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Update Docs; Update examples to allow Azure OpenAI setup (#154)

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* Update Docs; Update examples to allow Azure OpenAI setup

* update
This commit is contained in:
Eric Zhu
2024-06-28 23:15:46 -07:00
committed by GitHub
parent 4093102e51
commit f42361f57d
26 changed files with 295 additions and 579 deletions
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44
python/examples/README.md
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@@ -8,6 +8,8 @@ This directory contains examples and demos of how to use AGNext.
- `patterns`: Contains examples that illustrate how multi-agent patterns can be implemented in AGNext.
- `demos`: Contains interactive demos that showcase applications that can be built using AGNext.
See [Running the examples](#running-the-examples) for instructions on how to run the examples.
## Core examples
We provide examples to illustrate the core concepts of AGNext: agents, runtime, and message passing.
@@ -28,13 +30,11 @@ We provide examples to illustrate how to use tools in AGNext:
We provide examples to illustrate how multi-agent patterns can be implemented in AGNext:
- [`coder_executor_pub_sub.py`](patterns/coder_executor_pub_sub.py): An example of how to create a coder-executor reflection pattern using broadcast communication. This example creates a plot of stock prices using the Yahoo Finance API.
- [`coder_reviewer_direct.py`](patterns/coder_reviewer_direct.py): An example of how to create a coder-reviewer reflection pattern using direct communication.
- [`coder_reviewer_pub_sub.py`](patterns/coder_reviewer_pub_sub.py): An example of how to create a coder-reviewer reflection pattern using broadcast communication.
- [`group_chat_pub_sub.py`](patterns/group_chat_pub_sub.py): An example of how to create a round-robin group chat among three agents using broadcast communication.
- [`mixture_of_agents_direct.py`](patterns/mixture_of_agents_direct.py): An example of how to create a [mixture of agents](https://github.com/togethercomputer/moa) using direct communication.
- [`mixture_of_agents_pub_sub.py`](patterns/mixture_of_agents_pub_sub.py): An example of how to create a [mixture of agents](https://github.com/togethercomputer/moa) using broadcast communication.
- [`multi_agent_debate_pub_sub.py`](patterns/multi_agent_debate_pub_sub.py): An example of how to create a [sparse multi-agent debate](https://arxiv.org/abs/2406.11776) pattern using broadcast communication.
- [`coder_executor.py`](patterns/coder_executor.py): An example of how to create a coder-executor reflection pattern. This example creates a plot of stock prices using the Yahoo Finance API.
- [`coder_reviewer.py`](patterns/coder_reviewer.py): An example of how to create a coder-reviewer reflection pattern.
- [`group_chat.py`](patterns/group_chat.py): An example of how to create a round-robin group chat among three agents.
- [`mixture_of_agents.py`](patterns/mixture_of_agents.py): An example of how to create a [mixture of agents](https://github.com/togethercomputer/moa).
- [`multi_agent_debate.py`](patterns/multi_agent_debate.py): An example of how to create a [sparse multi-agent debate](https://arxiv.org/abs/2406.11776) pattern.
## Demos
@@ -50,14 +50,40 @@ We provide interactive demos that showcase applications that can be built using
the group chat pattern.
- [`chest_game.py`](demos/chess_game.py): an example with two chess player agents that executes its own tools to demonstrate tool use and reflection on tool use.
## Running the examples and demos
## Running the examples
First, you need a shell with AGNext and the examples dependencies installed. To do this, run:
### Prerequisites
First, you need a shell with AGNext and the examples dependencies installed.
To do this, in the example directory, run:
```bash
hatch shell
```
Then, you need to set the `OPENAI_API_KEY` environment variable to your OpenAI API key.
```bash
export OPENAI_API_KEY=your_openai_api_key
```
For Azure OpenAI API, you need to set the following environment variables:
```bash
export AZURE_OPENAI_API_KEY=your_azure_openai_api_key
export AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint
```
By default, OpenAI API is used.
To use Azure OpenAI API, set the `OPENAI_API_TYPE`
environment variable to `azure`.
```bash
export OPENAI_API_TYPE=azure
```
### Running
To run an example, just run the corresponding Python script. For example:
```bash

31
python/examples/common/utils.py
View File

@@ -1,10 +1,14 @@
from typing import List, Optional, Union
import os
from typing import Any, List, Optional, Union
from agnext.components.models import (
AssistantMessage,
AzureOpenAIChatCompletionClient,
ChatCompletionClient,
FunctionExecutionResult,
FunctionExecutionResultMessage,
LLMMessage,
OpenAIChatCompletionClient,
UserMessage,
)
from typing_extensions import Literal
@@ -96,3 +100,28 @@ def convert_messages_to_llm_messages(
raise AssertionError("unreachable")
return result
def get_chat_completion_client_from_envs(**kwargs: Any) -> ChatCompletionClient:
# Check API type.
api_type = os.getenv("OPENAI_API_TYPE", "openai")
if api_type == "openai":
# Check API key.
api_key = os.getenv("OPENAI_API_KEY")
if api_key is None:
raise ValueError("OPENAI_API_KEY is not set")
kwargs["api_key"] = api_key
return OpenAIChatCompletionClient(**kwargs)
elif api_type == "azure":
# Check Azure API key.
azure_api_key = os.getenv("AZURE_OPENAI_API_KEY")
if azure_api_key is None:
raise ValueError("AZURE_OPENAI_API_KEY is not set")
kwargs["api_key"] = azure_api_key
# Check Azure API endpoint.
azure_api_endpoint = os.getenv("AZURE_OPENAI_API_ENDPOINT")
if azure_api_endpoint is None:
raise ValueError("AZURE_OPENAI_API_ENDPOINT is not set")
kwargs["azure_endpoint"] = azure_api_endpoint
return AzureOpenAIChatCompletionClient(**kwargs) # type: ignore
raise ValueError(f"Unknown API type: {api_type}")

10
python/examples/core/one_agent_direct.py
View File

@@ -6,18 +6,23 @@ chat completion model, and returns the response to the main function.
"""
import asyncio
import os
import sys
from dataclasses import dataclass
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler
from agnext.components.models import (
ChatCompletionClient,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class Message:
@@ -41,7 +46,8 @@ class ChatCompletionAgent(TypeRoutedAgent):
async def main() -> None:
runtime = SingleThreadedAgentRuntime()
agent = runtime.register_and_get(
"chat_agent", lambda: ChatCompletionAgent("Chat agent", OpenAIChatCompletionClient(model="gpt-3.5-turbo"))
"chat_agent",
lambda: ChatCompletionAgent("Chat agent", get_chat_completion_client_from_envs(model="gpt-3.5-turbo")),
)
# Send a message to the agent.

11
python/examples/core/two_agents_pub_sub.py
View File

@@ -11,6 +11,8 @@ and publishes the response.
"""
import asyncio
import os
import sys
from dataclasses import dataclass
from typing import List
@@ -20,12 +22,15 @@ from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class Message:
@@ -76,7 +81,7 @@ async def main() -> None:
"Jack",
lambda: ChatCompletionAgent(
description="Jack a comedian",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
system_messages=[
SystemMessage("You are a comedian likes to make jokes. " "When you are done talking, say 'TERMINATE'.")
],
@@ -87,7 +92,7 @@ async def main() -> None:
"Cathy",
lambda: ChatCompletionAgent(
description="Cathy a poet",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
system_messages=[
SystemMessage("You are a poet likes to write poems. " "When you are done talking, say 'TERMINATE'.")
],

10
python/examples/demos/chat_room.py
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@@ -8,7 +8,7 @@ import sys
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler
from agnext.components.memory import ChatMemory
from agnext.components.models import ChatCompletionClient, OpenAIChatCompletionClient, SystemMessage
from agnext.components.models import ChatCompletionClient, SystemMessage
from agnext.core import AgentRuntime, CancellationToken
sys.path.append(os.path.abspath(os.path.dirname(__file__)))
@@ -16,7 +16,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.memory import BufferedChatMemory
from common.types import Message, TextMessage
from common.utils import convert_messages_to_llm_messages
from common.utils import convert_messages_to_llm_messages, get_chat_completion_client_from_envs
from utils import TextualChatApp, TextualUserAgent, start_runtime
@@ -102,7 +102,7 @@ def chat_room(runtime: AgentRuntime, app: TextualChatApp) -> None:
description="Alice in the chat room.",
background_story="Alice is a software engineer who loves to code.",
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
),
)
bob = runtime.register_and_get_proxy(
@@ -112,7 +112,7 @@ def chat_room(runtime: AgentRuntime, app: TextualChatApp) -> None:
description="Bob in the chat room.",
background_story="Bob is a data scientist who loves to analyze data.",
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
),
)
charlie = runtime.register_and_get_proxy(
@@ -122,7 +122,7 @@ def chat_room(runtime: AgentRuntime, app: TextualChatApp) -> None:
description="Charlie in the chat room.",
background_story="Charlie is a designer who loves to create art.",
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
),
)
app.welcoming_notice = f"""Welcome to the chat room demo with the following participants:

7
python/examples/demos/chess_game.py
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@@ -10,7 +10,7 @@ import sys
from typing import Annotated, Literal
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import OpenAIChatCompletionClient, SystemMessage
from agnext.components.models import SystemMessage
from agnext.components.tools import FunctionTool
from agnext.core import AgentRuntime
from chess import BLACK, SQUARE_NAMES, WHITE, Board, Move
@@ -22,6 +22,7 @@ from common.agents._chat_completion_agent import ChatCompletionAgent
from common.memory import BufferedChatMemory
from common.patterns._group_chat_manager import GroupChatManager
from common.types import TextMessage
from common.utils import get_chat_completion_client_from_envs
def validate_turn(board: Board, player: Literal["white", "black"]) -> None:
@@ -168,7 +169,7 @@ def chess_game(runtime: AgentRuntime) -> None: # type: ignore
),
],
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
tools=black_tools,
),
)
@@ -185,7 +186,7 @@ def chess_game(runtime: AgentRuntime) -> None: # type: ignore
),
],
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
tools=white_tools,
),
)

7
python/examples/demos/illustrator_critics.py
View File

@@ -6,7 +6,7 @@ import sys
import openai
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import OpenAIChatCompletionClient, SystemMessage
from agnext.components.models import SystemMessage
from agnext.core import AgentRuntime
sys.path.append(os.path.abspath(os.path.dirname(__file__)))
@@ -15,6 +15,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.agents import ChatCompletionAgent, ImageGenerationAgent
from common.memory import BufferedChatMemory
from common.patterns._group_chat_manager import GroupChatManager
from common.utils import get_chat_completion_client_from_envs
from utils import TextualChatApp, TextualUserAgent, start_runtime
@@ -42,7 +43,7 @@ def illustrator_critics(runtime: AgentRuntime, app: TextualChatApp) -> None:
),
],
memory=BufferedChatMemory(buffer_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo", max_tokens=500),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo", max_tokens=500),
),
)
illustrator = runtime.register_and_get_proxy(
@@ -70,7 +71,7 @@ def illustrator_critics(runtime: AgentRuntime, app: TextualChatApp) -> None:
),
],
memory=BufferedChatMemory(buffer_size=2),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
),
)
runtime.register(

13
python/examples/demos/software_consultancy.py
View File

@@ -17,7 +17,7 @@ import aiofiles
import aiohttp
import openai
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import OpenAIChatCompletionClient, SystemMessage
from agnext.components.models import SystemMessage
from agnext.components.tools import FunctionTool
from agnext.core import AgentRuntime
from markdownify import markdownify # type: ignore
@@ -30,6 +30,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.agents import ChatCompletionAgent
from common.memory import HeadAndTailChatMemory
from common.patterns._group_chat_manager import GroupChatManager
from common.utils import get_chat_completion_client_from_envs
from utils import TextualChatApp, TextualUserAgent, start_runtime
@@ -127,7 +128,7 @@ def software_consultancy(runtime: AgentRuntime, app: TextualChatApp) -> None: #
"Be concise and deliver now."
)
],
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
memory=HeadAndTailChatMemory(head_size=1, tail_size=10),
tools=[
FunctionTool(
@@ -167,7 +168,7 @@ def software_consultancy(runtime: AgentRuntime, app: TextualChatApp) -> None: #
"Be VERY concise."
)
],
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
memory=HeadAndTailChatMemory(head_size=1, tail_size=10),
tools=[
FunctionTool(
@@ -195,7 +196,7 @@ def software_consultancy(runtime: AgentRuntime, app: TextualChatApp) -> None: #
"Be concise and deliver now."
)
],
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
memory=HeadAndTailChatMemory(head_size=1, tail_size=10),
tools=[
FunctionTool(
@@ -227,7 +228,7 @@ def software_consultancy(runtime: AgentRuntime, app: TextualChatApp) -> None: #
"Be concise and deliver now."
)
],
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
memory=HeadAndTailChatMemory(head_size=1, tail_size=10),
tools=[
FunctionTool(
@@ -244,7 +245,7 @@ def software_consultancy(runtime: AgentRuntime, app: TextualChatApp) -> None: #
lambda: GroupChatManager(
description="A group chat manager.",
memory=HeadAndTailChatMemory(head_size=1, tail_size=10),
model_client=OpenAIChatCompletionClient(model="gpt-4-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo"),
participants=[developer, product_manager, ux_designer, illustrator, user_agent],
),
)

9
python/examples/patterns/coder_executor_pub_sub.py → python/examples/patterns/coder_executor.py
View File

@@ -13,7 +13,9 @@ otherwise, it generates a new code block and publishes a code execution task mes
"""
import asyncio
import os
import re
import sys
import uuid
from dataclasses import dataclass
from typing import Dict, List
@@ -25,12 +27,15 @@ from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class TaskMessage:
@@ -175,7 +180,7 @@ async def main(task: str, temp_dir: str) -> None:
runtime = SingleThreadedAgentRuntime()
# Register the agents.
runtime.register("coder", lambda: Coder(model_client=OpenAIChatCompletionClient(model="gpt-4-turbo")))
runtime.register("coder", lambda: Coder(model_client=get_chat_completion_client_from_envs(model="gpt-4-turbo")))
runtime.register("executor", lambda: Executor(executor=LocalCommandLineCodeExecutor(work_dir=temp_dir)))
# Publish the task message.

11
python/examples/patterns/coder_reviewer_pub_sub.py → python/examples/patterns/coder_reviewer.py
View File

@@ -13,7 +13,9 @@ a new code block and publishes a code review task message.
import asyncio
import json
import os
import re
import sys
import uuid
from dataclasses import dataclass
from typing import Dict, List, Union
@@ -24,12 +26,15 @@ from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class CodeWritingTask:
@@ -250,14 +255,14 @@ async def main() -> None:
"ReviewerAgent",
lambda: ReviewerAgent(
description="Code Reviewer",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
),
)
runtime.register(
"CoderAgent",
lambda: CoderAgent(
description="Coder",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
),
)
await runtime.publish_message(

249
python/examples/patterns/coder_reviewer_direct.py
View File

@@ -1,249 +0,0 @@
"""
This example shows how to use direct messaging to implement
a simple interaction between a coder and a reviewer agent.
1. The coder agent receives a code writing task message, generates a code block,
and sends a code review task message to the reviewer agent.
2. The reviewer agent receives the code review task message, reviews the code block,
and sends a code review result message to the coder agent.
3. The coder agent receives the code review result message, depending on the result:
if the code is approved, it sends a code writing result message; otherwise, it generates
a new code block and sends a code review task message.
4. The process continues until the coder agent receives an approved code review result message.
5. The main function prints the code writing result.
"""
import asyncio
import json
import re
from dataclasses import dataclass
from typing import List, Union
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler
from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import AgentId, CancellationToken
@dataclass
class CodeWritingTask:
task: str
@dataclass
class CodeWritingResult:
task: str
code: str
review: str
@dataclass
class CodeReviewTask:
code_writing_task: str
code_writing_scratchpad: str
code: str
@dataclass
class CodeReviewResult:
review: str
approved: bool
class ReviewerAgent(TypeRoutedAgent):
"""An agent that performs code review tasks."""
def __init__(
self,
description: str,
model_client: ChatCompletionClient,
) -> None:
super().__init__(description)
self._system_messages = [
SystemMessage(
content="""You are a code reviewer. You focus on correctness, efficiency and safety of the code.
Respond using the following JSON format:
{
"correctness": "<Your comments>",
"efficiency": "<Your comments>",
"safety": "<Your comments>",
"approval": "<APPROVE or REVISE>",
"suggested_changes": "<Your comments>"
}
""",
)
]
self._model_client = model_client
@message_handler
async def handle_code_review_task(
self, message: CodeReviewTask, cancellation_token: CancellationToken
) -> CodeReviewResult:
# Format the prompt for the code review.
prompt = f"""The problem statement is: {message.code_writing_task}
The code is:
```
{message.code}
```
Please review the code and provide feedback.
"""
# Generate a response using the chat completion API.
response = await self._model_client.create(
self._system_messages + [UserMessage(content=prompt, source=self.metadata["name"])]
)
assert isinstance(response.content, str)
# TODO: use structured generation library e.g. guidance to ensure the response is in the expected format.
# Parse the response JSON.
review = json.loads(response.content)
# Construct the review text.
review_text = "Code review:\n" + "\n".join([f"{k}: {v}" for k, v in review.items()])
approved = review["approval"].lower().strip() == "approve"
# Return the review result.
return CodeReviewResult(
review=review_text,
approved=approved,
)
class CoderAgent(TypeRoutedAgent):
"""An agent that performs code writing tasks."""
def __init__(
self,
description: str,
model_client: ChatCompletionClient,
reviewer: AgentId,
) -> None:
super().__init__(
description,
)
self._system_messages = [
SystemMessage(
content="""You are a proficient coder. You write code to solve problems.
Work with the reviewer to improve your code.
Always put all finished code in a single Markdown code block.
For example:
```python
def hello_world():
print("Hello, World!")
```
Respond using the following format:
Thoughts: <Your comments>
Code: <Your code>
""",
)
]
self._model_client = model_client
self._reviewer = reviewer
@message_handler
async def handle_code_writing_task(
self,
message: CodeWritingTask,
cancellation_token: CancellationToken,
) -> CodeWritingResult:
# Store the messages in a temporary memory for this request only.
memory: List[CodeWritingTask | CodeReviewTask | CodeReviewResult] = []
memory.append(message)
# Keep generating responses until the code is approved.
while not (isinstance(memory[-1], CodeReviewResult) and memory[-1].approved):
# Create a list of LLM messages to send to the model.
messages: List[LLMMessage] = [*self._system_messages]
for m in memory:
if isinstance(m, CodeReviewResult):
messages.append(UserMessage(content=m.review, source="Reviewer"))
elif isinstance(m, CodeReviewTask):
messages.append(AssistantMessage(content=m.code_writing_scratchpad, source="Coder"))
elif isinstance(m, CodeWritingTask):
messages.append(UserMessage(content=m.task, source="User"))
else:
raise ValueError(f"Unexpected message type: {m}")
# Generate a revision using the chat completion API.
response = await self._model_client.create(messages)
assert isinstance(response.content, str)
# Extract the code block from the response.
code_block = self._extract_code_block(response.content)
if code_block is None:
raise ValueError("Code block not found.")
# Create a code review task.
code_review_task = CodeReviewTask(
code_writing_task=message.task,
code_writing_scratchpad=response.content,
code=code_block,
)
# Store the code review task in the session memory.
memory.append(code_review_task)
# Send the code review task to the reviewer.
result = await self.send_message(code_review_task, self._reviewer)
# Store the review result in the session memory.
memory.append(await result)
# Obtain the request from previous messages.
review_request = next(m for m in reversed(memory) if isinstance(m, CodeReviewTask))
assert review_request is not None
# Publish the code writing result.
return CodeWritingResult(
task=message.task,
code=review_request.code,
review=memory[-1].review,
)
def _extract_code_block(self, markdown_text: str) -> Union[str, None]:
pattern = r"```(\w+)\n(.*?)\n```"
# Search for the pattern in the markdown text
match = re.search(pattern, markdown_text, re.DOTALL)
# Extract the language and code block if a match is found
if match:
return match.group(2)
return None
async def main() -> None:
runtime = SingleThreadedAgentRuntime()
reviewer = runtime.register_and_get(
"ReviewerAgent",
lambda: ReviewerAgent(
description="Code Reviewer",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
),
)
coder = runtime.register_and_get(
"CoderAgent",
lambda: CoderAgent(
description="Coder",
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
reviewer=reviewer,
),
)
result = await runtime.send_message(
message=CodeWritingTask(
task="Write a function to find the directory with the largest number of files using multi-processing."
),
recipient=coder,
)
while not result.done():
await runtime.process_next()
code_writing_result = result.result()
assert isinstance(code_writing_result, CodeWritingResult)
print("Code Writing Result:")
print("-" * 80)
print(f"Task:\n{code_writing_result.task}")
print("-" * 80)
print(f"Code:\n{code_writing_result.code}")
print("-" * 80)
print(f"Review:\n{code_writing_result.review}")
if __name__ == "__main__":
import logging
logging.basicConfig(level=logging.WARNING)
logging.getLogger("agnext").setLevel(logging.DEBUG)
asyncio.run(main())

13
python/examples/patterns/group_chat_pub_sub.py → python/examples/patterns/group_chat.py
View File

@@ -12,6 +12,8 @@ to the last message in the memory and publishes the response.
"""
import asyncio
import os
import sys
from dataclasses import dataclass
from typing import List
@@ -21,12 +23,15 @@ from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import AgentId, CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class Message:
@@ -113,7 +118,7 @@ async def main() -> None:
lambda: GroupChatParticipant(
description="A data scientist",
system_messages=[SystemMessage("You are a data scientist.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
),
)
agent2 = runtime.register_and_get(
@@ -121,7 +126,7 @@ async def main() -> None:
lambda: GroupChatParticipant(
description="An engineer",
system_messages=[SystemMessage("You are an engineer.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
),
)
agent3 = runtime.register_and_get(
@@ -129,7 +134,7 @@ async def main() -> None:
lambda: GroupChatParticipant(
description="An artist",
system_messages=[SystemMessage("You are an artist.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
),
)

16
python/examples/patterns/mixture_of_agents_pub_sub.py → python/examples/patterns/mixture_of_agents.py
View File

@@ -8,15 +8,21 @@ The reference agents handle each task independently and return the results to th
"""
import asyncio
import os
import sys
import uuid
from dataclasses import dataclass
from typing import Dict, List
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler
from agnext.components.models import ChatCompletionClient, OpenAIChatCompletionClient, SystemMessage, UserMessage
from agnext.components.models import ChatCompletionClient, SystemMessage, UserMessage
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class ReferenceAgentTask:
@@ -111,7 +117,7 @@ async def main() -> None:
lambda: ReferenceAgent(
description="Reference Agent 1",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=0.1),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo", temperature=0.1),
),
)
runtime.register(
@@ -119,7 +125,7 @@ async def main() -> None:
lambda: ReferenceAgent(
description="Reference Agent 2",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=0.5),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo", temperature=0.5),
),
)
runtime.register(
@@ -127,7 +133,7 @@ async def main() -> None:
lambda: ReferenceAgent(
description="Reference Agent 3",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=1.0),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo", temperature=1.0),
),
)
runtime.register(
@@ -139,7 +145,7 @@ async def main() -> None:
"...synthesize these responses into a single, high-quality response... Responses from models:"
)
],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
num_references=3,
),
)

146
python/examples/patterns/mixture_of_agents_direct.py
View File

@@ -1,146 +0,0 @@
"""
This example demonstrates the mixture of agents implemented using direct
messaging and async gathering of results.
Mixture of agents: https://github.com/togethercomputer/moa
The example consists of two types of agents: reference agents and an aggregator agent.
The aggregator agent distributes tasks to reference agents and aggregates the results.
The reference agents handle each task independently and return the results to the aggregator agent.
"""
import asyncio
from dataclasses import dataclass
from typing import List
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler
from agnext.components.models import ChatCompletionClient, OpenAIChatCompletionClient, SystemMessage, UserMessage
from agnext.core import AgentId, CancellationToken
@dataclass
class ReferenceAgentTask:
task: str
@dataclass
class ReferenceAgentTaskResult:
result: str
@dataclass
class AggregatorTask:
task: str
@dataclass
class AggregatorTaskResult:
result: str
class ReferenceAgent(TypeRoutedAgent):
"""The reference agent that handles each task independently."""
def __init__(
self,
description: str,
system_messages: List[SystemMessage],
model_client: ChatCompletionClient,
) -> None:
super().__init__(description)
self._system_messages = system_messages
self._model_client = model_client
@message_handler
async def handle_task(
self, message: ReferenceAgentTask, cancellation_token: CancellationToken
) -> ReferenceAgentTaskResult:
"""Handle a task message. This method sends the task to the model and respond with the result."""
task_message = UserMessage(content=message.task, source=self.metadata["name"])
response = await self._model_client.create(self._system_messages + [task_message])
assert isinstance(response.content, str)
return ReferenceAgentTaskResult(result=response.content)
class AggregatorAgent(TypeRoutedAgent):
"""The aggregator agent that distribute tasks to reference agents and aggregates the results."""
def __init__(
self,
description: str,
system_messages: List[SystemMessage],
model_client: ChatCompletionClient,
references: List[AgentId],
) -> None:
super().__init__(description)
self._system_messages = system_messages
self._model_client = model_client
self._references = references
@message_handler
async def handle_task(self, message: AggregatorTask, cancellation_token: CancellationToken) -> AggregatorTaskResult:
"""Handle a task message. This method sends the task to the reference agents
and aggregates the results."""
ref_task = ReferenceAgentTask(task=message.task)
results: List[ReferenceAgentTaskResult] = await asyncio.gather(
*[await self.send_message(ref_task, ref) for ref in self._references]
)
combined_result = "\n\n".join([r.result for r in results])
response = await self._model_client.create(
self._system_messages + [UserMessage(content=combined_result, source=self.metadata["name"])]
)
assert isinstance(response.content, str)
return AggregatorTaskResult(result=response.content)
async def main() -> None:
runtime = SingleThreadedAgentRuntime()
ref1 = runtime.register_and_get(
"ReferenceAgent1",
lambda: ReferenceAgent(
description="Reference Agent 1",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=0.1),
),
)
ref2 = runtime.register_and_get(
"ReferenceAgent2",
lambda: ReferenceAgent(
description="Reference Agent 2",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=0.5),
),
)
ref3 = runtime.register_and_get(
"ReferenceAgent3",
lambda: ReferenceAgent(
description="Reference Agent 3",
system_messages=[SystemMessage("You are a helpful assistant that can answer questions.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo", temperature=1.0),
),
)
agg = runtime.register_and_get(
"AggregatorAgent",
lambda: AggregatorAgent(
description="Aggregator Agent",
system_messages=[
SystemMessage(
"...synthesize these responses into a single, high-quality response... Responses from models:"
)
],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
references=[ref1, ref2, ref3],
),
)
result = await runtime.send_message(AggregatorTask(task="What are something fun to do in SF?"), agg)
while result.done() is False:
await runtime.process_next()
print(result.result())
if __name__ == "__main__":
import logging
logging.basicConfig(level=logging.WARNING)
logging.getLogger("agnext").setLevel(logging.DEBUG)
asyncio.run(main())

15
python/examples/patterns/multi_agent_debate_pub_sub.py → python/examples/patterns/multi_agent_debate.py
View File

@@ -32,7 +32,9 @@ to sample a random number of neighbors' responses to use.
import asyncio
import logging
import os
import re
import sys
import uuid
from dataclasses import dataclass
from typing import Dict, List
@@ -43,12 +45,15 @@ from agnext.components.models import (
AssistantMessage,
ChatCompletionClient,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
@@ -209,7 +214,7 @@ async def main(question: str) -> None:
runtime.register(
"MathSolver1",
lambda: MathSolver(
OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
neighbor_names=["MathSolver2", "MathSolver4"],
max_round=3,
),
@@ -217,7 +222,7 @@ async def main(question: str) -> None:
runtime.register(
"MathSolver2",
lambda: MathSolver(
OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
neighbor_names=["MathSolver1", "MathSolver3"],
max_round=3,
),
@@ -225,7 +230,7 @@ async def main(question: str) -> None:
runtime.register(
"MathSolver3",
lambda: MathSolver(
OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
neighbor_names=["MathSolver2", "MathSolver4"],
max_round=3,
),
@@ -233,7 +238,7 @@ async def main(question: str) -> None:
runtime.register(
"MathSolver4",
lambda: MathSolver(
OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
neighbor_names=["MathSolver1", "MathSolver3"],
max_round=3,
),

9
python/examples/tool-use/coding_one_agent_direct.py
View File

@@ -12,6 +12,8 @@ list of function calls.
import asyncio
import json
import os
import sys
from dataclasses import dataclass
from typing import List
@@ -24,13 +26,16 @@ from agnext.components.models import (
FunctionExecutionResult,
FunctionExecutionResultMessage,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.components.tools import PythonCodeExecutionTool, Tool
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class ToolExecutionTask:
@@ -130,7 +135,7 @@ async def main() -> None:
lambda: ToolEnabledAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
tools=tools,
),
)

9
python/examples/tool-use/coding_two_agent_pub_sub.py
View File

@@ -13,6 +13,8 @@ the results back to the tool use agent.
import asyncio
import json
import os
import sys
import uuid
from dataclasses import dataclass
from typing import Dict, List
@@ -26,13 +28,16 @@ from agnext.components.models import (
FunctionExecutionResult,
FunctionExecutionResultMessage,
LLMMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.components.tools import PythonCodeExecutionTool, Tool
from agnext.core import CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from common.utils import get_chat_completion_client_from_envs
@dataclass
class ToolExecutionTask:
@@ -192,7 +197,7 @@ async def main() -> None:
lambda: ToolUseAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
tools=tools,
),
)

5
python/examples/tool-use/custom_function_tool_one_agent_direct.py
View File

@@ -10,15 +10,16 @@ import sys
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import (
OpenAIChatCompletionClient,
SystemMessage,
)
from agnext.components.tools import FunctionTool
from typing_extensions import Annotated
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from coding_one_agent_direct import AIResponse, ToolEnabledAgent, UserRequest
from common.utils import get_chat_completion_client_from_envs
async def get_stock_price(ticker: str, date: Annotated[str, "The date in YYYY/MM/DD format."]) -> float:
@@ -36,7 +37,7 @@ async def main() -> None:
lambda: ToolEnabledAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=OpenAIChatCompletionClient(model="gpt-3.5-turbo"),
model_client=get_chat_completion_client_from_envs(model="gpt-3.5-turbo"),
tools=[
# Define a tool that gets the stock price.
FunctionTool(