Feature/cog 1312 integrating evaluation framework into dreamify (#562)

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

## Description
This PR contains eval framework changes due to the autooptimizer
integration

## 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**
  - Enhanced answer generation now returns structured answer details.
  - Search functionality accepts configurable prompt inputs.
  - Option to generate a metrics dashboard from evaluations.
- Corpus building tasks now support adjustable chunk settings for
greater flexibility.
- New task retrieval functionality allows for flexible task
configuration.
  - Introduced new methods for creating and managing metrics dashboards.

- **Refactor/Chore**
- Streamlined API signatures and reorganized module interfaces for
better consistency.
  - Updated import paths to reflect new module structure.

- **Tests**
- Updated test scenarios to align with new configurations and parameter
adjustments.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

.github/workflows/test_eval_framework.yml

@@ -14,7 +14,7 @@ jobs:
   run_eval_framework_test:
       uses: ./.github/workflows/reusable_python_example.yml
       with:
-        example-location: ./evals/eval_framework/run_eval.py
+        example-location: ./cognee/eval_framework/run_eval.py
       secrets:
         LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

cognee/api/v1/cognify/cognify_v2.py

@@ -112,8 +112,8 @@ def generate_dataset_name(dataset_name: str) -> str:
     return dataset_name.replace(".", "_").replace(" ", "_")
 
 
-async def get_default_tasks(
-    user: User = None, graph_model: BaseModel = KnowledgeGraph
+async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's comment)
+    user: User = None, graph_model: BaseModel = KnowledgeGraph, chunk_size=1024, chunker=TextChunker
 ) -> list[Task]:
     if user is None:
         user = await get_default_user()
@@ -126,7 +126,8 @@ async def get_default_tasks(
             Task(
                 extract_chunks_from_documents,
                 max_chunk_tokens=get_max_chunk_tokens(),
-                chunker=TextChunker,
+                chunker=chunker,
+                chunk_size=chunk_size,
             ),  # Extract text chunks based on the document type.
             Task(
                 extract_graph_from_data, graph_model=graph_model, task_config={"batch_size": 10}

cognee/api/v1/search/search_v2.py

@@ -12,6 +12,7 @@ async def search(
     query_type: SearchType = SearchType.GRAPH_COMPLETION,
     user: User = None,
     datasets: Union[list[str], str, None] = None,
+    system_prompt_path: str = "answer_simple_question.txt",
 ) -> list:
     # We use lists from now on for datasets
     if isinstance(datasets, str):
@@ -23,6 +24,8 @@ async def search(
     if user is None:
         raise UserNotFoundError
 
-    filtered_search_results = await search_function(query_text, query_type, datasets, user)
+    filtered_search_results = await search_function(
+        query_text, query_type, datasets, user, system_prompt_path=system_prompt_path
+    )
 
     return filtered_search_results

cognee/eval_framework/answer_generation/answer_generation_executor.py

@@ -3,20 +3,19 @@ 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_graph_completion": lambda query, system_prompt_path: cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION,
+        query_text=query,
+        system_prompt_path=system_prompt_path,
     ),
-    "cognee_completion": lambda query: cognee.search(
-        query_type=SearchType.COMPLETION, query_text=query
+    "cognee_completion": lambda query, system_prompt_path: cognee.search(
+        query_type=SearchType.COMPLETION, query_text=query, system_prompt_path=system_prompt_path
     ),
-    "cognee_summaries": lambda query: cognee.search(
-        query_type=SearchType.SUMMARIES, query_text=query
+    "graph_summary_completion": lambda query, system_prompt_path: cognee.search(
+        query_type=SearchType.GRAPH_SUMMARY_COMPLETION,
+        query_text=query,
+        system_prompt_path=system_prompt_path,
     ),
-    "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),
 }
 
 
@@ -25,13 +24,14 @@ class AnswerGeneratorExecutor:
         self,
         questions: List[Dict[str, str]],
         answer_resolver: Callable[[str], Awaitable[List[str]]],
+        system_prompt: str = "answer_simple_question.txt",
     ) -> List[Dict[str, str]]:
         answers = []
         for instance in questions:
             query_text = instance["question"]
             correct_answer = instance["answer"]
 
