Repository: x3n0cr4735/celi (language: python)

Issue:
Issue/#49 remove oldprocess_iteration  loop
This is code cleanup done in prep for moving the writing of output into tool implementations. 'process_iteration2' has been used in place of 'process_iteration', but both loops remained in the code. Now that we have to refactor to remove writing of the draft document, it makes sense to remove 'process_iteration'. (Otherwise, both would need to be refactored).


Discussion / hints:

Fix patch:
diff --git a/celi_framework/core/job_description.py b/celi_framework/core/job_description.py
index 47cde28..e9fa6e2 100644
--- a/celi_framework/core/job_description.py
+++ b/celi_framework/core/job_description.py
@@ -1,4 +1,9 @@
 from __future__ import annotations
+
+import datetime
+import json
+import logging
+import os
 from abc import ABC, abstractmethod
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Type
@@ -6,7 +11,9 @@
 from celi_framework.utils.llms import ToolDescription
 
 from pydantic import BaseModel, field_serializer
-from celi_framework.utils.utils import encode_class_type
+from celi_framework.utils.utils import encode_class_type, read_json_from_file, write_string_to_file
+
+logger = logging.getLogger(__name__)
 
 
 class Task(BaseModel):
@@ -60,9 +67,16 @@ def _update_references(self, item: Any, task_numbering: Dict[str, str]):
 class ToolImplementations(ABC):
     """Each public function in the class becomes a tool that the LLM can use.
 
-    There is one required function, `def get_schema(self) -> Dict[str, str]`.  This function returns a dictionary describing the document sections.  The processor will work through the sections, completing the defined tasks for each section.  Each dictionary can have any string values, but it is intended to be a section number followed by a section name.
+    There is one required function, `def get_schema(self) -> Dict[str, str]`.  This function returns a dictionary
+    describing the document sections.  The processor will work through the sections, completing the defined tasks for
+    each section.  Each dictionary can have any string values, but it is intended to be a section number followed by
+    a section name.
 
-    In addition to the `get_schema` function, the ToolImplementations class can have whatever other functions it needs to enable celi_framework.  Each function should be documented with type hints and a doc string.  The top section of the docstring will be included as the description of the overall function.  If the function takes arguments, there should be a section called "Args:" that contains a list of the arguments to the function and descriptions of each.  An example docstring is given below:
+    In addition to the `get_schema` function, the ToolImplementations class can have whatever other functions it needs
+    to enable celi_framework.  Each function should be documented with type hints and a doc string.  The top section of
+    the docstring will be included as the description of the overall function.  If the function takes arguments, there
+    should be a section called "Args:" that contains a list of the arguments to the function and descriptions of each.
+    An example docstring is given below:
 
         '''
         Extracts text from specified sections of documents.
@@ -80,10 +94,46 @@ class ToolImplementations(ABC):
 
     @abstractmethod
     def get_schema(self) -> Dict[str, str]:
-        "This function returns a dictionary describing the document sections.  The processor will work through the sections, completing the defined tasks for each section.  Each dictionary can have any string values, but it is intended to be a section number followed by a section name."
+        """This function returns a dictionary describing the document sections.  The processor will work through the
+        sections, completing the defined tasks for each section.  Each dictionary can have any string values,
+        but it is intended to be a section number followed by a section name."""
         pass
 
 
+class BaseDocToolImplementations(ToolImplementations, ABC):
+    """ A base class for ToolImplementations that draft documents.  Adds a standard `update_draft_doc` tool.
+    """
+
+    def __init__(self, drafts_dir: str = "target/celi_output/drafts"):
+        os.makedirs(drafts_dir, exist_ok=True)
+        self.draft_doc = (
+            f"{drafts_dir}/{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.json"
+        )
+        logger.info(f"Writing output to {self.draft_doc}")
+
+    def save_draft_section(self, doc_section: str, draft: str):
+        """Saves a draft of the current section.
+
+        This must be called with the final output before calling pop_context and moving on to the next section.
+
+        Args:
+            doc_section: The section name to save
+            draft: The draft text
+        """
+        logger.info(
+            f"Completed section {doc_section}.  Draft output is:\n{draft}",
+            extra={"color": "orange"},
+        )
+
+        logger.info(f"Adding section {doc_section} to {self.draft_doc}")
+        try:
+            current = read_json_from_file(self.draft_doc)
+        except FileNotFoundError:
+            current = {}
+        current[doc_section] = draft
+        write_string_to_file(json.dumps(current, indent=2), self.draft_doc)
+
+
 class JobDescription(BaseModel):
     """
     Specifies a configuration for MasterTemplateFactory
@@ -140,13 +190,15 @@ def generate_tool_description(method: Callable) -> ToolDescription:
     # Extracting parameters and their annotations, skipping 'self'
     sig = inspect.signature(method)
     parameters = {}
+    #logger.debug(f"Inspecting {method}: {sig}")
     for param_name, param in sig.parameters.items():
         if param_name == "self":  # Skip 'self' parameter
             continue
+        #logger.debug(f"Param {param}")
 
         if param.annotation == param.empty:
             param_type_dict = {"type": "string"}
-        elif param.annotation.__name__ == "str":
+        elif param.annotation == "str" or param.annotation.__name__ == "str":
             param_type_dict = {"type": "string"}
         elif param.annotation.__name__ == "List":
             param_type_dict = {"type": "array", "items": {"type": "string"}}
diff --git a/celi_framework/core/processor.py b/celi_framework/core/processor.py
index 79c94f2..6158f59 100644
--- a/celi_framework/core/processor.py
+++ b/celi_framework/core/processor.py
@@ -1,13 +1,16 @@
-"""
-This Python module, celi_framework.processor, is part of a larger system designed to automate the drafting of documents using language models.
-The module contains the ProcessRunner class, which is responsible for managing and orchestrating the drafting process.
+"""This Python module, celi_framework.processor, is part of a larger system designed to automate the drafting of
+documents using language models. The module contains the ProcessRunner class, which is responsible for managing and
+orchestrating the drafting process.
 
 The ProcessRunner class interacts with various utility functions and MongoDB for data storage and retrieval,
-optimizing the process through adaptive learning and feedback mechanisms. It also leverages a MasterTemplateFactory for dynamic task management,
-ensuring a structured approach to document creation based on a predefined configuration and schema.
+optimizing the process through adaptive learning and feedback mechanisms. It also leverages a MasterTemplateFactory
+for dynamic task management, ensuring a structured approach to document creation based on a predefined configuration
+and schema.
 
-The module must be run from main.py in the root directory. It imports various utility functions and classes from multiple modules,
-including llm_helper_funcs, llmcore_utils, TokenSplitter, token_counters, and exceptions. These imported functions and classes provide essential functionalities for parsing messages, managing tokens, handling exceptions, and splitting tokens, among others.
+The module must be run from main.py in the root directory. It imports various utility functions and classes from
+multiple modules, including llm_helper_funcs, llmcore_utils, TokenSplitter, token_counters, and exceptions. These
+imported functions and classes provide essential functionalities for parsing messages, managing tokens,
+handling exceptions, and splitting tokens, among others.
 
 The module also defines several constants related to token pricing and maximum spend.
 These constants are used to manage the costs associated with using language models for content generation.
@@ -15,57 +18,42 @@
 The ProcessRunner class is initialized with a master template, codex, id prefix, new JSON document, document directory,
 drafts directory, update queue, and a configuration. It also accepts an optional list of sections to skip.
 
-The class includes numerous methods for managing the drafting process, such as generating task instructions, saving templates,
-restarting the process, saving prompts and completions, dispatching function calls, running the main execution loop, processing iterations, retrieving responses, handling responses, updating prompt completions, checking token limits, handling draft settings, handling table settings, handling context popping, logging errors, and stopping the process.
+The class includes numerous methods for managing the drafting process, such as generating task instructions,
+saving templates, restarting the process, saving prompts and completions, dispatching function calls, running the
+main execution loop, processing iterations, retrieving responses, handling responses, updating prompt completions,
+checking token limits, handling draft settings, handling table settings, handling context popping, logging errors,
+and stopping the process.
 
-Each method in the ProcessRunner class plays a specific role in the drafting process,
-from initiating and managing the main execution loop to handling responses from the language model and updating the ongoing conversation context.
+Each method in the ProcessRunner class plays a specific role in the drafting process, from initiating and managing
+the main execution loop to handling responses from the language model and updating the ongoing conversation context.
 The class ensures that each interaction with the language model contributes towards the completion of the document,
-while also managing token usage to prevent excessive costs.
-"""
+while also managing token usage to prevent excessive costs."""
 
-import copy
-from dataclasses import asdict
 import datetime
 import json
 import os
-from queue import Queue
 from typing import Dict, List, Tuple
 
-from llm_core.splitters import TokenSplitter
+from openai.types.chat.chat_completion import Choice
 
 from celi_framework.core.job_description import (
     ToolImplementations,
     generate_tool_descriptions,
 )
 from celi_framework.core.mt_factory import MasterTemplateFactory
-from openai.types.chat.chat_completion import Choice
 from celi_framework.utils.codex import MongoDBUtilitySingleton
 from celi_framework.utils.llmcore_utils import (
-    FinalOutput,
-    IsPromptError,
     ParserFactory,
-    parse,
 )
 from celi_framework.utils.llms import ToolDescription, ask_split
 from celi_framework.utils.log import app_logger
 from celi_framework.utils.token_counters import (
     get_master_counter_instance,
-    token_counter_og,
 )
 from celi_framework.utils.utils import (
     create_new_timestamp,
-    generate_hash_id,
-    generate_prompt_and_completion_id,
-    generate_task_specific_id,
-    read_json_from_file,
-    write_string_to_file,
 )
 
-REQUEST_TOKEN_PRICE = 0.00001
-RESPONSE_TOKEN_PRICE = 0.00003
-MAX_SPEND = 400.0
-ONGOING_CHAT_TOKEN_LIMIT = 100000
 
 ChatMessageable = Tuple[str, str] | Dict[str, str]
 
@@ -76,42 +64,13 @@ class ProcessRunner:
 
     This class is central to automating the drafting process, utilizing natural language processing to generate, analyze, and refine text based on source material and expert guidance. It interacts closely with utility functions and MongoDB for data storage and retrieval, optimizing the process through adaptive learning and feedback mechanisms.
 
-    Attributes:
-        master_template_factory (MasterTemplateFactory): Factory instance to manage task templates.
-        keep_running (bool): Flag indicating if the process loop should continue running.
-        json_doc_new (dict): Template for new document content (Table of Contents).
-        doc_dir (str): Directory path for storing generated documents.
-        drafts_dir (str): Directory path for storing draft versions.
-        update_queue (Queue): Queue for processing updates and feedback from monitoring agents.
-        system_message (str): Instructional message for guiding the drafting process.
-        user_message (str): Placeholder for user-specific messages or instructions.
-        ongoing_chat (str): Accumulative chat history for context preservation.
-        token_counter (TokenCounter): Utility for tracking token usage against predefined limits.
-        new_drafts_dir (str): Directory for new drafts within the drafting session.
-        new_dir_templates (str): Directory for storing session-specific templates.
-
-    Methods:
-        process_skip_section_list(skip_section_list): Processes and skips specified sections from drafting.
-        generate_task_instructions(): Generates dynamic task instructions based on the current template and section.
-        save_template(): Saves the current task template to MongoDB with versioning for record-keeping and auditing.
-        restart(): Resets and restarts the drafting process, typically invoked after modifications to the template or in response to feedback.
-        save_prompt_and_completion(response): Parses and saves the interaction with the language model to MongoDB, handling versioning and error logging.
-        function_calls(name, arguments): Dispatches function calls to utility functions based on the name and provided arguments.
-        run(): Initiates and manages the main execution loop for processing drafting tasks.
-        process_iteration(): Manages a single iteration within the execution loop, handling task execution, response processing, and system updates.
-        get_response(): Retrieves responses from the language model based on current prompts and context.
-        handle_response(response): Processes language model responses, directing flow based on content type and action required.
-        handle_no_message_returned(): Handles scenarios where no message is returned from the language model.
-        handle_message_returned(message): Processes and logs messages returned from the language model.
-        handle_function_call(response): Executes function calls specified in language model responses.
-        update_prompt_completion(prompt_completion): Updates the ongoing chat with the latest prompt completion content.
-        check_token_limits(): Monitors and enforces token usage limits to prevent excessive costs.
-        check_chat_token_count(): Ensures the ongoing chat does not exceed token count limits, adjusting context as necessary.
-        handle_draft_setting(function_return): Processes and saves draft content based on function return values.
-        handle_table_setting(function_return): Handles the incorporation of table content into the draft based on function returns.
-        handle_pop_context(function_return): Manages context updates and section progression based on pop context function returns.
-        log_error_and_save_as_text(error, content, content_type): Logs errors and saves associated content for debugging.
-        stop(): Gracefully terminates the drafting process.
+    Args:
+        master_template (MasterTemplateFactory): Factory instance to manage task templates.
+        codex (MongoDBUtilitySingleton): Persistent storage - holds the LLM cache and CELI output
+        parser_factory (ParserFactory): Which LLM to use for parsing output
+        tool_implementations (ToolImplementations): Tools that CELI can call
+        llm_cache (bool): Whether to cache LLM requests
+        skip_section_list (List[str]): Optional list of sections to skip in the processing
 
