Create RAG Agents with Crew AI and LlamaIndex

 This video is a step-by-step tutorial to build RAG-powered AI agents using LlamaIndex, CrewAI and OpenAI on your own data.

Code:

!pip install llama-index-core

!pip install llama-index-readers-file

!pip install llama-index-embeddings-openai

!pip install llama-index-llms-llama-api

!pip install 'crewai[tools]'

import os

from crewai import Agent, Task, Crew, Process

from crewai_tools import LlamaIndexTool

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex

from llama_index.llms.openai import OpenAI

reader = SimpleDirectoryReader(input_files=["finance.csv"])

docs = reader.load_data()

docs[1].get_content()

from google.colab import userdata

os.environ['OPENAI_API_KEY']=userdata.get('OPENAI_API_KEY')

llm = OpenAI(model="gpt-4o")

index = VectorStoreIndex.from_documents(docs)

query_engine = index.as_query_engine(similarity_top_k=5, llm=llm)

query_tool = LlamaIndexTool.from_query_engine(

    query_engine,

    name="Finance Query Tool",

    description="Use this tool to lookup the financial data of products and their sales",

)

query_tool.args_schema.schema()

researcher = Agent(

    role="Senior Market Analyst",

    goal="Uncover insights about product sales trends",

    backstory="""You work at a market research firm.

  Your goal is to understand sales patterns across different product categories.""",

    verbose=True,

    allow_delegation=False,

    tools=[query_tool],

)

writer = Agent(

    role="Product Content Specialist",

    goal="Craft compelling content on product trends",

    backstory="""You are a renowned Content Specialist, known for your insightful and engaging articles.

  You transform complex sales data into compelling narratives.""",

    verbose=True,

    allow_delegation=False,

)

# Create tasks for your agents

task1 = Task(

    description="""Analyze the sales data of top 5 products in the last quarter.""",

    expected_output="Detailed sales report with trends and insights",

    agent=researcher,

)

task2 = Task(

    description="""Using the insights provided, develop an engaging blog

  post that highlights the top-selling products and their market trends.

  Your post should be informative yet accessible, catering to a casual audience.

  Make it sound cool, avoid complex words.""",

    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

)

result = crew.kickoff()

print("######################")

print(result)