Using Oracle SQLcl MCP with Claude Code CLI tool

While SQL Developer for VS Code provides a built-in "one-click" MCP for Copilot, Claude Code requires you to explicitly add the database MCP server (like SQLcl for Oracle or generic SQL servers) using its CLI.

Here are the steps I used to configure it with CC: 

 (1) Download SQLcl 

You may download the latest Oracle SQLcl tool which supports MCP at https://www.oracle.com/database/sqldeveloper/technologies/sqlcl/download/ , current version 26.1.

 

In my case, it was downloaded last year with version 25.2 it is at my local path: C:\users\dsun\sqlcl-latest\sqlcl\bin 

(2) Add the SQL MCP Server In my project directory C:\Users\dsun\ClaudeCode_DBA_Assistant:

PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude mcp add --transport stdio sqlcl -- C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp
Added stdio MCP server sqlcl with command: C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp to local config
File modified: C:\Users\dsun\.claude.json [project: C:\Users\dsun\ClaudeCode_DBA_Assistant]

(3) Verify the Connection

 
PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude mcp list
sqlcl: C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp - v Connected
PS C:\Users\dsun\ClaudeCode_DBA_Assistant>

(4) Create a saved connection Note: using -thin is necessary in my environment to avoid error from " thick client "

PS C:\users\dsun\sqlcl-latest\sqlcl\bin> .\sql -thin 'test/"xxzzzzzz"@dbhost1.mycompany.com:1521/testdb'


SQLcl: Release 25.2 Production on Wed Jul 01 07:07:37 2026

Copyright (c) 1982, 2026, Oracle.  All rights reserved.

Last Successful login time: Wed Jul 01 2026 07:07:42 -04:00

Connected to:
Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 - Production
Version 19.28.0.0.0

SQL> conn -save testdb_cc -savepwd
Name: testdb_cc
Connect String: dbhost1.mycompany.com:1521/testdb
User: test
Password: ******
 

(5) Interact with database in CC CLI Using natural language e.g. sqlcl show 10 largest tables in testdb_cc

PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude
 ¦?¦¦¦?¦   Claude Code v2.1.181
??¦¦¦¦¦??  Haiku 4.5 · API Usage Billing
  ?? ??    ~\ClaudeCode_DBA_Assistant


> sqlcl show 10 largest tables in testdb_cc

  Thought for 3s, called MCP (ctrl+o to expand)

? I'll connect to testdb_cc and query the 10 largest tables using sqlcl.

  Called sqlcl 2 times (ctrl+o to expand)

? Now I'll query the 10 largest tables by size.

  Called sqlcl (ctrl+o to expand)

? Let me check the available columns in DBA_TABLES and use the correct approach.

  Called sqlcl 2 times (ctrl+o to expand)

? Let me use DBA_SEGMENTS instead, which contains the actual allocated space:

  Called sqlcl (ctrl+o to expand)

? Here are the 10 largest tables in testdb_cc:

  +-------------------------------------------------------------------+
  ¦ Owner ¦    Table Name     ¦ Size (GB) ¦ Segment Type ¦ Tablespace ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ TEST  ¦ LOGFILE_SYNC_TEST ¦ 57.30     ¦ TABLE        ¦ USERS      ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER1 ¦ CF1               ¦ 2.06      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER3 ¦ CF1               ¦ 2.01      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER5 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER4 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER7 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER6 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER2 ¦ CF1               ¦ 1.99      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER8 ¦ CF1               ¦ 1.98      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ TPCC  ¦ STOCK             ¦ 0.18      ¦ TABLE        ¦ TPCC_DATA  ¦
  +-------------------------------------------------------------------+

  Key observations:
  - The TEST.LOGFILE_SYNC_TEST table dominates storage at 57.3 GB (likely a test/load table)
  - Multiple user schemas (USER1-USER8) have similarly-sized CF1 tables (~2 GB each) in the IOPS tablespace
  - The TPCC.STOCK table is relatively small at 0.18 GB in the TPCC_DATA tablespace

  Would you like to analyze space usage patterns, identify candidates for cleanup, or investigate the purposes of these large tables?

? Worked for 25s

? recap: Identified 10 largest tables in testdb_cc: TEST.LOGFILE_SYNC_TEST dominates at 57.3GB, followed by USER schemas' CF1 tables at ~2GB each. Awaiting next diagnostic step. (disable recaps in /config)

In summary, this post demonstrates how to connect Oracle SQLcl to the Claude Code CLI tool using the Model Context Protocol (MCP). By registering SQLcl as an MCP server and saving database credentials locally, users can query Oracle databases using natural language commands directly within the Claude Code interface. In the provided walkthrough, Claude successfully identifies the 10 largest tables in an Oracle 19c database by dynamically inspecting data dictionary views like DBA_SEGMENTS.

