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docs: major docs updates (#2897)
This commit is contained in:
Tony Kipkemboi
187
docs/tools/cloud-storage/bedrockinvokeagenttool.mdx
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187
docs/tools/cloud-storage/bedrockinvokeagenttool.mdx
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@@ -0,0 +1,187 @@
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---
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title: Bedrock Invoke Agent Tool
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description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
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icon: aws
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---
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|
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# `BedrockInvokeAgentTool`
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The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
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## Installation
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||||
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```bash
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uv pip install 'crewai[tools]'
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```
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|
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## Requirements
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|
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- AWS credentials configured (either through environment variables or AWS CLI)
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- `boto3` and `python-dotenv` packages
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- Access to Amazon Bedrock Agents
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## Usage
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Here's how to use the tool with a CrewAI agent:
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```python {2, 4-8}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize the tool
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agent_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id"
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)
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# Create a CrewAI agent that uses the tool
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aws_expert = Agent(
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role='AWS Service Expert',
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goal='Help users understand AWS services and quotas',
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backstory='I am an expert in AWS services and can provide detailed information about them.',
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tools=[agent_tool],
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verbose=True
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)
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# Create a task for the agent
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quota_task = Task(
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description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
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agent=aws_expert
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)
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# Create a crew with the agent
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crew = Crew(
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agents=[aws_expert],
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tasks=[quota_task],
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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print(result)
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```
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## Tool Arguments
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| Argument | Type | Required | Default | Description |
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|:---------|:-----|:---------|:--------|:------------|
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| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
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| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
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| **session_id** | `str` | No | timestamp | The unique identifier of the session |
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| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
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| **end_session** | `bool` | No | False | Whether to end the session after invocation |
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| **description** | `str` | No | None | Custom description for the tool |
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## Environment Variables
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```bash
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BEDROCK_AGENT_ID=your-agent-id # Alternative to passing agent_id
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BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
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AWS_REGION=your-aws-region # Defaults to us-west-2
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AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
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AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
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```
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## Advanced Usage
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### Multi-Agent Workflow with Session Management
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```python {2, 4-22}
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from crewai import Agent, Task, Crew, Process
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize tools with session management
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initial_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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followup_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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final_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id",
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end_session=True
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)
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# Create agents for different stages
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researcher = Agent(
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role='AWS Service Researcher',
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goal='Gather information about AWS services',
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backstory='I am specialized in finding detailed AWS service information.',
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tools=[initial_tool]
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)
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analyst = Agent(
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role='Service Compatibility Analyst',
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goal='Analyze service compatibility and requirements',
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backstory='I analyze AWS services for compatibility and integration possibilities.',
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tools=[followup_tool]
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)
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summarizer = Agent(
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role='Technical Documentation Writer',
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goal='Create clear technical summaries',
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backstory='I specialize in creating clear, concise technical documentation.',
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tools=[final_tool]
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)
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# Create tasks
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research_task = Task(
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description="Find all available AWS services in us-west-2 region.",
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agent=researcher
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)
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analysis_task = Task(
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description="Analyze which services support IPv6 and their implementation requirements.",
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agent=analyst
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)
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summary_task = Task(
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description="Create a summary of IPv6-compatible services and their key features.",
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agent=summarizer
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)
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# Create a crew with the agents and tasks
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crew = Crew(
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agents=[researcher, analyst, summarizer],
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tasks=[research_task, analysis_task, summary_task],
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process=Process.sequential,
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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```
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## Use Cases
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### Hybrid Multi-Agent Collaborations
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- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
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- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
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- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
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### Data Sovereignty and Compliance
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- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
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- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
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- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
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### Seamless AWS Service Integration