-            search_results = await answer_resolver(query_text)
+            search_results = await answer_resolver(query_text, system_prompt)
 
             answers.append(
                 {

cognee/eval_framework/answer_generation/run_question_answering_module.py

@@ -1,6 +1,7 @@
 import logging
 import json
-from evals.eval_framework.answer_generation.answer_generation_executor import (
+from typing import List
+from cognee.eval_framework.answer_generation.answer_generation_executor import (
     AnswerGeneratorExecutor,
     question_answering_engine_options,
 )
@@ -30,7 +31,9 @@ async def create_and_insert_answers_table(questions_payload):
         await session.commit()
 
 
-async def run_question_answering(params: dict) -> None:
+async def run_question_answering(
+    params: dict, system_prompt="answer_simple_question.txt"
+) -> List[dict]:
     if params.get("answering_questions"):
         logging.info("Question answering started...")
         try:
@@ -46,9 +49,17 @@ async def run_question_answering(params: dict) -> None:
         answers = await answer_generator.question_answering_non_parallel(
             questions=questions,
             answer_resolver=question_answering_engine_options[params["qa_engine"]],
+            system_prompt=system_prompt,
         )
         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...")
+
+        return answers
+    else:
+        logging.info(
+            "The question answering module was not executed as answering_questions is not enabled"
+        )
+        return []

cognee/eval_framework/benchmark_adapters/benchmark_adapters.py

@@ -1,10 +1,10 @@
 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.musique_adapter import MusiqueQAAdapter
-from evals.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
-from evals.eval_framework.benchmark_adapters.twowikimultihop_adapter import TwoWikiMultihopAdapter
+from cognee.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
+from cognee.eval_framework.benchmark_adapters.musique_adapter import MusiqueQAAdapter
+from cognee.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
+from cognee.eval_framework.benchmark_adapters.twowikimultihop_adapter import TwoWikiMultihopAdapter
 
 
 class BenchmarkAdapter(Enum):

cognee/eval_framework/benchmark_adapters/dummy_adapter.py

@@ -1,12 +1,12 @@
-from typing import Optional
+from typing import Optional, Any
 
-from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+from cognee.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[str], list[dict[str, str]]]:
+    ) -> tuple[list[str], list[dict[str, Any]]]:
         corpus_list = [
             "The cognee is an AI memory engine that supports different vector and graph databases",
             "Neo4j is a graph database supported by cognee",

cognee/eval_framework/benchmark_adapters/hotpot_qa_adapter.py

@@ -3,7 +3,7 @@ import os
 import json
 import random
 from typing import Optional, Any, List, Tuple
-from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+from cognee.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
 
 
 class HotpotQAAdapter(BaseBenchmarkAdapter):

cognee/eval_framework/benchmark_adapters/musique_adapter.py

@@ -6,7 +6,7 @@ import zipfile
 
 import gdown
 
-from evals.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
+from cognee.eval_framework.benchmark_adapters.base_benchmark_adapter import BaseBenchmarkAdapter
 
 
 class MusiqueQAAdapter(BaseBenchmarkAdapter):

cognee/eval_framework/benchmark_adapters/twowikimultihop_adapter.py

@@ -3,7 +3,7 @@ import os
 import json
 import random
 from typing import Optional, Any, List, Tuple
-from evals.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
+from cognee.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
 
 
 class TwoWikiMultihopAdapter(HotpotQAAdapter):

cognee/eval_framework/corpus_builder/corpus_builder_executor.py

@@ -2,8 +2,9 @@ import cognee
 import logging
 from typing import Optional, Tuple, List, Dict, Union, Any, Callable, Awaitable
 
-from evals.eval_framework.benchmark_adapters.benchmark_adapters import BenchmarkAdapter
-from evals.eval_framework.corpus_builder.task_getters.TaskGetters import TaskGetters
+from cognee.eval_framework.corpus_builder.task_getters.TaskGetters import TaskGetters
+from cognee.eval_framework.benchmark_adapters.benchmark_adapters import BenchmarkAdapter
+from cognee.modules.chunking.TextChunker import TextChunker
 from cognee.modules.pipelines.tasks.Task import Task
 from cognee.shared.utils import setup_logging
 
