Integrate Box Content with Llama Index
Python tools that connect Box content with Llama Index agents for search, metadata queries, text extraction, and Box AI-powered analysis.
Why it matters
Empower your Llama Index agents to access, understand, and utilize information stored in Box. This suite of tools provides an agnostic interface for seamless integration with any Llama Index agent.
Outcomes
What it gets done
Search Box resources by keywords and metadata.
Extract plain text content from supported Box file formats.
Analyze Box content using Box AI features and prompts.
Extract structured data from Box content using Box AI.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-tool-tools-box | bash Overview
Box Tools for Llama-Index
Box Tools for Llama-Index is a suite of Python tools that connects Box content with Llama Index agents. It provides five core capabilities: keyword-based Box Search, metadata-based search, text extraction from Box files, Box AI Prompt for content analysis, and Box AI Extract for structured data extraction using pre-trained models. Use these tools when building Llama Index agents that need to access, search, or analyze content stored in Box. They are ideal for scenarios requiring keyword searches, metadata queries, text extraction from Box files, or AI-powered content analysis within the Llama Index framework.
What it does
Box Tools for Llama-Index provides a suite of Python tools that integrate Box content management capabilities into Llama Index agents. The tools offer an agnostic interface that works within any agent built in the Llama Index framework, enabling agents to access, search, and analyze information stored in Box.
When to use - and when NOT to
Use these tools when you need to build Llama Index agents that interact with Box-hosted content, such as searching documents by keywords or metadata, extracting text from files, or leveraging Box AI features for content analysis. Use them when you want to empower agents to retrieve structured or unstructured data from Box repositories based on user queries or automated workflows.
Do not use these tools if your content is not stored in Box or if you need to work with content platforms other than Box.
Inputs and outputs
Users provide search keywords, metadata field specifications, file identifiers, user-defined prompts for AI analysis, or extraction parameters depending on the specific tool. The tools return search results matching the criteria, plain text content extracted from supported Box file formats, AI-generated analysis based on prompts, or structured data extracted using pre-trained Box AI models.
Integrations
These tools integrate with:
- Box Search: Search for Box resources based on keywords and various filtering options
- Search by Metadata: Search for Box resources based on specific metadata fields such as owner and creation date
- Text Extraction: Extract plain text content from supported Box file formats
- Box AI Prompt: Utilize Box AI features to analyze Box content based on user-defined prompts
- Box AI Extract: Extract structured data from Box content using pre-trained Box AI models
All tools work within the Llama Index framework and provide an agnostic interface compatible with any Llama Index agent.
Source README
Box Tools for Llama-Index
This repository provides a suite of Python tools for seamlessly integrating Box content with Llama Index. These tools offer an agnostic interface, allowing them to be used within any agent built within the Llama Index framework.
Available tools include:
- Box Search: Search for Box resources based on keywords and various filtering options.
- Search by Metadata: Search for Box resources based on specific metadata fields (e.g., owner, creation date).
- Text Extraction: Extract plain text content from supported Box file formats.
- Box AI Prompt: Utilize Box AI features to analyze Box content based on user-defined prompts.
- Box AI Extract: Extract structured data from Box content using pre-trained Box AI models.
By leveraging these tools, developers can empower their Llama Index agents to access, understand, and utilize valuable information stored within Box.
Check the links above for detailed usage instructions and examples for each tool.
FAQ
Common questions
Discussion
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