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

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Example: Basic Feedback Loop

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

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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)

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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")

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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.

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Example: Batch Feedback Collection

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

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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}")

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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.

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Example: Application Integration

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

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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

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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.

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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

Search Types

Learn about all search types including FEEDBACK

Core Concepts

Understand knowledge graph fundamentals

API Reference

Explore feedback API endpoints