feat: Cognee evaluation framework development (#498)

<!-- .github/pull_request_template.md -->

This PR contains the evaluation framework development for cognee

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Expanded evaluation framework now integrates asynchronous corpus
building, question answering, and performance evaluation with adaptive
benchmarks for improved metrics (correctness, exact match, and F1
score).

- **Infrastructure**
- Added database integration for persistent storage of questions,
answers, and metrics.
- Launched an interactive metrics dashboard featuring advanced
visualizations.
- Introduced an automated testing workflow for continuous quality
assurance.

- **Documentation**
  - Updated guidelines for generating concise, clear answers.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

.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 }}

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

cognee/modules/data/models/answers_base.py

@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class AnswersBase(DeclarativeBase):
+    pass

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))

cognee/modules/data/models/metrics_base.py

@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class MetricsBase(DeclarativeBase):
+    pass

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))

cognee/modules/data/models/questions_base.py

@@ -0,0 +1,5 @@
+from sqlalchemy.orm import DeclarativeBase
+
+
+class QuestionsBase(DeclarativeBase):
+    pass

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))

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

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...")

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

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

evals/eval_framework/benchmark_adapters/dummy_adapter.py

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+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

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

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

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()

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...")

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()

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

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,
+            ],
+        )

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

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

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

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

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"],
+        )

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"<h3>{metric} Details</h3>")
+        details_html.append("""
+            <table class="metric-table">
+                <tr>
+                    <th>Question</th>
+                    <th>Answer</th>
+                    <th>Golden Answer</th>
+                    <th>Reason</th>
+                    <th>Score</th>
+                </tr>
+        """)
+        for item in details:
+            details_html.append(
+                f"<tr>"
+                f"<td>{item['question']}</td>"
+                f"<td>{item['answer']}</td>"
+                f"<td>{item['golden_answer']}</td>"
+                f"<td>{item['reason']}</td>"
+                f"<td>{item['score']}</td>"
+                f"</tr>"
+            )
+        details_html.append("</table>")
+
+    html_template = f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>LLM Evaluation Dashboard {benchmark}</title>
+        <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 20px; }}
+            .chart {{ border: 1px solid #ddd; padding: 20px; margin-bottom: 30px; }}
+            .metric-table {{ border-collapse: collapse; width: 100%; margin-bottom: 30px; }}
+            .metric-table th, .metric-table td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+            .metric-table th {{ background-color: #f2f2f2; }}
+            h2 {{ color: #333; border-bottom: 2px solid #eee; padding-bottom: 10px; }}
+        </style>
+    </head>
+    <body>
+        <h1>LLM Evaluation Metrics Dashboard {benchmark}</h1>
+
+        <h2>Metrics Distribution</h2>
+        {"".join([f'<div class="chart">{fig}</div>' for fig in figures[: len(metrics_data)]])}
+
+        <h2>95% confidence interval for all the metrics</h2>
+        <div class="chart">{figures[-1]}</div>
+
+        <h2>Detailed Explanations</h2>
+        {"".join(details_html)}
+    </body>
+    </html>
+    """
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write(html_template)
+    return output_file

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")

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]
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 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
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 [[package]]
 name = "makefun"
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+dask = ["dask[dataframe] (>=2024.8)"]
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+
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "trove-classifiers (>=2024.10.12)"]
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
@@ -5506,6 +5520,24 @@ docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-a
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 type = ["mypy (>=1.11.2)"]
 
+[[package]]
+name = "plotly"
+version = "6.0.0"
+description = "An open-source, interactive data visualization library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+narwhals = ">=1.15.1"
+packaging = "*"
+
+[package.extras]
+express = ["numpy"]
+
 [[package]]
 name = "pluggy"
 version = "1.5.0"
@@ -6382,17 +6414,17 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-iso639"
-version = "2025.1.28"
+version = "2025.2.8"
 description = "ISO 639 language codes, names, and other associated information"
 optional = true
 python-versions = ">=3.9"
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 ]
 
 [package.extras]
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 [[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 = [
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 ]
 
 [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"

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}