Update App_Function_Libraries/Chunk_Lib.py

App_Function_Libraries/Chunk_Lib.py

@@ -1,467 +1,583 @@
-# Chunk_Lib.py
-#########################################
-# Chunking Library
-# This library is used to perform chunking of input files.
-# Currently, uses naive approaches. Nothing fancy.
-#
-####
-# Import necessary libraries
-import logging
-import re
-
-from typing import List, Optional, Tuple, Dict, Any
-
-from openai import OpenAI
-from tqdm import tqdm
-#
-# Import 3rd party
-from transformers import GPT2Tokenizer
-import nltk
-from nltk.tokenize import sent_tokenize, word_tokenize
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-#
-# Import Local
-from App_Function_Libraries.Tokenization_Methods_Lib import openai_tokenize
-from App_Function_Libraries.Utils import load_comprehensive_config
-
-
-#
-#######################################################################################################################
-# Function Definitions
-#
-
-# FIXME - Make sure it only downloads if it already exists, and does a check first.
-# Ensure NLTK data is downloaded
-def ntlk_prep():
-    nltk.download('punkt')
-
-# Load GPT2 tokenizer
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-
-# Load Config file for API keys
-config = load_comprehensive_config()
-openai_api_key = config.get('API', 'openai_api_key', fallback=None)
-
-def load_document(file_path):
-    with open(file_path, 'r') as file:
-        text = file.read()
-    return re.sub('\\s+', ' ', text).strip()
-
-
-def improved_chunking_process(text: str, chunk_options: Dict[str, Any]) -> List[Dict[str, Any]]:
-    chunk_method = chunk_options.get('method', 'words')
-    max_chunk_size = chunk_options.get('max_size', 300)
-    overlap = chunk_options.get('overlap', 0)
-    language = chunk_options.get('language', 'english')
-    adaptive = chunk_options.get('adaptive', False)
-    multi_level = chunk_options.get('multi_level', False)
-
-    if adaptive:
-        max_chunk_size = adaptive_chunk_size(text, max_chunk_size)
-
-    if multi_level:
-        chunks = multi_level_chunking(text, chunk_method, max_chunk_size, overlap, language)
-    else:
-        if chunk_method == 'words':
-            chunks = chunk_text_by_words(text, max_chunk_size, overlap)
-        elif chunk_method == 'sentences':
-            chunks = chunk_text_by_sentences(text, max_chunk_size, overlap, language)
-        elif chunk_method == 'paragraphs':
-            chunks = chunk_text_by_paragraphs(text, max_chunk_size, overlap)
-        elif chunk_method == 'tokens':
-            chunks = chunk_text_by_tokens(text, max_chunk_size, overlap)
-        else:
-            chunks = [text]  # No chunking applied
-
-    return [{'text': chunk, 'metadata': get_chunk_metadata(chunk, text)} for chunk in chunks]
-
-
-def adaptive_chunk_size(text: str, base_size: int) -> int:
-    # Simple adaptive logic: adjust chunk size based on text complexity
-    avg_word_length = sum(len(word) for word in text.split()) / len(text.split())
-    if avg_word_length > 6:  # Arbitrary threshold for "complex" text
-        return int(base_size * 0.8)  # Reduce chunk size for complex text
-    return base_size
-
-
-def multi_level_chunking(text: str, method: str, max_size: int, overlap: int, language: str) -> List[str]:
-    # First level: chunk by paragraphs
-    paragraphs = chunk_text_by_paragraphs(text, max_size * 2, overlap)
-
-    # Second level: chunk each paragraph further
-    chunks = []
-    for para in paragraphs:
-        if method == 'words':
-            chunks.extend(chunk_text_by_words(para, max_size, overlap))
-        elif method == 'sentences':
-            chunks.extend(chunk_text_by_sentences(para, max_size, overlap, language))
-        else:
-            chunks.append(para)
-
-    return chunks
-
-
-def chunk_text_by_words(text: str, max_words: int = 300, overlap: int = 0) -> List[str]:
-    words = text.split()
-    chunks = []
-    for i in range(0, len(words), max_words - overlap):
-        chunk = ' '.join(words[i:i + max_words])
-        chunks.append(chunk)
-    return post_process_chunks(chunks)
-
-
-def chunk_text_by_sentences(text: str, max_sentences: int = 10, overlap: int = 0, language: str = 'english') -> List[
-    str]:
-    nltk.download('punkt', quiet=True)
-    sentences = nltk.sent_tokenize(text, language=language)
-    chunks = []
-    for i in range(0, len(sentences), max_sentences - overlap):
-        chunk = ' '.join(sentences[i:i + max_sentences])
-        chunks.append(chunk)
-    return post_process_chunks(chunks)
-
-
-def chunk_text_by_paragraphs(text: str, max_paragraphs: int = 5, overlap: int = 0) -> List[str]:
-    paragraphs = re.split(r'\n\s*\n', text)
-    chunks = []
-    for i in range(0, len(paragraphs), max_paragraphs - overlap):
-        chunk = '\n\n'.join(paragraphs[i:i + max_paragraphs])
-        chunks.append(chunk)
-    return post_process_chunks(chunks)
-
-