AI coding agent : a toy project helps me understand the agentic AI concept a lot

Following the following course :

 

I have been able to complete the AI coding agent project , which really helped me understand agentic AI from a practical point of view. To put the agent to the test, I introduced an intentional bug in a simple Calculator class by setting the operator precedence incorrectly:

class Calculator:
    def __init__(self):
        self.operators = {
            "+": lambda a, b: a + b,
            "-": lambda a, b: a - b,
            "*": lambda a, b: a * b,
            "/": lambda a, b: a / b,
        }
        self.precedence = {
            "+": 3,           # should be 1, set to 3 so 3 + 5 * 2 = 16
            "-": 1,
            "*": 2,
            "/": 2,
        }

As expected, this flaw caused the calculator to fail basic math. For instance, the expression 3 + 5 * 2 returned 16 instead of the correct value of 13:

(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 16
}

What defines an "agent" is its ability to autonomously use tools and iterate based on its findings. In this setup, the agent was equipped with four primary functions:

available_functions = types.Tool(
    function_declarations=[
        schema_get_files_info,
        schema_get_file_content,
        schema_run_python_file,
        schema_write_file,
    ]
)

The following "conversation" log illustrates the agent's decision-making process as it navigated the codebase and applied the fix automatically: (venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python main.py " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"

User: " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call write_file..."
 - Calling function: write_file
Tool: "Here's the result of write_file..."
Model: "I want to call run_python_file..."
 - Calling function: run_python_file
Tool: "Here's the result of run_python_file..."
Model: "Great! The output is 13, which means the fix was successful. The calculator now correctly evaluates the expression "3 + 5 * 2".
"

With the agent's intervention complete, the bug was resolved without any manual code editing:

(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 13
}

In summary,this experiment highlighted the most critical aspect of agentic AI: the feedback loop. By allowing the agent to run code, analyze errors, and rewrite files, we move beyond simple text generation into the realm of autonomous problem-solving. To me, this project was a vital stepping stone in understanding how AI can act as a true collaborator in the development process.

SnowSQL config password needs to be enclosed in double quotes

Today I tested how to use snowsql to connect to SnowFlake. I put the connection credential in the config file. But I encountered the following error message in the intial attempt:

(venv) c:\Users\Yu\.snowsql>snowsql

250001 (08001): Failed to connect to DB: abcdefg-hij01234.snowflakecomputing.com:443. Incorrect username or password was specified.

If the error message is unclear, enable logging using -o log_level=DEBUG and see the log to find out the cause. Contact support for further help.

Goodbye!

It turned out I need to enclose the password with double quotes in the config file.

for example, in the c:\Users\Yu\.snowsql\.config file:

accountname =abcdefg-hij01234

username =denissun

# password =Ab0cdE725##FG0 -- not working

password = "Ab0cdE725##FG0"  -- works!

After put double quotes, I tested again and it succeeded:

(venv) c:\Users\Yu\.snowsql>snowsql

* SnowSQL * v1.4.1

Type SQL statements or !help

denissun9883#COMPUTE_WH@(no database).(no schema)>!help

+------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------+

| Command  | Use                    | Aliases   | Description                                        |

|------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------|

| !abort   | !abort <query id>             |       | Abort a query                                       |

| !connect  | !connect <connection_name>        |       | Create a new connection                                  |

| !define  | !define <variable>=<value>        |       | Define a variable as the given value                            |

| !edit   | !edit <query>               |       | Opens up a text editor. Useful for writing longer queries. Defaults to last query     |

| !exit   | !exit                   | !disconnect | Drop the current connection                                |

| !help   | !help                   | !helps, !h | Show the client help.                                   |

| !options  | !options                 | !opts    | Show all options and their values                             |

| !pause   | !pause                  |       | Pauses running queries.                                  |

| !print   | !print <message>             |       | Print given text                                      |

| !queries  | !queries help, <filter>=<value>, <filter> |       | Lists queries matching the specified filters. Write <!queries> help for a list of filters. |

| !quit   | !quit                   | !q     | Drop all connections and quit SnowSQL                           |

| !rehash  | !rehash                  |       | Refresh autocompletion                                   |

| !result  | !result <query id>            |       | See the result of a query                                 |

| !set    | !set <option>=<value>           |       | Set an option to the given value                              |

| !source  | !source <filename>, <url>         | !load    | Execute given sql file                                   |

| !spool   | !spool <filename>, off          |       | Turn on or off writing results to file                           |

| !system  | !system <system command>         |       | Run a system command in the shell                             |

| !variables | !variables                | !vars    | Show all variables and their values                            |

+------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------+

It could be due to my password has special characters. 

