Move GCE based data generation to its own package (#628)

Author: DavidKoleczek

…ssistant-evaluations/.vscode/launch.json …n/assistant-data-gen/.vscode/launch.jsonlibraries/python/assistant-evaluations/.vscode/launch.json renamed to libraries/python/assistant-data-gen/.vscode/launch.json libraries/python/assistant-evaluations/.vscode/launch.json renamed to libraries/python/assistant-data-gen/.vscode/launch.json

@@ -1,9 +1,8 @@
-# Evaluation
-This is a work-in-progress tool for evaluating Semantic Workbench Assistants for quality.
+# Data Generation
 
+This is a tool for generating data for testing Semantic Workbench assistants.
 
-## Automation and Data Generation
-There is currently one part to this which is automation to populate a Workbench conversation automatically without human intervention.
+The core functionality of this library is an automation to populate a Workbench conversation automatically without human intervention.
 This is implemented using a specialized version of the guided conversation engine (GCE).
 The GCE here focuses on the agenda and using an exact resource constraint to force the GCE to have a long running conversation.
 
@@ -12,7 +11,7 @@ There is also a quick `generate_scenario.py` script that can be used to generate
 ### Setup
 
 1. Run the workbench service running locally (at http://127.0.0.1:3000), an assistant service, and create the assistant you want to test.
-2. Have LLM provider configured. Check [pydantic_ai_utils.py](./assistant_evaluations/pydantic_ai_utils.py) for an example of how it is configured for Pydantic AI.
+2. Have LLM provider configured. Check [pydantic_ai_utils.py](./assistant_data_gen/pydantic_ai_utils.py) for an example of how it is configured for Pydantic AI.
    1. For example, create a `.env` file with your Azure OpenAI endpoint set as `ASSISTANT__AZURE_OPENAI_ENDPOINT=<your_endpoint>`
 3. Create a configuration file. See [document_assistant_example_config.yaml](./configs/document_assistant_example_config.yaml) for an example.
    1. The scenarios field is a list that allows you to specify multiple test scenarios (different conversation paths).
@@ -33,4 +32,3 @@ python scripts/generate_scenario.py --config path/to/custom_config.yaml
 
 ### Recommendations
 1. Be as specific as possible with your conversation flows. Generic conversation flows and/or resource constraints that are too high can lead to the agents getting stuck in a thank you loop.
-

…istant-evaluations/.vscode/settings.json …assistant-data-gen/.vscode/settings.jsonlibraries/python/assistant-evaluations/.vscode/settings.json renamed to libraries/python/assistant-data-gen/.vscode/settings.json libraries/python/assistant-evaluations/.vscode/settings.json renamed to libraries/python/assistant-data-gen/.vscode/settings.json

@@ -6,7 +6,7 @@
 import yaml
 from pydantic import BaseModel, Field
 
-from assistant_evaluations.gce.gce_agent import ResourceConstraintMode
+from assistant_data_gen.gce.gce_agent import ResourceConstraintMode
 
 
 class ScenarioConfig(BaseModel):

…es/python/assistant-evaluations/Makefile …aries/python/assistant-data-gen/Makefilelibraries/python/assistant-evaluations/Makefile renamed to libraries/python/assistant-data-gen/Makefile libraries/python/assistant-evaluations/Makefile renamed to libraries/python/assistant-data-gen/Makefile

@@ -40,7 +40,7 @@
 from pydantic_ai.providers.openai import OpenAIProvider
 from pydantic_ai.tools import ToolDefinition
 
-from assistant_evaluations.gce.prompts import (
+from assistant_data_gen.gce.prompts import (
     AGENDA_SYSTEM_PROMPT,
     CONVERSATION_SYSTEM_PROMPT,
     FIRST_USER_MESSAGE,
@@ -49,7 +49,7 @@
     TERMINATION_INSTRUCTIONS_EXACT,
     TERMINATION_INSTRUCTIONS_MAXIMUM,
 )
-from assistant_evaluations.pydantic_ai_utils import create_model
+from assistant_data_gen.pydantic_ai_utils import create_model
 
 
 class ResourceConstraintMode(Enum):