feat: Add incremental eval option to paramset (#446)

* QA eval dataset as argument, with hotpot and 2wikimultihop as options. Json schema validation for datasets.

* Load dataset file by filename, outsource utilities

* restructure metric selection

* Add comprehensiveness, diversity and empowerment metrics

* add promptfoo as an option

* refactor RAG solution in eval;2C

* LLM as a judge metrics implemented in a uniform way

* Use requests.get instead of wget

* clean up promptfoo config template

* minor fixes

* get promptfoo path instead of hardcoding

* minor fixes

* Add LLM as a judge prompts

* Support 4 different rag options in eval

* Minor refactor and logger usage

* feat: make tasks a configurable argument in the cognify function

* Run eval on a set of parameters and save results as json and png

* fix: add data points task

* script for running all param combinations

* enable context provider to get tasks as param

* bugfix in simple rag

* Incremental eval of cognee pipeline

* potential fix: single asyncio run

* temp fix: exclude insights

* Remove insights, have single asyncio run, refactor

* Include incremental eval in accepted paramsets

* minor fixes

* handle pipeline slices in utils

* Handle insights and customize search types

* Handle retrieved edges more safely

* bugfix

* fix simple rag

---------

Co-authored-by: lxobr <122801072+lxobr@users.noreply.github.com>
Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>

evals/eval_on_hotpot.py

@@ -9,7 +9,7 @@ from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
 from evals.qa_dataset_utils import load_qa_dataset
 from evals.qa_metrics_utils import get_metrics
-from evals.qa_context_provider_utils import qa_context_providers, create_cognee_context_getter
+from evals.qa_context_provider_utils import qa_context_providers, valid_pipeline_slices
 
 logger = logging.getLogger(__name__)
 
@@ -97,7 +97,7 @@ async def eval_on_QA_dataset(
 async def incremental_eval_on_QA_dataset(
     dataset_name_or_filename: str, num_samples, metric_name_list
 ):
-    pipeline_slice_names = ["base", "extract_chunks", "extract_graph", "summarize"]
+    pipeline_slice_names = valid_pipeline_slices.keys()
 
     incremental_results = {}
     for pipeline_slice_name in pipeline_slice_names:

evals/qa_context_provider_utils.py

@@ -5,6 +5,9 @@ from cognee.modules.retrieval.brute_force_triplet_search import brute_force_trip
 from cognee.tasks.completion.graph_query_completion import retrieved_edges_to_string
 from functools import partial
 from cognee.api.v1.cognify.cognify_v2 import get_default_tasks
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 async def get_raw_context(instance: dict) -> str:
@@ -24,6 +27,34 @@ async def cognify_instance(instance: dict, task_indices: list[int] = None):
     await cognee.cognify("QA", tasks=selected_tasks)
 
 
+def _insight_to_string(triplet: tuple) -> str:
+    if not (isinstance(triplet, tuple) and len(triplet) == 3):
+        logger.warning("Invalid input: Expected a tuple of length 3.")
+        return ""
+
+    node1, edge, node2 = triplet
+
+    if not (isinstance(node1, dict) and isinstance(edge, dict) and isinstance(node2, dict)):
+        logger.warning("Invalid input: Each element in the tuple must be a dictionary.")
+        return ""
+
+    node1_name = node1["name"] if "name" in node1 else "N/A"
+    node1_description = node1["description"] if "description" in node1 else node1["text"]
+    node1_string = f"name: {node1_name}, description: {node1_description}"
+    node2_name = node2["name"] if "name" in node2 else "N/A"
+    node2_description = node2["description"] if "description" in node2 else node2["text"]
+    node2_string = f"name: {node2_name}, description: {node2_description}"
+
+    edge_string = edge.get("relationship_name", "")
+
+    if not edge_string:
+        logger.warning("Missing required field: 'relationship_name' in edge dictionary.")
+        return ""
+
+    triplet_str = f"{node1_string} -- {edge_string} -- {node2_string}"
+    return triplet_str
+
+
 async def get_context_with_cognee(
     instance: dict,
     task_indices: list[int] = None,
@@ -33,9 +64,24 @@ async def get_context_with_cognee(
 
     search_results = []
     for search_type in search_types:
-        search_results += await cognee.search(search_type, query_text=instance["question"])
+        raw_search_results = await cognee.search(search_type, query_text=instance["question"])
 
