diff --git a/.github/workflows/test_eval_framework.yml b/.github/workflows/test_eval_framework.yml
new file mode 100644
index 000000000..54aa68dd0
--- /dev/null
+++ b/.github/workflows/test_eval_framework.yml
@@ -0,0 +1,22 @@
+name: test | eval_framework
+
+on:
+ workflow_dispatch:
+ pull_request:
+ types: [labeled, synchronize]
+
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ run_eval_framework_test:
+ uses: ./.github/workflows/reusable_python_example.yml
+ with:
+ example-location: ./evals/eval_framework/run_eval.py
+ secrets:
+ LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
+ GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
diff --git a/cognee/infrastructure/llm/prompts/answer_simple_question_benchmark.txt b/cognee/infrastructure/llm/prompts/answer_simple_question_benchmark.txt
new file mode 100644
index 000000000..4c3ccbf92
--- /dev/null
+++ b/cognee/infrastructure/llm/prompts/answer_simple_question_benchmark.txt
@@ -0,0 +1,9 @@
+You are a highly optimized question-answering system that extracts only the most essential information from the provided context.
+
+Rules:
+ • Minimize words – Use as few as possible
+ • Yes/no questions → Answer with only "yes" or "no"
+ • What/Who/Where questions → Answer with a single word or phrase (no full sentences).
+ • When questions → Give only the relevant date, time, or period.
+ • How/Why questions → Answer with the shortest possible phrase.
+ • No punctuation, just the answer, preferably in dry, concise lowercase
diff --git a/cognee/modules/data/models/answers_base.py b/cognee/modules/data/models/answers_base.py
new file mode 100644
index 000000000..1e28c52d9
--- /dev/null
+++ b/cognee/modules/data/models/answers_base.py
@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class AnswersBase(DeclarativeBase):
+ pass
diff --git a/cognee/modules/data/models/answers_data.py b/cognee/modules/data/models/answers_data.py
new file mode 100644
index 000000000..d1534a7d5
--- /dev/null
+++ b/cognee/modules/data/models/answers_data.py
@@ -0,0 +1,15 @@
+from datetime import datetime, timezone
+from uuid import uuid4
+from sqlalchemy import Column, DateTime, JSON, UUID
+
+from cognee.modules.data.models.answers_base import AnswersBase
+
+
+class Answers(AnswersBase):
+ __tablename__ = "eval_answers"
+
+ id = Column(UUID, primary_key=True, default=uuid4)
+
+ payload = Column(JSON, nullable=False)
+
+ created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
diff --git a/cognee/modules/data/models/metrics_base.py b/cognee/modules/data/models/metrics_base.py
new file mode 100644
index 000000000..fa30fd373
--- /dev/null
+++ b/cognee/modules/data/models/metrics_base.py
@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class MetricsBase(DeclarativeBase):
+ pass
diff --git a/cognee/modules/data/models/metrics_data.py b/cognee/modules/data/models/metrics_data.py
new file mode 100644
index 000000000..7f4236194
--- /dev/null
+++ b/cognee/modules/data/models/metrics_data.py
@@ -0,0 +1,15 @@
+from datetime import datetime, timezone
+from uuid import uuid4
+from sqlalchemy import Column, DateTime, JSON, UUID
+
+from cognee.modules.data.models.metrics_base import MetricsBase
+
+
+class Metrics(MetricsBase):
+ __tablename__ = "eval_metrics"
+
+ id = Column(UUID, primary_key=True, default=uuid4)
+
+ payload = Column(JSON, nullable=False)
+
+ created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
diff --git a/cognee/modules/data/models/questions_base.py b/cognee/modules/data/models/questions_base.py
new file mode 100644
index 000000000..81b898b67
--- /dev/null
+++ b/cognee/modules/data/models/questions_base.py
@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class QuestionsBase(DeclarativeBase):
+ pass
diff --git a/cognee/modules/data/models/questions_data.py b/cognee/modules/data/models/questions_data.py
new file mode 100644
index 000000000..7038083a8
--- /dev/null
+++ b/cognee/modules/data/models/questions_data.py
@@ -0,0 +1,15 @@
+from datetime import datetime, timezone
+from uuid import uuid4
+from sqlalchemy import Column, DateTime, JSON, UUID
+
+from cognee.modules.data.models.questions_base import QuestionsBase
+
+
+class Questions(QuestionsBase):
+ __tablename__ = "eval_questions"
+
+ id = Column(UUID, primary_key=True, default=uuid4)
+
+ payload = Column(JSON, nullable=False)
+
+ created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
