Add evals for cognee

evals/generate_test_set.py

@@ -22,8 +22,10 @@ dotenv.load_dotenv()
 
 dataset = EvaluationDataset()
 dataset.generate_goldens_from_docs(
-    document_paths=['soldiers_home.pdf'],
-    max_goldens_per_document=10
+    document_paths=['natural_language_processing.txt', 'soldiers_home.pdf', 'trump.txt'],
+    max_goldens_per_document=10,
+    num_evolutions=5,
+    enable_breadth_evolve=True,
 )
 
 

evals/simple_rag_vs_cognee_eval.py

@@ -13,14 +13,32 @@ from cognee.infrastructure.llm.get_llm_client import get_llm_client
 dataset = EvaluationDataset()
 dataset.add_test_cases_from_json_file(
     # file_path is the absolute path to you .json file
-    file_path="synthetic_data/20240519_185842.json",
-    input_key_name="query",
+    file_path="./synthetic_data/20240519_185842.json",
+    input_key_name="input",
     actual_output_key_name="actual_output",
     expected_output_key_name="expected_output",
-    context_key_name="context",
-    retrieval_context_key_name="retrieval_context",
+    context_key_name="context"
 )
 
+print(dataset)
+# from deepeval.synthesizer import Synthesizer
+#
+# synthesizer = Synthesizer(model="gpt-3.5-turbo")
+#
+# dataset = EvaluationDataset()
+# dataset.generate_goldens_from_docs(
+#     synthesizer=synthesizer,
+#     document_paths=['natural_language_processing.txt', 'soldiers_home.pdf', 'trump.txt'],
+#     max_goldens_per_document=10,
+#     num_evolutions=5,
+#     enable_breadth_evolve=True,
+# )
+
+
+print(dataset.goldens)
+print(dataset)
+
+
 
 
 import logging
@@ -29,43 +47,51 @@ from cognee.infrastructure import infrastructure_config
 
 logger = logging.getLogger(__name__)
 
-def AnswerModel(BaseModel):
+class AnswerModel(BaseModel):
     response:str
-def get_answer_base(content: str, response_model: Type[BaseModel]):
+def get_answer_base(content: str, context:str, response_model: Type[BaseModel]):
     llm_client = get_llm_client()
 
-    system_prompt = "Answer the following question: and use the context"
+    system_prompt = "THIS IS YOUR CONTEXT:" + str(context)
 
     return  llm_client.create_structured_output(content, system_prompt, response_model)
-def get_answer(content: str, model: Type[BaseModel]= AnswerModel):
+def get_answer(content: str,context, model: Type[BaseModel]= AnswerModel):
 
     try:
         return (get_answer_base(
             content,
+            context,
             model
         ))
     except Exception as error:
         logger.error("Error extracting cognitive layers from content: %s", error, exc_info = True)
         raise error
 
+def run_cognify_base_rag_and_search():
+    pass
+
+
+def run_cognify_and_search():
+    pass
 
 
 
-def convert_goldens_to_test_cases(goldens: List[Golden]) -> List[LLMTestCase]:
+def convert_goldens_to_test_cases(test_cases_raw: List[LLMTestCase]) -> List[LLMTestCase]:
     test_cases = []
-    for golden in goldens:
+    for case in test_cases_raw:
         test_case = LLMTestCase(
-            input=golden.input,
+            input=case.input,
             # Generate actual output using the 'input' and 'additional_metadata'
-            actual_output= get_answer(golden.input),
-            expected_output=golden.expected_output,
-            context=golden.context,
+            actual_output= str(get_answer(case.input, case.context).model_dump()['response']),
+            expected_output=case.expected_output,
+            context=case.context,
+            retrieval_context=["retrieval_context"],
         )
         test_cases.append(test_case)
     return test_cases
 
 # Data preprocessing before setting the dataset test cases
-dataset.test_cases = convert_goldens_to_test_cases(dataset.goldens)
+dataset.test_cases = convert_goldens_to_test_cases(dataset.test_cases)
 
 
 from deepeval.metrics import HallucinationMetric