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docs/concepts/knowledge.mdx
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---
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title: Knowledge
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description: What is knowledge in CrewAI and how to use it.
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icon: book
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---
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# Using Knowledge in CrewAI
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## Introduction
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The Knowledge class in CrewAI provides a powerful way to manage and query knowledge sources for your AI agents. This guide will show you how to implement knowledge management in your CrewAI projects.
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Additionally, we have specific tools for generate knowledge sources for strings, text files, PDF's, and Spreadsheets. You can expand on any source type by extending the `KnowledgeSource` class.
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## Basic Implementation
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Here's a simple example of how to use the Knowledge class:
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```python
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from crewai import Agent, Task, Crew, Knowledge, Process, LLM
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from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
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# Create a knowledge source
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content = "Users name is John. He is 30 years old and lives in San Francisco."
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string_source = StringKnowledgeSource(
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content=content, metadata={"preference": "personal"}
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)
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# Create a knowledge store with a list of sources and metadata
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knowledge = Knowledge(sources=[string_source], metadata={"preference": "personal"})
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llm = LLM(model="gpt-4o-mini", temperature=0)
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# Create an agent with the knowledge store
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agent = Agent(
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role="About User",
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goal="You know everything about the user.",
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backstory="""You are a master at understanding people and their preferences.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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task = Task(
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description="Answer the following questions about the user: {question}",
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expected_output="An answer to the question.",
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agent=agent,
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)
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crew = Crew(
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agents=[agent],
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tasks=[task],
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verbose=True,
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process=Process.sequential,
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knowledge=True, # Enable knowledge
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)
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result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
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```
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## Additionally:
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You can add more sources as well as not need to re-declare the knowledge store every kickoff.
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If you had sources from previous runs, you no longer need to declare the knowledge store.
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```python
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from crewai import Agent, Task, Crew, Process, LLM
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from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
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# Create a knowledge store with a list of sources and metadata
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llm = LLM(model="gpt-4o-mini", temperature=0)
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# Create an agent with the knowledge store
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agent = Agent(
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role="About User",
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goal="You know everything about the user.",
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backstory="""You are a master at understanding people and their preferences.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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)
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task = Task(
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description="Answer the following questions about the user: {question}",
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expected_output="An answer to the question.",
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agent=agent,
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)
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crew = Crew(
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agents=[agent],
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tasks=[task],
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verbose=True,
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process=Process.sequential,
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knowledge=True, # Enable knowledge
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)
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result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
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```
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