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- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
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- Enable CrewAI agents to interact with AWS services through natural language requests
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- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
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### Scalable Hybrid Agent Architectures
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- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
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- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
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### Cross-Organizational Agent Collaboration
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- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
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- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
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- Build agent ecosystems that span organizational boundaries while maintaining security and data control
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165
docs/tools/cloud-storage/bedrockkbretriever.mdx
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165
docs/tools/cloud-storage/bedrockkbretriever.mdx
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@@ -0,0 +1,165 @@
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---
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title: 'Bedrock Knowledge Base Retriever'
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description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
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icon: aws
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---
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# `BedrockKBRetrieverTool`
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The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
|
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## Installation
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||||
|
||||
```bash
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uv pip install 'crewai[tools]'
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||||
```
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||||
|
||||
## Requirements
|
||||
|
||||
- AWS credentials configured (either through environment variables or AWS CLI)
|
||||
- `boto3` and `python-dotenv` packages
|
||||
- Access to Amazon Bedrock Knowledge Base
|
||||
|
||||
## Usage
|
||||
|
||||
Here's how to use the tool with a CrewAI agent:
|
||||
|
||||
```python {2, 4-17}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
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# Initialize the tool
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kb_tool = BedrockKBRetrieverTool(
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knowledge_base_id="your-kb-id",
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number_of_results=5
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)
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# Create a CrewAI agent that uses the tool
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researcher = Agent(
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role='Knowledge Base Researcher',
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goal='Find information about company policies',
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backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
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tools=[kb_tool],
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verbose=True
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)
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# Create a task for the agent
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research_task = Task(
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description="Find our company's remote work policy and summarize the key points.",
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agent=researcher
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)
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# Create a crew with the agent
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crew = Crew(
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agents=[researcher],
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tasks=[research_task],
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verbose=2
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)
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|
||||
# Run the crew
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||||
result = crew.kickoff()
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print(result)
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```
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|
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## Tool Arguments
|
||||
|
||||
| Argument | Type | Required | Default | Description |
|
||||
|:---------|:-----|:---------|:---------|:-------------|
|
||||
| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
|
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| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
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| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
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| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
|
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| **next_token** | `str` | No | None | Token for pagination |
|
||||
|
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## Environment Variables
|
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|
||||
```bash
|
||||
BEDROCK_KB_ID=your-knowledge-base-id # Alternative to passing knowledge_base_id
|
||||
AWS_REGION=your-aws-region # Defaults to us-east-1
|
||||
AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
|
||||
AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
|
||||
```
|
||||
|
||||
## Response Format
|
||||
|
||||
The tool returns results in JSON format:
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||||
|
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```json
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||||
{
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||||
"results": [
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||||
{
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"content": "Retrieved text content",
|
||||
"content_type": "text",
|
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"source_type": "S3",
|
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"source_uri": "s3://bucket/document.pdf",
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"score": 0.95,
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"metadata": {
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"additional": "metadata"
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}
|
||||
}
|
||||
],
|
||||
"nextToken": "pagination-token",
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"guardrailAction": "NONE"
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||||
}
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```
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|
||||
## Advanced Usage
|
||||
|
||||
### Custom Retrieval Configuration
|
||||
|
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```python
|
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kb_tool = BedrockKBRetrieverTool(
|
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knowledge_base_id="your-kb-id",
|
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retrieval_configuration={
|
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"vectorSearchConfiguration": {
|
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"numberOfResults": 10,
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"overrideSearchType": "HYBRID"
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}
|
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}
|
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)
|
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|
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policy_expert = Agent(
|
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role='Policy Expert',
|
||||
goal='Analyze company policies in detail',
|
||||
backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
|
||||
tools=[kb_tool]
|
||||
)
|
||||
```
|
||||
|
||||
## Supported Data Sources
|
||||
|
||||
- Amazon S3
|
||||
- Confluence
|
||||
- Salesforce
|
||||
- SharePoint
|
||||
- Web pages
|
||||
- Custom document locations
|
||||
- Amazon Kendra
|
||||
- SQL databases
|
||||
|
||||
## Use Cases
|
||||
|
||||
### Enterprise Knowledge Integration
|
||||
- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
|
||||
- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
|
||||
- Create agents that can answer questions based on your internal documentation while maintaining data security
|
||||
|
||||
### Specialized Domain Knowledge
|
||||
- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
|
||||
- Leverage existing knowledge repositories that are already maintained in your AWS environment
|
||||
- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
|
||||
|
||||
### Data-Driven Decision Making
|
||||
- Ground CrewAI agent responses in your actual company data rather than general knowledge
|
||||
- Ensure agents provide recommendations based on your specific business context and documentation
|
||||
- Reduce hallucinations by retrieving factual information from your knowledge bases
|
||||
|
||||
### Scalable Information Access
|
||||
- Access terabytes of organizational knowledge without embedding it all into your models
|
||||
- Dynamically query only the relevant information needed for specific tasks
|
||||
- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
|
||||
|
||||
### Compliance and Governance
|
||||
- Ensure CrewAI agents provide responses that align with your company's approved documentation
|
||||
- Create auditable trails of information sources used by your agents
|
||||
- Maintain control over what information sources your agents can access
|
||||
50
docs/tools/cloud-storage/overview.mdx
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50
docs/tools/cloud-storage/overview.mdx
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@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: "Overview"
|
||||
description: "Interact with cloud services, storage systems, and cloud-based AI platforms"
|
||||
icon: "face-smile"
|
||||
---
|
||||
|
||||
These tools enable your agents to interact with cloud services, access cloud storage, and leverage cloud-based AI platforms for scalable operations.
|
||||
|
||||
## **Available Tools**
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="S3 Reader Tool" icon="cloud" href="/tools/cloud-storage/s3readertool">
|
||||
Read files and data from Amazon S3 buckets.
|
||||
</Card>
|
||||
|
||||
<Card title="S3 Writer Tool" icon="cloud-arrow-up" href="/tools/cloud-storage/s3writertool">
|
||||
Write and upload files to Amazon S3 storage.
|
||||
</Card>
|
||||
|
||||
<Card title="Bedrock Invoke Agent" icon="aws" href="/tools/cloud-storage/bedrockinvokeagenttool">
|
||||
Invoke Amazon Bedrock agents for AI-powered tasks.
|
||||
</Card>
|
||||
|
||||
<Card title="Bedrock KB Retriever" icon="database" href="/tools/cloud-storage/bedrockkbretriever">
|
||||
Retrieve information from Amazon Bedrock knowledge bases.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## **Common Use Cases**
|
||||
|
||||
- **File Storage**: Store and retrieve files from cloud storage systems
|
||||
- **Data Backup**: Backup important data to cloud storage
|
||||
- **AI Services**: Access cloud-based AI models and services
|
||||
- **Knowledge Retrieval**: Query cloud-hosted knowledge bases
|
||||
- **Scalable Operations**: Leverage cloud infrastructure for processing
|
||||
|
||||
```python
|
||||
from crewai_tools import S3ReaderTool, S3WriterTool, BedrockInvokeAgentTool
|
||||
|
||||
# Create cloud tools
|
||||
s3_reader = S3ReaderTool()
|
||||
s3_writer = S3WriterTool()
|
||||
bedrock_agent = BedrockInvokeAgentTool()
|
||||
|
||||
# Add to your agent
|
||||
agent = Agent(
|
||||
role="Cloud Operations Specialist",
|
||||
tools=[s3_reader, s3_writer, bedrock_agent],
|
||||
goal="Manage cloud resources and AI services"
|
||||
)
|
||||
144
docs/tools/cloud-storage/s3readertool.mdx
Normal file
144
docs/tools/cloud-storage/s3readertool.mdx
Normal file
@@ -0,0 +1,144 @@
|
||||
---
|
||||
title: S3 Reader Tool
|
||||
description: The `S3ReaderTool` enables CrewAI agents to read files from Amazon S3 buckets.
|
||||
icon: aws
|
||||
---
|
||||
|
||||
# `S3ReaderTool`
|
||||
|
||||
## Description
|
||||
|
||||
The `S3ReaderTool` is designed to read files from Amazon S3 buckets. This tool allows CrewAI agents to access and retrieve content stored in S3, making it ideal for workflows that require reading data, configuration files, or any other content stored in AWS S3 storage.
|
||||
|
||||
## Installation
|
||||
|
||||
To use this tool, you need to install the required dependencies:
|
||||
|
||||
```shell
|
||||
uv add boto3
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the `S3ReaderTool`, follow these steps:
|
||||
|
||||
1. **Install Dependencies**: Install the required packages using the command above.
|
||||
2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
|
||||
3. **Initialize the Tool**: Create an instance of the tool.
|
||||
4. **Specify S3 Path**: Provide the S3 path to the file you want to read.
