style: Fix import sorting in all files

Co-Authored-By: Joe Moura <joao@crewai.com>
style: Fix import sorting
2026-01-08 15:48:29 +00:00 · 2025-02-12 20:06:44 +00:00 · 2025-02-12 20:04:44 +00:00 · 2025-02-12 20:03:03 +00:00 · 2025-02-12 20:02:39 +00:00 · 2025-02-12 20:01:52 +00:00
20 changed files with 1038 additions and 680 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -21,4 +21,5 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
+agentops.log
+tests/cassettes/
--- a/docs/tools/faiss_search_tool.mdx
+++ b/docs/tools/faiss_search_tool.mdx
@@ -1,142 +0,0 @@
-# FAISS Search Tool
-
-The FAISS Search Tool enables efficient vector similarity search using Facebook AI Similarity Search (FAISS).
-
-## Usage
-
-```python
-from typing import List, Dict, Any
-from crewai import Agent
-from crewai.tools import FAISSSearchTool
-
-# Initialize tool
-search_tool = FAISSSearchTool(
-    index_type: str = "L2",  # or "IP" for inner product
-    dimension: int = 384,    # Match your embedder's dimension
-    embedder_config: Dict[str, Any] = {
-        "provider": "fastembed",
-        "model": "BAAI/bge-small-en-v1.5"
-    }
-)
-
-# Add documents (with error handling)
-try:
-    search_tool.add_texts([
-        "Document 1 content",
-        "Document 2 content",
-        # ...
-    ])
-except ValueError as e:
-    print(f"Failed to add documents: {e}")
-
-# Add large document sets efficiently
-try:
-    search_tool.add_texts_batch(
-        texts=["Doc 1", "Doc 2", ...],  # Large list of documents
-        batch_size=1000  # Process in batches to manage memory
-    )
-except ValueError as e:
-    print(f"Failed to add documents in batch: {e}")
-
-# Search with error handling
-try:
-    results = search_tool.run(
-        query="search query",
-        k=3,  # Number of results
-        score_threshold=0.6  # Minimum similarity score
-    )
-    for result in results:
-        print(f"Text: {result['text']}, Score: {result['score']}")
-except ValueError as e:
-    print(f"Search failed: {e}")
-
-# Create agent with tool
-agent = Agent(
-    role="researcher",
-    goal="Find relevant information",
-    tools=[search_tool]
-)
-```
-
-## Configuration
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| index_type | str | FAISS index type ("L2" or "IP") |
-| dimension | int | Embedding dimension |
-| embedder_config | dict | Embedder configuration |
-
-## Parameters
-
-### index_type
- `"L2"`: Euclidean distance (default)
- `"IP"`: Inner product similarity
-
-### dimension
-Default is 384, which matches the BAAI/bge-small-en-v1.5 model. Adjust this to match your chosen embedder model's output dimension.
-
-### embedder_config
-Configuration for the embedding model. Supports all CrewAI embedder providers:
- fastembed (default)
- openai
- google
- ollama
-
-## Error Handling
-
-The tool includes comprehensive error handling:
-
-```python
-# Invalid index type
-try:
-    tool = FAISSSearchTool(index_type="INVALID")
-except ValueError as e:
-    print(f"Invalid index type: {e}")
-
-# Empty query
-try:
-    results = tool.run(query="")
-except ValueError as e:
-    print(f"Invalid query: {e}")  # "Query cannot be empty"
-
-# Invalid k value
-try:
-    results = tool.run(query="test", k=0)
-except ValueError as e:
-    print(f"Invalid k: {e}")  # "k must be positive"
-
-# Invalid score threshold
-try:
-    results = tool.run(query="test", score_threshold=1.5)
-except ValueError as e:
-    print(f"Invalid threshold: {e}")  # "score_threshold must be between 0 and 1"
-```
-
-## Performance Considerations
-
-### Memory Management
-For large document sets, use batch processing to manage memory efficiently:
-```python
-# Process documents in batches
-tool.add_texts_batch(texts=large_document_list, batch_size=1000)
-```
-
-### Index Management
-Monitor and manage index size:
-```python
-# Check index size
-print(f"Current index size: {tool.index_size}")
-
-# Check if index is empty
-if tool.is_empty:
-    print("Index is empty")
-
-# Clear index if needed
-tool.clear_index()
-```
-
-### Performance Metrics
-The tool is optimized for performance:
- Search operations typically complete within 1 second for indices up to 1000 documents
- Batch processing helps manage memory for large document sets
- Input sanitization ensures query safety without significant overhead
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.102.0"
+version = "0.100.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -37,7 +37,6 @@ dependencies = [
    "tomli>=2.0.2",
    "blinker>=1.9.0",
    "json5>=0.10.0",
-    "faiss-cpu>=1.7.4,<2.0.0",
 ]

