meow

[agha64113-creator]

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Fixes #<issue_number_goes_here> 🦕

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[agha64113-creator]

meow

[agha64113-creator]

meow

[agha64113-creator]

Meow. ow I have a good understanding. I need to create a Go implementation of the cloud function. The problem statement shows an integration test file that needs to be created. But the integration test references Go types that don't exist yet: Config, NewClient, EngineDataType, EngineChunkType, SummaryType, and the client's Search, SearchWithPageSize, Close methods.
I need to create:

1. go/go.mod - Go module file
2. go/main.go - the cloud function implementation with the client
3. go/integration_test.go - the integration tests from the diff
4. The supporting types and methods that the tests expect
   Let me start building out the full implementation.

Progress update: Initial plan for Go cloud function implementation

• Create search/cloud-function/go/ directory structure
• Create go.mod for the Go module
• Create main.go with Config, types, Client, NewClient, Search, SearchWithPageSize, Close and HTTP handler
• Create integration_test.go with integration tests from problem statement
• Create README.md for the Go implementation
  Output
  $ git push -v origin copilot/add-license-comments
  Pushing to https://github.com/agha64113-creator/generative-ai
  To https://github.com/agha64113-creator/generative-ai
  = [up to date] copilot/add-license-comments -> copilot/add-license-comments
  updating local tracking ref 'refs/remotes/origin/copilot/add-license-comments'
  Everything up-to-date

Progress reported successfully.

General-purpose: Create Go cloud function for Vertex AI Search

Let me first look at the Python implementation to understand what needs to be mirrored.

Explore Python cloud function implementation
$ find /home/runner/work/generative-ai/generative-ai/search/cloud-function/python/ -type f | head -20 && echo "---" && ls /home/runner/work/generative-ai/generative-ai/search/cloud-function/
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/main.py
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/README.md
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/test_vertex_ai_search_client.py
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/requirements.txt
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/setup_env.sh
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/vertex_ai_search_client.py
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/.env.example
/home/runner/work/generative-ai/generative-ai/search/cloud-function/python/test_integration_vertex_search_client.py

README.md python
<exited with exit code 0>

View 2 files

search/cloud-function/python/main.py

1. Copyright 2024 Google LLC

3. Licensed under the Apache License, Version 2.0 (the "License");

4. you may not use this file except in compliance with the License.

5. You may obtain a copy of the License at

7. http://www.apache.org/licenses/LICENSE-2.0

9. Unless required by applicable law or agreed to in writing, software

10. distributed under the License is distributed on an "AS IS" BASIS,

11. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

12. See the License for the specific language governing permissions and

13. limitations under the License.

14. """
15. VertexAISearchClient for interacting with Google Cloud Vertex AI Search.
17. This module provides a client class for simplifying interactions with the
18. Vertex AI Search API. It handles configuration, query construction, and
19. result parsing.
21. Example usage:

22. config = VertexAISearchConfig(
    

23.     project_id="your-project",
    

24.     location="global",
    

25.     data_store_id="your-data-store",
    

26.     engine_data_type="UNSTRUCTURED",
    

27.     engine_chunk_type="CHUNK",
    

28.     summary_type="VERTEX_AI_SEARCH",
    

29. )
    

30. client = VertexAISearchClient(config)
    

31. results = client.search("your search query")
    

32. print(results)
    

33. """
34. from dataclasses import dataclass
35. import html
36. import json
37. import re
38. from typing import Any, Literal
40. from google.api_core.client_options import ClientOptions
41. from google.cloud import discoveryengine_v1alpha as discoveryengine
42. from google.cloud.discoveryengine_v1alpha.services.search_service.pagers import (

43. SearchPager,
    

44. )
45. from google.cloud.discoveryengine_v1alpha.types import SearchResponse

47. Define types using string literals, similar to enums.

48. EngineDataTypeStr = Literal["UNSTRUCTURED", "STRUCTURED", "WEBSITE", "BLENDED"]
49. EngineChunkTypeStr = Literal[

50. "DOCUMENT_WITH_SNIPPETS", "DOCUMENT_WITH_EXTRACTIVE_SEGMENTS", "CHUNK"
    

51. ]
52. SummaryTypeStr = Literal[

53. "NONE", "VERTEX_AI_SEARCH", "GENERATE_GROUNDED_ANSWERS", "GEMINI"
    

54. ]
57. @DataClass
58. class VertexAISearchConfig:

59. """Config for the Vertex AI Search data store."""
    

61. project_id: str
    

62. location: str
    

63. data_store_id: str
    

64. engine_data_type: EngineDataTypeStr | str
    

65. engine_chunk_type: EngineChunkTypeStr | str
    

66. summary_type: SummaryTypeStr | str
    

68. def __post_init__(self) -> None:
    

69.     """Validate and convert string inputs to appropriate types."""
    

70.     self.engine_data_type = self._validate_enum(
    

71.         self.engine_data_type, EngineDataTypeStr, "UNSTRUCTURED"
    

72.     )
    

73.     self.engine_chunk_type = self._validate_enum(
    

74.         self.engine_chunk_type, EngineChunkTypeStr, "CHUNK"
    

75.     )
    

76.     self.summary_type = self._validate_enum(
    

77.         self.summary_type, SummaryTypeStr, "VERTEX_AI_SEARCH"
    

78.     )
    

80. @staticmethod
    

81. def _validate_enum(value: str, enum_type: Any, default: str) -> str:
    

82.     """Validate and convert string to enum type."""
    

83.     if value in enum_type.__args__:
    

84.         return value
    

85.     print(f"Warning: Invalid value '{value}'. Using default: '{default}'")
    

86.     return default
    

88. def to_dict(self) -> dict[str, str]:
    

89.     """Convert the config to a dictionary."""
    

90.     return {
    

91.         "project_id": self.project_id,
    

92.         "location": self.location,
    

93.         "data_store_id": self.data_store_id,
    

94.         "engine_data_type": self.engine_data_type,
    

95.         "engine_chunk_type": self.engine_chunk_type,
    

96.         "summary_type": self.summary_type,
    

97.     }
    

100. class VertexAISearchClient: