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54 lines
1.8 KiB
Markdown
54 lines
1.8 KiB
Markdown
# LlamaIndexTool Documentation
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## Description
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This tool is designed to be a general wrapper around LlamaIndex tools and query engines, enabling you to leverage LlamaIndex resources
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in terms of RAG/agentic pipelines as tools to plug into CrewAI agents.
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## Installation
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To incorporate this tool into your project, follow the installation instructions below:
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```shell
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pip install 'crewai[tools]'
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```
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## Example
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The following example demonstrates how to initialize the tool and execute a search with a given query:
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```python
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from crewai_tools import LlamaIndexTool
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# Initialize the tool from a LlamaIndex Tool
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## Example 1: Initialize from FunctionTool
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from llama_index.core.tools import FunctionTool
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your_python_function = lambda ...: ...
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og_tool = FunctionTool.from_defaults(your_python_function, name="<name>", description='<description>')
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tool = LlamaIndexTool.from_tool(og_tool)
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## Example 2: Initialize from LlamaHub Tools
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from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
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wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
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wolfram_tools = wolfram_spec.to_tool_list()
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tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
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# Initialize Tool from a LlamaIndex Query Engine
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## NOTE: LlamaIndex has a lot of query engines, define whatever query engine you want
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query_engine = index.as_query_engine()
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query_tool = LlamaIndexTool.from_query_engine(
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query_engine,
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name="Uber 2019 10K Query Tool",
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description="Use this tool to lookup the 2019 Uber 10K Annual Report"
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)
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```
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## Steps to Get Started
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To effectively use the `LlamaIndexTool`, follow these steps:
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1. **Install CrewAI**: Confirm that the `crewai[tools]` package is installed in your Python environment.
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2. **Install and use LlamaIndex**: Follow LlamaIndex documentation (https://docs.llamaindex.ai/) to setup a RAG/agent pipeline.
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