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1856027 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | # -*- coding: utf-8 -*-
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Retrieval query to get relevant contexts."""
import re
from typing import List, Optional
from google.cloud import aiplatform_v1
from google.cloud.aiplatform import initializer
from vertexai.rag.utils import _gapic_utils
from vertexai.rag.utils import resources
def retrieval_query(
text: str,
rag_resources: Optional[List[resources.RagResource]] = None,
rag_retrieval_config: Optional[resources.RagRetrievalConfig] = None,
) -> aiplatform_v1.RetrieveContextsResponse:
"""Retrieve top k relevant docs/chunks.
Example usage:
```
import vertexai
vertexai.init(project="my-project")
config = vertexai.rag.rag_retrieval_config(
top_k=2,
filter=vertexai.rag.rag_retrieval_config.filter(
vector_distance_threshold=0.5
),
)
results = vertexai.rag.retrieval_query(
text="Why is the sky blue?",
rag_resources=[vertexai.rag.RagResource(
rag_corpus="projects/my-project/locations/us-central1/ragCorpora/rag-corpus-1",
rag_file_ids=["rag-file-1", "rag-file-2", ...],
)],
rag_retrieval_config=config,
)
```
Args:
text: The query in text format to get relevant contexts.
rag_resources: A list of RagResource. It can be used to specify corpus
only or ragfiles. Currently only support one corpus or multiple files
from one corpus. In the future we may open up multiple corpora support.
rag_retrieval_config: Optional. The config containing the retrieval
parameters, including similarity_top_k and vector_distance_threshold
Returns:
RetrieveContextsResonse.
"""
parent = initializer.global_config.common_location_path()
client = _gapic_utils.create_rag_service_client()
if rag_resources:
if len(rag_resources) > 1:
raise ValueError("Currently only support 1 RagResource.")
name = rag_resources[0].rag_corpus
else:
raise ValueError("rag_resources must be specified.")
data_client = _gapic_utils.create_rag_data_service_client()
if data_client.parse_rag_corpus_path(name):
rag_corpus_name = name
elif re.match("^{}$".format(_gapic_utils._VALID_RESOURCE_NAME_REGEX), name):
rag_corpus_name = parent + "/ragCorpora/" + name
else:
raise ValueError(
f"Invalid RagCorpus name: {name}. Proper format should be:"
" projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}"
)
if rag_resources:
gapic_rag_resource = (
aiplatform_v1.RetrieveContextsRequest.VertexRagStore.RagResource(
rag_corpus=rag_corpus_name,
rag_file_ids=rag_resources[0].rag_file_ids,
)
)
vertex_rag_store = aiplatform_v1.RetrieveContextsRequest.VertexRagStore(
rag_resources=[gapic_rag_resource],
)
else:
vertex_rag_store = aiplatform_v1.RetrieveContextsRequest.VertexRagStore(
rag_corpora=[rag_corpus_name],
)
# If rag_retrieval_config is not specified, set it to default values.
if not rag_retrieval_config:
api_retrival_config = aiplatform_v1.RagRetrievalConfig()
else:
# If rag_retrieval_config is specified, check for missing parameters.
api_retrival_config = aiplatform_v1.RagRetrievalConfig()
api_retrival_config.top_k = rag_retrieval_config.top_k
# Set vector_distance_threshold to config value if specified
if rag_retrieval_config.filter:
# Check if both vector_distance_threshold and vector_similarity_threshold
# are specified.
if (
rag_retrieval_config.filter
and rag_retrieval_config.filter.vector_distance_threshold
and rag_retrieval_config.filter.vector_similarity_threshold
):
raise ValueError(
"Only one of vector_distance_threshold or"
" vector_similarity_threshold can be specified at a time"
" in rag_retrieval_config."
)
api_retrival_config.filter.vector_distance_threshold = (
rag_retrieval_config.filter.vector_distance_threshold
)
api_retrival_config.filter.vector_similarity_threshold = (
rag_retrieval_config.filter.vector_similarity_threshold
)
query = aiplatform_v1.RagQuery(
text=text,
rag_retrieval_config=api_retrival_config,
)
request = aiplatform_v1.RetrieveContextsRequest(
vertex_rag_store=vertex_rag_store,
parent=parent,
query=query,
)
try:
response = client.retrieve_contexts(request=request)
except Exception as e:
raise RuntimeError("Failed in retrieving contexts due to: ", e) from e
return response
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