Get Recommended Chunks
Get recommendations of chunks similar to the positive samples in the request and dissimilar to the negative.
Authorizations
Headers
The dataset id or tracking_id to use for the request. We assume you intend to use an id if the value is a valid uuid.
The API version to use for this request. Defaults to V2 for orgs created after July 12, 2024 and V1 otherwise.
V1, V2 Body
Filters is a JSON object which can be used to filter chunks. This is useful for when you want to filter chunks by arbitrary metadata. Unlike with tag filtering, there is a performance hit for filtering on metadata.
The number of chunks to return. This is the number of chunks which will be returned in the response. The default is 10.
The ids of the chunks to be used as negative examples for the recommendation. The chunks in this array will be used to filter out similar chunks.
The tracking_ids of the chunks to be used as negative examples for the recommendation. The chunks in this array will be used to filter out similar chunks.
The ids of the chunks to be used as positive examples for the recommendation. The chunks in this array will be used to find similar chunks.
The tracking_ids of the chunks to be used as positive examples for the recommendation. The chunks in this array will be used to find similar chunks.
The type of recommendation to make. This lets you choose whether to recommend based off of semantic or fulltext similarity. The default is semantic.
semantic, fulltext, bm25 Set slim_chunks to true to avoid returning the content and chunk_html of the chunks. This is useful for when you want to reduce amount of data over the wire for latency improvement (typicall 10-50ms). Default is false.
Strategy to use for recommendations, either "average_vector" or "best_score". The default is "average_vector". The "average_vector" strategy will construct a single average vector from the positive and negative samples then use it to perform a pseudo-search. The "best_score" strategy is more advanced and navigates the HNSW with a heuristic of picking edges where the point is closer to the positive samples than it is the negatives.
average_vector, best_score User ID is the id of the user who is making the request. This is used to track user interactions with the recommendation results.