@@ -31,12 +32,14 @@ class CorpusBuilderExecutor:
         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]:
+    async def build_corpus(
+        self, limit: Optional[int] = None, chunk_size=1024, chunker=TextChunker
+    ) -> List[str]:
         self.load_corpus(limit=limit)
-        await self.run_cognee()
+        await self.run_cognee(chunk_size=chunk_size, chunker=chunker)
         return self.questions
 
-    async def run_cognee(self) -> None:
+    async def run_cognee(self, chunk_size=1024, chunker=TextChunker) -> None:
         setup_logging(logging.ERROR)
 
         await cognee.prune.prune_data()
@@ -44,5 +47,5 @@ class CorpusBuilderExecutor:
 
         await cognee.add(self.raw_corpus)
 
-        tasks = await self.task_getter()
+        tasks = await self.task_getter(chunk_size=chunk_size, chunker=TextChunker)
         await cognee.cognify(tasks=tasks)

cognee/eval_framework/corpus_builder/run_corpus_builder.py

@@ -1,14 +1,19 @@
 import logging
 import json
+from typing import List
+
+from unstructured.chunking.dispatch import chunk
+
 from cognee.infrastructure.files.storage import LocalStorage
-from evals.eval_framework.corpus_builder.corpus_builder_executor import CorpusBuilderExecutor
+from cognee.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,
 )
-from evals.eval_framework.corpus_builder.task_getters.TaskGetters import TaskGetters
+from cognee.modules.chunking.TextChunker import TextChunker
+from cognee.eval_framework.corpus_builder.task_getters.TaskGetters import TaskGetters
 
 
 async def create_and_insert_questions_table(questions_payload):
@@ -28,7 +33,7 @@ async def create_and_insert_questions_table(questions_payload):
         await session.commit()
 
 
-async def run_corpus_builder(params: dict) -> None:
+async def run_corpus_builder(params: dict, chunk_size=1024, chunker=TextChunker) -> List[dict]:
     if params.get("building_corpus_from_scratch"):
         logging.info("Corpus Builder started...")
 
@@ -42,7 +47,7 @@ async def run_corpus_builder(params: dict) -> None:
             task_getter=task_getter,
         )
         questions = await corpus_builder.build_corpus(
-            limit=params.get("number_of_samples_in_corpus")
+            limit=params.get("number_of_samples_in_corpus"), chunk_size=chunk_size, chunker=chunker
         )
         with open(params["questions_path"], "w", encoding="utf-8") as f:
             json.dump(questions, f, ensure_ascii=False, indent=4)
@@ -50,3 +55,5 @@ async def run_corpus_builder(params: dict) -> None:
         await create_and_insert_questions_table(questions_payload=questions)
 
         logging.info("Corpus Builder End...")
+
+        return questions

cognee/eval_framework/corpus_builder/task_getters/TaskGetters.py

@@ -2,7 +2,7 @@ from enum import Enum
 from typing import Callable, Awaitable, List
 from cognee.api.v1.cognify.cognify_v2 import get_default_tasks
 from cognee.modules.pipelines.tasks.Task import Task
-from evals.eval_framework.corpus_builder.task_getters.get_cascade_graph_tasks import (
+from cognee.eval_framework.corpus_builder.task_getters.get_cascade_graph_tasks import (
     get_cascade_graph_tasks,
 )
 

cognee/eval_framework/corpus_builder/task_getters/default_task_getter.py

@@ -0,0 +1,14 @@
+from cognee.api.v1.cognify.cognify_v2 import get_default_tasks
+from typing import List
+from cognee.eval_framework.corpus_builder.task_getters.base_task_getter import BaseTaskGetter
+from cognee.modules.pipelines.tasks.Task import Task
+from cognee.infrastructure.llm import get_max_chunk_tokens
+from cognee.modules.chunking.TextChunker import TextChunker
+
+
+class DefaultTaskGetter(BaseTaskGetter):
+    """Default task getter that retrieves tasks using the standard get_default_tasks function."""
+
+    async def get_tasks(self, chunk_size=1024, chunker=TextChunker) -> List[Task]:
+        """Retrieve default tasks asynchronously."""
+        return await get_default_tasks(chunk_size=chunk_size, chunker=chunker)

cognee/eval_framework/evaluation/deep_eval_adapter.py

@@ -1,9 +1,9 @@
 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 cognee.eval_framework.eval_config import EvalConfig
+from cognee.eval_framework.evaluation.base_eval_adapter import BaseEvalAdapter
+from cognee.eval_framework.evaluation.metrics.exact_match import ExactMatchMetric
+from cognee.eval_framework.evaluation.metrics.f1 import F1ScoreMetric
 from typing import Any, Dict, List
 
