Knowledge

from crewai import Agent, Task, Crew, Process, LLM
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

# Create a knowledge source
content = "Users name is John. He is 30 years old and lives in San Francisco."
string_source = StringKnowledgeSource(
    content=content,
)

# Create an LLM with a temperature of 0 to ensure deterministic outputs
llm = LLM(model="gpt-4o-mini", temperature=0)

# Create an agent with the knowledge store
agent = Agent(
    role="About User",
    goal="You know everything about the user.",
    backstory="""You are a master at understanding people and their preferences.""",
    verbose=True,
    allow_delegation=False,
    llm=llm,
)
task = Task(
    description="Answer the following questions about the user: {question}",
    expected_output="An answer to the question.",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
    process=Process.sequential,
    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
)

result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})

from crewai import LLM, Agent, Crew, Process, Task
from crewai.knowledge.source.crew_docling_source import CrewDoclingSource

# Create a knowledge source
content_source = CrewDoclingSource(
    file_paths=[
        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
    ],
)

# Create an LLM with a temperature of 0 to ensure deterministic outputs
llm = LLM(model="gpt-4o-mini", temperature=0)

# Create an agent with the knowledge store
agent = Agent(
    role="About papers",
    goal="You know everything about the papers.",
    backstory="""You are a master at understanding papers and their content.""",
    verbose=True,
    allow_delegation=False,
    llm=llm,
)
task = Task(
    description="Answer the following questions about the papers: {question}",
    expected_output="An answer to the question.",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
    process=Process.sequential,
    knowledge_sources=[
        content_source
    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
)

result = crew.kickoff(
    inputs={
        "question": "What is the reward hacking paper about? Be sure to provide sources."
    }
)

from crewai.knowledge.source.crew_docling_source import CrewDoclingSource

# Create a text file knowledge source
text_source = CrewDoclingSource(
    file_paths=["document.txt", "another.txt"]
)

# Create crew with text file source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[text_source]
)

crew = Crew(
    ...
    knowledge_sources=[text_source]
)

from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource

# Create a PDF knowledge source
pdf_source = PDFKnowledgeSource(
    file_paths=["document.pdf", "another.pdf"]
)

# Create crew with PDF knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[pdf_source]
)

crew = Crew(
    ...
    knowledge_sources=[pdf_source]
)

from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource

# Create a CSV knowledge source
csv_source = CSVKnowledgeSource(
    file_paths=["data.csv"]
)

# Create crew with CSV knowledge source or on agent level
agent = Agent(
    ...
    knowledge_sources=[csv_source]
)

crew = Crew(
    ...
    knowledge_sources=[csv_source]
)

from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource

# Create an Excel knowledge source
excel_source = ExcelKnowledgeSource(
    file_paths=["spreadsheet.xlsx"]
)

# Create crew with Excel knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[excel_source]
)

crew = Crew(
    ...
    knowledge_sources=[excel_source]
)

from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource

# Create a JSON knowledge source
json_source = JSONKnowledgeSource(
    file_paths=["data.json"]
)

# Create crew with JSON knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[json_source]
)

crew = Crew(
    ...
    knowledge_sources=[json_source]
)

from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

source = StringKnowledgeSource(
    content="Your content here",
    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
    chunk_overlap=200     # Overlap between chunks (default: 200)
)

crewai reset-memories --knowledge

from crewai import Agent, Task, Crew
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

# Create agent-specific knowledge about a product
product_specs = StringKnowledgeSource(
    content="""The XPS 13 laptop features:
    - 13.4-inch 4K display
    - Intel Core i7 processor
    - 16GB RAM
    - 512GB SSD storage
    - 12-hour battery life""",
    metadata={"category": "product_specs"}
)

# Create a support agent with product knowledge
support_agent = Agent(
    role="Technical Support Specialist",
    goal="Provide accurate product information and support.",
    backstory="You are an expert on our laptop products and specifications.",
    knowledge_sources=[product_specs]  # Agent-specific knowledge
)

# Create a task that requires product knowledge
support_task = Task(
    description="Answer this customer question: {question}",
    agent=support_agent
)

# Create and run the crew
crew = Crew(
    agents=[support_agent],
    tasks=[support_task]
)

# Get answer about the laptop's specifications
result = crew.kickoff(
    inputs={"question": "What is the storage capacity of the XPS 13?"}
)

# Fetch more articles
recent_news = SpaceNewsKnowledgeSource(
    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
    limit=20,  # Increase the number of articles
)

# Add search parameters
recent_news = SpaceNewsKnowledgeSource(
    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
    limit=10,
)