chore: use cot retriever only

cognee/tasks/feedback/create_enrichments.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Dict, List, Optional
+from typing import List
 from uuid import NAMESPACE_OID, uuid5
 
 from cognee.infrastructure.llm import LLMGateway

cognee/tasks/feedback/extract_feedback_interactions.py

@@ -1,21 +1,28 @@
 from __future__ import annotations
 
 from typing import Dict, List, Optional, Tuple
-from uuid import UUID
+from uuid import UUID, uuid5, NAMESPACE_OID
 
 from cognee.infrastructure.llm import LLMGateway
 from cognee.infrastructure.llm.prompts.read_query_prompt import read_query_prompt
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.databases.graph import get_graph_engine
-from uuid import uuid5, NAMESPACE_OID
 
-from .utils import filter_negative_feedback
 from .models import FeedbackEnrichment
 
 
 logger = get_logger("extract_feedback_interactions")
 
 
+def _filter_negative_feedback(feedback_nodes):
+    """Filter for negative sentiment feedback using precise sentiment classification."""
+    return [
+        (node_id, props)
+        for node_id, props in feedback_nodes
+        if (props.get("sentiment", "").casefold() == "negative" or props.get("score", 0) < 0)
+    ]
+
+
 def _get_normalized_id(node_id, props) -> str:
     """Return Cognee node id preference: props.id → props.node_id → raw node_id."""
     return str(props.get("id") or props.get("node_id") or node_id)
@@ -179,7 +186,7 @@ async def extract_feedback_interactions(
         return []
 
     feedback_nodes, interaction_nodes = _separate_feedback_and_interaction_nodes(graph_nodes)
-    negative_feedback_nodes = filter_negative_feedback(feedback_nodes)
+    negative_feedback_nodes = _filter_negative_feedback(feedback_nodes)
     if not negative_feedback_nodes:
         logger.info("No negative feedback found; returning empty list")
         return []

cognee/tasks/feedback/generate_improved_answers.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional
 from pydantic import BaseModel
 
 from cognee.infrastructure.llm import LLMGateway
 from cognee.infrastructure.llm.prompts.read_query_prompt import read_query_prompt
 from cognee.modules.graph.utils import resolve_edges_to_text
 from cognee.shared.logging_utils import get_logger
 
-from .utils import create_retriever
+from cognee.modules.retrieval.graph_completion_cot_retriever import GraphCompletionCotRetriever
 from .models import FeedbackEnrichment
 
 
@@ -91,11 +91,10 @@ async def _generate_improved_answer_for_single_interaction(
 
 async def generate_improved_answers(
     enrichments: List[FeedbackEnrichment],
-    retriever_name: str = "graph_completion_cot",
     top_k: int = 20,
     reaction_prompt_location: str = "feedback_reaction_prompt.txt",
 ) -> List[FeedbackEnrichment]:
-    """Generate improved answers using configurable retriever and LLM."""
+    """Generate improved answers using CoT retriever and LLM."""
     if not enrichments:
         logger.info("No enrichments provided; returning empty list")
         return []
@@ -104,9 +103,9 @@ async def generate_improved_answers(
         logger.error("Input data validation failed; missing required fields")
         return []
 
-    retriever = create_retriever(
-        retriever_name=retriever_name,
+    retriever = GraphCompletionCotRetriever(
         top_k=top_k,
+        save_interaction=False,
         user_prompt_path="graph_context_for_question.txt",
         system_prompt_path="answer_simple_question.txt",
     )

examples/python/feedback_enrichment_minimal_example.py

@@ -57,7 +57,7 @@ async def run_feedback_enrichment_memify(last_n: int = 5):
     # Instantiate tasks with their own kwargs
     extraction_tasks = [Task(extract_feedback_interactions, last_n=last_n)]
     enrichment_tasks = [
-        Task(generate_improved_answers, retriever_name="graph_completion_cot", top_k=20),
+        Task(generate_improved_answers, top_k=20),
         Task(create_enrichments),
         Task(extract_graph_from_data, graph_model=KnowledgeGraph, task_config={"batch_size": 10}),
         Task(add_data_points, task_config={"batch_size": 10}),