cognee/docs/ko/guides/feedback-system.md

Feedback System

Step-by-step guide to using feedback to improve Cognee's knowledge graphs

This guide shows you how to use Cognee's feedback system to improve search results and knowledge graph quality.

Before you start:

• Complete Quickstart to understand basic operations
• Read Search to understand search types
• Ensure you have LLM Providers configured for feedback processing

Example: Basic Feedback Loop

This example shows how to provide feedback to improve future search results.

Step 1: Perform Search with Interaction Saving

import cognee
from cognee import SearchType

# Search with interaction saving enabled
results = await cognee.search(
    query_text="What are the main themes in my data?",
    query_type=SearchType.GRAPH_COMPLETION,
    save_interaction=True  # Required for feedback
)

print("Search results:", results)

Step 2: Provide Positive Feedback

# Provide positive feedback
await cognee.search(
    query_text="Excellent analysis, very comprehensive and accurate!",
    query_type=SearchType.FEEDBACK,
    last_k=1  # Apply to last interaction
)

print("✅ Positive feedback applied")

Step 3: Provide Negative Feedback

# Provide constructive negative feedback
await cognee.search(
    query_text="This answer missed the key technical details I needed",
    query_type=SearchType.FEEDBACK,
    last_k=1
)

print("✅ Negative feedback applied")

Result: Feedback scores are applied to knowledge graph relationships to improve future results.

Example: Batch Feedback Collection

This example shows how to collect feedback on multiple recent interactions.

Step 1: Perform Multiple Searches

# Perform several searches
queries = [
    "What are the technical requirements?",
    "Summarize the project timeline",
    "Explain the architecture decisions"
]

for query in queries:
    results = await cognee.search(
        query_text=query,
        query_type=SearchType.GRAPH_COMPLETION,
        save_interaction=True
    )
    print(f"Results for '{query}': {results}")

Step 2: Provide Batch Feedback

# Provide feedback on multiple recent interactions
await cognee.search(
    query_text="The last few searches have been much more accurate and helpful",
    query_type=SearchType.FEEDBACK,
    last_k=3  # Apply to last 3 interactions
)

print("✅ Batch feedback applied to recent interactions")

Result: Multiple interactions are improved based on your feedback.

Example: Application Integration

This example shows how to integrate feedback collection in your application.

Step 1: Create Search Function with Feedback

async def search_with_feedback(query: str, user_feedback: str = None):
    # Perform search
    results = await cognee.search(
        query_text=query,
        query_type=SearchType.GRAPH_COMPLETION,
        save_interaction=True
    )
    
    # If user provides feedback, apply it
    if user_feedback:
        await cognee.search(
            query_text=user_feedback,
            query_type=SearchType.FEEDBACK,
            last_k=1
        )
        print("✅ Feedback collected and applied")
    
    return results

Step 2: Use in Your Application

# Search with immediate feedback
results = await search_with_feedback(
    "What are the security considerations?",
    "Great answer, very detailed and practical"
)

# Search without feedback
results = await search_with_feedback("What is the deployment process?")

Result: Integrated feedback collection in your application workflow.

Common Issues

Feedback not working:

• Ensure save_interaction=True in your search calls
• Check that you have recent interactions to apply feedback to
• Verify you're using SearchType.FEEDBACK for feedback calls

No improvement in results:

• Provide more specific feedback text
• Give feedback soon after receiving results
• Use positive feedback to reinforce good results

Performance concerns:

• Feedback requires LLM processing for sentiment analysis
• Consider batching multiple feedback calls
• Monitor LLM API quotas and rate limits

Integration challenges:

• Start with simple feedback collection
• Gradually add more sophisticated feedback patterns
• Test feedback effectiveness over time

Understand knowledge graph fundamentals Explore feedback API endpoints

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To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt