diff --git a/agntcy_iomapper/__init__.py b/agntcy_iomapper/__init__.py index 36bfc27..5bc9575 100644 --- a/agntcy_iomapper/__init__.py +++ b/agntcy_iomapper/__init__.py @@ -1,13 +1,11 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 # ruff: noqa: F401 -from .agent_iomapper import ( +from .base import ( AgentIOMapper, AgentIOMapperConfig, AgentIOMapperInput, AgentIOMapperOutput, -) -from .base import ( BaseIOMapper, BaseIOMapperConfig, BaseIOMapperInput, diff --git a/agntcy_iomapper/base/__init__.py b/agntcy_iomapper/base/__init__.py new file mode 100644 index 0000000..4288377 --- /dev/null +++ b/agntcy_iomapper/base/__init__.py @@ -0,0 +1,4 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +from .agent_iomapper import * +from .base import * diff --git a/agntcy_iomapper/agent_iomapper.py b/agntcy_iomapper/base/agent_iomapper.py similarity index 100% rename from agntcy_iomapper/agent_iomapper.py rename to agntcy_iomapper/base/agent_iomapper.py diff --git a/agntcy_iomapper/base.py b/agntcy_iomapper/base/base.py similarity index 59% rename from agntcy_iomapper/base.py rename to agntcy_iomapper/base/base.py index 383a974..982dd43 100644 --- a/agntcy_iomapper/base.py +++ b/agntcy_iomapper/base/base.py @@ -9,27 +9,62 @@ class ArgumentsDescription(BaseModel): + """ + ArgumentsDescription a pydantic model that defines + the details necessary to perfom io mapping between two agents + """ + json_schema: Schema | None = Field( default=None, description="Data format JSON schema" ) description: str | None = Field( default=None, description="Data (semantic) natural language description" ) + agent_manifest: dict[str, Any] | None = Field( + default=None, + description="Agent Manifest definition as per https://agntcy.github.io/acp-spec/openapi.html#model/agentmanifest", + ) @model_validator(mode="after") def _validate_obj(self) -> Self: - if self.json_schema is None and self.description is None: + if ( + self.json_schema is None + and self.description is None + and self.agent_manifest + ): raise ValueError( - 'Either the "schema" field and/or the "description" field must be specified.' + 'Either the "schema" field and/or the "description" or agent_manifest field must be specified.' ) return self class BaseIOMapperInput(BaseModel): - input: ArgumentsDescription = Field(description="Input data descriptions") - output: ArgumentsDescription = Field(description="Output data descriptions") + input: ArgumentsDescription = Field( + description="Input data descriptions", + ) + output: ArgumentsDescription = Field( + description="Output data descriptions", + ) data: Any = Field(description="Data to translate") + @model_validator(mode="after") + def _validate_obj(self) -> Self: + if self.input.agent_manifest is not None: + # given an input agents manifest map its ouput definition + # because the data to be mapped is the result of calling the input agent + self.input.json_schema = Schema.model_validate( + self.input.agent_manifest["specs"]["output"] + ) + + if self.output.agent_manifest: + # given an output agents manifest map its input definition + # because the data to be mapped would be mapped to it's input + self.output.json_schema = Schema.model_validate( + self.output.agent_manifest["specs"]["input"] + ) + + return self + class BaseIOMapperOutput(BaseModel): data: Any = Field(default=None, description="Data after translation") diff --git a/agntcy_iomapper/imperative/__init__.py b/agntcy_iomapper/imperative/__init__.py new file mode 100644 index 0000000..4b6694d --- /dev/null +++ b/agntcy_iomapper/imperative/__init__.py @@ -0,0 +1,8 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0" + +from .imperative import ( + ImperativeIOMapper, + ImperativeIOMapperInput, + ImperativeIOMapperOutput, +) diff --git a/agntcy_iomapper/imperative.py b/agntcy_iomapper/imperative/imperative.py similarity index 99% rename from agntcy_iomapper/imperative.py rename to agntcy_iomapper/imperative/imperative.py index 3260c26..c6a03e7 100644 --- a/agntcy_iomapper/imperative.py +++ b/agntcy_iomapper/imperative/imperative.py @@ -22,7 +22,7 @@ import jsonschema from jsonpath_ng.ext import parse -from .base import ( +from agntcy_iomapper.base import ( BaseIOMapper, BaseIOMapperConfig, BaseIOMapperInput, diff --git a/agntcy_iomapper/langgraph/__init__.py b/agntcy_iomapper/langgraph/__init__.py new file mode 100644 index 0000000..8426682 --- /dev/null +++ b/agntcy_iomapper/langgraph/__init__.py @@ -0,0 +1,11 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0" + +from .langgraph import ( + LangGraphIOMapper, + LangGraphIOMapperConfig, + LangGraphIOMapperInput, + LangGraphIOMapperOutput, +) + +from .create_langraph_iomapper import create_langraph_iomapper diff --git a/agntcy_iomapper/langgraph/create_langraph_iomapper.py b/agntcy_iomapper/langgraph/create_langraph_iomapper.py new file mode 100644 index 0000000..fa0409a --- /dev/null +++ b/agntcy_iomapper/langgraph/create_langraph_iomapper.py @@ -0,0 +1,22 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0" +from langchain_core.runnables import Runnable + +from .langgraph import ( + LangGraphIOMapper, + LangGraphIOMapperConfig, + LangGraphIOMapperInput, + LangGraphIOMapperOutput, +) + + +def create_langraph_iomapper( + config: LangGraphIOMapperConfig, +) -> Runnable[LangGraphIOMapperInput, LangGraphIOMapperOutput]: + """Creates a langgraph agent + Args: + config: The configuration of the llm that would be used during the mapping + Returns: + A runnable representing an agent. It returns as output the mapping result + """ + return LangGraphIOMapper(config).as_runnable() diff --git a/agntcy_iomapper/langgraph.py b/agntcy_iomapper/langgraph/langgraph.py similarity index 90% rename from agntcy_iomapper/langgraph.py rename to agntcy_iomapper/langgraph/langgraph.py index 255c9db..9daa816 100644 --- a/agntcy_iomapper/langgraph.py +++ b/agntcy_iomapper/langgraph/langgraph.py @@ -5,11 +5,11 @@ from langchain.chat_models import init_chat_model from langchain_core.language_models import BaseChatModel -from langchain_core.runnables import Runnable, RunnableConfig +from langchain_core.runnables import RunnableConfig from langgraph.utils.runnable import RunnableCallable from pydantic import Field -from .agent_iomapper import ( +from agntcy_iomapper.base import ( AgentIOMapper, AgentIOMapperConfig, AgentIOMapperInput, @@ -86,9 +86,3 @@ def invoke(self, state: dict[str, Any], config: RunnableConfig) -> dict: 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 index ae4c743..a84447b 100644 --- a/agntcy_iomapper/pydantic_ai.py +++ b/agntcy_iomapper/pydantic_ai.py @@ -18,7 +18,7 @@ from pydantic_ai.models.openai import OpenAIModel from typing_extensions import Self -from .agent_iomapper import ( +from agntcy_iomapper.base import ( AgentIOMapper, AgentIOMapperConfig, AgentIOMapperInput, diff --git a/docs/usage.md b/docs/usage.md index b5dd101..8e407db 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,7 +1,7 @@ # Usage -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 +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: ```python @@ -23,16 +23,16 @@ class BaseIOMapperConfig(BaseModel): validate_json_output: bool = Field(description="Validate output against JSON schema.") ``` -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) +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 +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 +it additionally supports specifying the model prompts for the translation. The configuration object provides a specification for the system and default user prompts: ```python @@ -54,11 +54,11 @@ class AgentIOMapperInput(BaseIOMapperInput): ) ``` -Further specification of models and their arguments is left to the underlying supported +Further specification of models and their arguments is left to the underlying supported packages: - * [Pydantic-AI](#pydantic-ai) - * [LangGraph](#langgraph) +- [Pydantic-AI](#pydantic-ai) +- [LangGraph](#langgraph) ### Pydantic-AI @@ -70,8 +70,8 @@ This project supports specifying model interations using [LangGraph](https://lan ## 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 +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 @@ -89,7 +89,7 @@ agents is omitted. } } - # the mapping object using jsonpath, note: the value of the mapping + # the mapping object using jsonpath, note: the value of the mapping # can be either a jsonpath or a function mapping_object = { "prof_question": "$.question", @@ -115,8 +115,8 @@ agents is omitted. ### 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. 