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refactor(agent): Agent modular refactoring (#1487)

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This commit is contained in:
Fangyin Cheng
2024-05-07 09:45:26 +08:00
committed by GitHub
parent 2a418f91e8
commit 863b5404dd
86 changed files with 4513 additions and 967 deletions
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33
docs/docs/agents/cookbook/calculator_with_agents.md
View File

@@ -7,7 +7,7 @@ In this example, we will show you how to use an agent as your calculator.
Install the required packages by running the following command:
```bash
pip install "dbgpt[agent]>=0.5.4rc0" -U
pip install "dbgpt[agent]>=0.5.6rc1" -U
pip install openai
```
@@ -18,36 +18,39 @@ Create a new Python file and add the following code:
```python
import asyncio
from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent
from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent
from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent
from dbgpt.model.proxy import OpenAILLMClient
async def main():
llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo")
context: AgentContext = AgentContext(conv_id="test123")
default_memory: GptsMemory = GptsMemory()
# Create an agent memory, default memory is ShortTermMemory
agent_memory: AgentMemory = AgentMemory()
# Create a code assistant agent
coder = (
await CodeAssistantAgent()
.bind(context)
.bind(LLMConfig(llm_client=llm_client))
.bind(default_memory)
.bind(agent_memory)
.build()
)
# Create a user proxy agent
user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()
user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build()
# Initiate a chat with the user proxy agent
await user_proxy.initiate_chat(
recipient=coder,
reviewer=user_proxy,
message="calculate the result of 321 * 123"
message="calculate the result of 321 * 123",
)
# Obtain conversation history messages between agents
print(await default_memory.one_chat_completions("test123"))
print(await agent_memory.gpts_memory.one_chat_completions("test123"))
if __name__ == "__main__":
asyncio.run(main())
```
@@ -55,6 +58,12 @@ if __name__ == "__main__":
You will see the following output:
````bash
Prompt manager is not available.
Prompt manager is not available.
Prompt manager is not available.
Prompt manager is not available.
Prompt manager is not available.
--------------------------------------------------------------------------------
User (to Turing)-[]:
@@ -62,7 +71,8 @@ User (to Turing)-[]:
--------------------------------------------------------------------------------
un_stream ai response: ```python
# Calculate the result of 321 * 123
# filename: calculate_multiplication.py
result = 321 * 123
print(result)
```
@@ -74,7 +84,7 @@ un_stream ai response: True
--------------------------------------------------------------------------------
Turing (to User)-[gpt-3.5-turbo]:
"```python\n# Calculate the result of 321 * 123\nresult = 321 * 123\nprint(result)\n```"
"```python\n# filename: calculate_multiplication.py\n\nresult = 321 * 123\nprint(result)\n```"
>>>>>>>>Turing Review info:
Pass(None)
>>>>>>>>Turing Action report:
@@ -85,7 +95,6 @@ execution succeeded,
--------------------------------------------------------------------------------
```agent-plans
[{"name": "calculate the result of 321 * 123", "num": 1, "status": "complete", "agent": "Human", "markdown": "```agent-messages\n[{\"sender\": \"CodeEngineer\", \"receiver\": \"Human\", \"model\": \"gpt-3.5-turbo\", \"markdown\": \"```vis-code\\n{\\\"exit_success\\\": true, \\\"language\\\": \\\"python\\\", \\\"code\\\": [[\\\"python\\\", \\\"# Calculate the result of 321 * 123\\\\nresult = 321 * 123\\\\nprint(result)\\\"]], \\\"log\\\": \\\"\\\\n39483\\\\n\\\"}\\n```\"}]\n```"}]
[{"name": "calculate the result of 321 * 123", "num": 1, "status": "complete", "agent": "Human", "markdown": "```agent-messages\n[{\"sender\": \"CodeEngineer\", \"receiver\": \"Human\", \"model\": \"gpt-3.5-turbo\", \"markdown\": \"```vis-code\\n{\\\"exit_success\\\": true, \\\"language\\\": \\\"python\\\", \\\"code\\\": [[\\\"python\\\", \\\"# filename: calculate_multiplication.py\\\\n\\\\nresult = 321 * 123\\\\nprint(result)\\\"]], \\\"log\\\": \\\"\\\\n39483\\\\n\\\"}\\n```\"}]\n```"}]
```
(dbgpt-agents-py3.11) (base) ➜ dbgpt-agents
````

278
docs/docs/agents/custom_agents.md
View File

@@ -10,7 +10,7 @@ summarizer.
Install the required packages by running the following command:
```bash
pip install "dbgpt[agent]>=0.5.4rc0" -U
pip install "dbgpt[agent]>=0.5.6rc1" -U
pip install openai
```
@@ -35,23 +35,25 @@ Before designing each Agent, it is necessary to define its role, identity, and
functional role. The specific definitions are as follows:
```python
from dbgpt.agent import ConversableAgent
from dbgpt.agent import ConversableAgent, ProfileConfig
class MySummarizerAgent(ConversableAgent):
# The name of the agent
name = "Aristotle"
# The profile of the agent
profile: str = "Summarizer"
# The core functional goals of the agent tell LLM what it can do with it.
goal: str = (
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
)
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc: str = (
"You can summarize provided text content according to user's questions"
" and output the summarization."
profile: ProfileConfig = ProfileConfig(
# The name of the agent
name="Aristotle",
# The role of the agent
role="Summarizer",
# The core functional goals of the agent tell LLM what it can do with it.
goal=(
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
),
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc=(
"You can summarize provided text content according to user's questions"
" and output the summarization."
