Knowledge example

meta_quest_knowledge/.gitignore

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+.env
+__pycache__/

meta_quest_knowledge/README.md

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+# PDF Knowledge Example
+
+This project demonstrates how to create a Crew of AI agents and tasks using crewAI. It uses a PDF knowledge source to answer user questions based on the content of the PDF. The PDF is loaded from a file and the knowledge source is initialized with it. The project also includes a custom task that uses the knowledge source to answer user questions. You can modify the question in the `main.py` file.
+
+## Installation
+
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+
+First, if you haven't already, install uv:
+
+```bash
+pip install uv
+```
+
+Next, navigate to your project directory and install the dependencies:
+
+(Optional) Lock the dependencies and install them by using the CLI command:
+```bash
+crewai install
+```
+### Customizing
+
+**Add your `OPENAI_API_KEY` into the `.env` file**
+
+- Modify `src/meta_quest_knowledge/config/agents.yaml` to define your agents
+- Modify `src/meta_quest_knowledge/config/tasks.yaml` to define your tasks
+- Modify `src/meta_quest_knowledge/crew.py` to add your own logic, tools and specific args
+- Modify `src/meta_quest_knowledge/main.py` to add custom inputs for your agents and tasks
+
+## Running the Project
+
+To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
+
+```bash
+$ crewai run
+```
+
+This command initializes the Crew, assembling the agents and assigning them tasks as defined in your configuration.
+
+## Additional Knowledge Sources
+
+Explore [Knowledge](https://docs.crewai.com/concepts/knowledge) documentation for more information on how to use different knowledge sources.
+You can select from multiple different knowledge sources such as:
+* Text files
+* PDFs
+* CSV & Excel files
+* JSON files
+* Sources supported by [docling](https://github.com/DS4SD/docling)

meta_quest_knowledge/knowledge/user_preference.txt

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+User name is John Doe.
+User is an AI Engineer.
+User is interested in AI Agents.
+User is based in San Francisco, California.

meta_quest_knowledge/pyproject.toml

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+[project]
+name = "Meta Quest Knowledge"
+version = "0.1.0"
+description = "Knowledge Example using crewAI"
+authors = [{ name = "Mike Plachta", email = "mike@crewai.com" }]
+requires-python = ">=3.10,<=3.13"
+dependencies = [
+    "crewai[tools]>=0.95.0,<1.0.0",
+]
+
+[project.scripts]
+meta_quest_knowledge = "meta_quest_knowledge.main:run"
+run_crew = "meta_quest_knowledge.main:run"
+train = "meta_quest_knowledge.main:train"
+replay = "meta_quest_knowledge.main:replay"
+test = "meta_quest_knowledge.main:test"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"

meta_quest_knowledge/src/meta_quest_knowledge/config/agents.yaml

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+meta_quest_expert:
+  role: >
+    Meta Quest Expert
+  goal: >
+    Provide the best possible answers to questions about Meta Quest
+  backstory: >
+    You're a seasoned expert in the world of Meta Quest. You're known for your
+    ability to provide the best possible answers to questions about this
+    cutting-edge technology, ensuring that your audience is well-informed and
+    satisfied with the latest advancements in the field.

meta_quest_knowledge/src/meta_quest_knowledge/config/tasks.yaml

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+answer_question_task:
+  description: >
+    Answer the user question with the most relevant information from the context and available knowledge sources.
+    Question: {question}
+
+    Do not answer questions that are not related to the context or knowledge sources.
+  expected_output: >
+    Best answer to the user question
+  agent: meta_quest_expert

meta_quest_knowledge/src/meta_quest_knowledge/crew.py

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+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
+
+# Knowledge sources
+pdf_source = PDFKnowledgeSource(
+    file_paths=["meta_quest_manual.pdf"]
+)
+
+@CrewBase
+class MetaQuestKnowledge():
+	"""MetaQuestKnowledge crew"""
+
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
+	@agent
+	def meta_quest_expert(self) -> Agent:
+		return Agent(
+			config=self.agents_config['meta_quest_expert'],
+			verbose=True
+		)
+
+	@task
+	def answer_question_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['answer_question_task'],
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the MetaQuestKnowledge crew"""
+
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+			knowledge_sources=[
+				pdf_source
+			]
+		)

meta_quest_knowledge/src/meta_quest_knowledge/main.py

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+#!/usr/bin/env python
+import sys
+import warnings
+
+from meta_quest_knowledge.crew import MetaQuestKnowledge
+
+warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
+
+# This main file is intended to be a way for you to run your
+# crew locally, so refrain from adding unnecessary logic into this file.
+# Replace with inputs you want to test with, it will automatically
+# interpolate any tasks and agents information
+
+def run():
+    """
+    Run the crew.
+    """
+    inputs = {
+        'question': 'How often should I take breaks?',
+    }
+    MetaQuestKnowledge().crew().kickoff(inputs=inputs)
+
+
+def train():
+    """
+    Train the crew for a given number of iterations.
+    """
+    inputs = {
+        'question': 'How often should I take breaks?',
+    }
+    try:
+        MetaQuestKnowledge().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while training the crew: {e}")
+
+def replay():
+    """
+    Replay the crew execution from a specific task.
+    """
+    try:
+        MetaQuestKnowledge().crew().replay(task_id=sys.argv[1])
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")
+
+def test():
+    """
+    Test the crew execution and returns the results.
+    """
+    inputs = {
+        'question': 'How often should I take breaks?',
+    }
+    try:
+        MetaQuestKnowledge().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")