diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 7ffcb2e9..d2702cbf 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -9,6 +9,7 @@ Examples examples/research_agent examples/validating_agent_output examples/advanced_output_handling + examples/customer_support examples/streaming_agent_output examples/event_driven autogen diff --git a/docs/source/examples/customer_support.rst b/docs/source/examples/customer_support.rst new file mode 100644 index 00000000..96b1157d --- /dev/null +++ b/docs/source/examples/customer_support.rst @@ -0,0 +1,73 @@ +Customer support chatbot with Ray Serve +======================================= + +This example demonstrates how to build a customer support chatbot using MotleyCrew and Ray Serve. +The chatbot is designed to answer customer queries based on a database of past issues and their resolutions. + +The code for this example can be found `here `_. +Also, see the `blog post `_ about this app. + +Key Components +-------------- + +1. Issue Database + + - Stores information about past issues and their solutions in a tree structure + - Intermediate nodes represent issue categories + - Leaf nodes represent individual issues + - Uses Kuzu to store and query the issue tree through our OGM (see :doc:`../knowledge_graph` for more details) + +2. AI Support Agent + + - Attempts to resolve customer issues based on past solutions + - Navigates the issue tree to find relevant information + - Can ask clarifying questions to the customer + - Proposes solutions or escalates to a human agent if necessary + +3. Agent Tools + + - IssueTreeViewTool: Allows the agent to navigate the issue tree + - CustomerChatTool: Enables the agent to ask additional questions to the customer + - ResolveIssueTool: Used to submit a solution or escalate to a human agent + +4. Ray Serve Deployment + + - Exposes the chatbot as an API + - Allows multiple customers to connect simultaneously + - Uses WebSockets over FastAPI for communication + +Implementation Details +---------------------- + +The support agent is implemented using the "every response is a tool call" design. +The agent loop can only end with a ResolveIssueTool call or when a constraint (e.g., number of iterations) is reached. +This is achieved by making the ResolveIssueTool an output handler. + +The Ray Serve deployment is configured using a simple decorator: + +.. code-block:: python + + @serve.deployment(num_replicas=3, ray_actor_options={"num_cpus": 1, "num_gpus": 0}) + class SupportAgentDeployment: + ... + +This setup allows for easy scaling and supports multiple simultaneous sessions balanced between replicas. + +Running the Example +------------------- + +The project includes sample issue data that can be used to populate the issue tree. + +To run this example: + +.. code-block:: bash + + git clone https://github.com/ShoggothAI/motleycrew.git + cd motleycrew + pip install -r requirements.txt + + python -m motleycrew.applications.customer_support.issue_tree # populate the issue tree + ray start --head + python -m motleycrew.applications.customer_support.ray_serve_app + +This example showcases the flexibility of MotleyCrew for building agent-based applications, allowing you to choose your preferred agent framework, orchestration model, and deployment solution. diff --git a/docs/source/examples/event_driven.nblink b/docs/source/examples/event_driven.nblink new file mode 100644 index 00000000..a2de2542 --- /dev/null +++ b/docs/source/examples/event_driven.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../examples/Event-driven orchestration for AI systems.ipynb" +} diff --git a/examples/old/delegation_crewai.py b/examples/old/delegation_crewai.py deleted file mode 100644 index 85be1f56..00000000 --- a/examples/old/delegation_crewai.py +++ /dev/null @@ -1,94 +0,0 @@ -import os -import sys -from pathlib import Path - -from dotenv import load_dotenv -from langchain_community.tools import DuckDuckGoSearchRun - -from motleycrew.agents.crewai import CrewAIMotleyAgent -from motleycrew.agents.langchain.legacy_react import LegacyReActMotleyAgent -from motleycrew.common import configure_logging -from motleycrew.tasks import SimpleTask - -WORKING_DIR = Path(os.path.realpath("..")) - -try: - from motleycrew import MotleyCrew -except ImportError: - # if we are running this from source - motleycrew_location = os.path.realpath(WORKING_DIR / "..") - sys.path.append(motleycrew_location) - - -def main(): - crew = MotleyCrew() - - search_tool = DuckDuckGoSearchRun() - - researcher = CrewAIMotleyAgent( - role="Senior Research Analyst", - goal="Uncover cutting-edge developments in AI and data science, doing web search if necessary", - backstory="""You work at a leading tech think tank. - Your expertise lies in identifying emerging trends. - You have a knack for dissecting complex data and presenting actionable insights.""", - description="Tool Research Analysis", - delegation=False, - verbose=True, - tools=[search_tool], - ) - - # You can give agents as tools to other agents - writer = LegacyReActMotleyAgent( - name="AI writer agent", - description="You are an experienced writer with a passion for technology.", - tools=[researcher], - verbose=True, - ) - - # Illustrator - - # Create tasks for your agents - - analysis_report_task = SimpleTask( - crew=crew, - name="produce comprehensive analysis report on AI advancements", - description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. - Identify key trends, breakthrough technologies, and potential industry impacts. - Your final answer MUST be a full analysis report""", - agent=researcher, - ) - - literature_summary_task = SimpleTask( - crew=crew, - name="provide a literature summary of recent papers on AI", - description="""Conduct a comprehensive literature review of the latest advancements in AI in 2024. - Identify key papers, researchers, and companies in the space. - Your final answer MUST be a full literature review with citations""", - agent=researcher, - ) - - blog_post_task = SimpleTask( - crew=crew, - name="produce blog post on AI advancements", - description="""Using the insights provided by a thorough web search, develop an engaging blog - post that highlights the most significant AI advancements. - Your post should be informative yet accessible, catering to a tech-savvy audience. - Make it sound cool, avoid complex words so it doesn't sound like AI. - Create a blog post of at least 4 paragraphs.""", - agent=writer, - ) - - # [analysis_report_task, literature_summary_task] >> blog_post_task - - # Get your crew to work! - result = crew.run() - - # Get the outputs of the task - print(blog_post_task.output) - return blog_post_task.output - - -if __name__ == "__main__": - configure_logging(verbose=True) - load_dotenv() - main() diff --git a/examples/old/image_generation_crewai.py b/examples/old/image_generation_crewai.py deleted file mode 100644 index ed922581..00000000 --- a/examples/old/image_generation_crewai.py +++ /dev/null @@ -1,62 +0,0 @@ -from dotenv import load_dotenv - -from motleycrew import MotleyCrew -from motleycrew.agents.crewai import CrewAIMotleyAgent -from motleycrew.common import configure_logging -from motleycrew.tasks import SimpleTask -from motleycrew.tools.image.dall_e import DallEImageGeneratorTool - - -def main(): - """Main function of running the example.""" - image_generator_tool = DallEImageGeneratorTool() - # For saving images locally use the line below - # image_generator_tool = DallEImageGeneratorTool(images_directory="images") - - illustrator = CrewAIMotleyAgent( - role="Illustrator", - goal="Create beautiful and insightful illustrations", - backstory="You are an expert in creating illustrations for all sorts of concepts and articles. " - "You do it by skillfully prompting a text-to-image model.\n" - "Your final answer MUST be the exact URL or filename of the illustration.", - verbose=True, - delegation=False, - tools=[image_generator_tool], - ) - - writer = CrewAIMotleyAgent( - role="Short stories writer", - goal="Write short stories for children", - backstory="You are an accomplished children's writer, known for your funny and interesting short stories.\n" - "Many parents around the world read your books to their children.", - verbose=True, - delegation=True, - tools=[illustrator], - ) - - # Create tasks for your agents - crew = MotleyCrew() - write_task = SimpleTask( - crew=crew, - name="write a short story about a cat", - description="Creatively write a short story of about 4 paragraphs " - "about a house cat that was smarter than its owners. \n" - "Write it in a cool and simple language, " - "making it intriguing yet suitable for children's comprehension.\n" - "You must include a fun illustration.