     The ProcessRunner class encapsulates the complexity of automating document drafting, providing a structured and adaptive framework for content generation, analysis, and refinement. It represents a key component in leveraging AI and NLP technologies for enhancing the efficiency and quality of documentation.
     """
@@ -122,19 +81,21 @@ def __init__(
         codex: MongoDBUtilitySingleton,
         parser_factory: ParserFactory,
         tool_implementations: ToolImplementations,
-        drafts_dir: str,
-        update_queue: Queue,
         llm_cache: bool,
-        skip_section_list: List[str] = [],
-    ):  # skip_section_list=SECTIONS_COMPLETED):
-        # Initialize MasterTemplateFactory # TODO -> Have this passed in, instead of initializing like this
+        skip_section_list=None,
+    ):
+        if skip_section_list is None:
+            skip_section_list = []
         self.master_template = master_template  # config and schema need to be defined # TODO -> Read latest version from codex?
         # TODO -> Have it be so that there is a manual template creation process which would be what's above (but with arg for manual create passed in),
         #  then rest of time have it just read from Mongo/codex
         self.builtin_tool_descriptions = [
             ToolDescription(
                 name="pop_context",
-                description='This used to signal to the outer layer that a pop is requested by the LLMs. Returns the next section number.\n\nThis function should not be called unless the "Final Document Review" has been completed and you are ready to move to the next section.\nIt will empty out the current chat history.',
+                description='This used to signal to the outer layer that a pop is requested by the LLMs. Returns the '
+                            'next section number.\n\nThis function should not be called unless the "Final Document '
+                            'Review" has been completed and you are ready to move to the next section.\nIt will empty '
+                            'out the current chat history.',
                 parameters={
                     "type": "object",
                     "properties": {
@@ -152,11 +113,6 @@ def __init__(
         self.parser_factory = parser_factory
         self.tool_implementations = tool_implementations
         self.keep_running = True
-        self.update_queue = update_queue
-        os.makedirs(drafts_dir, exist_ok=True)
-        self.draft_doc = (
-            f"{drafts_dir}/{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.json"
-        )
         self.completed_section = None
         self.sections_to_be_completed = list(
             self.master_template.schema.keys()
@@ -175,10 +131,10 @@ def __init__(
         self.tool_descriptions = generate_tool_descriptions(
             self.master_template.job_desc.tool_implementations_class
         )
-
+        self.pop_context_flag = False
         self.save_template()
 
-    def process_iteration2(self):
+    def process_iteration(self):
         app_logger.info("Started a new iteration", extra={"color": "orange"})
         self.pop_context_flag = False
         chat_len = len(self.ongoing_chat)
@@ -204,16 +160,7 @@ def process_iteration2(self):
         )
 
         if self.pop_context_flag:
-            try:
-                self.update_draft_doc(self.ongoing_chat)
-            except Exception as e:
-                app_logger.exception(
-                    f"Error updating draft doc, retrying", extra={"color": "red"}
-                )
-                self.pop_context_flag = False
-
-            if self.pop_context_flag:
-                self.handle_pop_context()
+            self.handle_pop_context()
 
 
     def format_chat_messages(self, msgs: List[ChatMessageable]):
@@ -224,27 +171,6 @@ def format_message_content(self, m: ChatMessageable):
             return f"{m['role'].capitalize()}:\n{m['content']}"
         return f"{m[0].capitalize()}:\n{m[1]}"
 
-    def update_draft_doc(self, msgs):
-        parsed_draft = parse(
-            self.parser_factory,
-            target_cls=FinalOutput,
-            msg=self.format_chat_messages(msgs[-8:]),
-        )
-        app_logger.info(
-            f"Completed section {parsed_draft.doc_section}.  Draft output is:\n{parsed_draft.draft}",
-            extra={"color": "orange"},
-        )
-        self.update_draft_doc_file(parsed_draft)
-
-        _id = generate_hash_id(msgs)
-        id = _id
-        draft_dict = {
-            "_id": _id,  # Assuming _id is defined elsewhere and unique to each draft
-            "id": id,  # Assuming id is defined elsewhere and unique to each draft
-            "draft_output": asdict(parsed_draft),
-        }
-        self.codex.save_document_with_versioning(draft_dict, collection_name="drafts")
-
     def process_skip_section_list(self, skip_section_list):
         """
         Filters out sections from the drafting process based on a provided list of sections to skip.
@@ -305,333 +231,6 @@ def save_template(self):
         # The method does not return anything
         return None
 
-    def save_prompt_and_completion(self, response):
-        """
-        Parses the language model's response and saves the interaction details to MongoDB. This method
-        handles the creation of a comprehensive document that includes the interaction's context, the
-        response, any identified function calls, and metadata such as timestamps and unique identifiers.
-        The document is versioned and saved for record-keeping, auditing, and potential future analysis.
-
-        This method also categorizes the response based on its content (e.g., draft content, function calls)
-        and handles errors or exceptions during parsing. It ensures that all significant interactions are
-        logged and stored in a structured format.
-
-        Args:
-            response (obj): The response object from the language model, encapsulating content, metadata,
-                            and any special instructions or function calls included in the response.
-
-        Returns:
-            str: The unique ID of the document saved in MongoDB, representing the specific interaction.
-
-        Raises:
-            ParsingException: If any errors occur during the parsing of the language model's response,
-                              indicating issues with understanding or processing the response content.
-
-        The method meticulously processes the response, checking for and executing function calls as necessary,
-        and prepares a document that captures the essence of the interaction. This document is then saved to
-        MongoDB, with a unique identifier returned for reference. Errors during parsing or saving trigger exceptions,
-        ensuring robust error handling and data integrity.
-        """
-
-        # Initialize variables with default values.
-        timestamp = create_new_timestamp(
-            ms=True
-        )  # Create a new timestamp for this operation.
-        task_list, task_desc = ["None"], "Tool Call"  # Default task information.
-        function_name, arguments = "None", "None"  # Default function call information.
-        response_msg = "None"  # Default value for response message.
-        prompt_exception = False
-
-        # try:
-        # Determine the type of response and extract relevant information.
-        if response.finish_reason == "tool_calls":
-            # Extract tool details if the finish reason is tool calls.
-            task_list = [
-                f"{_.function.name} with {json.dumps(_.function.arguments)}"
-                for _ in response.message.tool_calls
-            ]
-        elif response.message.content:
-            # Parse the direct message content for task details.
-            response_msg = response.message.content
-            # logger.info(f"Response message:\n{response_msg}", extra={"color": "green"})
-
-            # try:
-            # logger.info(
-            #     "Attempting to parse content",
-            #     extra={"color": "orange"},
-            # )
-            # parsed_msg = parse(self.parser_factory, Message, response_msg)
-            # # TODO -> Should we switch to parsed_msg.last_current_task_description_list (LIST)
-
-            # # Check if the task description matches the final output task, indicating a draft output.
-            # draft_output = self.master_template.job_desc.final_output_task in task_desc
-            # except Exception as e:
-            #     # Log and append context length issues encountered during parsing of prompt completion.
-            #     logger.exception(e, extra={"color": "red"})
-            #     error_message = f"{e}"
-            #     logger.error(error_message, extra={"color": "red"})
-            #     self.prompt_completion += f"\n{error_message}"
-            #     # prompt_exception = True
-
-        # except Exception as e:
-        #     # Log and append any errors encountered during parsing.
-        #     error_message = f"Error processing response: {e}"
-        #     logger.error(error_message, extra={"color": "red"})
-        #     self.prompt_completion += f"\n{error_message}"
-        #     raise ParsingException(f"Failed to parse response: {e}")
-
-        try:
-            # New chunking and error detection logic
-            # logger.info(
-            #     f"Self.prompt_completion = {self.prompt_completion}",
-            #     extra={"color": "green"},
-            # )
-            token_count = token_counter_og(string=str(self.prompt_completion))
-            app_logger.info(f"Self.completion token count = {token_count}")
-
-            # Initialize the TokenSplitter with the appropriate model and chunk size
-            splitter = TokenSplitter(
-                # model="mistral-7b-instruct-v0.1.Q5_K_M.gguf", DD This cause an error unknown encoding: mistral-7b-instruct-v0.1.Q5_K_M.gguf
-                chunk_size=4000,
-                chunk_overlap=50,
-            )
-
-            # Iterate through chunks generated by TokenSplitter
-            for chunk in splitter.chunkify(self.prompt_completion):
-                app_logger.info("Processing chunk...", extra={"color": "green"})
-
-                # Parse the current chunk for errors or exceptions
-                # parsed_prompt = parse_completion_for_error_with_llamacpp_parser(chunk)
-                parsed_prompt = parse(
-                    self.parser_factory, target_cls=IsPromptError, msg=chunk
-                )
-                app_logger.info(
-                    f"parse_completion_for_error:\n{parsed_prompt}",
-                    extra={"color": "orange"},
-                )
-
-                # Check if the parsed result indicates an error or exception
-                if (
-                    parsed_prompt.is_there_a_function_exception
-                    or parsed_prompt.is_there_a_function_error
-                ):
-                    prompt_exception = True
-                    app_logger.error(
-                        "Error or exception detected in prompt completion.",
-                        extra={"color": "red"},
-                    )
-                    self.prompt_completion += (
-                        "\nError or exception detected in prompt completion."
-                    )
-                    break
-        except Exception as e:
-            app_logger.exception(e)
-            app_logger.error(f"Error Parsing is not working:\n{e}")
-
-        # Generate unique IDs for the MongoDB document based on the task information.
-        unique_id, prompt_id, id = self.generate_document_ids(timestamp, task_list)
-
-        draft_output = self.pop_context_flag
-
-        # Prepare the MongoDB document with all extracted and parsed information.
-        document = self.prepare_document(
-            unique_id,
-            prompt_id,
-            id,
-            task_list,
-            task_desc,
-            draft_output,
-            response.finish_reason,
-            response_msg,
-            function_name,
-            arguments,
-            prompt_exception,
-            timestamp,
-        )
-
-        try:
-            # Attempt to save the prepared document to MongoDB with versioning.
-            self.codex.save_document_with_versioning(
-                document, collection_name="process_executions"
-            )
-            app_logger.info("Document saved with versioning.", extra={"color": "cyan"})
-        except Exception as e:
-            # Log and append any errors encountered during saving to MongoDB.
-            error_message = f"Failed to save document to MongoDB: {e}"
-            app_logger.error(error_message, extra={"color": "red"})
-            self.prompt_completion += f"\n{error_message}"
-
-        if draft_output:
-            # Prepare and save the draft document if a draft output is indicated.
-            draft = self.prepare_draft_dict(response_msg, document)
-            self.codex.save_document_with_versioning(draft, collection_name="drafts")
-            app_logger.info("Draft saved with versioning.", extra={"color": "green"})
-
-        # Return the unique ID of the main document saved or updated.
-        return document["_id"]
-
-    def generate_document_ids(self, timestamp, task_list):
-        """
-        Generates a set of unique identifiers for a new document based on the current timestamp and task list.
-        These identifiers are used to uniquely tag and track the document within the MongoDB database,
-        ensuring accurate versioning and retrieval.
-
-        The method produces three types of IDs:
-            1. A unique document ID that represents the specific interaction instance.
-            2. A prompt ID that identifies the set of prompts leading up to the current interaction.
-            3. A task-specific ID that links the document to a specific task within the process,
-                facilitating task-level tracking and analysis.
-
-        Args:
-            timestamp (str): A string representation of the current timestamp, used for generating time-based unique IDs.
-            task_list (list): A list of tasks that have been processed or are in processing, contributing to the context of the document.
-
-        Returns:
-            tuple: Contains three strings representing the unique document ID, prompt ID, and task-specific ID, respectively.
-
-        This method plays a critical role in the documentation and tracking system of the `ProcessRunner`,
-        ensuring that each piece of content generated and each interaction with the language model can be
-        uniquely identified and accessed.
-        """
-
-        unique_id = generate_prompt_and_completion_id(
-            system_message=self.system_message,
-            ongoing_chat=self.ongoing_chat,
-            prompt_completion=self.prompt_completion,
-            timestamp=timestamp,
-        )
-        prompt_id = generate_prompt_and_completion_id(
-            system_message=self.system_message,
-            ongoing_chat=self.ongoing_chat,
-        )
-
-        id = generate_task_specific_id(
-            document_type="document",  # TODO: Unhard code this
-            section_number=self.completed_section,
-            task=task_list[-1] if task_list else None,
-        )
-        return unique_id, prompt_id, id
-
-    def prepare_document(
-        self,
-        unique_id,
-        prompt_id,
-        id,
-        task_list,
-        task_desc,
-        draft_output,
-        finish_reason,
-        response_msg,
-        function_name,
-        arguments,
-        prompt_exception,
-        timestamp,
-    ):
-        """
-        Prepares a document for storage in MongoDB by organizing response data, interaction context,
-        and metadata into a structured format. This method consolidates the various pieces of information
-        related to a single interaction with the language model, including the response, any executed
-        function calls, and the overall context of the conversation.
-
-        Args:
-            unique_id (str): A unique identifier for the document, representing the specific interaction.
-            prompt_id (str): An identifier for the set of prompts leading up to the interaction.
-            id (str): A task-specific identifier linking the document to a particular task within the process.
-            task_list (list): The list of tasks involved in or relevant to the current interaction.
-            task_desc (str): A description of the current task or tasks being addressed.
-            draft_output (bool): Indicates whether the interaction resulted in draft content for the document.
-            finish_reason (str): The reason provided by the language model for completing the response.
-            response_msg (str): The content of the response from the language model.
-            function_name (str): The name of any function called during the interaction, if applicable.
-            arguments (str): A string representation of arguments passed to the called function, if any.
-            prompt_exception (bool): Indicates whether an exception occurred during prompt processing.
-            timestamp (str): The timestamp when the interaction occurred, used for versioning and tracking.
-
-        Returns:
-            dict: A dictionary representing the structured document ready for storage in MongoDB, including all relevant interaction details and metadata.
-
-        This method is crucial for ensuring that data from each interaction is accurately captured and stored in a manner that supports both immediate process needs and long-term analysis. The structured format facilitates querying, analysis, and retrieval of interactions based on various criteria.
-        """
-
-        document = {
-            "_id": unique_id,
-            "id": id,
-            "id_is": "task specific id",
-            "id_is_hash_of": "master_template + section_number + task",
-            "prompt_id": prompt_id,
-            "process_id": f"{self.codex._id}",
-            "template_id": self.master_template.id,
-            "finish_reason": finish_reason,
-            "document_section": self.current_section,
-            "task": task_list,
-            "task_desc": task_desc,
-            "draft_output": draft_output,
-            "system_message": self.system_message,
-            "ongoing_chat": self.ongoing_chat,
-            "prompt_completion": self.prompt_completion,
-            "prompt_exception": prompt_exception,
-            "response_msg": response_msg,
-            "function_name": function_name,
-            "function_arguments": arguments,
-            "timestamp": timestamp,
-        }
-        return document
-
-    def prepare_draft_dict(self, response_msg, document):
-        """
-        Prepares and structures the draft content based on the language model's response and additional document metadata.
-        This method formats the draft content, including any tables, figures, and references extracted from the response,
-        into a dictionary suitable for storage in MongoDB. It leverages parsing utilities to extract structured information
-        from the response message and associates this information with relevant document metadata.
-
-        Args:
-            response_msg (str): The content of the response from the language model, which includes draft text and potentially tables, figures, or references.
-            document (dict): A dictionary containing metadata about the interaction, including unique identifiers and task descriptions.
-
-        Returns:
-            dict: A dictionary representing the structured draft content, ready for storage in MongoDB. This includes extracted sections, tables, figures, references, and any comments or annotations identified in the response.
-
-        The returned dictionary includes keys for section content, comments, success flags indicating parsing success,
-        source material mappings, scope descriptions, and identifiers for tables, figures, and cross-references.
-        This structured approach facilitates later retrieval and analysis of draft content, supporting the iterative
-        refinement and compilation of the document.
-        """
-
-        parsed_draft = parse(
-            self.parser_factory, target_cls=FinalOutput, msg=response_msg
-        )
-        app_logger.info("THIS IS IMPORTANT!!!!!", extra={"color": "orange"})
-        app_logger.info(
-            f"parse_final_output_with_llamacpp_parser:\n{parsed_draft}",
-            extra={"color": "orange"},
-        )
-        _id = generate_hash_id(response_msg)
-        id = document["id"]
-
-        self.update_draft_doc_file(parsed_draft)
-
-        draft_dict = {
-            "_id": _id,  # Assuming _id is defined elsewhere and unique to each draft
-            "id": id,  # Assuming id is defined elsewhere and unique to each draft
-            "draft_output": asdict(parsed_draft),
-        }
-
-        return draft_dict
-
-    def update_draft_doc_file(self, output: FinalOutput):
-        app_logger.info(f"Adding section {output.doc_section} to {self.draft_doc}")
-        try:
-            current = read_json_from_file(self.draft_doc)
-        except FileNotFoundError:
-            current = {}
-        section_key = (
-            output.doc_section
-            if output.doc_section not in current
-            else (output.doc_section + " (2)")
-        )
-        current[section_key] = output.draft
-        write_string_to_file(json.dumps(current), self.draft_doc)
-
     def run(self):
         """
         Initiates and manages the main execution loop for drafting documents. This loop continues
@@ -666,232 +265,7 @@ def run(self):
         """
 
         while self.keep_running:
-            self.process_iteration2()
-
-    def process_iteration(self):
-        """
-        Manages a single iteration within the execution loop, orchestrating the processing of a task or interaction.
-        This method encapsulates the steps involved in generating a response from the language model, processing
-        that response, and preparing for subsequent actions based on the outcome.
-
-        The method performs the following key operations in sequence:
-        1. Retrieves a response from the language model based on the current context and instructions.
-        2. Processes the received response to understand and act upon any directives, content, or function calls it contains.
-        3. Updates the ongoing conversation context with the new information obtained from the response.
-        4. Checks for and handles any token usage limits to ensure compliance with operational constraints.
-        5. Saves the processed interaction to MongoDB, capturing the dialogue, decisions, and any generated content for record-keeping.
-
-        Each iteration represents a step forward in the drafting process, potentially advancing the document's content,
-        adjusting the drafting strategy based on feedback, or handling administrative tasks like context management
-        and token accounting.
-
-        Returns:
-            None. The primary effect of this method is the advancement of the drafting process through side effects
-            on class attributes and external data stores (e.g., MongoDB).
-
-        This method is crucial for maintaining the flow of the drafting process, ensuring that each interaction with
-        the language model is purposeful and that its outcomes are effectively integrated into the ongoing work. It
-        leverages other methods within the class to perform specific tasks, such as `get_response`, `handle_response`,
-        and `update_prompt_completion`.
-        """
-
-        # Log the start of a new iteration
-        app_logger.info("Started a new iteration", extra={"color": "orange"})
-        self.pop_context_flag = False
-
-        # Retrieve a response from the language model based on the current context and instructions
-        response = self.get_response()
-        app_logger.info(
-            f"Received response with finish reason {response.finish_reason}",
-            extra={"color": "orange"},
-        )
-
-        # Process the received response to understand and act upon any directives, content, or function calls it contains
-        prompt_completion = self.handle_response(response)
-        app_logger.info(
-            f"Handled response:\n{prompt_completion}", extra={"color": "green"}
-        )
-
-        # Update the ongoing conversation context with the new information obtained from the response
-        self.update_ongoing_chat(prompt_completion)
-        app_logger.info(
-            "Updated ongoing chat with new response", extra={"color": "orange"}
-        )
-
-        # Check for and handle any token usage limits to ensure compliance with operational constraints
-        self.check_token_limits()
-        app_logger.info("Checked token limits", extra={"color": "orange"})
-
-        # Count the tokens in the ongoing chat to ensure it does not exceed the limit
-        self.check_chat_token_count()
-        app_logger.info("Counted chat tokens", extra={"color": "orange"})
-
-        # Save the processed interaction to MongoDB, capturing the dialogue, decisions, and any generated content for record-keeping
-        doc_id = self.save_prompt_and_completion(response)
-        app_logger.info("Saved data to MongoDB", extra={"color": "orange"})
-
-        # Put the document ID in the update queue for monitoring
-        self.update_queue.put(("doc_save", doc_id))
-        app_logger.info(
-            f"Enqueued Doc ID {doc_id} for monitoring", extra={"color": "cyan"}
-        )
-
-        if self.pop_context_flag:
-            self.handle_pop_context()
-
-    def get_response(self) -> Choice:
-        """
-        Retrieves a response from the language model by submitting the current prompts and context. This method forms
-        the bridge between the drafting process and the language model, sending the constructed prompts to the model
-        and awaiting its response. The response is expected to further the drafting process, whether by generating text,
-        providing instructions, or executing a function call.
-
-        The method constructs a request that includes the ongoing conversation context and any specific instructions
-        encoded in the system and user messages. It then submits this request to the language model and waits for a
-        response, handling any errors or timeouts that may occur during the request.
-
-        Returns:
-            An object representing the language model's response. This object typically includes the generated text
-            response, metadata such as the reason for completion, and any function calls or instructions embedded within
-            the response text.
-
-        This response is crucial for advancing the drafting process, as it may contain the next piece of content to
-        be added to the document, instructions for the next steps, or commands to modify the drafting context. The
-        `get_response` method ensures that each interaction with the language model is captured and acted upon
-        according to the system's logic.
-        """
-
-        # Call LLM with system message and ongoing chat
-        return ask_split(
-            codex=self.codex if self.llm_cache else None,
-            user_prompt=self.ongoing_chat,  # type: ignore
-            system_message=self.system_message,
-            verbose=True,
-            timeout=None,
-            tool_descriptions=self.tool_implementations.tool_descriptions,
-        )
-
-    def handle_response(self, response: Choice):
-        """
-        Processes the response received from the language model, determining the appropriate course of action
-        based on the content and directives within the response. This method interprets the response to decide
-        whether to update the draft content, execute a function call, or adjust the conversation context.
-
-        The response from the language model can contain various types of information, including text content
-        for the draft, instructions for next steps, or commands for specific function calls. This method
-        categorizes the response appropriately and initiates the corresponding actions, such as updating
-        the draft content or handling embedded function calls.
-
-        Args:
-            response: An object representing the language model's response, which includes the response content,
-                      metadata such as the finish reason, and any embedded instructions or commands.
-
-        Returns:
-            str: A string representing the processed content to be appended to the ongoing conversation context.
-                 This could include new draft content, a summary of executed actions, or an indication of any
-                 required user input or decision-making.
-
-        The `handle_response` method is central to the operational logic of the `ProcessRunner`, ensuring that
-        each response from the language model is effectively integrated into the drafting process. It leverages
-        other methods within the class, such as `handle_no_message_returned`, `handle_message_returned`, and
-        `handle_function_call`, to address specific types of responses and actions required.
-        """
-        if response.finish_reason == "tool_calls":
-            prompt_completion = self.handle_function_call(response)
-        elif response.message.content:
-            # TODO -> We could add and drop prompt_completions to queue strategically
-            #  to keep only completions which are relevant to the tasks at hand
-            prompt_completion = self.handle_message_returned(response.message.content)
-        else:
-            prompt_completion = self.handle_no_message_returned()
-
-        return prompt_completion
-
-    def handle_no_message_returned(self):
-        """
-        Handles scenarios where the language model's response contains no message content. This situation can occur
-        when the model's output does not directly contribute to the drafting process or when an expected output is
-        missing due to model limitations or contextual constraints.
-
-        This method is invoked as a fallback mechanism to maintain the flow of the drafting process, ensuring that
-        even in the absence of direct content from the language model, the process can proceed with minimal disruption.
-        It may generate a placeholder message or prompt for further input, aiming to elicit more actionable guidance
-        or content on subsequent interactions.
-
-        Returns:
-            str: A placeholder message or instruction generated in response to the absence of a message from the
-                 language model. This output is designed to keep the conversation going and guide the user or system
-                 towards providing additional context or modifying the request to obtain a more useful response.
-
-        The method ensures continuity in the drafting process, preventing stalls due to incomplete or missing responses
-        and encouraging adjustments to the interaction strategy to better leverage the language model's capabilities.
-        """
-
-        app_logger.warning(
-            "No message was returned and no function was called.",
-            extra={"color": "red"},
-        )
-        return (
-            "\n\nNo message was returned from you (the writer) and no function was called.\n"
-            "As a writer, explain in detail what you want to do next."
-        )
-
-    def handle_message_returned(self, message):
-        """
-        Processes and integrates the message content returned from the language model into the drafting process.
-        This method is called when the language model provides actionable content or instructions within its response.
-
-        The returned message is evaluated for its relevance and utility in advancing the document's content or
-        addressing specific drafting tasks. Based on this evaluation, the method may update the draft content,
-        adjust the conversation context, or prompt further actions to refine or expand upon the provided content.
-
-        Args:
-            message (str): The content of the message returned by the language model, containing text, instructions,
-                           or commands to be integrated into the drafting process.
-
-        Returns:
-            str: The processed content, ready to be appended to the ongoing conversation context or used to update
-                 the draft document. This includes any modifications or annotations added to the original message
-                 to enhance its clarity or utility.
-
-        By effectively interpreting and utilizing the content provided by the language model, this method plays a
-        key role in maintaining the momentum of the drafting process and ensuring that each response contributes
-        towards the completion of the document.
-        """
-        return f"\nPrior message from you (the writer): \n{message}"
-
-    def handle_function_call(self, response):
-        """
-        Executes specific function calls embedded within the language model's response. This method is critical
-        for handling responses that contain instructions for actions beyond simple text generation, such as
-        retrieving additional data, performing analysis, or modifying the draft content in a particular manner.
-
-        Function calls are identified within the response based on predefined patterns or markers. Once detected,
-        this method dispatches the function call to the appropriate utility function within the system, passing
-        along any arguments specified in the response.
-
-        Args:
-            response: The response object from the language model, potentially containing one or more function
-                      calls along with the necessary arguments for execution.
-
-        Returns:
-            str: A summary or result of the executed function call, encapsulating any direct outputs, status
-                 messages, or indications of actions taken as a result of the function call. This information
-                 is used to update the ongoing conversation context or draft content.
-
-        This method ensures that the drafting process can dynamically adapt to the needs of the document, leveraging
-        the full range of capabilities offered by the language model and auxiliary utility functions to achieve
-        comprehensive and nuanced content development.
-        """
-
-        prompt_completion = (
-            self.handle_message_returned(response.message.content)
-            if response.message.content
-            else ""
-        )
-        tool_results = self.make_tool_calls(response)
-        prompt_completion += "\n\n".join(_["content"] for _ in tool_results)
-        return prompt_completion
+            self.process_iteration()
 
     def make_tool_calls(self, response: Choice):
         def make_tool_call(tool_call):
@@ -903,17 +277,17 @@ def make_tool_call(tool_call):
                 self.pop_context_flag = True
                 function_return = arguments["current_section_number"]
             else:
-                unknown_func = False
+                method_to_call = None
                 try:
                     method_to_call = getattr(self.tool_implementations, name)
                 except AttributeError:
-                    unknown_func = True
+                    pass
 
