Merge branch 'main' of github.com:crewAIInc/crewAI into knowledge

.github/workflows/tests.yml

@@ -26,7 +26,7 @@ jobs:
         run: uv python install 3.11.9
 
       - name: Install the project
-        run: uv sync --dev
+        run: uv sync --dev --all-extras
 
       - name: Run tests
-        run: uv run pytest tests
+        run: uv run pytest tests -vv

docs/concepts/knowledge.mdx

@@ -0,0 +1,75 @@
+---
+title: Knowledge
+description: What is knowledge in CrewAI and how to use it.
+icon: book
+---
+
+# Using Knowledge in CrewAI
+
+## Introduction
+
+The Knowledge class in CrewAI provides a powerful way to manage and query knowledge sources for your AI agents. This guide will show you how to implement knowledge management in your CrewAI projects.
+Additionally, we have specific tools for generate knowledge sources for strings, text files, PDF's, and Spreadsheets. You can expand on any source type by extending the `KnowledgeSource` class.
+
+## Basic Implementation
+
+Here's a simple example of how to use the Knowledge class:
+
+```python
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+# Create a knowledge source
+content = "Users name is John. He is 30 years old and lives in San Francisco."
+string_source = StringKnowledgeSource(
+    content=content, metadata={"preference": "personal"}
+)
+
+
+llm = LLM(model="gpt-4o-mini", temperature=0)
+  # Create an agent with the knowledge store
+agent = Agent(
+    role="About User",
+    goal="You know everything about the user.",
+    backstory="""You are a master at understanding people and their preferences.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=llm,
+)
+task = Task(
+    description="Answer the following questions about the user: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge={"sources": [string_source], "metadata": {"preference": "personal"}}, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
+```
+
+
+## Embedder Configuration
+
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
+
+```python
+...
+string_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"}
+)
+crew = Crew(
+    ...
+    knowledge={
+        "sources": [string_source],
+        "metadata": {"preference": "personal"},
+        "embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
+    },
+)
+```

pyproject.toml

@@ -39,6 +39,16 @@ Repository = "https://github.com/crewAIInc/crewAI"
 [project.optional-dependencies]
 tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
+fastembed = ["fastembed>=0.4.1"]
+pdfplumber = [
+    "pdfplumber>=0.11.4",
+]
+pandas = [
+    "pandas>=2.2.3",
+]
+openpyxl = [
+    "openpyxl>=3.1.5",
+]
 mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]

src/crewai/__init__.py

@@ -1,7 +1,9 @@
 import warnings
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.flow.flow import Flow
+from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
 from crewai.pipeline import Pipeline
 from crewai.process import Process
@@ -15,4 +17,14 @@ warnings.filterwarnings(
     module="pydantic.main",
 )
 __version__ = "0.80.0"
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
+__all__ = [
+    "Agent",
+    "Crew",
+    "Process",
+    "Task",
+    "Pipeline",
+    "Router",
+    "LLM",
+    "Flow",
+    "Knowledge",
+]

src/crewai/agent.py

@@ -11,8 +11,8 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.cli.constants import ENV_VARS
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools import BaseTool
+from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -52,6 +52,7 @@ class Agent(BaseAgent):
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
+            knowledge: The knowledge base of the agent.
             config: Dict representation of agent configuration.
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
@@ -272,6 +273,18 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
+        # Integrate the knowledge base
+        if self.crew and self.crew.knowledge:
+            knowledge_snippets = self.crew.knowledge.query([task.prompt()])
+            valid_snippets = [
+                result["context"] 
+                for result in knowledge_snippets 
+                if result and result.get("context")
+            ]
+            if valid_snippets:
+                formatted_knowledge = "\n".join(valid_snippets)
+                task_prompt += f"\n\nAdditional Information:\n{formatted_knowledge}"
+
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
 

src/crewai/cli/cli.py

@@ -136,6 +136,7 @@ def log_tasks_outputs() -> None:
 @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
 @click.option(
     "-k",
     "--kickoff-outputs",
@@ -143,17 +144,24 @@ def log_tasks_outputs() -> None:
     help="Reset LATEST KICKOFF TASK OUTPUTS",
 )
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
-def reset_memories(long, short, entities, kickoff_outputs, all):
+def reset_memories(
+    long: bool,
+    short: bool,
+    entities: bool,
+    knowledge: bool,
+    kickoff_outputs: bool,
+    all: bool,
+) -> None:
     """
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
     """
     try:
-        if not all and not (long or short or entities or kickoff_outputs):
+        if not all and not (long or short or entities or knowledge or kickoff_outputs):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 

src/crewai/cli/reset_memories_command.py

@@ -5,9 +5,17 @@ from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
-def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+def reset_memories_command(
+    long,
+    short,
+    entity,
+    knowledge,
+    kickoff_outputs,
+    all,
+) -> None:
     """
     Reset the crew memories.
 
@@ -17,6 +25,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
       entity (bool): Whether to reset the entity memory.
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
+      knowledge (bool): Whether to reset the knowledge.
     """
 
     try:
@@ -25,6 +34,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             EntityMemory().reset()
             LongTermMemory().reset()
             TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
         else:
             if long:
@@ -40,6 +50,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             if kickoff_outputs:
                 TaskOutputStorageHandler().reset()
                 click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

src/crewai/crew.py

@@ -27,6 +27,7 @@ from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.knowledge.knowledge import Knowledge
 from crewai.memory.user.user_memory import UserMemory
 from crewai.process import Process
 from crewai.task import Task
@@ -201,6 +202,10 @@ class Crew(BaseModel):
         default=[],
         description="List of execution logs for tasks",
     )
+    knowledge: Optional[Dict[str, Any]] = Field(
+        default=None, description="Knowledge for the crew. Add knowledge sources to the knowledge object."
+    )
+
 
     @field_validator("id", mode="before")
     @classmethod
@@ -275,6 +280,15 @@ class Crew(BaseModel):
                 self._user_memory = None
         return self
 
+    @model_validator(mode="after")
+    def create_crew_knowledge(self) -> "Crew":
+        if self.knowledge:
+            try:
+                self.knowledge = Knowledge(**self.knowledge) if isinstance(self.knowledge, dict) else self.knowledge
+            except (TypeError, ValueError) as e:
+                raise ValueError(f"Invalid knowledge configuration: {str(e)}")
+        return self
+
     @model_validator(mode="after")
     def check_manager_llm(self):
         """Validates that the language model is set when using hierarchical process."""

src/crewai/knowledge/embedder/base_embedder.py

@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+from typing import List
+
+import numpy as np
+
+
+class BaseEmbedder(ABC):
+    """
+    Abstract base class for text embedding models
+    """
+
+    @abstractmethod
+    def embed_chunks(self, chunks: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of text chunks
+
+        Args:
+            chunks: List of text chunks to embed
+
+        Returns:
+            Array of embeddings
+        """
+        pass
+
+    @abstractmethod
+    def embed_texts(self, texts: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of texts
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            Array of embeddings
+        """
+        pass
+
+    @abstractmethod
+    def embed_text(self, text: str) -> np.ndarray:
+        """
+        Generate embedding for a single text
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding array
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def dimension(self) -> int:
+        """Get the dimension of the embeddings"""
+        pass

src/crewai/knowledge/embedder/fastembed.py

@@ -0,0 +1,93 @@
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+
+from .base_embedder import BaseEmbedder
+
+try:
+    from fastembed_gpu import TextEmbedding  # type: ignore
+
+    FASTEMBED_AVAILABLE = True
+except ImportError:
+    try:
+        from fastembed import TextEmbedding
+
+        FASTEMBED_AVAILABLE = True
+    except ImportError:
+        FASTEMBED_AVAILABLE = False
+
+
+class FastEmbed(BaseEmbedder):
+    """
+    A wrapper class for text embedding models using FastEmbed
+    """
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        cache_dir: Optional[Union[str, Path]] = None,
+    ):
+        """
+        Initialize the embedding model
+
+        Args:
+            model_name: Name of the model to use
+            cache_dir: Directory to cache the model
+            gpu: Whether to use GPU acceleration
+        """
+        if not FASTEMBED_AVAILABLE:
+            raise ImportError(
+                "FastEmbed is not installed. Please install it with: "
+                "uv pip install fastembed or uv pip install fastembed-gpu for GPU support"
+            )
+
+        self.model = TextEmbedding(
+            model_name=model_name,
+            cache_dir=str(cache_dir) if cache_dir else None,
+        )
+
+    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of text chunks
+
+        Args:
+            chunks: List of text chunks to embed
+
+        Returns:
+            List of embeddings
+        """
+        embeddings = list(self.model.embed(chunks))
+        return embeddings
+
+    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of texts
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of embeddings
+        """
+        embeddings = list(self.model.embed(texts))
+        return embeddings
+
+    def embed_text(self, text: str) -> np.ndarray:
+        """
+        Generate embedding for a single text
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding array
+        """
+        return self.embed_texts([text])[0]
+
+    @property
+    def dimension(self) -> int:
+        """Get the dimension of the embeddings"""
+        # Generate a test embedding to get dimensions
+        test_embed = self.embed_text("test")
+        return len(test_embed)

src/crewai/knowledge/knowledge.py

@@ -0,0 +1,54 @@
+import os
+
+from typing import List, Optional, Dict, Any
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.logger import Logger
+from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
+os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
+
+
+class Knowledge(BaseModel):
+    """
+    Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
+    Args:
+        sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+        embedder_config: Optional[Dict[str, Any]] = None
+    """
+    sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    embedder_config: Optional[Dict[str, Any]] = None
+
+    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None, **data):
+        super().__init__(**data)
+        self.storage = KnowledgeStorage(embedder_config=embedder_config or None)
+
+        try:
+            for source in self.sources:
+                source.add()
+        except Exception as e:
+            Logger(verbose=True).log(
+                "warning",
+                f"Failed to init knowledge: {e}",
+                color="yellow",
+            )
+
+    def query(
+        self, query: List[str], limit: int = 3, preference: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Query across all knowledge sources to find the most relevant information.
+        Returns the top_k most relevant chunks.
+        """
+
+        results = self.storage.search(
+            query,
+            limit,
+            filter={"preference": preference} if preference else None,
+            score_threshold=DEFAULT_SCORE_THRESHOLD,
+        )
+        return results

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+from typing import Union, List
+
+from pydantic import Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from typing import Dict, Any
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+
+
+class BaseFileKnowledgeSource(BaseKnowledgeSource):
+    """Base class for knowledge sources that load content from files."""
+
+    file_path: Union[Path, List[Path]] = Field(...)
+    content: Dict[Path, str] = Field(init=False, default_factory=dict)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+
+    def model_post_init(self, _):
+        """Post-initialization method to load content."""
+        self.content = self.load_content()
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess file content. Should be overridden by subclasses."""
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+
+        for path in paths:
+            if not path.exists():
+                raise FileNotFoundError(f"File not found: {path}")
+            if not path.is_file():
+                raise ValueError(f"Path is not a file: {path}")
+        return {}
+
+    def save_documents(self, metadata: Dict[str, Any]):
+        """Save the documents to the storage."""
+        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks, chunk_metadatas)

src/crewai/knowledge/source/base_knowledge_source.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+
+import numpy as np
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+
+
+class BaseKnowledgeSource(BaseModel, ABC):
+    """Abstract base class for knowledge sources."""
+
+    chunk_size: int = 4000
+    chunk_overlap: int = 200
+    chunks: List[str] = Field(default_factory=list)
+    chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+    @abstractmethod
+    def load_content(self) -> Dict[Any, str]:
+        """Load and preprocess content from the source."""
+        pass
+
+    @abstractmethod
+    def add(self) -> None:
+        """Process content, chunk it, compute embeddings, and save them."""
+        pass
+
+    def get_embeddings(self) -> List[np.ndarray]:
+        """Return the list of embeddings for the chunks."""
+        return self.chunk_embeddings
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]
+
+    def save_documents(self, metadata: Dict[str, Any]):
+        """
+        Save the documents to the storage.
+        This method should be called after the chunks and embeddings are generated.
+        """
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -0,0 +1,44 @@
+import csv
+from typing import Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class CSVKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries CSV file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess CSV file content."""
+        super().load_content()  # Validate the file path
+
+        file_path = (
+            self.file_path[0] if isinstance(self.file_path, list) else self.file_path
+        )
+        file_path = Path(file_path) if isinstance(file_path, str) else file_path
+
+        with open(file_path, "r", encoding="utf-8") as csvfile:
+            reader = csv.reader(csvfile)
+            content = ""
+            for row in reader:
+                content += " ".join(row) + "\n"
+        return {file_path: content}
+
+    def add(self) -> None:
+        """
+        Add CSV file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -0,0 +1,56 @@
+from typing import Dict, List
+from pathlib import Path
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class ExcelKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries Excel file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess Excel file content."""
+        super().load_content()  # Validate the file path
+        pd = self._import_dependencies()
+
+        if isinstance(self.file_path, list):
+            file_path = self.file_path[0]
+        else:
+            file_path = self.file_path
+
+        df = pd.read_excel(file_path)
+        content = df.to_csv(index=False)
+        return {file_path: content}
+
+    def _import_dependencies(self):
+        """Dynamically import dependencies."""
+        try:
+            import openpyxl  # noqa
+            import pandas as pd
+
+            return pd
+        except ImportError as e:
+            missing_package = str(e).split()[-1]
+            raise ImportError(
+                f"{missing_package} is not installed. Please install it with: pip install {missing_package}"
+            )
+
+    def add(self) -> None:
+        """
+        Add Excel file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        # Convert dictionary values to a single string if content is a dictionary
+        if isinstance(self.content, dict):
+            content_str = "\n".join(str(value) for value in self.content.values())
+        else:
+            content_str = str(self.content)
+
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/json_knowledge_source.py

@@ -0,0 +1,54 @@
+import json
+from typing import Any, Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class JSONKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries JSON file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess JSON file content."""
+        super().load_content()  # Validate the file path
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+
+        content: Dict[Path, str] = {}
+        for path in paths:
+            with open(path, "r", encoding="utf-8") as json_file:
+                data = json.load(json_file)
+            content[path] = self._json_to_text(data)
+        return content
+
+    def _json_to_text(self, data: Any, level: int = 0) -> str:
+        """Recursively convert JSON data to a text representation."""
+        text = ""
+        indent = "  " * level
+        if isinstance(data, dict):
+            for key, value in data.items():
+                text += f"{indent}{key}: {self._json_to_text(value, level + 1)}\n"
+        elif isinstance(data, list):
+            for item in data:
+                text += f"{indent}- {self._json_to_text(item, level + 1)}\n"
+        else:
+            text += f"{str(data)}"
+        return text
+
+    def add(self) -> None:
+        """
+        Add JSON file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -0,0 +1,54 @@
+from typing import List, Dict
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class PDFKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries PDF file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess PDF file content."""
+        super().load_content()  # Validate the file paths
+        pdfplumber = self._import_pdfplumber()
+
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+        content = {}
+
+        for path in paths:
+            text = ""
+            with pdfplumber.open(path) as pdf:
+                for page in pdf.pages:
+                    page_text = page.extract_text()
+                    if page_text:
+                        text += page_text + "\n"
+            content[path] = text
+        return content
+
+    def _import_pdfplumber(self):
+        """Dynamically import pdfplumber."""
+        try:
+            import pdfplumber
+
+            return pdfplumber
+        except ImportError:
+            raise ImportError(
+                "pdfplumber is not installed. Please install it with: pip install pdfplumber"
+            )
+
+    def add(self) -> None:
+        """
+        Add PDF file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        for _, text in self.content.items():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/string_knowledge_source.py

@@ -0,0 +1,33 @@
+from typing import List
+
+from pydantic import Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+
+
+class StringKnowledgeSource(BaseKnowledgeSource):
+    """A knowledge source that stores and queries plain text content using embeddings."""
+
+    content: str = Field(...)
+
+    def model_post_init(self, _):
+        """Post-initialization method to validate content."""
+        self.load_content()
+
+    def load_content(self):
+        """Validate string content."""
+        if not isinstance(self.content, str):
+            raise ValueError("StringKnowledgeSource only accepts string content")
+
+    def add(self) -> None:
+        """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
+        new_chunks = self._chunk_text(self.content)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -0,0 +1,35 @@
+from typing import Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class TextFileKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries text file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess text file content."""
+        super().load_content()
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+        content = {}
+        for path in paths:
+            with path.open("r", encoding="utf-8") as f:
+                content[path] = f.read()  # type: ignore
+        return content
+
+    def add(self) -> None:
+        """
+        Add text file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        for _, text in self.content.items():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+from typing import Dict, Any, List, Optional
+
+
+class BaseKnowledgeStorage(ABC):
+    """Abstract base class for knowledge storage implementations."""
+
+    @abstractmethod
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        """Search for documents in the knowledge base."""
+        pass
+
+    @abstractmethod
+    def save(
+        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+    ) -> None:
+        """Save documents to the knowledge base."""
+        pass
+
+    @abstractmethod
+    def reset(self) -> None:
+        """Reset the knowledge base."""
+        pass

src/crewai/knowledge/storage/knowledge_storage.py

@@ -0,0 +1,132 @@
+import contextlib
+import io
+import logging
+import chromadb
+import os
+from crewai.utilities.paths import db_storage_path
+from typing import Optional, List
+from typing import Dict, Any
+from crewai.utilities import EmbeddingConfigurator
+from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+import hashlib
+
+
+@contextlib.contextmanager
+def suppress_logging(
+    logger_name="chromadb.segment.impl.vector.local_persistent_hnsw",
+    level=logging.ERROR,
+):
+    logger = logging.getLogger(logger_name)
+    original_level = logger.getEffectiveLevel()
+    logger.setLevel(level)
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
+        yield
+    logger.setLevel(original_level)
+
+
+class KnowledgeStorage(BaseKnowledgeStorage):
+    """
+    Extends Storage to handle embeddings for memory entries, improving
+    search efficiency.
+    """
+
+    collection: Optional[chromadb.Collection] = None
+
+    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None):
+        self._initialize_app(embedder_config or {})
+
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        with suppress_logging():
+            if self.collection:
+                fetched = self.collection.query(
+                    query_texts=query,
+                    n_results=limit,
+                    where=filter,
+                )
+                results = []
+                for i in range(len(fetched["ids"][0])):  # type: ignore
+                    result = {
+                        "id": fetched["ids"][0][i],  # type: ignore
+                        "metadata": fetched["metadatas"][0][i],  # type: ignore
+                        "context": fetched["documents"][0][i],  # type: ignore
+                        "score": fetched["distances"][0][i],  # type: ignore
+                    }
+                    if result["score"] >= score_threshold:  # type: ignore
+                        results.append(result)
+                return results
+            else:
+                raise Exception("Collection not initialized")
+
+    def _initialize_app(self, embedder_config: Optional[Dict[str, Any]] = None):
+        import chromadb
+        from chromadb.config import Settings
+
+        self._set_embedder_config(embedder_config)
+
+        chroma_client = chromadb.PersistentClient(
+            path=f"{db_storage_path()}/knowledge",
+            settings=Settings(allow_reset=True),
+        )
+
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_or_create_collection(name="knowledge")
+        except Exception:
+            raise Exception("Failed to create or get collection")
+
+    def reset(self):
+        if self.app:
+            self.app.reset()
+
+    def save(
+        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+    ):
+        if self.collection:
+            metadatas = [metadata] if isinstance(metadata, dict) else metadata
+
+            ids = [
+                hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
+            ]
+
+            self.collection.upsert(
+                documents=documents,
+                metadatas=metadatas,
+                ids=ids,
+            )
+        else:
+            raise Exception("Collection not initialized")
+
+    def _create_default_embedding_function(self):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    def _set_embedder_config(
+        self, embedder_config: Optional[Dict[str, Any]] = None
+    ) -> None:
+        """Set the embedding configuration for the knowledge storage.
+
+        Args:
+            embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
+                If None or empty, defaults to the default embedding function.
+        """
+        self.embedder_config = (
+            EmbeddingConfigurator().configure_embedder(embedder_config)
+            if embedder_config
+            else self._create_default_embedding_function()
+        )

src/crewai/memory/storage/rag_storage.py

@@ -4,13 +4,12 @@ import logging
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional, cast
 
-from chromadb import Documents, EmbeddingFunction, Embeddings
+from typing import Any, Dict, List, Optional
 from chromadb.api import ClientAPI
-from chromadb.api.types import validate_embedding_function
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities import EmbeddingConfigurator
 
 
 @contextlib.contextmanager
@@ -51,133 +50,8 @@ class RAGStorage(BaseRAGStorage):
         self._initialize_app()
 
     def _set_embedder_config(self):
-        if self.embedder_config is None:
-            self.embedder_config = self._create_default_embedding_function()
-
-        if isinstance(self.embedder_config, dict):
-            provider = self.embedder_config.get("provider")
-            config = self.embedder_config.get("config", {})
-            model_name = config.get("model")
-            if provider == "openai":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-                    model_name=model_name,
-                )
-            elif provider == "azure":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key"),
-                    api_base=config.get("api_base"),
-                    api_type=config.get("api_type", "azure"),
-                    api_version=config.get("api_version"),
-                    model_name=model_name,
-                )
-            elif provider == "ollama":
-                from chromadb.utils.embedding_functions.ollama_embedding_function import (
-                    OllamaEmbeddingFunction,
-                )
-
-                self.embedder_config = OllamaEmbeddingFunction(
-                    url=config.get("url", "http://localhost:11434/api/embeddings"),
-                    model_name=model_name,
-                )
-            elif provider == "vertexai":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleVertexEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleVertexEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
-                )
-            elif provider == "google":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleGenerativeAiEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleGenerativeAiEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
-                )
-            elif provider == "cohere":
-                from chromadb.utils.embedding_functions.cohere_embedding_function import (
-                    CohereEmbeddingFunction,
-                )
-
-                self.embedder_config = CohereEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
-                )
-            elif provider == "bedrock":
-                from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-                    AmazonBedrockEmbeddingFunction,
-                )
-
-                self.embedder_config = AmazonBedrockEmbeddingFunction(
-                    session=config.get("session"),
-                )
-            elif provider == "huggingface":
-                from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-                    HuggingFaceEmbeddingServer,
-                )
-
-                self.embedder_config = HuggingFaceEmbeddingServer(
-                    url=config.get("api_url"),
-                )
-            elif provider == "watson":
-                try:
-                    import ibm_watsonx_ai.foundation_models as watson_models
-                    from ibm_watsonx_ai import Credentials
-                    from ibm_watsonx_ai.metanames import (
-                        EmbedTextParamsMetaNames as EmbedParams,
-                    )
-                except ImportError as e:
-                    raise ImportError(
-                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-                    ) from e
-
-                class WatsonEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        if isinstance(input, str):
-                            input = [input]
-
-                        embed_params = {
-                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
-                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
-                        }
-
-                        embedding = watson_models.Embeddings(
-                            model_id=config.get("model"),
-                            params=embed_params,
-                            credentials=Credentials(
-                                api_key=config.get("api_key"), url=config.get("api_url")
-                            ),
-                            project_id=config.get("project_id"),
-                        )
-
-                        try:
-                            embeddings = embedding.embed_documents(input)
-                            return cast(Embeddings, embeddings)
-
-                        except Exception as e:
-                            print("Error during Watson embedding:", e)
-                            raise e
-
-                self.embedder_config = WatsonEmbeddingFunction()
-            else:
-                raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
-                )
-        else:
-            validate_embedding_function(self.embedder_config)
-            self.embedder_config = self.embedder_config
+        configurator = EmbeddingConfigurator()
+        self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
     def _initialize_app(self):
         import chromadb

src/crewai/utilities/__init__.py

@@ -10,6 +10,7 @@ from .rpm_controller import RPMController
 from .exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
+from .embedding_configurator import EmbeddingConfigurator
 
 __all__ = [
     "Converter",
@@ -23,4 +24,5 @@ __all__ = [
     "RPMController",
     "YamlParser",
     "LLMContextLengthExceededException",
+    "EmbeddingConfigurator",
 ]

src/crewai/utilities/constants.py

@@ -1,2 +1,3 @@
 TRAINING_DATA_FILE = "training_data.pkl"
 TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
+DEFAULT_SCORE_THRESHOLD = 0.35

src/crewai/utilities/embedding_configurator.py

@@ -0,0 +1,183 @@
+import os
+from typing import Any, Dict, cast
+from chromadb import EmbeddingFunction, Documents, Embeddings
+from chromadb.api.types import validate_embedding_function
+
+
+class EmbeddingConfigurator:
+    def __init__(self):
+        self.embedding_functions = {
+            "openai": self._configure_openai,
+            "azure": self._configure_azure,
+            "ollama": self._configure_ollama,
+            "vertexai": self._configure_vertexai,
+            "google": self._configure_google,
+            "cohere": self._configure_cohere,
+            "bedrock": self._configure_bedrock,
+            "huggingface": self._configure_huggingface,
+            "watson": self._configure_watson,
+        }
+
+    def configure_embedder(
+        self,
+        embedder_config: Dict[str, Any] | None = None,
+    ) -> EmbeddingFunction:
+        """Configures and returns an embedding function based on the provided config."""
+        if embedder_config is None:
+            return self._create_default_embedding_function()
+
+        provider = embedder_config.get("provider")
+        config = embedder_config.get("config", {})
+        model_name = config.get("model")
+
+        if isinstance(provider, EmbeddingFunction):
+            try:
+                validate_embedding_function(provider)
+                return provider
+            except Exception as e:
+                raise ValueError(f"Invalid custom embedding function: {str(e)}")
+
+        if provider not in self.embedding_functions:
+            raise Exception(
+                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
+            )
+
+        return self.embedding_functions[provider](config, model_name)
+
+    @staticmethod
+    def _create_default_embedding_function():
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    @staticmethod
+    def _configure_openai(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_azure(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key"),
+            api_base=config.get("api_base"),
+            api_type=config.get("api_type", "azure"),
+            api_version=config.get("api_version"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_ollama(config, model_name):
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
+
+        return OllamaEmbeddingFunction(
+            url=config.get("url", "http://localhost:11434/api/embeddings"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_vertexai(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
+
+        return GoogleVertexEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_google(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
+
+        return GoogleGenerativeAiEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_cohere(config, model_name):
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
+
+        return CohereEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_bedrock(config, model_name):
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
+
+        return AmazonBedrockEmbeddingFunction(
+            session=config.get("session"),
+        )
+
+    @staticmethod
+    def _configure_huggingface(config, model_name):
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
+
+        return HuggingFaceEmbeddingServer(
+            url=config.get("api_url"),
+        )
+
+    @staticmethod
+    def _configure_watson(config, model_name):
+        try:
+            import ibm_watsonx_ai.foundation_models as watson_models
+            from ibm_watsonx_ai import Credentials
+            from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
+        except ImportError as e:
+            raise ImportError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+            ) from e
+
+        class WatsonEmbeddingFunction(EmbeddingFunction):
+            def __call__(self, input: Documents) -> Embeddings:
+                if isinstance(input, str):
+                    input = [input]
+
+                embed_params = {
+                    EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                    EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                }
+
+                embedding = watson_models.Embeddings(
+                    model_id=config.get("model"),
+                    params=embed_params,
+                    credentials=Credentials(
+                        api_key=config.get("api_key"), url=config.get("api_url")
+                    ),
+                    project_id=config.get("project_id"),
+                )
+
+                try:
+                    embeddings = embedding.embed_documents(input)
+                    return cast(Embeddings, embeddings)
+                except Exception as e:
+                    print("Error during Watson embedding:", e)
+                    raise e
+
+        return WatsonEmbeddingFunction()

tests/agent_test.py

@@ -10,10 +10,11 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
+from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
-from crewai.tools import tool
 from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
@@ -1574,3 +1575,42 @@ def test_agent_execute_task_with_ollama():
     result = agent.execute_task(task)
     assert len(result.split(".")) == 2
     assert "AI" in result or "artificial intelligence" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_knowledge_sources():
+    # Create a knowledge source with some content
+    content = "Brandon's favorite color is blue and he likes Mexican food."
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    
+
+    with patch('crewai.knowledge.storage.knowledge_storage.KnowledgeStorage') as MockKnowledge:
+        mock_knowledge_instance = MockKnowledge.return_value
+        mock_knowledge_instance.sources = [string_source]
+        mock_knowledge_instance.query.return_value = [{
+            "content": content,
+            "metadata": {"preference": "personal"}
+        }]
+        
+        agent = Agent(
+            role="Information Agent",
+            goal="Provide information based on knowledge sources",
+            backstory="You have access to specific knowledge sources.",
+            llm=LLM(model="gpt-4o-mini"),
+        )
+
+        # Create a task that requires the agent to use the knowledge
+        task = Task(
+            description="What is Brandon's favorite color?",
+            expected_output="Brandon's favorite color.",
+            agent=agent,
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        result = crew.kickoff()
+
+        # Assert that the agent provides the correct information
+        assert "blue" in result.raw.lower()
+

tests/cassettes/test_agent_with_knowledge_sources.yaml

@@ -0,0 +1,115 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are Information Agent.
+      You have access to specific knowledge sources.\nYour personal goal is: Provide
+      information based on knowledge sources\nTo give my best complete final answer
+      to the task use the exact following format:\n\nThought: I now can give a great
+      answer\nFinal Answer: Your final answer must be the great and the most complete
+      as possible, it must be outcome described.\n\nI MUST use these formats, my job
+      depends on it!"}, {"role": "user", "content": "\nCurrent Task: What is Brandon''s
+      favorite color?\n\nThis is the expect criteria for your final answer: Brandon''s
+      favorite color.\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"], "stream": false}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '931'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.52.1
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.52.1
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.9
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA4xSQW7bMBC86xULXnqxAtmxI1e3FEWBtJekCXJpC4GmVhIdapcgqbhN4L8HlB1L
+        QVOgFwGa2RnOLPmcAAhdiQKEamVQnTXp5f3d9lsdbndh++C+757Or6/bm6uvn59WH/FGzKKCN1tU
+        4VV1prizBoNmOtDKoQwYXef5+SK7WK3ny4HouEITZY0N6ZLTTpNOF9limWZ5Ol8f1S1rhV4U8CMB
+        AHgevjEnVfhbFJDNXpEOvZcNiuI0BCAcm4gI6b32QVIQs5FUTAFpiH4FxDtQkqDRjwgSmhgbJPkd
+        OoCf9EWTNHA5/BfwyUmqmD54qOUjOx0QFBt2oD1sTI9n02Mc1r2XsSr1xhzx/Sm34cY63vgjf8Jr
+        Tdq3pUPpmWJGH9iKgd0nAL+G/fRvKgvruLOhDPyAFA3nF/nBT4zXMmHXRzJwkGaKr2bv+JUVBqmN
+        n2xYKKlarEbpeB2yrzRPiGTS+u8073kfmmtq/sd+JJRCG7AqrcNKq7eNxzGH8dX+a+y05SGw8H98
+        wK6sNTXorNOHN1PbMsuz1aZe5yoTyT55AQAA//8DAPaYLdRBAwAA
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 8e54a2a7d81467f7-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Wed, 20 Nov 2024 01:23:34 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=DoHo1Z11nN9bxkwZmJGnaxRhyrWE0UfyimYuUVRU6A4-1732065814-1.0.1.1-JVRvFrIJLHEq9OaFQS0qcgYcawE7t2XQ4Tpqd58n2Yfx3mvEqD34MJmooi1LtvdvjB2J8x1Rs.rCdXD.msLlKw;
+        path=/; expires=Wed, 20-Nov-24 01:53:34 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=n3RrNhFMqC3HtJ7n3e3agyxnM1YOQ6eKESz_eeXLtZA-1732065814630-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '344'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999790'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_8f1622677c64913753a595f679596614
+    status:
+      code: 200
+      message: OK
+version: 1

tests/cassettes/test_kickoff_for_each_error_handling.yaml

@@ -0,0 +1,232 @@
+interactions:
+- request:
+    body: !!binary |
+      Cv1YCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkS1FgKEgoQY3Jld2FpLnRl
+      bGVtZXRyeRLADQoQ5TzgW9QzcBbzMl1hJozLcxIIl3adf7U81wwqDENyZXcgQ3JlYXRlZDABOaAJ
+      txhffgkYQfiVuRhffgkYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5weXRob25fdmVy
+      c2lvbhIICgYzLjEyLjVKLgoIY3Jld19rZXkSIgogM2Y4ZDVjM2FiODgyZDY4NjlkOTNjYjgxZjBl
+      MmVkNGFKMQoHY3Jld19pZBImCiRjYjRiY2Q1Zi0xYWJkLTQyYmYtOGQ1OC02ZmEzMDU3ZDFjOTZK
+      HAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdf
+      bnVtYmVyX29mX3Rhc2tzEgIYA0obChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgCSpIFCgtjcmV3
+      X2FnZW50cxKCBQr/BFt7ImtleSI6ICI4YmQyMTM5YjU5NzUxODE1MDZlNDFmZDljNDU2M2Q3NSIs
+      ICJpZCI6ICI1ZThjNTM1MS1jNWVlLTRhZGUtODY5MC1kM2RhOWI1NzI5YzciLCAicm9sZSI6ICJS
+      ZXNlYXJjaGVyIiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6
+      IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwg
+      ImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZh
+      bHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119LCB7ImtleSI6ICI5
+      YTUwMTVlZjQ4OTVkYzYyNzhkNTQ4MThiYTQ0NmFmNyIsICJpZCI6ICJhMTcwODczOC0yYWE2LTRk
+      ZmYtODFlNy00OGFkMDNjNWFjY2QiLCAicm9sZSI6ICJTZW5pb3IgV3JpdGVyIiwgInZlcmJvc2U/
+      IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxs
+      aW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8i
+      OiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0
+      IjogMiwgInRvb2xzX25hbWVzIjogW119XUrbBQoKY3Jld190YXNrcxLMBQrJBVt7ImtleSI6ICI2
+      Nzg0OWZmNzE3ZGJhZGFiYTFiOTVkNWYyZGZjZWVhMSIsICJpZCI6ICIyNzkxNTMxMy0wNDBhLTRk
+      ZWItOTVkMy1mNWVmMzg2Mjk3NTEiLCAiYXN5bmNfZXhlY3V0aW9uPyI6IHRydWUsICJodW1hbl9p
+      bnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAiUmVzZWFyY2hlciIsICJhZ2VudF9rZXkiOiAi
+      OGJkMjEzOWI1OTc1MTgxNTA2ZTQxZmQ5YzQ1NjNkNzUiLCAidG9vbHNfbmFtZXMiOiBbXX0sIHsi
+      a2V5IjogImZjNTZkZWEzOGM5OTc0YjZmNTVhMmUyOGMxNDk5ODg2IiwgImlkIjogIjc3NzQ3MmVl
+      LWYzNzAtNDQyZS05NWMyLWVlMGVkYzZiMTgyZiIsICJhc3luY19leGVjdXRpb24/IjogZmFsc2Us
+      ICJodW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAiUmVzZWFyY2hlciIsICJhZ2Vu
+      dF9rZXkiOiAiOGJkMjEzOWI1OTc1MTgxNTA2ZTQxZmQ5YzQ1NjNkNzUiLCAidG9vbHNfbmFtZXMi
+      OiBbXX0sIHsia2V5IjogIjk0YTgyNmMxOTMwNTU5Njg2YmFmYjQwOWVlODM4NzZmIiwgImlkIjog
+      ImM4OWEzODA2LTg5MDItNGQ2My1iYzA0LTdjMzRhZTJmM2UxNyIsICJhc3luY19leGVjdXRpb24/
+      IjogZmFsc2UsICJodW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAiU2VuaW9yIFdy
+      aXRlciIsICJhZ2VudF9rZXkiOiAiOWE1MDE1ZWY0ODk1ZGM2Mjc4ZDU0ODE4YmE0NDZhZjciLCAi
+      dG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKOAgoQSqupTllrk2mxgu2AqenZUhIIspWxig2+
+      1M0qDFRhc2sgQ3JlYXRlZDABOcCj1BhffgkYQfhq1RhffgkYSi4KCGNyZXdfa2V5EiIKIDNmOGQ1
+      YzNhYjg4MmQ2ODY5ZDkzY2I4MWYwZTJlZDRhSjEKB2NyZXdfaWQSJgokY2I0YmNkNWYtMWFiZC00
+      MmJmLThkNTgtNmZhMzA1N2QxYzk2Si4KCHRhc2tfa2V5EiIKIDY3ODQ5ZmY3MTdkYmFkYWJhMWI5
+      NWQ1ZjJkZmNlZWExSjEKB3Rhc2tfaWQSJgokMjc5MTUzMTMtMDQwYS00ZGViLTk1ZDMtZjVlZjM4
+      NjI5NzUxegIYAYUBAAEAABKOAgoQ3dJesXQA5ISCqVgmwvBMgRIIdrWBiVQuihcqDFRhc2sgQ3Jl
+      YXRlZDABOdjAch1ffgkYQVh8cx1ffgkYSi4KCGNyZXdfa2V5EiIKIDNmOGQ1YzNhYjg4MmQ2ODY5
+      ZDkzY2I4MWYwZTJlZDRhSjEKB2NyZXdfaWQSJgokY2I0YmNkNWYtMWFiZC00MmJmLThkNTgtNmZh
+      MzA1N2QxYzk2Si4KCHRhc2tfa2V5EiIKIGZjNTZkZWEzOGM5OTc0YjZmNTVhMmUyOGMxNDk5ODg2
+      SjEKB3Rhc2tfaWQSJgokNzc3NDcyZWUtZjM3MC00NDJlLTk1YzItZWUwZWRjNmIxODJmegIYAYUB
+      AAEAABKOAgoQCBmV+4VbArZNiL5MaefbahII1fRxaC46KKgqDFRhc2sgQ3JlYXRlZDABOaDs4SNf
+      fgkYQai74iNffgkYSi4KCGNyZXdfa2V5EiIKIDNmOGQ1YzNhYjg4MmQ2ODY5ZDkzY2I4MWYwZTJl
+      ZDRhSjEKB2NyZXdfaWQSJgokY2I0YmNkNWYtMWFiZC00MmJmLThkNTgtNmZhMzA1N2QxYzk2Si4K
+      CHRhc2tfa2V5EiIKIDk0YTgyNmMxOTMwNTU5Njg2YmFmYjQwOWVlODM4NzZmSjEKB3Rhc2tfaWQS
+      JgokYzg5YTM4MDYtODkwMi00ZDYzLWJjMDQtN2MzNGFlMmYzZTE3egIYAYUBAAEAABKiBwoQhITI
+      U8q3JLgneRv1MZQY8RIIF2CpEmiZsP4qDENyZXcgQ3JlYXRlZDABOZDBCytffgkYQTDFDStffgkY
+      ShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5weXRob25fdmVyc2lvbhIICgYzLjEyLjVK
+      LgoIY3Jld19rZXkSIgogYTljYzVkNDMzOTViMjFiMTgxYzgwYmQ0MzUxY2NlYzhKMQoHY3Jld19p
+      ZBImCiQ2MTMwMWVmYS0yOGQ4LTQyNTItOWVjNi1iM2JmZDcyMWM0MzVKHAoMY3Jld19wcm9jZXNz
+      EgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tz
+      EgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgBStECCgtjcmV3X2FnZW50cxLBAgq+Alt7
+      ImtleSI6ICI4YmQyMTM5YjU5NzUxODE1MDZlNDFmZDljNDU2M2Q3NSIsICJpZCI6ICI3NWRjOTUw
+      OS02MjQ4LTQ0YWQtYTExZC1iZjdlZWVhOWI0NTQiLCAicm9sZSI6ICJSZXNlYXJjaGVyIiwgInZl
+      cmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlv
+      bl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVnYXRpb25fZW5h
+      YmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5
+      X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119XUr+AQoKY3Jld190YXNrcxLvAQrsAVt7Imtl
+      eSI6ICJlOWU2YjcyYWFjMzI2NDU5ZGQ3MDY4ZjBiMTcxN2MxYyIsICJpZCI6ICIxOTBlMGQ1Zi0y
+      NDg1LTQ3N2ItYWIxNC1kMTlmNDE5YTFlYjQiLCAiYXN5bmNfZXhlY3V0aW9uPyI6IHRydWUsICJo
+      dW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAiUmVzZWFyY2hlciIsICJhZ2VudF9r
+      ZXkiOiAiOGJkMjEzOWI1OTc1MTgxNTA2ZTQxZmQ5YzQ1NjNkNzUiLCAidG9vbHNfbmFtZXMiOiBb
+      XX1degIYAYUBAAEAABKOAgoQxgDNe1lQGKnixKPk3O1TDBIISyqKkjcA7OYqDFRhc2sgQ3JlYXRl
+      ZDABOfCYJCtffgkYQZAlJStffgkYSi4KCGNyZXdfa2V5EiIKIGE5Y2M1ZDQzMzk1YjIxYjE4MWM4
+      MGJkNDM1MWNjZWM4SjEKB2NyZXdfaWQSJgokNjEzMDFlZmEtMjhkOC00MjUyLTllYzYtYjNiZmQ3
+      MjFjNDM1Si4KCHRhc2tfa2V5EiIKIGU5ZTZiNzJhYWMzMjY0NTlkZDcwNjhmMGIxNzE3YzFjSjEK
+      B3Rhc2tfaWQSJgokMTkwZTBkNWYtMjQ4NS00NzdiLWFiMTQtZDE5ZjQxOWExZWI0egIYAYUBAAEA
+      ABK/DQoQwwR54Z8nOGgj2VSb63WRwhIIonLT+7Mwj00qDENyZXcgQ3JlYXRlZDABObDfvzhffgkY
+      QfBvwjhffgkYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5weXRob25fdmVyc2lvbhII
+      CgYzLjEyLjVKLgoIY3Jld19rZXkSIgogNjZhOTYwZGM2OWZmZjU3OGIyNmM2MWQ0ZjdjNWE5ZmVK
+      MQoHY3Jld19pZBImCiQxNThhMTkzOS01OWUzLTRlODgtYTRkYi04M2IzN2U5MjgxZWVKHAoMY3Jl
+      d19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdfbnVtYmVy
+      X29mX3Rhc2tzEgIYA0obChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgCSpIFCgtjcmV3X2FnZW50
+      cxKCBQr/BFt7ImtleSI6ICI4YmQyMTM5YjU5NzUxODE1MDZlNDFmZDljNDU2M2Q3NSIsICJpZCI6
+      ICI1ZThjNTM1MS1jNWVlLTRhZGUtODY5MC1kM2RhOWI1NzI5YzciLCAicm9sZSI6ICJSZXNlYXJj
+      aGVyIiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGws
+      ICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVn
+      YXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAi
+      bWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119LCB7ImtleSI6ICI5YTUwMTVl
+      ZjQ4OTVkYzYyNzhkNTQ4MThiYTQ0NmFmNyIsICJpZCI6ICJhMTcwODczOC0yYWE2LTRkZmYtODFl
+      Ny00OGFkMDNjNWFjY2QiLCAicm9sZSI6ICJTZW5pb3IgV3JpdGVyIiwgInZlcmJvc2U/IjogZmFs
+      c2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xs
+      bSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxz
+      ZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwg
+      InRvb2xzX25hbWVzIjogW119XUraBQoKY3Jld190YXNrcxLLBQrIBVt7ImtleSI6ICI5NDRhZWYw
+      YmFjODQwZjFjMjdiZDgzYTkzN2JjMzYxYiIsICJpZCI6ICIzN2FkNzI5MC04Yjg5LTRjNWEtYmNl
+      Zi03YzY0ZWJhMWM5NjciLCAiYXN5bmNfZXhlY3V0aW9uPyI6IHRydWUsICJodW1hbl9pbnB1dD8i
+      OiBmYWxzZSwgImFnZW50X3JvbGUiOiAiUmVzZWFyY2hlciIsICJhZ2VudF9rZXkiOiAiOGJkMjEz
+      OWI1OTc1MTgxNTA2ZTQxZmQ5YzQ1NjNkNzUiLCAidG9vbHNfbmFtZXMiOiBbXX0sIHsia2V5Ijog
+      ImZjNTZkZWEzOGM5OTc0YjZmNTVhMmUyOGMxNDk5ODg2IiwgImlkIjogIjZhMmViMGY2LTgwZTIt
+      NDkxOC05Zjk3LWVhNDY3OTNkMjI2YyIsICJhc3luY19leGVjdXRpb24/IjogdHJ1ZSwgImh1bWFu
+      X2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJSZXNlYXJjaGVyIiwgImFnZW50X2tleSI6
+      ICI4YmQyMTM5YjU5NzUxODE1MDZlNDFmZDljNDU2M2Q3NSIsICJ0b29sc19uYW1lcyI6IFtdfSwg
+      eyJrZXkiOiAiOTRhODI2YzE5MzA1NTk2ODZiYWZiNDA5ZWU4Mzg3NmYiLCAiaWQiOiAiZGQ2Yzkz
+      NzAtOGYwNC00ZDFmLThjODMtMmFiM2IyYzIwYWI3IiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxz
+      ZSwgImh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJTZW5pb3IgV3JpdGVyIiwg
+      ImFnZW50X2tleSI6ICI5YTUwMTVlZjQ4OTVkYzYyNzhkNTQ4MThiYTQ0NmFmNyIsICJ0b29sc19u
+      YW1lcyI6IFtdfV16AhgBhQEAAQAAErMHChBV+1WNQzpVlY6l4C/mUgHzEgi3vWQXjOQJ5CoMQ3Jl
+      dyBDcmVhdGVkMAE5sH1kPF9+CRhBaH1mPF9+CRhKGgoOY3Jld2FpX3ZlcnNpb24SCAoGMC44MC4w
+      ShoKDnB5dGhvbl92ZXJzaW9uEggKBjMuMTIuNUouCghjcmV3X2tleRIiCiBlZTY3NDVkN2M4YWU4
+      MmUwMGRmOTRkZTBmN2Y4NzExOEoxCgdjcmV3X2lkEiYKJDAwOThmODNmLTdkNTAtNGI2Mi1hYmIy
+      LTJlNTc0N2ZlMWE4OUocCgxjcmV3X3Byb2Nlc3MSDAoKc2VxdWVudGlhbEoRCgtjcmV3X21lbW9y
+      eRICEABKGgoUY3Jld19udW1iZXJfb2ZfdGFza3MSAhgBShsKFWNyZXdfbnVtYmVyX29mX2FnZW50
+      cxICGAFK2QIKC2NyZXdfYWdlbnRzEskCCsYCW3sia2V5IjogImYzMzg2ZjZkOGRhNzVhYTQxNmE2
+      ZTMxMDA1M2Y3Njk4IiwgImlkIjogIjEzODI4ZDViLWIyOWMtNDllMy05NWVhLTkyOGQ2ZmZhY2I0
+      NSIsICJyb2xlIjogInt0b3BpY30gUmVzZWFyY2hlciIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4
+      X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwg
+      ImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2VuYWJsZWQ/IjogZmFsc2UsICJhbGxv
+      d19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRyeV9saW1pdCI6IDIsICJ0b29sc19u
+      YW1lcyI6IFtdfV1KhwIKCmNyZXdfdGFza3MS+AEK9QFbeyJrZXkiOiAiMDZhNzMyMjBmNDE0OGE0
+      YmJkNWJhY2IwZDBiNDRmY2UiLCAiaWQiOiAiNWM4MjM1ZmYtNWVjNy00NzFhLWI4NWEtNWFkZjk3
+      YzJkYzI3IiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6IGZhbHNl
+      LCAiYWdlbnRfcm9sZSI6ICJ7dG9waWN9IFJlc2VhcmNoZXIiLCAiYWdlbnRfa2V5IjogImYzMzg2
+      ZjZkOGRhNzVhYTQxNmE2ZTMxMDA1M2Y3Njk4IiwgInRvb2xzX25hbWVzIjogW119XXoCGAGFAQAB
+      AAASjgIKEHOZ/a+LQwTLSkMO0sPwg9gSCL1SwQw1+0iKKgxUYXNrIENyZWF0ZWQwATl4kX48X34J
+      GEFIFn88X34JGEouCghjcmV3X2tleRIiCiBlZTY3NDVkN2M4YWU4MmUwMGRmOTRkZTBmN2Y4NzEx
+      OEoxCgdjcmV3X2lkEiYKJDAwOThmODNmLTdkNTAtNGI2Mi1hYmIyLTJlNTc0N2ZlMWE4OUouCgh0
+      YXNrX2tleRIiCiAwNmE3MzIyMGY0MTQ4YTRiYmQ1YmFjYjBkMGI0NGZjZUoxCgd0YXNrX2lkEiYK
+      JDVjODIzNWZmLTVlYzctNDcxYS1iODVhLTVhZGY5N2MyZGMyN3oCGAGFAQABAAASswcKEHZQCRd7
+      z4ZBCh4+06qs1r4SCNzrNsw+dn2zKgxDcmV3IENyZWF0ZWQwATlgWdtDX34JGEGIcN1DX34JGEoa
+      Cg5jcmV3YWlfdmVyc2lvbhIICgYwLjgwLjBKGgoOcHl0aG9uX3ZlcnNpb24SCAoGMy4xMi41Si4K
+      CGNyZXdfa2V5EiIKIGVlNjc0NWQ3YzhhZTgyZTAwZGY5NGRlMGY3Zjg3MTE4SjEKB2NyZXdfaWQS
+      JgokZTgzMTdjNzEtNmZiZS00MjI5LWE3MzctYTkxM2I0ZmU0ZTU0ShwKDGNyZXdfcHJvY2VzcxIM
+      CgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3X251bWJlcl9vZl90YXNrcxIC
+      GAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrZAgoLY3Jld19hZ2VudHMSyQIKxgJbeyJr
+      ZXkiOiAiZjMzODZmNmQ4ZGE3NWFhNDE2YTZlMzEwMDUzZjc2OTgiLCAiaWQiOiAiNjAwMzU5OTYt
+      ZWU1ZS00YmZhLThmODctMGM1ZTY0OTBlMmE4IiwgInJvbGUiOiAie3RvcGljfSBSZXNlYXJjaGVy
+      IiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGwsICJm
+      dW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVnYXRp
+      b25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAibWF4
+      X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119XUqHAgoKY3Jld190YXNrcxL4AQr1
+      AVt7ImtleSI6ICIwNmE3MzIyMGY0MTQ4YTRiYmQ1YmFjYjBkMGI0NGZjZSIsICJpZCI6ICI4MDc3
+      MDhjNS0yN2RkLTQ4ZDEtYTU0ZC1lZTRkNTZmMzBiZTQiLCAiYXN5bmNfZXhlY3V0aW9uPyI6IGZh
+      bHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9yb2xlIjogInt0b3BpY30gUmVzZWFy
+      Y2hlciIsICJhZ2VudF9rZXkiOiAiZjMzODZmNmQ4ZGE3NWFhNDE2YTZlMzEwMDUzZjc2OTgiLCAi
+      dG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKOAgoQtqHg5uy2kZsnJlJTYgmZoxIIlgUHkQ7m
+      LugqDFRhc2sgQ3JlYXRlZDABOTi470NffgkYQQg98ENffgkYSi4KCGNyZXdfa2V5EiIKIGVlNjc0
+      NWQ3YzhhZTgyZTAwZGY5NGRlMGY3Zjg3MTE4SjEKB2NyZXdfaWQSJgokZTgzMTdjNzEtNmZiZS00
+      MjI5LWE3MzctYTkxM2I0ZmU0ZTU0Si4KCHRhc2tfa2V5EiIKIDA2YTczMjIwZjQxNDhhNGJiZDVi
+      YWNiMGQwYjQ0ZmNlSjEKB3Rhc2tfaWQSJgokODA3NzA4YzUtMjdkZC00OGQxLWE1NGQtZWU0ZDU2
+      ZjMwYmU0egIYAYUBAAEAABKzBwoQpfKhpM9cCoiT5Mun1aoNQhII4HhX0QHHc/0qDENyZXcgQ3Jl
+      YXRlZDABORiH20lffgkYQcix3UlffgkYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5w
+      eXRob25fdmVyc2lvbhIICgYzLjEyLjVKLgoIY3Jld19rZXkSIgogZWU2NzQ1ZDdjOGFlODJlMDBk
+      Zjk0ZGUwZjdmODcxMThKMQoHY3Jld19pZBImCiRmZDk2MmQwMi0wNGY0LTQ3NDUtODc5YS02NTFm
+      MzFmMmZhOTZKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAA
+      ShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgB
+      StkCCgtjcmV3X2FnZW50cxLJAgrGAlt7ImtleSI6ICJmMzM4NmY2ZDhkYTc1YWE0MTZhNmUzMTAw
+      NTNmNzY5OCIsICJpZCI6ICIzNmZhMTEyZS02ZDVlLTRhMzgtODk0Yy01M2M5YjAzNTI5ODUiLCAi
+      cm9sZSI6ICJ7dG9waWN9IFJlc2VhcmNoZXIiLCAidmVyYm9zZT8iOiBmYWxzZSwgIm1heF9pdGVy
+      IjogMjAsICJtYXhfcnBtIjogbnVsbCwgImZ1bmN0aW9uX2NhbGxpbmdfbGxtIjogIiIsICJsbG0i
+      OiAiZ3B0LTRvLW1pbmkiLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxsb3dfY29k
+      ZV9leGVjdXRpb24/IjogZmFsc2UsICJtYXhfcmV0cnlfbGltaXQiOiAyLCAidG9vbHNfbmFtZXMi
+      OiBbXX1dSocCCgpjcmV3X3Rhc2tzEvgBCvUBW3sia2V5IjogIjA2YTczMjIwZjQxNDhhNGJiZDVi
+      YWNiMGQwYjQ0ZmNlIiwgImlkIjogIjY3NTE3ZjY1LThhYzMtNDIyZi1hMmJhLTM4NDcyZDRkYmZl
+      NSIsICJhc3luY19leGVjdXRpb24/IjogZmFsc2UsICJodW1hbl9pbnB1dD8iOiBmYWxzZSwgImFn
+      ZW50X3JvbGUiOiAie3RvcGljfSBSZXNlYXJjaGVyIiwgImFnZW50X2tleSI6ICJmMzM4NmY2ZDhk
+      YTc1YWE0MTZhNmUzMTAwNTNmNzY5OCIsICJ0b29sc19uYW1lcyI6IFtdfV16AhgBhQEAAQAAEo4C
+      ChAzGUzQMDZOgJ090im3887lEgik7+/nVnqntioMVGFzayBDcmVhdGVkMAE5UO7rSV9+CRhBWEDs
+      SV9+CRhKLgoIY3Jld19rZXkSIgogZWU2NzQ1ZDdjOGFlODJlMDBkZjk0ZGUwZjdmODcxMThKMQoH
+      Y3Jld19pZBImCiRmZDk2MmQwMi0wNGY0LTQ3NDUtODc5YS02NTFmMzFmMmZhOTZKLgoIdGFza19r
+      ZXkSIgogMDZhNzMyMjBmNDE0OGE0YmJkNWJhY2IwZDBiNDRmY2VKMQoHdGFza19pZBImCiQ2NzUx
+      N2Y2NS04YWMzLTQyMmYtYTJiYS0zODQ3MmQ0ZGJmZTV6AhgBhQEAAQAAErMHChCB1TPvVbWX62DF
+      102NfOHLEghdZ/LjI40W8SoMQ3JldyBDcmVhdGVkMAE5sJDUT19+CRhBEHXWT19+CRhKGgoOY3Jl
+      d2FpX3ZlcnNpb24SCAoGMC44MC4wShoKDnB5dGhvbl92ZXJzaW9uEggKBjMuMTIuNUouCghjcmV3
+      X2tleRIiCiBlZTY3NDVkN2M4YWU4MmUwMGRmOTRkZTBmN2Y4NzExOEoxCgdjcmV3X2lkEiYKJDUx
+      YmI1NGQ0LWM4MTAtNDA0Yy04MTQzLWVmNTgwMTlhN2Q2OEocCgxjcmV3X3Byb2Nlc3MSDAoKc2Vx
+      dWVudGlhbEoRCgtjcmV3X21lbW9yeRICEABKGgoUY3Jld19udW1iZXJfb2ZfdGFza3MSAhgBShsK
+      FWNyZXdfbnVtYmVyX29mX2FnZW50cxICGAFK2QIKC2NyZXdfYWdlbnRzEskCCsYCW3sia2V5Ijog
+      ImYzMzg2ZjZkOGRhNzVhYTQxNmE2ZTMxMDA1M2Y3Njk4IiwgImlkIjogIjRlNTExYTRhLTM1Yzkt
+      NDA0NC1iMzBlLWM4OGZjZTJiMzc5YiIsICJyb2xlIjogInt0b3BpY30gUmVzZWFyY2hlciIsICJ2
+      ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rp
+      b25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2Vu
+      YWJsZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRy
+      eV9saW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFtdfV1KhwIKCmNyZXdfdGFza3MS+AEK9QFbeyJr
+      ZXkiOiAiMDZhNzMyMjBmNDE0OGE0YmJkNWJhY2IwZDBiNDRmY2UiLCAiaWQiOiAiOTIzMWJmZjIt
+      ODFmOS00MjU4LTgyMDktMjkzMjUyOWI1ZjlmIiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxzZSwg
+      Imh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJ7dG9waWN9IFJlc2VhcmNoZXIi
+      LCAiYWdlbnRfa2V5IjogImYzMzg2ZjZkOGRhNzVhYTQxNmE2ZTMxMDA1M2Y3Njk4IiwgInRvb2xz
+      X25hbWVzIjogW119XXoCGAGFAQABAAASjgIKECTO19lNFYzBivlrqiZfSxASCIAH8VhjiPfQKgxU
+      YXNrIENyZWF0ZWQwATnIr+NPX34JGEHQAeRPX34JGEouCghjcmV3X2tleRIiCiBlZTY3NDVkN2M4
+      YWU4MmUwMGRmOTRkZTBmN2Y4NzExOEoxCgdjcmV3X2lkEiYKJDUxYmI1NGQ0LWM4MTAtNDA0Yy04
+      MTQzLWVmNTgwMTlhN2Q2OEouCgh0YXNrX2tleRIiCiAwNmE3MzIyMGY0MTQ4YTRiYmQ1YmFjYjBk
+      MGI0NGZjZUoxCgd0YXNrX2lkEiYKJDkyMzFiZmYyLTgxZjktNDI1OC04MjA5LTI5MzI1MjliNWY5
+      ZnoCGAGFAQABAAASswcKEHGb7KITfOkYfQT7CRjWfUcSCIn6YlQJ1QVbKgxDcmV3IENyZWF0ZWQw
+      ATmYH/BYX34JGEEgJ/JYX34JGEoaCg5jcmV3YWlfdmVyc2lvbhIICgYwLjgwLjBKGgoOcHl0aG9u
+      X3ZlcnNpb24SCAoGMy4xMi41Si4KCGNyZXdfa2V5EiIKIGVlNjc0NWQ3YzhhZTgyZTAwZGY5NGRl
+      MGY3Zjg3MTE4SjEKB2NyZXdfaWQSJgokZDc5Y2UyMWUtYmU1Ny00NTdiLWExMzEtNjZkMDFmZjQx
+      ZTI2ShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRj
+      cmV3X251bWJlcl9vZl90YXNrcxICGAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrZAgoL
+      Y3Jld19hZ2VudHMSyQIKxgJbeyJrZXkiOiAiZjMzODZmNmQ4ZGE3NWFhNDE2YTZlMzEwMDUzZjc2
+      OTgiLCAiaWQiOiAiNzRhNDUxNzgtNmExOS00N2RjLThlZjktZDdhZmQ5YzUwMDQ0IiwgInJvbGUi
+      OiAie3RvcGljfSBSZXNlYXJjaGVyIiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIw
+      LCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdw
+      dC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhl
+      Y3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119
+      XUqHAgoKY3Jld190YXNrcxL4AQr1AVt7ImtleSI6ICIwNmE3MzIyMGY0MTQ4YTRiYmQ1YmFjYjBk
+      MGI0NGZjZSIsICJpZCI6ICJjZWZiYjE1ZS01Y2M4LTQwZTctYTViMS03ODkzYjJlZGFkYmQiLCAi
+      YXN5bmNfZXhlY3V0aW9uPyI6IGZhbHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9y
+      b2xlIjogInt0b3BpY30gUmVzZWFyY2hlciIsICJhZ2VudF9rZXkiOiAiZjMzODZmNmQ4ZGE3NWFh
+      NDE2YTZlMzEwMDUzZjc2OTgiLCAidG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAAA==
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '11392'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Tue, 19 Nov 2024 22:14:39 GMT
+    status:
+      code: 200
+      message: OK
+version: 1

tests/knowledge/knowledge_test.py

@@ -0,0 +1,545 @@
+"""Test Knowledge creation and querying functionality."""
+
+from pathlib import Path
+from unittest.mock import patch
+
+from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
+from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def mock_vector_db():
+    """Mock vector database operations."""
+    with patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage") as mock:
+        # Mock the query method to return a predefined response
+        instance = mock.return_value
+        instance.query.return_value = [
+            {
+                "context": "Brandon's favorite color is blue and he likes Mexican food.",
+                "score": 0.9,
+            }
+        ]
+        instance.reset.return_value = None
+        yield instance
+
+
+@pytest.fixture(autouse=True)
+def reset_knowledge_storage(mock_vector_db):
+    """Fixture to reset knowledge storage before each test."""
+    yield
+
+
+def test_single_short_string(mock_vector_db):
+    # Create a knowledge base with a single short string
+    content = "Brandon's favorite color is blue and he likes Mexican food."
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [string_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What is Brandon's favorite color?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("blue" in result["context"].lower() for result in results)
+    # Verify the mock was called
+    mock_vector_db.query.assert_called_once()
+
+
+# @pytest.mark.vcr(filter_headers=["authorization"])
+def test_single_2k_character_string(mock_vector_db):
+    # Create a 2k character string with various facts about Brandon
+    content = (
+        "Brandon is a software engineer who lives in San Francisco. "
+        "He enjoys hiking and often visits the trails in the Bay Area. "
+        "Brandon has a pet dog named Max, who is a golden retriever. "
+        "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+        "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+        "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+        "Brandon plays the guitar and often performs at local open mic nights. "
+        "He is learning French and plans to visit Paris next year. "
+        "Brandon is passionate about technology and often attends tech meetups in the city. "
+        "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+        "Brandon's favorite color is blue, and he often wears blue shirts. "
+        "He enjoys cooking and often tries new recipes on weekends. "
+        "Brandon is a morning person and likes to start his day with a run in the park. "
+        "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+        "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+        "He is also a fan of board games and often hosts game nights at his place. "
+        "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+        "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+        "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+        "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+    )
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [string_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite movie?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("inception" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_short_strings(mock_vector_db):
+    # Create multiple short string sources
+    contents = [
+        "Brandon loves hiking.",
+        "Brandon has a dog named Max.",
+        "Brandon enjoys painting landscapes.",
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in contents
+    ]
+
+    # Mock the vector db query response
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon has a dog named Max.", "score": 0.9}
+    ]
+
+    mock_vector_db.sources = string_sources
+
+    # Perform a query
+    query = "What is the name of Brandon's pet?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("max" in result["context"].lower() for result in results)
+    # Verify the mock was called
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_2k_character_strings(mock_vector_db):
+    # Create multiple 2k character strings with various facts about Brandon
+    contents = [
+        (
+            "Brandon is a software engineer who lives in San Francisco. "
+            "He enjoys hiking and often visits the trails in the Bay Area. "
+            "Brandon has a pet dog named Max, who is a golden retriever. "
+            "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+            "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+            "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+            "Brandon plays the guitar and often performs at local open mic nights. "
+            "He is learning French and plans to visit Paris next year. "
+            "Brandon is passionate about technology and often attends tech meetups in the city. "
+            "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+            "Brandon's favorite color is blue, and he often wears blue shirts. "
+            "He enjoys cooking and often tries new recipes on weekends. "
+            "Brandon is a morning person and likes to start his day with a run in the park. "
+            "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+            "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+            "He is also a fan of board games and often hosts game nights at his place. "
+            "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+            "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+            "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+            "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+        (
+            "Brandon loves traveling and has visited over 20 countries. "
+            "He is fluent in Spanish and often practices with his friends. "
+            "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
+            "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
+            "Brandon is an avid cyclist and participates in local cycling events. "
+            "He is also a photographer and enjoys capturing landscapes and cityscapes. "
+            "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
+            "He is also a fan of virtual reality and owns a VR headset. "
+            "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
+            "He enjoys watching documentaries and learning about history and science. "
+            "Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
+            "He is also a fan of jazz music and often attends live performances. "
+            "Brandon is a member of a local running club and participates in marathons. "
+            "He is also a volunteer at a local animal shelter and helps with dog walking. "
+            "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
+            "He is also a fan of classic movies and has a collection of DVDs. "
+            "Brandon is a mentor for young professionals and enjoys giving career advice. "
+            "He is also a fan of puzzles and enjoys solving them in his free time. "
+            "Brandon's favorite sport is soccer, and he often plays with his friends. "
+            "He is also a fan of FC Barcelona and enjoys watching their matches. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in contents
+    ]
+
+    mock_vector_db.sources = string_sources
+    mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "the hitchhiker's guide to the galaxy" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+def test_single_short_file(mock_vector_db, tmpdir):
+    # Create a single short text file
+    content = "Brandon's favorite sport is basketball."
+    file_path = Path(tmpdir.join("short_file.txt"))
+    with open(file_path, "w") as f:
+        f.write(content)
+
+    file_source = TextFileKnowledgeSource(
+        file_path=file_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [file_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What sport does Brandon like?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("basketball" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_single_2k_character_file(mock_vector_db, tmpdir):
+    # Create a single 2k character text file with various facts about Brandon
+    content = (
+        "Brandon is a software engineer who lives in San Francisco. "
+        "He enjoys hiking and often visits the trails in the Bay Area. "
+        "Brandon has a pet dog named Max, who is a golden retriever. "
+        "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+        "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+        "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+        "Brandon plays the guitar and often performs at local open mic nights. "
+        "He is learning French and plans to visit Paris next year. "
+        "Brandon is passionate about technology and often attends tech meetups in the city. "
+        "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+        "Brandon's favorite color is blue, and he often wears blue shirts. "
+        "He enjoys cooking and often tries new recipes on weekends. "
+        "Brandon is a morning person and likes to start his day with a run in the park. "
+        "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+        "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+        "He is also a fan of board games and often hosts game nights at his place. "
+        "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+        "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+        "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+        "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+    ) * 2  # Repeat to ensure it's 2k characters
+    file_path = Path(tmpdir.join("long_file.txt"))
+    with open(file_path, "w") as f:
+        f.write(content)
+
+    file_source = TextFileKnowledgeSource(
+        file_path=file_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [file_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What is Brandon's favorite movie?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("inception" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_short_files(mock_vector_db, tmpdir):
+    # Create multiple short text files
+    contents = [
+        {
+            "content": "Brandon works as a software engineer.",
+            "metadata": {"category": "profession", "source": "occupation"},
+        },
+        {
+            "content": "Brandon lives in New York.",
+            "metadata": {"category": "city", "source": "personal"},
+        },
+        {
+            "content": "Brandon enjoys cooking Italian food.",
+            "metadata": {"category": "hobby", "source": "personal"},
+        },
+    ]
+    file_paths = []
+    for i, item in enumerate(contents):
+        file_path = Path(tmpdir.join(f"file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(item["content"])
+        file_paths.append((file_path, item["metadata"]))
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata=metadata)
+        for path, metadata in file_paths
+    ]
+    mock_vector_db.sources = file_sources
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon lives in New York.", "score": 0.9}
+    ]
+    # Perform a query
+    query = "What city does he reside in?"
+    results = mock_vector_db.query(query)
+    # Assert that the correct information is retrieved
+    assert any("new york" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_2k_character_files(mock_vector_db, tmpdir):
+    # Create multiple 2k character text files with various facts about Brandon
+    contents = [
+        (
+            "Brandon loves traveling and has visited over 20 countries. "
+            "He is fluent in Spanish and often practices with his friends. "
+            "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
+            "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
+            "Brandon is an avid cyclist and participates in local cycling events. "
+            "He is also a photographer and enjoys capturing landscapes and cityscapes. "
+            "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
+            "He is also a fan of virtual reality and owns a VR headset. "
+            "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
+            "He enjoys watching documentaries and learning about history and science. "
+            "Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
+            "He is also a fan of jazz music and often attends live performances. "
+            "Brandon is a member of a local running club and participates in marathons. "
+            "He is also a volunteer at a local animal shelter and helps with dog walking. "
+            "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
+            "He is also a fan of classic movies and has a collection of DVDs. "
+            "Brandon is a mentor for young professionals and enjoys giving career advice. "
+            "He is also a fan of puzzles and enjoys solving them in his free time. "
+            "Brandon's favorite sport is soccer, and he often plays with his friends. "
+            "He is also a fan of FC Barcelona and enjoys watching their matches. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+        (
+            "Brandon is a software engineer who lives in San Francisco. "
+            "He enjoys hiking and often visits the trails in the Bay Area. "
+            "Brandon has a pet dog named Max, who is a golden retriever. "
+            "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+            "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+            "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+            "Brandon plays the guitar and often performs at local open mic nights. "
+            "He is learning French and plans to visit Paris next year. "
+            "Brandon is passionate about technology and often attends tech meetups in the city. "
+            "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+            "Brandon's favorite color is blue, and he often wears blue shirts. "
+            "He enjoys cooking and often tries new recipes on weekends. "
+            "Brandon is a morning person and likes to start his day with a run in the park. "
+            "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+            "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+            "He is also a fan of board games and often hosts game nights at his place. "
+            "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+            "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+            "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+            "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+    ]
+    file_paths = []
+    for i, content in enumerate(contents):
+        file_path = Path(tmpdir.join(f"long_file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(content)
+        file_paths.append(file_path)
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        for path in file_paths
+    ]
+    mock_vector_db.sources = file_sources
+    mock_vector_db.query.return_value = [
+        {
+            "context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.",
+            "score": 0.9,
+        }
+    ]
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "the hitchhiker's guide to the galaxy" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_hybrid_string_and_files(mock_vector_db, tmpdir):
+    # Create string sources
+    string_contents = [
+        "Brandon is learning French.",
+        "Brandon visited Paris last summer.",
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in string_contents
+    ]
+
+    # Create file sources
+    file_contents = [
+        "Brandon prefers tea over coffee.",
+        "Brandon's favorite book is 'The Alchemist'.",
+    ]
+    file_paths = []
+    for i, content in enumerate(file_contents):
+        file_path = Path(tmpdir.join(f"file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(content)
+        file_paths.append(file_path)
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        for path in file_paths
+    ]
+
+    # Combine string and file sources
+    mock_vector_db.sources = string_sources + file_sources
+    mock_vector_db.query.return_value = [{"context": file_contents[1], "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("the alchemist" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_pdf_knowledge_source(mock_vector_db):
+    # Get the directory of the current file
+    current_dir = Path(__file__).parent
+    # Construct the path to the PDF file
+    pdf_path = current_dir / "crewai_quickstart.pdf"
+
+    # Create a PDFKnowledgeSource
+    pdf_source = PDFKnowledgeSource(
+        file_path=pdf_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [pdf_source]
+    mock_vector_db.query.return_value = [
+        {"context": "crewai create crew latest-ai-development", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "How do you create a crew?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "crewai create crew latest-ai-development" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_csv_knowledge_source(mock_vector_db, tmpdir):
+    """Test CSVKnowledgeSource with a simple CSV file."""
+
+    # Create a CSV file with sample data
+    csv_content = [
+        ["Name", "Age", "City"],
+        ["Brandon", "30", "New York"],
+        ["Alice", "25", "Los Angeles"],
+        ["Bob", "35", "Chicago"],
+    ]
+    csv_path = Path(tmpdir.join("data.csv"))
+    with open(csv_path, "w", encoding="utf-8") as f:
+        for row in csv_content:
+            f.write(",".join(row) + "\n")
+
+    # Create a CSVKnowledgeSource
+    csv_source = CSVKnowledgeSource(
+        file_path=csv_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [csv_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon is 30 years old.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "How old is Brandon?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("30" in result["context"] for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_json_knowledge_source(mock_vector_db, tmpdir):
+    """Test JSONKnowledgeSource with a simple JSON file."""
+
+    # Create a JSON file with sample data
+    json_data = {
+        "people": [
+            {"name": "Brandon", "age": 30, "city": "New York"},
+            {"name": "Alice", "age": 25, "city": "Los Angeles"},
+            {"name": "Bob", "age": 35, "city": "Chicago"},
+        ]
+    }
+    json_path = Path(tmpdir.join("data.json"))
+    with open(json_path, "w", encoding="utf-8") as f:
+        import json
+
+        json.dump(json_data, f)
+
+    # Create a JSONKnowledgeSource
+    json_source = JSONKnowledgeSource(
+        file_path=json_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [json_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Alice lives in Los Angeles.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "Where does Alice reside?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("los angeles" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_excel_knowledge_source(mock_vector_db, tmpdir):
+    """Test ExcelKnowledgeSource with a simple Excel file."""
+
+    # Create an Excel file with sample data
+    import pandas as pd
+
+    excel_data = {
+        "Name": ["Brandon", "Alice", "Bob"],
+        "Age": [30, 25, 35],
+        "City": ["New York", "Los Angeles", "Chicago"],
+    }
+    df = pd.DataFrame(excel_data)
+    excel_path = Path(tmpdir.join("data.xlsx"))
+    df.to_excel(excel_path, index=False)
+
+    # Create an ExcelKnowledgeSource
+    excel_source = ExcelKnowledgeSource(
+        file_path=excel_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [excel_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon is 30 years old.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "What is Brandon's age?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("30" in result["context"] for result in results)
+    mock_vector_db.query.assert_called_once()

tests/pipeline/test_pipeline.py

@@ -38,6 +38,7 @@ def mock_crew_factory():
 
         crew = MockCrew()
         crew.name = name
+        crew.knowledge = None
 
         task_output = TaskOutput(
             description="Test task", raw="Task output", agent="Test Agent"
@@ -67,6 +68,7 @@ def mock_crew_factory():
         crew.process = Process.sequential
         crew.config = None
         crew.cache = True
+        crew.embedder = None
 
         # Add non-empty agents and tasks
         mock_agent = MagicMock(spec=Agent)

uv.lock

@@ -638,6 +638,18 @@ dependencies = [
 agentops = [
     { name = "agentops" },
 ]
+fastembed = [
+    { name = "fastembed" },
+]
+openpyxl = [
+    { name = "openpyxl" },
+]
+pandas = [
+    { name = "pandas" },
+]
+pdfplumber = [
+    { name = "pdfplumber" },
+]
 mem0 = [
     { name = "mem0ai" },
 ]
@@ -674,6 +686,7 @@ requires-dist = [
     { name = "click", specifier = ">=8.1.7" },
     { name = "crewai-tools", specifier = ">=0.14.0" },
     { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
+    { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "jsonref", specifier = ">=1.1.0" },
@@ -681,9 +694,12 @@ requires-dist = [
     { name = "litellm", specifier = ">=1.44.22" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
+    { name = "openpyxl", marker = "extra == 'openpyxl'", specifier = ">=3.1.5" },
     { name = "opentelemetry-api", specifier = ">=1.22.0" },
     { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.22.0" },
     { name = "opentelemetry-sdk", specifier = ">=1.22.0" },
+    { name = "pandas", marker = "extra == 'pandas'", specifier = ">=2.2.3" },
+    { name = "pdfplumber", marker = "extra == 'pdfplumber'", specifier = ">=0.11.4" },
     { name = "pydantic", specifier = ">=2.4.2" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "pyvis", specifier = ">=0.3.2" },
@@ -927,6 +943,16 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/52/82/3d0355c22bc68cfbb8fbcf670da4c01b31bd7eb516974a08cf7533e89887/embedchain-0.1.125-py3-none-any.whl", hash = "sha256:f87b49732dc192c6b61221830f29e59cf2aff26d8f5d69df81f6f6cf482715c2", size = 211367 },
 ]
 
+[[package]]
+name = "et-xmlfile"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d3/38/af70d7ab1ae9d4da450eeec1fa3918940a5fafb9055e934af8d6eb0c2313/et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54", size = 17234 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/8b/5fe2cc11fee489817272089c4203e679c63b570a5aaeb18d852ae3cbba6a/et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa", size = 18059 },
+]
+
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -985,6 +1011,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c8/0c/92b468e4649e61eaa2d93a92e19a5b57a0f6cecaa236c53a76f3f72a4696/fastavro-1.9.7-cp312-cp312-win_amd64.whl", hash = "sha256:b6b2ccdc78f6afc18c52e403ee68c00478da12142815c1bd8a00973138a166d0", size = 487778 },
 ]
 
+[[package]]
+name = "fastembed"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "loguru" },
+    { name = "mmh3" },
+    { name = "numpy" },
+    { name = "onnx" },
+    { name = "onnxruntime" },
+    { name = "pillow" },
+    { name = "py-rust-stemmers" },
+    { name = "requests" },
+    { name = "tokenizers" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0c/75/0883d15b54016fa99a32cc333182bf862025bf0983daac417a1beabb53bf/fastembed-0.4.1.tar.gz", hash = "sha256:d5dcfffc3554dca48caf16eec35e38c20544c58e396a5d215f238d40c8442718", size = 40461 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/19/ae/1303f005be08ff31686a30421121680b864cc6d82f7cd82cee74a6ff9416/fastembed-0.4.1-py3-none-any.whl", hash = "sha256:f75f02468aafa8de474844f9fbaa89683a3dcfd76521fa83cfc3efc885db61f3", size = 65123 },
+]
+
 [[package]]
 name = "filelock"
 version = "3.16.1"
@@ -2042,6 +2090,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/22/f3/89a4d65d1b9286eb5ac6a6e92dd93523d92f3142a832e60c00d5cad64176/litellm-1.50.2-py3-none-any.whl", hash = "sha256:99cac60c78037946ab809b7cfbbadad53507bb2db8ae39391b4be215a0869fdd", size = 6318265 },
 ]
 
+[[package]]
+name = "loguru"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "win32-setctime", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9e/30/d87a423766b24db416a46e9335b9602b054a72b96a88a241f2b09b560fa8/loguru-0.7.2.tar.gz", hash = "sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac", size = 145103 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/0a/4f6fed21aa246c6b49b561ca55facacc2a44b87d65b8b92362a8e99ba202/loguru-0.7.2-py3-none-any.whl", hash = "sha256:003d71e3d3ed35f0f8984898359d65b79e5b21943f78af86aa5491210429b8eb", size = 62549 },
+]
+
 [[package]]
 name = "mako"
 version = "1.3.6"
@@ -2310,74 +2371,58 @@ wheels = [
 
 [[package]]
 name = "mmh3"
-version = "5.0.1"
+version = "4.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e2/08/04ad6419f072ea3f51f9a0f429dd30f5f0a0b02ead7ca11a831117b6f9e8/mmh3-5.0.1.tar.gz", hash = "sha256:7dab080061aeb31a6069a181f27c473a1f67933854e36a3464931f2716508896", size = 32008 }
+sdist = { url = "https://files.pythonhosted.org/packages/63/96/aa247e82878b123468f0079ce2ac77e948315bab91ce45d2934a62e0af95/mmh3-4.1.0.tar.gz", hash = "sha256:a1cf25348b9acd229dda464a094d6170f47d2850a1fcb762a3b6172d2ce6ca4a", size = 26357 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fa/b9/9a91b0a0e330557cdbf51fc43ca0ba306633f2ec6d2b15e871e288592a32/mmh3-5.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f0a4b4bf05778ed77d820d6e7d0e9bd6beb0c01af10e1ce9233f5d2f814fcafa", size = 52867 },
-    { url = "https://files.pythonhosted.org/packages/da/28/6b37f0d6707872764e1af49f327b0940b6a3ad995d91b3839b90ba35f559/mmh3-5.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac7a391039aeab95810c2d020b69a94eb6b4b37d4e2374831e92db3a0cdf71c6", size = 38352 },
-    { url = "https://files.pythonhosted.org/packages/76/84/a98f59a620b522f218876a0630b02fc345ecf078f6393595756ddb3aa0b5/mmh3-5.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3a2583b5521ca49756d8d8bceba80627a9cc295f255dcab4e3df7ccc2f09679a", size = 38214 },
-    { url = "https://files.pythonhosted.org/packages/35/cb/4980c7eb6cd31f49d1913a4066562bc9e0af28526750f1232be9688a9cd4/mmh3-5.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:081a8423fe53c1ac94f87165f3e4c500125d343410c1a0c5f1703e898a3ef038", size = 93502 },
-    { url = "https://files.pythonhosted.org/packages/65/f3/29726296fadeaf06134a6978f7c453dfa562cf2f0f1faf9ae28b9b8ef76e/mmh3-5.0.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b8b4d72713799755dc8954a7d36d5c20a6c8de7b233c82404d122c7c7c1707cc", size = 98394 },
-    { url = "https://files.pythonhosted.org/packages/35/fd/e181f4f4b250f7b63ee27a7d65e5e290a3ea0e26cc633f4bfd906f04558b/mmh3-5.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:389a6fd51efc76d3182d36ec306448559c1244f11227d2bb771bdd0e6cc91321", size = 98052 },
-    { url = "https://files.pythonhosted.org/packages/61/5c/8a5d838da3eb3fb91035ef5eaaea469abab4e8e3fae55607c27a1a07d162/mmh3-5.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:39f4128edaa074bff721b1d31a72508cba4d2887ee7867f22082e1fe9d4edea0", size = 86320 },
-    { url = "https://files.pythonhosted.org/packages/10/80/3f33a8f4de12cea322607da1a84d001513affb741b3c3cc1277ecb85d34b/mmh3-5.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d5d23a94d91aabba3386b3769048d5f4210fdfef80393fece2f34ba5a7b466c", size = 93232 },
-    { url = "https://files.pythonhosted.org/packages/9e/1c/d0ce5f498493be4de2e7e7596e1cbf63315a4c0bb8bb94e3c37c4fad965d/mmh3-5.0.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:16347d038361f8b8f24fd2b7ef378c9b68ddee9f7706e46269b6e0d322814713", size = 93590 },
-    { url = "https://files.pythonhosted.org/packages/d9/66/770b5ad35b5a2eb7965f3fcaeaa76148e59543575d2e27b80690c1b0795c/mmh3-5.0.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:6e299408565af7d61f2d20a5ffdd77cf2ed902460fe4e6726839d59ba4b72316", size = 88433 },
-    { url = "https://files.pythonhosted.org/packages/14/58/e0d258b18749d8640233976493716a40aa27352dcb1cea941836357dac24/mmh3-5.0.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:42050af21ddfc5445ee5a66e73a8fc758c71790305e3ee9e4a85a8e69e810f94", size = 99339 },
-    { url = "https://files.pythonhosted.org/packages/38/26/7267146122deb584cf377975b994d80c6d72c4c8d0e8eedff4d0cc5cd4c8/mmh3-5.0.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:2ae9b1f5ef27ec54659920f0404b7ceb39966e28867c461bfe83a05e8d18ddb0", size = 93944 },
-    { url = "https://files.pythonhosted.org/packages/8d/6b/df60b14a2dd383d8848f6f35496c86c7003be3ffb236789e98d002c542c6/mmh3-5.0.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:50c2495a02045f3047d71d4ae9cdd7a15efc0bcbb7ff17a18346834a8e2d1d19", size = 92798 },
-    { url = "https://files.pythonhosted.org/packages/0a/3f/d5fecf13915163a15b449e5cc89232a4df90e836ecad1c38121318119d27/mmh3-5.0.1-cp310-cp310-win32.whl", hash = "sha256:c028fa77cddf351ca13b4a56d43c1775652cde0764cadb39120b68f02a23ecf6", size = 39185 },
-    { url = "https://files.pythonhosted.org/packages/74/8e/4bb5ade332a87de633cda21dae09d6002d69601f2b93e9f40302ab2d9acf/mmh3-5.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:c5e741e421ec14400c4aae30890515c201f518403bdef29ae1e00d375bb4bbb5", size = 39766 },
-    { url = "https://files.pythonhosted.org/packages/16/2b/cd5cfa4d7ad40a37655af491f9270909d63fc27bcf0558ec36000ee5347f/mmh3-5.0.1-cp310-cp310-win_arm64.whl", hash = "sha256:b17156d56fabc73dbf41bca677ceb6faed435cc8544f6566d72ea77d8a17e9d0", size = 36540 },
-    { url = "https://files.pythonhosted.org/packages/fb/8a/f3b9cf8b7110fef0f130158d7602af6f5b09f2cf568130814b7c92e2507b/mmh3-5.0.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9a6d5a9b1b923f1643559ba1fc0bf7a5076c90cbb558878d3bf3641ce458f25d", size = 52867 },
-    { url = "https://files.pythonhosted.org/packages/bf/06/f466e0da3c5bd6fbb1e047f70fd4e9e9563d0268aa56de511f363478dbf2/mmh3-5.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3349b968be555f7334bbcce839da98f50e1e80b1c615d8e2aa847ea4a964a012", size = 38349 },
-    { url = "https://files.pythonhosted.org/packages/13/f0/2d3daca276a4673f82af859e4b0b18befd4e6e54f1017ba48ea9735b2f1b/mmh3-5.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1bd3c94b110e55db02ab9b605029f48a2f7f677c6e58c09d44e42402d438b7e1", size = 38211 },
-    { url = "https://files.pythonhosted.org/packages/e3/56/a2d203ca97702d4e045ac1a46a608393da1a1dddb24f81de664dae940518/mmh3-5.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d47ba84d48608f79adbb10bb09986b6dc33eeda5c2d1bd75d00820081b73bde9", size = 95104 },
-    { url = "https://files.pythonhosted.org/packages/ec/45/c7c8ae64e3ae024776a0ce5377c16c6741a3359f3e9505fc35fc5012beb2/mmh3-5.0.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c0217987a8b8525c8d9170f66d036dec4ab45cfbd53d47e8d76125791ceb155e", size = 100049 },
-    { url = "https://files.pythonhosted.org/packages/d5/74/681113776fe406c09870ab2152ffbd214a15bbc8f1d1da9ad73ce594b878/mmh3-5.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2797063a34e78d1b61639a98b0edec1c856fa86ab80c7ec859f1796d10ba429", size = 99671 },
-    { url = "https://files.pythonhosted.org/packages/bf/4f/dbb8be18ce9b6ff8df14bc14348c0404b3091fb51df9c673ebfcf5877db3/mmh3-5.0.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8bba16340adcbd47853a2fbe5afdb397549e8f2e79324ff1dced69a3f8afe7c3", size = 87549 },
-    { url = "https://files.pythonhosted.org/packages/5f/82/274d646f3f604c35b7e3d4eb7f3ff08b3bdc6a2c87d797709bb6f084a611/mmh3-5.0.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:282797957c9f60b51b9d768a602c25f579420cc9af46feb77d457a27823d270a", size = 94780 },
-    { url = "https://files.pythonhosted.org/packages/c9/a1/f094ca8b8fb5e2ac53201070bda42b0fee80ceb92c153eb99a1453e3aed3/mmh3-5.0.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e4fb670c29e63f954f9e7a2cdcd57b36a854c2538f579ef62681ccbaa1de2b69", size = 90430 },
-    { url = "https://files.pythonhosted.org/packages/d9/23/4732ba68c6ef7242b69bb53b9e1bcb2ef065d68ed85fd26e829fb911ab5a/mmh3-5.0.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:8ee7d85438dc6aff328e19ab052086a3c29e8a9b632998a49e5c4b0034e9e8d6", size = 89451 },
-    { url = "https://files.pythonhosted.org/packages/3c/c5/daea5d534fcf20b2399c2a7b1cd00a8d29d4d474247c15c2c94548a1a272/mmh3-5.0.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b7fb5db231f3092444bc13901e6a8d299667126b00636ffbad4a7b45e1051e2f", size = 94703 },
-    { url = "https://files.pythonhosted.org/packages/5e/4a/34d5691e7be7c63c34181387bc69bdcc0005ca93c8b562d68cb5775e0e78/mmh3-5.0.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:c100dd441703da5ec136b1d9003ed4a041d8a1136234c9acd887499796df6ad8", size = 91054 },
-    { url = "https://files.pythonhosted.org/packages/5c/3a/ab31bb5e9e1a19a4a997593cbe6ce56710308218ff36c7f76d40ff9c8d2e/mmh3-5.0.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:71f3b765138260fd7a7a2dba0ea5727dabcd18c1f80323c9cfef97a7e86e01d0", size = 89571 },
-    { url = "https://files.pythonhosted.org/packages/0b/79/b986bb067dbfcba6879afe6e723aad1bd53f223450532dd9a4606d0af389/mmh3-5.0.1-cp311-cp311-win32.whl", hash = "sha256:9a76518336247fd17689ce3ae5b16883fd86a490947d46a0193d47fb913e26e3", size = 39187 },
-    { url = "https://files.pythonhosted.org/packages/48/69/97029eda3df0f84edde16a496a2e71bac508fc5d1f0a31e163da071e2670/mmh3-5.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:336bc4df2e44271f1c302d289cc3d78bd52d3eed8d306c7e4bff8361a12bf148", size = 39766 },
-    { url = "https://files.pythonhosted.org/packages/c7/51/538f2b8412303281d8ce2a9a5c4ea84ff81f06de98af0b7c72059727a3bb/mmh3-5.0.1-cp311-cp311-win_arm64.whl", hash = "sha256:af6522722fbbc5999aa66f7244d0986767a46f1fb05accc5200f75b72428a508", size = 36540 },
-    { url = "https://files.pythonhosted.org/packages/75/c7/5b52d0882e7c0dccfaf8786a648e2b26c5307c594abe5cbe98c092607c97/mmh3-5.0.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:f2730bb263ed9c388e8860438b057a53e3cc701134a6ea140f90443c4c11aa40", size = 52907 },
-    { url = "https://files.pythonhosted.org/packages/01/b5/9609fa353c27188292748db033323c206f3fc6fbfa124bccf6a42af0da08/mmh3-5.0.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6246927bc293f6d56724536400b85fb85f5be26101fa77d5f97dd5e2a4c69bf2", size = 38389 },
-    { url = "https://files.pythonhosted.org/packages/33/99/49bf3c86244857b3b250c2f54aff22a5a78ef12258af556fa39bb1e80699/mmh3-5.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fbca322519a6e6e25b6abf43e940e1667cf8ea12510e07fb4919b48a0cd1c411", size = 38204 },
-    { url = "https://files.pythonhosted.org/packages/f8/04/8860cab35b48aaefe40cf88344437e79ddc93cf7ff745dacd1cd56a2be1e/mmh3-5.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eae8c19903ed8a1724ad9e67e86f15d198a7a1271a4f9be83d47e38f312ed672", size = 95091 },
-    { url = "https://files.pythonhosted.org/packages/fa/e9/4ac56001a5bab6d26aa3dfabeddea6d7f037fd2972c76803259f51a5af75/mmh3-5.0.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a09fd6cc72c07c0c07c3357714234b646d78052487c4a3bd5f7f6e08408cff60", size = 100055 },
-    { url = "https://files.pythonhosted.org/packages/18/e8/7d5fd73f559c423ed5b72f940130c27803a406ee0ffc32ef5422f733df67/mmh3-5.0.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2ff8551fee7ae3b11c5d986b6347ade0dccaadd4670ffdb2b944dee120ffcc84", size = 99764 },
-    { url = "https://files.pythonhosted.org/packages/54/d8/c0d89da6c729feec997a9b3b68698894cef12359ade0da95eba9e03b1d5d/mmh3-5.0.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e39694c73a5a20c8bf36dfd8676ed351e5234d55751ba4f7562d85449b21ef3f", size = 87650 },
-    { url = "https://files.pythonhosted.org/packages/dd/41/ec0ee3fd5124c83cb767dcea8569bb326f8981cc88c991e3e4e948a31e24/mmh3-5.0.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eba6001989a92f72a89c7cf382fda831678bd780707a66b4f8ca90239fdf2123", size = 94976 },
-    { url = "https://files.pythonhosted.org/packages/8e/fa/e8059199fe6fbb2fd6494302904cb1209b2f8b6899d58059858a280e89a5/mmh3-5.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0771f90c9911811cc606a5c7b7b58f33501c9ee896ed68a6ac22c7d55878ecc0", size = 90485 },
-    { url = "https://files.pythonhosted.org/packages/3a/a0/eb9da5f93dea3f44b8e970f013279d1543ab210ccf63bb030830968682aa/mmh3-5.0.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:09b31ed0c0c0920363e96641fac4efde65b1ab62b8df86293142f35a254e72b4", size = 89554 },
-    { url = "https://files.pythonhosted.org/packages/e7/e8/5803181eac4e015b4caf307af22fea74292dca48e580d93afe402dcdc138/mmh3-5.0.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5cf4a8deda0235312db12075331cb417c4ba163770edfe789bde71d08a24b692", size = 94872 },
-    { url = "https://files.pythonhosted.org/packages/ed/f9/4d55063f9dcaed41524f078a85989efdf1d335159af5e70af29942ebae67/mmh3-5.0.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:41f7090a95185ef20ac018581a99337f0cbc84a2135171ee3290a9c0d9519585", size = 91326 },
-    { url = "https://files.pythonhosted.org/packages/80/75/0a5acab5291480acd939db80e94448ac937fc7fbfddc0a67b3e721ebfc9c/mmh3-5.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b97b5b368fb7ff22194ec5854f5b12d8de9ab67a0f304728c7f16e5d12135b76", size = 89810 },
-    { url = "https://files.pythonhosted.org/packages/9b/fd/eb1a3573cda74d4c2381d10ded62c128e869954ced1881c15e2bcd97a48f/mmh3-5.0.1-cp312-cp312-win32.whl", hash = "sha256:842516acf04da546f94fad52db125ee619ccbdcada179da51c326a22c4578cb9", size = 39206 },
-    { url = "https://files.pythonhosted.org/packages/66/e8/542ed252924002b84c43a68a080cfd4facbea0d5df361e4f59637638d3c7/mmh3-5.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:d963be0dbfd9fca209c17172f6110787ebf78934af25e3694fe2ba40e55c1e2b", size = 39799 },
-    { url = "https://files.pythonhosted.org/packages/bd/25/ff2cd36c82a23afa57a05cdb52ab467a911fb12c055c8a8238c0d426cbf0/mmh3-5.0.1-cp312-cp312-win_arm64.whl", hash = "sha256:a5da292ceeed8ce8e32b68847261a462d30fd7b478c3f55daae841404f433c15", size = 36537 },
-    { url = "https://files.pythonhosted.org/packages/09/e0/fb19c46265c18311b422ba5ce3e18046ad45c48cfb213fd6dbec23ae6b51/mmh3-5.0.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:673e3f1c8d4231d6fb0271484ee34cb7146a6499fc0df80788adb56fd76842da", size = 52909 },
-    { url = "https://files.pythonhosted.org/packages/c3/94/54fc591e7a24c7ce2c531ecfc5715cff932f9d320c2936550cc33d67304d/mmh3-5.0.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f795a306bd16a52ad578b663462cc8e95500b3925d64118ae63453485d67282b", size = 38396 },
-    { url = "https://files.pythonhosted.org/packages/1f/9a/142bcc9d0d28fc8ae45bbfb83926adc069f984cdf3495a71534cc22b8e27/mmh3-5.0.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5ed57a5e28e502a1d60436cc25c76c3a5ba57545f250f2969af231dc1221e0a5", size = 38207 },
-    { url = "https://files.pythonhosted.org/packages/f8/5b/f1c9110aa70321bb1ee713f17851b9534586c63bc25e0110e4fc03ae2450/mmh3-5.0.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:632c28e7612e909dbb6cbe2fe496201ada4695b7715584005689c5dc038e59ad", size = 94988 },
-    { url = "https://files.pythonhosted.org/packages/87/e5/4dc67e7e0e716c641ab0a5875a659e37258417439590feff5c3bd3ff4538/mmh3-5.0.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:53fd6bd525a5985e391c43384672d9d6b317fcb36726447347c7fc75bfed34ec", size = 99969 },
-    { url = "https://files.pythonhosted.org/packages/ac/68/d148327337687c53f04ad9ceaedfa9ad155ee0111d0cb06220f044d66720/mmh3-5.0.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dceacf6b0b961a0e499836af3aa62d60633265607aef551b2a3e3c48cdaa5edd", size = 99662 },
-    { url = "https://files.pythonhosted.org/packages/13/79/782adb6df6397947c1097b1e94b7f8d95629a4a73df05cf7207bd5148c1f/mmh3-5.0.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8f0738d478fdfb5d920f6aff5452c78f2c35b0eff72caa2a97dfe38e82f93da2", size = 87606 },
-    { url = "https://files.pythonhosted.org/packages/f2/c2/0404383281df049d0e4ccf07fabd659fc1f3da834df6708d934116cbf45d/mmh3-5.0.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e70285e7391ab88b872e5bef632bad16b9d99a6d3ca0590656a4753d55988af", size = 94836 },
-    { url = "https://files.pythonhosted.org/packages/c8/33/fda67c5f28e4c2131891cf8cbc3513cfc55881e3cfe26e49328e38ffacb3/mmh3-5.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:27e5fc6360aa6b828546a4318da1a7da6bf6e5474ccb053c3a6aa8ef19ff97bd", size = 90492 },
-    { url = "https://files.pythonhosted.org/packages/64/2f/0ed38aefe2a87f30bb1b12e5b75dc69fcffdc16def40d1752d6fc7cbbf96/mmh3-5.0.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:7989530c3c1e2c17bf5a0ec2bba09fd19819078ba90beedabb1c3885f5040b0d", size = 89594 },
-    { url = "https://files.pythonhosted.org/packages/95/ab/6e7a5e765fc78e3dbd0a04a04cfdf72e91eb8e31976228e69d82c741a5b4/mmh3-5.0.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:cdad7bee649950da7ecd3cbbbd12fb81f1161072ecbdb5acfa0018338c5cb9cf", size = 94929 },
-    { url = "https://files.pythonhosted.org/packages/74/51/f748f00c072006f4a093d9b08853a0e2e3cd5aeaa91343d4e2d942851978/mmh3-5.0.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e143b8f184c1bb58cecd85ab4a4fd6dc65a2d71aee74157392c3fddac2a4a331", size = 91317 },
-    { url = "https://files.pythonhosted.org/packages/df/a1/21ee8017a7feb0270c49f756ff56da9f99bd150dcfe3b3f6f0d4b243423d/mmh3-5.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e5eb12e886f3646dd636f16b76eb23fc0c27e8ff3c1ae73d4391e50ef60b40f6", size = 89861 },
-    { url = "https://files.pythonhosted.org/packages/c2/d2/46a6d070de4659bdf91cd6a62d659f8cc547dadee52b6d02bcbacb3262ed/mmh3-5.0.1-cp313-cp313-win32.whl", hash = "sha256:16e6dddfa98e1c2d021268e72c78951234186deb4df6630e984ac82df63d0a5d", size = 39201 },
-    { url = "https://files.pythonhosted.org/packages/ed/07/316c062f09019b99b248a4183c5333f8eeebe638345484774908a8f2c9c0/mmh3-5.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:d3ffb792d70b8c4a2382af3598dad6ae0c5bd9cee5b7ffcc99aa2f5fd2c1bf70", size = 39807 },
-    { url = "https://files.pythonhosted.org/packages/9d/d3/f7e6d7d062b8d7072c3989a528d9d47486ee5d5ae75250f6e26b4976d098/mmh3-5.0.1-cp313-cp313-win_arm64.whl", hash = "sha256:122fa9ec148383f9124292962bda745f192b47bfd470b2af5fe7bb3982b17896", size = 36539 },
+    { url = "https://files.pythonhosted.org/packages/ef/5a/8609dc74421858f7e94a89dc69221ab9b2c14d0d63a139b46ec190eedc44/mmh3-4.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:be5ac76a8b0cd8095784e51e4c1c9c318c19edcd1709a06eb14979c8d850c31a", size = 39433 },
+    { url = "https://files.pythonhosted.org/packages/93/6c/e7a0f07c7082c76964b1ff46aa852f36e2ec6a9c3530dec0afa0b3162fc2/mmh3-4.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:98a49121afdfab67cd80e912b36404139d7deceb6773a83620137aaa0da5714c", size = 29280 },
+    { url = "https://files.pythonhosted.org/packages/76/84/60ca728ec7d7e1779a98000d64941c6221786124b4f07bf105a627055890/mmh3-4.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5259ac0535874366e7d1a5423ef746e0d36a9e3c14509ce6511614bdc5a7ef5b", size = 30130 },
+    { url = "https://files.pythonhosted.org/packages/2a/22/f2ec190b491f712d9ef5ea6252204b6f05255ac9af54a7b505adc3128aed/mmh3-4.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5950827ca0453a2be357696da509ab39646044e3fa15cad364eb65d78797437", size = 68837 },
+    { url = "https://files.pythonhosted.org/packages/ae/b9/c1e8065671e1d2f4e280c9c57389e74964f4a5792cac26717ad592002c7d/mmh3-4.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1dd0f652ae99585b9dd26de458e5f08571522f0402155809fd1dc8852a613a39", size = 72275 },
+    { url = "https://files.pythonhosted.org/packages/6b/18/92bbdb102ab2b4e80084e927187d871758280eb067c649693e42bfc6d0d1/mmh3-4.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:99d25548070942fab1e4a6f04d1626d67e66d0b81ed6571ecfca511f3edf07e6", size = 70919 },
+    { url = "https://files.pythonhosted.org/packages/e2/cd/391ce1d1bb559871a5d3a6bbb30b82bf51d3e3b42c4e8589cccb201953da/mmh3-4.1.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:53db8d9bad3cb66c8f35cbc894f336273f63489ce4ac416634932e3cbe79eb5b", size = 65885 },
+    { url = "https://files.pythonhosted.org/packages/03/87/4b01a43336bd506478850d1bc3d180648b2d26b4acf1fc4bf1df72bf562f/mmh3-4.1.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75da0f615eb55295a437264cc0b736753f830b09d102aa4c2a7d719bc445ec05", size = 67610 },
+    { url = "https://files.pythonhosted.org/packages/e8/12/b464149a1b7181c7ce431ebf3d24fa994863f2f1abc75b78d202dde966e0/mmh3-4.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b926b07fd678ea84b3a2afc1fa22ce50aeb627839c44382f3d0291e945621e1a", size = 74888 },
+    { url = "https://files.pythonhosted.org/packages/fc/3e/f4eb45a23fc17b970394c1fe74eba157514577ae2d63757684241651d754/mmh3-4.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:c5b053334f9b0af8559d6da9dc72cef0a65b325ebb3e630c680012323c950bb6", size = 72969 },
+    { url = "https://files.pythonhosted.org/packages/c0/3b/83934fd9494371357da0ca026d55ad427c199d611b97b6ffeecacfd8e720/mmh3-4.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:5bf33dc43cd6de2cb86e0aa73a1cc6530f557854bbbe5d59f41ef6de2e353d7b", size = 80338 },
+    { url = "https://files.pythonhosted.org/packages/b6/c4/5bcd709ea7269173d7e925402f05e05cf12194ef53cc9912a5ad166f8ded/mmh3-4.1.0-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:fa7eacd2b830727ba3dd65a365bed8a5c992ecd0c8348cf39a05cc77d22f4970", size = 76580 },
+    { url = "https://files.pythonhosted.org/packages/da/6a/4c0680d64475e551d7f4cc78bf0fd247c711ed2717f6bb311934993d1e69/mmh3-4.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:42dfd6742b9e3eec599f85270617debfa0bbb913c545bb980c8a4fa7b2d047da", size = 75325 },
+    { url = "https://files.pythonhosted.org/packages/70/bc/e2ed99e580b3dd121f6462147bd5f521c57b3c81c692aa2d416b0678c89f/mmh3-4.1.0-cp310-cp310-win32.whl", hash = "sha256:2974ad343f0d39dcc88e93ee6afa96cedc35a9883bc067febd7ff736e207fa47", size = 31235 },
+    { url = "https://files.pythonhosted.org/packages/73/2b/3aec865da7feb52830782d9fb7c54115cc18815680c244301adf9080622f/mmh3-4.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:74699a8984ded645c1a24d6078351a056f5a5f1fe5838870412a68ac5e28d865", size = 31271 },
+    { url = "https://files.pythonhosted.org/packages/17/2a/925439189ccf562bdcb839aed6263d718359f0c376d673beb3b83d3864ac/mmh3-4.1.0-cp310-cp310-win_arm64.whl", hash = "sha256:f0dc874cedc23d46fc488a987faa6ad08ffa79e44fb08e3cd4d4cf2877c00a00", size = 30147 },
+    { url = "https://files.pythonhosted.org/packages/2e/d6/86beea107e7e9700df9522466346c23a2f54faa81337c86fd17002aa95a6/mmh3-4.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:3280a463855b0eae64b681cd5b9ddd9464b73f81151e87bb7c91a811d25619e6", size = 39427 },
+    { url = "https://files.pythonhosted.org/packages/1c/08/65fa5489044e2afc304e8540c6c607d5d7b136ddc5cd8315c13de0adc34c/mmh3-4.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:97ac57c6c3301769e757d444fa7c973ceb002cb66534b39cbab5e38de61cd896", size = 29281 },
+    { url = "https://files.pythonhosted.org/packages/b3/aa/98511d3ea3f6ba958136d913be3be3c1009be935a20ecc7b2763f0a605b6/mmh3-4.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a7b6502cdb4dbd880244818ab363c8770a48cdccecf6d729ade0241b736b5ec0", size = 30130 },
+    { url = "https://files.pythonhosted.org/packages/3c/b7/1a93f81643435b0e57f1046c4ffe46f0214693eaede0d9b0a1a236776e70/mmh3-4.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:52ba2da04671a9621580ddabf72f06f0e72c1c9c3b7b608849b58b11080d8f14", size = 69072 },
+    { url = "https://files.pythonhosted.org/packages/45/9e/2ff70246aefd9cf146bc6a420c28ed475a0d1a325f31ee203be02f9215d4/mmh3-4.1.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5a5fef4c4ecc782e6e43fbeab09cff1bac82c998a1773d3a5ee6a3605cde343e", size = 72470 },
+    { url = "https://files.pythonhosted.org/packages/dc/cb/57bc1fdbdbe6837aebfca982494e23e2498ee2a89585c9054713b22e4167/mmh3-4.1.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5135358a7e00991f73b88cdc8eda5203bf9de22120d10a834c5761dbeb07dd13", size = 71251 },
+    { url = "https://files.pythonhosted.org/packages/4d/c2/46d7d2721b69fbdfd30231309e6395f62ff6744e5c00dd8113b9faa06fba/mmh3-4.1.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cff9ae76a54f7c6fe0167c9c4028c12c1f6de52d68a31d11b6790bb2ae685560", size = 66035 },
+    { url = "https://files.pythonhosted.org/packages/6f/a4/7ba4bcc838818bcf018e26d118d5ddb605c23c4fad040dc4d811f1cfcb04/mmh3-4.1.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f6f02576a4d106d7830ca90278868bf0983554dd69183b7bbe09f2fcd51cf54f", size = 67844 },
+    { url = "https://files.pythonhosted.org/packages/71/ed/8e80d1038e7bb15eaf739711d1fc36f2341acb6b1b95fa77003f2799c91e/mmh3-4.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:073d57425a23721730d3ff5485e2da489dd3c90b04e86243dd7211f889898106", size = 76724 },
+    { url = "https://files.pythonhosted.org/packages/1c/22/a6a70ca81f0ce8fe2f3a68d89c1184c2d2d0fbe0ee305da50e972c5ff9fa/mmh3-4.1.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:71e32ddec7f573a1a0feb8d2cf2af474c50ec21e7a8263026e8d3b4b629805db", size = 75004 },
+    { url = "https://files.pythonhosted.org/packages/73/20/abe50b605760f1f5b6e0b436c650649e69ca478d0f41b154f300367c09e4/mmh3-4.1.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7cbb20b29d57e76a58b40fd8b13a9130db495a12d678d651b459bf61c0714cea", size = 82230 },
+    { url = "https://files.pythonhosted.org/packages/45/80/a1fc99d3ee50b573df0bfbb1ad518463af78d2ebca44bfca3b3f9473d651/mmh3-4.1.0-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:a42ad267e131d7847076bb7e31050f6c4378cd38e8f1bf7a0edd32f30224d5c9", size = 78679 },
+    { url = "https://files.pythonhosted.org/packages/9e/51/6c9ee2ddf3b386f45ff83b6926a5e826635757d91dab04cbf16eee05f9a7/mmh3-4.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:4a013979fc9390abadc445ea2527426a0e7a4495c19b74589204f9b71bcaafeb", size = 77382 },
+    { url = "https://files.pythonhosted.org/packages/ee/fa/4b377f244c27fac5f0343cc4dc0d2eb0a08049afc8d5322d07be7461a768/mmh3-4.1.0-cp311-cp311-win32.whl", hash = "sha256:1d3b1cdad7c71b7b88966301789a478af142bddcb3a2bee563f7a7d40519a00f", size = 31232 },
+    { url = "https://files.pythonhosted.org/packages/d1/b0/500ef56c29b276d796bfdb47c16d34fa18a68945e4d730a6fa7d483583ed/mmh3-4.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:0dc6dc32eb03727467da8e17deffe004fbb65e8b5ee2b502d36250d7a3f4e2ec", size = 31276 },
+    { url = "https://files.pythonhosted.org/packages/cc/84/94795e6e710c3861f8f355a12be9c9f4b8433a538c983e75bd4c00496a8a/mmh3-4.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:9ae3a5c1b32dda121c7dc26f9597ef7b01b4c56a98319a7fe86c35b8bc459ae6", size = 30142 },
+    { url = "https://files.pythonhosted.org/packages/18/45/b4d41e86b00eed8c500adbe0007129861710e181c7f49c507ef6beae9496/mmh3-4.1.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0033d60c7939168ef65ddc396611077a7268bde024f2c23bdc283a19123f9e9c", size = 39495 },
+    { url = "https://files.pythonhosted.org/packages/a6/d4/f041b8704cb8d1aad3717105daa582e29818b78a540622dfed84cd00d88f/mmh3-4.1.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d6af3e2287644b2b08b5924ed3a88c97b87b44ad08e79ca9f93d3470a54a41c5", size = 29334 },
+    { url = "https://files.pythonhosted.org/packages/cb/bb/8f75378e1a83b323f9ed06248333c383e7dac614c2f95e1419965cb91693/mmh3-4.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d82eb4defa245e02bb0b0dc4f1e7ee284f8d212633389c91f7fba99ba993f0a2", size = 30144 },
+    { url = "https://files.pythonhosted.org/packages/3e/50/5e36c1945bd83e780a37361fc1999fc4c5a59ecc10a373557fdf0e58eb1f/mmh3-4.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba245e94b8d54765e14c2d7b6214e832557e7856d5183bc522e17884cab2f45d", size = 69094 },
+    { url = "https://files.pythonhosted.org/packages/70/c7/6ae37e7519a938226469476b84bcea2650e2a2cc7a848e6a206ea98ecee3/mmh3-4.1.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bb04e2feeabaad6231e89cd43b3d01a4403579aa792c9ab6fdeef45cc58d4ec0", size = 72611 },
+    { url = "https://files.pythonhosted.org/packages/5e/47/6613f69f57f1e5045e66b22fae9c2fb39ef754c455805d3917f6073e316e/mmh3-4.1.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1e3b1a27def545ce11e36158ba5d5390cdbc300cfe456a942cc89d649cf7e3b2", size = 71462 },
+    { url = "https://files.pythonhosted.org/packages/e0/0a/e423db18ce7b479c4b96381a112b443f0985c611de420f95c58a9f934080/mmh3-4.1.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce0ab79ff736d7044e5e9b3bfe73958a55f79a4ae672e6213e92492ad5e734d5", size = 66165 },
+    { url = "https://files.pythonhosted.org/packages/4c/7b/bfeb68bee5bddc8baf7ef630b93edc0a533202d84eb076dbb6c77e7e5fd5/mmh3-4.1.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b02268be6e0a8eeb8a924d7db85f28e47344f35c438c1e149878bb1c47b1cd3", size = 68088 },
+    { url = "https://files.pythonhosted.org/packages/d4/a6/b82e30143997c05776887f5177f724e3b714aa7e7346fbe2ec70f52abcd0/mmh3-4.1.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:deb887f5fcdaf57cf646b1e062d56b06ef2f23421c80885fce18b37143cba828", size = 76241 },
+    { url = "https://files.pythonhosted.org/packages/6c/60/a3d5872cf7610fcb13e36c472476020c5cf217b23c092bad452eb7784407/mmh3-4.1.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:99dd564e9e2b512eb117bd0cbf0f79a50c45d961c2a02402787d581cec5448d5", size = 74538 },
+    { url = "https://files.pythonhosted.org/packages/f6/d5/742173a94c78f4edab71c04097f6f9150c47f8fd034d592f5f34a9444719/mmh3-4.1.0-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:08373082dfaa38fe97aa78753d1efd21a1969e51079056ff552e687764eafdfe", size = 81793 },
+    { url = "https://files.pythonhosted.org/packages/d0/7a/a1db0efe7c67b761d83be3d50e35ef26628ef56b3b8bc776d07412ee8b16/mmh3-4.1.0-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:54b9c6a2ea571b714e4fe28d3e4e2db37abfd03c787a58074ea21ee9a8fd1740", size = 78217 },
+    { url = "https://files.pythonhosted.org/packages/b3/78/1ff8da7c859cd09704e2f500588d171eda9688fcf6f29e028ef261262a16/mmh3-4.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a7b1edf24c69e3513f879722b97ca85e52f9032f24a52284746877f6a7304086", size = 77052 },
+    { url = "https://files.pythonhosted.org/packages/ed/c7/cf16ace81fc9fbe54a75c914306252af26c6ea485366bb3b579bf6e3dbb8/mmh3-4.1.0-cp312-cp312-win32.whl", hash = "sha256:411da64b951f635e1e2284b71d81a5a83580cea24994b328f8910d40bed67276", size = 31277 },
+    { url = "https://files.pythonhosted.org/packages/d2/0b/b3b1637dca9414451edf287fd91e667e7231d5ffd7498137fe011951fc0a/mmh3-4.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:bebc3ecb6ba18292e3d40c8712482b4477abd6981c2ebf0e60869bd90f8ac3a9", size = 31318 },
+    { url = "https://files.pythonhosted.org/packages/dd/6c/c0f06040c58112ccbd0df989055ede98f7c1a1f392dc6a3fc63ec6c124ec/mmh3-4.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:168473dd608ade6a8d2ba069600b35199a9af837d96177d3088ca91f2b3798e3", size = 30147 },
 ]
 
 [[package]]
@@ -2572,6 +2617,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl", hash = "sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca", size = 151688 },
 ]
 
+[[package]]
+name = "onnx"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "protobuf" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/54/0e385c26bf230d223810a9c7d06628d954008a5e5e4b73ee26ef02327282/onnx-1.17.0.tar.gz", hash = "sha256:48ca1a91ff73c1d5e3ea2eef20ae5d0e709bb8a2355ed798ffc2169753013fd3", size = 12165120 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2e/29/57053ba7787788ac75efb095cfc1ae290436b6d3a26754693cd7ed1b4fac/onnx-1.17.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:38b5df0eb22012198cdcee527cc5f917f09cce1f88a69248aaca22bd78a7f023", size = 16645616 },
+    { url = "https://files.pythonhosted.org/packages/75/0d/831807a18db2a5e8f7813848c59272b904a4ef3939fe4d1288cbce9ea735/onnx-1.17.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d545335cb49d4d8c47cc803d3a805deb7ad5d9094dc67657d66e568610a36d7d", size = 15908420 },
+    { url = "https://files.pythonhosted.org/packages/dd/5b/c4f95dbe652d14aeba9afaceb177e9ffc48ac3c03048dd3f872f26f07e34/onnx-1.17.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3193a3672fc60f1a18c0f4c93ac81b761bc72fd8a6c2035fa79ff5969f07713e", size = 16046244 },
+    { url = "https://files.pythonhosted.org/packages/08/a9/c1f218085043dccc6311460239e253fa6957cf12ee4b0a56b82014938d0b/onnx-1.17.0-cp310-cp310-win32.whl", hash = "sha256:0141c2ce806c474b667b7e4499164227ef594584da432fd5613ec17c1855e311", size = 14423516 },
+    { url = "https://files.pythonhosted.org/packages/0e/d3/d26ebf590a65686dde6b27fef32493026c5be9e42083340d947395f93405/onnx-1.17.0-cp310-cp310-win_amd64.whl", hash = "sha256:dfd777d95c158437fda6b34758f0877d15b89cbe9ff45affbedc519b35345cf9", size = 14528496 },
+    { url = "https://files.pythonhosted.org/packages/e5/a9/8d1b1d53aec70df53e0f57e9f9fcf47004276539e29230c3d5f1f50719ba/onnx-1.17.0-cp311-cp311-macosx_12_0_universal2.whl", hash = "sha256:d6fc3a03fc0129b8b6ac03f03bc894431ffd77c7d79ec023d0afd667b4d35869", size = 16647991 },
+    { url = "https://files.pythonhosted.org/packages/7b/e3/cc80110e5996ca61878f7b4c73c7a286cd88918ff35eacb60dc75ab11ef5/onnx-1.17.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f01a4b63d4e1d8ec3e2f069e7b798b2955810aa434f7361f01bc8ca08d69cce4", size = 15908949 },
+    { url = "https://files.pythonhosted.org/packages/b1/2f/91092557ed478e323a2b4471e2081fdf88d1dd52ae988ceaf7db4e4506ff/onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4a183c6178be001bf398260e5ac2c927dc43e7746e8638d6c05c20e321f8c949", size = 16048190 },
+    { url = "https://files.pythonhosted.org/packages/ac/59/9ea23fc22d0bb853133f363e6248e31bcbc6c1c90543a3938c00412ac02a/onnx-1.17.0-cp311-cp311-win32.whl", hash = "sha256:081ec43a8b950171767d99075b6b92553901fa429d4bc5eb3ad66b36ef5dbe3a", size = 14424299 },
+    { url = "https://files.pythonhosted.org/packages/51/a5/19b0dfcb567b62e7adf1a21b08b23224f0c2d13842aee4d0abc6f07f9cf5/onnx-1.17.0-cp311-cp311-win_amd64.whl", hash = "sha256:95c03e38671785036bb704c30cd2e150825f6ab4763df3a4f1d249da48525957", size = 14529142 },
+    { url = "https://files.pythonhosted.org/packages/b4/dd/c416a11a28847fafb0db1bf43381979a0f522eb9107b831058fde012dd56/onnx-1.17.0-cp312-cp312-macosx_12_0_universal2.whl", hash = "sha256:0e906e6a83437de05f8139ea7eaf366bf287f44ae5cc44b2850a30e296421f2f", size = 16651271 },
+    { url = "https://files.pythonhosted.org/packages/f0/6c/f040652277f514ecd81b7251841f96caa5538365af7df07f86c6018cda2b/onnx-1.17.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d955ba2939878a520a97614bcf2e79c1df71b29203e8ced478fa78c9a9c63c2", size = 15907522 },
+    { url = "https://files.pythonhosted.org/packages/3d/7c/67f4952d1b56b3f74a154b97d0dd0630d525923b354db117d04823b8b49b/onnx-1.17.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f3fb5cc4e2898ac5312a7dc03a65133dd2abf9a5e520e69afb880a7251ec97a", size = 16046307 },
+    { url = "https://files.pythonhosted.org/packages/ae/20/6da11042d2ab870dfb4ce4a6b52354d7651b6b4112038b6d2229ab9904c4/onnx-1.17.0-cp312-cp312-win32.whl", hash = "sha256:317870fca3349d19325a4b7d1b5628f6de3811e9710b1e3665c68b073d0e68d7", size = 14424235 },
+    { url = "https://files.pythonhosted.org/packages/35/55/c4d11bee1fdb0c4bd84b4e3562ff811a19b63266816870ae1f95567aa6e1/onnx-1.17.0-cp312-cp312-win_amd64.whl", hash = "sha256:659b8232d627a5460d74fd3c96947ae83db6d03f035ac633e20cd69cfa029227", size = 14530453 },
+]
+
 [[package]]
 name = "onnxruntime"
 version = "1.19.2"
@@ -2621,6 +2693,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ad/31/28a83e124e9f9dd04c83b5aeb6f8b1770f45addde4dd3d34d9a9091590ad/openai-1.52.1-py3-none-any.whl", hash = "sha256:f23e83df5ba04ee0e82c8562571e8cb596cd88f9a84ab783e6c6259e5ffbfb4a", size = 386945 },
 ]
 
+[[package]]
+name = "openpyxl"
+version = "3.1.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "et-xmlfile" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/f9/88d94a75de065ea32619465d2f77b29a0469500e99012523b91cc4141cd1/openpyxl-3.1.5.tar.gz", hash = "sha256:cf0e3cf56142039133628b5acffe8ef0c12bc902d2aadd3e0fe5878dc08d1050", size = 186464 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c0/da/977ded879c29cbd04de313843e76868e6e13408a94ed6b987245dc7c8506/openpyxl-3.1.5-py2.py3-none-any.whl", hash = "sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2", size = 250910 },
+]
+
 [[package]]
 name = "opentelemetry-api"
 version = "1.27.0"
@@ -2935,6 +3019,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08", size = 31191 },
 ]
 
+[[package]]
+name = "pdfminer-six"
+version = "20231228"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "charset-normalizer" },
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/31/b1/a43e3bd872ded4deea4f8efc7aff1703fca8c5455d0c06e20506a06a44ff/pdfminer.six-20231228.tar.gz", hash = "sha256:6004da3ad1a7a4d45930cb950393df89b068e73be365a6ff64a838d37bcb08c4", size = 7362505 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/eb/9c/e46fe7502b32d7db6af6e36a9105abb93301fa1ec475b5ddcba8b35ae23a/pdfminer.six-20231228-py3-none-any.whl", hash = "sha256:e8d3c3310e6fbc1fe414090123ab01351634b4ecb021232206c4c9a8ca3e3b8f", size = 5614515 },
+]
+
+[[package]]
+name = "pdfplumber"
+version = "0.11.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pdfminer-six" },
+    { name = "pillow" },
+    { name = "pypdfium2" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ca/f0/457bda3629dfa5b01c645519fe30230e1739751f6645e23fca2dabf6c2e5/pdfplumber-0.11.4.tar.gz", hash = "sha256:147b55cde2351fcb9523b46b09cc771eea3602faecfb60d463c6bf951694fbe8", size = 113305 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/87/415cb472981a8d2e36beeeadf074ebb686cc2bfe8d18de973232da291bd5/pdfplumber-0.11.4-py3-none-any.whl", hash = "sha256:6150f0678c7aaba974ac09839c17475d6c0c4d126b5f92cb85154885f31c6d73", size = 59182 },
+]
+
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -2949,69 +3060,61 @@ wheels = [
 
 [[package]]
 name = "pillow"
-version = "11.0.0"
+version = "10.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a5/26/0d95c04c868f6bdb0c447e3ee2de5564411845e36a858cfd63766bc7b563/pillow-11.0.0.tar.gz", hash = "sha256:72bacbaf24ac003fea9bff9837d1eedb6088758d41e100c1552930151f677739", size = 46737780 }
+sdist = { url = "https://files.pythonhosted.org/packages/cd/74/ad3d526f3bf7b6d3f408b73fde271ec69dfac8b81341a318ce825f2b3812/pillow-10.4.0.tar.gz", hash = "sha256:166c1cd4d24309b30d61f79f4a9114b7b2313d7450912277855ff5dfd7cd4a06", size = 46555059 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/98/fb/a6ce6836bd7fd93fbf9144bf54789e02babc27403b50a9e1583ee877d6da/pillow-11.0.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:6619654954dc4936fcff82db8eb6401d3159ec6be81e33c6000dfd76ae189947", size = 3154708 },
-    { url = "https://files.pythonhosted.org/packages/6a/1d/1f51e6e912d8ff316bb3935a8cda617c801783e0b998bf7a894e91d3bd4c/pillow-11.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b3c5ac4bed7519088103d9450a1107f76308ecf91d6dabc8a33a2fcfb18d0fba", size = 2979223 },
-    { url = "https://files.pythonhosted.org/packages/90/83/e2077b0192ca8a9ef794dbb74700c7e48384706467067976c2a95a0f40a1/pillow-11.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a65149d8ada1055029fcb665452b2814fe7d7082fcb0c5bed6db851cb69b2086", size = 4183167 },
-    { url = "https://files.pythonhosted.org/packages/0e/74/467af0146970a98349cdf39e9b79a6cc8a2e7558f2c01c28a7b6b85c5bda/pillow-11.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88a58d8ac0cc0e7f3a014509f0455248a76629ca9b604eca7dc5927cc593c5e9", size = 4283912 },
-    { url = "https://files.pythonhosted.org/packages/85/b1/d95d4f7ca3a6c1ae120959605875a31a3c209c4e50f0029dc1a87566cf46/pillow-11.0.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:c26845094b1af3c91852745ae78e3ea47abf3dbcd1cf962f16b9a5fbe3ee8488", size = 4195815 },
-    { url = "https://files.pythonhosted.org/packages/41/c3/94f33af0762ed76b5a237c5797e088aa57f2b7fa8ee7932d399087be66a8/pillow-11.0.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:1a61b54f87ab5786b8479f81c4b11f4d61702830354520837f8cc791ebba0f5f", size = 4366117 },
-    { url = "https://files.pythonhosted.org/packages/ba/3c/443e7ef01f597497268899e1cca95c0de947c9bbf77a8f18b3c126681e5d/pillow-11.0.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:674629ff60030d144b7bca2b8330225a9b11c482ed408813924619c6f302fdbb", size = 4278607 },
-    { url = "https://files.pythonhosted.org/packages/26/95/1495304448b0081e60c0c5d63f928ef48bb290acee7385804426fa395a21/pillow-11.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:598b4e238f13276e0008299bd2482003f48158e2b11826862b1eb2ad7c768b97", size = 4410685 },
-    { url = "https://files.pythonhosted.org/packages/45/da/861e1df971ef0de9870720cb309ca4d553b26a9483ec9be3a7bf1de4a095/pillow-11.0.0-cp310-cp310-win32.whl", hash = "sha256:9a0f748eaa434a41fccf8e1ee7a3eed68af1b690e75328fd7a60af123c193b50", size = 2249185 },
-    { url = "https://files.pythonhosted.org/packages/d5/4e/78f7c5202ea2a772a5ab05069c1b82503e6353cd79c7e474d4945f4b82c3/pillow-11.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:a5629742881bcbc1f42e840af185fd4d83a5edeb96475a575f4da50d6ede337c", size = 2566726 },
-    { url = "https://files.pythonhosted.org/packages/77/e4/6e84eada35cbcc646fc1870f72ccfd4afacb0fae0c37ffbffe7f5dc24bf1/pillow-11.0.0-cp310-cp310-win_arm64.whl", hash = "sha256:ee217c198f2e41f184f3869f3e485557296d505b5195c513b2bfe0062dc537f1", size = 2254585 },
-    { url = "https://files.pythonhosted.org/packages/f0/eb/f7e21b113dd48a9c97d364e0915b3988c6a0b6207652f5a92372871b7aa4/pillow-11.0.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1c1d72714f429a521d8d2d018badc42414c3077eb187a59579f28e4270b4b0fc", size = 3154705 },
-    { url = "https://files.pythonhosted.org/packages/25/b3/2b54a1d541accebe6bd8b1358b34ceb2c509f51cb7dcda8687362490da5b/pillow-11.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:499c3a1b0d6fc8213519e193796eb1a86a1be4b1877d678b30f83fd979811d1a", size = 2979222 },
-    { url = "https://files.pythonhosted.org/packages/20/12/1a41eddad8265c5c19dda8fb6c269ce15ee25e0b9f8f26286e6202df6693/pillow-11.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c8b2351c85d855293a299038e1f89db92a2f35e8d2f783489c6f0b2b5f3fe8a3", size = 4190220 },
-    { url = "https://files.pythonhosted.org/packages/a9/9b/8a8c4d07d77447b7457164b861d18f5a31ae6418ef5c07f6f878fa09039a/pillow-11.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f4dba50cfa56f910241eb7f883c20f1e7b1d8f7d91c750cd0b318bad443f4d5", size = 4291399 },
-    { url = "https://files.pythonhosted.org/packages/fc/e4/130c5fab4a54d3991129800dd2801feeb4b118d7630148cd67f0e6269d4c/pillow-11.0.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:5ddbfd761ee00c12ee1be86c9c0683ecf5bb14c9772ddbd782085779a63dd55b", size = 4202709 },
-    { url = "https://files.pythonhosted.org/packages/39/63/b3fc299528d7df1f678b0666002b37affe6b8751225c3d9c12cf530e73ed/pillow-11.0.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:45c566eb10b8967d71bf1ab8e4a525e5a93519e29ea071459ce517f6b903d7fa", size = 4372556 },
-    { url = "https://files.pythonhosted.org/packages/c6/a6/694122c55b855b586c26c694937d36bb8d3b09c735ff41b2f315c6e66a10/pillow-11.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b4fd7bd29610a83a8c9b564d457cf5bd92b4e11e79a4ee4716a63c959699b306", size = 4287187 },
-    { url = "https://files.pythonhosted.org/packages/ba/a9/f9d763e2671a8acd53d29b1e284ca298bc10a595527f6be30233cdb9659d/pillow-11.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:cb929ca942d0ec4fac404cbf520ee6cac37bf35be479b970c4ffadf2b6a1cad9", size = 4418468 },
-    { url = "https://files.pythonhosted.org/packages/6e/0e/b5cbad2621377f11313a94aeb44ca55a9639adabcaaa073597a1925f8c26/pillow-11.0.0-cp311-cp311-win32.whl", hash = "sha256:006bcdd307cc47ba43e924099a038cbf9591062e6c50e570819743f5607404f5", size = 2249249 },
-    { url = "https://files.pythonhosted.org/packages/dc/83/1470c220a4ff06cd75fc609068f6605e567ea51df70557555c2ab6516b2c/pillow-11.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:52a2d8323a465f84faaba5236567d212c3668f2ab53e1c74c15583cf507a0291", size = 2566769 },
-    { url = "https://files.pythonhosted.org/packages/52/98/def78c3a23acee2bcdb2e52005fb2810ed54305602ec1bfcfab2bda6f49f/pillow-11.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:16095692a253047fe3ec028e951fa4221a1f3ed3d80c397e83541a3037ff67c9", size = 2254611 },
-    { url = "https://files.pythonhosted.org/packages/1c/a3/26e606ff0b2daaf120543e537311fa3ae2eb6bf061490e4fea51771540be/pillow-11.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d2c0a187a92a1cb5ef2c8ed5412dd8d4334272617f532d4ad4de31e0495bd923", size = 3147642 },
-    { url = "https://files.pythonhosted.org/packages/4f/d5/1caabedd8863526a6cfa44ee7a833bd97f945dc1d56824d6d76e11731939/pillow-11.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:084a07ef0821cfe4858fe86652fffac8e187b6ae677e9906e192aafcc1b69903", size = 2978999 },
-    { url = "https://files.pythonhosted.org/packages/d9/ff/5a45000826a1aa1ac6874b3ec5a856474821a1b59d838c4f6ce2ee518fe9/pillow-11.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8069c5179902dcdce0be9bfc8235347fdbac249d23bd90514b7a47a72d9fecf4", size = 4196794 },
-    { url = "https://files.pythonhosted.org/packages/9d/21/84c9f287d17180f26263b5f5c8fb201de0f88b1afddf8a2597a5c9fe787f/pillow-11.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f02541ef64077f22bf4924f225c0fd1248c168f86e4b7abdedd87d6ebaceab0f", size = 4300762 },
-    { url = "https://files.pythonhosted.org/packages/84/39/63fb87cd07cc541438b448b1fed467c4d687ad18aa786a7f8e67b255d1aa/pillow-11.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:fcb4621042ac4b7865c179bb972ed0da0218a076dc1820ffc48b1d74c1e37fe9", size = 4210468 },
-    { url = "https://files.pythonhosted.org/packages/7f/42/6e0f2c2d5c60f499aa29be14f860dd4539de322cd8fb84ee01553493fb4d/pillow-11.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:00177a63030d612148e659b55ba99527803288cea7c75fb05766ab7981a8c1b7", size = 4381824 },
-    { url = "https://files.pythonhosted.org/packages/31/69/1ef0fb9d2f8d2d114db982b78ca4eeb9db9a29f7477821e160b8c1253f67/pillow-11.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8853a3bf12afddfdf15f57c4b02d7ded92c7a75a5d7331d19f4f9572a89c17e6", size = 4296436 },
-    { url = "https://files.pythonhosted.org/packages/44/ea/dad2818c675c44f6012289a7c4f46068c548768bc6c7f4e8c4ae5bbbc811/pillow-11.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3107c66e43bda25359d5ef446f59c497de2b5ed4c7fdba0894f8d6cf3822dafc", size = 4429714 },
-    { url = "https://files.pythonhosted.org/packages/af/3a/da80224a6eb15bba7a0dcb2346e2b686bb9bf98378c0b4353cd88e62b171/pillow-11.0.0-cp312-cp312-win32.whl", hash = "sha256:86510e3f5eca0ab87429dd77fafc04693195eec7fd6a137c389c3eeb4cfb77c6", size = 2249631 },
-    { url = "https://files.pythonhosted.org/packages/57/97/73f756c338c1d86bb802ee88c3cab015ad7ce4b838f8a24f16b676b1ac7c/pillow-11.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:8ec4a89295cd6cd4d1058a5e6aec6bf51e0eaaf9714774e1bfac7cfc9051db47", size = 2567533 },
-    { url = "https://files.pythonhosted.org/packages/0b/30/2b61876e2722374558b871dfbfcbe4e406626d63f4f6ed92e9c8e24cac37/pillow-11.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:27a7860107500d813fcd203b4ea19b04babe79448268403172782754870dac25", size = 2254890 },
-    { url = "https://files.pythonhosted.org/packages/63/24/e2e15e392d00fcf4215907465d8ec2a2f23bcec1481a8ebe4ae760459995/pillow-11.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:bcd1fb5bb7b07f64c15618c89efcc2cfa3e95f0e3bcdbaf4642509de1942a699", size = 3147300 },
-    { url = "https://files.pythonhosted.org/packages/43/72/92ad4afaa2afc233dc44184adff289c2e77e8cd916b3ddb72ac69495bda3/pillow-11.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0e038b0745997c7dcaae350d35859c9715c71e92ffb7e0f4a8e8a16732150f38", size = 2978742 },
-    { url = "https://files.pythonhosted.org/packages/9e/da/c8d69c5bc85d72a8523fe862f05ababdc52c0a755cfe3d362656bb86552b/pillow-11.0.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ae08bd8ffc41aebf578c2af2f9d8749d91f448b3bfd41d7d9ff573d74f2a6b2", size = 4194349 },
-    { url = "https://files.pythonhosted.org/packages/cd/e8/686d0caeed6b998351d57796496a70185376ed9c8ec7d99e1d19ad591fc6/pillow-11.0.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d69bfd8ec3219ae71bcde1f942b728903cad25fafe3100ba2258b973bd2bc1b2", size = 4298714 },
-    { url = "https://files.pythonhosted.org/packages/ec/da/430015cec620d622f06854be67fd2f6721f52fc17fca8ac34b32e2d60739/pillow-11.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:61b887f9ddba63ddf62fd02a3ba7add935d053b6dd7d58998c630e6dbade8527", size = 4208514 },
-    { url = "https://files.pythonhosted.org/packages/44/ae/7e4f6662a9b1cb5f92b9cc9cab8321c381ffbee309210940e57432a4063a/pillow-11.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:c6a660307ca9d4867caa8d9ca2c2658ab685de83792d1876274991adec7b93fa", size = 4380055 },
-    { url = "https://files.pythonhosted.org/packages/74/d5/1a807779ac8a0eeed57f2b92a3c32ea1b696e6140c15bd42eaf908a261cd/pillow-11.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:73e3a0200cdda995c7e43dd47436c1548f87a30bb27fb871f352a22ab8dcf45f", size = 4296751 },
-    { url = "https://files.pythonhosted.org/packages/38/8c/5fa3385163ee7080bc13026d59656267daaaaf3c728c233d530e2c2757c8/pillow-11.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fba162b8872d30fea8c52b258a542c5dfd7b235fb5cb352240c8d63b414013eb", size = 4430378 },
-    { url = "https://files.pythonhosted.org/packages/ca/1d/ad9c14811133977ff87035bf426875b93097fb50af747793f013979facdb/pillow-11.0.0-cp313-cp313-win32.whl", hash = "sha256:f1b82c27e89fffc6da125d5eb0ca6e68017faf5efc078128cfaa42cf5cb38798", size = 2249588 },
-    { url = "https://files.pythonhosted.org/packages/fb/01/3755ba287dac715e6afdb333cb1f6d69740a7475220b4637b5ce3d78cec2/pillow-11.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:8ba470552b48e5835f1d23ecb936bb7f71d206f9dfeee64245f30c3270b994de", size = 2567509 },
-    { url = "https://files.pythonhosted.org/packages/c0/98/2c7d727079b6be1aba82d195767d35fcc2d32204c7a5820f822df5330152/pillow-11.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:846e193e103b41e984ac921b335df59195356ce3f71dcfd155aa79c603873b84", size = 2254791 },
-    { url = "https://files.pythonhosted.org/packages/eb/38/998b04cc6f474e78b563716b20eecf42a2fa16a84589d23c8898e64b0ffd/pillow-11.0.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:4ad70c4214f67d7466bea6a08061eba35c01b1b89eaa098040a35272a8efb22b", size = 3150854 },
-    { url = "https://files.pythonhosted.org/packages/13/8e/be23a96292113c6cb26b2aa3c8b3681ec62b44ed5c2bd0b258bd59503d3c/pillow-11.0.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:6ec0d5af64f2e3d64a165f490d96368bb5dea8b8f9ad04487f9ab60dc4bb6003", size = 2982369 },
-    { url = "https://files.pythonhosted.org/packages/97/8a/3db4eaabb7a2ae8203cd3a332a005e4aba00067fc514aaaf3e9721be31f1/pillow-11.0.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c809a70e43c7977c4a42aefd62f0131823ebf7dd73556fa5d5950f5b354087e2", size = 4333703 },
-    { url = "https://files.pythonhosted.org/packages/28/ac/629ffc84ff67b9228fe87a97272ab125bbd4dc462745f35f192d37b822f1/pillow-11.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:4b60c9520f7207aaf2e1d94de026682fc227806c6e1f55bba7606d1c94dd623a", size = 4412550 },
-    { url = "https://files.pythonhosted.org/packages/d6/07/a505921d36bb2df6868806eaf56ef58699c16c388e378b0dcdb6e5b2fb36/pillow-11.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:1e2688958a840c822279fda0086fec1fdab2f95bf2b717b66871c4ad9859d7e8", size = 4461038 },
-    { url = "https://files.pythonhosted.org/packages/d6/b9/fb620dd47fc7cc9678af8f8bd8c772034ca4977237049287e99dda360b66/pillow-11.0.0-cp313-cp313t-win32.whl", hash = "sha256:607bbe123c74e272e381a8d1957083a9463401f7bd01287f50521ecb05a313f8", size = 2253197 },
-    { url = "https://files.pythonhosted.org/packages/df/86/25dde85c06c89d7fc5db17940f07aae0a56ac69aa9ccb5eb0f09798862a8/pillow-11.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5c39ed17edea3bc69c743a8dd3e9853b7509625c2462532e62baa0732163a904", size = 2572169 },
-    { url = "https://files.pythonhosted.org/packages/51/85/9c33f2517add612e17f3381aee7c4072779130c634921a756c97bc29fb49/pillow-11.0.0-cp313-cp313t-win_arm64.whl", hash = "sha256:75acbbeb05b86bc53cbe7b7e6fe00fbcf82ad7c684b3ad82e3d711da9ba287d3", size = 2256828 },
-    { url = "https://files.pythonhosted.org/packages/36/57/42a4dd825eab762ba9e690d696d894ba366e06791936056e26e099398cda/pillow-11.0.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:1187739620f2b365de756ce086fdb3604573337cc28a0d3ac4a01ab6b2d2a6d2", size = 3119239 },
-    { url = "https://files.pythonhosted.org/packages/98/f7/25f9f9e368226a1d6cf3507081a1a7944eddd3ca7821023377043f5a83c8/pillow-11.0.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:fbbcb7b57dc9c794843e3d1258c0fbf0f48656d46ffe9e09b63bbd6e8cd5d0a2", size = 2950803 },
-    { url = "https://files.pythonhosted.org/packages/59/01/98ead48a6c2e31e6185d4c16c978a67fe3ccb5da5c2ff2ba8475379bb693/pillow-11.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d203af30149ae339ad1b4f710d9844ed8796e97fda23ffbc4cc472968a47d0b", size = 3281098 },
-    { url = "https://files.pythonhosted.org/packages/51/c0/570255b2866a0e4d500a14f950803a2ec273bac7badc43320120b9262450/pillow-11.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21a0d3b115009ebb8ac3d2ebec5c2982cc693da935f4ab7bb5c8ebe2f47d36f2", size = 3323665 },
-    { url = "https://files.pythonhosted.org/packages/0e/75/689b4ec0483c42bfc7d1aacd32ade7a226db4f4fac57c6fdcdf90c0731e3/pillow-11.0.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:73853108f56df97baf2bb8b522f3578221e56f646ba345a372c78326710d3830", size = 3310533 },
-    { url = "https://files.pythonhosted.org/packages/3d/30/38bd6149cf53da1db4bad304c543ade775d225961c4310f30425995cb9ec/pillow-11.0.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:e58876c91f97b0952eb766123bfef372792ab3f4e3e1f1a2267834c2ab131734", size = 3414886 },
-    { url = "https://files.pythonhosted.org/packages/ec/3d/c32a51d848401bd94cabb8767a39621496491ee7cd5199856b77da9b18ad/pillow-11.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:224aaa38177597bb179f3ec87eeefcce8e4f85e608025e9cfac60de237ba6316", size = 2567508 },
+    { url = "https://files.pythonhosted.org/packages/0e/69/a31cccd538ca0b5272be2a38347f8839b97a14be104ea08b0db92f749c74/pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:4d9667937cfa347525b319ae34375c37b9ee6b525440f3ef48542fcf66f2731e", size = 3509271 },
+    { url = "https://files.pythonhosted.org/packages/9a/9e/4143b907be8ea0bce215f2ae4f7480027473f8b61fcedfda9d851082a5d2/pillow-10.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:543f3dc61c18dafb755773efc89aae60d06b6596a63914107f75459cf984164d", size = 3375658 },
+    { url = "https://files.pythonhosted.org/packages/8a/25/1fc45761955f9359b1169aa75e241551e74ac01a09f487adaaf4c3472d11/pillow-10.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7928ecbf1ece13956b95d9cbcfc77137652b02763ba384d9ab508099a2eca856", size = 4332075 },
+    { url = "https://files.pythonhosted.org/packages/5e/dd/425b95d0151e1d6c951f45051112394f130df3da67363b6bc75dc4c27aba/pillow-10.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4d49b85c4348ea0b31ea63bc75a9f3857869174e2bf17e7aba02945cd218e6f", size = 4444808 },
+    { url = "https://files.pythonhosted.org/packages/b1/84/9a15cc5726cbbfe7f9f90bfb11f5d028586595907cd093815ca6644932e3/pillow-10.4.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:6c762a5b0997f5659a5ef2266abc1d8851ad7749ad9a6a5506eb23d314e4f46b", size = 4356290 },
+    { url = "https://files.pythonhosted.org/packages/b5/5b/6651c288b08df3b8c1e2f8c1152201e0b25d240e22ddade0f1e242fc9fa0/pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:a985e028fc183bf12a77a8bbf36318db4238a3ded7fa9df1b9a133f1cb79f8fc", size = 4525163 },
+    { url = "https://files.pythonhosted.org/packages/07/8b/34854bf11a83c248505c8cb0fcf8d3d0b459a2246c8809b967963b6b12ae/pillow-10.4.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:812f7342b0eee081eaec84d91423d1b4650bb9828eb53d8511bcef8ce5aecf1e", size = 4463100 },
+    { url = "https://files.pythonhosted.org/packages/78/63/0632aee4e82476d9cbe5200c0cdf9ba41ee04ed77887432845264d81116d/pillow-10.4.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:ac1452d2fbe4978c2eec89fb5a23b8387aba707ac72810d9490118817d9c0b46", size = 4592880 },
+    { url = "https://files.pythonhosted.org/packages/df/56/b8663d7520671b4398b9d97e1ed9f583d4afcbefbda3c6188325e8c297bd/pillow-10.4.0-cp310-cp310-win32.whl", hash = "sha256:bcd5e41a859bf2e84fdc42f4edb7d9aba0a13d29a2abadccafad99de3feff984", size = 2235218 },
+    { url = "https://files.pythonhosted.org/packages/f4/72/0203e94a91ddb4a9d5238434ae6c1ca10e610e8487036132ea9bf806ca2a/pillow-10.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:ecd85a8d3e79cd7158dec1c9e5808e821feea088e2f69a974db5edf84dc53141", size = 2554487 },
+    { url = "https://files.pythonhosted.org/packages/bd/52/7e7e93d7a6e4290543f17dc6f7d3af4bd0b3dd9926e2e8a35ac2282bc5f4/pillow-10.4.0-cp310-cp310-win_arm64.whl", hash = "sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1", size = 2243219 },
+    { url = "https://files.pythonhosted.org/packages/a7/62/c9449f9c3043c37f73e7487ec4ef0c03eb9c9afc91a92b977a67b3c0bbc5/pillow-10.4.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0a9ec697746f268507404647e531e92889890a087e03681a3606d9b920fbee3c", size = 3509265 },
+    { url = "https://files.pythonhosted.org/packages/f4/5f/491dafc7bbf5a3cc1845dc0430872e8096eb9e2b6f8161509d124594ec2d/pillow-10.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfe91cb65544a1321e631e696759491ae04a2ea11d36715eca01ce07284738be", size = 3375655 },
+    { url = "https://files.pythonhosted.org/packages/73/d5/c4011a76f4207a3c151134cd22a1415741e42fa5ddecec7c0182887deb3d/pillow-10.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5dc6761a6efc781e6a1544206f22c80c3af4c8cf461206d46a1e6006e4429ff3", size = 4340304 },
+    { url = "https://files.pythonhosted.org/packages/ac/10/c67e20445a707f7a610699bba4fe050583b688d8cd2d202572b257f46600/pillow-10.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e84b6cc6a4a3d76c153a6b19270b3526a5a8ed6b09501d3af891daa2a9de7d6", size = 4452804 },
+    { url = "https://files.pythonhosted.org/packages/a9/83/6523837906d1da2b269dee787e31df3b0acb12e3d08f024965a3e7f64665/pillow-10.4.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:bbc527b519bd3aa9d7f429d152fea69f9ad37c95f0b02aebddff592688998abe", size = 4365126 },
+    { url = "https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319", size = 4533541 },
+    { url = "https://files.pythonhosted.org/packages/f4/7c/01b8dbdca5bc6785573f4cee96e2358b0918b7b2c7b60d8b6f3abf87a070/pillow-10.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:59291fb29317122398786c2d44427bbd1a6d7ff54017075b22be9d21aa59bd8d", size = 4471616 },
+    { url = "https://files.pythonhosted.org/packages/c8/57/2899b82394a35a0fbfd352e290945440e3b3785655a03365c0ca8279f351/pillow-10.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:416d3a5d0e8cfe4f27f574362435bc9bae57f679a7158e0096ad2beb427b8696", size = 4600802 },
+    { url = "https://files.pythonhosted.org/packages/4d/d7/a44f193d4c26e58ee5d2d9db3d4854b2cfb5b5e08d360a5e03fe987c0086/pillow-10.4.0-cp311-cp311-win32.whl", hash = "sha256:7086cc1d5eebb91ad24ded9f58bec6c688e9f0ed7eb3dbbf1e4800280a896496", size = 2235213 },
+    { url = "https://files.pythonhosted.org/packages/c1/d0/5866318eec2b801cdb8c82abf190c8343d8a1cd8bf5a0c17444a6f268291/pillow-10.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:cbed61494057c0f83b83eb3a310f0bf774b09513307c434d4366ed64f4128a91", size = 2554498 },
+    { url = "https://files.pythonhosted.org/packages/d4/c8/310ac16ac2b97e902d9eb438688de0d961660a87703ad1561fd3dfbd2aa0/pillow-10.4.0-cp311-cp311-win_arm64.whl", hash = "sha256:f5f0c3e969c8f12dd2bb7e0b15d5c468b51e5017e01e2e867335c81903046a22", size = 2243219 },
+    { url = "https://files.pythonhosted.org/packages/05/cb/0353013dc30c02a8be34eb91d25e4e4cf594b59e5a55ea1128fde1e5f8ea/pillow-10.4.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:673655af3eadf4df6b5457033f086e90299fdd7a47983a13827acf7459c15d94", size = 3509350 },
+    { url = "https://files.pythonhosted.org/packages/e7/cf/5c558a0f247e0bf9cec92bff9b46ae6474dd736f6d906315e60e4075f737/pillow-10.4.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:866b6942a92f56300012f5fbac71f2d610312ee65e22f1aa2609e491284e5597", size = 3374980 },
+    { url = "https://files.pythonhosted.org/packages/84/48/6e394b86369a4eb68b8a1382c78dc092245af517385c086c5094e3b34428/pillow-10.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29dbdc4207642ea6aad70fbde1a9338753d33fb23ed6956e706936706f52dd80", size = 4343799 },
+    { url = "https://files.pythonhosted.org/packages/3b/f3/a8c6c11fa84b59b9df0cd5694492da8c039a24cd159f0f6918690105c3be/pillow-10.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf2342ac639c4cf38799a44950bbc2dfcb685f052b9e262f446482afaf4bffca", size = 4459973 },
+    { url = "https://files.pythonhosted.org/packages/7d/1b/c14b4197b80150fb64453585247e6fb2e1d93761fa0fa9cf63b102fde822/pillow-10.4.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:f5b92f4d70791b4a67157321c4e8225d60b119c5cc9aee8ecf153aace4aad4ef", size = 4370054 },
+    { url = "https://files.pythonhosted.org/packages/55/77/40daddf677897a923d5d33329acd52a2144d54a9644f2a5422c028c6bf2d/pillow-10.4.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:86dcb5a1eb778d8b25659d5e4341269e8590ad6b4e8b44d9f4b07f8d136c414a", size = 4539484 },
+    { url = "https://files.pythonhosted.org/packages/40/54/90de3e4256b1207300fb2b1d7168dd912a2fb4b2401e439ba23c2b2cabde/pillow-10.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:780c072c2e11c9b2c7ca37f9a2ee8ba66f44367ac3e5c7832afcfe5104fd6d1b", size = 4477375 },
+    { url = "https://files.pythonhosted.org/packages/13/24/1bfba52f44193860918ff7c93d03d95e3f8748ca1de3ceaf11157a14cf16/pillow-10.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:37fb69d905be665f68f28a8bba3c6d3223c8efe1edf14cc4cfa06c241f8c81d9", size = 4608773 },
+    { url = "https://files.pythonhosted.org/packages/55/04/5e6de6e6120451ec0c24516c41dbaf80cce1b6451f96561235ef2429da2e/pillow-10.4.0-cp312-cp312-win32.whl", hash = "sha256:7dfecdbad5c301d7b5bde160150b4db4c659cee2b69589705b6f8a0c509d9f42", size = 2235690 },
+    { url = "https://files.pythonhosted.org/packages/74/0a/d4ce3c44bca8635bd29a2eab5aa181b654a734a29b263ca8efe013beea98/pillow-10.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:1d846aea995ad352d4bdcc847535bd56e0fd88d36829d2c90be880ef1ee4668a", size = 2554951 },
+    { url = "https://files.pythonhosted.org/packages/b5/ca/184349ee40f2e92439be9b3502ae6cfc43ac4b50bc4fc6b3de7957563894/pillow-10.4.0-cp312-cp312-win_arm64.whl", hash = "sha256:e553cad5179a66ba15bb18b353a19020e73a7921296a7979c4a2b7f6a5cd57f9", size = 2243427 },
+    { url = "https://files.pythonhosted.org/packages/c3/00/706cebe7c2c12a6318aabe5d354836f54adff7156fd9e1bd6c89f4ba0e98/pillow-10.4.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8bc1a764ed8c957a2e9cacf97c8b2b053b70307cf2996aafd70e91a082e70df3", size = 3525685 },
+    { url = "https://files.pythonhosted.org/packages/cf/76/f658cbfa49405e5ecbfb9ba42d07074ad9792031267e782d409fd8fe7c69/pillow-10.4.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6209bb41dc692ddfee4942517c19ee81b86c864b626dbfca272ec0f7cff5d9fb", size = 3374883 },
+    { url = "https://files.pythonhosted.org/packages/46/2b/99c28c4379a85e65378211971c0b430d9c7234b1ec4d59b2668f6299e011/pillow-10.4.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bee197b30783295d2eb680b311af15a20a8b24024a19c3a26431ff83eb8d1f70", size = 4339837 },
+    { url = "https://files.pythonhosted.org/packages/f1/74/b1ec314f624c0c43711fdf0d8076f82d9d802afd58f1d62c2a86878e8615/pillow-10.4.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ef61f5dd14c300786318482456481463b9d6b91ebe5ef12f405afbba77ed0be", size = 4455562 },
+    { url = "https://files.pythonhosted.org/packages/4a/2a/4b04157cb7b9c74372fa867096a1607e6fedad93a44deeff553ccd307868/pillow-10.4.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:297e388da6e248c98bc4a02e018966af0c5f92dfacf5a5ca22fa01cb3179bca0", size = 4366761 },
+    { url = "https://files.pythonhosted.org/packages/ac/7b/8f1d815c1a6a268fe90481232c98dd0e5fa8c75e341a75f060037bd5ceae/pillow-10.4.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e4db64794ccdf6cb83a59d73405f63adbe2a1887012e308828596100a0b2f6cc", size = 4536767 },
+    { url = "https://files.pythonhosted.org/packages/e5/77/05fa64d1f45d12c22c314e7b97398ffb28ef2813a485465017b7978b3ce7/pillow-10.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bd2880a07482090a3bcb01f4265f1936a903d70bc740bfcb1fd4e8a2ffe5cf5a", size = 4477989 },
+    { url = "https://files.pythonhosted.org/packages/12/63/b0397cfc2caae05c3fb2f4ed1b4fc4fc878f0243510a7a6034ca59726494/pillow-10.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4b35b21b819ac1dbd1233317adeecd63495f6babf21b7b2512d244ff6c6ce309", size = 4610255 },
+    { url = "https://files.pythonhosted.org/packages/7b/f9/cfaa5082ca9bc4a6de66ffe1c12c2d90bf09c309a5f52b27759a596900e7/pillow-10.4.0-cp313-cp313-win32.whl", hash = "sha256:551d3fd6e9dc15e4c1eb6fc4ba2b39c0c7933fa113b220057a34f4bb3268a060", size = 2235603 },
+    { url = "https://files.pythonhosted.org/packages/01/6a/30ff0eef6e0c0e71e55ded56a38d4859bf9d3634a94a88743897b5f96936/pillow-10.4.0-cp313-cp313-win_amd64.whl", hash = "sha256:030abdbe43ee02e0de642aee345efa443740aa4d828bfe8e2eb11922ea6a21ea", size = 2554972 },
+    { url = "https://files.pythonhosted.org/packages/48/2c/2e0a52890f269435eee38b21c8218e102c621fe8d8df8b9dd06fabf879ba/pillow-10.4.0-cp313-cp313-win_arm64.whl", hash = "sha256:5b001114dd152cfd6b23befeb28d7aee43553e2402c9f159807bf55f33af8a8d", size = 2243375 },
+    { url = "https://files.pythonhosted.org/packages/38/30/095d4f55f3a053392f75e2eae45eba3228452783bab3d9a920b951ac495c/pillow-10.4.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5b4815f2e65b30f5fbae9dfffa8636d992d49705723fe86a3661806e069352d4", size = 3493889 },
+    { url = "https://files.pythonhosted.org/packages/f3/e8/4ff79788803a5fcd5dc35efdc9386af153569853767bff74540725b45863/pillow-10.4.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:8f0aef4ef59694b12cadee839e2ba6afeab89c0f39a3adc02ed51d109117b8da", size = 3346160 },
+    { url = "https://files.pythonhosted.org/packages/d7/ac/4184edd511b14f760c73f5bb8a5d6fd85c591c8aff7c2229677a355c4179/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9f4727572e2918acaa9077c919cbbeb73bd2b3ebcfe033b72f858fc9fbef0026", size = 3435020 },
+    { url = "https://files.pythonhosted.org/packages/da/21/1749cd09160149c0a246a81d646e05f35041619ce76f6493d6a96e8d1103/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff25afb18123cea58a591ea0244b92eb1e61a1fd497bf6d6384f09bc3262ec3e", size = 3490539 },
+    { url = "https://files.pythonhosted.org/packages/b6/f5/f71fe1888b96083b3f6dfa0709101f61fc9e972c0c8d04e9d93ccef2a045/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:dc3e2db6ba09ffd7d02ae9141cfa0ae23393ee7687248d46a7507b75d610f4f5", size = 3476125 },
+    { url = "https://files.pythonhosted.org/packages/96/b9/c0362c54290a31866c3526848583a2f45a535aa9d725fd31e25d318c805f/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:02a2be69f9c9b8c1e97cf2713e789d4e398c751ecfd9967c18d0ce304efbf885", size = 3579373 },
+    { url = "https://files.pythonhosted.org/packages/52/3b/ce7a01026a7cf46e5452afa86f97a5e88ca97f562cafa76570178ab56d8d/pillow-10.4.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:0755ffd4a0c6f267cccbae2e9903d95477ca2f77c4fcf3a3a09570001856c8a5", size = 2554661 },
 ]
 
 [[package]]
@@ -3228,6 +3331,48 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378", size = 98708 },
 ]
 
+[[package]]
+name = "py-rust-stemmers"
+version = "0.1.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f4/8a/c7481c6e324da825f13bafb362dbca47dbf8a7dd1a3a3502f47cdb05bfa9/py_rust_stemmers-0.1.3.tar.gz", hash = "sha256:ad796d47874181a25addb505a04245e34620bd7a0c5055671f52d9ce993253e2", size = 8676 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3e/ed/4c85aa5f2046f7c34db174b89f92d24daaa347a149343f43614a6329c006/py_rust_stemmers-0.1.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:8b4861673bc690a5830a5d84d61c64a95ede86f79c9952df66e99e0559fe8264", size = 287578 },
+    { url = "https://files.pythonhosted.org/packages/72/7c/b3df3222e375cb838572952217cedf3d7925f85f3449c3c87142417e9fab/py_rust_stemmers-0.1.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b0d2108c758e8081064cbbb7fc70d3cdfd32e0cccf7d051c1d888d16c91c1e78", size = 273908 },
+    { url = "https://files.pythonhosted.org/packages/48/d2/2c422476a6e21d9adbf4355b306269ac396eaa853efc896afdb2c628a334/py_rust_stemmers-0.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fdf43a726b81dd5439a98973200546660e10379e805bb6fd6366dbd8d0857666", size = 309863 },
+    { url = "https://files.pythonhosted.org/packages/ff/4f/42cd09a77639f3b0b2d662cbbc19248355ce40ba69eaac796007aae37b7e/py_rust_stemmers-0.1.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:03acb3d89f8090f67698d2c64172492618585927dfb56d0b5f6070ff54269940", size = 313215 },
+    { url = "https://files.pythonhosted.org/packages/8a/2c/39bfcdf674c799cb486fd1f10a9ce1599030884b47f2819aabb39db0398a/py_rust_stemmers-0.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3f8cd1139a641ed53e9a1d7f25ae9cf3757cae96a2b0ce0d9399332ec8b148f", size = 323524 },
+    { url = "https://files.pythonhosted.org/packages/95/b4/38e66537da1864538912aae92f8285badf8201bccdddfdbe06c3c27e99ac/py_rust_stemmers-0.1.3-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:0a5906aa2eec31f647b94d6cc9b2b065bf77ca31be095fcbb1b412ba42f0e473", size = 323903 },
+    { url = "https://files.pythonhosted.org/packages/78/a5/7f219ff3547bfc1337b00761c6cd857fe51b90014b9d51aeba325e33d548/py_rust_stemmers-0.1.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b89fe8e55201604e89bdbd7559b19337ef9ae703a5545878d37664507c1067e9", size = 485483 },
+    { url = "https://files.pythonhosted.org/packages/66/59/43c89cb1388a9c508d28868ce04900d0f3b4457a74b1c61411c9306a3aa4/py_rust_stemmers-0.1.3-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:0d43981b272c73709d3885ed096a332b2a160db2317fbe16cc9ef3b1d974d39a", size = 567275 },
+    { url = "https://files.pythonhosted.org/packages/7d/3a/08722448c51e7b926b8f40a55f363e92236a89b761e89e5ee76b0e11baa8/py_rust_stemmers-0.1.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:1b379c3901a87ee63d7cbb01a68ece78af7040e0c3e3d52fe7b108bfa399feb2", size = 488902 },
+    { url = "https://files.pythonhosted.org/packages/c3/74/41efa33c0eb008eb2b1337f40021debf487e8cea5dbe4af97241a43d54b7/py_rust_stemmers-0.1.3-cp310-none-win_amd64.whl", hash = "sha256:0f571ee0f2a4b2314d4cd8ef26af83e1fd24ea3e3ff97407d536184167f05957", size = 208973 },
+    { url = "https://files.pythonhosted.org/packages/da/3b/f61826b786ed06f195c80b542abe082dcdd1747341c1194f6f782d566a02/py_rust_stemmers-0.1.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:2d8b8e6b6d5839a168dae510a00ff4662c7d0a22d12f24fe81caa0ac59265711", size = 287577 },
+    { url = "https://files.pythonhosted.org/packages/59/fd/322bf0dbc142ae71516c06c2026f4ac0a4685f108a873935581b7eef3d9d/py_rust_stemmers-0.1.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:02b347ab8fe686a88aef0432060471d501b37a6b9a868e7c50bffcd382269cf2", size = 273910 },
+    { url = "https://files.pythonhosted.org/packages/10/34/02aa64046e4a21b1dd5f7d602fb33b1c79bd0dd57c8ebfe5897efcf62ac3/py_rust_stemmers-0.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4a65b429eb1282934a1cc3c1b2698ae32a6dc00d6be00dd747e688c642eb110", size = 309863 },
+    { url = "https://files.pythonhosted.org/packages/10/a4/f4fd2afc713b0497b76023c6e491f356962213bd518f148cbd28b7144e78/py_rust_stemmers-0.1.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9fbbb37e0df579859b42b3f850aa08fe829d190d32c6338349eccb0e762b74c6", size = 313218 },
+    { url = "https://files.pythonhosted.org/packages/98/78/f64e096df43d730fb5f6e2201e6d6ca05ed18e94946f11cdeddd0205f099/py_rust_stemmers-0.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d6f9790fe1e9962787817b1894486df7e0b5fc59e4adad423e189530530fae11", size = 323525 },
+    { url = "https://files.pythonhosted.org/packages/21/38/09beb9ca8ec3af8dbfd441f77fc003472ca900f678d1eb25839db08df691/py_rust_stemmers-0.1.3-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:fd5d7388f807f584b4c55bfbe608ef40cff0024c1dc54de95d28265395065d02", size = 323903 },
+    { url = "https://files.pythonhosted.org/packages/fc/63/08af5678a0cb0f6c5a462def7aec0c32f3742574ee36ddd660103d13bc86/py_rust_stemmers-0.1.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:72a7b810d8d376c03f0ccebe146f04cbf4c6c97bd74e489b0ddf1342eb40970c", size = 485484 },
+    { url = "https://files.pythonhosted.org/packages/33/a7/740b8dd06cb48ed397d65cabda9d38c2c310869c3bf51b0e0a347cb7fc8f/py_rust_stemmers-0.1.3-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:658784c0072f7aae67c726be9acac40dd27b29416356c63a3a760a9499a93513", size = 567275 },
+    { url = "https://files.pythonhosted.org/packages/6e/75/e785900047b4fc5773d0bea37c565825df26de81f25ab2d341ecaa2f55f5/py_rust_stemmers-0.1.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e6afcd19da56d4182eecb43bdb6c5b9686370063f2538df877fc23f1d16f909e", size = 488906 },
+    { url = "https://files.pythonhosted.org/packages/5b/ee/86ee4eb3188f45cf0831318dab9afddc231ae71b8fecc0dbbc79eb885ded/py_rust_stemmers-0.1.3-cp311-none-win_amd64.whl", hash = "sha256:47211ac6252eb484f5067d30b1812667936deffcef89b4b0acd2efe881a99aed", size = 208976 },
+    { url = "https://files.pythonhosted.org/packages/cc/08/f9c9ef78c7dca7a69c451b1df754195e02a3a1e7a450becdce687102aae7/py_rust_stemmers-0.1.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:a36bfbd9219a55bdf5aa9c5d74b8a3741cb092495190ca18551dc39f57272d57", size = 287577 },
+    { url = "https://files.pythonhosted.org/packages/50/3a/5c518bc2761f8a873b1ec9333f7f74a8f58e7e8b39d5de065038427b114b/py_rust_stemmers-0.1.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ca1ab04ff2fa15a1d0685007293ffdf4679dcfdc02fc5b36c1af0111670908a1", size = 273906 },
+    { url = "https://files.pythonhosted.org/packages/b4/ae/3cae1a65a99687e4bf830ab733b3adde13e458a7908b6826dd9025c8c5c3/py_rust_stemmers-0.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ccaa08251b9cb421429976d56365ddf9db63b5a8ac4e7817723fb0b62adf8b19", size = 309864 },
+    { url = "https://files.pythonhosted.org/packages/a9/f2/b4167a4a64b0bade1695b32e4bd13ca752085d43559670fd7173cfb59b9e/py_rust_stemmers-0.1.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6262b40f989c0b0bcb3eaef5511268ba63703428c4ab1aa9353a58c8572735b7", size = 313217 },
+    { url = "https://files.pythonhosted.org/packages/54/ff/f27e0762a74668bf520525d7bad8daa4dd621ef5b3155c464c5bd8a7dd3f/py_rust_stemmers-0.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a073701b492ef900cee5185961c23006ba13fa6126cf716f241c929adbdfad6e", size = 323525 },
+    { url = "https://files.pythonhosted.org/packages/d3/f2/2f4599ef5481be24378a23f93af405b4ca968450873d48d0a56ba925d7b5/py_rust_stemmers-0.1.3-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:39c75f10da70380076b68398d84cdc42b42966180bdb8216b81d21a824278b50", size = 323903 },
+    { url = "https://files.pythonhosted.org/packages/dd/84/1aea103917659abc12456ce061621557eed0a44e174270908e3fb28f2cc3/py_rust_stemmers-0.1.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:34f7d92abc85f0f0b1fa407410b3f2daaf2c36b8277a2ffff2ff0beb2f2acc2f", size = 485487 },
+    { url = "https://files.pythonhosted.org/packages/bd/67/16d48e7f02b285b39028aa47f847b3a279c903bc5cd49c8012ea90255317/py_rust_stemmers-0.1.3-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:fbb9f7933239a57d1d9c0fcdfbe0c5283a081e9e64ddc48ed878783be3d52b2b", size = 567278 },
+    { url = "https://files.pythonhosted.org/packages/ad/1c/cb8cc9680f8aa04f96cb5c814887b3bb8d23a2e9abf460ef861ae16bfe50/py_rust_stemmers-0.1.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:921803a6f8259f10bf348ac0e32a767c28ab587c9ad5c3b1ee593a4bbbe98d39", size = 488907 },
+    { url = "https://files.pythonhosted.org/packages/cd/29/88217de06239e3e526fa6286a11e3662d94acb0be4216c1310301a252dab/py_rust_stemmers-0.1.3-cp312-none-win_amd64.whl", hash = "sha256:576206b540575e81bb84a0f620b7a8529f5e89b0b2ec7d4487f3183789dd5cfd", size = 208980 },
+    { url = "https://files.pythonhosted.org/packages/f1/45/e1ec9e76b4462e70fa42f6ac8be9f1bfe6565c1c260b9e5824e772157edf/py_rust_stemmers-0.1.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:59eacf7687738b20886a7c0ceeae999d501902b4e6234cf11eecd2f45f2c26bb", size = 288041 },
+    { url = "https://files.pythonhosted.org/packages/4a/5b/eb594ca68715c23dd3b8f52dd700c10cbdd8133faaaf19886962c8f97c90/py_rust_stemmers-0.1.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:e39d5d273e13aec2f07a2c3ea0050b3bf3aaa7b6e9f6bef3d4e728ab49979ae8", size = 274089 },
+    { url = "https://files.pythonhosted.org/packages/79/55/b62b14cdeb7268a818f21e4c8cfd543261c563dc9bd89ba7116293ce3008/py_rust_stemmers-0.1.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f95b25138431c4a457d684c49c6de5ff0c1852cf1cb3657e187ea63610fc7c21", size = 310373 },
+    { url = "https://files.pythonhosted.org/packages/a4/71/f0b7131505013eaaa4fbfcd821b30b36431d01b7fe96951d84721cdb4ef8/py_rust_stemmers-0.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1cc9df57dff15d12d7fec65a541af6fdcefd40ea5f7ebd48ad5202a1b9a56f89", size = 324052 },
+]
+
 [[package]]
 name = "pyarrow"
 version = "17.0.0"
@@ -3444,6 +3589,26 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/48/8f/9bbf22ba6a00001a45dbc54337e5bbbd43e7d8f34c8158c92cddc45736af/pypdf-5.0.1-py3-none-any.whl", hash = "sha256:ff8a32da6c7a63fea9c32fa4dd837cdd0db7966adf6c14f043e3f12592e992db", size = 294470 },
 ]
 
+[[package]]
+name = "pypdfium2"
+version = "4.30.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/14/838b3ba247a0ba92e4df5d23f2bea9478edcfd72b78a39d6ca36ccd84ad2/pypdfium2-4.30.0.tar.gz", hash = "sha256:48b5b7e5566665bc1015b9d69c1ebabe21f6aee468b509531c3c8318eeee2e16", size = 140239 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/9a/c8ff5cc352c1b60b0b97642ae734f51edbab6e28b45b4fcdfe5306ee3c83/pypdfium2-4.30.0-py3-none-macosx_10_13_x86_64.whl", hash = "sha256:b33ceded0b6ff5b2b93bc1fe0ad4b71aa6b7e7bd5875f1ca0cdfb6ba6ac01aab", size = 2837254 },
+    { url = "https://files.pythonhosted.org/packages/21/8b/27d4d5409f3c76b985f4ee4afe147b606594411e15ac4dc1c3363c9a9810/pypdfium2-4.30.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:4e55689f4b06e2d2406203e771f78789bd4f190731b5d57383d05cf611d829de", size = 2707624 },
+    { url = "https://files.pythonhosted.org/packages/11/63/28a73ca17c24b41a205d658e177d68e198d7dde65a8c99c821d231b6ee3d/pypdfium2-4.30.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e6e50f5ce7f65a40a33d7c9edc39f23140c57e37144c2d6d9e9262a2a854854", size = 2793126 },
+    { url = "https://files.pythonhosted.org/packages/d1/96/53b3ebf0955edbd02ac6da16a818ecc65c939e98fdeb4e0958362bd385c8/pypdfium2-4.30.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3d0dd3ecaffd0b6dbda3da663220e705cb563918249bda26058c6036752ba3a2", size = 2591077 },
+    { url = "https://files.pythonhosted.org/packages/ec/ee/0394e56e7cab8b5b21f744d988400948ef71a9a892cbeb0b200d324ab2c7/pypdfium2-4.30.0-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cc3bf29b0db8c76cdfaac1ec1cde8edf211a7de7390fbf8934ad2aa9b4d6dfad", size = 2864431 },
+    { url = "https://files.pythonhosted.org/packages/65/cd/3f1edf20a0ef4a212a5e20a5900e64942c5a374473671ac0780eaa08ea80/pypdfium2-4.30.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1f78d2189e0ddf9ac2b7a9b9bd4f0c66f54d1389ff6c17e9fd9dc034d06eb3f", size = 2812008 },
+    { url = "https://files.pythonhosted.org/packages/c8/91/2d517db61845698f41a2a974de90762e50faeb529201c6b3574935969045/pypdfium2-4.30.0-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:5eda3641a2da7a7a0b2f4dbd71d706401a656fea521b6b6faa0675b15d31a163", size = 6181543 },
+    { url = "https://files.pythonhosted.org/packages/ba/c4/ed1315143a7a84b2c7616569dfb472473968d628f17c231c39e29ae9d780/pypdfium2-4.30.0-py3-none-musllinux_1_1_i686.whl", hash = "sha256:0dfa61421b5eb68e1188b0b2231e7ba35735aef2d867d86e48ee6cab6975195e", size = 6175911 },
+    { url = "https://files.pythonhosted.org/packages/7a/c4/9e62d03f414e0e3051c56d5943c3bf42aa9608ede4e19dc96438364e9e03/pypdfium2-4.30.0-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:f33bd79e7a09d5f7acca3b0b69ff6c8a488869a7fab48fdf400fec6e20b9c8be", size = 6267430 },
+    { url = "https://files.pythonhosted.org/packages/90/47/eda4904f715fb98561e34012826e883816945934a851745570521ec89520/pypdfium2-4.30.0-py3-none-win32.whl", hash = "sha256:ee2410f15d576d976c2ab2558c93d392a25fb9f6635e8dd0a8a3a5241b275e0e", size = 2775951 },
+    { url = "https://files.pythonhosted.org/packages/25/bd/56d9ec6b9f0fc4e0d95288759f3179f0fcd34b1a1526b75673d2f6d5196f/pypdfium2-4.30.0-py3-none-win_amd64.whl", hash = "sha256:90dbb2ac07be53219f56be09961eb95cf2473f834d01a42d901d13ccfad64b4c", size = 2892098 },
+    { url = "https://files.pythonhosted.org/packages/be/7a/097801205b991bc3115e8af1edb850d30aeaf0118520b016354cf5ccd3f6/pypdfium2-4.30.0-py3-none-win_arm64.whl", hash = "sha256:119b2969a6d6b1e8d55e99caaf05290294f2d0fe49c12a3f17102d01c441bd29", size = 2752118 },
+]
+
 [[package]]
 name = "pypika"
 version = "0.48.9"
@@ -4743,6 +4908,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/56/27/96a5cd2626d11c8280656c6c71d8ab50fe006490ef9971ccd154e0c42cd2/websockets-13.1-py3-none-any.whl", hash = "sha256:a9a396a6ad26130cdae92ae10c36af09d9bfe6cafe69670fd3b6da9b07b4044f", size = 152134 },
 ]
 
+[[package]]
+name = "win32-setctime"
+version = "1.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/dd/f95a13d2b235a28d613ba23ebad55191514550debb968b46aab99f2e3a30/win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2", size = 3676 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0a/e6/a7d828fef907843b2a5773ebff47fb79ac0c1c88d60c0ca9530ee941e248/win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad", size = 3604 },
+]
+
 [[package]]
 name = "wrapt"
 version = "1.16.0"