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agntcy_iomapper.agent_iomapper import AgentIOMapperInput, AgentIOMapperOutput -from agntcy_iomapper.base import ArgumentsDescription + +from agntcy_iomapper.base import ( + AgentIOMapperInput, + AgentIOMapperOutput, + ArgumentsDescription, +) AGENTIO_TEST_PARAMETERS = [ ( 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?", - ), + input=ArgumentsDescription(description="Text in English."), + output=ArgumentsDescription(description="Text in French."), + data="Would you please tell me where I can buy some snails?", + ), AgentIOMapperOutput( - data = "Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", + data="Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", ), ), ( AgentIOMapperInput( - input = ArgumentsDescription(description=""), - output = ArgumentsDescription(description=""), - data = "Would you please tell me where I can buy some snails?", + 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 }}", - ), + ), AgentIOMapperOutput( - data = "Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", + data="Pourriez-vous s'il vous plaît me dire où je peux acheter des escargots?", ), ), ( AgentIOMapperInput( - input = ArgumentsDescription(json_schema=Schema( - properties={ - "description": Schema( - type=DataType.STRING, - description="A description of the object.", - example="It is a right triangle with a hypotenuse of 5 cm.", - ), - }, - )), - output = ArgumentsDescription(json_schema=Schema( - properties={ - "length": Schema( - type=DataType.STRING, - description="The length as a measure with units.", - example="5 cm", - ), - "width": Schema( - type=DataType.STRING, - description="The width as a measure with units.", - example="5 cm", - ), - "height": Schema( - type=DataType.STRING, - description="The height as a measure with units.", - example="5 cm", - ), - }, - )), - data = {"description": "The object is a purple cube with sides of 1 m."}, - ), + input=ArgumentsDescription( + json_schema=Schema( + properties={ + "description": Schema( + type=DataType.STRING, + description="A description of the object.", + example="It is a right triangle with a hypotenuse of 5 cm.", + ), + }, + ) + ), + output=ArgumentsDescription( + json_schema=Schema( + properties={ + "length": Schema( + type=DataType.STRING, + description="The length as a measure with units.", + example="5 cm", + ), + "width": Schema( + type=DataType.STRING, + description="The width as a measure with units.", + example="5 cm", + ), + "height": Schema( + type=DataType.STRING, + description="The height as a measure with units.", + example="5 cm", + ), + }, + ) + ), + data={"description": "The object is a purple cube with sides of 1 m."}, + ), AgentIOMapperOutput( - data = {"length": "1 m", "width": "1 m", "height": "1 m"}, + data={"length": "1 m", "width": "1 m", "height": "1 m"}, ), ), ( AgentIOMapperInput( - input = ArgumentsDescription(json_schema=Schema( - properties={ - "description": Schema( - type=DataType.STRING, - description="A description of the object.", - example="It is a blue, right triangle with a hypotenuse of 5 cm.", - ), - }, - )), - output = ArgumentsDescription(json_schema=Schema( - properties={ - "length": Schema( - type=DataType.NUMBER, - description="The length as a number without units.", - example="5", - ), - "width": Schema( - type=DataType.NUMBER, - description="The width as a number without units.", - example="5", - ), - "height": Schema( - type=DataType.NUMBER, - description="The height as a number without units.", - example="5", - ), - }, - )), - data = {"description": "The object is a purple cube with sides of 1.5 m."