-                if unknown_func:
+                if not method_to_call:
                     app_logger.error(
                         f"Unknown function name: {name}", extra={"color": "red"}
                     )
-                    function_return = "Error: Called unknown function name: {name}"
+                    function_return = f"Error: Called unknown function name: {name}"
                 else:
                     function_return = method_to_call(**arguments)
             function_log = f"Call to function {name} with arguments {arguments} returned\n{function_return}"
@@ -921,137 +295,12 @@ def make_tool_call(tool_call):
 
         return [make_tool_call(_) for _ in response.message.tool_calls]
 
-    def update_ongoing_chat(self, prompt_completion):
-        """
-        Updates the ongoing conversation context with the latest response or content generated by the language model.
-        This method is essential for integrating new information into the drafting process, ensuring that each
-        language model interaction contributes to the evolving document draft and maintains the continuity of the conversation.
-
-        Args:
-            prompt_completion (str): The content or instructions to be added to the ongoing conversation context.
-                                      This can include text generated by the language model, summaries of executed
-                                      actions, or other relevant information that supports the drafting process.
-
-        Modifies:
-            This method directly updates the `ongoing_chat` attribute by appending the new `prompt_completion` content,
-            ensuring that the updated context is used in subsequent interactions with the language model.
-
-        Returns:
-            None. The primary effect of this method is the modification of the class's internal state, specifically
-            the updating of the conversation context to include the latest interactions.
-
-        The updated conversation context plays a critical role in informing future prompts and interactions with the
-        language model, allowing for a dynamic and responsive drafting process that adapts to new information and
-        instructions as the document develops.
-        """
-
-        self.prompt_completion = copy.deepcopy(prompt_completion)
-        self.ongoing_chat += prompt_completion
-
-    def check_token_limits(self):
-        """
-        Monitors and enforces the predefined token usage limits to manage costs associated with the language model's API calls.
-        This method calculates the total cost of tokens consumed in requests and responses, comparing it against the
-        maximum allowable spend (MAX_SPEND). If the total cost exceeds this limit, the method triggers a halt in the
-        drafting process to prevent further charges.
-
-        The token costs are determined by multiplying the number of tokens used in requests and responses by their respective
-        prices (REQUEST_TOKEN_PRICE and RESPONSE_TOKEN_PRICE). This approach ensures transparent and controlled usage of the
-        language model, aligning with budget constraints.
-
-        Modifies:
-            This method potentially modifies the `keep_running` attribute, setting it to False if the token usage exceeds
-            the specified maximum spend, effectively stopping the process.
-
-        Returns:
-            None. The primary function of this method is to perform a check and potentially modify the process's
-            execution state based on the outcome.
-
-        This method is a critical component of the operational management within the `ProcessRunner`, ensuring that
-        the drafting process remains cost-effective and within budgetary limits. It exemplifies prudent resource
-        management, safeguarding against unexpected expenditures.
-        """
-
-        # Get the total number of request and response tokens used so far from the token counter
-        request_tokens, response_tokens = self.token_counter.get_total_tokens()
-
-        # Log the total number of request and response tokens
-        app_logger.info(
-            f"Total Request Tokens: {request_tokens}, Total Response Tokens: {response_tokens}",
-            extra={"color": "dark_grey"},
-        )
-
-        # Calculate the total cost of the tokens used in requests and responses by multiplying the number of tokens by their respective prices
-        total_cost = (
-            REQUEST_TOKEN_PRICE * request_tokens
-            + RESPONSE_TOKEN_PRICE * response_tokens
-        )
-
-        # Check if the total cost exceeds the maximum allowable spend
-        if total_cost > MAX_SPEND:
-            # If the total cost exceeds the maximum spend, log an error message
-            app_logger.error("Max Spend Reached!", extra={"color": "red"})
-
-            # Set the keep_running flag to False to stop the drafting process
-            self.keep_running = False
-
-            # Exit the program (this line may be removed later when we are confident about stopping and need to keep state)
-            exit()  # TODO <- To be removed later when we are confident about stoping and need to keep state (?)
-
-    def check_chat_token_count(self):
-        """
-        Evaluates the token count of the ongoing conversation context to ensure it remains within the limits
-        acceptable by the language model's API. This method is crucial for maintaining the effectiveness and
-        efficiency of interactions with the language model, as exceeding token count limits can lead to truncated
-        responses or failed requests.
-
-        If the token count exceeds a predefined threshold, this method initiates measures to reduce the context size,
-        such as invoking the `pop_context` function to manage the conversation history or summarizing the context to
-        maintain essential information while discarding older or less relevant content.
-
-        Returns:
-            None. The focus of this method is on evaluating and potentially modifying the `ongoing_chat` attribute
-            to ensure compliance with token count constraints.
-
-        Through active context management, this method supports the continuous and uninterrupted operation of the
-        drafting process, ensuring that each interaction with the language model is optimized for relevance and
-        compliance with technical constraints.
-        """
-
-        # Calculate the token count of the ongoing chat by calling the token_counter_og function
-        chat_token_count = token_counter_og(self.ongoing_chat)
-
-        # Log the token count of the ongoing chat
-        app_logger.info(
-            f"Token count of ongoing context: {chat_token_count}",
-            extra={"color": "white"},
-        )
-
-        # Check if the token count of the ongoing chat exceeds the limit of 100000 tokens
-        if chat_token_count > ONGOING_CHAT_TOKEN_LIMIT:
-            # If the token count exceeds the limit, append a message to the ongoing chat to stop the current action
-            # and call the pop_context function with the current section number as the argument
-            self.ongoing_chat += (
-                "\n\nStop what you are doing and call function pop_context "
-                "with current section number as the value passed to the function."
-            )
-
-            # Log an error message indicating that a pop request had to be sent because the token count for the ongoing chat exceeded the limit
-            app_logger.error(
-                "Had to send pop request because tokens for ongoing chat = {chat_token_count}"
-            )
-
     def handle_pop_context(self):
         """
         Adjusts the drafting context in response to a 'pop context' function call, which is used to manage the conversation
         history and focus on relevant content. This method may be triggered by specific directives in the language model's
         response or as part of context management strategies to optimize the interaction with the language model.
 
-        Args:
-            function_return: The outcome or data associated with the 'pop context' function call. While this method does
-                                not typically process complex return values, the argument is included to maintain consistency
-                                in function call handling.
-
         Returns:
             str: A summary message reflecting the action taken to adjust the context. This may include confirmation that
                     older or less relevant conversation history has been truncated or summarized to maintain focus and efficiency.
@@ -1068,8 +317,8 @@ def handle_pop_context(self):
         assert self.current_section in self.sections_to_be_completed
         self.sections_to_be_completed.remove(self.current_section)
         if self.sections_to_be_completed:
+            next_section = self.sections_to_be_completed[0]
             try:
-                next_section = self.sections_to_be_completed[0]
                 next_user_msg = (
                     f"Proceed to document section {next_section}, and do Task #1."
                 )
diff --git a/celi_framework/core/runner.py b/celi_framework/core/runner.py
index 7686212..54e4a6d 100644
--- a/celi_framework/core/runner.py
+++ b/celi_framework/core/runner.py
@@ -16,14 +16,12 @@
 @dataclass
 class Directories:
     output_dir: str
-    drafts_dir: str
     evaluations_dir: str
 
     @staticmethod
     def create(output_dir: str) -> Directories:
         return Directories(
             output_dir=output_dir,
-            drafts_dir=f"{output_dir}/drafts",
             evaluations_dir=f"{output_dir}/evaluations",
         )
 
@@ -69,8 +67,6 @@ def run_celi(celi_config: CELIConfig):
         codex=codex,
         parser_factory=parser_factory,
         tool_implementations=celi_config.tool_implementations,
-        drafts_dir=celi_config.directories.drafts_dir,
-        update_queue=update_queue,
         llm_cache=celi_config.llm_cache,
     )
 
@@ -97,4 +93,4 @@ def run_celi(celi_config: CELIConfig):
     # Wait for threads to finish
     [_.join() for _ in threads]
     app_logger.info("Threads completed. Exiting.")
-    return process_runner.draft_doc
+    return
diff --git a/celi_framework/examples/reporting_template/tools.py b/celi_framework/examples/reporting_template/tools.py
index bb0ad42..aaed623 100644
--- a/celi_framework/examples/reporting_template/tools.py
+++ b/celi_framework/examples/reporting_template/tools.py
@@ -16,7 +16,7 @@
 from attr import dataclass
 from celi_framework.core.job_description import (
     ToolImplementations,
-    generate_tool_descriptions,
+    generate_tool_descriptions, BaseDocToolImplementations,
 )
 from celi_framework.core.templates import (
     make_draft_setting_output_prompt,
@@ -38,7 +38,7 @@ class Document:
     content: Dict[str, str]
 
 
-class ReportingToolImplementations(ToolImplementations):
+class ReportingToolImplementations(BaseDocToolImplementations):
     doc_dir: str
 
     def __init__(self, doc_dir: str):
diff --git a/celi_framework/examples/wikipedia/eval/run_eval.py b/celi_framework/examples/wikipedia/eval/run_eval.py
index 98c8971..ea1446f 100644
--- a/celi_framework/examples/wikipedia/eval/run_eval.py
+++ b/celi_framework/examples/wikipedia/eval/run_eval.py
@@ -1,14 +1,7 @@
-from celi_framework.logging_setup import setup_logging
-
-setup_logging()
-
-import json
-import argparse
 import copy
+import json
 import logging
-import logging.config
 import os
-
 from functools import lru_cache
 from pathlib import Path
 from typing import List
@@ -24,8 +17,9 @@
     load_content_from_wikipedia_url,
 )
 from celi_framework.examples.wikipedia.tools import WikipediaToolImplementations
-from celi_framework.main import instantiate_with_argparse_args
-from celi_framework.utils.utils import get_obj_by_name, read_json_from_file, str2bool
+from celi_framework.logging_setup import setup_logging
+from celi_framework.main import instantiate_with_argparse_args, setup_standard_args
+from celi_framework.utils.utils import get_obj_by_name, read_json_from_file
 
 logger = logging.getLogger(__name__)
 
@@ -33,49 +27,7 @@
 def get_config():
     load_dotenv()
 
-    parser = argparse.ArgumentParser(description="Run the document generator.")
-
-    def bool_opt(opt: str, env_var: str, help: str):
-        parser.add_argument(
-            opt,
-            action="store_true",
-            default=str2bool(os.getenv(env_var, "False")),
-            help=help,
-        )
-
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default=os.getenv("OUTPUT_DIR"),
-        help="Output directory path",
-    )
-    parser.add_argument(
-        "--db-url",
-        type=str,
-        default=os.getenv("DB_URL", "mongodb://localhost:27017/"),
-        help="Mongo DB URL",
-    )
-    parser.add_argument(
-        "--db-name", type=str, default="celi", help="Mongo database name"
-    )
-    bool_opt(
-        "--external-db", "EXTERNAL_DB", "Set to True if using an existing mongo server."
-    )
-    parser.add_argument(
-        "--parser-model-class",
-        type=str,
-        default=os.getenv("PARSER_MODEL_CLASS", "llm_core.parsers.LLaMACPPParser"),
-    )
-    parser.add_argument(
-        "--parser-model-name",
-        type=str,
-        default=os.getenv("PARSER_MODEL_NAME", "mixtral-8x7b-v0.1.Q5_K_M.gguf"),
-    )
-    bool_opt("--no-cache", "NO_CACHE", "Set to True to turn off LLM caching")
-    bool_opt(
-        "--no-monitor", "NO_MONITOR", "Set to True to turn off the monitoring thread"
-    )
-
+    parser = setup_standard_args()
     args = parser.parse_args()
 
     directories = Directories.create(args.output_dir)
@@ -119,7 +71,8 @@ def run_test(config: CELIConfig, example: str, target: str):
         config.tool_implementations = WikipediaToolImplementations(
             example, target, ignore_updates=True
         )
-        draft_doc_path = run_celi(config)
+        run_celi(config)
+        draft_doc_path = config.tool_implementations.draft_doc
         draft_doc_sections = read_json_from_file(draft_doc_path)
         draft_doc = "\n".join(f"{k}\n{v}" for k, v in draft_doc_sections.items())
         result = evaluate(draft_doc, target)
@@ -153,12 +106,12 @@ def evaluate(generated_doc: str, target_url: str):
     )
     ground_truth_doc = format_content(target_dict)
 
-    results = bertscore.compute(
+    eval_results = bertscore.compute(
         predictions=[generated_doc],
         references=[ground_truth_doc],
         model_type="distilbert-base-uncased",
     )
-    return results
+    return eval_results
 
