Get Interaction
Get a specific interaction.
Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Path Parameters
Response
Successful Response
Response model for a stored interaction.
Extends SearchInteraction with system-assigned fields.
ID of the document/feature that was interacted with. REQUIRED. This is the SAME value as the document_id returned in retriever execute results — you can pass it as either feature_id or document_id (the latter is accepted as an alias). Used to track which specific items users engage with.
"doc_abc123"
"prod_xyz789"
"feat_12345"
List of interaction types that occurred. REQUIRED. Multiple types can be recorded simultaneously (e.g., VIEW + CLICK + LONG_VIEW for a result the user engaged with). Use the InteractionType enum values.
1Types of user interactions with search results.
These interaction types are used to track user behavior and improve retrieval quality through Learning to Rank (LTR), collaborative filtering, and embedding fine-tuning.
Values: IMPRESSION: Result was rendered on screen (passive signal, no user action) VIEW: User viewed a search result CLICK: User clicked on a search result DWELL: User lingered on a result (weaker than long_view, stronger than view) POSITIVE_FEEDBACK: User explicitly marked result as relevant/helpful NEGATIVE_FEEDBACK: User explicitly marked result as not relevant PURCHASE: User purchased the item (high-value conversion signal) ADD_TO_CART: User added item to cart (mid-funnel signal) WISHLIST: User saved item for later (engagement signal) LONG_VIEW: User spent significant time viewing (dwell time) SHARE: User shared the result (strong engagement signal) BOOKMARK: User bookmarked the result QUERY_REFINEMENT: User modified their search query ZERO_RESULTS: Query yielded no results (helps identify gaps) FILTER_TOGGLE: User modified filters (helps understand intent) SKIP: User skipped over result to click something lower (negative signal) RETURN_TO_RESULTS: User quickly returned to results (negative signal)
Usage in Retrieval Optimization: - LTR (Learning to Rank): Train models to predict click-through rates - Collaborative Filtering: Find similar users/items based on interactions - Embedding Fine-tuning: Adjust embeddings based on what users actually click - Query Understanding: Analyze refinements and zero-result queries - Result Quality: Identify poorly-performing results via skip/return patterns
impression, view, click, dwell, positive_feedback, negative_feedback, purchase, add_to_cart, wishlist, long_view, share, bookmark, query_refinement, zero_results, filter_toggle, skip, return_to_results ["click"]["positive_feedback", "click", "long_view"]["negative_feedback"]["purchase", "click"]Position in search results where interaction occurred (0-indexed). REQUIRED. Critical for Learning to Rank - helps identify position bias. E.g., position=0 means first result, position=9 means 10th result. Higher engagement at lower positions suggests higher quality.
x >= 00
1
5
15
Additional context about the interaction. NOT REQUIRED. Can include device, duration, viewport info, etc. Use this to enrich interaction data with application-specific context.
{
"device": "mobile",
"duration_ms": 5000,
"page": "search_results",
"viewport_position": 0.75
}Customer's authenticated user identifier. NOT REQUIRED. Persists across sessions for long-term tracking. Enables personalization and user-specific metrics. Use your application's user ID format.
"user_abc123"
Temporary identifier for a single search session. NOT REQUIRED. Typically 30min-1hr duration. Tracks anonymous and authenticated users within a session. Use to group related queries and understand search journeys. Also used by learned fusion (auto-tune) for within-session real-time adaptation: interactions sharing a session_id allow the bandit to update feature weights mid-session without waiting for batch aggregation.
"sess_abc123"
ID of the retriever execution that generated these results. NOT REQUIRED but HIGHLY RECOMMENDED for training and optimization. Links the interaction back to the exact search query, pipeline configuration, and stage execution that produced the results the user saw. Essential for: fine-tuning embeddings, training rerankers, query understanding, and tracing which pipeline configs produce better user engagement. Retrieve from the retriever execution response and pass to interactions.
"exec_abc123xyz"
ID of the retriever that was executed. NOT REQUIRED but RECOMMENDED for multi-retriever analytics. Enables comparing performance across different retriever configurations. If execution_id is provided, retriever_id can be inferred from the execution record.
"ret_abc123"
Snapshot of the query input that generated these results. HIGHLY RECOMMENDED for training optimization. Storing the query directly enables 10-100x faster training data extraction by avoiding expensive joins to execution records. Use the same format as retriever query input (e.g., {'text': '...', 'filters': {...}}). Essential for: embedding fine-tuning (query-document pairs), query expansion learning, and analyzing which query patterns lead to better engagement. NOT REQUIRED but strongly recommended for production use cases involving model training.
{ "text": "wireless headphones" }Initial retrieval score of this document when shown to the user. HIGHLY RECOMMENDED for Learning to Rank (LTR). This is a critical feature for reranker training - helps the model learn how to adjust initial scores based on user engagement. Should match the score from the retriever execution results. NOT REQUIRED but strongly recommended for LTR and reranker training.
0.95
Total number of results shown to the user in this search. NOT REQUIRED but useful for context. Helps understand interaction patterns - clicking position 5 of 10 results is different from position 5 of 100 results. Useful for position bias correction and CTR analysis.
x >= 110
Feature URI that produced the clicked result (e.g. 'mixpeek://text_extractor@v1/embedding'). Required for learned fusion — interactions without this field do not contribute to weight learning.
"mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"
When the interaction actually happened (ISO 8601). OPTIONAL. Omit for live interactions — the server stamps 'now'. Supply this ONLY to backfill historical interactions (e.g. migrating existing click logs) so learned-fusion temporal decay weights them by their TRUE age. A naive datetime is interpreted as UTC; a future value is clamped to now. Backfilled events bypass the real-time session cache.
"2026-01-15T10:30:00Z"
Unique identifier for this interaction record. System-assigned UUID. Use this to reference the interaction in subsequent requests. Null when the interaction was deduplicated (see deduplicated field).
"int_abc123xyz789"
True when this interaction was a duplicate of a recently recorded interaction with the same (execution_id, feature_id, interaction_type) tuple and was silently dropped. The interaction_id will be null.
ISO 8601 timestamp when the interaction was recorded. System-assigned. Used for time-based analysis, training data recency weighting, and temporal trends in user behavior.
"2025-01-15T10:30:00Z"
Non-blocking warning about the interaction. Present when the request was accepted but may not behave as intended (e.g. missing feature_uri for learned fusion).