}, - ), + input=ArgumentsDescription( + json_schema=Schema( + properties={ + "description": Schema( + type=DataType.STRING, + description="A description of the object.", + example="It is a blue, right triangle with a hypotenuse of 5 cm.", + ), + }, + ) + ), + output=ArgumentsDescription( + json_schema=Schema( + properties={ + "length": Schema( + type=DataType.NUMBER, + description="The length as a number without units.", + example="5", + ), + "width": Schema( + type=DataType.NUMBER, + description="The width as a number without units.", + example="5", + ), + "height": Schema( + type=DataType.NUMBER, + description="The height as a number without units.", + example="5", + ), + }, + ) + ), + data={"description": "The object is a purple cube with sides of 1.5 m."}, + ), AgentIOMapperOutput( - data = {"length": 1.5, "width": 1.5, "height": 1.5}, + data={"length": 1.5, "width": 1.5, "height": 1.5}, ), ), ] diff --git a/tests/conftest.py b/tests/conftest.py index 2fa09d0..b8806b4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,24 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 -from dotenv import load_dotenv, find_dotenv +import pytest +from dotenv import find_dotenv, load_dotenv +from langchain_openai.chat_models.azure import AzureChatOpenAI + +from agntcy_iomapper.langgraph import LangGraphIOMapperConfig + + +@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, + ) + load_dotenv(dotenv_path=find_dotenv(usecwd=True)) diff --git a/tests/data/manifests/agent_mailreviewer.json b/tests/data/manifests/agent_mailreviewer.json new file mode 100644 index 0000000..9bc907d --- /dev/null +++ b/tests/data/manifests/agent_mailreviewer.json @@ -0,0 +1,56 @@ +{ + "metadata": { + "ref": { + "name": "org.agntcy.email_reviewer", + "version": "0.0.1" + }, + "description": "In charge of reviewing and correcting emails addressed to a specific audience" + }, + "specs": { + "input": { + "type": "object", + "properties": { + "email": { + "type": "string" + }, + "audience": { + "type": "string", + "enum": ["general", "technical", "business", "academic"] + } + }, + "required": ["email", "audience"] + }, + "output": { + "type": "object", + "properties": { + "correct": { + "type": "boolean" + }, + "corrected_email": { + "type": "string" + } + }, + "required": ["correct"] + }, + "capabilities": { + "threads": false, + "interrupts": false, + "callbacks": false + }, + "config": {}, + "interrupts": [] + }, + "dependencies": [], + "deployments": [ + { + "type": "source_code", + "name": "src", + "url": "file://.", + "framework_config": { + "framework_type": "llamaindex", + "name": "./workflows", + "path": "workflows:email_reviewer" + } + } + ] +} diff --git a/tests/data/manifests/agent_mailwriter.json b/tests/data/manifests/agent_mailwriter.json new file mode 100644 index 0000000..283b6bc --- /dev/null +++ b/tests/data/manifests/agent_mailwriter.json @@ -0,0 +1,84 @@ +{ + "metadata": { + "ref": { + "name": "org.agntcy.mailcomposer", + "version": "0.0.1" + }, + "description": "Offer a chat interface to compose an email for a marketing campaign. Final output is the email that could be used for the campaign" + }, + "specs": { + "input": { + "type": "object", + "properties": { + "messages": { + "type": "array", + "items": { + "type": "object", + "properties": { + "type": { + "type": "string", + "enum": ["human", "assistant"] + }, + "content": { + "type": "string" + } + } + } + }, + "is_completed": { + "type": "boolean" + } + } + }, + "output": { + "type": "object", + "properties": { + "messages": { + "type": "array", + "items": { + "type": "object", + "properties": { + "type": { + "type": "string", + "enum": ["human", "assistant"] + }, + "final_email": { + "type": "string" + } + } + } + }, + "is_completed": { + "type": "boolean" + } + } + }, + "config": { + "type": "object", + "description": "The configuration of the agent", + "properties": { + "test": { + "type": "boolean" + } + } + }, + "capabilities": { + "threads": false, + "interrupts": false, + "callbacks": false + }, + "interrupts": [] + }, + "dependencies": [], + "deployments": [ + { + "type": "source_code", + "name": "src", + "url": "file://.", + "framework_config": { + "framework_type": "langgraph", + "graph": "./mailcomposer/mailcomposer.py:graph" + } + } + ] +} diff --git a/tests/test_agent_iomapper_from_manifest.py b/tests/test_agent_iomapper_from_manifest.py new file mode 100644 index 0000000..c18df0e --- /dev/null +++ b/tests/test_agent_iomapper_from_manifest.py @@ -0,0 +1,130 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0" +import json +from pathlib import Path + +import pytest +from deepdiff import diff +from langgraph.graph import END, START, StateGraph + +from agntcy_iomapper.base import ( + AgentIOMapperInput, + AgentIOMapperOutput, + ArgumentsDescription, +) +from agntcy_iomapper.langgraph import create_langraph_iomapper +from tests.util import State, compare_outputs_async + +HERE = Path(__file__).parent +mailwriter_manifest = json.load( + open(HERE / "./data/manifests/agent_mailwriter.json", "rb") +) +mailreviewer_manifest = json.load( + open(HERE / "./data/manifests/agent_mailreviewer.json") +) + +input_data = { + "instructions": "The recipient is a candidate for a job. Please tell the candidate that we selected them to have an online interview. Ask if the candidate is interested and say that details about the schedule will be communicated in further emails.", + "emailchain": [ + { + "direction": "sent", + "subject": "Job application", + "content": "Dear candidate, we are pleased to inform you that we selected you to have an online interview. Are you interested? Details about the schedule will be communicated in further emails.", + }, + ], + "context": "An experienced software developer", +} + +input_data_multiple_exchange = { + "instructions": "The recipient is a candidate for a job. Please tell the candidate that we selected them to have an online interview. Ask if the candidate is interested and say that details about the schedule will be communicated in further emails.", + "emailchain": [ + { + "direction": "sent", + "subject": "Initial Approach", + "content": "Dear Jonh, we've found your profile online and think that the follwoing position we're recruting for might be of your interest 'Job details'", + }, + { + "direction": "received", + "subject": "RE:Initial", + "content": "Dear candidate, we are pleased to inform you that we selected you to have an online interview. Are you interested? Details about the schedule will be communicated in further emails.", + }, + { + "direction": "sent", + "subject": "Interview Schedule", + "content": "We're very pleased that you are willing to move forward with your applicaiton, and this emails serves as an invitation for the next step which is a online assessment.", + }, + ], + "context": "An experienced software developer", +} +expected_output = { + "email": "Dear candidate, we are pleased to inform you that we selected you to have an online interview. Are you interested? Details about the schedule will be communicated in further emails.", + "audience": "general", +} + +expected_output_chain = { + "email": "Dear candidate, we are pleased to inform you that we selected you to have an online interview. Are you interested? Details about the schedule will be communicated in further emails.", + "audience": "general", +} + + +@pytest.mark.parametrize( + "source_agent_manifest, target_agent_manifest, input_data, expected_output", + [ + (mailwriter_manifest, mailreviewer_manifest, input_data, expected_output), + ( + mailwriter_manifest, + mailreviewer_manifest, + input_data_multiple_exchange, + expected_output_chain, + ), + ], +) +@pytest.mark.llm +async def test_mapping_from_manifest( + llm_iomapper_config, + source_agent_manifest, + target_agent_manifest, + input_data, + expected_output, +): + # Convert the schemas to pydantic objects representing input and output respectively + # Invoke + llmIOMapper = create_langraph_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() + + input = AgentIOMapperInput( + input=ArgumentsDescription( + description="Agent that does write