 
 @lru_cache()
@@ -167,13 +120,15 @@ def load_eval_model():
 
 
 if __name__ == "__main__":
-    config = get_config()
+    setup_logging()
+
+    celi_config = get_config()
 
     test_sets = read_json_from_file(Path(__file__).parent / "test_sets.json")
-    result = [
+    results = [
         _
         for test_set_name, test_set in test_sets.items()
-        for _ in run_test_set(config, test_set_name, test_set)
+        for _ in run_test_set(celi_config, test_set_name, test_set)
     ]
-    ret = pd.DataFrame.from_records(result)
+    ret = pd.DataFrame.from_records(results)
     print(ret)
diff --git a/celi_framework/examples/wikipedia/job_description.py b/celi_framework/examples/wikipedia/job_description.py
index 2302879..4f58b66 100644
--- a/celi_framework/examples/wikipedia/job_description.py
+++ b/celi_framework/examples/wikipedia/job_description.py
@@ -49,6 +49,8 @@
             "description": """Determine if any additional information is required to draft the section and call the ask_question_about_target function to gather that information.
             Feel free to ask as many questions as you need and keep working on this task until you have the information you need.  This is especially important if
             the initial references turn out not to be useful.
+            If you ask questions, make sure they are specific and include the names of what you are asking about.  For 
+            example, ask "What are major events in Henry Thoreau's life" instead of "What are major events in the author's life?"
             If you don't have any questions, just move on to the next section.""",
             "example_call": '{"prompt": "What is unusual about when this band formed?"}',
         },
@@ -63,7 +65,11 @@
     Task(
         task_name="Draft New Document Section",
         details={
-            "description": "Draft a new section analogous to the revised example section, ensuring alignment with its structure, format, and scope (from {{TaskRef:Understand Differentiation}} output).  Use the section structure you defined in {{TaskRef:Define subsections for this section}}. However, the details should be related to the target and not the example document.",
+            "description": "Draft a new section analogous to the revised example section, ensuring alignment with its "
+                           "structure, format, and scope (from {{TaskRef:Understand Differentiation}} output).  Use "
+                           "the section structure you defined in {{TaskRef:Define subsections for this section}}. "
+                           "However, the details should be related to the target and not the example document."
+                           "Call the save_draft_section tool to save the draft.",
             "guidelines": [
                 "Clearly identify the section number and section heading/title at the top of the content.",
                 "The new section should have its unique scope and purpose, distinct from the example section.",
@@ -79,55 +85,16 @@
             ],
         },
     ),
-    # Task(
-    #     task_name="Final Section Review",
-    #     details={
-    #         "description": "Review the final text for this section and provide a PASS/FAIL decision based on the success criteria.",
-    #         "prerequisite_tasks": ["Draft New Document Section"],
-    #         "instructions": [
-    #             "Evaluate the success of the new section draft",
-    #             "Document the review outcome",
-    #             "Print the final review output.",
-    #         ],
-    #         "success_flag_criteria": {
-    #             "FAIL": [
-    #                 "The section includes information beyond what's relevant as indicated by {{TaskRef:Understand Differentiation}}.",
-    #                 "Any mention of specific drugs or combination therapies not relevant to the new study context.",
-    #                 "Information in the new section is redundant with the sections analyzed in {{TaskRef:Understand Differentiation}}.",
-    #                 "The draft does not reflect the structure, format, or detail level of the revised example section (output of {{TaskRef:Handle Treatment Details}}).",
-    #                 "Missing or incorrectly referenced tables or figures from the new reference materials.",
-    #                 "Significant deviation from the new reference materials, suggesting misalignment with the study's current focus.",
-    #                 "Inconsistent use of verb tenses where required.",
-    #             ],
-    #             "PASS": [
-    #                 "The section appropriately includes information within the scope defined by {{TaskRef:Understand Differentiation}}.",
-    #                 "The draft logically follows what the section heading describes.",
-    #                 "The draft focuses exclusively on the new study context.",
-    #                 "The draft meets criteria for completeness, accuracy, and is aligned with the revised example and the new reference materials.",
-    #             ],
-    #         },
-    #         "additional_instructions": "Offer feedback on the process of differentiating from other sections, the use of source materials, and the rationale for selected source mappings.",
-    #         "output_format": {
-    #             "Section Review": "[SECTION NUMBER] - [SECTION HEADING]",
-    #             "Draft Review": "[content here]",
-    #             "Comments": "[comments here]",
-    #             "Success Flag": "[FAIL/PASS]",
-    #             "Source Mapping Review": "[REVIEW OF UTILIZED SOURCES]",
-    #             "Tables": "[LIST OF REFERENCED TABLES]",
-    #             "Figures": "[LIST OF REFERENCED FIGURES]",
-    #             "Cross-References": "[REFERENCES TO OTHER DOCUMENT SECTIONS (FROM EXAMPLE DOCUMENT)]",
-    #             "Scope of Section Review": "[REFLECTION ON FOLLOWING THE SCOPE GUIDELINES SET OUT IN {{TaskRef:Understand Differentiation}} OUTPUT]",
-    #         },
-    #     },
-    # ),
     Task(
         task_name="Prepare for Next Document Section",
         details={
-            "description": "SIgnal that you have completed the draft by calling the pop_context function and prepare to start drafting the next section of the document.",
+            "description": "Signal that you have completed the draft by calling the pop_context function and prepare "
+                           "to start drafting the next section of the document.",
             "function_call": "Use the pop_context function with the argument value = current section identifier.",
             "example_call": "{{'current_section_identifier': ['1.2']}}",
             "instructions": [
-                "Announce completion: 'Proceed to the next section of the document, [current section identifier] has been completed.'"
+                "Announce completion: 'Proceed to the next section of the document, [current section identifier] has "
+                "been completed.'"
             ],
         },
     ),
diff --git a/celi_framework/examples/wikipedia/loader.py b/celi_framework/examples/wikipedia/loader.py
index 5a9735b..1c8d580 100644
--- a/celi_framework/examples/wikipedia/loader.py
+++ b/celi_framework/examples/wikipedia/loader.py
@@ -9,6 +9,7 @@
     "ContentHierarchyNode",
     "ContentReference",
     "load_content_from_wikipedia_url",
+    "format_content",
 ]
 
 
diff --git a/celi_framework/examples/wikipedia/tools.py b/celi_framework/examples/wikipedia/tools.py
index 3077984..0c57236 100644
--- a/celi_framework/examples/wikipedia/tools.py
+++ b/celi_framework/examples/wikipedia/tools.py
@@ -1,24 +1,16 @@
-from dataclasses import asdict, dataclass
 import json
 import logging
-from platform import node
 import re
+from dataclasses import asdict, dataclass
 from typing import Any, Dict, List, Optional
-from llama_index.core import VectorStoreIndex
+
 from llama_index.core.base.response.schema import Response
 from llama_index.core.schema import MetadataMode, QueryBundle, NodeWithScore, TextNode
 from llama_index.vector_stores.chroma.base import ChromaVectorStore
-from numpy import isin
 
-from celi_framework.core.job_description import ToolImplementations
-from celi_framework.core.templates import (
-    make_draft_setting_output_prompt,
-    make_table_setting_output_prompt,
-)
+from celi_framework.core.job_description import BaseDocToolImplementations
 from celi_framework.examples.wikipedia.index import get_wikipedia_index
 from celi_framework.utils.codex import MongoDBUtilitySingleton
-from celi_framework.utils.llms import quick_ask
-from celi_framework.utils.token_counters import get_master_counter_instance
 from celi_framework.utils.utils import format_toc, get_section_context_as_text
 
 logger = logging.getLogger(__name__)
@@ -37,11 +29,19 @@ def get_content(self):
 
 
 @dataclass
-class WikipediaToolImplementations(ToolImplementations):
+class WikipediaToolImplementations(BaseDocToolImplementations):
+    """If ignore_updates is set, then a cached version of that URL will be used, even if it is out of date.
+    Otherwise, a cache will only be used if it hasn't expired and the content hasn't changed."""
     example_url: str
     target_url: str
     ignore_updates: bool = False
-    """If ignore_updates is set, then a cached version of that URL will be used, even if it is out of date.  Otherwise, a cache will only be used if it hasn't expired and the content hasn't changed."""
+
+    def __init__(self, example_url: str, target_url: str, ignore_updates: bool, drafts_dir: str = "target/celi_output/drafts"):
+        super().__init__(drafts_dir=drafts_dir)
+        self.example_url = example_url
+        self.target_url = target_url
+        self.ignore_updates = ignore_updates
+        self.__post_init__()
 
     def __post_init__(self):
         self.example_page = self._page_title(self.example_url)
diff --git a/celi_framework/logging_config.json b/celi_framework/logging_config.json
index 7ee4dc5..2384bb9 100644
--- a/celi_framework/logging_config.json
+++ b/celi_framework/logging_config.json
@@ -1,5 +1,6 @@
 {
     "version": 1,
+    "disable_existing_loggers": false,
     "formatters": {
         "simple": {
             "format": "%(asctime)s %(name)s %(levelname)s - %(message)s"
diff --git a/celi_framework/main.py b/celi_framework/main.py
index b961dc2..af9adbf 100644
--- a/celi_framework/main.py
+++ b/celi_framework/main.py
@@ -1,5 +1,5 @@
 """
-This Python script establishes a multi-threaded environment for document processing and monitoring within a given system. It is designed to efficiently manage and monitor the processing of documents by utilizing a combination of custom classes, threading, and resource management techniques. The script integrates several components, including:
+This Python script establishes a multithreaded environment for document processing and monitoring within a given system. It is designed to efficiently manage and monitor the processing of documents by utilizing a combination of custom classes, threading, and resource management techniques. The script integrates several components, including:
 
 - `ProcessRunner`: A class responsible for managing the processing pipeline of documents. It uses MongoDB for storage and retrieval of documents and employs a queue for managing updates.
 - `MonitoringAgent`: A class tasked with monitoring the progress and status of document processing, ensuring that system performance is maintained and any issues are promptly addressed.
@@ -14,16 +14,8 @@
 The script is structured to initiate and manage parallel threads dedicated to document processing and system monitoring, demonstrating an effective pattern for building scalable and responsive Python applications. By separating concerns into distinct threads and utilizing a shared queue for communication, it achieves a high degree of concurrency and efficiency in processing tasks.
 """
 
-if __name__ == "__main__":
-    # This has to be run before any loggers are created.
-    from celi_framework.logging_setup import setup_logging
-
-    setup_logging()
-
 import argparse
-from dataclasses import asdict
 import inspect
-import logging
 import logging.config
 import os
 from typing import Type
@@ -31,8 +23,7 @@
 from dotenv import load_dotenv
 
 from celi_framework.core.runner import CELIConfig, Directories, MongoDBConfig, run_celi
-from celi_framework.utils.codex import MongoDBUtilitySingleton
-from celi_framework.utils.llmcore_utils import new_parser_factory
+from celi_framework.logging_setup import setup_logging
 from celi_framework.utils.utils import get_obj_by_name, read_json_from_file, str2bool
 
 logger = logging.getLogger(__name__)
@@ -40,8 +31,54 @@
 
 def get_config():
     load_dotenv("./.env")
-    logger.info(f"Tool config env. var is {os.getenv('TOOL_CONFIG_JSON', '<not set>')}")
 
+    parser = setup_standard_args()
+
+    args = parser.parse_args()
+
+    directories = Directories.create(args.output_dir)
+    mongo_config = instantiate_with_argparse_args(args, MongoDBConfig)
+
+    job_description = get_obj_by_name(args.job_description)
+
+    # If the tool_config_json file doesn't exist, try to find it relative to the root of the installed package.
+    # This allows examples packaged with celi to work correctly.
+    if args.tool_config_json:
+        if os.path.exists(args.tool_config_json):
+            tool_config_json = args.tool_config_json
+        else:
+            # Find the root of the installed package
+            root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+            tool_config_json = os.path.join(root_dir, args.tool_config_json)
+            if not os.path.exists(tool_config_json):
+                raise FileNotFoundError(
+                    f"Could not find {args.tool_config_json} or {tool_config_json}"
+                )
+        tool_config = read_json_from_file(tool_config_json)
+    else:
+        tool_config = {}
+
+    tool_implementations = job_description.tool_implementations_class(**tool_config)
+
+    llm_cache = not args.no_cache
+    use_monitor = not args.no_monitor
+
+    # Instantiate the class, passing parser_model as a parameter
+    parser_cls = get_obj_by_name(args.parser_model_class)
+
+    return CELIConfig(  # noqa: F821
+        mongo_config=mongo_config,
+        directories=directories,
+        job_description=job_description,
+        tool_implementations=tool_implementations,
+        parser_cls=parser_cls,
+        parser_model_name=args.parser_model_name,
+        llm_cache=llm_cache,
+        use_monitor=use_monitor,
+    )
+
+
+def setup_standard_args():
     parser = argparse.ArgumentParser(description="Run the document generator.")
 
     def bool_opt(opt: str, env_var: str, help: str):
@@ -58,7 +95,6 @@ def bool_opt(opt: str, env_var: str, help: str):
         default=os.getenv("OUTPUT_DIR"),
         help="Output directory path",
     )
-
     parser.add_argument(
         "--db-url",
         type=str,
@@ -104,49 +140,7 @@ def bool_opt(opt: str, env_var: str, help: str):
         "NO_MONITOR",
         "Set to True to turn off the monitoring thread",
     )
-
-    args = parser.parse_args()
-
-    directories = Directories.create(args.output_dir)
-    mongo_config = instantiate_with_argparse_args(args, MongoDBConfig)
-
-    job_description = get_obj_by_name(args.job_description)
-
-    # If the tool_config_json file doesn't exist, try to find it relative to the root of the installed package.
-    # This allows examples packaged with celi to work correctly.
-    if args.tool_config_json:
-        if os.path.exists(args.tool_config_json):
-            tool_config_json = args.tool_config_json
-        else:
-            # Find the root of the installed package
-            root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-            tool_config_json = os.path.join(root_dir, args.tool_config_json)
-            if not os.path.exists(tool_config_json):
-                raise FileNotFoundError(
-                    f"Could not find {args.tool_config_json} or {tool_config_json}"
-                )
-        tool_config = read_json_from_file(tool_config_json)
-    else:
-        tool_config = {}
-
-    tool_implementations = job_description.tool_implementations_class(**tool_config)
-
-    llm_cache = not args.no_cache
-    use_monitor = not args.no_monitor
-
-    # Instantiate the class, passing parser_model as a parameter
-    parser_cls = get_obj_by_name(args.parser_model_class)
-
-    return CELIConfig(  # noqa: F821
-        mongo_config=mongo_config,
-        directories=directories,
-        job_description=job_description,
-        tool_implementations=tool_implementations,
-        parser_cls=parser_cls,
-        parser_model_name=args.parser_model_name,
-        llm_cache=llm_cache,
-        use_monitor=use_monitor,
-    )
+    return parser
 
 
 def instantiate_with_argparse_args(args: argparse.Namespace, cls: Type):
@@ -163,6 +157,9 @@ def instantiate_with_argparse_args(args: argparse.Namespace, cls: Type):
 
 
 if __name__ == "__main__":
+    setup_logging()
     logger.debug("Starting CELI")
     config = get_config()
     run_celi(config)
+
+
diff --git a/poetry.lock b/poetry.lock
index 04ae4d2..5827466 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,87 +2,87 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.9.3"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"},
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"},
-    {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:82e6aa28dd46374f72093eda8bcd142f7771ee1eb9d1e223ff0fa7177a96b4a5"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f56455b0c2c7cc3b0c584815264461d07b177f903a04481dfc33e08a89f0c26b"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bca77a198bb6e69795ef2f09a5f4c12758487f83f33d63acde5f0d4919815768"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e083c285857b78ee21a96ba1eb1b5339733c3563f72980728ca2b08b53826ca5"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab40e6251c3873d86ea9b30a1ac6d7478c09277b32e14745d0d3c6e76e3c7e29"},
-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:df822ee7feaaeffb99c1a9e5e608800bd8eda6e5f18f5cfb0dc7eeb2eaa6bbec"},
-    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:acef0899fea7492145d2bbaaaec7b345c87753168589cc7faf0afec9afe9b747"},
-    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:cd73265a9e5ea618014802ab01babf1940cecb90c9762d8b9e7d2cc1e1969ec6"},
-    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:a78ed8a53a1221393d9637c01870248a6f4ea5b214a59a92a36f18151739452c"},
-    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:6b0e029353361f1746bac2e4cc19b32f972ec03f0f943b390c4ab3371840aabf"},
-    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7cf5c9458e1e90e3c390c2639f1017a0379a99a94fdfad3a1fd966a2874bba52"},
-    {file = "aiohttp-3.9.3-cp310-cp310-win32.whl", hash = "sha256:3e59c23c52765951b69ec45ddbbc9403a8761ee6f57253250c6e1536cacc758b"},
-    {file = "aiohttp-3.9.3-cp310-cp310-win_amd64.whl", hash = "sha256:055ce4f74b82551678291473f66dc9fb9048a50d8324278751926ff0ae7715e5"},
-    {file = "aiohttp-3.9.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6b88f9386ff1ad91ace19d2a1c0225896e28815ee09fc6a8932fded8cda97c3d"},
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 [[package]]
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+    {file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:31adb9cbb8737a581a843e13df22ffb7c84638342de3708a98d5c986770f2834"},
+    {file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:551b320396e1d05e49cc18dd77d970accd52b322441628aca04801bbd1d52a73"},
+    {file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:6717543d2c110a155e6821ce5670c1f512f602eabb77dba95717ca76af79867d"},
+    {file = "bcrypt-4.1.3-cp39-abi3-win32.whl", hash = "sha256:6004f5229b50f8493c49232b8e75726b568535fd300e5039e255d919fc3a07f2"},
+    {file = "bcrypt-4.1.3-cp39-abi3-win_amd64.whl", hash = "sha256:2505b54afb074627111b5a8dc9b6ae69d0f01fea65c2fcaea403448c503d3991"},
+    {file = "bcrypt-4.1.3-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:cb9c707c10bddaf9e5ba7cdb769f3e889e60b7d4fea22834b261f51ca2b89fed"},
+    {file = "bcrypt-4.1.3-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:9f8ea645eb94fb6e7bea0cf4ba121c07a3a182ac52876493870033141aa687bc"},
+    {file = "bcrypt-4.1.3-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:f44a97780677e7ac0ca393bd7982b19dbbd8d7228c1afe10b128fd9550eef5f1"},
+    {file = "bcrypt-4.1.3-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d84702adb8f2798d813b17d8187d27076cca3cd52fe3686bb07a9083930ce650"},
+    {file = "bcrypt-4.1.3.tar.gz", hash = "sha256:2ee15dd749f5952fe3f0430d0ff6b74082e159c50332a1413d51b5689cf06623"},
 ]
 
 [package.extras]
@@ -611,13 +611,13 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.4.24"
+version = "0.5.0"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.4.24-py3-none-any.whl", hash = "sha256:3a08e237a4ad28b5d176685bd22429a03717fe09d35022fb230d516108da01da"},
-    {file = "chromadb-0.4.24.tar.gz", hash = "sha256:a5c80b4e4ad9b236ed2d4899a5b9e8002b489293f2881cb2cadab5b199ee1c72"},
+    {file = "chromadb-0.5.0-py3-none-any.whl", hash = "sha256:8193dc65c143b61d8faf87f02c44ecfa778d471febd70de517f51c5d88a06009"},
+    {file = "chromadb-0.5.0.tar.gz", hash = "sha256:7954af614a9ff7b2902ddbd0a162f33f7ec0669e2429903905c4f7876d1f766f"},
 ]
 
 [package.dependencies]
@@ -638,7 +638,6 @@ opentelemetry-sdk = ">=1.2.0"
 orjson = ">=3.9.12"
 overrides = ">=7.3.1"
 posthog = ">=2.4.0"
-pulsar-client = ">=3.1.0"
 pydantic = ">=1.9"
 pypika = ">=0.48.9"
 PyYAML = ">=6.0.0"
@@ -789,13 +788,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.4"
+version = "0.6.5"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
-python-versions = ">=3.7,<4.0"
+python-versions = "<4.0,>=3.7"
 files = [
-    {file = "dataclasses_json-0.6.4-py3-none-any.whl", hash = "sha256:f90578b8a3177f7552f4e1a6e535e84293cd5da421fcce0642d49c0d7bdf8df2"},
-    {file = "dataclasses_json-0.6.4.tar.gz", hash = "sha256:73696ebf24936560cca79a2430cbc4f3dd23ac7bf46ed17f38e5e5e7657a6377"},
+    {file = "dataclasses_json-0.6.5-py3-none-any.whl", hash = "sha256:f49c77aa3a85cac5bf5b7f65f4790ca0d2be8ef4d92c75e91ba0103072788a39"},
+    {file = "dataclasses_json-0.6.5.tar.gz", hash = "sha256:1c287594d9fcea72dc42d6d3836cf14848c2dc5ce88f65ed61b36b57f515fe26"},
 ]
 
 [package.dependencies]
@@ -804,21 +803,21 @@ typing-inspect = ">=0.4.0,<1"
 
 [[package]]
 name = "datasets"
-version = "2.18.0"
+version = "2.19.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.18.0-py3-none-any.whl", hash = "sha256:f1bbf0e2896917a914de01cbd37075b14deea3837af87ad0d9f697388ccaeb50"},
-    {file = "datasets-2.18.0.tar.gz", hash = "sha256:cdf8b8c6abf7316377ba4f49f9589a4c74556d6b481afd0abd2284f3d69185cb"},
+    {file = "datasets-2.19.0-py3-none-any.whl", hash = "sha256:f57c5316e123d4721b970c68c1cb856505f289cda58f5557ffe745b49c011a8e"},
+    {file = "datasets-2.19.0.tar.gz", hash = "sha256:0b47e08cc7af2c6800a42cadc4657b22a0afc7197786c8986d703c08d90886a6"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.9"
 filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
-huggingface-hub = ">=0.19.4"
+fsspec = {version = ">=2023.1.0,<=2024.3.1", extras = ["http"]}
+huggingface-hub = ">=0.21.2"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
@@ -834,15 +833,15 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
 quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+tensorflow = ["tensorflow (>=2.6.0)"]
+tensorflow-gpu = ["tensorflow (>=2.6.0)"]
+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
@@ -984,15 +983,30 @@ files = [
     {file = "docutils-0.20.1.tar.gz", hash = "sha256:f08a4e276c3a1583a86dce3e34aba3fe04d02bba2dd51ed16106244e8a923e3b"},
 ]
 
+[[package]]
+name = "email-validator"
+version = "2.1.1"
+description = "A robust email address syntax and deliverability validation library."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "email_validator-2.1.1-py3-none-any.whl", hash = "sha256:97d882d174e2a65732fb43bfce81a3a834cbc1bde8bf419e30ef5ea976370a05"},
+    {file = "email_validator-2.1.1.tar.gz", hash = "sha256:200a70680ba08904be6d1eef729205cc0d687634399a5924d842533efb824b84"},
+]
+
+[package.dependencies]
+dnspython = ">=2.0.0"
+idna = ">=2.0.0"
+
 [[package]]
 name = "evaluate"
-version = "0.4.1"
+version = "0.4.2"
 description = "HuggingFace community-driven open-source library of evaluation"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "evaluate-0.4.1-py3-none-any.whl", hash = "sha256:3ff079ab09572c0a2c1e6d749887c19f6783ab993320412cd39f6fe501d28510"},
-    {file = "evaluate-0.4.1.tar.gz", hash = "sha256:d721d9f2059ced79770d8a0509e954fbd1bbac96a8f9160e29888d8073cda3d9"},
+    {file = "evaluate-0.4.2-py3-none-any.whl", hash = "sha256:5fdcaf8a086b075c2b8e2c5898f501224b020b0ac7d07be76536e47e661c0c65"},
+    {file = "evaluate-0.4.2.tar.gz", hash = "sha256:851ab767df8ec4031366c512eb88d8174adfba65d2c8c4c9bfdfe9c702212234"},
 ]
 
 [package.dependencies]
@@ -1005,30 +1019,29 @@ numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
 requests = ">=2.19.0"
-responses = "<0.19"
 tqdm = ">=4.62.1"
 xxhash = "*"
 
 [package.extras]
-dev = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+dev = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy (>=1.10.0)", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
 docs = ["s3fs"]
 evaluator = ["scipy (>=1.7.1)", "transformers"]
 quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
 template = ["cookiecutter", "gradio (>=3.0.0)"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+tests = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy (>=1.10.0)", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
 torch = ["torch"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
-    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -1050,32 +1063,56 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.110.1"
+version = "0.111.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.110.1-py3-none-any.whl", hash = "sha256:5df913203c482f820d31f48e635e022f8cbfe7350e4830ef05a3163925b1addc"},
-    {file = "fastapi-0.110.1.tar.gz", hash = "sha256:6feac43ec359dfe4f45b2c18ec8c94edb8dc2dfc461d417d9e626590c071baad"},
+    {file = "fastapi-0.111.0-py3-none-any.whl", hash = "sha256:97ecbf994be0bcbdadedf88c3150252bed7b2087075ac99735403b1b76cc8fc0"},
+    {file = "fastapi-0.111.0.tar.gz", hash = "sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7"},
 ]
 
 [package.dependencies]
+email_validator = ">=2.0.0"
+fastapi-cli = ">=0.0.2"
+httpx = ">=0.23.0"
+jinja2 = ">=2.11.2"
+orjson = ">=3.2.1"
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
+python-multipart = ">=0.0.7"
 starlette = ">=0.37.2,<0.38.0"
 typing-extensions = ">=4.8.0"
+ujson = ">=4.0.1,<4.0.2 || >4.0.2,<4.1.0 || >4.1.0,<4.2.0 || >4.2.0,<4.3.0 || >4.3.0,<5.0.0 || >5.0.0,<5.1.0 || >5.1.0"
+uvicorn = {version = ">=0.12.0", extras = ["standard"]}
 
 [package.extras]
-all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+all = ["email_validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+
+[[package]]
+name = "fastapi-cli"
+version = "0.0.2"
+description = "Run and manage FastAPI apps from the command line with FastAPI CLI. 🚀"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fastapi_cli-0.0.2-py3-none-any.whl", hash = "sha256:d7a8ec89fd52ad16c52de9fe7299e4b22c7a32e1bf28aa886e5517e4927423dd"},
+    {file = "fastapi_cli-0.0.2.tar.gz", hash = "sha256:589565ba758432632eadcf7b950e0ec76bb283b549784d9df17f261a8a9de476"},
+]
+
+[package.dependencies]
+fastapi = "*"
+typer = ">=0.12.3"
+uvicorn = {version = ">=0.29.0", extras = ["standard"]}
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
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+    {file = "grpcio-1.63.0-cp38-cp38-win_amd64.whl", hash = "sha256:6a1a3642d76f887aa4009d92f71eb37809abceb3b7b5a1eec9c554a246f20e3a"},
+    {file = "grpcio-1.63.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:75f701ff645858a2b16bc8c9fc68af215a8bb2d5a9b647448129de6e85d52bce"},
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+    {file = "grpcio-1.63.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:259e11932230d70ef24a21b9fb5bb947eb4703f57865a404054400ee92f42f5d"},
+    {file = "grpcio-1.63.0-cp39-cp39-win32.whl", hash = "sha256:a44624aad77bf8ca198c55af811fd28f2b3eaf0a50ec5b57b06c034416ef2d0a"},
+    {file = "grpcio-1.63.0-cp39-cp39-win_amd64.whl", hash = "sha256:166e5c460e5d7d4656ff9e63b13e1f6029b122104c1633d5f37eaea348d7356d"},
+    {file = "grpcio-1.63.0.tar.gz", hash = "sha256:f3023e14805c61bc439fb40ca545ac3d5740ce66120a678a3c6c2c55b70343d1"},
 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.62.1)"]
+protobuf = ["grpcio-tools (>=1.63.0)"]
 
