dev trulens_evaluetor

examples/graph_evaluation_example.py

@@ -0,0 +1,24 @@
+import os
+from scrapegraphai.evaluetor import TrulensEvaluator
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Define the configuration for the language model
+openai_key = os.getenv("OPENAI_APIKEY")
+
+llm_config = {
+    "api_key": openai_key,
+    "model_name": "gpt-3.5-turbo",
+}
+
+list_of_inputs = [
+    ("List me all the titles and project descriptions", "https://perinim.github.io/projects/", llm_config),
+    ("Who is the author of the project?", "https://perinim.github.io/projects/", llm_config),
+    ("What is the project about?", "https://perinim.github.io/projects/", llm_config)
+]
+
+# Create the TrulensEvaluator instance
+trulens_evaluator = TrulensEvaluator()
+# Evaluate SmartScraperGraph on the list of inputs
+trulens_evaluator.evaluate(list_of_inputs)

requirements.txt

@@ -9,3 +9,4 @@ pandas==2.0.3
 python-dotenv==1.0.1
 tiktoken>=0.5.2,<0.6.0
 tqdm==4.66.1
+trulens_eval==0.23.0

scrapegraphai/evaluetor/__init__.py

@@ -0,0 +1,4 @@
+""" 
+__init__.py file for evaluetor folder
+"""
+from .trulens_evaluetor import TrulensEvaluator

scrapegraphai/evaluetor/trulens_evaluetor.py

@@ -0,0 +1,71 @@
+import os
+from scrapegraphai.graphs import SmartScraperGraph
+from openai import OpenAI
+from trulens_eval import Feedback, OpenAI as fOpenAI, Tru, Provider, Select, TruBasicApp
+
+class TrulensEvaluator:
+    """
+    Class for evaluating Trulens using SmartScraperGraph.
+
+    Attributes:
+
+        tru_llm_standalone_recorder: TruBasicApp instance for recording.
+
+    Methods:
+        evaluate: Evaluates Trulens using SmartScraperGraph.
+        llm_standalone: Standalone function for Trulens evaluation.
+    """
+
+    def __init__(self):
+        standalone = StandAlone()
+        f_custom_function = Feedback(standalone.json_complaint).on(
+            my_text_field=Select.RecordOutput
+        )
+        os.environ["OPENAI_API_KEY"] = os.environ["OPENAI_APIKEY"]
+        client = OpenAI()
+        tru = Tru()
+        tru.reset_database()
+        fopenai = fOpenAI()
+        f_relevance = Feedback(self.fopenai.relevance).on_input_output()
+        tru_llm_standalone_recorder = TruBasicApp(self.llm_standalone, app_id="smart_scraper_evaluator", feedbacks=[self.f_relevance, self.f_custom_function])
+
+    def evaluate(self, graph_params : list[tuple[str, str, dict]]):
+        """
+        Evaluates Trulens using SmartScraperGraph and starts the dashboard.
+
+        Args:
+            graph_params: List of tuples containing graph parameters.
+
+        Returns:
+            None
+        """
+        with self.tru_llm_standalone_recorder as recording:
+            for params in graph_params:
+                output = SmartScraperGraph(*params).run()
+                self.tru_llm_standalone_recorder.app(params[0], output)
+        self.tru.run_dashboard()
+
+    def llm_standalone(self, prompt, response):
+        """
+        Standalone function for Trulens evaluation. Private method.
+
+        Args:
+            prompt: Prompt for evaluation.
+            response: Response from evaluation.
+
+        Returns:
+            str: Response as a string.
+        """
+        print(f"Prompt: {prompt}")
+        return str(response)
+
+"""
+Class for standalone Trulens evaluation. Personalise
+"""
+class StandAlone(Provider):
+    def json_complaint(self, my_text_field: str) -> float:
+        if '{' in my_text_field and '}' in my_text_field and ':' in my_text_field:
+            return 1.0
+        else:
+            return 0.0
+