 

cognee/eval_framework/evaluation/direct_llm_eval_adapter.py

@@ -1,9 +1,9 @@
 from typing import Any, Dict, List
 from pydantic import BaseModel
 from cognee.infrastructure.llm.get_llm_client import get_llm_client
-from evals.eval_framework.evaluation.base_eval_adapter import BaseEvalAdapter
+from cognee.eval_framework.evaluation.base_eval_adapter import BaseEvalAdapter
 from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
-from evals.eval_framework.eval_config import EvalConfig
+from cognee.eval_framework.eval_config import EvalConfig
 
 
 class CorrectnessEvaluation(BaseModel):

cognee/eval_framework/evaluation/evaluation_executor.py

@@ -1,5 +1,5 @@
 from typing import List, Dict, Any, Union
-from evals.eval_framework.evaluation.evaluator_adapters import EvaluatorAdapter
+from cognee.eval_framework.evaluation.evaluator_adapters import EvaluatorAdapter
 
 
 class EvaluationExecutor:

cognee/eval_framework/evaluation/evaluator_adapters.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Type
-from evals.eval_framework.evaluation.deep_eval_adapter import DeepEvalAdapter
-from evals.eval_framework.evaluation.direct_llm_eval_adapter import DirectLLMEvalAdapter
+from cognee.eval_framework.evaluation.deep_eval_adapter import DeepEvalAdapter
+from cognee.eval_framework.evaluation.direct_llm_eval_adapter import DirectLLMEvalAdapter
 
 
 class EvaluatorAdapter(Enum):

cognee/eval_framework/evaluation/run_evaluation_module.py

@@ -1,8 +1,9 @@
 import logging
 import json
-from evals.eval_framework.evaluation.evaluation_executor import EvaluationExecutor
-from evals.eval_framework.analysis.metrics_calculator import calculate_metrics_statistics
-from evals.eval_framework.analysis.dashboard_generator import create_dashboard
+from typing import List
+from cognee.eval_framework.evaluation.evaluation_executor import EvaluationExecutor
+from cognee.eval_framework.analysis.metrics_calculator import calculate_metrics_statistics
+from cognee.eval_framework.analysis.dashboard_generator import create_dashboard
 from cognee.infrastructure.files.storage import LocalStorage
 from cognee.infrastructure.databases.relational.get_relational_engine import (
     get_relational_engine,
@@ -50,13 +51,14 @@ async def execute_evaluation(params: dict) -> None:
 
     await create_and_insert_metrics_table(metrics)
     logging.info("Evaluation completed")
+    return metrics
 
 
-async def run_evaluation(params: dict) -> None:
+async def run_evaluation(params: dict) -> List[dict]:
     """Run each step of the evaluation pipeline based on configuration flags."""
     # Step 1: Evaluate answers if requested
     if params.get("evaluating_answers"):
-        await execute_evaluation(params)
+        metrics = await execute_evaluation(params)
     else:
         logging.info("Skipping evaluation as evaluating_answers is False")
 
