diff --git a/Makefile b/Makefile index 2c7b13b..2d32060 100644 --- a/Makefile +++ b/Makefile @@ -44,7 +44,7 @@ setup: set_python_env # ============================ setup_test: set_python_env - @poetry install --with=test + @poetry install --with=test --all-extras test: setup_test poetry run pytest -vvrx diff --git a/agntcy_iomapper/__init__.py b/agntcy_iomapper/__init__.py index 92b9c86..36bfc27 100644 --- a/agntcy_iomapper/__init__.py +++ b/agntcy_iomapper/__init__.py @@ -1,15 +1,20 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 # ruff: noqa: F401 +from .agent_iomapper import ( + AgentIOMapper, + AgentIOMapperConfig, + AgentIOMapperInput, + AgentIOMapperOutput, +) from .base import ( BaseIOMapper, - IOMapperInput, - IOMapperOutput, - IOModelSettings, + BaseIOMapperConfig, + BaseIOMapperInput, + BaseIOMapperOutput, ) -from .imperative import ImperativeIOMapper -from .iomapper import ( - AgentIOMapper, - IOMapperConfig, - IOModelArgs, +from .imperative import ( + ImperativeIOMapper, + ImperativeIOMapperInput, + ImperativeIOMapperOutput, ) diff --git a/agntcy_iomapper/iomapper.py b/agntcy_iomapper/agent_iomapper.py similarity index 52% rename from agntcy_iomapper/iomapper.py rename to agntcy_iomapper/agent_iomapper.py index 88a73f4..b05ca4b 100644 --- a/agntcy_iomapper/iomapper.py +++ b/agntcy_iomapper/agent_iomapper.py @@ -1,55 +1,38 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 -import argparse -import asyncio import json import logging import re -from typing import ClassVar, TypedDict +from abc import abstractmethod +from typing import ClassVar -import aiofiles import jsonschema -from dotenv import find_dotenv, load_dotenv from jinja2 import Environment from jinja2.sandbox import SandboxedEnvironment -from pydantic import BaseModel, Field, model_validator -from pydantic_ai import Agent -from typing_extensions import Self +from pydantic import Field -from .base import BaseIOMapper, IOMapperInput, IOMapperOutput, IOModelSettings -from .supported_agents import get_supported_agent +from .base import ( + BaseIOMapper, + BaseIOMapperConfig, + BaseIOMapperInput, + BaseIOMapperOutput, +) logger = logging.getLogger(__name__) -class IOModelArgs(TypedDict, total=False): - base_url: str - api_version: str - azure_endpoint: str - azure_ad_token: str - project: str - organization: str +class AgentIOMapperInput(BaseIOMapperInput): + message_template: str | None = Field( + max_length=4096, + default=None, + description="Message (user) to send to LLM to effect translation.", + ) -class IOMapperConfig(BaseModel): - models: dict[str, IOModelArgs] = Field( - default={"azure:gpt-4o-mini": IOModelArgs()}, - description="LLM configuration to use for translation", - ) - default_model: str | None = Field( - default="azure:gpt-4o-mini", - description="Default arguments to LLM completion function by configured model.", - ) - default_model_settings: dict[str, IOModelSettings] = Field( - default={"azure:gpt-4o-mini": IOModelSettings(seed=42, temperature=0.8)}, - description="LLM configuration to use for translation", - ) - validate_json_input: bool = Field( - default=False, description="Validate input against JSON schema." - ) - validate_json_output: bool = Field( - default=False, description="Validate output against JSON schema." - ) +AgentIOMapperOutput = BaseIOMapperOutput + + +class AgentIOMapperConfig(BaseIOMapperConfig): system_prompt_template: str = Field( max_length=4096, default="You are a translation machine. You translate both natural language and object formats for computers.", @@ -61,19 +44,6 @@ class IOMapperConfig(BaseModel): description="Default user message template. This can be overridden by the message request.", ) - @model_validator(mode="after") - def _validate_obj(self) -> Self: - if self.models and self.default_model not in self.models: - raise ValueError( - f"default model {self.default_model} not present in configured models" - ) - # Fill out defaults to eliminate need for checking. - for model_name in self.models.keys(): - if model_name not in self.default_model_settings: - self.default_model_settings[model_name] = IOModelSettings() - - return self - class AgentIOMapper(BaseIOMapper): _json_search_pattern: ClassVar[re.Pattern] = re.compile( @@ -82,11 +52,13 @@ class AgentIOMapper(BaseIOMapper): def __init__( self, - config: IOMapperConfig, + config: AgentIOMapperConfig | None = None, jinja_env: Environment | None = None, jinja_env_async: Environment | None = None, ): - self.config = config + if config is None: + config = AgentIOMapperConfig() + super().__init__(config) if jinja_env is not None and jinja_env.is_async: raise ValueError("Async Jinja env passed to jinja_env argument") @@ -136,41 +108,19 @@ def _check_jinja_env(self, enable_async: bool): self.config.message_template ) - def _get_render_env(self, input: IOMapperInput) -> dict[str, str]: + def _get_render_env(self, input: AgentIOMapperInput) -> dict[str, str]: return { "input": input.input, "output": input.output, "data": input.data, } - def _get_model_settings(self, input: IOMapperInput): - model_name = input.model or self.config.default_model - if model_name not in self.config.models: - raise ValueError(f"requested model {model_name} not found") - elif input.model_settings is None: - return self.config.default_model_settings[model_name] - else: - model_settings = self.config.default_model_settings[model_name].copy() - model_settings.update(input.model_settings) - return model_settings - - def _get_agent( - self, is_async: bool, input: IOMapperInput, system_prompt: str - ) -> Agent: - model_name = input.model or self.config.default_model - if model_name not in self.config.models: - raise ValueError(f"requested model {model_name} not found") - - return get_supported_agent( - model_name, - model_args=self.config.models[model_name], - system_prompt=system_prompt, - ) - - def _get_output(self, input: IOMapperInput, outputs: str) -> IOMapperOutput: + def _get_output( + self, input: AgentIOMapperInput, outputs: str + ) -> AgentIOMapperOutput: if input.output.json_schema is None: # If there is no schema, quote the chars for JSON. - return IOMapperOutput.model_validate_json( + return AgentIOMapperOutput.model_validate_json( f'{{"data": {json.dumps(outputs)} }}' ) @@ -179,9 +129,9 @@ def _get_output(self, input: IOMapperInput, outputs: str) -> IOMapperOutput: if matches: outputs = matches[-1] - return IOMapperOutput.model_validate_json(f'{{"data": {outputs} }}') + return AgentIOMapperOutput.model_validate_json(f'{{"data": {outputs} }}') - def _validate_input(self, input: IOMapperInput) -> None: + def _validate_input(self, input: AgentIOMapperInput) -> None: if self.config.validate_json_input and input.input.json_schema is not None: jsonschema.validate( instance=input.data, @@ -190,7 +140,9 @@ def _validate_input(self, input: IOMapperInput) -> None: ), ) - def _validate_output(self, input: IOMapperInput, output: IOMapperOutput) -> None: + def _validate_output( + self, input: AgentIOMapperInput, output: AgentIOMapperOutput + ) -> None: if self.config.validate_json_output and input.output.json_schema is not None: output_schema = input.output.json_schema.model_dump( exclude_none=True, mode="json" @@ -201,34 +153,46 @@ def _validate_output(self, input: IOMapperInput, output: IOMapperOutput) -> None schema=output_schema, ) - def invoke(self, input: IOMapperInput) -> IOMapperOutput: + def invoke(self, input: AgentIOMapperInput, **kwargs) -> AgentIOMapperOutput: self._validate_input(input) self._check_jinja_env(False) render_env = self._get_render_env(input) system_prompt = self.prompt_template.render(render_env) - agent = self._get_agent(False, input, system_prompt) if input.message_template is not None: logging.info(f"User template supplied on input: {input.message_template}") user_template = self.jinja_env.from_string(input.message_template) else: user_template = self.user_template - response = agent.run_sync( - user_prompt=user_template.render(render_env), - model_settings=self._get_model_settings(input), + user_prompt = user_template.render(render_env) + + outputs = self._invoke( + input, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + **kwargs, ) - outputs = response.data logging.debug(f"The LLM returned: {outputs}") output = self._get_output(input, outputs) self._validate_output(input, output) return output - async def ainvoke(self, input: IOMapperInput) -> IOMapperOutput: + @abstractmethod + def _invoke( + self, input: AgentIOMapperInput, messages: list[dict[str, str]], **kwargs + ) -> str: + """Invoke internal model to process messages. + Args: + messages: the messages to send to the LLM + """ + + async def ainvoke(self, input: AgentIOMapperInput, **kwargs) -> AgentIOMapperOutput: self._validate_input(input) self._check_jinja_env(True) render_env = self._get_render_env(input) system_prompt = await self.prompt_template_async.render_async(render_env) - agent = self._get_agent(True, input, system_prompt) if input.message_template is not None: logging.info(f"User template supplied on input: {input.message_template}") @@ -237,53 +201,26 @@ async def ainvoke(self, input: IOMapperInput) -> IOMapperOutput: ) else: user_template_async = self.user_template_async - response = await agent.run( - user_prompt=await user_template_async.render_async(render_env), - model_settings=self._get_model_settings(input), + user_prompt = await user_template_async.render_async(render_env) + + outputs = await self._ainvoke( + input, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + **kwargs, ) - outputs = response.data logging.debug(f"The LLM returned: {outputs}") output = self._get_output(input, outputs) self._validate_output(input, output) return output - -async def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--inputfile", help="Inputfile", required=True) - parser.add_argument("--configfile", help="Configuration file", required=True) - parser.add_argument("--outputfile", help="Output file", required=True) - args = parser.parse_args() - logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO) - - jinja_env = SandboxedEnvironment( - loader=None, - enable_async=True, - autoescape=False, - ) - - async with aiofiles.open(args.configfile, "r") as fp: - configs = await fp.read() - - config = IOMapperConfig.model_validate_json(configs) - logging.info(f"Loaded config from {args.configfile}: {config.model_dump_json()}") - - async with aiofiles.open(args.inputfile, "r") as fp: - inputs = await fp.read() - - input = IOMapperInput.model_validate_json(inputs) - logging.info(f"Loaded input from {args.inputfile}: {input.model_dump_json()}") - - p = AgentIOMapper(config, jinja_env) - output = await p.ainvoke(input) - outputs = output.model_dump_json() - - async with aiofiles.open(args.outputfile, "w") as fp: - await fp.write(outputs) - - logging.info(f"Dumped output to {args.outputfile}: {outputs}") - - -if __name__ == "__main__": - load_dotenv(dotenv_path=find_dotenv(usecwd=True)) - asyncio.run(main()) + @abstractmethod + async def _ainvoke( + self, input: AgentIOMapperInput, messages: list[dict[str, str]], **kwargs + ) -> str: + """Async invoke internal model to process messages. + Args: + messages: the messages to send to the LLM + """ diff --git a/agntcy_iomapper/base.py b/agntcy_iomapper/base.py index 1d25e03..383a974 100644 --- a/agntcy_iomapper/base.py +++ b/agntcy_iomapper/base.py @@ -1,7 +1,7 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 from abc import ABC, abstractmethod -from typing import Any, TypedDict +from typing import Any from openapi_pydantic import Schema from pydantic import BaseModel, Field, model_validator @@ -25,50 +25,41 @@ def _validate_obj(self) -> Self: return self -class IOModelSettings(TypedDict, total=False): - max_tokens: int - temperature: float - top_p: float - parallel_tool_calls: bool - seed: int - presence_penalty: float - frequency_penalty: float - logit_bias: dict[str, int] - - -class IOMapperInput(BaseModel): +class BaseIOMapperInput(BaseModel): input: ArgumentsDescription = Field(description="Input data descriptions") output: ArgumentsDescription = Field(description="Output data descriptions") data: Any = Field(description="Data to translate") - message_template: str | None = Field( - max_length=4096, - default=None, - description="Message (user) to send to LLM to effect translation.", - ) - model_settings: IOModelSettings | None = Field( - default=None, - description="Specific arguments for LLM transformation.", - ) - model: str | None = Field( - default=None, - description="Specific model out of those configured to handle request.", - ) -class IOMapperOutput(BaseModel): +class BaseIOMapperOutput(BaseModel): data: Any = Field(default=None, description="Data after translation") error: str | None = Field( max_length=4096, default=None, description="Description of error on failure." ) +class BaseIOMapperConfig(BaseModel): + validate_json_input: bool = Field( + default=False, description="Validate input against JSON schema." + ) + validate_json_output: bool = Field( + default=False, description="Validate output against JSON schema." + ) + + class BaseIOMapper(ABC): """Abstract base class for interfacing with io mapper. All io mappers wrappers inherited from