emails", + agent_manifest=source_agent_manifest, + ), + output=ArgumentsDescription( + description="Agent that reviews emails", + agent_manifest=target_agent_manifest, + ), + data=input_data, + ) + + expected_mapp = AgentIOMapperOutput(data=expected_output) + + output_state = await graph.ainvoke(State(input=input)) + 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 + + print(output.data) + + outputs = output.model_dump(exclude_unset=True, exclude_none=True) + expects = expected_mapp.model_dump(exclude_unset=True, exclude_none=True) + mapdiff = diff.DeepDiff(outputs, expects) + assert len(mapdiff.affected_paths) == 0 diff --git a/tests/test_imperative_iomapper.py b/tests/test_imperative_iomapper.py index af2f8fd..d9aa9ad 100644 --- a/tests/test_imperative_iomapper.py +++ b/tests/test_imperative_iomapper.py @@ -5,8 +5,8 @@ import pytest from openapi_pydantic import Schema -from agntcy_iomapper.imperative import ImperativeIOMapper, ImperativeIOMapperInput from agntcy_iomapper.base import ArgumentsDescription +from agntcy_iomapper.imperative import ImperativeIOMapper, ImperativeIOMapperInput @pytest.mark.parametrize( diff --git a/tests/test_langgraph_agent_iomapper.py b/tests/test_langgraph_agent_iomapper.py index 8187d59..d15a3e6 100644 --- a/tests/test_langgraph_agent_iomapper.py +++ b/tests/test_langgraph_agent_iomapper.py @@ -1,41 +1,24 @@ # 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 +import pytest +from deepdiff import diff +from langgraph.graph import END, START, StateGraph +from agntcy_iomapper.langgraph import ( + create_langraph_iomapper, +) from tests.agentio_data import AGENTIO_TEST_PARAMETERS +from tests.util import State, compare_outputs, compare_outputs_async -@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.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) + llmIOMapper = create_langraph_iomapper(llm_iomapper_config) workflow = StateGraph(State) workflow.add_node("mapper", llmIOMapper) @@ -47,8 +30,10 @@ async def test_agentio_mapping_async(llm_iomapper_config, input, expected_output 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) + if isinstance(output.data, str): + equalp = await compare_outputs_async( + llm_iomapper_config.llm, output.data, expected_output.data + ) assert equalp return @@ -57,12 +42,14 @@ async def test_agentio_mapping_async(llm_iomapper_config, input, expected_output mapdiff = diff.DeepDiff(outputs, expects) assert len(mapdiff.affected_paths) == 0 -@pytest.mark.parametrize ( - "input, expected_output", AGENTIO_TEST_PARAMETERS, + +@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) + llmIOMapper = create_langraph_iomapper(llm_iomapper_config) workflow = StateGraph(State) workflow.add_node("mapper", llmIOMapper) @@ -74,8 +61,10 @@ def test_agentio_mapping(llm_iomapper_config, input, expected_output): 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) + if isinstance(output.data, str): + equalp = compare_outputs( + llm_iomapper_config.llm, output.data, expected_output.data + ) assert equalp return @@ -83,44 +72,3 @@ def test_agentio_mapping(llm_iomapper_config, input, expected_output): 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 index 7e5ea7e..9d5b521 100644 --- a/tests/test_pydantic_ai_agent_iomapper.py +++ b/tests/test_pydantic_ai_agent_iomapper.py @@ -1,15 +1,22 @@ # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 -from deepdiff import diff +import re + import pytest +from deepdiff import diff 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 agntcy_iomapper.pydantic_ai import ( + AgentIOModelArgs, + AgentModelSettings, + PydanticAIAgentIOMapperConfig, + PydanticAIIOAgentIOMapper, + get_supported_agent, +) from tests.agentio_data import AGENTIO_TEST_PARAMETERS + @pytest.fixture def jinja_env() -> SandboxedEnvironment: return SandboxedEnvironment( @@ -18,6 +25,7 @@ def jinja_env() -> SandboxedEnvironment: autoescape=False, ) + @pytest.fixture def jinja_env_async() -> SandboxedEnvironment: return SandboxedEnvironment( @@ -26,6 +34,7 @@ def jinja_env_async() -> SandboxedEnvironment: autoescape=False, ) + @pytest.fixture def