Crew AI with Bedrock LLMs

agents.yaml

bedrock_agent:
  role: >
    Bedrock AI Assistant
  goal: >
    Help users generate responses using AWS Bedrock's LLM.
  backstory: >
    You are an advanced AI assistant powered by AWS Bedrock. You excel at processing
    and generating meaningful insights from textual data.
  tools:
    - bedrock_tool

config.ini

[DEFAULT]
OPENAI_API_KEY=a-string-without-any-quotes

[DB]
DB_USERNAME=a-string-without-any-quotes
DB_PASSWORD=a-string-without-any-quotes
DB_HOST=a-string-without-any-quotes
DB_NAME=a-string-without-any-quotes

[AWS]
AWS_REGION=a-string-without-any-quotes
AWS_PROFILE=a-string-without-any-quotes
AWS_ACCESS_KEY_ID=a-string-without-any-quotes
AWS_SECRET_ACCESS_KEY=a-string-without-any-quotes

crew_ai_with_bedrock_llm.py

"""Using Bedrock LLMs"""

import configparser
import yaml
from crewai import LLM, Agent, Crew, Task

CONFIG_FILENAME = ".config.ini"

class ConfigHelper:
    """ConfigHelper"""

    def __init__(self, config_filename):
        self.config = configparser.ConfigParser()
        self.config.read(config_filename)

    def get_value_by_section_and_key(self, section, key):
        """get_value_by_section_and_key"""
        return self.config.get(section, key)

    def get_all_details_of_section(self, section) -> dict:
        """get_all_details_of_section"""
        return dict(self.config.items(section))

cfg = ConfigHelper(CONFIG_FILENAME)
default_cfgs = cfg.get_all_details_of_section("DEFAULT")
db_cfgs = cfg.get_all_details_of_section("DB")
aws_cfgs = cfg.get_all_details_of_section("AWS")

llm_bedrock_claude = LLM(
    model="anthropic.claude-3-sonnet-20240229-v1:0",
    aws_access_key_id=aws_cfgs["aws_access_key_id"],
    aws_secret_access_key=aws_cfgs["aws_secret_access_key"],
    aws_region_name=aws_cfgs["aws_region"],
)

# Import agents and tasks from YAML
# Define file paths for YAML configurations
files = {"agents": "config/agents.yaml", "tasks": "config/tasks.yaml"}

# Load configurations from YAML files
configs = {}
for config_type, file_path in files.items():
    with open(file_path, "r") as file:
        configs[config_type] = yaml.safe_load(file)

# Assign loaded configurations to specific variables
agents_config = configs["agents"]
tasks_config = configs["tasks"]

# Create Agent objects
agents = [
    Agent(
        role=agent_data["role"],
        goal=agent_data["goal"],
        backstory=agent_data["backstory"],
        llm=llm_bedrock_claude,
        tools=[],
        verbose=False,
    )
    for agent_data in agents_config.values()
]

# Create Task objects
tasks = [
    Task(
        description=task_data["description"],
        expected_output=task_data["expected_output"],
        agent=agents[0],
        verbose=False,
    )
    for task_data in tasks_config.values()
]

# Initialize the crew
crew = Crew(
    agents=agents,
    tasks=tasks,
)

# Kick off the crew
result = crew.kickoff(
    inputs={"input_prompt": "Explain quantum computing in simple terms."}
)
print(result)


print("******************* COSTING *******************")
print("Total tokens: ", crew.usage_metrics.total_tokens)
print("Prompt tokens: ", crew.usage_metrics.prompt_tokens)
print("Output tokens: ", crew.usage_metrics.completion_tokens)

tasks.yaml

generate_response_task:
  description: >
    Use AWS Bedrock's LLM to generate a response to the given input prompt.

    Input Prompt:
    {input_prompt}

  expected_output: >
    A detailed and coherent response from Bedrock's LLM.

Project directory structure

.
├── config
│   ├── __init__.py
│   ├── agents.yaml
│   └── tasks.yaml
├── config.ini
├── crew_ai_with_bedrock_llm.py
├── pip-requirements.txt