-def chunk_text_by_tokens(text: str, max_tokens: int = 1000, overlap: int = 0) -> List[str]:
-    # This is a simplified token-based chunking. For more accurate tokenization,
-    # consider using a proper tokenizer like GPT-2 TokenizerFast
-    words = text.split()
-    chunks = []
-    current_chunk = []
-    current_token_count = 0
-
-    for word in words:
-        word_token_count = len(word) // 4 + 1  # Rough estimate of token count
-        if current_token_count + word_token_count > max_tokens and current_chunk:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = current_chunk[-overlap:] if overlap > 0 else []
-            current_token_count = sum(len(w) // 4 + 1 for w in current_chunk)
-
-        current_chunk.append(word)
-        current_token_count += word_token_count
-
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
-
-    return post_process_chunks(chunks)
-
-
-def post_process_chunks(chunks: List[str]) -> List[str]:
-    return [chunk.strip() for chunk in chunks if chunk.strip()]
-
-
-def get_chunk_metadata(chunk: str, full_text: str) -> Dict[str, Any]:
-    start_index = full_text.index(chunk)
-    return {
-        'start_index': start_index,
-        'end_index': start_index + len(chunk),
-        'word_count': len(chunk.split()),
-        'char_count': len(chunk)
-    }
-
-
-# Hybrid approach, chunk each sentence while ensuring total token size does not exceed a maximum number
-def chunk_text_hybrid(text, max_tokens=1000):
-    sentences = nltk.tokenize.sent_tokenize(text)
-    chunks = []
-    current_chunk = []
-    current_length = 0
-
-    for sentence in sentences:
-        tokens = tokenizer.encode(sentence)
-        if current_length + len(tokens) <= max_tokens:
-            current_chunk.append(sentence)
-            current_length += len(tokens)
-        else:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = [sentence]
-            current_length = len(tokens)
-
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
-
-    return chunks
-
-# Thanks openai
-def chunk_on_delimiter(input_string: str,
-                       max_tokens: int,
-                       delimiter: str) -> List[str]:
-    chunks = input_string.split(delimiter)
-    combined_chunks, _, dropped_chunk_count = combine_chunks_with_no_minimum(
-        chunks, max_tokens, chunk_delimiter=delimiter, add_ellipsis_for_overflow=True)
-    if dropped_chunk_count > 0:
-        print(f"Warning: {dropped_chunk_count} chunks were dropped due to exceeding the token limit.")
-    combined_chunks = [f"{chunk}{delimiter}" for chunk in combined_chunks]
-    return combined_chunks
-
-
-def recursive_summarize_chunks(chunks, summarize_func, custom_prompt):
-    summarized_chunks = []
-    current_summary = ""
-
-    for i, chunk in enumerate(chunks):
-        if i == 0:
-            current_summary = summarize_func(chunk, custom_prompt)
-        else:
-            combined_text = current_summary + "\n\n" + chunk
-            current_summary = summarize_func(combined_text, custom_prompt)
-
-        summarized_chunks.append(current_summary)
-
-    return summarized_chunks
-
-
-# Sample text for testing
-sample_text = """
-Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence 
-concerned with the interactions between computers and human language, in particular how to program computers 
-to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" 
-the contents of documents, including the contextual nuances of the language within them. The technology can then 
-accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
-
-Challenges in natural language processing frequently involve speech recognition, natural language understanding, 
-and natural language generation.
-
-Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled 
-"Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.
-"""
-
-# Example usage of different chunking methods
-# print("Chunking by words:")
-# print(chunk_text_by_words(sample_text, max_words=50))
-#
-# print("\nChunking by sentences:")
-# print(chunk_text_by_sentences(sample_text, max_sentences=2))
-#
-# print("\nChunking by paragraphs:")
-# print(chunk_text_by_paragraphs(sample_text, max_paragraphs=1))
-#
-# print("\nChunking by tokens:")
-# print(chunk_text_by_tokens(sample_text, max_tokens=50))
-#
-# print("\nHybrid chunking:")
-# print(chunk_text_hybrid(sample_text, max_tokens=50))
-
-
-
-#######################################################################################################################
-#
-# Experimental Semantic Chunking
-#
-
-# Chunk text into segments based on semantic similarity
-def count_units(text, unit='tokens'):
-    if unit == 'words':
-        return len(text.split())
-    elif unit == 'tokens':
-        return len(word_tokenize(text))
-    elif unit == 'characters':
-        return len(text)
-    else:
-        raise ValueError("Invalid unit. Choose 'words', 'tokens', or 'characters'.")
-
-
-def semantic_chunking(text, max_chunk_size=2000, unit='words'):
-    nltk.download('punkt', quiet=True)
-    sentences = sent_tokenize(text)
-    vectorizer = TfidfVectorizer()
-    sentence_vectors = vectorizer.fit_transform(sentences)
-
-    chunks = []
-    current_chunk = []
-    current_size = 0
-
-    for i, sentence in enumerate(sentences):
-        sentence_size = count_units(sentence, unit)
-        if current_size + sentence_size > max_chunk_size and current_chunk:
-            chunks.append(' '.join(current_chunk))
-            overlap_size = count_units(' '.join(current_chunk[-3:]), unit)  # Use last 3 sentences for overlap
-            current_chunk = current_chunk[-3:]  # Keep last 3 sentences for overlap
-            current_size = overlap_size
-
-        current_chunk.append(sentence)
-        current_size += sentence_size
-
-        if i + 1 < len(sentences):
-            current_vector = sentence_vectors[i]
-            next_vector = sentence_vectors[i + 1]
-            similarity = cosine_similarity(current_vector, next_vector)[0][0]
-            if similarity < 0.5 and current_size >= max_chunk_size // 2:
-                chunks.append(' '.join(current_chunk))
-                overlap_size = count_units(' '.join(current_chunk[-3:]), unit)
-                current_chunk = current_chunk[-3:]
-                current_size = overlap_size
-
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
-
-    return chunks
-
-
-def semantic_chunk_long_file(file_path, max_chunk_size=1000, overlap=100):
-    try:
-        with open(file_path, 'r', encoding='utf-8') as file:
-            content = file.read()
-
-        chunks = semantic_chunking(content, max_chunk_size, overlap)
-        return chunks
-    except Exception as e:
-        logging.error(f"Error chunking text file: {str(e)}")
-        return None
-#######################################################################################################################
-
-
-
-
-
-
-#######################################################################################################################
-#
-# OpenAI Rolling Summarization
-#
-
-client = OpenAI(api_key=openai_api_key)
-def get_chat_completion(messages, model='gpt-4-turbo'):
-    response = client.chat.completions.create(
-        model=model,
-        messages=messages,
-        temperature=0,
-    )
-    return response.choices[0].message.content
-
-
-# This function combines text chunks into larger blocks without exceeding a specified token count.
-#   It returns the combined chunks, their original indices, and the number of dropped chunks due to overflow.
-def combine_chunks_with_no_minimum(
-        chunks: List[str],
-        max_tokens: int,
-        chunk_delimiter="\n\n",
-        header: Optional[str] = None,
-        add_ellipsis_for_overflow=False,
-) -> Tuple[List[str], List[int]]:
-    dropped_chunk_count = 0
-    output = []  # list to hold the final combined chunks
-    output_indices = []  # list to hold the indices of the final combined chunks
-    candidate = (
-        [] if header is None else [header]
-    )  # list to hold the current combined chunk candidate
-    candidate_indices = []
-    for chunk_i, chunk in enumerate(chunks):
-        chunk_with_header = [chunk] if header is None else [header, chunk]
-        # FIXME MAKE NOT OPENAI SPECIFIC
-        if len(openai_tokenize(chunk_delimiter.join(chunk_with_header))) > max_tokens:
-            print(f"warning: chunk overflow")
-            if (
-                    add_ellipsis_for_overflow
-                    # FIXME MAKE NOT OPENAI SPECIFIC
-                    and len(openai_tokenize(chunk_delimiter.join(candidate + ["..."]))) <= max_tokens
-            ):
-                candidate.append("...")
-                dropped_chunk_count += 1
-            continue  # this case would break downstream assumptions
-        # estimate token count with the current chunk added
-        # FIXME MAKE NOT OPENAI SPECIFIC
-        extended_candidate_token_count = len(openai_tokenize(chunk_delimiter.join(candidate + [chunk])))
-        # If the token count exceeds max_tokens, add the current candidate to output and start a new candidate
-        if extended_candidate_token_count > max_tokens:
-            output.append(chunk_delimiter.join(candidate))
-            output_indices.append(candidate_indices)
-            candidate = chunk_with_header  # re-initialize candidate
-            candidate_indices = [chunk_i]
-        # otherwise keep extending the candidate
-        else:
-            candidate.append(chunk)
-            candidate_indices.append(chunk_i)
-    # add the remaining candidate to output if it's not empty
-    if (header is not None and len(candidate) > 1) or (header is None and len(candidate) > 0):
-        output.append(chunk_delimiter.join(candidate))
-        output_indices.append(candidate_indices)
-    return output, output_indices, dropped_chunk_count
-
-
-def rolling_summarize(text: str,
-                      detail: float = 0,
-                      model: str = 'gpt-4-turbo',
-                      additional_instructions: Optional[str] = None,
-                      minimum_chunk_size: Optional[int] = 500,
-                      chunk_delimiter: str = ".",
-                      summarize_recursively=False,
-                      verbose=False):
-    """
-    Summarizes a given text by splitting it into chunks, each of which is summarized individually.