diff --git a/evals/eval_framework/__init__.py b/evals/eval_framework/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evals/eval_framework/answer_generation/__init__.py b/evals/eval_framework/answer_generation/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evals/eval_framework/answer_generation/answer_generation_executor.py b/evals/eval_framework/answer_generation/answer_generation_executor.py
new file mode 100644
index 000000000..a5b18c8e7
--- /dev/null
+++ b/evals/eval_framework/answer_generation/answer_generation_executor.py
@@ -0,0 +1,44 @@
+import cognee
+from typing import List, Dict, Callable, Awaitable
+from cognee.api.v1.search import SearchType
+
+question_answering_engine_options: Dict[str, Callable[[str], Awaitable[List[str]]]] = {
+ "cognee_graph_completion": lambda query: cognee.search(
+ query_type=SearchType.GRAPH_COMPLETION, query_text=query
+ ),
+ "cognee_completion": lambda query: cognee.search(
+ query_type=SearchType.COMPLETION, query_text=query
+ ),
+ "cognee_summaries": lambda query: cognee.search(
+ query_type=SearchType.SUMMARIES, query_text=query
+ ),
+ "cognee_insights": lambda query: cognee.search(
+ query_type=SearchType.INSIGHTS, query_text=query
+ ),
+ "cognee_chunks": lambda query: cognee.search(query_type=SearchType.CHUNKS, query_text=query),
+ "cognee_code": lambda query: cognee.search(query_type=SearchType.CODE, query_text=query),
+}
+
+
+class AnswerGeneratorExecutor:
+ async def question_answering_non_parallel(
+ self,
+ questions: List[Dict[str, str]],
+ answer_resolver: Callable[[str], Awaitable[List[str]]],
+ ) -> List[Dict[str, str]]:
+ answers = []
+ for instance in questions:
+ query_text = instance["question"]
+ correct_answer = instance["answer"]
+
+ search_results = await answer_resolver(query_text)
+
+ answers.append(
+ {
+ "question": query_text,
+ "answer": search_results[0],
+ "golden_answer": correct_answer,
+ }
+ )
+
+ return answers
diff --git a/evals/eval_framework/answer_generation/run_question_answering_module.py b/evals/eval_framework/answer_generation/run_question_answering_module.py
new file mode 100644
index 000000000..3ad5e78ac
--- /dev/null
+++ b/evals/eval_framework/answer_generation/run_question_answering_module.py
@@ -0,0 +1,54 @@
+import logging
+import json
+from evals.eval_framework.answer_generation.answer_generation_executor import (
+ AnswerGeneratorExecutor,
+ question_answering_engine_options,
+)
+from cognee.infrastructure.files.storage import LocalStorage
+from cognee.infrastructure.databases.relational.get_relational_engine import (
+ get_relational_engine,
+ get_relational_config,
+)
+from cognee.modules.data.models.answers_base import AnswersBase
+from cognee.modules.data.models.answers_data import Answers
+
+
+async def create_and_insert_answers_table(questions_payload):
+ relational_config = get_relational_config()
+ relational_engine = get_relational_engine()
+
+ if relational_engine.engine.dialect.name == "sqlite":
+ LocalStorage.ensure_directory_exists(relational_config.db_path)
+
+ async with relational_engine.engine.begin() as connection:
+ if len(AnswersBase.metadata.tables.keys()) > 0:
+ await connection.run_sync(AnswersBase.metadata.create_all)
+
+ async with relational_engine.get_async_session() as session:
+ data_point = Answers(payload=questions_payload)
+ session.add(data_point)
+ await session.commit()
+
+
+async def run_question_answering(params: dict) -> None:
+ if params.get("answering_questions"):
+ logging.info("Question answering started...")
+ try:
+ with open(params["questions_path"], "r", encoding="utf-8") as f:
+ questions = json.load(f)
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Could not find the file: {params['questions_path']}")
+ except json.JSONDecodeError as e:
+ raise ValueError(f"Error decoding JSON from {params['questions_path']}: {e}")
+
+ logging.info(f"Loaded {len(questions)} questions from {params['questions_path']}")
+ answer_generator = AnswerGeneratorExecutor()
+ answers = await answer_generator.question_answering_non_parallel(
+ questions=questions,
+ answer_resolver=question_answering_engine_options[params["qa_engine"]],
+ )
+ with open(params["answers_path"], "w", encoding="utf-8") as f:
+ json.dump(answers, f, ensure_ascii=False, indent=4)
+
+ await create_and_insert_answers_table(answers)
+ logging.info("Question answering End...")
