Documentation Index

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BedrockKBRetrieverTool

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Installation

uv pip install 'crewai[tools]'

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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

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Usage

from crewai import Agent, Task, Crew
from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool

# Initialize the tool
kb_tool = BedrockKBRetrieverTool(
    knowledge_base_id="your-kb-id",
    number_of_results=5
)

# Create a CrewAI agent that uses the tool
researcher = Agent(
    role='Knowledge Base Researcher',
    goal='Find information about company policies',
    backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
    tools=[kb_tool],
    verbose=True
)

# Create a task for the agent
research_task = Task(
    description="Find our company's remote work policy and summarize the key points.",
    agent=researcher
)

# Create a crew with the agent
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=2
)

# Run the crew
result = crew.kickoff()
print(result)   

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Tool Arguments

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Environment Variables

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

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Response Format

{
  "results": [
    {
      "content": "Retrieved text content",
      "content_type": "text",
      "source_type": "S3",
      "source_uri": "s3://bucket/document.pdf",
      "score": 0.95,
      "metadata": {
        "additional": "metadata"
      }
    }
  ],
  "nextToken": "pagination-token",
  "guardrailAction": "NONE"
}

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Advanced Usage

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Custom Retrieval Configuration

kb_tool = BedrockKBRetrieverTool(
    knowledge_base_id="your-kb-id",
    retrieval_configuration={
        "vectorSearchConfiguration": {
            "numberOfResults": 10,
            "overrideSearchType": "HYBRID"
        }
    }
)

policy_expert = Agent(
    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]
)

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Supported Data Sources

• Amazon S3
• Confluence
• Salesforce
• SharePoint
• Web pages
• Custom document locations
• Amazon Kendra
• SQL databases

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Use Cases

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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

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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

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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

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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

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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