-    The level of detail in the summary can be adjusted, and the process can optionally be made recursive.
-
-    Parameters:
-        - text (str): The text to be summarized.
-        - detail (float, optional): A value between 0 and 1
-            indicating the desired level of detail in the summary. 0 leads to a higher level summary, and 1 results in a more
-            detailed summary. Defaults to 0.
-        - additional_instructions (Optional[str], optional): Additional instructions to provide to the
-            model for customizing summaries. - minimum_chunk_size (Optional[int], optional): The minimum size for text
-            chunks. Defaults to 500.
-        - chunk_delimiter (str, optional): The delimiter used to split the text into chunks. Defaults to ".".
-        - summarize_recursively (bool, optional): If True, summaries are generated recursively, using previous summaries for context.
-        - verbose (bool, optional): If True, prints detailed information about the chunking process.
-    Returns:
-    - str: The final compiled summary of the text.
-
-    The function first determines the number of chunks by interpolating between a minimum and a maximum chunk count
-    based on the `detail` parameter. It then splits the text into chunks and summarizes each chunk. If
-    `summarize_recursively` is True, each summary is based on the previous summaries, adding more context to the
-    summarization process. The function returns a compiled summary of all chunks.
-    """
-
-    # check detail is set correctly
-    assert 0 <= detail <= 1
-
-    # interpolate the number of chunks based to get specified level of detail
-    max_chunks = len(chunk_on_delimiter(text, minimum_chunk_size, chunk_delimiter))
-    min_chunks = 1
-    num_chunks = int(min_chunks + detail * (max_chunks - min_chunks))
-
-    # adjust chunk_size based on interpolated number of chunks
-    # FIXME MAKE NOT OPENAI SPECIFIC
-    document_length = len(openai_tokenize(text))
-    chunk_size = max(minimum_chunk_size, document_length // num_chunks)
-    text_chunks = chunk_on_delimiter(text, chunk_size, chunk_delimiter)
-    if verbose:
-        print(f"Splitting the text into {len(text_chunks)} chunks to be summarized.")
-        # FIXME MAKE NOT OPENAI SPECIFIC
-        print(f"Chunk lengths are {[len(openai_tokenize(x)) for x in text_chunks]}")
-
-    # set system message - FIXME
-    system_message_content = "Rewrite this text in summarized form."
-    if additional_instructions is not None:
-        system_message_content += f"\n\n{additional_instructions}"
-
-    accumulated_summaries = []
-    for i, chunk in enumerate(tqdm(text_chunks)):
-        if summarize_recursively and accumulated_summaries:
-            # Combine previous summary with current chunk for recursive summarization
-            combined_text = accumulated_summaries[-1] + "\n\n" + chunk
-            user_message_content = f"Previous summary and new content to summarize:\n\n{combined_text}"
-        else:
-            user_message_content = chunk
-
-        messages = [
-            {"role": "system", "content": system_message_content},
-            {"role": "user", "content": user_message_content}
-        ]
-
-        response = get_chat_completion(messages, model=model)
-        accumulated_summaries.append(response)
-
-    final_summary = '\n\n'.join(accumulated_summaries)
-    return final_summary
-
-
-
+# Chunk_Lib.py
+#########################################
+# Chunking Library
+# This library is used to perform chunking of input files.
+# Currently, uses naive approaches. Nothing fancy.
+#
+####
+# Import necessary libraries
+import logging
+import re
+
+from typing import List, Optional, Tuple, Dict, Any
+
+from openai import OpenAI
+from tqdm import tqdm
+#
+# Import 3rd party
+from transformers import GPT2Tokenizer
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+#
+# Import Local
+from App_Function_Libraries.Tokenization_Methods_Lib import openai_tokenize
+from App_Function_Libraries.Utils import load_comprehensive_config
+
+
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+# FIXME - Make sure it only downloads if it already exists, and does a check first.
+# Ensure NLTK data is downloaded
+def ntlk_prep():
+    nltk.download('punkt')
+
+# Load GPT2 tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# Load Config file for API keys
+config = load_comprehensive_config()
+openai_api_key = config.get('API', 'openai_api_key', fallback=None)
+
+def load_document(file_path):
+    with open(file_path, 'r') as file:
+        text = file.read()
+    return re.sub('\\s+', ' ', text).strip()
+
+
+def improved_chunking_process(text: str, chunk_options: Dict[str, Any]) -> List[Dict[str, Any]]:
+    chunk_method = chunk_options.get('method', 'words')
+    max_chunk_size = chunk_options.get('max_size', 300)
+    overlap = chunk_options.get('overlap', 0)
+    language = chunk_options.get('language', 'english')
+    adaptive = chunk_options.get('adaptive', False)
+    multi_level = chunk_options.get('multi_level', False)
+
+    if adaptive:
+        max_chunk_size = adaptive_chunk_size(text, max_chunk_size)
+
+    if multi_level:
+        chunks = multi_level_chunking(text, chunk_method, max_chunk_size, overlap, language)
+    else:
+        if chunk_method == 'words':
+            chunks = chunk_text_by_words(text, max_chunk_size, overlap)
+        elif chunk_method == 'sentences':
+            chunks = chunk_text_by_sentences(text, max_chunk_size, overlap, language)
+        elif chunk_method == 'paragraphs':
+            chunks = chunk_text_by_paragraphs(text, max_chunk_size, overlap)
+        elif chunk_method == 'tokens':
+            chunks = chunk_text_by_tokens(text, max_chunk_size, overlap)
+        elif chunk_method == 'chapters':
+            return chunk_ebook_by_chapters(text, chunk_options)
+        else:
+            # No chunking applied
+            chunks = [text]
+
+    return [{'text': chunk, 'metadata': get_chunk_metadata(chunk, text)} for chunk in chunks]
+
+
+def adaptive_chunk_size(text: str, base_size: int) -> int:
+    # Simple adaptive logic: adjust chunk size based on text complexity
+    avg_word_length = sum(len(word) for word in text.split()) / len(text.split())
+    if avg_word_length > 6:  # Arbitrary threshold for "complex" text
+        return int(base_size * 0.8)  # Reduce chunk size for complex text
+    return base_size
+
+
+def multi_level_chunking(text: str, method: str, max_size: int, overlap: int, language: str) -> List[str]:
+    # First level: chunk by paragraphs
+    paragraphs = chunk_text_by_paragraphs(text, max_size * 2, overlap)
+
+    # Second level: chunk each paragraph further
+    chunks = []
+    for para in paragraphs:
+        if method == 'words':
+            chunks.extend(chunk_text_by_words(para, max_size, overlap))
+        elif method == 'sentences':
+            chunks.extend(chunk_text_by_sentences(para, max_size, overlap, language))
+        else:
+            chunks.append(para)
+
+    return chunks
+
+
+def chunk_text_by_words(text: str, max_words: int = 300, overlap: int = 0) -> List[str]:
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), max_words - overlap):
+        chunk = ' '.join(words[i:i + max_words])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_sentences(text: str, max_sentences: int = 10, overlap: int = 0, language: str = 'english') -> List[
+    str]:
+    nltk.download('punkt', quiet=True)
+    sentences = nltk.sent_tokenize(text, language=language)
+    chunks = []
+    for i in range(0, len(sentences), max_sentences - overlap):
+        chunk = ' '.join(sentences[i:i + max_sentences])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_paragraphs(text: str, max_paragraphs: int = 5, overlap: int = 0) -> List[str]:
+    paragraphs = re.split(r'\n\s*\n', text)
+    chunks = []
+    for i in range(0, len(paragraphs), max_paragraphs - overlap):
+        chunk = '\n\n'.join(paragraphs[i:i + max_paragraphs])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_tokens(text: str, max_tokens: int = 1000, overlap: int = 0) -> List[str]:
+    # This is a simplified token-based chunking. For more accurate tokenization,
+    # consider using a proper tokenizer like GPT-2 TokenizerFast
+    words = text.split()
+    chunks = []
+    current_chunk = []
+    current_token_count = 0
+
+    for word in words:
+        word_token_count = len(word) // 4 + 1  # Rough estimate of token count
+        if current_token_count + word_token_count > max_tokens and current_chunk:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = current_chunk[-overlap:] if overlap > 0 else []
+            current_token_count = sum(len(w) // 4 + 1 for w in current_chunk)
+
+        current_chunk.append(word)
+        current_token_count += word_token_count
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return post_process_chunks(chunks)
+
+
+def post_process_chunks(chunks: List[str]) -> List[str]:
+    return [chunk.strip() for chunk in chunks if chunk.strip()]
+
+
+def get_chunk_metadata(chunk: str, full_text: str, chunk_type: str = "generic", chapter_number: Optional[int] = None, chapter_pattern: Optional[str] = None) -> Dict[str, Any]:
+    start_index = full_text.index(chunk)
+    metadata = {
+        'start_index': start_index,
+        'end_index': start_index + len(chunk),
+        'word_count': len(chunk.split()),
+        'char_count': len(chunk),
+        'chunk_type': chunk_type
+    }
+    if chunk_type == "chapter":
+        metadata['chapter_number'] = chapter_number
+        metadata['chapter_pattern'] = chapter_pattern
+    return metadata
+
+
+# Hybrid approach, chunk each sentence while ensuring total token size does not exceed a maximum number
+def chunk_text_hybrid(text, max_tokens=1000):
+    sentences = nltk.tokenize.sent_tokenize(text)
+    chunks = []
+    current_chunk = []
+    current_length = 0
+
+    for sentence in sentences:
+        tokens = tokenizer.encode(sentence)
+        if current_length + len(tokens) <= max_tokens:
+            current_chunk.append(sentence)
+            current_length += len(tokens)
+        else:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = [sentence]
+            current_length = len(tokens)
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return chunks
+
+# Thanks openai
+def chunk_on_delimiter(input_string: str,
+                       max_tokens: int,
+                       delimiter: str) -> List[str]:
+    chunks = input_string.split(delimiter)
+    combined_chunks, _, dropped_chunk_count = combine_chunks_with_no_minimum(
+        chunks, max_tokens, chunk_delimiter=delimiter, add_ellipsis_for_overflow=True)
+    if dropped_chunk_count > 0:
+        print(f"Warning: {dropped_chunk_count} chunks were dropped due to exceeding the token limit.")