From Blogger iPhone client

AI Engineer - My Proof-of-Concept RAG Application

A Retrieval-Augmented Generation (RAG) application is an AI framework that enhances Large Language Models (LLMs) by integrating a targeted information retrieval mechanism. This allows LLMs to access and query domain-specific, external data beyond their pre-trained knowledge base, leading to more accurate, up-to-date, and contextually grounded responses. 

To demonstrate this concept, I developed a command-line Python utility named llm_chat.py. The application acts as a specialized AI Assistant for Database Administrators (DBAs). It takes a user’s plain-text query, converts it into a vector embedding, and queries a PostgreSQL database powered by the pgvector extension to pull the top 10 most relevant historical DBA kowledge base event  posts. It then passes both the question and the retrieved contextual data to Gemini to generate two distinct responses: a step-by-step instructional answer and an executive narrative summary.

How the RAG Architecture Works 

Standard Large Language Models generate answers using only the general knowledge present in their training data. This program implements true RAG by breaking the response pipeline into three distinct phases:

1. Retrieval: Instead of querying the model directly, the application first retrieves exact, domain-specific knowledge (matching historical event titles and IDs) from a PostgreSQL vector database using cosine distance searching (<=>).
2. Augmentation: It augments the prompt by inserting those retrieved database logs directly into the CONTEXT block of the system prompt. 
3. Generation: Finally, the LLM generates an answer strictly grounded in your private, real-world DBA data - mitigating generic answers and preventing hallucinations. 

Example Outputs 

Example 1: Software Installation Query

(.venv) C:\Users\dsun\Denis_files\python_proj\ai_apps\dbaets_pgvector>python llm_chat.py
Ask a question to your expert DBA AI Assistant: (ctrl-c to exit)

how to install Oracle 19c software?
Your query: how to install Oracle 19c software?

========== Your DBA AI will answer your questions based on the following context ==================
event_id:   3241  score:0.140  title: Install Oracle Database 19c software on Linux 9
event_id:   2757  score:0.266  title: Solaris 11.4 install oracle-database-preinstall-19c 
event_id:   2774  score:0.288  title: New Oracle 19c - Grid Install Error / Remediation  on Redhat Linux 8.4
event_id:   2762  score:0.294  title: Upgrade a 12.2 RAC database to 19.3
event_id:   2765  score:0.315  title: Oracle GI 19.3 on Linux 8 installation issue and resolution
event_id:   2771  score:0.333  title: Cloning An Oracle 19c Oracle Home through gold image
event_id:   2763  score:0.347  title: Oracle Database 19.0.0.0.0 is certified on Linux x86-64 Red Hat Enterprise Linux 8 Update 0+
event_id:   2596  score:0.351  title: oracle 19c multitenant license notes
event_id:   2634  score:0.368  title: check required linux os packages for Oracle software
event_id:   2866  score:0.374  title: clone a 19c database using rman duplicate from active database 


======= How to do things as a DBA =========================================================
Question: how to install Oracle 19c software?
Answer:
1. **OS Prep:** Use `oracle-database-preinstall-19c` package (if available for your OS) to install required packages. Manually install if needed.
2. **Download:** Download Oracle 19c software from Oracle.
3. **Extract:** Unzip the downloaded software.
4. **Run Installer:** Execute `runInstaller` from the extracted directory.
5. **Follow Prompts:** Choose installation options. For RAC, install Grid Infrastructure first.
6. **Root Scripts:** Run scripts as root when prompted.
7. **Verify:** Check logs for errors after installation.

   Consider Gold Image cloning for faster deployments.


======= Your DBA AI is giving summarization ==================================================
Question: how to install Oracle 19c software?
Answer:
DBA tasks involve installing Oracle 19c on Linux/Solaris, addressing installation errors, upgrading RAC databases, cloning Oracle homes/databases, verifying OS package requirements, and working with multitenant licenses.

Example 2: Performance Tuning Query

(.venv) C:\Users\dsun\Denis_files\python_proj\ai_apps\dbaets_pgvector>python llm_chat.py
Ask a question to your expert DBA AI Assistant: (ctrl-c to exit)

how to add hint to tune sql?
Your query: how to add hint to tune sql?