\n" - "Your final answer MUST be the full story with the illustration URL or filename attached.", - agent=writer, - ) - - # Get your crew to work! - crew.run() - - print(write_task.output) - return write_task.output - - -if __name__ == "__main__": - configure_logging(verbose=True) - - load_dotenv() - main() diff --git a/examples/old/math_crewai.py b/examples/old/math_crewai.py deleted file mode 100644 index 0b18ade6..00000000 --- a/examples/old/math_crewai.py +++ /dev/null @@ -1,60 +0,0 @@ -from dotenv import load_dotenv - -from motleycrew import MotleyCrew -from motleycrew.agents.crewai import CrewAIMotleyAgent -from motleycrew.common import configure_logging -from motleycrew.tasks import SimpleTask -from motleycrew.tools.code import PythonREPLTool - - -def main(): - """Main function of running the example.""" - repl_tool = PythonREPLTool() - - # Define your agents with roles and goals - solver1 = CrewAIMotleyAgent( - role="High School Math Teacher", - goal="Generate great solutions to math problems", - backstory="""You are a high school math teacher with a passion for problem-solving. - To solve a math problem, you first reason about it, step by step, then generate the code to solve it exactly, - using sympy, then use the REPL tool to evaluate that code, and then - use the output to generate a human-readable solution.""", - verbose=True, - delegation=False, - tools=[repl_tool], - ) - - solver = solver1 - - problems = [ - "Problem: If $725x + 727y = 1500$ and $729x+ 731y = 1508$, " - "what are the values of $x$, $y$, and $x - y$ ?", - "Drei Personen erhalten zusammen Fr. 2450. Die erste Person erh\u00e4lt Fr. 70 mehr als " - "die zweite, aber Fr. 60 weniger als die dritte.\nWie viel erh\u00e4lt jede Person? ", - "Find all numbers $a$ for which the graph of $y=x^2+a$ and the graph of $y=ax$ intersect. " - "Express your answer in interval notation.", - ] - - # Create tasks for your agents - crew = MotleyCrew() - task = SimpleTask( - crew=crew, - name="solve math problem", - description=f"""Create a nice human-readable solution to the following problem: - {problems[1]} - IN THE SAME LANGUAGE AS THE PROBLEM""", - agent=solver, - ) - - # Get your crew to work! - crew.run() - - print(task.output) - return task.output - - -if __name__ == "__main__": - configure_logging(verbose=True) - - load_dotenv() - main() diff --git a/examples/old/research_agent.py b/examples/old/research_agent.py deleted file mode 100644 index 09cc2ee2..00000000 --- a/examples/old/research_agent.py +++ /dev/null @@ -1,65 +0,0 @@ -from pathlib import Path -import shutil -import os - -import kuzu -from dotenv import load_dotenv - -# This assumes you have a .env file in the examples folder, containing your OpenAI key -load_dotenv() - -WORKING_DIR = Path(os.path.realpath("..")) - -from motleycrew import MotleyCrew -from motleycrew.storage import MotleyKuzuGraphStore -from motleycrew.common import configure_logging -from motleycrew.applications.research_agent.question_task import QuestionTask -from motleycrew.applications.research_agent.answer_task import AnswerTask -from motleycrew.tools.simple_retriever_tool import SimpleRetrieverTool -from motleycrew.common import AsyncBackend -from motleycache import enable_cache - -configure_logging(verbose=True) -enable_cache() - -DATA_DIR = os.path.realpath(os.path.join(WORKING_DIR, "mahabharata/text/TinyTales")) -PERSIST_DIR = WORKING_DIR / "storage" -query_tool = SimpleRetrieverTool(DATA_DIR, PERSIST_DIR, return_strings_only=True) - -# Create a new graph store -shutil.rmtree(PERSIST_DIR / "kuzu_db", ignore_errors=True) -db = kuzu.Database(str(PERSIST_DIR / "kuzu_db")) -graph_store = MotleyKuzuGraphStore(db) - -crew = MotleyCrew(async_backend=AsyncBackend.ASYNCIO, graph_store=graph_store) - -QUESTION = "Why did Arjuna kill Karna, his half-brother?" -MAX_ITER = 10 -ANSWER_LENGTH = 200 - - -# We need to pass the crew to the Tasks so they have access to the graph store -# and the crew is aware of them - -# The question task is responsible for new question generation -question_task = QuestionTask( - crew=crew, - question=QUESTION, - query_tool=query_tool, - max_iter=MAX_ITER, - allow_async_units=True, -) - -# The answer task is responsible for rolling the answers up the tree -answer_task = AnswerTask(answer_length=ANSWER_LENGTH, crew=crew) - -# Only kick off the answer task once the question task is done -question_task >> answer_task - -# And now run the recipes -done_items = crew.run() - -# Print the final answer -question = done_items[-1].question -print(question.question) -print(question.answer) diff --git a/examples/old/single_llama_index.py b/examples/old/single_llama_index.py deleted file mode 100644 index f7704f89..00000000 --- a/examples/old/single_llama_index.py +++ /dev/null @@ -1,50 +0,0 @@ -from dotenv import load_dotenv -from langchain_community.tools import DuckDuckGoSearchRun - - -from motleycrew import MotleyCrew -from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent -from motleycrew.common import configure_logging -from motleycrew.tasks import SimpleTask -from motleycrew.common.exceptions import InvalidOutput -from motleycrew.common import AsyncBackend - -from langchain_core.tools import StructuredTool - - -def main(): - """Main function of running the example.""" - search_tool = DuckDuckGoSearchRun() - - - # TODO: add LlamaIndex native tools - researcher = ReActLlamaIndexMotleyAgent( - prompt_prefix="Your goal is to uncover cutting-edge developments in AI and data science", - tools=[search_tool], - verbose=True, - ) - - crew = MotleyCrew(async_backend=AsyncBackend.NONE) - - # Create tasks for your agents - task = SimpleTask( - crew=crew, - name="produce comprehensive analysis report on AI advancements", - description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. - Identify key trends, breakthrough technologies, and potential industry impacts. - Your final answer MUST be a full analysis report""", - agent=researcher, - ) - - # Get your crew to work! - crew.run() - - print(task.output) - return task.output - - -if __name__ == "__main__": - configure_logging(verbose=True) - - load_dotenv() - main() diff --git a/examples/research_agent/research_agent_main.py b/examples/research_agent/research_agent_main.py deleted file mode 100644 index d349812f..00000000 --- a/examples/research_agent/research_agent_main.py +++ /dev/null @@ -1,73 +0,0 @@ -import os -import platform -import shutil -from pathlib import Path - -import kuzu -from dotenv import load_dotenv - -from motleycrew import MotleyCrew -from motleycrew.applications.research_agent.answer_task import AnswerTask -from motleycrew.applications.research_agent.question_task import QuestionTask -from motleycrew.common import LLMFramework, configure_logging -from motleycrew.common.llms import init_llm -from motleycrew.storage import MotleyKuzuGraphStore -from motleycrew.tools.simple_retriever_tool import SimpleRetrieverTool - -WORKING_DIR = Path(__file__).parent -if "Dropbox" in WORKING_DIR.parts and platform.system() == "Windows": - # On Windows, kuzu has file locking issues with Dropbox - DB_PATH = os.path.realpath(os.path.expanduser("~") + "/Documents/research_db") -else: - DB_PATH = os.path.realpath(WORKING_DIR / "research_db") - -DATA_DIR = os.path.realpath(os.path.join(WORKING_DIR, "../../../mahabharata/text/TinyTales")) -PERSIST_DIR = WORKING_DIR / "storage" - -QUESTION = "Why did Arjuna kill Karna, his half-brother?" -MAX_ITER = 2 -ANSWER_LENGTH = 200 - - -def main(): - llm = init_llm( - llm_framework=LLMFramework.LANGCHAIN - ) # throughout this project, we use LangChain's LLM wrappers - - load_dotenv() - configure_logging(verbose=True) - - shutil.rmtree(DB_PATH) - - # You can pass any LlamaIndex embedding to the retriever tool, default is OpenAI's text-embedding-ada-002 - query_tool = SimpleRetrieverTool(DATA_DIR, PERSIST_DIR, return_strings_only=True) - - db = kuzu.Database(DB_PATH) - graph_store = MotleyKuzuGraphStore(db) - crew = MotleyCrew(graph_store=graph_store) - - question_task = QuestionTask( - crew=crew, - question=QUESTION, - query_tool=query_tool, - max_iter=MAX_ITER, - llm=llm, - ) - answer_task = AnswerTask(answer_length=ANSWER_LENGTH, crew=crew, llm=llm) - - question_task >> answer_task - - done_tasks = crew.run() - - final_answer = done_tasks[-1].question - - print("Question: ", final_answer.question) - print("Answer: ", final_answer.answer) - print() - print("To explore the graph:") - print(f"docker run -p 8000:8000 -v {DB_PATH}:/database --rm kuzudb/explorer:latest") - print("MATCH (A)-[r]->(B) RETURN *;") - - -if __name__ == "__main__": - main() diff --git a/poetry.lock b/poetry.lock index 39f449b7..fb72dee7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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