+    combined_chunks = [f"{chunk}{delimiter}" for chunk in combined_chunks]
+    return combined_chunks
+
+# ????FIXME
+def recursive_summarize_chunks(chunks, summarize_func, custom_prompt, temp=None, system_prompt=None):
+    summarized_chunks = []
+    current_summary = ""
+
+    logging.debug(f"recursive_summarize_chunks: Summarizing {len(chunks)} chunks recursively...")
+    logging.debug(f"recursive_summarize_chunks:  temperature is @ {temp}")
+    for i, chunk in enumerate(chunks):
+        if i == 0:
+            current_summary = summarize_func(chunk, custom_prompt, temp, system_prompt)
+        else:
+            combined_text = current_summary + "\n\n" + chunk
+            current_summary = summarize_func(combined_text, custom_prompt, temp, system_prompt)
+
+        summarized_chunks.append(current_summary)
+
+    return summarized_chunks
+
+
+# Sample text for testing
+sample_text = """
+Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence 
+concerned with the interactions between computers and human language, in particular how to program computers 
+to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" 
+the contents of documents, including the contextual nuances of the language within them. The technology can then 
+accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
+
+Challenges in natural language processing frequently involve speech recognition, natural language understanding, 
+and natural language generation.
+
+Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled 
+"Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.
+"""
+
+# Example usage of different chunking methods
+# print("Chunking by words:")
+# print(chunk_text_by_words(sample_text, max_words=50))
+#
+# print("\nChunking by sentences:")
+# print(chunk_text_by_sentences(sample_text, max_sentences=2))
+#
+# print("\nChunking by paragraphs:")
+# print(chunk_text_by_paragraphs(sample_text, max_paragraphs=1))
+#
+# print("\nChunking by tokens:")
+# print(chunk_text_by_tokens(sample_text, max_tokens=50))
+#
+# print("\nHybrid chunking:")
+# print(chunk_text_hybrid(sample_text, max_tokens=50))
+
+
+
+#######################################################################################################################
+#
+# Experimental Semantic Chunking
+#
+
+# Chunk text into segments based on semantic similarity
+def count_units(text, unit='tokens'):
+    if unit == 'words':
+        return len(text.split())
+    elif unit == 'tokens':
+        return len(word_tokenize(text))
+    elif unit == 'characters':
+        return len(text)
+    else:
+        raise ValueError("Invalid unit. Choose 'words', 'tokens', or 'characters'.")
+
+
+def semantic_chunking(text, max_chunk_size=2000, unit='words'):
+    nltk.download('punkt', quiet=True)
+    sentences = sent_tokenize(text)
+    vectorizer = TfidfVectorizer()
+    sentence_vectors = vectorizer.fit_transform(sentences)
+
+    chunks = []
+    current_chunk = []
+    current_size = 0
+
+    for i, sentence in enumerate(sentences):
+        sentence_size = count_units(sentence, unit)
+        if current_size + sentence_size > max_chunk_size and current_chunk:
+            chunks.append(' '.join(current_chunk))
+            overlap_size = count_units(' '.join(current_chunk[-3:]), unit)  # Use last 3 sentences for overlap
+            current_chunk = current_chunk[-3:]  # Keep last 3 sentences for overlap
+            current_size = overlap_size
+
+        current_chunk.append(sentence)
+        current_size += sentence_size
+
+        if i + 1 < len(sentences):
+            current_vector = sentence_vectors[i]
+            next_vector = sentence_vectors[i + 1]
+            similarity = cosine_similarity(current_vector, next_vector)[0][0]
+            if similarity < 0.5 and current_size >= max_chunk_size // 2:
+                chunks.append(' '.join(current_chunk))
+                overlap_size = count_units(' '.join(current_chunk[-3:]), unit)
+                current_chunk = current_chunk[-3:]
+                current_size = overlap_size
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return chunks
+
+
+def semantic_chunk_long_file(file_path, max_chunk_size=1000, overlap=100):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            content = file.read()
+
+        chunks = semantic_chunking(content, max_chunk_size, overlap)
+        return chunks
+    except Exception as e:
+        logging.error(f"Error chunking text file: {str(e)}")
+        return None
+#######################################################################################################################
+
+
+
+
+
+
+#######################################################################################################################
+#
+# OpenAI Rolling Summarization
+#
+
+client = OpenAI(api_key=openai_api_key)
+def get_chat_completion(messages, model='gpt-4-turbo'):
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=0,
+    )
+    return response.choices[0].message.content
+
+
+# This function combines text chunks into larger blocks without exceeding a specified token count.