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to use the `S3ReaderTool` to read a file from an S3 bucket:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools.aws.s3 import S3ReaderTool
|
||||
|
||||
# Initialize the tool
|
||||
s3_reader_tool = S3ReaderTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
file_reader_agent = Agent(
|
||||
role="File Reader",
|
||||
goal="Read files from S3 buckets",
|
||||
backstory="An expert in retrieving and processing files from cloud storage.",
|
||||
tools=[s3_reader_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Example task to read a configuration file
|
||||
read_task = Task(
|
||||
description="Read the configuration file from {my_bucket} and summarize its contents.",
|
||||
expected_output="A summary of the configuration file contents.",
|
||||
agent=file_reader_agent,
|
||||
)
|
||||
|
||||
# Create and run the crew
|
||||
crew = Crew(agents=[file_reader_agent], tasks=[read_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `S3ReaderTool` accepts the following parameter when used by an agent:
|
||||
|
||||
- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
|
||||
|
||||
## AWS Credentials
|
||||
|
||||
The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
|
||||
|
||||
- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
|
||||
- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
|
||||
- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
|
||||
|
||||
## Usage
|
||||
|
||||
When using the `S3ReaderTool` with an agent, the agent will need to provide the S3 file path:
|
||||
|
||||
```python Code
|
||||
# Example of using the tool with an agent
|
||||
file_reader_agent = Agent(
|
||||
role="File Reader",
|
||||
goal="Read files from S3 buckets",
|
||||
backstory="An expert in retrieving and processing files from cloud storage.",
|
||||
tools=[s3_reader_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create a task for the agent to read a specific file
|
||||
read_config_task = Task(
|
||||
description="Read the application configuration file from {my_bucket} and extract the database connection settings.",
|
||||
expected_output="The database connection settings from the configuration file.",
|
||||
agent=file_reader_agent,
|
||||
)
|
||||
|
||||
# Run the task
|
||||
crew = Crew(agents=[file_reader_agent], tasks=[read_config_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The `S3ReaderTool` includes error handling for common S3 issues:
|
||||
|
||||
- Invalid S3 path format
|
||||
- Missing or inaccessible files
|
||||
- Permission issues
|
||||
- AWS credential problems
|
||||
|
||||
When an error occurs, the tool will return an error message that includes details about the issue.
|
||||
|
||||
## Implementation Details
|
||||
|
||||
The `S3ReaderTool` uses the AWS SDK for Python (boto3) to interact with S3:
|
||||
|
||||
```python Code
|
||||
class S3ReaderTool(BaseTool):
|
||||
name: str = "S3 Reader Tool"
|
||||
description: str = "Reads a file from Amazon S3 given an S3 file path"
|
||||
|
||||
def _run(self, file_path: str) -> str:
|
||||
try:
|
||||
bucket_name, object_key = self._parse_s3_path(file_path)
|
||||
|
||||
s3 = boto3.client(
|
||||
's3',
|
||||
region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
|
||||
aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
|
||||
aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
|
||||
)
|
||||
|
||||
# Read file content from S3
|
||||
response = s3.get_object(Bucket=bucket_name, Key=object_key)
|
||||
file_content = response['Body'].read().decode('utf-8')
|
||||
|
||||
return file_content
|
||||
except ClientError as e:
|
||||
return f"Error reading file from S3: {str(e)}"
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `S3ReaderTool` provides a straightforward way to read files from Amazon S3 buckets. By enabling agents to access content stored in S3, it facilitates workflows that require cloud-based file access. This tool is particularly useful for data processing, configuration management, and any task that involves retrieving information from AWS S3 storage.
|
||||
150
docs/tools/cloud-storage/s3writertool.mdx
Normal file
150
docs/tools/cloud-storage/s3writertool.mdx
Normal file
@@ -0,0 +1,150 @@
|
||||
---
|
||||
title: S3 Writer Tool
|
||||
description: The `S3WriterTool` enables CrewAI agents to write content to files in Amazon S3 buckets.
|
||||
icon: aws
|
||||
---
|
||||
|
||||
# `S3WriterTool`
|
||||
|
||||
## Description
|
||||
|
||||
The `S3WriterTool` is designed to write content to files in Amazon S3 buckets. This tool allows CrewAI agents to create or update files in S3, making it ideal for workflows that require storing data, saving configuration files, or persisting any other content to AWS S3 storage.
|
||||
|
||||
## Installation
|
||||
|
||||
To use this tool, you need to install the required dependencies:
|
||||
|
||||
```shell
|
||||
uv add boto3
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the `S3WriterTool`, follow these steps:
|
||||
|
||||
1. **Install Dependencies**: Install the required packages using the command above.
|
||||
2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
|
||||
3. **Initialize the Tool**: Create an instance of the tool.
|
||||
4. **Specify S3 Path and Content**: Provide the S3 path where you want to write the file and the content to be written.