),
)
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -70,39 +72,41 @@ be bound if there are some special requirements). which mainly includes:
So, we can define the constraints of the agent's prompt as follows:
```python
from dbgpt.agent import ConversableAgent
from dbgpt.agent import ConversableAgent, ProfileConfig
class MySummarizerAgent(ConversableAgent):
# The name of the agent
name = "Aristotle"
# The profile of the agent
profile: str = "Summarizer"
# The core functional goals of the agent tell LLM what it can do with it.
goal: str = (
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
)
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc: str = (
"You can summarize provided text content according to user's questions"
" and output the summarization."
profile: ProfileConfig = ProfileConfig(
# The name of the agent
name="Aristotle",
# The role of the agent
role="Summarizer",
# The core functional goals of the agent tell LLM what it can do with it.
goal=(
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
),
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc=(
"You can summarize provided text content according to user's questions"
" and output the summarization."
),
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {{ param_name }}.
constraints=[
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{{ not_related_message }}'!!.",
]
)
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {param_name}.
constraints: list[str] = [
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{not_related_message}'!!.",
]
def __init__(self, **kwargs):
super().__init__(**kwargs)
```
@@ -114,50 +118,52 @@ to assemble the values, and the following interface (`_init_reply_message`) need
overloaded and implemented:
```python
from dbgpt.agent import ConversableAgent, AgentMessage
from dbgpt.agent import AgentMessage, ConversableAgent, ProfileConfig
NOT_RELATED_MESSAGE = "Did not find the information you want."
class MySummarizerAgent(ConversableAgent):
# The name of the agent
name = "Aristotle"
# The profile of the agent
profile: str = "Summarizer"
# The core functional goals of the agent tell LLM what it can do with it.
goal: str = (
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
)
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc: str = (
"You can summarize provided text content according to user's questions"
" and output the summarization."
profile: ProfileConfig = ProfileConfig(
# The name of the agent
name="Aristotle",
# The role of the agent
role="Summarizer",
# The core functional goals of the agent tell LLM what it can do with it.
goal=(
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
),
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc=(
"You can summarize provided text content according to user's questions"
" and output the summarization."
),
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {{ param_name }}.
constraints=[
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{{ not_related_message }}'!!.",
],
)
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {param_name}.
constraints: list[str] = [
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{not_related_message}'!!.",
]
def __init__(self, **kwargs):
super().__init__(**kwargs)
def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage:
reply_message = super()._init_reply_message(received_message)
# Fill in the dynamic parameters in the prompt template
reply_message.context = {
"not_related_message": NOT_RELATED_MESSAGE
}
reply_message.context = {"not_related_message": NOT_RELATED_MESSAGE}
return reply_message
```
@@ -343,7 +349,7 @@ class SummaryAction(Action[SummaryActionInput]):
### Binding Action to Agent
After the development and definition of agent and cction are completed,
After the development and definition of agent and action are completed,
bind the action to the corresponding agent.
```python
@@ -405,7 +411,7 @@ After the custom agent is created, you can use it in the following way:
import asyncio
from dbgpt.agent import AgentContext, ConversableAgent, GptsMemory, LLMConfig, UserProxyAgent
from dbgpt.agent import AgentContext, ConversableAgent, AgentMemory, LLMConfig, UserProxyAgent
from dbgpt.model.proxy import OpenAILLMClient
class MySummarizerAgent(ConversableAgent):
@@ -415,17 +421,17 @@ async def main():
llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo")
context: AgentContext = AgentContext(conv_id="summarize")
default_memory: GptsMemory = GptsMemory()
agent_memory: AgentMemory = AgentMemory()
summarizer = (
await MySummarizerAgent()
.bind(context)
.bind(LLMConfig(llm_client=llm_client))
.bind(default_memory)
.bind(agent_memory)
.build()
)
user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build()
user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
await user_proxy.initiate_chat(
@@ -451,7 +457,7 @@ async def main():
Nuclear electric rocket
""",
)
print(await default_memory.one_chat_completions("summarize"))
print(await agent_memory.gpts_memory.one_chat_completions("summarize"))
if __name__ == "__main__":
asyncio.run(main())
@@ -467,11 +473,12 @@ from dbgpt.agent import (
Action,
ActionOutput,
AgentContext,
AgentMemory,
AgentMessage,
AgentResource,
ConversableAgent,
GptsMemory,
LLMConfig,
ProfileConfig,
ResourceType,
UserProxyAgent,
)
@@ -481,7 +488,6 @@ from dbgpt.model.proxy import OpenAILLMClient
from dbgpt.vis import Vis
from pydantic import BaseModel, Field
NOT_RELATED_MESSAGE = "Did not find the information you want."
CHECK_RESULT_SYSTEM_MESSAGE = (
@@ -496,37 +502,42 @@ CHECK_RESULT_SYSTEM_MESSAGE = (
"by TERMINATE. For instance: False|Some important concepts in the input are "
"not summarized. TERMINATE"
)
class MySummarizerAgent(ConversableAgent):
# The name of the agent
name = "Aristotle"
# The profile of the agent
profile: str = "Summarizer"
# The core functional goals of the agent tell LLM what it can do with it.
goal: str = (
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
)
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc: str = (
"You can summarize provided text content according to user's questions"
" and output the summarization."
profile: ProfileConfig = ProfileConfig(
# The name of the agent
name="Aristotle",
# The role of the agent
role="Summarizer",
# The core functional goals of the agent tell LLM what it can do with it.
goal=(
"Summarize answer summaries based on user questions from provided "
"resource information or from historical conversation memories."
),
# Introduction and description of the agent, used for task assignment and display.
# If it is empty, the goal content will be used.
desc=(
"You can summarize provided text content according to user's questions"
" and output the summarization."
),
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {{ param_name }}.
constraints=[
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{{ not_related_message }}'!!.",
],
)
# Refer to the following. It can contain multiple constraints and reasoning
# restriction logic, and supports the use of parameter template {param_name}.
constraints: list[str] = [
"Prioritize the summary of answers to user questions from the improved resource"
" text. If no relevant information is found, summarize it from the historical "
"dialogue memory given. It is forbidden to make up your own.",
"You need to first detect user's question that you need to answer with your"
" summarization.",
"Extract the provided text content used for summarization.",
"Then you need to summarize the extracted text content.",
"Output the content of summarization ONLY related to user's question. The "
"output language must be the same to user's question language.",
"If you think the provided text content is not related to user questions at "
"all, ONLY output '{not_related_message}'!!.",
]
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._init_actions([SummaryAction])
@@ -534,11 +545,9 @@ class MySummarizerAgent(ConversableAgent):
def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage:
reply_message = super()._init_reply_message(received_message)
# Fill in the dynamic parameters in the prompt template
reply_message.context = {
"not_related_message": NOT_RELATED_MESSAGE
}
reply_message.context = {"not_related_message": NOT_RELATED_MESSAGE}
return reply_message
def prepare_act_param(self) -> Dict[str, Any]:
return {"action_extra_param_key": "this is extra param"}
@@ -565,7 +574,7 @@ class MySummarizerAgent(ConversableAgent):
],
prompt=CHECK_RESULT_SYSTEM_MESSAGE,
)
fail_reason = ""
if check_result and (
"true" in check_result.lower() or "yes" in check_result.lower()
@@ -595,6 +604,7 @@ class SummaryActionInput(BaseModel):
description="The summary content",
)
class SummaryAction(Action[SummaryActionInput]):
def __init__(self):
super().__init__()
@@ -604,13 +614,13 @@ class SummaryAction(Action[SummaryActionInput]):
# The resource type that the current Agent needs to use
# here we do not need to use resources, just return None
return None
@property
def render_protocol(self) -> Optional[Vis]:
# The visualization rendering protocol that the current Agent needs to use
# here we do not need to use visualization rendering, just return None
return None
@property
def out_model_type(self):
return SummaryActionInput
@@ -624,14 +634,16 @@ class SummaryAction(Action[SummaryActionInput]):
**kwargs,
) -> ActionOutput:
"""Perform the action.
The entry point for actual execution of Action. Action execution will be
automatically initiated after model inference.
The entry point for actual execution of Action. Action execution will be
automatically initiated after model inference.
"""
extra_param = kwargs.get("action_extra_param_key", None)
try:
# Parse the input message
param: SummaryActionInput = self._input_convert(ai_message, SummaryActionInput)
param: SummaryActionInput = self._input_convert(
ai_message, SummaryActionInput
)
except Exception:
return ActionOutput(
is_exe_success=False,
@@ -640,7 +652,7 @@ class SummaryAction(Action[SummaryActionInput]):
)
# Check if the summary content is not related to user questions
if param.summary and cmp_string_equal(
param.summary,
param.summary,
NOT_RELATED_MESSAGE,
ignore_case=True,
ignore_punctuation=True,
@@ -662,18 +674,17 @@ async def main():
llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo")
context: AgentContext = AgentContext(conv_id="summarize")
default_memory: GptsMemory = GptsMemory()
agent_memory: AgentMemory = AgentMemory()
summarizer = (
await MySummarizerAgent()
.bind(context)
.bind(LLMConfig(llm_client=llm_client))
.bind(default_memory)
.bind(agent_memory)
.build()
)
user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build()
user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
await user_proxy.initiate_chat(
recipient=summarizer,
@@ -698,7 +709,8 @@ async def main():
Nuclear electric rocket
""",
)
print(await default_memory.one_chat_completions("summarize"))
print(await agent_memory.gpts_memory.one_chat_completions("summarize"))
if __name__ == "__main__":
asyncio.run(main())