+#   It returns the combined chunks, their original indices, and the number of dropped chunks due to overflow.
+def combine_chunks_with_no_minimum(
+        chunks: List[str],
+        max_tokens: int,
+        chunk_delimiter="\n\n",
+        header: Optional[str] = None,
+        add_ellipsis_for_overflow=False,
+) -> Tuple[List[str], List[int]]:
+    dropped_chunk_count = 0
+    output = []  # list to hold the final combined chunks
+    output_indices = []  # list to hold the indices of the final combined chunks
+    candidate = (
+        [] if header is None else [header]
+    )  # list to hold the current combined chunk candidate
+    candidate_indices = []
+    for chunk_i, chunk in enumerate(chunks):
+        chunk_with_header = [chunk] if header is None else [header, chunk]
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        if len(openai_tokenize(chunk_delimiter.join(chunk_with_header))) > max_tokens:
+            print(f"warning: chunk overflow")
+            if (
+                    add_ellipsis_for_overflow
+                    # FIXME MAKE NOT OPENAI SPECIFIC
+                    and len(openai_tokenize(chunk_delimiter.join(candidate + ["..."]))) <= max_tokens
+            ):
+                candidate.append("...")
+                dropped_chunk_count += 1
+            continue  # this case would break downstream assumptions
+        # estimate token count with the current chunk added
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        extended_candidate_token_count = len(openai_tokenize(chunk_delimiter.join(candidate + [chunk])))
+        # If the token count exceeds max_tokens, add the current candidate to output and start a new candidate
+        if extended_candidate_token_count > max_tokens:
+            output.append(chunk_delimiter.join(candidate))
+            output_indices.append(candidate_indices)
+            candidate = chunk_with_header  # re-initialize candidate
+            candidate_indices = [chunk_i]
+        # otherwise keep extending the candidate
+        else:
+            candidate.append(chunk)
+            candidate_indices.append(chunk_i)
+    # add the remaining candidate to output if it's not empty
+    if (header is not None and len(candidate) > 1) or (header is None and len(candidate) > 0):
+        output.append(chunk_delimiter.join(candidate))
+        output_indices.append(candidate_indices)
+    return output, output_indices, dropped_chunk_count
+
+
+def rolling_summarize(text: str,
+                      detail: float = 0,
+                      model: str = 'gpt-4-turbo',
+                      additional_instructions: Optional[str] = None,
+                      minimum_chunk_size: Optional[int] = 500,
+                      chunk_delimiter: str = ".",
+                      summarize_recursively=False,
+                      verbose=False):
+    """
+    Summarizes a given text by splitting it into chunks, each of which is summarized individually.
+    The level of detail in the summary can be adjusted, and the process can optionally be made recursive.
+
+    Parameters:
+        - text (str): The text to be summarized.
+        - detail (float, optional): A value between 0 and 1
+            indicating the desired level of detail in the summary. 0 leads to a higher level summary, and 1 results in a more
+            detailed summary. Defaults to 0.
+        - additional_instructions (Optional[str], optional): Additional instructions to provide to the
+            model for customizing summaries. - minimum_chunk_size (Optional[int], optional): The minimum size for text
+            chunks. Defaults to 500.
+        - chunk_delimiter (str, optional): The delimiter used to split the text into chunks. Defaults to ".".
+        - summarize_recursively (bool, optional): If True, summaries are generated recursively, using previous summaries for context.
+        - verbose (bool, optional): If True, prints detailed information about the chunking process.
+    Returns:
+    - str: The final compiled summary of the text.
+
+    The function first determines the number of chunks by interpolating between a minimum and a maximum chunk count
+    based on the `detail` parameter. It then splits the text into chunks and summarizes each chunk. If
+    `summarize_recursively` is True, each summary is based on the previous summaries, adding more context to the
+    summarization process. The function returns a compiled summary of all chunks.
+    """
+
+    # check detail is set correctly
+    assert 0 <= detail <= 1
+
+    # interpolate the number of chunks based to get specified level of detail
+    max_chunks = len(chunk_on_delimiter(text, minimum_chunk_size, chunk_delimiter))
+    min_chunks = 1
+    num_chunks = int(min_chunks + detail * (max_chunks - min_chunks))
+
+    # adjust chunk_size based on interpolated number of chunks
+    # FIXME MAKE NOT OPENAI SPECIFIC
+    document_length = len(openai_tokenize(text))
+    chunk_size = max(minimum_chunk_size, document_length // num_chunks)
+    text_chunks = chunk_on_delimiter(text, chunk_size, chunk_delimiter)
+    if verbose:
+        print(f"Splitting the text into {len(text_chunks)} chunks to be summarized.")