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to use the `S3WriterTool` to write content to a file in an S3 bucket:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools.aws.s3 import S3WriterTool
|
||||
|
||||
# Initialize the tool
|
||||
s3_writer_tool = S3WriterTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
file_writer_agent = Agent(
|
||||
role="File Writer",
|
||||
goal="Write content to files in S3 buckets",
|
||||
backstory="An expert in storing and managing files in cloud storage.",
|
||||
tools=[s3_writer_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Example task to write a report
|
||||
write_task = Task(
|
||||
description="Generate a summary report of the quarterly sales data and save it to {my_bucket}.",
|
||||
expected_output="Confirmation that the report was successfully saved to S3.",
|
||||
agent=file_writer_agent,
|
||||
)
|
||||
|
||||
# Create and run the crew
|
||||
crew = Crew(agents=[file_writer_agent], tasks=[write_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/reports/quarterly-summary.txt"})
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `S3WriterTool` accepts the following parameters when used by an agent:
|
||||
|
||||
- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
|
||||
- **content**: Required. The content to write to the file.
|
||||
|
||||
## AWS Credentials
|
||||
|
||||
The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
|
||||
|
||||
- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
|
||||
- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
|
||||
- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
|
||||
|
||||
## Usage
|
||||
|
||||
When using the `S3WriterTool` with an agent, the agent will need to provide both the S3 file path and the content to write:
|
||||
|
||||
```python Code
|
||||
# Example of using the tool with an agent
|
||||
file_writer_agent = Agent(
|
||||
role="File Writer",
|
||||
goal="Write content to files in S3 buckets",
|
||||
backstory="An expert in storing and managing files in cloud storage.",
|
||||
tools=[s3_writer_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create a task for the agent to write a specific file
|
||||
write_config_task = Task(
|
||||
description="""
|
||||
Create a configuration file with the following database settings:
|
||||
- host: db.example.com
|
||||
- port: 5432
|
||||
- username: app_user
|
||||
- password: secure_password
|
||||
|
||||
Save this configuration as JSON to {my_bucket}.
|
||||
""",
|
||||
expected_output="Confirmation that the configuration file was successfully saved to S3.",
|
||||
agent=file_writer_agent,
|
||||
)
|
||||
|
||||
# Run the task
|
||||
crew = Crew(agents=[file_writer_agent], tasks=[write_config_task])
|
||||
result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/db-config.json"})
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The `S3WriterTool` includes error handling for common S3 issues:
|
||||
|
||||
- Invalid S3 path format
|
||||
- Permission issues (e.g., no write access to the bucket)
|
||||
- AWS credential problems
|
||||
- Bucket does not exist
|
||||
|
||||
When an error occurs, the tool will return an error message that includes details about the issue.
|
||||
|
||||
## Implementation Details
|
||||
|
||||
The `S3WriterTool` uses the AWS SDK for Python (boto3) to interact with S3:
|
||||
|
||||
```python Code
|
||||
class S3WriterTool(BaseTool):
|
||||
name: str = "S3 Writer Tool"
|
||||
description: str = "Writes content to a file in Amazon S3 given an S3 file path"
|
||||
|
||||
def _run(self, file_path: str, content: str) -> str:
|
||||
try:
|
||||
bucket_name, object_key = self._parse_s3_path(file_path)
|
||||
|
||||
s3 = boto3.client(
|
||||
's3',
|
||||
region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
|
||||
aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
|
||||
aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
|
||||
)
|
||||
|
||||
s3.put_object(Bucket=bucket_name, Key=object_key, Body=content.encode('utf-8'))
|
||||
return f"Successfully wrote content to {file_path}"
|
||||
except ClientError as e:
|
||||
return f"Error writing file to S3: {str(e)}"
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `S3WriterTool` provides a straightforward way to write content to files in Amazon S3 buckets. By enabling agents to create and update files in S3, it facilitates workflows that require cloud-based file storage. This tool is particularly useful for data persistence, configuration management, report generation, and any task that involves storing information in AWS S3 storage.
|
||||
Reference in New Issue
Block a user