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This commit is contained in:
Brandon Hancock
2025-01-06 14:25:05 -05:00
parent cea5a28db1
commit 33ca1e3a9e
7 changed files with 27 additions and 121 deletions
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10
src/crewai/cli/crew_chat.py
View File

@@ -27,9 +27,17 @@ def run_chat():
crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
system_message = build_system_message(crew_chat_inputs)
# Call the LLM to generate the introductory message
introductory_message = chat_llm.call(
messages=[{"role": "system", "content": system_message}]
)
click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
messages = [
{"role": "system", "content": system_message},
{"role": "assistant", "content": introductory_message},
]
available_functions = {
crew_chat_inputs.crew_name: create_tool_function(crew, messages),
}
@@ -77,6 +85,8 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
"If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
"After calling the tool, be prepared to take user feedback and make adjustments as needed. "
"If you are ever unsure about a user's request or need clarification, ask the user for more information."
"Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
"For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
f"\nCrew Name: {crew_chat_inputs.crew_name}"
f"\nCrew Description: {crew_chat_inputs.crew_description}"
)

86
src/crewai/cli/fetch_crew_inputs.py
View File

@@ -1,86 +0,0 @@
import json
import subprocess
from typing import Optional
import click
from packaging import version
from crewai.cli.utils import read_toml
from crewai.cli.version import get_crewai_version
from crewai.types.crew_chat import ChatInputs
def fetch_crew_inputs() -> Optional[ChatInputs]:
"""
Fetch the crew's ChatInputs (a structure containing crew_description and input fields)
by running "uv run fetch_chat_inputs", which prints JSON representing a ChatInputs object.
This function will parse that JSON and return a ChatInputs instance.
If the output is empty or invalid, an empty ChatInputs object is returned.
"""
command = ["uv", "run", "fetch_chat_inputs"]
crewai_version = get_crewai_version()
min_required_version = "0.87.0"
pyproject_data = read_toml()
crew_name = pyproject_data.get("project", {}).get("name", None)
# If you're on an older poetry-based setup and version < min_required_version
if pyproject_data.get("tool", {}).get("poetry") and (
version.parse(crewai_version) < version.parse(min_required_version)
):
click.secho(
f"You are running an older version of crewAI ({crewai_version}) that uses poetry pyproject.toml.\n"
f"Please run `crewai update` to update your pyproject.toml to use uv.",
fg="red",
)
try:
result = subprocess.run(command, capture_output=True, text=True, check=True)
stdout_lines = result.stdout.strip().splitlines()
# Find the line that contains the JSON data
json_line = next(
(
line
for line in stdout_lines
if line.startswith("{") and line.endswith("}")
),
None,
)
if not json_line:
click.echo(
"No valid JSON output received from `fetch_chat_inputs` command.",
err=True,
)
return None
try:
raw_data = json.loads(json_line)
chat_inputs = ChatInputs(**raw_data)
if crew_name:
chat_inputs.crew_name = crew_name
return chat_inputs
except json.JSONDecodeError as e:
click.echo(
f"Unable to parse JSON from `fetch_chat_inputs` output: {e}\nOutput: {repr(json_line)}",
err=True,
)
return None
except subprocess.CalledProcessError as e:
click.echo(f"An error occurred while fetching chat inputs: {e}", err=True)
click.echo(e.output, err=True, nl=True)
if pyproject_data.get("tool", {}).get("poetry"):
click.secho(
"It's possible that you are using an old version of crewAI that uses poetry.\n"
"Please run `crewai update` to update your pyproject.toml to use uv.",
fg="yellow",
)
except Exception as e:
click.echo(f"An unexpected error occurred: {e}", err=True)
return None

7
src/crewai/cli/templates/crew/main.py
View File

@@ -1,10 +1,8 @@
#!/usr/bin/env python
import sys
import json
import warnings
from {{folder_name}}.crew import {{crew_name}}
from crewai.utilities.llm_utils import create_llm
warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -15,13 +13,10 @@ warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
def run():
"""
Run the crew, allowing CLI overrides for required inputs.
Usage example:
uv run run_crew -- --topic="New Topic" --some_other_field="Value"
Run the crew.
"""
inputs = {
'topic': 'AI LLMs'
# Add any other default fields here
}
try:

4
src/crewai/crew.py
View File

@@ -209,10 +209,6 @@ class Crew(BaseModel):
default=None,
description="LLM used to handle chatting with the crew.",
)
chat_inputs: Optional[ChatInputs] = Field(
default=None,
description="Holds descriptions of the crew as well as named inputs for chat usage.",
)
_knowledge: Optional[Knowledge] = PrivateAttr(
default=None,
)

11
src/crewai/llm.py
View File

@@ -146,7 +146,6 @@ class LLM:
self.callbacks = callbacks
self.context_window_size = 0
# For safety, we disable passing init params to next calls
litellm.drop_params = True
self.set_callbacks(callbacks)
@@ -247,40 +246,36 @@ class LLM:
function_name = tool_call.function.name
if function_name in available_functions:
# Parse arguments
try:
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
logging.warning(f"Failed to parse function arguments: {e}")
return text_response # Fallback to text response
return text_response
fn = available_functions[function_name]
try:
# Call the actual tool function
result = fn(**function_args)
# Return the result directly
return result
except Exception as e:
logging.error(
f"Error executing function '{function_name}': {e}"
)
return text_response # Fallback to text response
return text_response
else:
logging.warning(
f"Tool call requested unknown function '{function_name}'"
)
return text_response # Fallback to text response
return text_response
except Exception as e:
# Check if context length was exceeded, otherwise log
if not LLMContextLengthExceededException(
str(e)
)._is_context_limit_error(str(e)):
logging.error(f"LiteLLM call failed: {str(e)}")
# Re-raise the exception
raise
def supports_function_calling(self) -> bool:

28
src/crewai/task.py
View File

@@ -393,7 +393,7 @@ class Task(BaseModel):
self.retry_count += 1
context = self.i18n.errors("validation_error").format(
guardrail_result_error=guardrail_result.error,
task_output=task_output.raw
task_output=task_output.raw,
)
printer = Printer()
printer.print(
@@ -431,9 +431,7 @@ class Task(BaseModel):
content = (
json_output
if json_output
else pydantic_output.model_dump_json()
if pydantic_output
else result
else pydantic_output.model_dump_json() if pydantic_output else result
)
self._save_file(content)
@@ -453,11 +451,12 @@ class Task(BaseModel):
tasks_slices = [self.description, output]
return "\n".join(tasks_slices)
def interpolate_inputs_and_add_conversation_history(self, inputs: Dict[str, Union[str, int, float]]) -> None:
def interpolate_inputs_and_add_conversation_history(
self, inputs: Dict[str, Union[str, int, float]]
) -> None:
"""Interpolate inputs into the task description, expected output, and output file path.
Add conversation history if present.
Args:
inputs: Dictionary mapping template variables to their values.
Supported value types are strings, integers, and floats.
@@ -497,16 +496,15 @@ class Task(BaseModel):
input_string=self._original_output_file, inputs=inputs
)
except (KeyError, ValueError) as e:
raise ValueError(f"Error interpolating output_file path: {str(e)}") from e
raise ValueError(
f"Error interpolating output_file path: {str(e)}"
) from e
if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
# Fetch the conversation history instruction using self.i18n.slice
conversation_instruction = self.i18n.slice(
"conversation_history_instruction"
)
print("crew_chat_messages:", inputs["crew_chat_messages"])
# Ensure that inputs["crew_chat_messages"] is a string
crew_chat_messages_json = str(inputs["crew_chat_messages"])
try:
@@ -515,15 +513,15 @@ class Task(BaseModel):
print("An error occurred while parsing crew chat messages:", e)
raise
# Process the messages to build conversation history
conversation_history = "\n".join(
f"{msg['role'].capitalize()}: {msg['content']}"
for msg in crew_chat_messages
if isinstance(msg, dict) and "role" in msg and "content" in msg
)
# Add the instruction and conversation history to the description
self.description += f"\n\n{conversation_instruction}\n\n{conversation_history}"
self.description += (
f"\n\n{conversation_instruction}\n\n{conversation_history}"
)
def interpolate_only(
self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]

2
src/crewai/utilities/llm_utils.py
View File

@@ -34,7 +34,6 @@ def create_llm(
if isinstance(llm_value, str):
try:
created_llm = LLM(model=llm_value)
print(f"LLM created with model='{llm_value}'")
return created_llm
except Exception as e:
print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
@@ -197,7 +196,6 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
# Try creating the LLM
try:
new_llm = LLM(**llm_params)
print(f"LLM created with model='{model_name}'")
return new_llm
except Exception as e:
print(