item, field): try: if 'expression' in field: return eval(field['expression'], {}, item) elif 'function' in field: return field['function'](item) except Exception as e: if self.verbose: print(f"Error computing field {field['name']}: {str(e)}") return field.get('default') def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: """ Run the extraction strategy on a combined HTML content. How it works: 1. Combines multiple HTML sections using the `DEL` delimiter. 2. Calls the `extract` method with the combined HTML. Args: url (str): The URL of the page being processed. sections (List[str]): A list of HTML sections. *q: Additional positional arguments. **kwargs: Additional keyword arguments for custom extraction. Returns: List[Dict[str, Any]]: A list of extracted items. """ combined_html = self.DEL.join(sections) return self.extract(url, combined_html, **kwargs) @abstractmethod def _get_element_text(self, element) -> str: """Get text content from element""" pass @abstractmethod def _get_element_html(self, element) -> str: """Get HTML content from element""" pass @abstractmethod def _get_element_attribute(self, element, attribute: str): """Get attribute value from element""" pass class JsonCssExtractionStrategy(JsonElementExtractionStrategy): """ Concrete implementation of `JsonElementExtractionStrategy` using CSS selectors. How it works: 1. Parses HTML content with BeautifulSoup. 2. Selects elements using CSS selectors defined in the schema. 3. Extracts field data and applies transformations as defined. Attributes: schema (Dict[str, Any]): The schema defining the extraction rules. verbose (bool): Enables verbose logging for debugging purposes. Methods: _parse_html(html_content): Parses HTML content into a BeautifulSoup object. _get_base_elements(parsed_html, selector): Selects base elements using a CSS selector. _get_elements(element, selector): Selects child elements using a CSS selector. _get_element_text(element): Extracts text content from a BeautifulSoup element. _get_element_html(element): Extracts the raw HTML content of a BeautifulSoup element. _get_element_attribute(element, attribute): Retrieves an attribute value from a BeautifulSoup element. """ def __init__(self, schema: Dict[str, Any], **kwargs): kwargs['input_format'] = 'html' # Force HTML input super().__init__(schema, **kwargs) def _parse_html(self, html_content: str): return BeautifulSoup(html_content, 'html.parser') def _get_base_elements(self, parsed_html, selector: str): return parsed_html.select(selector) def _get_elements(self, element, selector: str): selected = element.select_one(selector) return [selected] if selected else [] def _get_element_text(self, element) -> str: return element.get_text(strip=True) def _get_element_html(self, element) -> str: return str(element) def _get_element_attribute(self, element, attribute: str): return element.get(attribute) class JsonXPathExtractionStrategy(JsonElementExtractionStrategy): """ Concrete implementation of `JsonElementExtractionStrategy` using XPath selectors. How it works: 1. Parses HTML content into an lxml tree. 2. Selects elements using XPath expressions. 3. Converts CSS selectors to XPath when needed. Attributes: schema (Dict[str, Any]): The schema defining the extraction rules. verbose (bool): Enables verbose logging for debugging purposes. Methods: _parse_html(html_content): Parses HTML content into an lxml tree. _get_base_elements(parsed_html, selector): Selects base elements using an XPath selector. _css_to_xpath(css_selector): Converts a CSS selector to an XPath expression. _get_elements(element, selector): Selects child elements using an XPath selector. _get_element_text(element): Extracts text content from an lxml element. _get_element_html(element): Extracts the raw HTML content of an lxml element. _get_element_attribute(element, attribute): Retrieves an attribute value from an lxml element. """ def __init__(self, schema: Dict[str, Any], **kwargs): kwargs['input_format'] = 'html' # Force HTML input super().__init__(schema, **kwargs) def _parse_html(self, html_content: str): return html.fromstring(html_content) def _get_base_elements(self, parsed_html, selector: str): return parsed_html.xpath(selector) def _css_to_xpath(self, css_selector: str) -> str: """Convert CSS selector to XPath if needed""" if '/' in css_selector: # Already an XPath return css_selector return self._basic_css_to_xpath(css_selector) def _basic_css_to_xpath(self, css_selector: str) -> str: """Basic CSS to XPath conversion for common cases""" if ' > ' in css_selector: parts = css_selector.split(' > ') return '//' + '/'.join(parts) if ' ' in css_selector: parts = css_selector.split(' ') return '//' + '//'.join(parts) return '//' + css_selector def _get_elements(self, element, selector: str): xpath = self._css_to_xpath(selector) if not xpath.startswith('.'): xpath = '.' + xpath return element.xpath(xpath) def _get_element_text(self, element) -> str: return ''.join(element.xpath('.//text()')).strip() def _get_element_html(self, element) -> str: return etree.tostring(element, encoding='unicode') def _get_element_attribute(self, element, attribute: str): return element.get(attribute) ####################################################### # Strategies based on the extraction of specific types# ####################################################### class TopicExtractionStrategy(ExtractionStrategy): def __init__(self, num_keywords: int = 3, **kwargs): """ Initialize the topic extraction strategy with parameters for topic segmentation. :param num_keywords: Number of keywords to represent each topic segment. """ import nltk super().