BaseIOMapper. """ + def __init__( + self, + config: BaseIOMapperConfig | None = None, + ): + self.config = config if config is not None else BaseIOMapperConfig() + @abstractmethod - def invoke(self, input: IOMapperInput) -> IOMapperOutput: + def invoke(self, input: BaseIOMapperInput) -> BaseIOMapperOutput: """Pass input data to be mapped and returned represented in the output schema Args: @@ -76,7 +67,7 @@ def invoke(self, input: IOMapperInput) -> IOMapperOutput: """ @abstractmethod - async def ainvoke(self, input: IOMapperInput) -> IOMapperOutput: + async def ainvoke(self, input: BaseIOMapperInput) -> BaseIOMapperOutput: """Pass input data to be mapped and returned represented in the output schema Args: diff --git a/agntcy_iomapper/imperative.py b/agntcy_iomapper/imperative.py index a56477a..3260c26 100644 --- a/agntcy_iomapper/imperative.py +++ b/agntcy_iomapper/imperative.py @@ -22,34 +22,49 @@ import jsonschema from jsonpath_ng.ext import parse -from agntcy_iomapper.base import BaseIOMapper, IOMapperInput, IOMapperOutput +from .base import ( + BaseIOMapper, + BaseIOMapperConfig, + BaseIOMapperInput, + BaseIOMapperOutput, +) logger = logging.getLogger(__name__) +ImperativeIOMapperInput = BaseIOMapperInput +ImperativeIOMapperOutput = BaseIOMapperOutput + + class ImperativeIOMapper(BaseIOMapper): field_mapping: dict[str, Union[str, Callable]] """A dictionary for where the keys are fields of the output object and values are JSONPath (strings) representing how the mapping """ - def __init__(self, field_mapping: dict[str, Union[str, Callable]] | None) -> None: - super().__init__() + def __init__( + self, + field_mapping: dict[str, Union[str, Callable]] | None, + config: BaseIOMapperConfig | None = None, + ) -> None: + super().__init__(config) self.field_mapping = field_mapping - def invoke(self, input: IOMapperInput) -> IOMapperOutput | None: + def invoke(self, input: ImperativeIOMapperInput) -> ImperativeIOMapperOutput | None: if input.data is None: return None if self.field_mapping is None: - return IOMapperOutput(data=input.data) + return ImperativeIOMapperOutput(data=input.data) data = self._imperative_map(input) - return IOMapperOutput(data=data) + return ImperativeIOMapperOutput(data=data) - def ainvoke(self, input: IOMapperInput) -> IOMapperOutput | None: + def ainvoke( + self, input: ImperativeIOMapperInput + ) -> ImperativeIOMapperOutput | None: return self.invoke(input) - def _imperative_map(self, input_definition: IOMapperInput) -> Any: + def _imperative_map(self, input_definition: ImperativeIOMapperInput) -> Any: """ Converts input data to a desired output type. diff --git a/agntcy_iomapper/langgraph.py b/agntcy_iomapper/langgraph.py new file mode 100644 index 0000000..255c9db --- /dev/null +++ b/agntcy_iomapper/langgraph.py @@ -0,0 +1,94 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +import logging +from typing import Any + +from langchain.chat_models import init_chat_model +from langchain_core.language_models import BaseChatModel +from langchain_core.runnables import Runnable, RunnableConfig +from langgraph.utils.runnable import RunnableCallable +from pydantic import Field + +from .agent_iomapper import ( + AgentIOMapper, + AgentIOMapperConfig, + AgentIOMapperInput, + AgentIOMapperOutput, +) + +logger = logging.getLogger(__name__) + +LangGraphIOMapperInput = AgentIOMapperInput +LangGraphIOMapperOutput = AgentIOMapperOutput + + +class LangGraphIOMapperConfig(AgentIOMapperConfig): + llm: BaseChatModel | str = Field( + default="anthropic:claude-3-5-sonnet-latest", + description="Model to use for translation as LangChain description or model class.", + ) + + +class _LangGraphAgentIOMapper(AgentIOMapper): + def __init__( + self, + config: LangGraphIOMapperConfig | None = None, + **kwargs, + ): + if config is None: + config = LangGraphIOMapperConfig() + super().__init__(config, **kwargs) + if isinstance(config.llm, str): + self.llm = init_chat_model(config.llm) + else: + self.llm = config.llm + + def _invoke( + self, + input: LangGraphIOMapperInput, + messages: list[dict[str, str]], + *, + config: RunnableConfig | None = None, + **kwargs, + ) -> str: + response = self.llm.invoke(messages, config, **kwargs) + return response.content + + async def _ainvoke( + self, + input: LangGraphIOMapperOutput, + messages: list[dict[str, str]], + *, + config: RunnableConfig | None = None, + **kwargs, + ) -> str: + response = await self.llm.ainvoke(messages, config, **kwargs) + return response.content + + +class LangGraphIOMapper: + def __init__(self, config: LangGraphIOMapperConfig): + self._iomapper = _LangGraphAgentIOMapper(config) + + async def ainvoke(self, state: dict[str, Any], config: RunnableConfig) -> dict: + response = await self._iomapper.ainvoke(input=state["input"], config=config) + if response is not None: + return {"output": response} + else: + return {} + + def invoke(self, state: dict[str, Any], config: RunnableConfig) -> dict: + response = self._iomapper.invoke(input=state["input"], config=config) + if response is not None: + return {"output": response} + else: + return {} + + def as_runnable(self): + return RunnableCallable(self.invoke, self.ainvoke, name="extract", trace=False) + + +def create_iomapper( + config: LangGraphIOMapperConfig, +) -> Runnable[LangGraphIOMapperInput, LangGraphIOMapperOutput]: + return LangGraphIOMapper(config).as_runnable() diff --git a/agntcy_iomapper/pydantic_ai.py b/agntcy_iomapper/pydantic_ai.py new file mode 100644 index 0000000..ae4c743 --- /dev/null +++ b/agntcy_iomapper/pydantic_ai.py @@ -0,0 +1,230 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +import logging +from typing import Any, Literal, TypedDict + +from openai import AsyncAzureOpenAI +from pydantic import Field, model_validator +from pydantic_ai import Agent +from pydantic_ai.messages import ( + ModelMessage, + ModelRequest, + ModelResponse, + SystemPromptPart, + TextPart, + UserPromptPart, +) +from pydantic_ai.models import KnownModelName +from pydantic_ai.models.openai import OpenAIModel +from typing_extensions import Self + +from .agent_iomapper import ( + AgentIOMapper, + AgentIOMapperConfig, + AgentIOMapperInput, + AgentIOMapperOutput, +) + +logger = logging.getLogger(__name__) + + +class AgentIOModelArgs(TypedDict, total=False): + base_url: str + api_version: str + azure_endpoint: str + azure_ad_token: str + project: str + organization: str + + +class AgentModelSettings(TypedDict, total=False): + max_tokens: int + temperature: float + top_p: float + parallel_tool_calls: bool + seed: int + presence_penalty: float + frequency_penalty: float + logit_bias: dict[str, int] + + +class PydanticAIAgentIOMapperInput(AgentIOMapperInput): + model_settings: AgentModelSettings | None = Field( + default=None, + description="Specific arguments for LLM transformation.", + ) + model: str | None = Field( + default=None, + description="Specific model out of those configured to handle request.", + ) + + +PydanticAIAgentIOMapperOutput = AgentIOMapperOutput + + +class PydanticAIAgentIOMapperConfig(AgentIOMapperConfig): + models: dict[str, AgentIOModelArgs] = Field( + default={"azure:gpt-4o-mini": AgentIOModelArgs()}, + description="LLM configuration to use for translation", + ) + default_model: str | None = Field( + default="azure:gpt-4o-mini", + description="Default arguments to LLM completion function by configured model.", + ) + default_model_settings: dict[str, AgentModelSettings] = Field( + default={"azure:gpt-4o-mini": AgentModelSettings(seed=42, temperature=0.8)}, + description="LLM configuration to use for translation", + ) + + @model_validator(mode="after") + def _validate_obj(self) -> Self: + if self.models and self.default_model not in self.models: + raise ValueError( + f"default model {self.default_model} not present in configured models" + ) + # Fill out defaults to eliminate need for checking. + for model_name in self.models.keys(): + if model_name not in self.default_model_settings: + self.default_model_settings[model_name] = AgentModelSettings() + + return self + + +SupportedModelName = ( + KnownModelName + | Literal[ + "azure:gpt-4o-mini", + "azure:gpt-4o", + "azure:gpt-4", + ] +) + + +def get_supported_agent( + model_name: SupportedModelName, + model_args: dict[str, Any] = {}, + **kwargs, +) -> Agent: + """ + Creates and returns an `Agent` instance for the given model. + + Args: + model_name (SupportedModelName): The name of the model to be used. + If the name starts with "azure:", an `AsyncAzureOpenAI` client is used. + model_args (dict[str, Any], optional): Additional arguments for model + initialization. Defaults to an empty dictionary. + **kwargs: Additional keyword arguments passed to the `Agent` constructor. + + Returns: + Agent: An instance of the `Agent` class configured with the specified model. + + Notes: + - The `pydantic-ai` package does not currently pass `model_args` to the + inferred model in the constructor, but this behavior might change in + the future. + """ + if model_name.startswith("azure:"): + client = AsyncAzureOpenAI(**model_args) + model = OpenAIModel(model_name[6:], openai_client=client) + return Agent(model, **kwargs) + + return Agent(model_name, **kwargs) + + +class PydanticAIIOAgentIOMapper(AgentIOMapper): + def __init__( + self, + config: PydanticAIAgentIOMapperConfig, + **kwargs, + ): + super().__init__(config, **kwargs) + + def _get_model_settings(self, input: PydanticAIAgentIOMapperInput): + if hasattr(input, "model") and input.model is not None: + model_name = input.model + else: + model_name = self.config.default_model + + if model_name not in self.config.models: + raise ValueError(f"requested model {model_name} not found") + elif hasattr(input, "model_settings") and input.model_settings is not None: + model_settings = self.config.default_model_settings[model_name].copy() + model_settings.update(input.model_settings) + return model_settings + else: + return self.config.default_model_settings[model_name] + + def _get_agent( + self, input: PydanticAIAgentIOMapperInput, system_prompt: str + ) -> Agent: + if hasattr(input, "model") and input.model is not None: + model_name = input.model + else: + model_name = self.config.default_model + + if model_name not in self.config.models: + raise ValueError(f"requested model {model_name} not found") + + return get_supported_agent( + model_name, + model_args=self.config.models[model_name], + system_prompt=system_prompt, + ) + + def _get_prompts( + self, messages: list[dict[str, str]] + ) -> tuple[str, str, list[ModelMessage]]: + system_prompt = "" + user_prompt = "" + message_history = [] + + for msg in messages: + role = msg.get("role", "user") + + if role.lower() == "system": + system_prompt = msg.get("content", "") + message_history.append( + ModelRequest(parts=[SystemPromptPart(content=system_prompt)]) + ) + elif role.lower() == "user": + user_prompt = msg.get("content", "") + message_history.append( + ModelRequest(parts=[UserPromptPart(content=user_prompt)]) + ) + elif role.lower() == "assistant": + content = msg.get("content", "") + message_history.append(ModelResponse(parts=[TextPart(content=content)])) + + return (system_prompt, user_prompt, message_history) + + def _invoke( + self, + input: PydanticAIAgentIOMapperInput, + messages: list[dict[str, str]], + **kwargs, + ) -> str: + system_prompt, user_prompt, message_history = self._get_prompts(messages) + + agent = self._get_agent(input, system_prompt) + response = agent.run_sync( + user_prompt, + model_settings=self._get_model_settings(input), + message_history=message_history, + ) + return response.data + + async def _ainvoke( + self, + input: PydanticAIAgentIOMapperInput, + messages: list[dict[str, str]], + **kwargs, + ) -> str: + system_prompt, user_prompt, message_history = self._get_prompts(messages) + + agent = self._get_agent(input, system_prompt) + response = await agent.run( + user_prompt, + model_settings=self._get_model_settings(input), + message_history=message_history, + ) + return response.data diff --git a/agntcy_iomapper/supported_agents.py b/agntcy_iomapper/supported_agents.py deleted file mode 100644 index a18f9d7..0000000 --- a/agntcy_iomapper/supported_agents.py +++ /dev/null @@ -1,48 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. -# SPDX-License-Identifier: Apache-2.0 -from typing import Any, Literal - -from openai import AsyncAzureOpenAI -from pydantic_ai import Agent -from pydantic_ai.models import KnownModelName -from pydantic_ai.models.openai import OpenAIModel - -SupportedModelName = ( - KnownModelName - | Literal[ - "azure:gpt-4o-mini", - "azure:gpt-4o", - "azure:gpt-4", - ] -) - - -def get_supported_agent( - model_name: SupportedModelName, - model_args: dict[str, Any] = {}, - **kwargs, -) -> Agent: - """ - Creates and returns an `Agent` instance for the given model. - - Args: - model_name (SupportedModelName): The name of the model to be used. - If the name starts with "azure:", an `AsyncAzureOpenAI` client is used. - model_args (dict[str, Any], optional): Additional arguments for model - initialization. Defaults to an empty dictionary. - **kwargs: Additional keyword arguments passed to the `Agent` constructor. - - Returns: - Agent: An instance of the `Agent` class configured with the specified model. - - Notes: - - The `pydantic-ai` package does not currently pass `model_args` to the - inferred model in the constructor, but this behavior might change in - the future. - """ - if model_name.startswith("azure:"): - client = AsyncAzureOpenAI(**model_args) - model = OpenAIModel(model_name[6:], openai_client=client) - return Agent(model, **kwargs) - - return Agent(model_name, **kwargs) diff --git a/docs/_navbar.md b/docs/_navbar.md new file mode 100644 index 0000000..95e3bb4 --- /dev/null +++ b/docs/_navbar.md @@ -0,0 +1,6 @@ +* [Getting Started](#getting-started) +* [Usage](usage#usage) + * [Pydantic-AI Agentic IOMapper](usage#pydantic-ai) + * [LangGraph Agentic IOMapper](usage#langgraph) + * [JSON Deterministic IOMapper](usage#use-imperative--deterministic-mapper) +* [Contributing](CONTRIBUTING#how-to-contribute) \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index da200ef..fb0e96d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -15,7 +15,8 @@ name: 'iomapper-agnt', repo: 'agntcy/iomapper-agnt', homepage : 'README.md', - coverpage: true + coverpage: true, + loadNavbar: true } diff --git a/docs/usage.md b/docs/usage.md index 923fb1c..b5dd101 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,18 +1,84 @@ # Usage -### Use Agent IO Mapper +The Agntcy IO Mapper functions provided an easy to use package for mapping output from +one agent to another. The data can be described in JSON or with natural language. The +(incomplete) [pydantic](https://docs.pydantic.dev/latest/) models follow: -:TODO +```python +class ArgumentsDescription(BaseModel): + json_schema: Schema | None = Field(description="Data format JSON schema") + description: str | None = Field(description="Data (semantic) natural language description") + +class BaseIOMapperInput(BaseModel): + input: ArgumentsDescription = Field(description="Input data descriptions") + output: ArgumentsDescription = Field(description="Output data descriptions") + data: Any = Field(description="Data to translate") -### Use Determenistic +class BaseIOMapperOutput(BaseModel): + data: Any = Field(description="Data after translation") + error: str | None = Field(description="Description of error on failure.") + +class BaseIOMapperConfig(BaseModel): + validate_json_input: bool = Field(description="Validate input against JSON schema.") + validate_json_output: bool = Field(description="Validate output against JSON schema.") +``` -The code snippet below illustrates a fully functional deterministic mapping that transforms the output of one agent into input for a second agent. The code for the agents is omitted. +There are several different ways to leverage the IO Mapper functions in Python. There +is an [agentic interface](#use-agent-io-mapper) using models that can be invoked on +different AI platforms and a [imperative interface](#use-imperative--deterministic-io-mapper) +that does deterministic JSON remapping without using any AI models. + +## Use Agent IO Mapper + +The Agent IO Mapper uses an LLM/model to transform the inputs (typically output of the +first agent) to match the desired output (typically the input of a second agent). As such, +it additionally supports specifying the model prompts for the translation. The configuration +object provides a specification for the system and default user prompts: ```python - #define schema for the origin agent +class AgentIOMapperConfig(BaseIOMapperConfig): + system_prompt_template: str = Field( + description="System prompt Jinja2 template used with LLM service for translation." + ) + message_template: str = Field( + description="Default user message template. This can be overridden by the message request." + ) +``` + +and the input object supports overriding the user prompt for the requested translation: + +```python +class AgentIOMapperInput(BaseIOMapperInput): + message_template: str | None = Field( + description="Message (user) to send to LLM to effect translation.", + ) +``` + +Further specification of models and their arguments is left to the underlying supported +packages: + * [Pydantic-AI](#pydantic-ai) + * [LangGraph](#langgraph) + + +### Pydantic-AI + +One of the supported platforms for managing the model interactions is [Pydantic-AI](https://ai.pydantic.dev/). + +### LangGraph + +This project supports specifying model interations using [LangGraph](https://langchain-ai.github.io/langgraph/). + +## Use Imperative / Deterministic IO Mapper + +The code snippet below illustrates a fully functional deterministic mapping that +transforms the output of one agent into input for a second agent. The code for the +agents is omitted. + +```python + # define schema for the origin agent input_schema = {"question": {"type": "string"}} - #define schema to witch the input should be converted to + # define schema to witch the input should be converted to output_schema = { "quiz": { "type": "object", @@ -23,7 +89,8 @@ The code snippet below illustrates a fully functional deterministic mapping that } } - #the mapping object using jsonpath, note: the value of the mapping can be either a jsonpath or a function + # the mapping object using jsonpath, note: the value of the mapping + # can be either a jsonpath or a function mapping_object = { "prof_question": "$.question", "due_date": lambda _: datetime.now().strftime("%x"), @@ -38,26 +105,21 @@ The code snippet below illustrates a fully functional deterministic mapping that ), data={"question": output_prof}, ) - #instantiate the mapper + # instantiate the mapper imerative_mapp = ImperativeIOMapper( field_mapping=mapping_object, ) - #get the mapping result and send to the other agent + # get the mapping result and send to the other agent mapping_result = imerative_mapp.invoke(input=input) - - - ``` ### Use Examples -1. To run the examples we strongly recommend that a [virtual environment is created](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/) +1. To run the examples we strongly recommend that a + [virtual environment is created](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/) 2. Install the requirements file -3. from within examples folder run: +3. From within examples folder run: -```sh +```shell make run_imperative_example ``` - -[GitHub](https://github.com/agntcy/iomapper-agnt/) -[Get Started](#getting-started) diff --git a/examples/langgraph.py b/examples/langgraph.py new file mode 100644 index 0000000..1c5f094 --- /dev/null +++ b/examples/langgraph.py @@ -0,0 +1,2 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 diff --git a/poetry.lock b/poetry.lock index 65b75eb..604b72e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -12,6 +12,140 @@ files = [ {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, ] +[[package]] +name = "aiohappyeyeballs" +version = "2.4.6" +description = "Happy Eyeballs for asyncio" +optional = true +python-versions = ">=3.9" +groups = ["main"] +markers = "extra == \"langgraph\"" +files = [ + {file = "aiohappyeyeballs-2.4.6-py3-none-any.whl", hash = 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"sha256:b2d8c62d08e7255f68f7a740bae85b3c9b8e5466baa9cbf7f57f1cde0ac6bc09"}, +] + +[package.dependencies] +cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""} + +[package.extras] +cffi = ["cffi (>=1.11)"] + +[extras] +langgraph = ["langchain", "langchain-openai", "langgraph"] +pydantic-ai = ["pydantic-ai"] + [metadata] lock-version = "2.1" python-versions = ">=3.9.0,<4.0" -content-hash = "82a760aaaf5ca496cda5a81e4fe66c4f7754afd5ddb7666d434243563cb89a76" +content-hash = "98052fbf8b8e9e4bfbed072dd075c3d0160082ba056dc2172392a5792da397b7" diff --git a/pyproject.toml b/pyproject.toml index a03999e..eda0bb0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "agntcy-iomapper" -version = "0.1.0" +version = "0.2.0" license = "Apache-2.0" description = "A tool to transform output from one agent to the input of another." readme = "README.md" @@ -19,11 +19,17 @@ python-dotenv = "^1.0.1" aiofiles = "^24.1.0" jinja2 = "^3.1.5" openapi-pydantic = "^0.5.1" -pydantic-ai = "^0.0.23" +pydantic-ai = { version = "^0.0.23", optional = true } jsonschema = { extras = ["format"], version = "^4.23.0" } jsonpath-ng = "^1.7.0" pydantic = "^2.10.6" +langchain = { version = "^0.3.19", optional = true } +langgraph = { version = "^0.2.73", optional = true } +langchain-openai = { version = "^0.3.6", optional = true } +[tool.poetry.extras] +langgraph = [ "langchain", "langgraph", "langchain-openai" ] +pydantic-ai = [ "pydantic-ai" ] [tool.poetry.group.test.dependencies] pytest = "*" diff --git a/tests/test_simple.py b/tests/agentio_data.py similarity index 67% rename from tests/test_simple.py rename to tests/agentio_data.py index 6a199fd..a5ff2be 100644 --- a/tests/test_simple.py +++ b/tests/agentio_data.py @@ -1,37 +1,33 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 -from deepdiff import diff -import pytest from openapi_pydantic import DataType, Schema - -from agntcy_iomapper import AgentIOMapper, IOMapperInput, IOMapperOutput +from agntcy_iomapper.agent_iomapper import AgentIOMapperInput, AgentIOMapperOutput from agntcy_iomapper.base import ArgumentsDescription -from utils import compare_outputs, compare_outputs_async -SIMPLE_TEST_PARAMETERS = [ +AGENTIO_TEST_PARAMETERS = [ ( - IOMapperInput( + AgentIOMapperInput( input = ArgumentsDescription(description="Text in English."), output = ArgumentsDescription(description="Text in French."), data = "Would you please tell me where I can buy some snails?", ), - IOMapperOutput( + AgentIOMapperOutput( data = "Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", ), ), ( - IOMapperInput( + AgentIOMapperInput( input = ArgumentsDescription(description=""), output = ArgumentsDescription(description=""), data = "Would you please tell me where I can buy some snails?", message_template="Translate from English to French the following data: {{ data }}", ), - IOMapperOutput( + AgentIOMapperOutput( data = "Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", ), ), ( - IOMapperInput( + AgentIOMapperInput( input = ArgumentsDescription(json_schema=Schema( properties={ "description": Schema( @@ -62,12 +58,12 @@ )), data = {"description": "The object is a purple cube with sides of 1 m."}, ), - IOMapperOutput( + AgentIOMapperOutput( data = {"length": "1 m", "width": "1 m", "height": "1 m"}, ), ), ( - IOMapperInput( + AgentIOMapperInput( input = ArgumentsDescription(json_schema=Schema( properties={ "description": Schema( @@ -98,43 +94,8 @@ )), data = {"description": "The object is a purple cube with sides of 1.5 m."}, ), - IOMapperOutput( + AgentIOMapperOutput( data = {"length": 1.5, "width": 1.5, "height": 1.5}, ), ), ] - -@pytest.mark.parametrize ( - "input, expected_output", SIMPLE_TEST_PARAMETERS, -) -@pytest.mark.llm -async def test_simple_mapping_async(llm_iomapper_config, jinja_env_async, input, expected_output): - llmIOMapper = AgentIOMapper(llm_iomapper_config, jinja_env_async=jinja_env_async) - output = await llmIOMapper.ainvoke(input) - if isinstance(output.data, str): - equalp = await compare_outputs_async(llmIOMapper, output.data, expected_output.data) - assert equalp - return - - outputs = output.model_dump(exclude_unset=True, exclude_none=True) - expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) - mapdiff = diff.DeepDiff(outputs, expects) - assert len(mapdiff.affected_paths) == 0 - -@pytest.mark.parametrize ( - "input, expected_output", SIMPLE_TEST_PARAMETERS, -) -@pytest.mark.llm -def test_simple_mapping(llm_iomapper_config, jinja_env, input, expected_output): - llmIOMapper = AgentIOMapper(llm_iomapper_config, jinja_env=jinja_env) - output = llmIOMapper.invoke(input) - if isinstance(output.data, str): - equalp = compare_outputs(llmIOMapper, output.data, expected_output.data) - assert equalp - return - - outputs = output.model_dump(exclude_unset=True, exclude_none=True) - expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) - mapdiff = diff.DeepDiff(outputs, expects) - assert len(mapdiff.affected_paths) == 0 - diff --git a/tests/conftest.py b/tests/conftest.py index fd0b4ec..2fa09d0 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,52 +1,5 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 -import pytest - from dotenv import load_dotenv, find_dotenv -from jinja2.sandbox import SandboxedEnvironment -from agntcy_iomapper import IOMapperConfig, IOModelSettings, IOModelArgs load_dotenv(dotenv_path=find_dotenv(usecwd=True)) - -@pytest.fixture -def jinja_env() -> SandboxedEnvironment: - return SandboxedEnvironment( - loader=None, - enable_async=False, - autoescape=False, - ) - -@pytest.fixture -def jinja_env_async() -> SandboxedEnvironment: - return SandboxedEnvironment( - loader=None, - enable_async=True, - autoescape=False, - ) - -@pytest.fixture -def no_llm_iomapper_config() -> IOMapperConfig: - return IOMapperConfig( - models = {}, - validate_json_input=True, - validate_json_output=True, - ) - -@pytest.fixture -def llm_iomapper_config() -> IOMapperConfig: - return IOMapperConfig( - models={ - "azure:gpt-4o-mini": IOModelArgs( - api_version="2024-07-01-preview", - azure_endpoint="https://smith-project-agents.openai.azure.com", - ), - }, - default_model_settings={ - "azure:gpt-4o-mini": IOModelSettings( - seed=42, - temperature=0, - ), - }, - validate_json_input=True, - validate_json_output=True, - ) diff --git a/tests/test_determenistic_iomapper.py b/tests/test_imperative_iomapper.py similarity index 97% rename from tests/test_determenistic_iomapper.py rename to tests/test_imperative_iomapper.py index 0e3e2e8..af2f8fd 100644 --- a/tests/test_determenistic_iomapper.py +++ b/tests/test_imperative_iomapper.py @@ -5,8 +5,8 @@ import pytest from openapi_pydantic import Schema -from agntcy_iomapper import ImperativeIOMapper -from agntcy_iomapper.base import ArgumentsDescription, IOMapperInput +from agntcy_iomapper.imperative import ImperativeIOMapper, ImperativeIOMapperInput +from agntcy_iomapper.base import ArgumentsDescription @pytest.mark.parametrize( @@ -182,7 +182,7 @@ def test_imperative_iomapp( field_mapping=field_mapping, ) - input = IOMapperInput( + input = ImperativeIOMapperInput( input=ArgumentsDescription(json_schema=Schema.model_validate(input_schema)), output=ArgumentsDescription(json_schema=Schema.model_validate(output_schema)), data=input_value, diff --git a/tests/test_langgraph_agent_iomapper.py b/tests/test_langgraph_agent_iomapper.py new file mode 100644 index 0000000..8187d59 --- /dev/null +++ b/tests/test_langgraph_agent_iomapper.py @@ -0,0 +1,126 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +from deepdiff import diff +import pytest +import re +from typing import TypedDict +from langchain_core.language_models import BaseChatModel +from langgraph.graph import StateGraph, START, END +from langchain_openai.chat_models.azure import AzureChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +from agntcy_iomapper.langgraph import LangGraphIOMapperConfig, create_iomapper, LangGraphIOMapperInput, LangGraphIOMapperOutput + +from tests.agentio_data import AGENTIO_TEST_PARAMETERS + +@pytest.fixture +def llm_iomapper_config() -> LangGraphIOMapperConfig: + return LangGraphIOMapperConfig( + llm=AzureChatOpenAI( + model="gpt-4o-mini", + api_version="2024-07-01-preview", + seed=42, + temperature=0, + ), + validate_json_input=True, + validate_json_output=True, + ) + +class State(TypedDict): + input: LangGraphIOMapperInput + output: LangGraphIOMapperOutput + +@pytest.mark.parametrize ( + "input, expected_output", AGENTIO_TEST_PARAMETERS, +) +@pytest.mark.llm +async def test_agentio_mapping_async(llm_iomapper_config, input, expected_output): + llmIOMapper = create_iomapper(llm_iomapper_config) + + workflow = StateGraph(State) + workflow.add_node("mapper", llmIOMapper) + workflow.add_edge(START, "mapper") + workflow.add_edge("mapper", END) + graph = workflow.compile() + + output_state = await graph.ainvoke(State(input=input)) + print(f"output_state: {output_state}") + output = output_state["output"] + + if isinstance(output.data, str): + equalp = await compare_outputs_async(llm_iomapper_config.llm, output.data, expected_output.data) + assert equalp + return + + outputs = output.model_dump(exclude_unset=True, exclude_none=True) + expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) + mapdiff = diff.DeepDiff(outputs, expects) + assert len(mapdiff.affected_paths) == 0 + +@pytest.mark.parametrize ( + "input, expected_output", AGENTIO_TEST_PARAMETERS, +) +@pytest.mark.llm +def test_agentio_mapping(llm_iomapper_config, input, expected_output): + llmIOMapper = create_iomapper(llm_iomapper_config) + + workflow = StateGraph(State) + workflow.add_node("mapper", llmIOMapper) + workflow.add_edge(START, "mapper") + workflow.add_edge("mapper", END) + graph = workflow.compile() + + output_state = graph.invoke(State(input=input)) + print(f"output_state: {output_state}") + output = output_state["output"] + + if isinstance(output.data, str): + equalp = compare_outputs(llm_iomapper_config.llm, output.data, expected_output.data) + assert equalp + return + + outputs = output.model_dump(exclude_unset=True, exclude_none=True) + expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) + mapdiff = diff.DeepDiff(outputs, expects) + assert len(mapdiff.affected_paths) == 0 + + +__COMPARE_SYSTEM_PROMPT="""You are comparing two texts for similarity. +First, write out in a step by step manner your reasoning to be sure that your conclusion is correct. Avoid simply stating the correct answer at the outset. Then print only a single choice from [true, false] (without quotes or punctuation) on its own line corresponding to the correct answer. At the end, repeat just the answer by itself on a new line. +""" +__COMPARE_USER_PROMPT="""Here is the data: +[BEGIN DATA] +************ +[First text]: {text1} +************ +[Second text]: {text2} +************ +[END DATA] + +Compare the factual content of the texts. Ignore any differences in style, grammar, or punctuation. +Answer the question by selecting one of the following options: +(true) The first text is largely the same as the second and fully consistent with it. +(false) There is a disagreement between the first text and the second. + +Reasoning +""" + +async def compare_outputs_async(llm: BaseChatModel, text1: str, text2: str) -> bool: + user_prompt = __COMPARE_USER_PROMPT.format(**locals()) + response = await llm.ainvoke([ + SystemMessage(content=__COMPARE_SYSTEM_PROMPT), + HumanMessage(content=user_prompt), + ]) + matches = re.search("(true|false)\\s*$", response.content, re.IGNORECASE) + match = matches.group(1) + return match is not None and match.startswith("t") + +def compare_outputs(llm: BaseChatModel, text1: str, text2: str) -> bool: + user_prompt = __COMPARE_USER_PROMPT.format(**locals()) + response = llm.invoke([ + SystemMessage(content=__COMPARE_SYSTEM_PROMPT), + HumanMessage(content=user_prompt), + ]) + matches = re.search("(true|false)\\s*$", response.content, re.IGNORECASE) + match = matches.group(1) + return match is not None and match.startswith("t") diff --git a/tests/test_pydantic_ai_agent_iomapper.py b/tests/test_pydantic_ai_agent_iomapper.py new file mode 100644 index 0000000..7e5ea7e --- /dev/null +++ b/tests/test_pydantic_ai_agent_iomapper.py @@ -0,0 +1,127 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +from deepdiff import diff +import pytest +from jinja2.sandbox import SandboxedEnvironment + +from agntcy_iomapper.pydantic_ai import PydanticAIIOAgentIOMapper, PydanticAIAgentIOMapperConfig, AgentModelSettings, AgentIOModelArgs, get_supported_agent +from agntcy_iomapper import AgentIOMapper +import re + +from tests.agentio_data import AGENTIO_TEST_PARAMETERS + +@pytest.fixture +def jinja_env() -> SandboxedEnvironment: + return SandboxedEnvironment( + loader=None, + enable_async=False, + autoescape=False, + ) + +@pytest.fixture +def jinja_env_async() -> SandboxedEnvironment: + return SandboxedEnvironment( + loader=None, + enable_async=True, + autoescape=False, + ) + +@pytest.fixture +def llm_iomapper_config() -> PydanticAIAgentIOMapperConfig: + return PydanticAIAgentIOMapperConfig( + models={ + "azure:gpt-4o-mini": AgentIOModelArgs( + api_version="2024-07-01-preview", + azure_endpoint="https://smith-project-agents.openai.azure.com", + ), + }, + default_model_settings={ + "azure:gpt-4o-mini": AgentModelSettings( + seed=42, + temperature=0, + ), + }, + validate_json_input=True, + validate_json_output=True, + ) + +@pytest.mark.parametrize ( + "input, expected_output", AGENTIO_TEST_PARAMETERS, +) +@pytest.mark.llm +async def test_agent_mapping_async(llm_iomapper_config, jinja_env_async, input, expected_output): + llmIOMapper = PydanticAIIOAgentIOMapper(llm_iomapper_config, jinja_env_async=jinja_env_async) + output = await llmIOMapper.ainvoke(input) + if isinstance(output.data, str): + equalp = await compare_outputs_async(llmIOMapper, output.data, expected_output.data) + assert equalp + return + + outputs = output.model_dump(exclude_unset=True, exclude_none=True) + expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) + mapdiff = diff.DeepDiff(outputs, expects) + assert len(mapdiff.affected_paths) == 0 + +@pytest.mark.parametrize ( + "input, expected_output", AGENTIO_TEST_PARAMETERS, +) +@pytest.mark.llm +def test_agent_mapping(llm_iomapper_config, jinja_env, input, expected_output): + llmIOMapper = PydanticAIIOAgentIOMapper(llm_iomapper_config, jinja_env=jinja_env) + output = llmIOMapper.invoke(input) + if isinstance(output.data, str): + equalp = compare_outputs(llmIOMapper, output.data, expected_output.data) + assert equalp + return + + outputs = output.model_dump(exclude_unset=True, exclude_none=True) + expects = expected_output.model_dump(exclude_unset=True, exclude_none=True) + mapdiff = diff.DeepDiff(outputs, expects) + assert len(mapdiff.affected_paths) == 0 + + +__COMPARE_SYSTEM_PROMPT="""You are comparing two texts for similarity. +First, write out in a step by step manner your reasoning to be sure that your conclusion is correct. Avoid simply stating the correct answer at the outset. Then print only a single choice from [true, false] (without quotes or punctuation) on its own line corresponding to the correct answer. At the end, repeat just the answer by itself on a new line. +""" +__COMPARE_USER_PROMPT="""Here is the data: +[BEGIN DATA] +************ +[First text]: {text1} +************ +[Second text]: {text2} +************ +[END DATA] + +Compare the factual content of the texts. Ignore any differences in style, grammar, or punctuation. +Answer the question by selecting one of the following options: +(true) The first text is largely the same as the second and fully consistent with it. +(false) There is a disagreement between the first text and the second. + +Reasoning +""" + +async def compare_outputs_async(iomapper: AgentIOMapper, text1: str, text2: str) -> bool: + model_name = iomapper.config.default_model + agent = get_supported_agent( + model_name, + system_prompt=__COMPARE_SYSTEM_PROMPT, + model_args=iomapper.config.models[model_name], + ) + user_prompt = __COMPARE_USER_PROMPT.format(**locals()) + response = await agent.run(user_prompt=user_prompt, model_settings=iomapper.config.default_model_settings[model_name]) + matches = re.search("(true|false)\\s*$", response.data, re.IGNORECASE) + match = matches.group(1) + return match is not None and match.startswith("t") + +def compare_outputs(iomapper: AgentIOMapper, text1: str, text2: str) -> bool: + model_name = iomapper.config.default_model + agent = get_supported_agent( + model_name, + system_prompt=__COMPARE_SYSTEM_PROMPT, + model_args=iomapper.config.models[model_name], + ) + user_prompt = __COMPARE_USER_PROMPT.format(**locals()) + response = agent.run_sync(user_prompt=user_prompt, model_settings=iomapper.config.default_model_settings[model_name]) + matches = re.search("(true|false)\\s*$", response.data, re.IGNORECASE) + match = matches.group(1) + return match is not None and match.startswith("t") diff --git a/tests/utils.py b/tests/utils.py deleted file mode 100644 index 75e35d3..0000000 --- a/tests/utils.py +++ /dev/null @@ -1,51 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. -# SPDX-License-Identifier: Apache-2.0 -from agntcy_iomapper import AgentIOMapper -from agntcy_iomapper.supported_agents import get_supported_agent -import re - -__COMPARE_SYSTEM_PROMPT="""You are comparing two texts for similarity. -First, write out in a step by step manner your reasoning to be sure that your conclusion is correct. Avoid simply stating the correct answer at the outset. Then print only a single choice from [true, false] (without quotes or punctuation) on its own line corresponding to the correct answer. At the end, repeat just the answer by itself on a new line. -""" -__COMPARE_USER_PROMPT="""Here is the data: -[BEGIN DATA] -************ -[First text]: {text1} -************ -[Second text]: {text2} -************ -[END DATA] - -Compare the factual content of the texts. Ignore any differences in style, grammar, or punctuation. -Answer the question by selecting one of the following options: -(true) The first text is largely the same as the second and fully consistent with it. -(false) There is a disagreement between the first text and the second. - -Reasoning -""" - -async def compare_outputs_async(iomapper: AgentIOMapper, text1: str, text2: str) -> bool: - model_name = iomapper.config.default_model - agent = get_supported_agent( - model_name, - system_prompt=__COMPARE_SYSTEM_PROMPT, - model_args=iomapper.config.models[model_name], - ) - user_prompt = __COMPARE_USER_PROMPT.format(**locals()) - response = await agent.run(user_prompt=user_prompt, model_settings=iomapper.config.default_model_settings[model_name]) - matches = re.search("(true|false)\\s*$", response.data, re.IGNORECASE) - match = matches.group(1) - return match is not None and match.startswith("t") - -def compare_outputs(iomapper: AgentIOMapper, text1: str, text2: str) -> bool: - model_name = iomapper.config.default_model - agent = get_supported_agent( - model_name, - system_prompt=__COMPARE_SYSTEM_PROMPT, - model_args=iomapper.config.models[model_name], - ) - user_prompt = __COMPARE_USER_PROMPT.format(**locals()) - response = agent.run_sync(user_prompt=user_prompt, model_settings=iomapper.config.default_model_settings[model_name]) - matches = re.search("(true|false)\\s*$", response.data, re.IGNORECASE) - match = matches.group(1) - return match is not None and match.startswith("t")