 [[package]]
 name = "h11"
@@ -1583,13 +1612,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.22.2"
+version = "0.23.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.22.2-py3-none-any.whl", hash = "sha256:3429e25f38ccb834d310804a3b711e7e4953db5a9e420cc147a5e194ca90fd17"},
-    {file = "huggingface_hub-0.22.2.tar.gz", hash = "sha256:32e9a9a6843c92f253ff9ca16b9985def4d80a93fb357af5353f770ef74a81be"},
+    {file = "huggingface_hub-0.23.0-py3-none-any.whl", hash = "sha256:075c30d48ee7db2bba779190dc526d2c11d422aed6f9044c5e2fdc2c432fdb91"},
+    {file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
 ]
 
 [package.dependencies]
@@ -1602,16 +1631,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "minijinja (>=1.0)"]
 quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -1631,13 +1660,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
@@ -1696,6 +1725,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
+]
+
 [[package]]
 name = "ipykernel"
 version = "6.29.4"
@@ -1731,13 +1774,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.23.0"
+version = "8.24.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.23.0-py3-none-any.whl", hash = "sha256:07232af52a5ba146dc3372c7bf52a0f890a23edf38d77caef8d53f9cdc2584c1"},
-    {file = "ipython-8.23.0.tar.gz", hash = "sha256:7468edaf4f6de3e1b912e57f66c241e6fd3c7099f2ec2136e239e142e800274d"},
+    {file = "ipython-8.24.0-py3-none-any.whl", hash = "sha256:d7bf2f6c4314984e3e02393213bab8703cf163ede39672ce5918c51fe253a2a3"},
+    {file = "ipython-8.24.0.tar.gz", hash = "sha256:010db3f8a728a578bb641fdd06c063b9fb8e96a9464c63aec6310fbcb5e80501"},
 ]
 
 [package.dependencies]
@@ -1751,7 +1794,7 @@ prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5.13.0"
-typing-extensions = {version = "*", markers = "python_version < \"3.12\""}
+typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
 all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
@@ -1764,7 +1807,7 @@ nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath"]
+test = ["pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
@@ -1805,24 +1848,24 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.4.0"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.4.0-py3-none-any.whl", hash = "sha256:42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7"},
-    {file = "joblib-1.4.0.tar.gz", hash = "sha256:1eb0dc091919cd384490de890cb5dfd538410a6d4b3b54eef09fb8c50b409b1c"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.21.1"
+version = "4.22.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
-    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
+    {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"},
+    {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"},
 ]
 
 [package.dependencies]
@@ -2047,12 +2090,12 @@ tornado = {version = "*", markers = "python_version > \"2.7\""}
 
 [[package]]
 name = "llama-cpp-python"
-version = "0.2.61"
+version = "0.2.69"
 description = "Python bindings for the llama.cpp library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "llama_cpp_python-0.2.61.tar.gz", hash = "sha256:2d554259a66040f5daae7f3cf7e43b44971dc49f10225a9ba196eb2a49778bd4"},
+    {file = "llama_cpp_python-0.2.69.tar.gz", hash = "sha256:b37e864b4d9f7ac286a3e926d87afab2f136ae9290e11088f7a205b80d3c04a9"},
 ]
 
 [package.dependencies]
@@ -2069,19 +2112,19 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
 
 [[package]]
 name = "llama-index"
-version = "0.10.28"
+version = "0.10.34"
 description = "Interface between LLMs and your data"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index-0.10.28-py3-none-any.whl", hash = "sha256:2fe2857af6c2d9cefdd978931ebd09f78265a57df2d9d094d0c78869dd0715b8"},
-    {file = "llama_index-0.10.28.tar.gz", hash = "sha256:2ecf9d6da489cc3f99b1adaea50b1549efa55e04cd050f332da784a87c2db846"},
+    {file = "llama_index-0.10.34-py3-none-any.whl", hash = "sha256:da2fe7bf0cde25070ba144e2b8ca0fc0e0d42bff1b32521dc1a28ae4ab7c9c32"},
+    {file = "llama_index-0.10.34.tar.gz", hash = "sha256:29c882665fc3a78df8682224620919dc42a95e372fd3afc3c8504822a69892f9"},
 ]
 
 [package.dependencies]
 llama-index-agent-openai = ">=0.1.4,<0.3.0"
 llama-index-cli = ">=0.1.2,<0.2.0"
-llama-index-core = ">=0.10.28,<0.11.0"
+llama-index-core = ">=0.10.34,<0.11.0"
 llama-index-embeddings-openai = ">=0.1.5,<0.2.0"
 llama-index-indices-managed-llama-cloud = ">=0.1.2,<0.2.0"
 llama-index-legacy = ">=0.9.48,<0.10.0"
@@ -2094,29 +2137,29 @@ llama-index-readers-llama-parse = ">=0.1.2,<0.2.0"
 
 [[package]]
 name = "llama-index-agent-openai"
-version = "0.2.2"
+version = "0.2.3"
 description = "llama-index agent openai integration"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_agent_openai-0.2.2-py3-none-any.whl", hash = "sha256:fa8cbc2c7be5a465848f8d5b432db01c55f07dfa06357edb7fb77fb17d534d1e"},
-    {file = "llama_index_agent_openai-0.2.2.tar.gz", hash = "sha256:12063dd932c74015796f973986cc52d783f51fda38e4ead72a56d0fd195925ee"},
+    {file = "llama_index_agent_openai-0.2.3-py3-none-any.whl", hash = "sha256:3782b24dd611364e391672dadc8308efd58d731a097c34a40e29f28c3abc5034"},
+    {file = "llama_index_agent_openai-0.2.3.tar.gz", hash = "sha256:c899d90b32036656a8ef86d0f0378d4168e00eb2d75a10901eab58ba5b2656a4"},
 ]
 
 [package.dependencies]
-llama-index-core = ">=0.10.1,<0.11.0"
+llama-index-core = ">=0.10.30,<0.11.0"
 llama-index-llms-openai = ">=0.1.5,<0.2.0"
 openai = ">=1.14.0"
 
 [[package]]
 name = "llama-index-cli"
-version = "0.1.11"
+version = "0.1.12"
 description = "llama-index cli"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_cli-0.1.11-py3-none-any.whl", hash = "sha256:44bc32af6d9bc0b523ad284f24fa1ec17288491243fe6d7c7b4770b3245dbb84"},
-    {file = "llama_index_cli-0.1.11.tar.gz", hash = "sha256:5de707e125aa877d70c61da70cc44fea72a9f7adb77f413b51f73b1deffdd750"},
+    {file = "llama_index_cli-0.1.12-py3-none-any.whl", hash = "sha256:d80d546786f02d3f16f6183b8e86b22b8b5c33a1500923659f2ccbff8d5df634"},
+    {file = "llama_index_cli-0.1.12.tar.gz", hash = "sha256:3cf1f706c3c69c6b1aab07fca7faad3959db1709808efd50491b669d38b0b580"},
 ]
 
 [package.dependencies]
@@ -2126,13 +2169,13 @@ llama-index-llms-openai = ">=0.1.1,<0.2.0"
 
 [[package]]
 name = "llama-index-core"
-version = "0.10.28"
+version = "0.10.34"
 description = "Interface between LLMs and your data"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_core-0.10.28-py3-none-any.whl", hash = "sha256:73e2ec86ea42a0efdc73dcfe46e03ba39ab7725699dcd791464c160d24a02cb1"},
-    {file = "llama_index_core-0.10.28.tar.gz", hash = "sha256:b454640396daa2678b638299e2a5e2539fa3548afd280d45d224fd3b303c20f4"},
+    {file = "llama_index_core-0.10.34-py3-none-any.whl", hash = "sha256:073eb6f2617686826065c68b0934a2cd127bf6f762220601c00d20712b48d586"},
+    {file = "llama_index_core-0.10.34.tar.gz", hash = "sha256:d41beeff56f7581d924f2a27c0020f291827b15a18bb7bd80f3ab38ccc95e4ca"},
 ]
 
 [package.dependencies]
@@ -2142,7 +2185,7 @@ deprecated = ">=1.2.9.3"
 dirtyjson = ">=1.0.8,<2.0.0"
 fsspec = ">=2023.5.0"
 httpx = "*"
-llamaindex-py-client = ">=0.1.16,<0.2.0"
+llamaindex-py-client = ">=0.1.18,<0.2.0"
 nest-asyncio = ">=1.5.8,<2.0.0"
 networkx = ">=3.0"
 nltk = ">=3.8.1,<4.0.0"
@@ -2170,13 +2213,13 @@ query-tools = ["guidance (>=0.0.64,<0.0.65)", "jsonpath-ng (>=1.6.0,<2.0.0)", "l
 
 [[package]]
 name = "llama-index-embeddings-openai"
-version = "0.1.7"
+version = "0.1.9"
 description = "llama-index embeddings openai integration"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_embeddings_openai-0.1.7-py3-none-any.whl", hash = "sha256:6023925ed1487b0688323d21711efbf8880e82ed3b87ef413255c3dc63a2f2fe"},
-    {file = "llama_index_embeddings_openai-0.1.7.tar.gz", hash = "sha256:c71cc9820680c4cedfc9845dc87b94f6851d1ccce1e486fc91298f8fa8d9f27d"},
+    {file = "llama_index_embeddings_openai-0.1.9-py3-none-any.whl", hash = "sha256:fbd16d6197b91f4dbdc6d0707e573cc224ac2b0a48d5b370c6232dd8a2282473"},
+    {file = "llama_index_embeddings_openai-0.1.9.tar.gz", hash = "sha256:0fd292b2f9a0ad4534a790d6374726bc885853188087eb018167dcf239643924"},
 ]
 
 [package.dependencies]
@@ -2184,18 +2227,18 @@ llama-index-core = ">=0.10.1,<0.11.0"
 
 [[package]]
 name = "llama-index-indices-managed-llama-cloud"
-version = "0.1.5"
+version = "0.1.6"
 description = "llama-index indices llama-cloud integration"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_indices_managed_llama_cloud-0.1.5-py3-none-any.whl", hash = "sha256:79f636cb6f4fabb12fec153564110f7f4dfda3cacb087793a5fec988484d7d2c"},
-    {file = "llama_index_indices_managed_llama_cloud-0.1.5.tar.gz", hash = "sha256:47cdde9f06bbddd508f0efcf41de425e85171ac2c8fda8a5fb2a89673e1c8c71"},
+    {file = "llama_index_indices_managed_llama_cloud-0.1.6-py3-none-any.whl", hash = "sha256:cba33e1a3677b2a2ae7f239119acbf6dc3818f105edc92315729842b56fbc949"},
+    {file = "llama_index_indices_managed_llama_cloud-0.1.6.tar.gz", hash = "sha256:74b3b0e9ebf9d348d3054f9fc0c657031acceb9351c31116ad8d5a7ae4729f5c"},
 ]
 
 [package.dependencies]
 llama-index-core = ">=0.10.0,<0.11.0"
-llamaindex-py-client = ">=0.1.13,<0.2.0"
+llamaindex-py-client = ">=0.1.19,<0.2.0"
 
 [[package]]
 name = "llama-index-legacy"
@@ -2238,13 +2281,13 @@ query-tools = ["guidance (>=0.0.64,<0.0.65)", "jsonpath-ng (>=1.6.0,<2.0.0)", "l
 
 [[package]]
 name = "llama-index-llms-openai"
-version = "0.1.15"
+version = "0.1.16"
 description = "llama-index llms openai integration"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_llms_openai-0.1.15-py3-none-any.whl", hash = "sha256:401ba9db1549e4287b73749dee57b11db1e0ffa814bb1464475aea1ff84442c7"},
-    {file = "llama_index_llms_openai-0.1.15.tar.gz", hash = "sha256:6bdbf307b1d43a9a7c2a52f72ba7db61cb96d904f99e7ea5d889dd7818f10814"},
+    {file = "llama_index_llms_openai-0.1.16-py3-none-any.whl", hash = "sha256:4a9c0fe969302731907c8fa31631812397637e114a44ebbad11fd6c59def9315"},
+    {file = "llama_index_llms_openai-0.1.16.tar.gz", hash = "sha256:313bbc17c241992430a6bf686a1b1edc4276c8256ad6b0550aa1bea1e0fed1a6"},
 ]
 
 [package.dependencies]
@@ -2267,13 +2310,13 @@ llama-index-llms-openai = ">=0.1.1,<0.2.0"
 