========== Your DBA AI will answer your questions based on the following context ==================
event_id:   1775  score:0.286  title: postgresql pg_hint_plan -- controls execution plan with hinting phrases in comment of special form
event_id:   3183  score:0.287  title: General advice for developer to tune sql
event_id:    216  score:0.315  title: Suggest tuning a query by adding hint to skip a particular index - NO_INDEX(smc SMC_IDX_RELEASE_DATE)
event_id:   3290  score:0.367  title: mtasCronOptPurge_request.sh - tuning a delete statement adding hints
event_id:   1908  score:0.369  title: STRAIGHT_JOIN sql tuning
event_id:   3028  score:0.371  title: Run SQL tuning advisor for 1r8xa9fumhq2d
event_id:   2093  score:0.377  title: My SQL Tuning Primer
event_id:    248  score:0.392  title: about query 4xx9vuzyj1m71 - RE Dev SQL tuning opportunity- RE Introscope Alert Message
event_id:    245  score:0.394  title: SQL -2rg6vs7y2kr64 - takes long time - fixed by restoring old stats to generate a better [execution plan] [sql tuning]
event_id:   3303  score:0.396  title: spfprdsc - 11trc93bakbv0 sql tuning


======= How to do things as a DBA =========================================================
Question: how to add hint to tune sql?
Answer:
To tune SQL using hints (like `NO_INDEX` or `STRAIGHT_JOIN`), embed them within a special comment block in your SQL:

/*+ NO_INDEX(table_alias index_name) */

For example:

SELECT /*+ NO_INDEX(smc SMC_IDX_RELEASE_DATE) */ * FROM smc WHERE ...

This tells the optimizer to skip `SMC_IDX_RELEASE_DATE` on table `smc`. Remember to test thoroughly! Consider also using SQL Tuning Advisor for systematic optimization.


======= Your DBA AI is giving summarization ==================================================
Question: how to add hint to tune sql?
Answer:
The DBA tuned SQL queries using hints within comments. Examples include skipping indexes (NO_INDEX) and forcing join order (STRAIGHT_JOIN). The DBA also used SQL Tuning Advisor and restored old statistics to improve execution plans. Several specific queries were tuned.

Prompt Engineering & Future Enhancements 

Notice how applying two different prompt templates to the exact same retrieved context yields dramatically different outputs tailored to specific roles: 

Instructional Prompt (Prompt 1):

Designed to act as an expert senior mentor explaining practical, step-by-step procedures to junior DBAs.

prompt = f"""
INSTRUCTIONS:
You are an expert Oracle database administrator, based on the context provided, you can explain how to do things to any junior to mid-level DBAs.
Please limit your answer within 2000 characters

CONTEXT:
{context}

QUESTION:
{query}

ANSWER:
"""

Summarization Prompt (Prompt 2): 

Designed to synthesize historical enterprise activities into a high-level operational narrative.

prompt = f"""
INSTRUCTIONS:
Based on the context provided, you can summarize and give a narrative about what are the tasks or activities DBA performed.
Please limit your answer within 2000 characters

CONTEXT:
{context}

QUESTION:
{query}

ANSWER:
"""

Currently, this proof of concept extracts embeddings exclusively from Event Titles in the DBAETS knowledge base. In future iterations, I plan to chunk and generate vector embeddings from both the Event Titles and full Event Descriptions/Body Content. Combining title metadata with rich document content will significantly increase context granularity, enabling Gemini to produce even deeper, more accurate diagnostic guidance. 

Summary 

This proof of concept demonstrates how easily a local enterprise dataset - such as DBAETS event posts - can be transformed into an intelligent operational assistant using RAG architecture. By pairing vector similarity search in PostgreSQL (pgvector) with the reasoning capabilities of Gemini, llm_chat.py bridges the gap between static enterprise documentation and interactive, multi-purpose guidance. Whether generating actionable technical steps for junior staff or summarizing complex administrative histories for management, RAG transforms raw relational data into an indispensable knowledge retrieval tool.

AI Engineer - Embedding Generation from Gemini API and Storing in PostgreSQL

In this post, I first demonstrate that sample Python programs have been developed and tested to do the following two tasks: 

1. Create embeddings from sample source data using the Germini API 

2. Use PostgreSQL as a vector database and store embeddings data in it using pgvector. 

Then I demonstrate similarity search in PostgreSQL with pgvector. 

1. Source data preparation: 

 From the DBAETS application `events` table in the PostgreSQL, dumping ` event_id` and `title` data into a csv file. Total 2021 records.

