pydantic ai agent 框架
pydantic 团队也开始搞ai agent 框架了，目前属于beta 版
使用pydantic ai 的一些原因（来自官方介绍）
- 来自pydantic团队，不少llm python sdk 都使用了此包
- 模型无关，尽管当前支持的还有限，但是提供了简单接口可以扩展
- 类型安全
- 支持基于普通python 代码的控制流以及agent 组合，和以前开发python 项目代码的方式类似
- 支持结构化输出
- 流响应
- 类型安全的依赖注入系统
- logfire 集成，可以方便的进行监控以及调试
一个简单示例
来自官方文档,可以看到代码更加符合python的编码方式
from dataclasses import dataclass
from pydantic import BaseModel, Field
from pydantic_ai import Agent, RunContext
from bank_database import DatabaseConn
# SupportDependencies is used to pass data, connections, and logic into the model that will be needed when running
# system prompt and tool functions. Dependency injection provides a type-safe way to customise the behavior of your agents.
@dataclass
class SupportDependencies:
customer_id: int
db: DatabaseConn
# This pydantic model defines the structure of the result returned by the agent.
class SupportResult(BaseModel):
support_advice: str = Field(description='Advice returned to the customer')
block_card: bool = Field(description="Whether to block the customer's card")
risk: int = Field(description='Risk level of query', ge=0, le=10)
# This agent will act as first-tier support in a bank.
# Agents are generic in the type of dependencies they accept and the type of result they return.
# In this case, the support agent has type `Agent[SupportDependencies, SupportResult]`.
support_agent = Agent(
'openai:gpt-4o',
deps_type=SupportDependencies,
# The response from the agent will, be guaranteed to be a SupportResult,
# if validation fails the agent is prompted to try again.
result_type=SupportResult,
system_prompt=(
'You are a support agent in our bank, give the '
'customer support and judge the risk level of their query.'
),
)
# Dynamic system prompts can can make use of dependency injection.
# Dependencies are carried via the `RunContext` argument, which is parameterized with the `deps_type` from above.
# If the type annotation here is wrong, static type checkers will catch it.
@support_agent.system_prompt
async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str:
customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id)
return f"The customer's name is {customer_name!r}"
# `tool` let you register functions which the LLM may call while responding to a user.
# Again, dependencies are carried via `RunContext`, any other arguments become the tool schema passed to the LLM.
# Pydantic is used to validate these arguments, and errors are passed back to the LLM so it can retry.
@support_agent.tool
async def customer_balance(
ctx: RunContext[SupportDependencies], include_pending: bool
) -> float:
"""Returns the customer's current account balance."""
# The docstring of a tool is also passed to the LLM as the description of the tool.
# Parameter descriptions are extracted from the docstring and added to the parameter schema sent to the LLM.
balance = await ctx.deps.db.customer_balance(
id=ctx.deps.customer_id,
include_pending=include_pending,
)
return balance
... # In a real use case, you'd add more tools and a longer system prompt
async def main():
deps = SupportDependencies(customer_id=123, db=DatabaseConn())
# Run the agent asynchronously, conducting a conversation with the LLM until a final response is reached.
# Even in this fairly simple case, the agent will exchange multiple messages with the LLM as tools are called to retrieve a result.
result = await support_agent.run('What is my balance?', deps=deps)
# The result will be validated with Pydantic to guarantee it is a `SupportResult`, since the agent is generic,
# it'll also be typed as a `SupportResult` to aid with static type checking.
print(result.data)
"""
support_advice='Hello John, your current account balance, including pending transactions, is $123.45.' block_card=False risk=1
"""
result = await support_agent.run('I just lost my card!', deps=deps)
print(result.data)
"""
support_advice="I'm sorry to hear that, John. We are temporarily blocking your card to prevent unauthorized transactions." block_card=True risk=8
"""
说明
尽管pydantic ai目前比较新，但是设计上还是很不错的，很值得尝试下，这个很pydantic