diff --git a/evals/eval_framework/benchmark_adapters/__init__.py b/evals/eval_framework/benchmark_adapters/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evals/eval_framework/benchmark_adapters/base_benchmark_adapter.py b/evals/eval_framework/benchmark_adapters/base_benchmark_adapter.py
new file mode 100644
index 000000000..325126b7b
--- /dev/null
+++ b/evals/eval_framework/benchmark_adapters/base_benchmark_adapter.py
@@ -0,0 +1,8 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+
+class BaseBenchmarkAdapter(ABC):
+ @abstractmethod
+ def load_corpus(self, limit: Optional[int] = None, seed: int = 42) -> List[str]:
+ pass
diff --git a/evals/eval_framework/benchmark_adapters/benchmark_adapters.py b/evals/eval_framework/benchmark_adapters/benchmark_adapters.py
new file mode 100644
index 000000000..6d7033caa
--- /dev/null
+++ b/evals/eval_framework/benchmark_adapters/benchmark_adapters.py
@@ -0,0 +1,21 @@
+from enum import Enum
+from typing import Type
+
+from evals.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
+from evals.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
+from evals.eval_framework.benchmark_adapters.twowikimultihop_adapter import TwoWikiMultihopAdapter
+
+
+class BenchmarkAdapter(Enum):
+ DUMMY = ("Dummy", DummyAdapter)
+ HOTPOTQA = ("HotPotQA", HotpotQAAdapter)
+ TWOWIKIMULTIHOP = ("TwoWikiMultiHop", TwoWikiMultihopAdapter)
+
+ def __new__(cls, adapter_name: str, adapter_class: Type):
+ obj = object.__new__(cls)
+ obj._value_ = adapter_name
+ obj.adapter_class = adapter_class
+ return obj
+
+ def __str__(self):
+ return self.value
diff --git a/evals/eval_framework/benchmark_adapters/dummy_adapter.py b/evals/eval_framework/benchmark_adapters/dummy_adapter.py
new file mode 100644
index 000000000..3f35a8be6
--- /dev/null
+++ b/evals/eval_framework/benchmark_adapters/dummy_adapter.py
@@ -0,0 +1,22 @@
+from typing import Optional, Union, Any, LiteralString
+
+from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+
+
+class DummyAdapter(BaseBenchmarkAdapter):
+ def load_corpus(
+ self, limit: Optional[int] = None, seed: int = 42
+ ) -> tuple[list[Union[LiteralString, str]], list[dict[str, str]]]:
+ corpus_list = [
+ "The cognee is an AI memory engine that supports different vector and graph databases",
+ "Neo4j is a graph database supported by cognee",
+ ]
+ question_answer_pairs = [
+ {
+ "answer": "Yes",
+ "question": "Is Neo4j supported by cognee?",
+ "type": "dummy",
+ }
+ ]
+
+ return corpus_list, question_answer_pairs
diff --git a/evals/eval_framework/benchmark_adapters/hotpot_qa_adapter.py b/evals/eval_framework/benchmark_adapters/hotpot_qa_adapter.py
new file mode 100644
index 000000000..3cf4753e1
--- /dev/null
+++ b/evals/eval_framework/benchmark_adapters/hotpot_qa_adapter.py
@@ -0,0 +1,51 @@
+import requests
+import os
+import json
+import random
+from typing import Optional, Union, Any, LiteralString
+from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+
+
+class HotpotQAAdapter(BaseBenchmarkAdapter):
+ dataset_info = {
+ "filename": "hotpot_benchmark.json",
+ "url": "http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json",
+ # train: "http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_train_v1.1.json" delete file after changing the url
+ # distractor test: "http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json" delete file after changing the url
+ }
+
+ def load_corpus(
+ self, limit: Optional[int] = None, seed: int = 42
+ ) -> tuple[list[Union[LiteralString, str]], list[dict[str, Any]]]:
+ filename = self.dataset_info["filename"]
+
+ if os.path.exists(filename):
+ with open(filename, "r", encoding="utf-8") as f:
+ corpus_json = json.load(f)
+ else:
+ response = requests.get(self.dataset_info["url"])
+ response.raise_for_status()
+ corpus_json = response.json()
+
+ with open(filename, "w", encoding="utf-8") as f:
+ json.dump(corpus_json, f, ensure_ascii=False, indent=4)
+
+ if limit is not None and 0 < limit < len(corpus_json):
+ random.seed(seed)
+ corpus_json = random.sample(corpus_json, limit)
+
+ corpus_list = []
+ question_answer_pairs = []
+ for item in corpus_json:
+ for title, sentences in item["context"]:
+ corpus_list.append(" ".join(sentences))
+
+ question_answer_pairs.append(
+ {
+ "question": item["question"],
+ "answer": item["answer"].lower(),
+ "level": item["level"],
+ }
+ )
+
+ return corpus_list, question_answer_pairs
diff --git a/evals/eval_framework/benchmark_adapters/twowikimultihop_adapter.py b/evals/eval_framework/benchmark_adapters/twowikimultihop_adapter.py
new file mode 100644
index 000000000..0fbe3f156
--- /dev/null
+++ b/evals/eval_framework/benchmark_adapters/twowikimultihop_adapter.py
@@ -0,0 +1,49 @@
+import requests
+import os
+import json
+import random
+from typing import Optional, Union, Any, LiteralString
+from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+
+
+class TwoWikiMultihopAdapter(BaseBenchmarkAdapter):
+ dataset_info = {
+ "filename": "2wikimultihop_dev.json",
+ "URL": "https://huggingface.co/datasets/voidful/2WikiMultihopQA/resolve/main/dev.json",
+ }
+
+ def load_corpus(
+ self, limit: Optional[int] = None, seed: int = 42
+ ) -> tuple[list[Union[LiteralString, str]], list[dict[str, Any]]]:
+ filename = self.dataset_info["filename"]
+
+ if os.path.exists(filename):
+ with open(filename, "r", encoding="utf-8") as f:
+ corpus_json = json.load(f)
+ else:
+ response = requests.get(self.dataset_info["URL"])
+ response.raise_for_status()
+ corpus_json = response.json()
+
+ with open(filename, "w", encoding="utf-8") as f:
+ json.dump(corpus_json, f, ensure_ascii=False, indent=4)
+
+ if limit is not None and 0 < limit < len(corpus_json):
+ random.seed(seed)
+ corpus_json = random.sample(corpus_json, limit)
+
+ corpus_list = []
+ question_answer_pairs = []
+ for dict in corpus_json:
+ for title, sentences in dict["context"]:
+ corpus_list.append(" ".join(sentences))
+
+ question_answer_pairs.append(
+ {
+ "question": dict["question"],
+ "answer": dict["answer"].lower(),
+ "type": dict["type"],
+ }
+ )
+
+ return corpus_list, question_answer_pairs
diff --git a/evals/eval_framework/corpus_builder/__init__.py b/evals/eval_framework/corpus_builder/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evals/eval_framework/corpus_builder/corpus_builder_executor.py b/evals/eval_framework/corpus_builder/corpus_builder_executor.py
new file mode 100644
index 000000000..49abc9489
--- /dev/null
+++ b/evals/eval_framework/corpus_builder/corpus_builder_executor.py
@@ -0,0 +1,43 @@
+import cognee
+import logging
+from typing import Optional, Tuple, List, Dict, Union, Any
+
+from evals.eval_framework.benchmark_adapters.benchmark_adapters import BenchmarkAdapter
+from cognee.shared.utils import setup_logging
+
+
+class CorpusBuilderExecutor:
+ def __init__(self, benchmark: Union[str, Any] = "Dummy") -> None:
+ if isinstance(benchmark, str):
+ try:
+ adapter_enum = BenchmarkAdapter(benchmark)
+ except ValueError:
+ raise ValueError(f"Unsupported benchmark: {benchmark}")
+ self.adapter = adapter_enum.adapter_class()
+ else:
+ self.adapter = benchmark
+
+ self.raw_corpus = None
+ self.questions = None
+
+ def load_corpus(self, limit: Optional[int] = None) -> Tuple[List[Dict], List[str]]:
+ self.raw_corpus, self.questions = self.adapter.load_corpus(limit=limit)
+ return self.raw_corpus, self.questions
+
+ async def build_corpus(self, limit: Optional[int] = None) -> List[str]:
+ self.load_corpus(limit=limit)
+ await self.run_cognee()
+ return self.questions
+
+ async def run_cognee(self) -> None:
+ setup_logging(logging.ERROR)
+
+ # Pruning system and databases.
+ await cognee.prune.prune_data()
+ await cognee.prune.prune_system(metadata=True)
+
+ # Adding corpus elements to the cognee metastore.
+ await cognee.add(self.raw_corpus)
+
+ # Running cognify to build the knowledge graph.
+ await cognee.cognify()
diff --git a/evals/eval_framework/corpus_builder/run_corpus_builder.py b/evals/eval_framework/corpus_builder/run_corpus_builder.py
new file mode 100644
index 000000000..01ce58537
--- /dev/null
+++ b/evals/eval_framework/corpus_builder/run_corpus_builder.py
@@ -0,0 +1,42 @@
+import logging
+import json
+from cognee.infrastructure.files.storage import LocalStorage
+from evals.eval_framework.corpus_builder.corpus_builder_executor import CorpusBuilderExecutor
+from cognee.modules.data.models.questions_base import QuestionsBase
+from cognee.modules.data.models.questions_data import Questions
+from cognee.infrastructure.databases.relational.get_relational_engine import (
+ get_relational_engine,
+ get_relational_config,
+)
+
+
+async def create_and_insert_questions_table(questions_payload):
+ relational_config = get_relational_config()
+ relational_engine = get_relational_engine()
+
+ if relational_engine.engine.dialect.name == "sqlite":
+ LocalStorage.ensure_directory_exists(relational_config.db_path)
+
+ async with relational_engine.engine.begin() as connection:
+ if len(QuestionsBase.metadata.tables.keys()) > 0:
+ await connection.run_sync(QuestionsBase.metadata.create_all)
+
+ async with relational_engine.get_async_session() as session:
+ data_point = Questions(payload=questions_payload)
+ session.add(data_point)
+ await session.commit()
+
+
+async def run_corpus_builder(params: dict) -> None:
+ if params.get("building_corpus_from_scratch"):
+ logging.info("Corpus Builder started...")
+ corpus_builder = CorpusBuilderExecutor(benchmark=params["benchmark"])
+ questions = await corpus_builder.build_corpus(
+ limit=params.get("number_of_samples_in_corpus")
+ )
+ with open(params["questions_path"], "w", encoding="utf-8") as f:
+ json.dump(questions, f, ensure_ascii=False, indent=4)
+
+ await create_and_insert_questions_table(questions_payload=questions)
+
+ logging.info("Corpus Builder End...")
