crew.py

from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task

# from newsletter_gen.tools.research import SearchAndContents, FindSimilar, GetContents
from research import SearchAndContents, FindSimilar, GetContents # JB

from langchain_anthropic import ChatAnthropic
from langchain_groq import ChatGroq
from datetime import datetime
import streamlit as st
from typing import Union, List, Tuple, Dict
from langchain_core.agents import AgentFinish
import json
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import HuggingFaceEndpoint # JB
import os

# JB:
# https://python.langchain.com/v0.2/docs/integrations/chat/ollama/
# LangChain supports many other chat models. Here, we're using Ollama
from langchain_community.chat_models import ChatOllama

# To get rid of the telemetry error messages, try:
# Connection Timeout Error with telemetry.crewai.com #254
# https://github.com/joaomdmoura/crewAI/issues/254
# os.environ["OTEL_SDK_DISABLED"] = "true"
os.environ["OTEL_SDK_DISABLED"] = "true"
# SUCCES:
# DIT LIJKT INDERDAAD DE TELEMETRY ERROR MESSAGES IN DE VS CODE TERMINAL TE VOORKOMEN !!!!!!!!!!
# Wel in die terminal nog deze korte messages:
# 2024-06-14 02:20:17,425 - 25632 - __init__.py-__init__:1218 - WARNING: SDK is disabled.



@CrewBase
class NewsletterGenCrew:
    """NewsletterGen crew"""

    # agents_config = "config/agents.yaml"
    # tasks_config = "config/tasks.yaml"
    agents_config = "agents.yaml"
    tasks_config  = "tasks.yaml"

    def llm(self):
        # llm = ChatAnthropic(model_name="claude-3-sonnet-20240229", max_tokens=4096) # ORIGINAL
        # llm = ChatAnthropic(model_name="claude-3-sonnet-20240229", 
        #                    # max_tokens=4096,
        #                    cache=True,
        #                    api_key="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"
        #                    ) # JB
        # https://console.anthropic.com/dashboard
        # https://console.anthropic.com/settings/keys
        # jb_anthropic_key_2_13-06-2024: 
        # ANTHROPIC_API_KEY=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
        # https://console.anthropic.com/settings/usage
        #
        # BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Claude API. Please go to Plans & Billing to upgrade or purchase credits.'}}

        # llm = ChatGroq(model="llama3-70b-8192") 
        # https://console.groq.com/docs/rate-limits
        # llm = ChatGroq(model="mixtral-8x7b-32768") # JB 13-06-2024 - geeft af en toe rate limit errors
        # IN HF SPACES STREAMLIT APP: BadRequestError: Error code: 400 - {'error': {'message': 'Organization has been restricted. Please reach out to support if you believe this was in error.', 'type': 'invalid_request_error', 'code': 'organization_restricted'}}

        llm = ChatGoogleGenerativeAI(google_api_key=os.getenv("GOOGLE_API_KEY"))

        # https://python.langchain.com/v0.2/docs/integrations/chat/ollama/
        # supports many more optional parameters. Hover on your `ChatOllama(...)`
        # class to view the latest available supported parameters
        # llm = ChatOllama(model="llama3")
        # llm = ChatOllama(model="mistral:latest")
        # check if ollama is running and which LLMs can then be used, run this in Anaconda cmd admin window: 
        # ollama list
        # OUTPUT EXAMPLE:
        # (newsletter-gen-py3.11) (base) C:\Users\jfhmb\EXA_CREWAI\exa-crewai-master\exa-crewai-master>ollama list
        # NAME                            ID              SIZE    MODIFIED
        # llama3:latest                   365c0bd3c000    4.7 GB  3 days ago
        # nomic-embed-text:latest         0a109f422b47    274 MB  3 days ago
        # crewai-llama3:latest            d952d07761cd    4.7 GB  10 days ago
        # llama3:8b                       365c0bd3c000    4.7 GB  10 days ago
        # mistral:latest                  61e88e884507    4.1 GB  6 weeks ago
        # mxbai-embed-large:latest        468836162de7    669 MB  6 weeks ago
        #
        # OLLAMA LOGS:
        # C:\Users\jfhmb\AppData\Local\Ollama
        #
        # Running ollama on Hugging Face Spaces #2833
        # https://github.com/ollama/ollama/issues/2833


        # HUGGING FACE LLMs
        # https://python.langchain.com/v0.2/docs/integrations/chat/huggingface/
        # HUGGINGFACEHUB_API_TOKEN
        # https://huggingface.co/docs/hub/security-tokens
        # https://huggingface.co/settings/tokens
        # %pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2
        # 1. Instantiate an LLM
        # HuggingFaceEndpoint
        # from langchain_huggingface import HuggingFaceEndpoint
        #
        #llm = HuggingFaceEndpoint(
        #    repo_id="meta-llama/Meta-Llama-3-70B-Instruct",
        #    task="text-generation",
        #    max_new_tokens=512,
        #    do_sample=False,
        #    repetition_penalty=1.03,
        #)
        # BadRequestError: (Request ID: ots-pfsrtb04xa7oVcKIc) Bad request: Model requires a Pro subscription; check out hf.co/pricing to learn more. Make sure to include your HF token in your query.
        # API Reference:HuggingFaceEndpoint


        print("JB: in class NewsletterGenCrew - using llm: ", llm)

        return llm

    def step_callback(
        self,
        agent_output: Union[str, List[Tuple[Dict, str]], AgentFinish],
        agent_name,
        *args,
    ):
        with st.chat_message("AI"):
            # Try to parse the output if it is a JSON string
            if isinstance(agent_output, str):
                try:
                    agent_output = json.loads(agent_output)
                except json.JSONDecodeError:
                    pass

            if isinstance(agent_output, list) and all(
                isinstance(item, tuple) for item in agent_output
            ):

                for action, description in agent_output:
                    # Print attributes based on assumed structure
                    st.write(f"Agent Name: {agent_name}")
                    st.write(f"Tool used: {getattr(action, 'tool', 'Unknown')}")
                    st.write(f"Tool input: {getattr(action, 'tool_input', 'Unknown')}")
                    st.write(f"{getattr(action, 'log', 'Unknown')}")
                    with st.expander("Show observation"):
                        st.markdown(f"Observation\n\n{description}")

            # Check if the output is a dictionary as in the second case
            elif isinstance(agent_output, AgentFinish):
                st.write(f"Agent Name: {agent_name}")
                output = agent_output.return_values
                st.write(f"I finished my task:\n{output['output']}")

            # Handle unexpected formats
            else:
                st.write(type(agent_output))
                st.write(agent_output)

    @agent
    def researcher(self) -> Agent:
        return Agent(
            config=self.agents_config["researcher"],
            tools=[SearchAndContents(), FindSimilar(), GetContents()],
            verbose=True,
            llm=self.llm(),
            step_callback=lambda step: self.step_callback(step, "Research Agent"),
        )

    @agent
    def editor(self) -> Agent:
        return Agent(
            config=self.agents_config["editor"],
            verbose=True,
            tools=[SearchAndContents(), FindSimilar(), GetContents()],
            llm=self.llm(),
            step_callback=lambda step: self.step_callback(step, "Chief Editor"),
        )

    @agent
    def designer(self) -> Agent:
        return Agent(
            config=self.agents_config["designer"],
            verbose=True,
            allow_delegation=False,
            llm=self.llm(),
            step_callback=lambda step: self.step_callback(step, "HTML Writer"),
        )

    @task
    def research_task(self) -> Task:
        return Task(
            config=self.tasks_config["research_task"],
            agent=self.researcher(),
            output_file=f"logs/{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_research_task.md",
        )

    @task
    def edit_task(self) -> Task:
        return Task(
            config=self.tasks_config["edit_task"],
            agent=self.editor(),
            output_file=f"logs/{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_edit_task.md",
        )

    @task
    def newsletter_task(self) -> Task:
        return Task(
            config=self.tasks_config["newsletter_task"],
            agent=self.designer(),
            output_file=f"logs/{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_newsletter_task.html",
        )

    @crew
    def crew(self) -> Crew:
        """Creates the NewsletterGen crew"""
        return Crew(
            agents=self.agents,  # Automatically created by the @agent decorator
            tasks=self.tasks,  # Automatically created by the @task decorator
            process=Process.sequential,
            verbose=2,
            # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
        )