mirror of
https://github.com/crewAIInc/crewAI.git
synced 2025-12-16 04:18:35 +00:00
14a36d3f5e
* initial knowledge * WIP * Adding core knowledge sources * Improve types and better support for file paths * added additional sources * fix linting * update yaml to include optional deps * adding in lorenze feedback * ensure embeddings are persisted * improvements all around Knowledge class * return this * properly reset memory * properly reset memory+knowledge * consolodation and improvements * linted * cleanup rm unused embedder * fix test * fix duplicate * generating cassettes for knowledge test * updated default embedder * None embedder to use default on pipeline cloning * improvements * fixed text_file_knowledge * mypysrc fixes * type check fixes * added extra cassette * just mocks * linted * mock knowledge query to not spin up db * linted * verbose run * put a flag * fix * adding docs * better docs * improvements from review * more docs * linted * rm print * more fixes * clearer docs * added docstrings and type hints for cli --------- Co-authored-by: João Moura <joaomdmoura@gmail.com> Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
76 lines
2.4 KiB
Plaintext
76 lines
2.4 KiB
Plaintext
---
|
|
title: Knowledge
|
|
description: What is knowledge in CrewAI and how to use it.
|
|
icon: book
|
|
---
|
|
|
|
# Using Knowledge in CrewAI
|
|
|
|
## Introduction
|
|
|
|
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.
|
|
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.
|
|
|
|
## Basic Implementation
|
|
|
|
Here's a simple example of how to use the Knowledge class:
|
|
|
|
```python
|
|
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, metadata={"preference": "personal"}
|
|
)
|
|
|
|
|
|
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], "metadata": {"preference": "personal"}}, # 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?"})
|
|
```
|
|
|
|
|
|
## Embedder Configuration
|
|
|
|
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.
|
|
|
|
```python
|
|
...
|
|
string_source = StringKnowledgeSource(
|
|
content="Users name is John. He is 30 years old and lives in San Francisco.",
|
|
metadata={"preference": "personal"}
|
|
)
|
|
crew = Crew(
|
|
...
|
|
knowledge={
|
|
"sources": [string_source],
|
|
"metadata": {"preference": "personal"},
|
|
"embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
|
|
},
|
|
)
|
|
```
|