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RecommendGroupChunksRequest.md

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RecommendGroupChunksRequest

Properties

Name Type Description Notes
filters ChunkFilter [optional]
group_size int The number of chunks to fetch for each group. This is the number of chunks which will be returned in the response for each group. The default is 3. If this is set to a large number, we recommend setting slim_chunks to true to avoid returning the content and chunk_html of the chunks so as to reduce latency due to content download and serialization. [optional]
limit int The number of groups to return. This is the number of groups which will be returned in the response. The default is 10. [optional]
negative_group_ids List[str] The ids of the groups to be used as negative examples for the recommendation. The groups in this array will be used to filter out similar groups. [optional]
negative_group_tracking_ids List[str] The ids of the groups to be used as negative examples for the recommendation. The groups in this array will be used to filter out similar groups. [optional]
positive_group_ids List[str] The ids of the groups to be used as positive examples for the recommendation. The groups in this array will be used to find similar groups. [optional]
positive_group_tracking_ids List[str] The ids of the groups to be used as positive examples for the recommendation. The groups in this array will be used to find similar groups. [optional]
recommend_type str The type of recommendation to make. This lets you choose whether to recommend based off of `semantic` or `fulltext` similarity. The default is `semantic`. [optional]
slim_chunks bool 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. [optional]
strategy str 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. [optional]

Example

from trieve_py_client.models.recommend_group_chunks_request import RecommendGroupChunksRequest

# TODO update the JSON string below
json = "{}"
# create an instance of RecommendGroupChunksRequest from a JSON string
recommend_group_chunks_request_instance = RecommendGroupChunksRequest.from_json(json)
# print the JSON string representation of the object
print(RecommendGroupChunksRequest.to_json())

# convert the object into a dict
recommend_group_chunks_request_dict = recommend_group_chunks_request_instance.to_dict()
# create an instance of RecommendGroupChunksRequest from a dict
recommend_group_chunks_request_form_dict = recommend_group_chunks_request.from_dict(recommend_group_chunks_request_dict)

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