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# 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
import warnings
from google.cloud import aiplatform_v1beta1
from google.cloud.aiplatform import initializer
from vertexai.preview.rag.utils import _gapic_utils
from vertexai.preview.rag.utils import resources
def retrieval_query(
text: str,
rag_resources: Optional[List[resources.RagResource]] = None,
rag_corpora: Optional[List[str]] = None,
similarity_top_k: Optional[int] = None,
vector_distance_threshold: Optional[float] = None,
vector_search_alpha: Optional[float] = None,
rag_retrieval_config: Optional[resources.RagRetrievalConfig] = None,
) -> aiplatform_v1beta1.RetrieveContextsResponse:
"""Retrieve top k relevant docs/chunks.
Example usage:
```
import vertexai
vertexai.init(project="my-project")
# Using deprecated parameters
results = vertexai.preview.rag.retrieval_query(
text="Why is the sky blue?",
rag_resources=[vertexai.preview.rag.RagResource(
rag_corpus="projects/my-project/locations/us-central1/ragCorpora/rag-corpus-1",
rag_file_ids=["rag-file-1", "rag-file-2", ...],
)],
similarity_top_k=2,
vector_distance_threshold=0.5,
vector_search_alpha=0.5,
)
# Using RagRetrievalConfig. Equivalent to the above example.
config = vertexai.preview.rag.RagRetrievalConfig(
top_k=2,
filter=vertexai.preview.rag.Filter(
vector_distance_threshold=0.5
),
hybrid_search=vertexai.preview.rag.rag_retrieval_config.hybrid_search(
alpha=0.5
),
ranking=vertex.preview.rag.Ranking(
llm_ranker=vertexai.preview.rag.LlmRanker(
model_name="gemini-1.5-flash-002"
)
)
)
results = vertexai.preview.rag.retrieval_query(
text="Why is the sky blue?",
rag_resources=[vertexai.preview.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_corpora: If rag_resources is not specified, use rag_corpora as a list
of rag corpora names. Deprecated. Use rag_resources instead.
similarity_top_k: The number of contexts to retrieve. Deprecated. Use
rag_retrieval_config.top_k instead.
vector_distance_threshold: Optional. Only return contexts with vector
distance smaller than the threshold. Deprecated. Use
rag_retrieval_config.filter.vector_distance_threshold instead.
vector_search_alpha: Optional. Controls the weight between dense and
sparse vector search results. The range is [0, 1], where 0 means
sparse vector search only and 1 means dense vector search only.
The default value is 0.5. Deprecated. Use
rag_retrieval_config.hybrid_search.alpha instead.
rag_retrieval_config: Optional. The config containing the retrieval
parameters, including top_k, vector_distance_threshold,
and alpha.
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
elif rag_corpora:
if len(rag_corpora) > 1:
raise ValueError("Currently only support 1 RagCorpus.")
name = rag_corpora[0]
warnings.warn(
f"rag_corpora is deprecated. Please use rag_resources instead."
f" After {resources.DEPRECATION_DATE} using"
" rag_corpora will raise error",
DeprecationWarning,
)
else:
raise ValueError("rag_resources or rag_corpora 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: {rag_corpora}. Proper format should be:"
" projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}"
)
if rag_resources:
gapic_rag_resource = (
aiplatform_v1beta1.RetrieveContextsRequest.VertexRagStore.RagResource(
rag_corpus=rag_corpus_name,
rag_file_ids=rag_resources[0].rag_file_ids,
)
)
vertex_rag_store = aiplatform_v1beta1.RetrieveContextsRequest.VertexRagStore(
rag_resources=[gapic_rag_resource],
)
else:
vertex_rag_store = aiplatform_v1beta1.RetrieveContextsRequest.VertexRagStore(
rag_corpora=[rag_corpus_name],
)
# Check for deprecated parameters and raise warnings.
if similarity_top_k:
# If similarity_top_k is specified, throw deprecation warning.
warnings.warn(
"similarity_top_k is deprecated. Please use"
" rag_retrieval_config.top_k instead."
f" After {resources.DEPRECATION_DATE} using"
" similarity_top_k will raise error",
DeprecationWarning,
)
if vector_search_alpha:
# If vector_search_alpha is specified, throw deprecation warning.
warnings.warn(
"vector_search_alpha is deprecated. Please use"
" rag_retrieval_config.alpha instead."
f" After {resources.DEPRECATION_DATE} using"
" vector_search_alpha will raise error",
DeprecationWarning,
)
if vector_distance_threshold:
# If vector_distance_threshold is specified, throw deprecation warning.
warnings.warn(
"vector_distance_threshold is deprecated. Please use"
" rag_retrieval_config.filter.vector_distance_threshold instead."
f" After {resources.DEPRECATION_DATE} using"
" vector_distance_threshold will raise error",
DeprecationWarning,
)
# If rag_retrieval_config is not specified, set it to default values.
if not rag_retrieval_config:
api_retrival_config = aiplatform_v1beta1.RagRetrievalConfig(
top_k=similarity_top_k,
hybrid_search=aiplatform_v1beta1.RagRetrievalConfig.HybridSearch(
alpha=vector_search_alpha,
),
filter=aiplatform_v1beta1.RagRetrievalConfig.Filter(
vector_distance_threshold=vector_distance_threshold
),
)
else:
# If rag_retrieval_config is specified, check for missing parameters.
api_retrival_config = aiplatform_v1beta1.RagRetrievalConfig()
# Set top_k to config value if specified
if rag_retrieval_config.top_k:
api_retrival_config.top_k = rag_retrieval_config.top_k
else:
api_retrival_config.top_k = similarity_top_k
# Set alpha to config value if specified
if (
rag_retrieval_config.hybrid_search
and rag_retrieval_config.hybrid_search.alpha
):
api_retrival_config.hybrid_search.alpha = (
rag_retrieval_config.hybrid_search.alpha
)
else:
api_retrival_config.hybrid_search.alpha = vector_search_alpha
# 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."
)
# Set vector_distance_threshold to config value if specified
if (
rag_retrieval_config.filter
and rag_retrieval_config.filter.vector_distance_threshold
):
api_retrival_config.filter.vector_distance_threshold = (
rag_retrieval_config.filter.vector_distance_threshold
)
else:
api_retrival_config.filter.vector_distance_threshold = (
vector_distance_threshold
)
# Set vector_similarity_threshold to config value if specified
if (
rag_retrieval_config.filter
and rag_retrieval_config.filter.vector_similarity_threshold
):
api_retrival_config.filter.vector_similarity_threshold = (
rag_retrieval_config.filter.vector_similarity_threshold
)
if (
rag_retrieval_config.ranking
and rag_retrieval_config.ranking.rank_service
and rag_retrieval_config.ranking.llm_ranker
):
raise ValueError("Only one of rank_service and llm_ranker can be set.")
if rag_retrieval_config.ranking and rag_retrieval_config.ranking.rank_service:
api_retrival_config.ranking.rank_service.model_name = (
rag_retrieval_config.ranking.rank_service.model_name
)
elif rag_retrieval_config.ranking and rag_retrieval_config.ranking.llm_ranker:
api_retrival_config.ranking.llm_ranker.model_name = (
rag_retrieval_config.ranking.llm_ranker.model_name
)
query = aiplatform_v1beta1.RagQuery(
text=text,
rag_retrieval_config=api_retrival_config,
)
request = aiplatform_v1beta1.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
|