@@ -67,18 +69,7 @@ async def run_evaluation(params: dict) -> None:
             json_data=params["metrics_path"], aggregate_output_path=params["aggregate_metrics_path"]
         )
         logging.info("Metrics calculation completed")
+        return metrics
     else:
         logging.info("Skipping metrics calculation as calculate_metrics is False")
-
-    # Step 3: Generate dashboard if requested
-    if params.get("dashboard"):
-        logging.info("Generating dashboard...")
-        create_dashboard(
-            metrics_path=params["metrics_path"],
-            aggregate_metrics_path=params["aggregate_metrics_path"],
-            output_file=params["dashboard_path"],
-            benchmark=params["benchmark"],
-        )
-        logging.info(f"Dashboard generated at {params['dashboard_path']}")
-    else:
-        logging.info("Skipping dashboard generation as dashboard is False")
+        return []

cognee/eval_framework/metrics_dashboard.py

@@ -0,0 +1,172 @@
+import json
+import plotly.graph_objects as go
+from typing import Dict, List, Tuple
+from collections import defaultdict
+
+
+def create_distribution_plots(metrics_data: Dict[str, List[float]]) -> List[str]:
+    """Create distribution histogram plots for each metric."""
+    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))
+    return figures
+
+
+def create_ci_plot(ci_results: Dict[str, Tuple[float, float, float]]) -> str:
+    """Create confidence interval bar plot."""
+    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",
+    )
+    return fig.to_html(full_html=False)
+
+
+def generate_details_html(metrics_data: List[Dict]) -> List[str]:
+    """Generate HTML for detailed metric information."""
+    details_html = []
+    metric_details = {}
+
+    # Organize metrics by type
+    for entry in metrics_data:
+        for metric, values in entry["metrics"].items():
+            if metric not in metric_details:
+                metric_details[metric] = []
+            metric_details[metric].append(
+                {
+                    "question": entry["question"],
+                    "answer": entry["answer"],
+                    "golden_answer": entry["golden_answer"],
+                    "reason": values.get("reason", ""),
+                    "score": values["score"],
+                }
+            )
+
+    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>")
+    return details_html
+
+
+def get_dashboard_html_template(
+    figures: List[str], details_html: List[str], benchmark: str = ""
+) -> str:
+    """Generate the complete HTML dashboard template."""
+    return 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[:-1]])}
+
+        <h2>95% confidence interval for all the metrics</h2>
+        <div class="chart">{figures[-1]}</div>
+
+        <h2>Detailed Explanations</h2>
+        {"".join(details_html)}
+    </body>
+    </html>
+    """
+
+
+def create_dashboard(
+    metrics_path: str,
+    aggregate_metrics_path: str,
+    output_file: str = "dashboard_with_ci.html",
+    benchmark: str = "",
+) -> str:
+    """Create and save the dashboard with all visualizations."""
+    # Read metrics files
+    with open(metrics_path, "r") as f:
+        metrics_data = json.load(f)
+    with open(aggregate_metrics_path, "r") as f:
+        aggregate_data = json.load(f)
+
+    # Extract data for visualizations
+    metrics_by_type = defaultdict(list)
+    for entry in metrics_data:
+        for metric, values in entry["metrics"].items():
+            metrics_by_type[metric].append(values["score"])
+
+    # Generate visualizations
+    distribution_figures = create_distribution_plots(metrics_by_type)
+    ci_plot = create_ci_plot(
+        {
+            metric: (data["mean"], data["ci_lower"], data["ci_upper"])
+            for metric, data in aggregate_data.items()
+        }
+    )
+
+    # Combine all figures
+    figures = distribution_figures + [ci_plot]
+
+    # Generate HTML components
+    details_html = generate_details_html(metrics_data)
+    dashboard_html = get_dashboard_html_template(figures, details_html, benchmark)
+
+    # Write to file
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write(dashboard_html)
+
+    return output_file

cognee/eval_framework/modal_run_eval.py

@@ -4,12 +4,12 @@ import json
 import asyncio
 import datetime
 import 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 (
+from cognee.eval_framework.eval_config import EvalConfig
+from cognee.eval_framework.corpus_builder.run_corpus_builder import run_corpus_builder
+from cognee.eval_framework.answer_generation.run_question_answering_module import (
     run_question_answering,
 )
-from evals.eval_framework.evaluation.run_evaluation_module import run_evaluation
+from cognee.eval_framework.evaluation.run_evaluation_module import run_evaluation
 
 logger = logging.getLogger(__name__)
 

cognee/eval_framework/run_eval.py

@@ -1,13 +1,14 @@
 import logging
 import asyncio
 from cognee.shared.utils import setup_logging
-from evals.eval_framework.eval_config import EvalConfig
+from cognee.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 (
+from cognee.eval_framework.corpus_builder.run_corpus_builder import run_corpus_builder
+from cognee.eval_framework.answer_generation.run_question_answering_module import (
     run_question_answering,
 )
-from evals.eval_framework.evaluation.run_evaluation_module import run_evaluation
+from cognee.eval_framework.evaluation.run_evaluation_module import run_evaluation
+from cognee.eval_framework.metrics_dashboard import create_dashboard
 