 [[package]]
 name = "llama-index-program-openai"
-version = "0.1.5"
+version = "0.1.6"
 description = "llama-index program openai integration"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {file = "llama_index_program_openai-0.1.5.tar.gz", hash = "sha256:c33aa2d2876ad0ff1f9a2a755d4e7d4917240847d0174e7b2d0b8474499bb700"},
+    {file = "llama_index_program_openai-0.1.6-py3-none-any.whl", hash = "sha256:4660b338503537c5edca1e0dab606af6ce372b4f1b597e2833c6b602447c5d8d"},
+    {file = "llama_index_program_openai-0.1.6.tar.gz", hash = "sha256:c6a4980c5ea826088b28b4dee3367edb20221e6d05eb0e05019049190131d772"},
 ]
 
 [package.dependencies]
@@ -2299,22 +2342,24 @@ llama-index-program-openai = ">=0.1.1,<0.2.0"
 
 [[package]]
 name = "llama-index-readers-file"
-version = "0.1.16"
+version = "0.1.20"
 description = "llama-index readers file integration"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_readers_file-0.1.16-py3-none-any.whl", hash = "sha256:1a3f3bc1f09c6894318a5392edc43b118fabc2f2820f446c51cad73676662f73"},
-    {file = "llama_index_readers_file-0.1.16.tar.gz", hash = "sha256:7f25b8fddaf1a91bac06c66727b913b48e26ef68714fcd5197051b3f710f4cfa"},
+    {file = "llama_index_readers_file-0.1.20-py3-none-any.whl", hash = "sha256:055999f86ad11130445bfe10c0886ddecb63e1b727678ab4ba58602282443289"},
+    {file = "llama_index_readers_file-0.1.20.tar.gz", hash = "sha256:1258c016bc8ee1efe37a3b081384ba47dbca13b92c205d3154ba235fc09cc2fb"},
 ]
 
 [package.dependencies]
 beautifulsoup4 = ">=4.12.3,<5.0.0"
 llama-index-core = ">=0.10.1,<0.11.0"
-pymupdf = ">=1.23.21,<2.0.0"
 pypdf = ">=4.0.1,<5.0.0"
 striprtf = ">=0.0.26,<0.0.27"
 
+[package.extras]
+pymupdf = ["pymupdf (>=1.23.21,<2.0.0)"]
+
 [[package]]
 name = "llama-index-readers-llama-parse"
 version = "0.1.4"
@@ -2332,42 +2377,42 @@ llama-parse = ">=0.4.0,<0.5.0"
 
 [[package]]
 name = "llama-index-vector-stores-chroma"
-version = "0.1.6"
+version = "0.1.8"
 description = "llama-index vector_stores chroma integration"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_index_vector_stores_chroma-0.1.6-py3-none-any.whl", hash = "sha256:506b1cb9a7a552ecb3afa70ddf479c1c683fcbfe7313654e3543e62e3ec07eae"},
-    {file = "llama_index_vector_stores_chroma-0.1.6.tar.gz", hash = "sha256:6dff3dc9d79c4039fa46cd528e5e4b6ded6473e5ef632424134491007da4ebdb"},
+    {file = "llama_index_vector_stores_chroma-0.1.8-py3-none-any.whl", hash = "sha256:77f5081a08bcede4fafb3c47b15b3bd5cacaef8d038750207d1858f73bc2e255"},
+    {file = "llama_index_vector_stores_chroma-0.1.8.tar.gz", hash = "sha256:9c574baf370faf456bcb67b9d5ea273a6fa1f2b4fd205a59c47b68112364b9e7"},
 ]
 
 [package.dependencies]
-chromadb = ">=0.4.22,<0.5.0"
+chromadb = ">=0.4.0,<0.6.0"
 llama-index-core = ">=0.10.1,<0.11.0"
 
 [[package]]
 name = "llama-parse"
-version = "0.4.0"
+version = "0.4.2"
 description = "Parse files into RAG-Optimized formats."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "llama_parse-0.4.0-py3-none-any.whl", hash = "sha256:52b450c9bb94746c5f86867f54fd2e74670d89b385dcb879eb93490a42fda628"},
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+    {file = "llama_parse-0.4.2-py3-none-any.whl", hash = "sha256:5ce0390141f216dcd88c1123fea7f2a4f561d177f791a97217a3db3509dec4ff"},
+    {file = "llama_parse-0.4.2.tar.gz", hash = "sha256:fa04c09730b102155f6505de9cf91998c86d334581f0f12597c5eb47ca5db859"},
 ]
 
 [package.dependencies]
-llama-index-core = ">=0.10.7"
+llama-index-core = ">=0.10.29"
 
 [[package]]
 name = "llamaindex-py-client"
-version = "0.1.16"
+version = "0.1.19"
 description = ""
 optional = false
-python-versions = "<4.0,>=3.8"
+python-versions = "<4,>=3.8"
 files = [
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-    {file = "llamaindex_py_client-0.1.16.tar.gz", hash = "sha256:e99bbc0855e6caaa75eba219cdb3cf6c943ae94fa15ccbb68a3a08d452fd6380"},
+    {file = "llamaindex_py_client-0.1.19-py3-none-any.whl", hash = "sha256:fd9416fd78b97209bf323bc3c7fab314499778563e7274f10853ad560563d10e"},
+    {file = "llamaindex_py_client-0.1.19.tar.gz", hash = "sha256:73f74792bb8c092bae6dc626627a09ac13a099fa8d10f8fcc83e17a2b332cca7"},
 ]
 
 [package.dependencies]
@@ -2640,13 +2685,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.21.1"
+version = "3.21.2"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "marshmallow-3.21.1.tar.gz", hash = "sha256:4e65e9e0d80fc9e609574b9983cf32579f305c718afb30d7233ab818571768c3"},
+    {file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"},
+    {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
 ]
 
 [package.dependencies]
@@ -2654,7 +2699,7 @@ packaging = ">=17.0"
 
 [package.extras]
 dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-version-warning (==1.1.2)"]
+docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -2707,13 +2752,13 @@ python-dateutil = ">=2.7"
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.6"
+version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
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+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
 ]
 
 [package.dependencies]
@@ -2749,6 +2794,24 @@ files = [
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 ]
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "mmh3"
 version = "4.1.0"
@@ -3239,12 +3302,13 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -3287,36 +3351,36 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "onnxruntime"
-version = "1.17.1"
+version = "1.17.3"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
@@ -3329,13 +3393,13 @@ sympy = "*"
 
 [[package]]
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-version = "1.16.2"
+version = "1.25.2"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
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 [package.dependencies]
@@ -3365,39 +3429,24 @@ files = [
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 importlib-metadata = ">=6.0,<=7.0"
 
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-
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-version = "1.24.0"
+version = "1.11.1"
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-opentelemetry-proto = "1.24.0"
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+opentelemetry-sdk = ">=1.11,<2.0"
 
 [package.extras]
 test = ["pytest-grpc"]
@@ -3462,17 +3511,17 @@ instruments = ["fastapi (>=0.58,<1.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.24.0"
+version = "1.11.1"
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-python-versions = ">=3.8"
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 [package.dependencies]
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+protobuf = ">=3.13.0"
 
 [[package]]
 name = "opentelemetry-sdk"
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+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -3784,28 +3828,29 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
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 python-versions = ">=3.8"
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 [[package]]
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 description = "plugin and hook calling mechanisms for python"
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 python-versions = ">=3.8"
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 [package.extras]
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 python-versions = ">=3.8"
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 [[package]]
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-certifi = "*"
-
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-
 [[package]]
 name = "pure-eval"
 version = "0.2.2"
@@ -3990,51 +3988,51 @@ tiktoken = "*"
 
 [[package]]
 name = "pyarrow"
-version = "15.0.2"
+version = "16.0.0"
 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 dnspython = ">=1.16.0,<3.0.0"
 
 [package.extras]
-aws = ["pymongo-auth-aws (<2.0.0)"]
-encryption = ["certifi", "pymongo[aws]", "pymongocrypt (>=1.6.0,<2.0.0)"]
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 gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
 ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
 snappy = ["python-snappy"]
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 name = "pymupdf"
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 description = "A high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -4416,18 +4391,15 @@ files = [
 
 [[package]]
 name = "pyproject-hooks"
-version = "1.0.0"
+version = "1.1.0"
 description = "Wrappers to call pyproject.toml-based build backend hooks."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
-[package.dependencies]
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-
 [[package]]
 name = "pyreadline3"
 version = "3.4.1"
@@ -4441,13 +4413,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "8.1.1"
+version = "8.2.0"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -4733,13 +4714,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "referencing"
-version = "0.34.0"
+version = "0.35.1"
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 python-versions = ">=3.8"
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 name = "regex"
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 description = "Alternative regular expression module, to replace re."
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 [[package]]
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 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
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-
 [[package]]
 name = "rich"
 version = "13.7.1"
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@@ -5204,7 +5143,7 @@ paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
 pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
 quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
 tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
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 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
@@ -5293,18 +5232,18 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
 [[package]]
 name = "setuptools"
-version = "69.2.0"
+version = "69.5.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
@@ -5364,20 +5303,20 @@ files = [
 
 [[package]]
 name = "sphinx"
-version = "7.2.6"
+version = "7.3.7"
 description = "Python documentation generator"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
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 babel = ">=2.9"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.18.1,<0.21"
+docutils = ">=0.18.1,<0.22"
 imagesize = ">=1.3"
 Jinja2 = ">=3.0"
 packaging = ">=21.0"
@@ -5390,11 +5329,12 @@ sphinxcontrib-htmlhelp = ">=2.0.0"
 sphinxcontrib-jsmath = "*"
 sphinxcontrib-qthelp = "*"
 sphinxcontrib-serializinghtml = ">=1.1.9"
+tomli = {version = ">=2", markers = "python_version < \"3.11\""}
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["docutils-stubs", "flake8 (>=3.5.0)", "flake8-simplify", "isort", "mypy (>=0.990)", "ruff", "sphinx-lint", "types-requests"]
-test = ["cython (>=3.0)", "filelock", "html5lib", "pytest (>=4.6)", "setuptools (>=67.0)"]
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+test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=6.0)", "setuptools (>=67.0)"]
 
 [[package]]
 name = "sphinx-autobuild"
@@ -5417,22 +5357,22 @@ test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "sphinx-autodoc-typehints"
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+version = "2.1.0"
 description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 optional = false
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 ]
 
 [package.dependencies]
-sphinx = ">=7.1.2"
+sphinx = ">=7.3.5"
 
 [package.extras]
-docs = ["furo (>=2023.9.10)"]
+docs = ["furo (>=2024.1.29)"]
 numpy = ["nptyping (>=2.5)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.8)"]
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 [[package]]
 name = "sphinx-basic-ng"
@@ -5579,60 +5519,60 @@ sphinx = ">=4.0"
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.29"
+version = "2.0.30"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
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+[[package]]
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+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
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+
+[package.dependencies]
+huggingface-hub = ">=0.16.4,<1.0"
 
 [package.extras]
 dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
 [[package]]
 name = "tomli"
@@ -5942,42 +5885,38 @@ files = [
 
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 name = "torch"
-version = "2.2.2"
+version = "2.3.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -5988,10 +5927,10 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
 nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -6020,13 +5959,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.2"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
-    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
 ]
 
 [package.dependencies]
@@ -6040,28 +5979,28 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.2"
+version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"},
-    {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"},
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "transformers"
-version = "4.39.3"
+version = "4.40.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.39.3-py3-none-any.whl", hash = "sha256:7838034a12cca3168247f9d2d1dba6724c9de3ae0f73a108258c6b8fc5912601"},
-    {file = "transformers-4.39.3.tar.gz", hash = "sha256:2586e5ff4150f122716fc40f5530e92871befc051848fbe82600969c535b762d"},
+    {file = "transformers-4.40.1-py3-none-any.whl", hash = "sha256:9d5ee0c8142a60501faf9e49a0b42f8e9cb8611823bce4f195a9325a6816337e"},
+    {file = "transformers-4.40.1.tar.gz", hash = "sha256:55e1697e6f18b58273e7117bb469cdffc11be28995462d8d5e422fef38d2de36"},
 ]
 
 [package.dependencies]
@@ -6073,21 +6012,21 @@ pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.14,<0.19"
+tokenizers = ">=0.19,<0.20"
 tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
 deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 docs-specific = ["hf-doc-builder"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -6108,32 +6047,32 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6198,6 +6137,80 @@ files = [
     {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
+[[package]]
+name = "ujson"
+version = "5.9.0"
+description = "Ultra fast JSON encoder and decoder for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "ujson-5.9.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b1c0991c4fe256f5fdb19758f7eac7f47caac29a6c57d0de16a19048eb86bad"},
+    {file = "ujson-5.9.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2a8ea0f55a1396708e564595aaa6696c0d8af532340f477162ff6927ecc46e21"},
+    {file = "ujson-5.9.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:07e0cfdde5fd91f54cd2d7ffb3482c8ff1bf558abf32a8b953a5d169575ae1cd"},
+    {file = "ujson-5.9.0.tar.gz", hash = "sha256:89cc92e73d5501b8a7f48575eeb14ad27156ad092c2e9fc7e3cf949f07e75532"},
+]
+
 [[package]]
 name = "url-normalize"
 version = "1.4.3"
@@ -6399,17 +6412,17 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.7.0"
+version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
-    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 


Test patch:
diff --git a/conftest.py b/conftest.py
index 377feee..543246f 100644
--- a/conftest.py
+++ b/conftest.py
@@ -1,8 +1,4 @@
-import atexit
-import json
-import logging.config
 import os
-from pathlib import Path
 
 from dotenv import load_dotenv
 
@@ -11,5 +7,4 @@
 load_dotenv()
 os.environ["PYTHONASYNCIODEBUG"] = "1"
 
-# Set up logging
 setup_logging()