__init__(**kwargs) self.num_keywords = num_keywords self.tokenizer = nltk.TextTilingTokenizer() def extract_keywords(self, text: str) -> List[str]: """ Extract keywords from a given text segment using simple frequency analysis. :param text: The text segment from which to extract keywords. :return: A list of keyword strings. """ import nltk # Tokenize the text and compute word frequency words = nltk.word_tokenize(text) freq_dist = nltk.FreqDist(words) # Get the most common words as keywords keywords = [word for (word, _) in freq_dist.most_common(self.num_keywords)] return keywords def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]: """ Extract topics from HTML content using TextTiling for segmentation and keyword extraction. :param url: The URL of the webpage. :param html: The HTML content of the webpage. :param provider: The provider to be used for extraction (not used here). :param api_token: Optional API token for the provider (not used here). :return: A list of dictionaries representing the topics. """ # Use TextTiling to segment the text into topics segmented_topics = html.split(self.DEL) # Split by lines or paragraphs as needed # Prepare the output as a list of dictionaries topic_list = [] for i, segment in enumerate(segmented_topics): # Extract keywords for each segment keywords = self.extract_keywords(segment) topic_list.append({ "index": i, "content": segment, "keywords": keywords }) return topic_list def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: """ Process sections using topic segmentation and keyword extraction. :param url: The URL of the webpage. :param sections: List of sections (strings) to process. :param provider: The provider to be used for extraction (not used here). :param api_token: Optional API token for the provider (not used here). :return: A list of processed JSON blocks. """ # Concatenate sections into a single text for coherent topic segmentation return self.extract(url, self.DEL.join(sections), **kwargs) class ContentSummarizationStrategy(ExtractionStrategy): def __init__(self, model_name: str = "sshleifer/distilbart-cnn-12-6", **kwargs): """ Initialize the content summarization strategy with a specific model. :param model_name: The model to use for summarization. """ super().__init__(**kwargs) from transformers import pipeline self.summarizer = pipeline("summarization", model=model_name) def extract(self, url: str, text: str, provider: str = None, api_token: Optional[str] = None) -> List[Dict[str, Any]]: """ Summarize a single section of text. :param url: The URL of the webpage. :param text: A section of text to summarize. :param provider: The provider to be used for extraction (not used here). :param api_token: Optional API token for the provider (not used here). :return: A dictionary with the summary. """ try: summary = self.summarizer(text, max_length=130, min_length=30, do_sample=False) return {"summary": summary[0]['summary_text']} except Exception as e: print(f"Error summarizing text: {e}") return {"summary": text} # Fallback to original text if summarization fails def run(self, url: str, sections: List[str], provider: str = None, api_token: Optional[str] = None) -> List[Dict[str, Any]]: """ Process each section in parallel to produce summaries. :param url: The URL of the webpage. :param sections: List of sections (strings) to summarize. :param provider: The provider to be used for extraction (not used here). :param api_token: Optional API token for the provider (not used here). :return: A list of dictionaries with summaries for each section. """ # Use a ThreadPoolExecutor to summarize in parallel summaries = [] with ThreadPoolExecutor() as executor: # Create a future for each section's summarization future_to_section = {executor.submit(self.extract, url, section, provider, api_token): i for i, section in enumerate(sections)} for future in as_completed(future_to_section): section_index = future_to_section[future] try: summary_result = future.result() summaries.append((section_index, summary_result)) except Exception as e: print(f"Error processing section {section_index}: {e}") summaries.append((section_index, {"summary": sections[section_index]})) # Fallback to original text # Sort summaries by the original section index to maintain order summaries.sort(key=lambda x: x[0]) return [summary for _, summary in summaries] ####################################################### # Deprecated strategies ####################################################### class _JsonCssExtractionStrategy(ExtractionStrategy): def __init__(self, schema: Dict[str, Any], **kwargs): kwargs['input_format'] = 'html' # Force HTML input super().__init__(**kwargs) self.schema = schema def extract(self, url: str, html: str, *q, **kwargs) -> List[Dict[str, Any]]: soup = BeautifulSoup(html, 'html.parser') base_elements = soup.select(self.schema['baseSelector']) results = [] for element in base_elements: # Extract base element attributes first item = {} if 'baseFields' in self.schema: for field in self.schema['baseFields']: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value # Then extract child fields field_data = self._extract_item(element, self.schema['fields']) item.update(field_data) results.append(item) return results def _extract_field(self, element, field): try: if field['type'] == 'nested': nested_element = element.select_one(field['selector']) return self._extract_item(nested_element, field['fields']) if nested_element else {} if field['type'] == 'list': elements = element.select(field['selector']) return [self._extract_list_item(el, field['fields']) for el in elements] if field['type'] == 'nested_list': elements = element.select(field['selector']) return [self._extract_item(el, field['fields']) for el in elements] return self._extract_single_field(element, field) except Exception as e: if self.verbose: print(f"Error extracting field {field['name']}: {str(e)}") return field.get('default') def _extract_list_item(self, element, fields): item = {} for field in fields: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value return item def _extract_single_field(self, element, field): if 'selector' in field: selected = element.select_one(field['selector']) if not selected: return field.get('default') else: selected = element value = None if field['type'] == 'text': value = selected.get_text(strip=True) elif field['type'] == 'attribute': value = selected.get(field['attribute']) elif field['type'] == 'html': value = str(selected) elif field['type'] == 'regex': text = selected.get_text(strip=True) match = re.search(field['pattern'], text) value = match.group(1) if match else None if 'transform' in field: value = self._apply_transform(value, field['transform']) return value if value is not None else field.get('default') def _extract_item(self, element, fields): item = {} for field in fields: if field['type'] == 'computed': value = self._compute_field(item, field) else: value = self._extract_field(element, field) if value is not None: item[field['name']] = value return item def _apply_transform(self, value, transform): if transform == 'lowercase': return value.lower() elif transform == 'uppercase': return value.upper() elif transform == 'strip': return value.strip() return value def _compute_field(self, item, field): try: if 'expression' in field: return eval(field['expression'], {}, item) elif 'function' in field: return field['function'](item) except Exception as e: if self.verbose: print(f"Error computing field {field['name']}: {str(e)}") return field.get('default') def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: combined_html = self.DEL.join(sections) return self.extract(url, combined_html, **kwargs) class _JsonXPathExtractionStrategy(ExtractionStrategy): def __init__(self, schema: Dict[str, Any], **kwargs): kwargs['input_format'] = 'html' # Force HTML input super().__init__(**kwargs) self.schema = schema def extract(self, url: str, html_content: str, *q, **kwargs) -> List[Dict[str, Any]]: tree = html.fromstring(html_content) base_xpath = self.schema['baseSelector'] base_elements = tree.xpath(base_xpath) results = [] for element in base_elements: # Extract base element attributes first item = {} if 'baseFields' in self.schema: for field in self.schema['baseFields']: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value # Then extract child fields field_data = self._extract_item(element, self.schema['fields']) item.update(field_data) results.append(item) return results def _css_to_xpath(self, css_selector: str) -> str: """Convert CSS selector to XPath if needed""" if '/' in css_selector: # Already an XPath return css_selector else: # Fallback to basic conversion for common cases return self._basic_css_to_xpath(css_selector) def _basic_css_to_xpath(self, css_selector: str) -> str: """Basic CSS to XPath conversion for common cases""" # Handle basic cases if ' > ' in css_selector: parts = css_selector.split(' > ') return '//' + '/'.join(parts) if ' ' in css_selector: parts = css_selector.split(' ') return '//' + '//'.join(parts) return '//' + css_selector def _extract_field(self, element, field): try: if field['type'] == 'nested': xpath = self._css_to_xpath(field['selector']) nested_element = element.xpath(xpath)[0] if element.xpath(xpath) else None return self._extract_item(nested_element, field['fields']) if nested_element is not None else {} if field['type'] == 'list': xpath = self._css_to_xpath(field['selector']) elements = element.xpath(xpath) return [self._extract_list_item(el, field['fields']) for el in elements] if field['type'] == 'nested_list': xpath = self._css_to_xpath(field['selector']) elements = element.xpath(xpath) return [self._extract_item(el, field['fields']) for el in elements] return self._extract_single_field(element, field) except Exception as e: if self.verbose: print(f"Error extracting field {field['name']}: {str(e)}") return field.get('default') def _extract_list_item(self, element, fields): item = {} for field in fields: value = self._extract_single_field(element, field) if value is not None: item[field['name']] = value return item def _extract_single_field(self, element, field): if 'selector' in field: xpath = self._css_to_xpath(field['selector']) selected = element.xpath(xpath) if not selected: return field.get('default') selected = selected[0] else: selected = element value = None if field['type'] == 'text': value = ''.join(selected.xpath('.//text()')).strip() elif field['type'] == 'attribute': value = selected.get(field['attribute']) elif field['type'] == 'html': value = etree.tostring(selected, encoding='unicode') elif field['type'] == 'regex': text = ''.join(selected.xpath('.//text()')).strip() match = re.search(field['pattern'], text) value = match.group(1) if match else None if 'transform' in field: value = self._apply_transform(value, field['transform']) return value if value is not None else field.get('default') def _extract_item(self, element, fields): item = {} for field in fields: if field['type'] == 'computed': value = self._compute_field(item, field) else: value = self._extract_field(element, field) if value is not None: item[field['name']] = value return item def _apply_transform(self, value, transform): if transform == 'lowercase': return value.lower() elif transform == 'uppercase': return value.upper() elif transform == 'strip': return value.strip() return value def _compute_field(self, item, field): try: if 'expression' in field: return eval(field['expression'], {}, item) elif 'function' in field: return field['function'](item) except Exception as e: if self.verbose: print(f"Error computing field {field['name']}: {str(e)}") return field.get('default') def run(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]: combined_html = self.DEL.join(sections) return self.extract(url, combined_html, **kwargs)