diff --git a/evals/eval_framework/eval_config.py b/evals/eval_framework/eval_config.py
new file mode 100644
index 000000000..5651a3e47
--- /dev/null
+++ b/evals/eval_framework/eval_config.py
@@ -0,0 +1,56 @@
+from functools import lru_cache
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List
+
+
+class EvalConfig(BaseSettings):
+ # Corpus builder params
+ building_corpus_from_scratch: bool = True
+ number_of_samples_in_corpus: int = 1
+ benchmark: str = "Dummy" # Options: 'HotPotQA', 'Dummy', 'TwoWikiMultiHop'
+
+ # Question answering params
+ answering_questions: bool = True
+ qa_engine: str = (
+ "cognee_completion" # Options: 'cognee_completion' or 'cognee_graph_completion'
+ )
+
+ # Evaluation params
+ evaluating_answers: bool = True
+ evaluation_engine: str = "DeepEval"
+ evaluation_metrics: List[str] = ["correctness", "EM", "f1"]
+ deepeval_model: str = "gpt-4o-mini"
+
+ # Visualization
+ dashboard: bool = True
+
+ # file paths
+ questions_path: str = "questions_output.json"
+ answers_path: str = "answers_output.json"
+ metrics_path: str = "metrics_output.json"
+ dashboard_path: str = "dashboard.html"
+
+ model_config = SettingsConfigDict(env_file=".env", extra="allow")
+
+ def to_dict(self) -> dict:
+ return {
+ "building_corpus_from_scratch": self.building_corpus_from_scratch,
+ "number_of_samples_in_corpus": self.number_of_samples_in_corpus,
+ "benchmark": self.benchmark,
+ "answering_questions": self.answering_questions,
+ "qa_engine": self.qa_engine,
+ "evaluating_answers": self.evaluating_answers,
+ "evaluation_engine": self.evaluation_engine,
+ "evaluation_metrics": self.evaluation_metrics,
+ "dashboard": self.dashboard,
+ "questions_path": self.questions_path,
+ "answers_path": self.answers_path,
+ "metrics_path": self.metrics_path,
+ "dashboard_path": self.dashboard_path,
+ "deepeval_model": self.deepeval_model,
+ }
+
+
+@lru_cache
+def get_llm_config():
+ return EvalConfig()
diff --git a/evals/eval_framework/evaluation/__init__.py b/evals/eval_framework/evaluation/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evals/eval_framework/evaluation/base_eval_adapter.py b/evals/eval_framework/evaluation/base_eval_adapter.py
new file mode 100644
index 000000000..76fa4d602
--- /dev/null
+++ b/evals/eval_framework/evaluation/base_eval_adapter.py
@@ -0,0 +1,10 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+
+class BaseEvalAdapter(ABC):
+ @abstractmethod
+ async def evaluate_answers(
+ self, data: List[Dict[str, Any]], evaluator_metrics: List[str]
+ ) -> List[Dict[str, Any]]:
+ pass
diff --git a/evals/eval_framework/evaluation/deep_eval_adapter.py b/evals/eval_framework/evaluation/deep_eval_adapter.py
new file mode 100644
index 000000000..ec0cc41f3
--- /dev/null
+++ b/evals/eval_framework/evaluation/deep_eval_adapter.py
@@ -0,0 +1,60 @@
+from deepeval.metrics import GEval
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+from evals.eval_framework.eval_config import EvalConfig
+from evals.eval_framework.evaluation.base_eval_adapter import BaseEvalAdapter
+from evals.eval_framework.evaluation.metrics.exact_match import ExactMatchMetric
+from evals.eval_framework.evaluation.metrics.f1 import F1ScoreMetric
+from typing import Any, Dict, List
+
+
+class DeepEvalAdapter(BaseEvalAdapter):
+ def __init__(self):
+ self.g_eval_metrics = {
+ "correctness": self.g_eval_correctness(),
+ "EM": ExactMatchMetric(),
+ "f1": F1ScoreMetric(),
+ }
+
+ async def evaluate_answers(
+ self, answers: List[Dict[str, Any]], evaluator_metrics: List[str]
+ ) -> List[Dict[str, Any]]:
+ # evaluator_metrics contains all the necessary metrics that are gonna be evaluated dynamically
+ for metric in evaluator_metrics:
+ if metric not in self.g_eval_metrics:
+ raise ValueError(f"Unsupported metric: {metric}")
+
+ results = []
+ for answer in answers:
+ test_case = LLMTestCase(
+ input=answer["question"],
+ actual_output=answer["answer"],
+ expected_output=answer["golden_answer"],
+ )
+ metric_results = {}
+ for metric in evaluator_metrics:
+ metric_to_calculate = self.g_eval_metrics[metric]
+ metric_to_calculate.measure(test_case)
+ metric_results[metric] = {
+ "score": metric_to_calculate.score,
+ "reason": metric_to_calculate.reason,
+ }
+ results.append({**answer, "metrics": metric_results})
+
+ return results
+
+ def g_eval_correctness(self):
+ return GEval(
+ name="Correctness",
+ criteria="Determine whether the actual output is factually correct based on the expected output.",
+ model=EvalConfig().to_dict()["deepeval_model"],
+ evaluation_steps=[
+ "Check whether the facts in 'actual output' contradicts any facts in 'expected output'",
+ "You should also heavily penalize omission of detail",
+ "Vague language, or contradicting OPINIONS, are OK",
+ ],
+ evaluation_params=[
+ LLMTestCaseParams.INPUT,
+ LLMTestCaseParams.ACTUAL_OUTPUT,
+ LLMTestCaseParams.EXPECTED_OUTPUT,
+ ],
+ )
diff --git a/evals/eval_framework/evaluation/evaluation_executor.py b/evals/eval_framework/evaluation/evaluation_executor.py
new file mode 100644
index 000000000..becee8f4e
--- /dev/null
+++ b/evals/eval_framework/evaluation/evaluation_executor.py
@@ -0,0 +1,20 @@
+from typing import List, Dict, Any, Union
+from evals.eval_framework.evaluation.evaluator_adapters import EvaluatorAdapter
+
+
+class EvaluationExecutor:
+ def __init__(self, evaluator_engine: Union[str, EvaluatorAdapter, Any] = "DeepEval") -> None:
+ if isinstance(evaluator_engine, str):
+ try:
+ adapter_enum = EvaluatorAdapter(evaluator_engine)
+ except ValueError:
+ raise ValueError(f"Unsupported evaluator: {evaluator_engine}")
+ self.eval_adapter = adapter_enum.adapter_class()
+ elif isinstance(evaluator_engine, EvaluatorAdapter):
+ self.eval_adapter = evaluator_engine.adapter_class()
+ else:
+ self.eval_adapter = evaluator_engine
+
+ async def execute(self, answers: List[Dict[str, str]], evaluator_metrics: Any) -> Any:
+ metrics = await self.eval_adapter.evaluate_answers(answers, evaluator_metrics)
+ return metrics
diff --git a/evals/eval_framework/evaluation/evaluator_adapters.py b/evals/eval_framework/evaluation/evaluator_adapters.py
new file mode 100644
index 000000000..1b78dee28
--- /dev/null
+++ b/evals/eval_framework/evaluation/evaluator_adapters.py
@@ -0,0 +1,16 @@
+from enum import Enum
+from typing import Type
+from evals.eval_framework.evaluation.deep_eval_adapter import DeepEvalAdapter
+
+
+class EvaluatorAdapter(Enum):
+ DEEPEVAL = ("DeepEval", DeepEvalAdapter)
+
+ def __new__(cls, adapter_name: str, adapter_class: Type):
+ obj = object.__new__(cls)
+ obj._value_ = adapter_name
+ obj.adapter_class = adapter_class
+ return obj
+
+ def __str__(self):
+ return self.value
diff --git a/evals/eval_framework/evaluation/metrics/exact_match.py b/evals/eval_framework/evaluation/metrics/exact_match.py
new file mode 100644
index 000000000..51709c345
--- /dev/null
+++ b/evals/eval_framework/evaluation/metrics/exact_match.py
@@ -0,0 +1,15 @@
+from deepeval.test_case import LLMTestCase
+from typing import Optional
+
+
+class ExactMatchMetric:
+ def __init__(self) -> None:
+ self.score: Optional[float] = None
+ self.reason: Optional[str] = None
+
+ def measure(self, test_case: "LLMTestCase") -> float:
+ actual = test_case.actual_output.strip().lower() if test_case.actual_output else ""
+ expected = test_case.expected_output.strip().lower() if test_case.expected_output else ""
+ self.score = 1.0 if actual == expected else 0.0
+ self.reason = "Exact match" if self.score == 1.0 else "Not an exact match"
+ return self.score
diff --git a/evals/eval_framework/evaluation/metrics/f1.py b/evals/eval_framework/evaluation/metrics/f1.py
new file mode 100644
index 000000000..953df59e4
--- /dev/null
+++ b/evals/eval_framework/evaluation/metrics/f1.py
@@ -0,0 +1,46 @@
+from collections import Counter
+from deepeval.test_case import LLMTestCase
+import re
+from typing import Optional, Any
+
+
+class F1ScoreMetric:
+ def __init__(self) -> None:
+ self.score: Optional[float] = None
+ self.reason: Optional[str] = None
+
+ def measure(self, test_case: "LLMTestCase") -> float:
+ actual = (test_case.actual_output or "").lower()
+ expected = (test_case.expected_output or "").lower()
+
+ actual_tokens = [
+ re.sub(r"\W+", "", token.strip())
+ for token in actual.split()
+ if re.sub(r"\W+", "", token.strip())
+ ]
+
+ expected_tokens = [
+ re.sub(r"\W+", "", token.strip())
+ for token in expected.split()
+ if re.sub(r"\W+", "", token.strip())
+ ]
+
+ if not actual_tokens and not expected_tokens:
+ self.score = 1.0
+ self.reason = "Both actual and expected are empty"
+ return self.score
+
+ actual_counts = Counter(actual_tokens)
+ expected_counts = Counter(expected_tokens)
+
+ tp = sum(min(actual_counts[word], expected_counts[word]) for word in actual_counts)
+ fp = sum(actual_counts[word] for word in actual_counts) - tp
+ fn = sum(expected_counts[word] for word in expected_counts) - tp
+
+ precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+ recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+
+ f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0
+ self.score = f1
+ self.reason = f"F1: {f1:.2f} (Precision: {precision:.2f}, Recall: {recall:.2f})"
+ return self.score
diff --git a/evals/eval_framework/evaluation/run_evaluation_module.py b/evals/eval_framework/evaluation/run_evaluation_module.py
new file mode 100644
index 000000000..351d253f6
--- /dev/null
+++ b/evals/eval_framework/evaluation/run_evaluation_module.py
@@ -0,0 +1,59 @@
+import logging
+import json
+from evals.eval_framework.evaluation.evaluation_executor import EvaluationExecutor
+from evals.eval_framework.metrics_dashboard import generate_metrics_dashboard
+from cognee.infrastructure.files.storage import LocalStorage
+from cognee.infrastructure.databases.relational.get_relational_engine import (
+ get_relational_engine,
+ get_relational_config,
+)
+from cognee.modules.data.models.metrics_data import Metrics
+from cognee.modules.data.models.metrics_base import MetricsBase
+
+
+async def create_and_insert_metrics_table(questions_payload):
+ relational_config = get_relational_config()
+ relational_engine = get_relational_engine()
+
+ if relational_engine.engine.dialect.name == "sqlite":
+ LocalStorage.ensure_directory_exists(relational_config.db_path)
+
+ async with relational_engine.engine.begin() as connection:
+ if len(MetricsBase.metadata.tables.keys()) > 0:
+ await connection.run_sync(MetricsBase.metadata.create_all)
+
+ async with relational_engine.get_async_session() as session:
+ data_point = Metrics(payload=questions_payload)
+ session.add(data_point)
+ await session.commit()
+
+
+async def run_evaluation(params: dict) -> None:
+ if params.get("evaluating_answers"):
+ logging.info("Evaluation started...")
+ try:
+ with open(params["answers_path"], "r", encoding="utf-8") as f:
+ answers = json.load(f)
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Could not find the file: {params['answers_path']}")
+ except json.JSONDecodeError as e:
+ raise ValueError(f"Error decoding JSON from {params['answers_path']}: {e}")
+
+ logging.info(f"Loaded {len(answers)} answers from {params['answers_path']}")
+ evaluator = EvaluationExecutor(evaluator_engine=params["evaluation_engine"])
+ metrics = await evaluator.execute(
+ answers=answers, evaluator_metrics=params["evaluation_metrics"]
+ )
+ with open(params["metrics_path"], "w", encoding="utf-8") as f:
+ json.dump(metrics, f, ensure_ascii=False, indent=4)
+
+ await create_and_insert_metrics_table(metrics)
+
+ logging.info("Evaluation End...")
+
+ if params.get("dashboard"):
+ generate_metrics_dashboard(
+ json_data=params["metrics_path"],
+ output_file=params["dashboard_path"],
+ benchmark=params["benchmark"],
+ )
diff --git a/evals/eval_framework/metrics_dashboard.py b/evals/eval_framework/metrics_dashboard.py
new file mode 100644
index 000000000..739f3f605
--- /dev/null
+++ b/evals/eval_framework/metrics_dashboard.py
@@ -0,0 +1,148 @@
+import json
+from collections import defaultdict
+import plotly.graph_objects as go
+import numpy as np
+
+
+def bootstrap_ci(scores, num_samples=10000, confidence_level=0.95):
+ means = []
+ n = len(scores)
+ for _ in range(num_samples):
+ sample = np.random.choice(scores, size=n, replace=True)
+ means.append(np.mean(sample))
+
+ lower_bound = np.percentile(means, (1 - confidence_level) / 2 * 100)
+ upper_bound = np.percentile(means, (1 + confidence_level) / 2 * 100)
+ return np.mean(scores), lower_bound, upper_bound
+
+
+def generate_metrics_dashboard(json_data, output_file="dashboard_with_ci.html", benchmark=""):
+ try:
+ with open(json_data, "r", encoding="utf-8") as f:
+ data = json.load(f)
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Could not find the file: {json_data}")
+ except json.JSONDecodeError as e:
+ raise ValueError(f"Error decoding JSON from {json_data}: {e}")
+
+ metrics_data = defaultdict(list)
+ metric_details = defaultdict(list)
+
+ for entry in data:
+ for metric, values in entry["metrics"].items():
+ score = values["score"]
+ metrics_data[metric].append(score)
+ if "reason" in values:
+ metric_details[metric].append(
+ {
+ "question": entry["question"],
+ "answer": entry["answer"],
+ "golden_answer": entry["golden_answer"],
+ "reason": values["reason"],
+ "score": score,
+ }
+ )
+
+ figures = []
+
+ for metric, scores in metrics_data.items():
+ fig = go.Figure()
+ fig.add_trace(go.Histogram(x=scores, name=metric, nbinsx=10, marker_color="#1f77b4"))
+
+ fig.update_layout(
+ title=f"{metric} Score Distribution",
+ xaxis_title="Score",
+ yaxis_title="Count",
+ bargap=0.1,
+ template="seaborn",
+ )
+ figures.append(fig.to_html(full_html=False))
+
+ ci_results = {}
+ for metric, scores in metrics_data.items():
+ mean_score, lower, upper = bootstrap_ci(scores)
+ ci_results[metric] = (mean_score, lower, upper)
+
+ # Bar chart with confidence intervals
+ fig = go.Figure()
+ for metric, (mean_score, lower, upper) in ci_results.items():
+ fig.add_trace(
+ go.Bar(
+ x=[metric],
+ y=[mean_score],
+ error_y=dict(
+ type="data",
+ array=[upper - mean_score],
+ arrayminus=[mean_score - lower],
+ visible=True,
+ ),
+ name=metric,
+ )
+ )
+
+ fig.update_layout(
+ title="95% confidence interval for all the metrics",
+ xaxis_title="Metric",
+ yaxis_title="Score",
+ template="seaborn",
+ )
+ figures.append(fig.to_html(full_html=False))
+
+ details_html = []
+ for metric, details in metric_details.items():
+ details_html.append(f"{metric} Details
")
+ details_html.append("""
+
+
+ | Question |
+ Answer |
+ Golden Answer |
+ Reason |
+ Score |
+
+ """)
+ for item in details:
+ details_html.append(
+ f""
+ f"| {item['question']} | "
+ f"{item['answer']} | "
+ f"{item['golden_answer']} | "
+ f"{item['reason']} | "
+ f"{item['score']} | "
+ f"
"
+ )
+ details_html.append("
")
+
+ html_template = f"""
+
+
+
+ LLM Evaluation Dashboard {benchmark}
+
+
+
+
+ LLM Evaluation Metrics Dashboard {benchmark}
+
+ Metrics Distribution
+ {"".join([f'{fig}
' for fig in figures[: len(metrics_data)]])}
+
+ 95% confidence interval for all the metrics
+ {figures[-1]}
+
+ Detailed Explanations
+ {"".join(details_html)}
+
+
+ """
+
+ with open(output_file, "w", encoding="utf-8") as f:
+ f.write(html_template)
+ return output_file
diff --git a/evals/eval_framework/run_eval.py b/evals/eval_framework/run_eval.py
new file mode 100644
index 000000000..7a8653b58
--- /dev/null
+++ b/evals/eval_framework/run_eval.py
@@ -0,0 +1,41 @@
+import logging
+import asyncio
+from cognee.shared.utils import setup_logging
+from evals.eval_framework.eval_config import EvalConfig
+
+from evals.eval_framework.corpus_builder.run_corpus_builder import run_corpus_builder
+from evals.eval_framework.answer_generation.run_question_answering_module import (
+ run_question_answering,
+)
+from evals.eval_framework.evaluation.run_evaluation_module import run_evaluation
+
+# Configure logging
+setup_logging(logging.INFO)
+
+# Define parameters and file paths.
