CollectionUpdateRequest

Properties

Name	Type	Description	Notes
name	str	Name of the collection	[optional]
description	str	Description of the collection	[optional]
rag_type	str	RAG type options: * `auto` - Automatically select the best rag_type. * `llm_only` LLM Only - Answer the query without any supporting document contexts. Requires 1 LLM call. * `rag` RAG (Retrieval Augmented Generation) - Use supporting document contexts to answer the query. Requires 1 LLM call. * `hyde1` LLM Only + RAG composite - HyDE RAG (Hypothetical Document Embedding). Use ‘LLM Only’ response to find relevant contexts from a collection for generating a response. Requires 2 LLM calls. * `hyde2` HyDE + RAG composite - Use the ‘HyDE RAG’ response to find relevant contexts from a collection for generating a response. Requires 3 LLM calls. * `rag+` Summary RAG - Like RAG, but uses more context and recursive summarization to overcome LLM context limits. Keeps all retrieved chunks, puts them in order, adds neighboring chunks, then uses the summary API to get the answer. Can require several LLM calls. * `all_data` All Data RAG - Like Summary RAG, but includes all document chunks. Uses recursive summarization to overcome LLM context limits. Can require several LLM calls.	[optional]

Example

from h2ogpte.rest_async.models.collection_update_request import CollectionUpdateRequest

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

# convert the object into a dict
collection_update_request_dict = collection_update_request_instance.to_dict()
# create an instance of CollectionUpdateRequest from a dict
collection_update_request_from_dict = CollectionUpdateRequest.from_dict(collection_update_request_dict)