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* added knowledge to agent level * linted * added doc * added from suggestions * added test * fixes from discussion * fix docs * fix test * rm cassette for knowledge_sources test as its a mock and update agent doc string * fix test * rm unused * linted
106 lines
3.5 KiB
Plaintext
106 lines
3.5 KiB
Plaintext
---
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title: Knowledge
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description: Understand what knowledge is in CrewAI and how to effectively 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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Knowledge in CrewAI serves as a foundational component for enriching AI agents with contextual and relevant information. It enables agents to access and utilize structured data sources during their execution processes, making them more intelligent and responsive.
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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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## What is Knowledge?
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The `Knowledge` class in CrewAI manages various sources that store information, which can be queried and retrieved by AI agents. This modular approach allows you to integrate diverse data formats such as text, PDFs, spreadsheets, and more into your AI workflows.
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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, 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 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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)
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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_sources=[string_source], # Enable knowledge by adding the sources here.
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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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## Appending Knowledge Sources To Individual Agents
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Sometimes you may want to append knowledge sources to an individual agent. You can do this by setting the `knowledge` parameter in the `Agent` class.
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```python
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agent = Agent(
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...
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knowledge_sources=[
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StringKnowledgeSource(
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content="Users name is John. He is 30 years old and lives in San Francisco.",
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metadata={"preference": "personal"},
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)
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],
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)
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```
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## Agent Level Knowledge Sources
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You can also append knowledge sources to an individual agent by setting the `knowledge_sources` parameter in the `Agent` class.
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```python
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string_source = StringKnowledgeSource(
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content="Users name is John. He is 30 years old and lives in San Francisco.",
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metadata={"preference": "personal"},
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)
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agent = Agent(
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...
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knowledge_sources=[string_source],
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)
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```
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## Embedder Configuration
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You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
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```python
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...
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string_source = StringKnowledgeSource(
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content="Users name is John. He is 30 years old and lives in San Francisco.",
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metadata={"preference": "personal"}
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)
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crew = Crew(
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...
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knowledge_sources=[string_source],
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embedder_config={"provider": "ollama", "config": {"model": "nomic-embed-text:latest"}},
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)
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```
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