llm_iomapper_config() -> PydanticAIAgentIOMapperConfig: return PydanticAIAgentIOMapperConfig( @@ -37,7 +46,7 @@ def llm_iomapper_config() -> PydanticAIAgentIOMapperConfig: }, default_model_settings={ "azure:gpt-4o-mini": AgentModelSettings( - seed=42, + seed=42, temperature=0, ), }, @@ -45,15 +54,23 @@ def llm_iomapper_config() -> PydanticAIAgentIOMapperConfig: validate_json_output=True, ) -@pytest.mark.parametrize ( - "input, expected_output", AGENTIO_TEST_PARAMETERS, + +@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) +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) + if isinstance(output.data, str): + equalp = await compare_outputs_async( + llmIOMapper, output.data, expected_output.data + ) assert equalp return @@ -62,14 +79,16 @@ async def test_agent_mapping_async(llm_iomapper_config, jinja_env_async, input, mapdiff = diff.DeepDiff(outputs, expects) assert len(mapdiff.affected_paths) == 0 -@pytest.mark.parametrize ( - "input, expected_output", AGENTIO_TEST_PARAMETERS, + +@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): + if isinstance(output.data, str): equalp = compare_outputs(llmIOMapper, output.data, expected_output.data) assert equalp return @@ -80,10 +99,10 @@ def test_agent_mapping(llm_iomapper_config, jinja_env, input, expected_output): assert len(mapdiff.affected_paths) == 0 -__COMPARE_SYSTEM_PROMPT="""You are comparing two texts for similarity. +__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: +__COMPARE_USER_PROMPT = """Here is the data: [BEGIN DATA] ************ [First text]: {text1} @@ -100,28 +119,38 @@ def test_agent_mapping(llm_iomapper_config, jinja_env, input, expected_output): Reasoning """ -async def compare_outputs_async(iomapper: AgentIOMapper, text1: str, text2: str) -> bool: + +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_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]) + 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_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]) + response = agent.run_sync( + user_prompt=user_prompt, + model_settings=iomapper.config.default_model_settings[model_name], + ) matches = re.search("(true|false)\\s*$", response.data, re.IGNORECASE) match = matches.group(1) return match is not None and match.startswith("t") diff --git a/tests/util.py b/tests/util.py new file mode 100644 index 0000000..2b49ec0 --- /dev/null +++ b/tests/util.py @@ -0,0 +1,65 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import re +from typing import TypedDict + +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import HumanMessage, SystemMessage + +from agntcy_iomapper.langgraph import ( + LangGraphIOMapperInput, + LangGraphIOMapperOutput, +) + + +class State(TypedDict): + input: LangGraphIOMapperInput + output: LangGraphIOMapperOutput + + +__COMPARE_SYSTEM_PROMPT = """You are comparing two texts for similarity. +First, write out in a step by step manner your reasoning to be sure that your conclusion is correct. Avoid simply stating the correct answer at the outset. Then print only a single choice from [true, false] (without quotes or punctuation) on its own line corresponding to the correct answer. At the end, repeat just the answer by itself on a new line. +""" +__COMPARE_USER_PROMPT = """Here is the data: +[BEGIN DATA] +************ +[First text]: {text1} +************ +[Second text]: {text2} +************ +[END DATA] + +Compare the factual content of the texts. Ignore any differences in style, grammar, or punctuation. +Answer the question by selecting one of the following options: +(true) The first text is largely the same as the second and fully consistent with it. +(false) There is a disagreement between the first text and the second. + +Reasoning +""" + + +async def compare_outputs_async(llm: BaseChatModel, text1: str, text2: str) -> bool: + user_prompt = __COMPARE_USER_PROMPT.format(**locals()) + response = await llm.ainvoke( + [ + SystemMessage(content=__COMPARE_SYSTEM_PROMPT), + HumanMessage(content=user_prompt), + ] + ) + matches = re.search("(true|false)\\s*$", 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")