+eval_params = EvalConfig().to_dict()
+
+questions_file = "questions_output.json"
+answers_file = "answers_output.json"
+metrics_file = "metrics_output.json"
+dashboard_path = "dashboard.html"
+
+
+async def main():
+ # Corpus builder
+ await run_corpus_builder(eval_params)
+
+ # Question answering
+ await run_question_answering(eval_params)
+
+ # Metrics calculation + dashboard
+ await run_evaluation(eval_params)
+
+
+if __name__ == "__main__":
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ loop.run_until_complete(main())
+ finally:
+ print("Done")
diff --git a/poetry.lock b/poetry.lock
index 28c751af7..0c6d89eaa 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
[[package]]
name = "aiofiles"
@@ -987,73 +987,68 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
[[package]]
name = "coverage"
-version = "7.6.10"
+version = "7.6.11"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.9"
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[package.extras]
-dev = ["black (==24.10.0)", "build (==1.2.2)", "flake8 (==7.1.1)", "mypy (==1.14.1)", "pytest (==8.3.4)", "requests (==2.32.3)", "twine (==6.1.0)"]
+dev = ["black (==25.1.0)", "build (==1.2.2)", "flake8 (==7.1.1)", "mypy (==1.15.0)", "pytest (==8.3.4)", "requests (==2.32.3)", "twine (==6.1.0)"]
[[package]]
name = "python-json-logger"
@@ -7092,13 +7124,13 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
[[package]]
name = "rich-argparse"
-version = "1.6.0"
+version = "1.7.0"
description = "Rich help formatters for argparse and optparse"
optional = false
python-versions = ">=3.8"
files = [
- {file = "rich_argparse-1.6.0-py3-none-any.whl", hash = "sha256:fbe70a1d821b3f2fa8958cddf0cae131870a6e9faa04ab52b409cb1eda809bd7"},
- {file = "rich_argparse-1.6.0.tar.gz", hash = "sha256:092083c30da186f25bcdff8b1d47fdfb571288510fb051e0488a72cc3128de13"},
+ {file = "rich_argparse-1.7.0-py3-none-any.whl", hash = "sha256:b8ec8943588e9731967f4f97b735b03dc127c416f480a083060433a97baf2fd3"},
+ {file = "rich_argparse-1.7.0.tar.gz", hash = "sha256:f31d809c465ee43f367d599ccaf88b73bc2c4d75d74ed43f2d538838c53544ba"},
]
[package.dependencies]
@@ -8296,13 +8328,13 @@ files = [
[[package]]
name = "types-setuptools"
-version = "75.8.0.20250110"
+version = "75.8.0.20250210"
description = "Typing stubs for setuptools"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "types_setuptools-75.8.0.20250110-py3-none-any.whl", hash = "sha256:a9f12980bbf9bcdc23ecd80755789085bad6bfce4060c2275bc2b4ca9f2bc480"},
- {file = "types_setuptools-75.8.0.20250110.tar.gz", hash = "sha256:96f7ec8bbd6e0a54ea180d66ad68ad7a1d7954e7281a710ea2de75e355545271"},
+ {file = "types_setuptools-75.8.0.20250210-py3-none-any.whl", hash = "sha256:a217d7b4d59be04c29e23d142c959a0f85e71292fd3fc4313f016ca11f0b56dc"},
+ {file = "types_setuptools-75.8.0.20250210.tar.gz", hash = "sha256:c1547361b2441f07c94e25dce8a068e18c611593ad4b6fdd727b1a8f5d1fda33"},
]
[[package]]
@@ -9153,4 +9185,4 @@ weaviate = ["weaviate-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10.0,<3.13"
-content-hash = "c37eade13e7debb1bd186b899ab35265ada938f76c6de1f5ba29a9d8c707f3eb"
+content-hash = "a213bb86ac98ac7b327e40eafae1ef038ff74071dea144b8c005eb86b252612f"
diff --git a/pyproject.toml b/pyproject.toml
index fed03fe7d..7d41c8daa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -80,6 +80,7 @@ nltk = "3.9.1"
google-generativeai = {version = "^0.8.4", optional = true}
parso = {version = "^0.8.4", optional = true}
jedi = {version = "^0.19.2", optional = true}
+plotly = "^6.0.0"
mistral-common = {version = "^1.5.2", optional = true}