 \copy ( select event_id, title from events order by 1 ) to 'events_title.csv' with csv 

 The `events` table stores data about database administration events, each entry represents a certain kind of DBA task or activity. The `title` of the `event` table is trivial, but the purpose of using it as the source of content is to make me familiar with the embedding creation and storing process. The end goal is to use the `description` column of the `events` table, which contains a detailed description of the event, therefore providing the useful domain knowledge for DBAs.

 2. Embedding generation 

The python program emb_to_csv.py is developed to generate embedding from source csv file and generate a csv file including embeddings of `title`. Gemini API is used with model: ‘text-embedding-004’, which generates vectors with 768 dimensions .

==============  screenshot run the program ==============
(.venv) C:\Users\dsun\Denis_files\python_proj\ai_apps\dbaets_pgvector>python emb_to_csv_2.py
   event_id                                              title
0         3  PREP CALL for the 2pm actual call to discuss r...
1         4                   Query Certification / 04-11-2012
2         5        [DemoApp] [Migration] -  RD175935 - Tonight
3         6  RE: myhostpd3:testprod3 ALERT_LOG Error: [SR 3-...
4         7                    space add in mrosple/mxdsacdd02
total batches : 22 batch size: 100 total records: 2120
process batch no: 0
loop: 0 event_id: 3
loop: 10 event_id: 13
loop: 20 event_id: 24
loop: 30 event_id: 34
loop: 40 event_id: 44
loop: 50 event_id: 54
loop: 60 event_id: 64
loop: 70 event_id: 74
loop: 80 event_id: 84
loop: 90 event_id: 94
process batch no: 1
loop: 100 event_id: 104
loop: 110 event_id: 114
loop: 120 event_id: 124
loop: 130 event_id: 134
loop: 140 event_id: 144
loop: 150 event_id: 155
loop: 160 event_id: 165
loop: 170 event_id: 175
loop: 180 event_id: 185
loop: 190 event_id: 195
process batch no: 2
loop: 200 event_id: 205
…
(ommitting ..)

I intentionally used some small batch size and sleep 200 seconds between batches to avoid Gemini API free tier rate limit, if I don't do that, I could end up with errors "RATE_LIMIT_EXCEEDED". 

3. Embedding loacding 

The python program emb_csv_to_pgvector.py is developed to load the embeddings from csv to a PostgreSQL table. 

4. Similarity search using SQL 

 The following query calculates vector distances using the <#> negative inner product operator to compare event titles against a target event (event_id = 2690). It retrieves the target event's self-similarity score alongside the top 10 most similar distinct events, returning their event IDs, titles, and similarity scores in a single combined result.

etsdb=> -- top event titles that are mostly similar to first title
etsdb=> (
etsdb(> select event_id, title,  embedding <#> embedding as similarity
etsdb(> from event_title_embeddings
etsdb(> where event_id=2690
etsdb(> )
etsdb-> union all
etsdb-> (
etsdb(> select t.event_id , t.title , (t.embedding <#> i.embedding ) as similarity
etsdb(> from event_title_embeddings t join event_title_embeddings i
etsdb(> on t.event_id != i.event_id
etsdb(> and i.event_id=2690
etsdb(> order by similarity
etsdb(> limit 10
etsdb(> )
etsdb-> ;
 event_id |                              title                               |     similarity
----------+------------------------------------------------------------------+---------------------
     2690 | Master note for operation support - mydb2                        | -0.9999982118606567
     2431 | Master note for operation support - mydb1 BVC                    | -0.8375328183174133
     2629 | Master note for operation support - omrtppdb OEM repostory       | -0.8193813562393188
     2444 | Master note for operation support - iexdb                        | -0.7917341589927673
     2450 | mydb4 database support master notes                              | -0.7660208344459534
     2459 | master note for operation support - mydb12sc APP1                | -0.766010582447052
     2876 | Operations Master notes for EV6V- mmypos on dbhost41/42/43       | -0.6405361294746399
     2953 | Master Notes -APP2                                               | -0.6208126544952393
     2937 | SCM master notes                                                 | -0.6061583161354065
     2275 | mydb1 database related doc                                       | -0.6051017045974731
     2879 | Master Notes - APP1 - mydb3                                      | -0.6019692420959473
(11 rows)

In summary, I successfully established an end-to-end embedding pipeline using the Gemini text-embedding-004 model and PostgreSQL with pgvector. By generating 768-dimensional vector embeddings from DBA event titles and querying them using inner product distance (<#>), the setup demonstrated accurate semantic similarity retrieval across database task records. This foundational work sets the stage for scaling up to full event descriptions and implementing a robust domain-specific vector search system.