meows???? So who's game.

[agha64113-creator]

Excellent! Now let me check the API files to see the structure of user profiles:

Check API for user_profiles structure

$ cd /home/runner/work/generative-ai/generative-ai/gemini/sample-apps/genwealth && grep -A 20 -B 5 "user_profiles\|risk_profile\|bio" api/index.ts | head -60
database-files/llm.sql:118:        SELECT CONCAT(E'List 3 words that best describe the type of investment this person is looking for based on their QUESTION, RISK_PROFILE, and BIO. \n\nQUESTION:\n', prompt, E'\n\nRISK_PROFILE: \n', risk_profile, E'\n\nBIO: \n', bio) INTO extractive_prompt FROM user_profiles WHERE id = uid;

<exited with exit code 0>

Find SELECT queries on user_profiles

$ cd /home/runner/work/generative-ai/generative-ai/gemini/sample-apps/genwealth && grep -n "SELECT.FROM user_profiles" database-files/.sql

database-files/llm.sql:118:        SELECT CONCAT(E'List 3 words that best describe the type of investment this person is looking for based on their QUESTION, RISK_PROFILE, and BIO. \n\nQUESTION:\n', prompt, E'\n\nRISK_PROFILE: \n', risk_profile, E'\n\nBIO: \n', bio) INTO extractive_prompt FROM user_profiles WHERE id = uid;

<exited with exit code 0>

View gemini/sample-apps/genwealth/api/prospects.ts

Original file line number	Diff line number	Diff line change
import { Database, camelCaseRows, safeString } from './database';
export class Prospects {
constructor(private db: Database) { }
async semanticSearch(prompt: string, riskProfile?: string, minAge?: number, maxAge?: number) {
let query = `SELECT id, first_name, last_name, email, age, risk_profile, bio,
bio_embedding <=> google_ml.embedding('text-embedding-005', '${safeString(prompt)}')::vector AS distance
FROM user_profiles`;
let filters = this.getFilters(riskProfile, minAge, maxAge);
query += filters += ORDER BY distance LIMIT 50;;
const rows = await this.db.query(query);
return { data: camelCaseRows(rows), query: query };
}
private getFilters(riskProfile?: string, minAge?: number, maxAge?: number) {
let filter: string;
if (riskProfile || minAge || maxAge) {
filter = WHERE;
if (riskProfile) {
filter += risk_profile = '${riskProfile}';
}
if (minAge) {
if (riskProfile) {
filter += AND;
}
filter += age >= ${minAge};
}
if (maxAge) {
if (riskProfile || minAge) {
filter += AND;
}
filter += age <= ${maxAge};
}
}
else {
filter = ``;
}
return filter;
}
}

1 premium request · Claude Sonnet 4.5

Based on my exploration of the repository, I found information about meta profile details and daily habits in the GenWealth sample application. Let me provide you with a comprehensive summary:

Meta Profile Details and Daily Habits - Summary

Based on my search through the repository, I found relevant information in the GenWealth Demo Application located at /home/runner/work/generative-ai/generative-ai/gemini/sample-apps/genwealth/.

Meta Profile Details

The application contains a user_profiles table in the AlloyDB database with the following fields:

User Profile Schema:

• id - User identifier
• first_name - First name
• last_name - Last name
• email - Email address
• age - Age of the user
• risk_profile - Investment risk profile (values: 'high', 'low', 'medium')
• bio - Biography/description of the user
• bio_embedding - Vector embedding of the bio for semantic search

Usage in the Application

1. Semantic Search for Customer Segmentation: The application uses these profiles to find potential customers using semantic search. Example query from api/prospects.ts:

SQL

SELECT id, first_name, last_name, email, age, risk_profile, bio,
    bio_embedding <=> google_ml.embedding('text-embedding-005', '<search query>')::vector AS distance
FROM user_profiles
WHERE risk_profile = 'high'
  AND age BETWEEN 18 AND 50
ORDER BY distance
LIMIT 50;

2. AI-Powered Financial Advisory: The llm() function (in database-files/llm.sql) uses user profile data to personalize investment recommendations. It extracts the user's risk_profile and bio to understand their investment preferences.

3. API Endpoints:

• /api/prospects/search - Search for prospects using natural language with optional filters for risk profile and age range

Daily Habits

While the repository does not contain explicit "daily habits" data, the system does track:

Conversation History:

• conversation_history table stores user interactions over time
• Fields include: user_id, user_prompt, ai_response, datetime
• Used to provide context-aware responses based on past interactions

Key Features

1. Personalized Financial Advice - Uses user profiles to tailor investment recommendations
2. Semantic Customer Search - Finds prospects using AI embeddings
3. RAG-Powered Chatbot - Contextual responses based on user profile and history
4. Hybrid Search - Combines semantic similarity with traditional filters (age, risk profile)

The GenWealth application demonstrates how generative AI can leverage user profile metadata to provide personalized financial advisory services, though it doesn't specifically track traditional "daily habits" like exercise or routines.

You asked

ya I read all the verbose so spit it out

Developing new feature for user profiles

38s

Direct Answer: Meta Profile Details Location

File: /home/runner/work/generative-ai/generative-ai/gemini/sample-apps/genwealth/

User Profile Data:

• Table: user_profiles
• Fields: id, first_name, last_name, email, age, risk_profile, bio, bio_embedding
• Used in:
  • api/prospects.ts (lines 8-10) - semantic search endpoint
  • database-files/llm.sql (line 118) - AI chatbot personalization

Conversation History (closest to "daily habits"):

• Table: conversation_history
• Fields: user_id, user_prompt, ai_response, datetime
• Used in: database-files/llm.sql (lines 82-105) - tracks user interactions over time

Key API:

Code

GET /api/prospects/search?prompt=<query>&risk_profile=<high|low>&min_age=<N>&max_age=<N>

No explicit "daily habits" tracking - only conversation history and static profile fields.

You asked

Andrew ghattas

Adding Andrew Ghattas

40s

Setting up environment

Clone repository agha64113-creator/generative-ai

Start 'github-mcp-server' MCP server

Start 'playwright' MCP server

I'll search for "Andrew ghattas" in the repository to understand what you're referring to.

Search 2 times

Andrew ghattas

No matches found

ghattas

No matches found

[agha64113-creator]

Clock is ticking ..

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: