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from langchain_community.llms import HuggingFaceHub
#from langchain_community.llms import HuggingFaceHub
llm_zephyr_7b_beta = HuggingFaceHub(
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools.tools import SerperDevTool
#from crewai_tools import SerperDevTool
search_tool = SerperDevTool()
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting actionable insights.""",
verbose=True,
allow_delegation=False,
tools=[search_tool],
max_execution_time=5,
max_iter=15, # Optional
max_rpm=None, # Optional
llm=llm_zephyr_7b_beta
# You can pass an optional llm attribute specifying what mode you wanna use.
# It can be a local model through Ollama / LM Studio or a remote
# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
#
# import os
# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
#
# OR
#
# from langchain_openai import ChatOpenAI
# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
max_execution_time=5,
max_iter=15, # Optional
max_rpm=None, # Optional
allow_delegation=True,
llm=llm_zephyr_7b_beta
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.""",
expected_output="Full analysis report in bullet points",
agent=researcher
)
task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
expected_output="Full blog post of at least 4 paragraphs",
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=2, # You can set it to 1 or 2 to different logging levels
)
# Get your crew to work!
#result = crew.kickoff()
#print("######################")
#print(result)
##################
###### other models:
# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
# "bn22/Mistral-7B-Instruct-v0.1-sharded"
# "HuggingFaceH4/zephyr-7b-beta"
# function for loading 4-bit quantized model
def load_model( ):
model = HuggingFaceHub(
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
)
return model
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from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
# middlewares to allow cross orgin communications
app.add_middleware(
CORSMiddleware,
allow_origins=['*'],
allow_credentials=True,
allow_methods=['*'],
allow_headers=['*'],
)
@app.post("/generate/")
def generate(user_input, history=[]):
print("######################")
result = crew.kickoff()
print("######################")
print(result)
return result
# load the model asynchronously on startup and save it into memory
@app.on_event("startup")
async def startup():
# Get your crew to work!
# result = crew.kickoff()
print("######################")
# print(result)