-    search_results_str = "\n".join([context_item["text"] for context_item in search_results])
+        if search_type == SearchType.INSIGHTS:
+            res_list = [_insight_to_string(edge) for edge in raw_search_results]
+        else:
+            res_list = [
+                context_item.get("text", "")
+                for context_item in raw_search_results
+                if isinstance(context_item, dict)
+            ]
+            if all(not text for text in res_list):
+                logger.warning(
+                    "res_list contains only empty strings: No valid 'text' entries found in raw_search_results."
+                )
+
+        search_results += res_list
+
+    search_results_str = "\n".join(search_results)
 
     return search_results_str
 
@@ -47,11 +93,7 @@ def create_cognee_context_getter(
 
 
 async def get_context_with_simple_rag(instance: dict) -> str:
-    await cognee.prune.prune_data()
-    await cognee.prune.prune_system(metadata=True)
-
-    for title, sentences in instance["context"]:
-        await cognee.add("\n".join(sentences), dataset_name="QA")
+    await cognify_instance(instance)
 
     vector_engine = get_vector_engine()
     found_chunks = await vector_engine.search("document_chunk_text", instance["question"], limit=5)
@@ -72,10 +114,14 @@ async def get_context_with_brute_force_triplet_search(instance: dict) -> str:
 
 
 valid_pipeline_slices = {
-    "base": [0, 1, 5],
-    "extract_chunks": [0, 1, 2, 5],
-    "extract_graph": [0, 1, 2, 3, 5],
-    "summarize": [0, 1, 2, 3, 4, 5],
+    "extract_graph": {
+        "slice": [0, 1, 2, 3, 5],
+        "search_types": [SearchType.INSIGHTS, SearchType.CHUNKS],
+    },
+    "summarize": {
+        "slice": [0, 1, 2, 3, 4, 5],
+        "search_types": [SearchType.INSIGHTS, SearchType.SUMMARIES, SearchType.CHUNKS],
+    },
 }
 
 qa_context_providers = {
@@ -84,6 +130,8 @@ qa_context_providers = {
     "simple_rag": get_context_with_simple_rag,
     "brute_force": get_context_with_brute_force_triplet_search,
 } | {
-    name: create_cognee_context_getter(task_indices=slice)
-    for name, slice in valid_pipeline_slices.items()
+    name: create_cognee_context_getter(
+        task_indices=value["slice"], search_types=value["search_types"]
+    )
+    for name, value in valid_pipeline_slices.items()
 }

evals/run_qa_eval.py

@@ -1,5 +1,5 @@
 import asyncio
-from evals.eval_on_hotpot import eval_on_QA_dataset
+from evals.eval_on_hotpot import eval_on_QA_dataset, incremental_eval_on_QA_dataset
 from evals.qa_eval_utils import get_combinations, save_results_as_image
 import argparse
 from pathlib import Path
@@ -15,19 +15,26 @@ async def run_evals_on_paramset(paramset: dict, out_path: str):
         num_samples = params["num_samples"]
         rag_option = params["rag_option"]
 
-        result = await eval_on_QA_dataset(
-            dataset,
-            rag_option,
-            num_samples,
-            paramset["metric_names"],
-        )
-
         if dataset not in results:
             results[dataset] = {}
         if num_samples not in results[dataset]:
             results[dataset][num_samples] = {}
 
-        results[dataset][num_samples][rag_option] = result
+        if rag_option == "cognee_incremental":
+            result = await incremental_eval_on_QA_dataset(
+                dataset,
+                num_samples,
+                paramset["metric_names"],
+            )
+            results[dataset][num_samples] |= result
+        else:
+            result = await eval_on_QA_dataset(
+                dataset,
+                rag_option,
+                num_samples,
+                paramset["metric_names"],
+            )
+            results[dataset][num_samples][rag_option] = result
 
         with open(json_path, "w") as file:
             json.dump(results, file, indent=1)