Advanced Ticketing System - 1. Search relevant tickets with LLM generated summary with RAG technology. 2. LLM answers your query before returning search results. 3. Use Google Analytics to collect user's click activities and feedbacks to the search engine.

1. Proprietary
Advanced Ticketing
System
with Generative AI
Michael Chi
Solutions Architect, Google Cloud

2. Proprietary
Michael Chi
Solutions Architect, J/APAC, Google Cloud

3. Advanced Ticketing
System
w/ Generative AI

4. Why…
Advanced
Ticketing System

5. Question:
Does Langchain Supports
Vertex AI ?

6. Question:
Does Langchain Supports
Vertex AI ?

7. How…
Advanced
Ticketing System

8. I want…
1 Improve accuracy
2 Improve temporal
relevance
3 Easy Feedback
process
Text
Generation
Multi-Source Search &
Ranking
GenAI
Summary
GenAI
Command
Grounded
Generation
User
Command
Query Generation
Index
Score
Search
Cloud SQL
User Query
Retrieval Augmented Language Model Architecture
Generated Text &

9. Confidential + Proprietary
VectorStore
Documents Embedding
Fetch Relevant (to your query) Documents

10. Confidential + Proprietary
VectorStore
Documents Embedding
Fetch Relevant (to your query) Documents

11. Confidential + Proprietary
VectorStore
Documents Embedding
Fetch Relevant (to your query) Documents

12. Confidential + Proprietary
VectorStore
Documents Embedding
PROMPT:
{question}
{content}
Fetch Relevant (to your query) Documents

13. DEMO
Demo一下，你就懂了！
by Bard

14. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart
No PR /
Q&A

15. 看Code!
看code，你的超能力！
by Bard

16. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

17. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

18. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

19. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

20. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

21. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

22. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

23. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

24. Data Processing
Google
Analytics
Searching…
PostgreSQL
w/ PGVector
Summary
BigQuery
Embedding
Search Query
Embedding
PostgreSQL
w/ PGVector
Data Source
github & Doc
Data Ingestion User Interaction Feedback Loop
Process Chart

25. Pre-Generate Summary
vs
Summarization on-the-fly
Considerations

26. Full content summary
vs
Segmentary summary
Considerations

27. Confidential + Proprietary
Documents
Split Chunks
Chunk 1 Chunk 2 Chunk 3
Embedding 1 Embedding 2 Embedding 3
Build Index
LLM
Question
Query
Embedding
Semantic
Search
Document
Database
Document
Chunks
Answer
LLM to
Summarize
Ingestion Runtime

28. RETRIEVAL_DOCUMENT vs
RETRIEVAL_QUERY vs
SEMANTIC_SIMILARITY vs
NONE
Considerations

30. Considerations
Cosine distance
vs
Euclidean distance
vs
Native Inner Product

31. Cosine
較不易受向
量大小影響
計算效率較
差
Euclidean Native Inner Product
易受向量大
小影響(遠)
計算效率較
好
易受向量大
小影響（近)
Considerations

32. Cosine
文本相似度
計算
例如，計算
兩篇文章的
相似度。
圖像相似度
計算：例如
，計算兩張
圖像的相似
度。
商品相似度
計算：例如
，計算兩種
商品的相似
度。
Euclidean Native Inner Product
地理位置搜
索
例如，尋找
距離某個位
置最近的餐
廳。
圖像搜索:
例如，尋找
與某張圖像
相似的圖像
文本搜索：
例如，尋找
與某個文本
相似的文
本。
異常點檢測
：例如，檢
測出數據中
的異常值。
聚類分析：
例如，將數
據分為不同
的類別。
降維：例如
，將高維數
據降維到低
維。
Considerations
資料量少的情況下, Cosine與Euclidean其實差異不大

33. 你這個
Issue可以
寫詳細一點
嗎?
Bonus!
求你！

34. SELECT
issue_url,
issue_title,
issue_body,
comment_body,
state,
state_reason,
1 - (issue_title_embedding <=> issue_body_embedding)
AS issue_similarity,
1 - (issue_title_embedding <=> comment_embedding)
AS issue_comments_similarity,
summary
FROM gh_issues_dr_20231213_bugfix
where
1 - (issue_title_embedding <=> comment_embedding) < 0.65
and
1 - (issue_title_embedding <=> issue_body_embedding) < 0.65
ORDER BY issue_similarity asc LIMIT 10

35. 你的腦洞，我的Ticketing
Thank You
你的腦洞，我的Ticketing
Bard