 # Configure logging
 setup_logging(logging.INFO)
@@ -31,6 +32,15 @@ async def main():
     # Metrics calculation + dashboard
     await run_evaluation(eval_params)
 
+    if eval_params.get("dashboard"):
+        logging.info("Generating dashboard...")
+        create_dashboard(
+            metrics_path=eval_params["metrics_path"],
+            aggregate_metrics_path=eval_params["aggregate_metrics_path"],
+            output_file=eval_params["dashboard_path"],
+            benchmark=eval_params["benchmark"],
+        )
+
 
 if __name__ == "__main__":
     loop = asyncio.new_event_loop()

cognee/modules/retrieval/base_retriever.py

@@ -14,8 +14,3 @@ class BaseRetriever(ABC):
     async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
         """Generates a response using the query and optional context."""
         pass
-
-    @classmethod
-    def as_search(cls) -> Callable:
-        """Creates a search function from the retriever class."""
-        return lambda query: cls().get_completion(query)

cognee/modules/search/methods/search.py

@@ -25,11 +25,14 @@ async def search(
     query_type: SearchType,
     datasets: list[str],
     user: User,
+    system_prompt_path="answer_simple_question.txt",
 ):
     query = await log_query(query_text, query_type.value, user.id)
 
     own_document_ids = await get_document_ids_for_user(user.id, datasets)
-    search_results = await specific_search(query_type, query_text, user)
+    search_results = await specific_search(
+        query_type, query_text, user, system_prompt_path=system_prompt_path
+    )
 
     filtered_search_results = []
 
@@ -45,15 +48,23 @@ async def search(
     return filtered_search_results
 
 
-async def specific_search(query_type: SearchType, query: str, user: User) -> list:
+async def specific_search(
+    query_type: SearchType, query: str, user: User, system_prompt_path="answer_simple_question.txt"
+) -> list:
     search_tasks: dict[SearchType, Callable] = {
-        SearchType.SUMMARIES: SummariesRetriever.as_search(),
-        SearchType.INSIGHTS: InsightsRetriever.as_search(),
-        SearchType.CHUNKS: ChunksRetriever.as_search(),
-        SearchType.COMPLETION: CompletionRetriever.as_search(),
-        SearchType.GRAPH_COMPLETION: GraphCompletionRetriever.as_search(),
-        SearchType.GRAPH_SUMMARY_COMPLETION: GraphSummaryCompletionRetriever.as_search(),
-        SearchType.CODE: CodeRetriever.as_search(),
+        SearchType.SUMMARIES: SummariesRetriever().get_completion,
+        SearchType.INSIGHTS: InsightsRetriever().get_completion,
+        SearchType.CHUNKS: ChunksRetriever().get_completion,
+        SearchType.COMPLETION: CompletionRetriever(
+            system_prompt_path=system_prompt_path
+        ).get_completion,
+        SearchType.GRAPH_COMPLETION: GraphCompletionRetriever(
+            system_prompt_path=system_prompt_path
+        ).get_completion,
+        SearchType.GRAPH_SUMMARY_COMPLETION: GraphSummaryCompletionRetriever(
+            system_prompt_path=system_prompt_path
+        ).get_completion,
+        SearchType.CODE: CodeRetriever().get_completion,
     }
 
     search_task = search_tasks.get(query_type)

cognee/tests/unit/eval_framework/answer_generation_test.py

@@ -1,8 +1,8 @@
 import pytest
-from evals.eval_framework.answer_generation.answer_generation_executor import (
+from cognee.eval_framework.answer_generation.answer_generation_executor import (
     AnswerGeneratorExecutor,
 )
-from evals.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
+from cognee.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
 from unittest.mock import AsyncMock
 
 
@@ -12,16 +12,13 @@ async def test_answer_generation():
     corpus_list, qa_pairs = DummyAdapter().load_corpus(limit=limit)
 
     mock_answer_resolver = AsyncMock()
-    mock_answer_resolver.side_effect = lambda query: ["mock_answer"]
+    mock_answer_resolver.side_effect = lambda query, system_prompt: ["mock_answer"]
 
     answer_generator = AnswerGeneratorExecutor()
     answers = await answer_generator.question_answering_non_parallel(
-        questions=qa_pairs,
-        answer_resolver=mock_answer_resolver,
+        questions=qa_pairs, answer_resolver=mock_answer_resolver, system_prompt="test.txt"
     )
 
-    mock_answer_resolver.assert_called_once_with(qa_pairs[0]["question"])
-
     assert len(answers) == len(qa_pairs)
     assert answers[0]["question"] == qa_pairs[0]["question"], (
         "AnswerGeneratorExecutor is passing the question incorrectly"

cognee/tests/unit/eval_framework/benchmark_adapters_test.py

@@ -1,8 +1,8 @@
 import pytest
-from evals.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
-from evals.eval_framework.benchmark_adapters.musique_adapter import MusiqueQAAdapter
-from evals.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
-from evals.eval_framework.benchmark_adapters.twowikimultihop_adapter import TwoWikiMultihopAdapter
+from cognee.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
+from cognee.eval_framework.benchmark_adapters.musique_adapter import MusiqueQAAdapter
+from cognee.eval_framework.benchmark_adapters.dummy_adapter import DummyAdapter
+from cognee.eval_framework.benchmark_adapters.twowikimultihop_adapter import TwoWikiMultihopAdapter
 from unittest.mock import patch, mock_open
 
 

cognee/tests/unit/eval_framework/corpus_builder_test.py

@@ -1,5 +1,5 @@
 import pytest
-from evals.eval_framework.corpus_builder.corpus_builder_executor import CorpusBuilderExecutor
+from cognee.eval_framework.corpus_builder.corpus_builder_executor import CorpusBuilderExecutor
 from cognee.infrastructure.databases.graph import get_graph_engine
 from unittest.mock import AsyncMock, patch
 

cognee/tests/unit/eval_framework/dashboard_test.py

@@ -3,7 +3,7 @@ import json
 import os
 
 
-from evals.eval_framework.analysis.dashboard_generator import (
+from cognee.eval_framework.analysis.dashboard_generator import (
     create_distribution_plots,
     create_ci_plot,
     generate_details_html,

cognee/tests/unit/eval_framework/deepeval_adapter_test.py

@@ -1,13 +1,13 @@
 import pytest
 from unittest.mock import patch, MagicMock
-from evals.eval_framework.eval_config import EvalConfig
+from cognee.eval_framework.eval_config import EvalConfig
 import sys
 
 with patch.dict(
     sys.modules,
     {"deepeval": MagicMock(), "deepeval.metrics": MagicMock(), "deepeval.test_case": MagicMock()},
 ):
-    from evals.eval_framework.evaluation.deep_eval_adapter import DeepEvalAdapter
+    from cognee.eval_framework.evaluation.deep_eval_adapter import DeepEvalAdapter
 
 
 @pytest.fixture

cognee/tests/unit/eval_framework/metrics_test.py

@@ -4,15 +4,15 @@ import sys
 from unittest.mock import patch, MagicMock
 import unittest
 import numpy as np
-from evals.eval_framework.analysis.metrics_calculator import bootstrap_ci
+from cognee.eval_framework.analysis.metrics_calculator import bootstrap_ci
 
 
 with patch.dict(
     sys.modules,
     {"deepeval": MagicMock(), "deepeval.test_case": MagicMock()},
 ):
-    from evals.eval_framework.evaluation.metrics.exact_match import ExactMatchMetric
-    from evals.eval_framework.evaluation.metrics.f1 import F1ScoreMetric
+    from cognee.eval_framework.evaluation.metrics.exact_match import ExactMatchMetric
+    from cognee.eval_framework.evaluation.metrics.f1 import F1ScoreMetric
 
 
 class MockTestCase: