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title, description
| title | description |
|---|---|
| Using LlamaIndex Tools | Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more. |
Using LlamaIndex Tools
!!! info "LlamaIndex Integration" CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. Here are the available built-in tools offered by LlamaIndex.
from crewai import Agent
from crewai_tools import LlamaIndexTool
# Example 1: Initialize from FunctionTool
from llama_index.core.tools import FunctionTool
your_python_function = lambda ...: ...
og_tool = FunctionTool.from_defaults(your_python_function, name="<name>", description='<description>')
tool = LlamaIndexTool.from_tool(og_tool)
# Example 2: Initialize from LlamaHub Tools
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
wolfram_tools = wolfram_spec.to_tool_list()
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
# Example 3: Initialize Tool from a LlamaIndex Query Engine
query_engine = index.as_query_engine()
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Uber 2019 10K Query Tool",
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
)
# Create and assign the tools to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[tool, *tools, query_tool]
)
# rest of the code ...
Steps to Get Started
To effectively use the LlamaIndexTool, follow these steps:
-
Package Installation: Confirm that the
crewai[tools]package is installed in your Python environment.pip install 'crewai[tools]' -
Install and Use LlamaIndex: Follow the LlamaIndex documentation LlamaIndex Documentation to set up a RAG/agent pipeline.