 [project.urls]
@@ -46,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
-tools = ["crewai-tools>=0.36.0"]
+tools = ["crewai-tools>=0.32.1"]
 embeddings = [
    "tiktoken~=0.7.0"
 ]
--- a/src/crewai/init.py
+++ b/src/crewai/init.py
@@ -14,7 +14,7 @@ warnings.filterwarnings(
    category=UserWarning,
    module="pydantic.main",
 )
-__version__ = "0.102.0"
+__version__ = "0.100.1"
 __all__ = [
    "Agent",
    "Crew",
--- a/src/crewai/agents/crew_agent_executor.py
+++ b/src/crewai/agents/crew_agent_executor.py
@@ -21,6 +21,9 @@ from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
    LLMContextLengthExceededException,
 )
+from crewai.utilities.exceptions.feedback_processing_exception import (
+    FeedbackProcessingError,
+)
 from crewai.utilities.logger import Logger
 from crewai.utilities.training_handler import CrewTrainingHandler

@@ -487,17 +490,40 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
        return CrewAgentParser(agent=self.agent).parse(answer)

    def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        """Format a message with role and content.
+        
+        Args:
+            prompt (str): The message content
+            role (str): The message role (default: "user")
+            
+        Returns:
+            Dict[str, str]: Formatted message with role and content
+            
+        Raises:
+            FeedbackProcessingError: If prompt is empty or exceeds max length
+        """
+        if not prompt or not prompt.strip():
+            raise FeedbackProcessingError("Feedback message cannot be empty")
+        if len(prompt) > 8192:  # Standard context window size
+            raise FeedbackProcessingError("Feedback message exceeds maximum length")
+            
        prompt = prompt.rstrip()
        return {"role": role, "content": prompt}

    def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
        """Handle human feedback with different flows for training vs regular use.

+        This method processes human feedback by either handling it as training data
+        or as regular feedback requiring potential multiple iterations.
+
        Args:
-            formatted_answer: The initial AgentFinish result to get feedback on
+            formatted_answer (AgentFinish): The initial AgentFinish result to get feedback on

        Returns:
            AgentFinish: The final answer after processing feedback
+            
+        Raises:
+            FeedbackProcessingError: If feedback processing fails
        """
        human_feedback = self._ask_human_input(formatted_answer.output)

@@ -511,44 +537,97 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
        return bool(self.crew and self.crew._train)

    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
+        self,
+        initial_answer: AgentFinish,
+        feedback: str,
    ) -> AgentFinish:
-        """Process feedback for training scenarios with single iteration."""
-        self._printer.print(
-            content="\nProcessing training feedback.\n",
-            color="yellow",
-        )
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+        """Process feedback for training scenarios with single iteration.
+        
+        Args:
+            initial_answer (AgentFinish): The initial answer to improve
+            feedback (str): The feedback to process
+            
+        Returns:
+            AgentFinish: The improved answer after processing feedback
+            
+        Raises:
+            FeedbackProcessingError: If feedback processing fails
+        """
+        try:
+            self._printer.print(
+                content="\nProcessing training feedback.\n",
+                color="yellow",
            )
-        )
-        improved_answer = self._invoke_loop()
-        self._handle_crew_training_output(improved_answer)
-        self.ask_for_human_input = False
-        return improved_answer
+            self._handle_crew_training_output(initial_answer, feedback)
+            improved_answer = self._process_feedback_iteration(feedback)
+            self._handle_crew_training_output(improved_answer)
+            self.ask_for_human_input = False
+            return improved_answer
+        except Exception as e:
+            error_msg = f"Failed to process training feedback: {str(e)}"
+            self._printer.print(
+                content=error_msg,
+                color="red"
+            )
+            raise FeedbackProcessingError(error_msg, original_error=e)

    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
+        self,
+        current_answer: AgentFinish,
+        initial_feedback: str,
    ) -> AgentFinish:
-        """Process feedback for regular use with potential multiple iterations."""
-        feedback = initial_feedback
-        answer = current_answer
+        """Process feedback for regular use with potential multiple iterations.

-        while self.ask_for_human_input:
-            response = self._get_llm_feedback_response(feedback)
+        This method handles the iterative feedback process where the agent continues
+        to improve its answer based on user feedback until no more changes are needed.

-            if not self._feedback_requires_changes(response):
-                self.ask_for_human_input = False
-            else:
-                answer = self._process_feedback_iteration(feedback)
-                feedback = self._ask_human_input(answer.output)
+        Args:
+            current_answer (AgentFinish): The current answer from the agent
+            initial_feedback (str): The initial feedback from the user

-        return answer
+        Returns:
+            AgentFinish: The final answer after processing all feedback iterations
+            
+        Raises:
+            FeedbackProcessingError: If feedback processing or validation fails
+        """
+        try:
+            feedback = initial_feedback
+            answer = current_answer
+
+            while self.ask_for_human_input:
+                # Add feedback message with user role using standard formatter
+                feedback_msg = self._i18n.slice("feedback_message").format(feedback=feedback)
+                self.messages.append(self._format_msg(feedback_msg, role="user"))
+                
+                response = self._get_llm_feedback_response(feedback)
+                if not self._feedback_requires_changes(response):
+                    self.ask_for_human_input = False
+                else:
+                    answer = self._process_feedback_iteration(feedback)
+                    feedback = self._ask_human_input(answer.output)
+
+            return answer
+        except Exception as e:
+            error_msg = f"Failed to process feedback: {str(e)}"
+            self._printer.print(
+                content=error_msg,
+                color="red"
+            )
+            raise FeedbackProcessingError(error_msg, original_error=e)

    def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
-        """Get LLM classification of whether feedback requires changes."""
+        """Get LLM classification of whether feedback requires changes.
+        
+        Args:
+            feedback (str): The feedback to classify
+            
+        Returns:
+            Optional[str]: The LLM's response indicating if changes are needed
+            
+        Raises:
+            FeedbackProcessingError: If LLM call fails after max retries
+        """
        prompt = self._i18n.slice("human_feedback_classification").format(
            feedback=feedback
        )
@@ -561,21 +640,45 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
            except Exception as error:
                self._log_feedback_error(retry, error)

+        error_msg = f"Failed to get LLM feedback response after {MAX_LLM_RETRY} retries"
        self._log_max_retries_exceeded()
-        return None
+        raise FeedbackProcessingError(error_msg)

    def _feedback_requires_changes(self, response: Optional[str]) -> bool:
-        """Determine if feedback response indicates need for changes."""
+        """Determine if feedback response indicates need for changes.
+        
+        Args:
+            response (Optional[str]): The LLM's response to feedback classification
+            
+        Returns:
+            bool: True if feedback requires changes, False otherwise
+        """
        return response == "true" if response else False

    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process a single feedback iteration."""
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+        """Process a single feedback iteration.
+        
+        Args:
+            feedback (str): The feedback to process from the user
+            
+        Returns:
+            AgentFinish: The processed agent response after incorporating feedback
+        """
+        try:
+            # Add feedback instructions with user role
+            self.messages.append(
+                self._format_msg(
+                    self._i18n.slice("feedback_instructions").format(feedback=feedback),
+                    role="user"
+                )
            )
-        )
-        return self._invoke_loop()
+            return self._invoke_loop()
+        except Exception as e:
+            self._printer.print(
+                content=f"Error processing feedback iteration: {str(e)}",
+                color="red"
+            )
+            raise

    def _log_feedback_error(self, retry_count: int, error: Exception) -> None:
        """Log feedback processing errors."""
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/src/crewai/cli/templates/crew/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0"
+    "crewai[tools]>=0.100.1,<1.0.0"
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/flow/pyproject.toml
+++ b/src/crewai/cli/templates/flow/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0",
+    "crewai[tools]>=0.100.1,<1.0.0",
 ]

 [project.scripts]
--- a/src/crewai/cli/templates/tool/pyproject.toml
+++ b/src/crewai/cli/templates/tool/pyproject.toml
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0"
+    "crewai[tools]>=0.100.1"
 ]

 [tool.crewai]
--- a/src/crewai/flow/flow.py
+++ b/src/crewai/flow/flow.py
@@ -1,5 +1,4 @@
 import asyncio
-import copy
 import inspect
 import logging
 from typing import (
@@ -395,6 +394,7 @@ class FlowMeta(type):
                or hasattr(attr_value, "__trigger_methods__")
                or hasattr(attr_value, "__is_router__")
            ):
+
                # Register start methods
                if hasattr(attr_value, "__is_start_method__"):
                    start_methods.append(attr_name)
@@ -569,9 +569,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
            f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
        )

-    def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
-
    @property
    def state(self) -> T:
        return self._state
@@ -743,7 +740,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
            event=FlowStartedEvent(
                type="flow_started",
                flow_name=self.__class__.__name__,
-                inputs=inputs,
            ),
        )
        self._log_flow_event(
@@ -807,18 +803,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
    async def _execute_method(
        self, method_name: str, method: Callable, *args: Any, **kwargs: Any
    ) -> Any:
-        dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (kwargs or {})
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionStartedEvent(
-                type="method_execution_started",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                params=dumped_params,
-                state=self._copy_state(),
-            ),
-        )
-
        result = (
            await method(*args, **kwargs)
            if asyncio.iscoroutinefunction(method)
@@ -828,18 +812,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
        self._method_execution_counts[method_name] = (
            self._method_execution_counts.get(method_name, 0) + 1
        )
-
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionFinishedEvent(
-                type="method_execution_finished",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                state=self._copy_state(),
-                result=result,
-            ),
-        )
-
        return result

    async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -978,6 +950,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
        """
        try:
            method = self._methods[listener_name]
+
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionStartedEvent(
+                    type="method_execution_started",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
            sig = inspect.signature(method)
            params = list(sig.parameters.values())
            method_params = [p for p in params if p.name != "self"]
@@ -989,6 +971,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
            else:
                listener_result = await self._execute_method(listener_name, method)

+            self.event_emitter.send(
+                self,
+                event=MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
            # Execute listeners (and possibly routers) of this listener
            await self._execute_listeners(listener_name, listener_result)

--- a/src/crewai/flow/flow_events.py
+++ b/src/crewai/flow/flow_events.py
@@ -1,8 +1,6 @@
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Any, Dict, Optional, Union
-
-from pydantic import BaseModel
+from typing import Any, Optional


@dataclass
@@ -17,21 +15,17 @@ class Event:

@dataclass
 class FlowStartedEvent(Event):
-    inputs: Optional[Dict[str, Any]] = None
+    pass


@dataclass
 class MethodExecutionStartedEvent(Event):
    method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    params: Optional[Dict[str, Any]] = None


@dataclass
 class MethodExecutionFinishedEvent(Event):
    method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    result: Any = None


@dataclass
--- a/src/crewai/tools/init.py
+++ b/src/crewai/tools/init.py
@@ -1,4 +1 @@
 from .base_tool import BaseTool, tool
-from .faiss_search_tool import FAISSSearchTool
-
-__all__ = ["BaseTool", "tool", "FAISSSearchTool"]
--- a/src/crewai/tools/faiss_search_tool.py
+++ b/src/crewai/tools/faiss_search_tool.py
@@ -1,175 +0,0 @@
-import logging
-import re
-from typing import Any, Dict, List, Optional
-
-import faiss
-import numpy as np
-from pydantic import BaseModel, Field
-
-from crewai.tools import BaseTool
-from crewai.utilities import EmbeddingConfigurator
-
-logger = logging.getLogger(__name__)
-
-class FAISSSearchTool(BaseTool):
-    """FAISS vector similarity search tool for efficient document search."""
-    
-    model_config = {"extra": "allow"}
-    
-    name: str = "FAISS Search Tool"
-    description: str = "Search through documents using FAISS vector similarity search"
-    embedder_config: Optional[Dict[str, Any]] = Field(default=None)
-    dimension: int = Field(default=384)  # Default for BAAI/bge-small-en-v1.5
-    texts: List[str] = Field(default_factory=list)
-    index_type: str = Field(default="L2")
-    index: Any = Field(default=None)  # FAISS index instance
-    embedder: Any = Field(default=None)  # Embedder instance
-    
-    def __init__(
-        self,
-        index_type: str = "L2",
-        dimension: int = 384,
-        embedder_config: Optional[Dict[str, Any]] = None,
-    ):
-        """Initialize FAISS search tool.
-        
-        Args:
-            index_type: Type of FAISS index ("L2" or "IP")
-            dimension: Embedding dimension
-            embedder_config: Configuration for the embedder
-        """
-        super().__init__()
-        self.dimension = dimension
-        self.embedder_config = embedder_config
-        self.index_type = index_type
-        self.index = self._create_index(index_type)
-        self._initialize_embedder()
-
-    def _create_index(self, index_type: str) -> faiss.Index:
-        """Create FAISS index of specified type.
-        
-        Args:
-            index_type: Type of index ("L2" or "IP")
-            
-        Returns:
-            FAISS index instance
-            
-        Raises:
-            ValueError: If index_type is not supported
-        """
-        if index_type == "L2":
-            return faiss.IndexFlatL2(self.dimension)
-        elif index_type == "IP":
-            return faiss.IndexFlatIP(self.dimension)
-        else:
-            raise ValueError(f"Unsupported index type: {index_type}")
-
-    def _initialize_embedder(self):
-        """Initialize the embedder using the provided configuration."""
-        from crewai.knowledge.embedder.fastembed import FastEmbed
-        self.embedder = FastEmbed()
-
-    def _sanitize_query(self, query: str) -> str:
-        """Remove potentially harmful characters from query.
-        
-        Args:
-            query: Input query string
-            
-        Returns:
-            Sanitized query string
-        """
-        return re.sub(r'[^\w\s]', '', query)
-
-    def _run(
-        self,
-        query: str,
-        k: int = 3,
-        score_threshold: float = 0.6
-    ) -> List[Dict[str, Any]]:
-        """Search for similar texts using FAISS.
-        
-        Args:
-            query: Search query
-            k: Number of results to return
-            score_threshold: Minimum similarity score threshold
-            
-        Returns:
-            List of dictionaries containing matched texts and scores
-            
-        Raises:
-            ValueError: If input parameters are invalid
-        """
-        if not query.strip():
-            raise ValueError("Query cannot be empty")
-        if k < 1:
-            raise ValueError("k must be positive")
-        if not 0 <= score_threshold <= 1:
-            raise ValueError("score_threshold must be between 0 and 1")
-            
-        logger.debug(f"Searching for query: {query} with k={k}")
-        query = self._sanitize_query(query)
-        query_embedding = self.embedder.embed_text(query)
-        
-        D, I = self.index.search(
-            np.array([query_embedding], dtype=np.float32),
-            k
-        )
-        
-        results = []
-        for i, (dist, idx) in enumerate(zip(D[0], I[0])):
-            if idx < len(self.texts):
-                score = 1.0 / (1.0 + dist)  # Convert distance to similarity score
-                if score >= score_threshold:
-                    results.append({
-                        "text": self.texts[idx],
-                        "score": score
-                    })
-        return results
-
-    def add_texts(self, texts: List[str]) -> None:
-        """Add texts to the search index.
-        
-        Args:
-            texts: List of texts to add
-            
-        Raises:
-            ValueError: If embedding or indexing fails
-        """
-        try:
-            embeddings = self.embedder.embed_texts(texts)
-            self.index.add(np.array(embeddings, dtype=np.float32))
-            self.texts.extend(texts)
-        except Exception as e:
-            raise ValueError(f"Failed to add texts: {str(e)}")
-            
-    def add_texts_batch(self, texts: List[str], batch_size: int = 1000) -> None:
-        """Add texts in batches to prevent memory issues.
-        
-        Args:
-            texts: List of texts to add
-            batch_size: Size of each batch
-            
-        Raises:
-            ValueError: If batch_size is invalid
-        """
-        if batch_size < 1:
-            raise ValueError("batch_size must be positive")
-            
-        for i in range(0, len(texts), batch_size):
-            batch = texts[i:i + batch_size]
-            self.add_texts(batch)
-            
-    def clear_index(self) -> None:
-        """Clear the index and stored texts."""
-        self.index = self._create_index(self.index_type)
-        self.texts = []
-        
-    @property
-    def index_size(self) -> int:
-        """Return number of vectors in index."""
-        return len(self.texts)
-        
-    @property
-    def is_empty(self) -> bool:
-        """Check if index is empty."""
-        return len(self.texts) == 0
--- a/src/crewai/utilities/exceptions/init.py
+++ b/src/crewai/utilities/exceptions/init.py
@@ -0,0 +1,5 @@
+"""Exceptions module for CrewAI."""
+
+from .feedback_processing_exception import FeedbackProcessingError
+
+__all__ = ["FeedbackProcessingError"]
--- a/src/crewai/utilities/exceptions/feedback_processing_exception.py
+++ b/src/crewai/utilities/exceptions/feedback_processing_exception.py
@@ -0,0 +1,8 @@
+from typing import Optional
+
+
+class FeedbackProcessingError(Exception):
+    """Exception raised when feedback processing fails."""
+    def __init__(self, message: str, original_error: Optional[Exception] = None):
+        self.original_error = original_error
+        super().__init__(message)
--- a/tests/agent_test.py
+++ b/tests/agent_test.py
@@ -2,7 +2,7 @@

 import os
 from unittest import mock
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch

 import pytest

@@ -19,6 +19,9 @@ from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
+from crewai.utilities.exceptions.feedback_processing_exception import (
+    FeedbackProcessingError,
+)


 def test_agent_llm_creation_with_env_vars():
@@ -1001,6 +1004,53 @@ def test_agent_human_input():
        assert mock_human_input.call_count == 2  # Should have asked for feedback twice
        assert output.strip().lower() == "hello"  # Final output should be 'Hello'

+        # Verify message format for human feedback
+        messages = agent.agent_executor.messages
+        feedback_messages = [m for m in messages if "Feedback:" in m.get("content", "")]
+        assert len(feedback_messages) == 2  # Two feedback messages
+        for msg in feedback_messages:
+            assert msg["role"] == "user"  # All feedback messages should have user role
+
+
+@pytest.fixture
+def mock_executor():
+    """Create a mock executor for testing."""
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory"
+    )
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        human_input=True,
+        agent=agent
+    )
+    executor = CrewAgentExecutor(
+        agent=agent,
+        task=task,
+        llm=agent.llm,
+        crew=None,
+        prompt="",
+        max_iter=1,
+        tools=[],
+        tools_names=[],
+        stop_words=[],
+        tools_description="",
+        tools_handler=None
+    )
+    return executor
+
+def test_empty_feedback_handling(mock_executor):
+    """Test that empty feedback is properly handled."""
+    with pytest.raises(FeedbackProcessingError):
+        mock_executor._format_msg("")
+
+def test_long_feedback_handling(mock_executor):
+    """Test that very long feedback is properly handled."""
+    very_long_feedback = "x" * 10000
+    with pytest.raises(FeedbackProcessingError):
+        mock_executor._format_msg(very_long_feedback)

 def test_interpolate_inputs():
    agent = Agent(
--- a/tests/cassettes/test_empty_feedback_handling.yaml
+++ b/tests/cassettes/test_empty_feedback_handling.yaml
@@ -0,0 +1,307 @@
+interactions:
+- request:
+    body: !!binary |
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--- a/tests/cassettes/test_long_feedback_handling.yaml
+++ b/tests/cassettes/test_long_feedback_handling.yaml
@@ -0,0 +1,87 @@
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+- request:
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--- a/tests/flow_test.py
+++ b/tests/flow_test.py
@@ -1,18 +1,11 @@
 """Test Flow creation and execution basic functionality."""

 import asyncio
-from datetime import datetime

 import pytest
 from pydantic import BaseModel

 from crewai.flow.flow import Flow, and_, listen, or_, router, start
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)


 def test_simple_sequential_flow():
@@ -405,218 +398,3 @@ def test_router_with_multiple_conditions():

    # final_step should run after router_and
    assert execution_order.index("log_final_step") > execution_order.index("router_and")
-
-
-def test_unstructured_flow_event_emission():
-    """Test that the correct events are emitted during unstructured flow
-    execution with all fields validated."""
-
-    class PoemFlow(Flow):
-        @start()
-        def prepare_flower(self):
-            self.state["flower"] = "roses"
-            return "foo"
-
-        @start()
-        def prepare_color(self):
-            self.state["color"] = "red"
-            return "bar"
-
-        @listen(prepare_color)
-        def write_first_sentence(self):
-            return f"{self.state['flower']} are {self.state['color']}"
-
-        @listen(write_first_sentence)
-        def finish_poem(self, first_sentence):
-            separator = self.state.get("separator", "\n")
-            return separator.join([first_sentence, "violets are blue"])
-
-        @listen(finish_poem)
-        def save_poem_to_database(self):
-            # A method without args/kwargs to ensure events are sent correctly
-            pass
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = PoemFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"separator": ", "})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "PoemFlow"
-    assert event_log[0].inputs == {"separator": ", "}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    # Asserting for concurrent start method executions in a for loop as you
-    # can't guarantee ordering in asynchronous executions
-    for i in range(1, 5):
-        event = event_log[i]
-        assert isinstance(event.state, dict)
-        assert isinstance(event.state["id"], str)
-
-        if event.method_name == "prepare_flower":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "foo"
-                assert event.state["flower"] == "roses"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_flower"
-        elif event.method_name == "prepare_color":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "bar"
-                assert event.state["color"] == "red"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_color"
-        else:
-            assert False, f"Unexpected method {event.method_name} in prepare events"
-
-    assert isinstance(event_log[5], MethodExecutionStartedEvent)
-    assert event_log[5].method_name == "write_first_sentence"
-    assert event_log[5].params == {}
-    assert isinstance(event_log[5].state, dict)
-    assert event_log[5].state["flower"] == "roses"
-    assert event_log[5].state["color"] == "red"
-    assert event_log[5].state["separator"] == ", "
-
-    assert isinstance(event_log[6], MethodExecutionFinishedEvent)
-    assert event_log[6].method_name == "write_first_sentence"
-    assert event_log[6].result == "roses are red"
-
-    assert isinstance(event_log[7], MethodExecutionStartedEvent)
-    assert event_log[7].method_name == "finish_poem"
-    assert event_log[7].params == {"_0": "roses are red"}
-    assert isinstance(event_log[7].state, dict)
-    assert event_log[7].state["flower"] == "roses"
-    assert event_log[7].state["color"] == "red"
-
-    assert isinstance(event_log[8], MethodExecutionFinishedEvent)
-    assert event_log[8].method_name == "finish_poem"
-    assert event_log[8].result == "roses are red, violets are blue"
-
-    assert isinstance(event_log[9], MethodExecutionStartedEvent)
-    assert event_log[9].method_name == "save_poem_to_database"
-    assert event_log[9].params == {}
-    assert isinstance(event_log[9].state, dict)
-    assert event_log[9].state["flower"] == "roses"
-    assert event_log[9].state["color"] == "red"
-
-    assert isinstance(event_log[10], MethodExecutionFinishedEvent)
-    assert event_log[10].method_name == "save_poem_to_database"
-    assert event_log[10].result is None
-
-    assert isinstance(event_log[11], FlowFinishedEvent)
-    assert event_log[11].flow_name == "PoemFlow"
-    assert event_log[11].result is None
-    assert isinstance(event_log[11].timestamp, datetime)
-
-
-def test_structured_flow_event_emission():
-    """Test that the correct events are emitted during structured flow
-    execution with all fields validated."""
-
-    class OnboardingState(BaseModel):
-        name: str = ""
-        sent: bool = False
-
-    class OnboardingFlow(Flow[OnboardingState]):
-        @start()
-        def user_signs_up(self):
-            self.state.sent = False
-
-        @listen(user_signs_up)
-        def send_welcome_message(self):
-            self.state.sent = True
-            return f"Welcome, {self.state.name}!"
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = OnboardingFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"name": "Anakin"})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "OnboardingFlow"
-    assert event_log[0].inputs == {"name": "Anakin"}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "user_signs_up"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "user_signs_up"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "send_welcome_message"
-    assert event_log[3].params == {}
-    assert getattr(event_log[3].state, "sent") is False
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "send_welcome_message"
-    assert getattr(event_log[4].state, "sent") is True
-    assert event_log[4].result == "Welcome, Anakin!"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "OnboardingFlow"
-    assert event_log[5].result == "Welcome, Anakin!"
-    assert isinstance(event_log[5].timestamp, datetime)
-
-
-def test_stateless_flow_event_emission():
-    """Test that the correct events are emitted stateless during flow execution
-    with all fields validated."""
-
-    class StatelessFlow(Flow):
-        @start()
-        def init(self):
-            pass
-
-        @listen(init)
-        def process(self):
-            return "Deeds will not be less valiant because they are unpraised."
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = StatelessFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff()
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "StatelessFlow"
-    assert event_log[0].inputs is None
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "init"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "init"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "process"
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "process"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "StatelessFlow"
-    assert (
-        event_log[5].result
-        == "Deeds will not be less valiant because they are unpraised."
-    )
-    assert isinstance(event_log[5].timestamp, datetime)
--- a/tests/tools/test_faiss_search_tool.py
+++ b/tests/tools/test_faiss_search_tool.py
@@ -1,46 +0,0 @@
-import time
-
-import numpy as np
-import pytest
-
-from crewai.tools import FAISSSearchTool
-
-def test_faiss_search_tool_initialization():
-    tool = FAISSSearchTool()
-    assert tool.name == "FAISS Search Tool"
-    assert tool.dimension == 384
-
-def test_faiss_search_with_texts():
-    tool = FAISSSearchTool()
-    texts = [
-        "The quick brown fox",
-        "jumps over the lazy dog",
-        "A completely different text"
-    ]
-    tool.add_texts(texts)
-    
-    results = tool.run(
-        query="quick fox",
-        k=2,
-        score_threshold=0.5
-    )
-    
-    assert len(results) > 0
-    assert isinstance(results[0]["text"], str)
-    assert isinstance(results[0]["score"], float)
-
-def test_faiss_search_threshold_filtering():
-    tool = FAISSSearchTool()
-    texts = ["Text A", "Text B", "Text C"]
-    tool.add_texts(texts)
-    
-    results = tool.run(
-        query="Something completely different",
-        score_threshold=0.99  # High threshold
-    )
-    
-    assert len(results) == 0  # No results above threshold
-
-def test_invalid_index_type():
-    with pytest.raises(ValueError):
-        FAISSSearchTool(index_type="INVALID")
--- a/uv.lock
+++ b/uv.lock
@@ -198,6 +198,15 @@ wheels = [
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 ]

+[[package]]
+name = "asn1crypto"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
+wheels = [
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+]
+
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -219,6 +228,15 @@ wheels = [
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 ]

+[[package]]
+name = "atpublic"
+version = "5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5d/18/b1d247792440378abeeb0853f9daa2a127284b68776af6815990be7fcdb0/atpublic-5.0.tar.gz", hash = "sha256:d5cb6cbabf00ec1d34e282e8ce7cbc9b74ba4cb732e766c24e2d78d1ad7f723f", size = 14646 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/03/2cb0e5326e19b7d877bc9c3a7ef436a30a06835b638580d1f5e21a0409ed/atpublic-5.0-py3-none-any.whl", hash = "sha256:b651dcd886666b1042d1e38158a22a4f2c267748f4e97fde94bc492a4a28a3f3", size = 5207 },
+]
+
 [[package]]
 name = "attrs"
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Author	SHA1	Message	Date
Devin AI	97449a5f80	style: Fix import sorting in all files Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:06:44 +00:00
Devin AI	17ff29bc8e	style: Fix import sorting Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:04:44 +00:00
Devin AI	b7f5064f31	style: Fix code formatting - Fix method parameter formatting - Add trailing commas for consistency Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:03:03 +00:00
Devin AI	c10665c897	refactor: Improve feedback message formatting - Use i18n for feedback message formatting - Enhance method documentation - Improve code organization Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:02:39 +00:00
Devin AI	0fd72fc5d9	refactor: Improve training feedback handling - Add comprehensive docstrings - Add error handling for training feedback - Add proper type hints Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:01:52 +00:00
Devin AI	8195aaedea	docs: Enhance method documentation and type hints - Add comprehensive docstrings - Improve type hints clarity - Add missing error documentation Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:01:22 +00:00
Devin AI	3ffe98bf39	refactor: Improve feedback handling documentation and error handling - Add comprehensive docstrings - Enhance error handling in LLM feedback response - Add proper type hints Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:00:56 +00:00
Devin AI	e65a6b523a	refactor: Improve feedback handling code quality - Add type hints and docstrings - Enhance error handling in feedback processing - Standardize message formatting Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 20:00:23 +00:00
Devin AI	d842529b13	chore: Add test cassettes to gitignore Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 19:55:54 +00:00
Devin AI	a304c9573b	feat: Add feedback validation and error handling - Add FeedbackProcessingError for feedback handling - Add validation for empty and long feedback messages - Add test coverage for edge cases Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 19:54:02 +00:00
Devin AI	f8a5df9d5a	feat: Add feedback validation and error handling - Add FeedbackProcessingError for feedback handling - Add validation for empty and long feedback messages - Add test coverage for edge cases Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 19:53:41 +00:00
Devin AI	3dd79fed82	feat: Add FeedbackProcessingError for feedback handling - Add custom exception for feedback processing errors - Add proper error inheritance and docstrings - Make exception importable via __init__.py Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 17:36:24 +00:00
Devin AI	b7d8fadfad	refactor: Improve feedback handling code quality - Use _format_msg consistently for feedback messages - Add comprehensive type hints - Improve method documentation - Add error handling for feedback processing Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 17:33:50 +00:00
Devin AI	537aac0785	fix: Add proper role to human feedback messages for LiteLLM - Update _handle_human_feedback to set user role for feedback messages - Add test coverage for message format - Fixes #2111 Co-Authored-By: Joe Moura <joao@crewai.com>	2025-02-12 17:30:59 +00:00