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        print(f"Chunk lengths are {[len(openai_tokenize(x)) for x in text_chunks]}")
+
+    # set system message - FIXME
+    system_message_content = "Rewrite this text in summarized form."
+    if additional_instructions is not None:
+        system_message_content += f"\n\n{additional_instructions}"
+
+    accumulated_summaries = []
+    for i, chunk in enumerate(tqdm(text_chunks)):
+        if summarize_recursively and accumulated_summaries:
+            # Combine previous summary with current chunk for recursive summarization
+            combined_text = accumulated_summaries[-1] + "\n\n" + chunk
+            user_message_content = f"Previous summary and new content to summarize:\n\n{combined_text}"
+        else:
+            user_message_content = chunk
+
+        messages = [
+            {"role": "system", "content": system_message_content},
+            {"role": "user", "content": user_message_content}
+        ]
+
+        response = get_chat_completion(messages, model=model)
+        accumulated_summaries.append(response)
+
+    final_summary = '\n\n'.join(accumulated_summaries)
+    return final_summary
+
+#
+#
+#######################################################################################################################
+#
+# Ebook Chapter Chunking
+
+
+def chunk_ebook_by_chapters(text: str, chunk_options: Dict[str, Any]) -> List[Dict[str, Any]]:
+    max_chunk_size = chunk_options.get('max_size', 300)
+    overlap = chunk_options.get('overlap', 0)
+    custom_pattern = chunk_options.get('custom_chapter_pattern', None)
+
+    # List of chapter heading patterns to try, in order
+    chapter_patterns = [
+        custom_pattern,
+        r'^#{1,2}\s+',  # Markdown style: '# ' or '## '
+        r'^Chapter\s+\d+',  # 'Chapter ' followed by numbers
+        r'^\d+\.\s+',  # Numbered chapters: '1. ', '2. ', etc.
+        r'^[A-Z\s]+$'  # All caps headings
+    ]
+
+    chapter_positions = []
+    used_pattern = None
+
+    for pattern in chapter_patterns:
+        if pattern is None:
+            continue
+        chapter_regex = re.compile(pattern, re.MULTILINE | re.IGNORECASE)
+        chapter_positions = [match.start() for match in chapter_regex.finditer(text)]
+        if chapter_positions:
+            used_pattern = pattern
+            break
+
+    # If no chapters found, return the entire content as one chunk
+    if not chapter_positions:
+        return [{'text': text, 'metadata': get_chunk_metadata(text, text, chunk_type="whole_document")}]
+
+    # Split content into chapters
+    chunks = []
+    for i in range(len(chapter_positions)):
+        start = chapter_positions[i]
+        end = chapter_positions[i + 1] if i + 1 < len(chapter_positions) else None
+        chapter = text[start:end]
+
+        # Apply overlap if specified
+        if overlap > 0 and i > 0:
+            overlap_start = max(0, start - overlap)
+            chapter = text[overlap_start:end]
+
+        chunks.append(chapter)
+
+    # Post-process chunks
+    processed_chunks = post_process_chunks(chunks)
+
+    # Add metadata to chunks
+    return [{'text': chunk, 'metadata': get_chunk_metadata(chunk, text, chunk_type="chapter", chapter_number=i + 1,
+                                                           chapter_pattern=used_pattern)}
+            for i, chunk in enumerate(processed_chunks)]
+
+
+# # Example usage
+# if __name__ == "__main__":
+#     sample_ebook_content = """
+# # Chapter 1: Introduction
+#
+# This is the introduction.
+#
+# ## Section 1.1
+#
+# Some content here.
+#
+# # Chapter 2: Main Content
+#
+# This is the main content.
+#
+# ## Section 2.1
+#
+# More content here.
+#
+# CHAPTER THREE
+#
+# This is the third chapter.
+#
+# 4. Fourth Chapter
+#
+# This is the fourth chapter.
+# """
+#
+#     chunk_options = {
+#         'method': 'chapters',
+#         'max_size': 500,
+#         'overlap': 50,
+#         'custom_chapter_pattern': r'^CHAPTER\s+[A-Z]+'  # Custom pattern for 'CHAPTER THREE' style
+#     }
+#
+#     chunked_chapters = improved_chunking_process(sample_ebook_content, chunk_options)
+#
+#     for i, chunk in enumerate(chunked_chapters, 1):
+#         print(f"Chunk {i}:")
+#         print(chunk['text'])
+#         print(f"Metadata: {chunk['metadata']}\n")
+
+
+
+
+#
+# End of Chunking Library
+#######################################################################################################################