crewAI/docs/concepts/knowledge.mdx

---
title: Knowledge
description: Understand what knowledge is in CrewAI and how to effectively use it.
icon: book
---

# Using Knowledge in CrewAI

## What is Knowledge?

Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. Think of it as giving your agents a reference library they can consult while working.

<Info>
  Key benefits of using Knowledge:
  - Enhance agents with domain-specific information
  - Support decisions with real-world data
  - Maintain context across conversations
  - Ground responses in factual information
</Info>

## Supported Knowledge Sources

CrewAI supports various types of knowledge sources out of the box:

<CardGroup cols={2}>
  <Card title="Text Sources" icon="text">
    - Raw strings
    - Text files (.txt)
    - PDF documents
  </Card>
  <Card title="Structured Data" icon="table">
    - CSV files
    - Excel spreadsheets
    - JSON documents
  </Card>
</CardGroup>

## Quick Start

Here's a simple example using string-based knowledge:

```python
from crewai import Agent, Task, Crew
from crewai.knowledge import StringKnowledgeSource

# 1. Create a knowledge source
product_info = StringKnowledgeSource(
    content="""Our product X1000 has the following features:
    - 10-hour battery life
    - Water-resistant
    - Available in black and silver
    Price: $299.99""",
    metadata={"category": "product"}
)

# 2. Create an agent with knowledge
sales_agent = Agent(
    role="Sales Representative",
    goal="Accurately answer customer questions about products",
    backstory="Expert in product features and customer service",
    knowledge_sources=[product_info]  # Attach knowledge to agent
)

# 3. Create a task
answer_task = Task(
    description="Answer: What colors is the X1000 available in and how much does it cost?",
    agent=sales_agent
)

# 4. Create and run the crew
crew = Crew(
    agents=[sales_agent],
    tasks=[answer_task]
)

result = crew.kickoff()
```

## Knowledge Configuration

### Collection Names

Knowledge sources are organized into collections for better management:

```python
# Create knowledge sources with specific collections
tech_specs = StringKnowledgeSource(
    content="Technical specifications...",
    collection_name="product_tech_specs"
)

pricing_info = StringKnowledgeSource(
    content="Pricing information...",
    collection_name="product_pricing"
)
```

### Metadata and Filtering

Add metadata to organize and filter knowledge:

```python
knowledge_source = StringKnowledgeSource(
    content="Product details...",
    metadata={
        "category": "electronics",
        "product_line": "premium",
        "last_updated": "2024-03"
    }
)
```

### Chunking Configuration

Control how your content is split for processing:

```python
knowledge_source = PDFKnowledgeSource(
    file_path="product_manual.pdf",
    chunk_size=2000,     # Characters per chunk
    chunk_overlap=200    # Overlap between chunks
)
```

## Advanced Usage

### Custom Knowledge Sources

Create your own knowledge source by extending the base class:

```python
from crewai.knowledge.source import BaseKnowledgeSource

class APIKnowledgeSource(BaseKnowledgeSource):
    def __init__(self, api_endpoint: str, **kwargs):
        super().__init__(**kwargs)
        self.api_endpoint = api_endpoint
    
    def load_content(self):
        # Implement API data fetching
        response = requests.get(self.api_endpoint)
        return response.json()
    
    def add(self):
        content = self.load_content()
        # Process and store content
        self.save_documents({"source": "api"})
```

### Embedder Configuration

Customize the embedding process:

```python
crew = Crew(
    agents=[agent],
    tasks=[task],
    knowledge_sources=[source],
    embedder_config={
        "model": "BAAI/bge-small-en-v1.5",
        "normalize": True,
        "max_length": 512
    }
)
```

## Best Practices

<AccordionGroup>
  <Accordion title="Content Organization">
    - Use meaningful collection names
    - Add detailed metadata for filtering
    - Keep chunk sizes appropriate for your content
    - Consider content overlap for context preservation
  </Accordion>
  
  <Accordion title="Performance Tips">
    - Use smaller chunk sizes for precise retrieval
    - Implement metadata filtering for faster searches
    - Choose appropriate embedding models for your use case
    - Cache frequently accessed knowledge
  </Accordion>
  
  <Accordion title="Error Handling">
    - Validate knowledge source content
    - Handle missing or corrupted files
    - Monitor embedding generation
    - Implement fallback options
  </Accordion>
</AccordionGroup>

## Common Issues and Solutions

<AccordionGroup>
  <Accordion title="Content Not Found">
    If agents can't find relevant information:
    - Check chunk sizes
    - Verify knowledge source loading
    - Review metadata filters
    - Test with simpler queries first
  </Accordion>
  
  <Accordion title="Performance Issues">
    If knowledge retrieval is slow:
    - Reduce chunk sizes
    - Optimize metadata filtering
    - Consider using a lighter embedding model
    - Cache frequently accessed content
  </Accordion>
</AccordionGroup>