Feedback System

Feedback on recall answers is handled via Sessions: you record Q&A in a session, then attach feedback to specific entries using cognee.session.add_feedback and cognee.session.delete_feedback. Before you start:

Complete Quickstart and Sessions
Run recall() with a session_id so that Q&A entries are stored
Ensure caching is enabled

Record feedback on a session Q&A

Run recall() with session_id so the interaction is stored.
Get the session history with cognee.session.get_session and identify the qa_id of the entry you want to rate.
Call cognee.session.add_feedback with that qa_id, and optionally feedback_text and feedback_score (1–5).
To make feedback influence future retrieval, run improve() with the relevant session_ids. If you are already writing new content with remember(), self_improvement=True can trigger this in the background automatically.
To clear feedback, use cognee.session.delete_feedback(session_id=..., qa_id=...). Both add_feedback and delete_feedback return True on success and False only when the entry was not found or caching is disabled — every other failure raises, see Return contract and errors.

get_session returns a list of SessionQAEntry objects. Each entry has: qa_id, question, answer, context, time, feedback_text, feedback_score. Entries are in chronological order (oldest first); use entries[-1] for the most recent. Pass optional user for multi-tenant or permission-scoped usage.

import cognee
from cognee import SearchType

# Run recall with a session so the Q&A is stored
results = await cognee.recall(
    query_text="What are the main themes in my data?",
    query_type=SearchType.GRAPH_COMPLETION,
    session_id="my_session",
)
print(results)

# Get session history; entries are chronological, so latest is entries[-1]
entries = await cognee.session.get_session(session_id="my_session", last_n=5)
latest = entries[-1]
qa_id = latest.qa_id

# Add feedback for that Q&A (feedback_score must be 1–5)
ok = await cognee.session.add_feedback(
    session_id="my_session",
    qa_id=qa_id,
    feedback_text="Very helpful and accurate",
    feedback_score=5,
)
# ok is True if the entry was found and updated

# Push feedback into the graph
await cognee.improve(
    dataset="main_dataset",
    session_ids=["my_session"],
)

# Recall again with feedback_influence > 0 to surface the weighted results
improved_results = await cognee.recall(
    query_text="What are the main themes in my data?",
    query_type=SearchType.GRAPH_COMPLETION,
    feedback_influence=0.5,
)
print(improved_results)

The per-call feedback_influence defaults to the DEFAULT_FEEDBACK_INFLUENCE environment variable, which is 0.0 (off) by default — so the learned feedback signal does not change ranking until you opt in. Set DEFAULT_FEEDBACK_INFLUENCE (e.g. 0.1) to activate the signal globally, or pass feedback_influence per call to recall() / search() to override it. Setting it back to 0.0 restores the prior baseline.

Personalize ranking per user

The feedback weights above are a global signal: every user’s ratings move the same weights for everyone. To let one user’s ratings nudge only that user’s results, turn on per-user preference personalization:

Enable it in your .env — it is off by default:
PERSONALIZATION_ENABLED="true"
Rate answers. The same add_feedback(..., feedback_score=1..5) call from above feeds personalization too; alternatively, with AUTO_FEEDBACK on, a rating is inferred when the user’s next message clearly judges the previous answer (“that was exactly right”).
Run improve() with the session — the same call that applies global feedback weights also folds the rated turns into that user’s preference weights.
Recall again as the same user: ranking in graph, hybrid, and RAG completion is nudged toward what they rated up, by at most PERSONALIZATION_INFLUENCE (default 0.3, i.e. 30%).

# 2. Rate an answer (or let AUTO_FEEDBACK infer a rating from the conversation)
await cognee.session.add_feedback(
    session_id="my_session",
    qa_id=qa_id,
    feedback_score=5,
)

# 3. Fold ratings into this user's preference weights
await cognee.improve(
    dataset="main_dataset",
    session_ids=["my_session"],
)

# 4. Recall as the same user — ranking now leans toward what they rated up
results = await cognee.recall(
    query_text="What are the main themes in my data?",
    query_type=SearchType.GRAPH_COMPLETION,
)

Unlike feedback_influence, there is no per-call knob: the nudge applies whenever a user is in context and exactly one dataset resolves, and its strength is set by the PERSONALIZATION_INFLUENCE environment variable. How ratings become weights, how they decay, and how each retriever applies them is covered in User Preferences.

Feedback API Reference

`add_feedback()`

Attach a rating and optional text comment to a stored Q&A entry.

Parameter	Type	Description
`session_id`	`str`	Target session that contains the Q&A entry.
`qa_id`	`str`	Target entry ID. You can get this value from `entry.qa_id` on a `SessionQAEntry` returned by `get_session()`.
`feedback_text`	`Optional[str]`	Optional free-form feedback comment.
`feedback_score`	`Optional[int]`	Optional integer rating from `1` to `5`.
`user`	`Optional[User]`	Optional session owner; resolves automatically when `None`.

Returns True if feedback was stored successfully, False if the entry was not found or caching is disabled. See Return contract and errors.

`delete_feedback()`

Clear both feedback_text and feedback_score for an existing Q&A entry without deleting the entry itself.

Parameter	Type	Description
`session_id`	`str`	Target session that contains the Q&A entry.
`qa_id`	`str`	Target entry ID whose feedback should be cleared.
`user`	`Optional[User]`	Optional session owner; resolves automatically when `None`.

Returns True if feedback was cleared, False if the entry was not found or caching is disabled. See Return contract and errors. When calling add_feedback(), provide at least one of feedback_text or feedback_score. If you pass feedback_score, it must be an integer between 1 and 5.

Return contract and errors

False means exactly two things: the Q&A entry was not found, or caching is disabled. It never means “something went wrong”. Everything else raises instead of being reported as False — an unreachable cache, a misconfigured backend, or invalid parameters (an empty session_id or qa_id raises SessionParameterValidationError). Earlier releases wrapped these calls in a catch-all that returned False, so a rating recorded while the cache hiccuped was silently dropped and looked identical to a bad qa_id. The same contract applies to cognee.session.add_frequency_weights(), which records the graph nodes and edges an answer used so improve() can raise their frequency weights. Handle the two outcomes separately:

from cognee.infrastructure.databases.exceptions import SessionParameterValidationError

try:
    ok = await cognee.session.add_feedback(
        session_id="my_session",
        qa_id=entry.qa_id,
        feedback_score=5,
    )
except SessionParameterValidationError:
    # Empty session_id or qa_id — a caller bug, fix the ids.
    raise
# Any other exception (unreachable cache, misconfigured backend) also propagates:
# the feedback was NOT recorded, and the call is safe to retry once the cause is fixed.

if not ok:
    print("No such Q&A entry, or caching is disabled (check the CACHING setting).")

The cognee-cli feedback add and cognee-cli feedback delete commands report the two cases separately and exit non-zero on both — a not-found entry names the ids and points at the CACHING setting, while an infrastructure failure surfaces the underlying error. Earlier releases exited 0 with a generic message in both cases, so scripts that only checked the exit status silently accepted lost feedback.

Example

cleared = await cognee.session.delete_feedback(
    session_id="my_session",
    qa_id="some-qa-id",
)
print("Feedback cleared:", cleared)

Sessions

Enable conversation memory with sessions

Sessions and Caching

How sessions and caching work

Improve

Enrich the graph and bridge session memory

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Record feedback on a session Q&A

Personalize ranking per user

Feedback API Reference

`add_feedback()`

`delete_feedback()`

Return contract and errors

Example

Sessions

Sessions and Caching

Improve

​Record feedback on a session Q&A

​Personalize ranking per user

​Feedback API Reference

​add_feedback()

​delete_feedback()

​Return contract and errors

​Example

Sessions

Sessions and Caching

Improve

Record feedback on a session Q&A

Personalize ranking per user

Feedback API Reference

`add_feedback()`

`delete_feedback()`

Return contract and errors

Example